Abstract:
  Solving Partially Observable Markov Decision Processes (POMDPs) is hard.
Learning optimal controllers for POMDPs when the model is unknown is harder.
Online learning of optimal controllers for unknown POMDPs, which requires
efficient learning using regret-minimizing algorithms that effectively tradeoff
exploration and exploitation, is even harder, and no solution exists currently.
In this paper, we consider infinite-horizon average-cost POMDPs with unknown
transition model, though a known observation model. We propose a natural
posterior sampling-based reinforcement learning algorithm (PSRL-POMDP) and show
that it achieves a regret bound of $O(\log T)$, where $T$ is the time horizon,
when the parameter set is finite. In the general case (continuous parameter
set), we show that the algorithm achieves $O (T^{2/3})$ regret under two
technical assumptions. To the best of our knowledge, this is the first online
RL algorithm for POMDPs and has sub-linear regret.


Abstract:
  Let $D$ be a $k$-regular bipartite tournament on $n$ vertices. We show that,
for every $p$ with $2 \le p \le n/2-2$, $D$ has a cycle $C$ of length $2p$ such
that $D \setminus C$ is hamiltonian unless $D$ is isomorphic to the special
digraph $F_{4k}$. This statement was conjectured by Manoussakis, Song and Zhang
[K. Zhang, Y. Manoussakis, and Z. Song. Complementary cycles containing a fixed
arc in diregular bipartite tournaments. Discrete Mathematics,
133(1-3):325--328,1994]. In the same paper, the conjecture was proved for $p=2$
and more recently Bai, Li and He gave a proof for $p=3$ [Y. Bai, H. Li, and W.
He. Complementary cycles in regular bipartite tournaments. Discrete
Mathematics, 333:14--27, 2014].


Abstract:
  Missing time-series data is a prevalent practical problem. Imputation methods
in time-series data often are applied to the full panel data with the purpose
of training a model for a downstream out-of-sample task. For example, in
finance, imputation of missing returns may be applied prior to training a
portfolio optimization model. Unfortunately, this practice may result in a
look-ahead-bias in the future performance on the downstream task. There is an
inherent trade-off between the look-ahead-bias of using the full data set for
imputation and the larger variance in the imputation from using only the
training data. By connecting layers of information revealed in time, we propose
a Bayesian posterior consensus distribution which optimally controls the
variance and look-ahead-bias trade-off in the imputation. We demonstrate the
benefit of our methodology both in synthetic and real financial data.


Abstract:
  Recent studies have witnessed that self-supervised methods based on view
synthesis obtain clear progress on multi-view stereo (MVS). However, existing
methods rely on the assumption that the corresponding points among different
views share the same color, which may not always be true in practice. This may
lead to unreliable self-supervised signal and harm the final reconstruction
performance. To address the issue, we propose a framework integrated with more
reliable supervision guided by semantic co-segmentation and data-augmentation.
Specially, we excavate mutual semantic from multi-view images to guide the
semantic consistency. And we devise effective data-augmentation mechanism which
ensures the transformation robustness by treating the prediction of regular
samples as pseudo ground truth to regularize the prediction of augmented
samples. Experimental results on DTU dataset show that our proposed methods
achieve the state-of-the-art performance among unsupervised methods, and even
compete on par with supervised methods. Furthermore, extensive experiments on
Tanks&Temples dataset demonstrate the effective generalization ability of the
proposed method.


Abstract:
  Nearly a decade ago, Azrieli and Shmaya introduced the class of
$\lambda$-Lipschitz games in which every player's payoff function is
$\lambda$-Lipschitz with respect to the actions of the other players. They
showed that such games admit $\epsilon$-approximate pure Nash equilibria for
certain settings of $\epsilon$ and $\lambda$. They left open, however, the
question of how hard it is to find such an equilibrium. In this work, we
develop a query-efficient reduction from more general games to Lipschitz games.
We use this reduction to show a query lower bound for any randomized algorithm
finding $\epsilon$-approximate pure Nash equilibria of $n$-player,
binary-action, $\lambda$-Lipschitz games that is exponential in
$\frac{n\lambda}{\epsilon}$. In addition, we introduce ``Multi-Lipschitz
games,'' a generalization involving player-specific Lipschitz values, and
provide a reduction from finding equilibria of these games to finding
equilibria of Lipschitz games, showing that the value of interest is the sum of
the individual Lipschitz parameters. Finally, we provide an exponential lower
bound on the deterministic query complexity of finding $\epsilon$-approximate
correlated equilibria of $n$-player, $m$-action, $\lambda$-Lipschitz games for
strong values of $\epsilon$, motivating the consideration of explicitly
randomized algorithms in the above results. Our proof is arguably simpler than
those previously used to show similar results.


Abstract:
  The WIMP proposed here yields the observed abundance of dark matter, and is
consistent with the current limits from direct detection, indirect detection,
and collider experiments, if its mass is $\sim 72$ GeV/$c^2$. It is also
consistent with analyses of the gamma rays observed by Fermi-LAT from the
Galactic center (and other sources), and of the antiprotons observed by AMS-02,
in which the excesses are attributed to dark matter annihilation. These
successes are shared by the inert doublet model (IDM), but the phenomenology is
very different: The dark matter candidate of the IDM has first-order gauge
couplings to other new particles, whereas the present candidate does not. In
addition to indirect detection through annihilation products, it appears that
the present particle can be observed in the most sensitive direct-detection and
collider experiments currently being planned.


Abstract:
  It has been shown that the performance of neural machine translation (NMT)
drops starkly in low-resource conditions, often requiring large amounts of
auxiliary data to achieve competitive results. An effective method of
generating auxiliary data is back-translation of target language sentences. In
this work, we present a case study of Tigrinya where we investigate several
back-translation methods to generate synthetic source sentences. We find that
in low-resource conditions, back-translation by pivoting through a
higher-resource language related to the target language proves most effective
resulting in substantial improvements over baselines.


Abstract:
  This paper proposes architectures that facilitate the extrapolation of
emotional expressions in deep neural network (DNN)-based text-to-speech (TTS).
In this study, the meaning of "extrapolate emotional expressions" is to borrow
emotional expressions from others, and the collection of emotional speech
uttered by target speakers is unnecessary. Although a DNN has potential power
to construct DNN-based TTS with emotional expressions and some DNN-based TTS
systems have demonstrated satisfactory performances in the expression of the
diversity of human speech, it is necessary and troublesome to collect emotional
speech uttered by target speakers. To solve this issue, we propose
architectures to separately train the speaker feature and the emotional feature
and to synthesize speech with any combined quality of speakers and emotions.
The architectures are parallel model (PM), serial model (SM), auxiliary input
model (AIM), and hybrid models (PM&AIM and SM&AIM). These models are trained
through emotional speech uttered by few speakers and neutral speech uttered by
many speakers. Objective evaluations demonstrate that the performances in the
open-emotion test provide insufficient information. They make a comparison with
those in the closed-emotion test, but each speaker has their own manner of
expressing emotion. However, subjective evaluation results indicate that the
proposed models could convey emotional information to some extent. Notably, the
PM can correctly convey sad and joyful emotions at a rate of >60%.


Abstract:
  The baroclinic annular mode (BAM) is a leading-order mode of the eddy-kinetic
energy in the Southern Hemisphere exhibiting. oscillatory behavior at
intra-seasonal time scales. The oscillation mechanism has been linked to
transient eddy-mean flow interactions that remain poorly understood. Here we
demonstrate that the finite memory effect in eddy-heat flux dependence on the
large-scale flow can explain the origin of the BAM's oscillatory behavior. We
represent the eddy memory effect by a delayed integral kernel that leads to a
generalized Langevin equation for the planetary-scale heat equation. Using a
mathematical framework for the interactions between planetary and
synoptic-scale motions, we derive a reduced dynamical model of the BAM - a
stochastically-forced oscillator with a period proportional to the geometric
mean between the eddy-memory time scale and the diffusive eddy equilibration
timescale. Our model provides a formal justification for the previously
proposed phenomenological model of the BAM and could be used to explicitly
diagnose the memory kernel and improve our understanding of transient eddy-mean
flow interactions in the atmosphere.


Abstract:
  Spelling irregularities, known now as spelling mistakes, have been found for
several centuries. As humans, we are able to understand most of the misspelled
words based on their location in the sentence, perceived pronunciation, and
context. Unlike humans, computer systems do not possess the convenient auto
complete functionality of which human brains are capable. While many programs
provide spelling correction functionality, many systems do not take context
into account. Moreover, Artificial Intelligence systems function in the way
they are trained on. With many current Natural Language Processing (NLP)
systems trained on grammatically correct text data, many are vulnerable against
adversarial examples, yet correctly spelled text processing is crucial for
learning. In this paper, we investigate how spelling errors can be corrected in
context, with a pre-trained language model BERT. We present two experiments,
based on BERT and the edit distance algorithm, for ranking and selecting
candidate corrections. The results of our experiments demonstrated that when
combined properly, contextual word embeddings of BERT and edit distance are
capable of effectively correcting spelling errors.


Abstract:
  In game theory, mechanism design is concerned with the design of incentives
so that a desired outcome of the game can be achieved. In this paper, we study
the design of incentives so that a desirable equilibrium is obtained, for
instance, an equilibrium satisfying a given temporal logic property -- a
problem that we call equilibrium design. We base our study on a framework where
system specifications are represented as temporal logic formulae, games as
quantitative concurrent game structures, and players' goals as mean-payoff
objectives. In particular, we consider system specifications given by LTL and
GR(1) formulae, and show that implementing a mechanism to ensure that a given
temporal logic property is satisfied on some/every Nash equilibrium of the
game, whenever such a mechanism exists, can be done in PSPACE for LTL
properties and in NP/$\Sigma^{P}_{2}$ for GR(1) specifications. We also study
the complexity of various related decision and optimisation problems, such as
optimality and uniqueness of solutions, and show that the complexities of all
such problems lie within the polynomial hierarchy. As an application,
equilibrium design can be used as an alternative solution to the rational
synthesis and verification problems for concurrent games with mean-payoff
objectives whenever no solution exists, or as a technique to repair, whenever
possible, concurrent games with undesirable rational outcomes (Nash equilibria)
in an optimal way.


Abstract:
  Fonts have had trends throughout their history, not only in when they were
invented but also in their usage and popularity. In this paper, we attempt to
specifically find the trends in font usage using robust regression on a large
collection of text images. We utilize movie posters as the source of fonts for
this task because movie posters can represent time periods by using their
release date. In addition, movie posters are documents that are carefully
designed and represent a wide range of fonts. To understand the relationship
between the fonts of movie posters and time, we use a regression Convolutional
Neural Network (CNN) to estimate the release year of a movie using an isolated
title text image. Due to the difficulty of the task, we propose to use of a
hybrid training regimen that uses a combination of Mean Squared Error (MSE) and
Tukey's biweight loss. Furthermore, we perform a thorough analysis on the
trends of fonts through time.


Abstract:
  Using characteristics to treat advection terms in time-dependent PDEs leads
to a class of schemes, e.g., semi-Lagrangian and Lagrange-Galerkin schemes,
which preserve stability under large Courant numbers, and may therefore be
appealing in many practical situations. Unfortunately, the need of locating the
feet of characteristics may cause a serious drop of efficiency in the case of
unstructured space grids, and thus prevent the use of large time-step schemes
on complex geometries. In this paper, we perform an in-depth analysis of the
main recipes available for characteristic location, and propose a technique to
improve the efficiency of this phase, using additional information related to
the advecting vector field. This results in a clear improvement of execution
times in the unstructured case, thus extending the range of applicability of
large time-step schemes.


Abstract:
  In this paper, we construct a sequence $(c_k)_{k\in\mathbb{Z}_{\geq 1}}$ of
symplectic capacities based on the Chiu-Tamarkin complex $C_{T,\ell}$, a
$\mathbb{Z}/\ell\mathbb{Z}$-equivariant invariant coming from the microlocal
theory of sheaves. We compute $(c_k)_{k\in\mathbb{Z}_{\geq 1}}$ for convex
toric domains, which are the same as the Gutt-Hutchings capacities. On the
other hand, our method works for the contact embedding problem. We define a
sequence of "contact capacities" $([c]_k)_{k\in\mathbb{Z}_{\geq 1}}$ on the
prequantized contact manifold $\mathbb{R}^{2d}\times S^1$, which could derive
some embedding obstructions of prequantized convex toric domains.


Abstract:
  We prove that all homology 3-spheres are $J_4$-equivalent, i.e. that any
homology 3-sphere can be obtained from one another by twisting one of its
Heegaard splittings by an element of the mapping class group acting trivially
on the fourth nilpotent quotient of the fundamental group of the gluing
surface. We do so by exhibiting an element of $J_4$, the fourth term of the
Johnson filtration of the mapping class group, on which (the core of) the
Casson invariant takes the value $1$. In particular, this provides an explicit
example of an element of $J_4$ that is not a commutator of length $2$ in the
Torelli group.


Abstract:
  This paper presents an approach to deal with safety of dynamical systems in
presence of multiple non-convex unsafe sets. While optimal control and model
predictive control strategies can be employed in these scenarios, they suffer
from high computational complexity in case of general nonlinear systems.
Leveraging control barrier functions, on the other hand, results in
computationally efficient control algorithms. Nevertheless, when safety
guarantees have to be enforced alongside stability objectives, undesired
asymptotically stable equilibrium points have been shown to arise. We propose a
computationally efficient optimization-based approach which allows us to ensure
safety of dynamical systems without introducing undesired equilibria even in
presence of multiple non-convex unsafe sets. The developed control algorithm is
showcased in simulation and in a real robot navigation application.


Abstract:
  Advances in synthetic biology and nanotechnology have contributed to the
design of tools that can be used to control, reuse, modify, and re-engineer
cells' structure, as well as enabling engineers to effectively use biological
cells as programmable substrates to realize Bio-Nano Things (biological
embedded computing devices). Bio-NanoThings are generally tiny, non-intrusive,
and concealable devices that can be used for in-vivo applications such as
intra-body sensing and actuation networks, where the use of artificial devices
can be detrimental. Such (nano-scale) devices can be used in various healthcare
settings such as continuous health monitoring, targeted drug delivery, and
nano-surgeries. These services can also be grouped to form a collaborative
network (i.e., nanonetwork), whose performance can potentially be improved when
connected to higher bandwidth external networks such as the Internet, say via
5G. However, to realize the IoBNT paradigm, it is also important to seamlessly
connect the biological environment with the technological landscape by having a
dynamic interface design to convert biochemical signals from the human body
into an equivalent electromagnetic signal (and vice versa). This,
unfortunately, risks the exposure of internal biological mechanisms to
cyber-based sensing and medical actuation, with potential security and privacy
implications. This paper comprehensively reviews bio-cyber interface for IoBNT
architecture, focusing on bio-cyber interfacing options for IoBNT like
biologically inspired bio-electronic devices, RFID enabled implantable chips,
and electronic tattoos. This study also identifies known and potential security
and privacy vulnerabilities and mitigation strategies for consideration in
future IoBNT designs and implementations.


Abstract:
  In this paper, we introduce a novel deep neural network suitable for
multi-scale analysis and propose efficient model-agnostic methods that help the
network extract information from high-frequency domains to reconstruct clearer
images. Our model can be applied to multi-scale image enhancement problems
including denoising, deblurring and single image super-resolution. Experiments
on SIDD, Flickr2K, DIV2K, and REDS datasets show that our method achieves
state-of-the-art performance on each task. Furthermore, we show that our model
can overcome the over-smoothing problem commonly observed in existing
PSNR-oriented methods and generate more natural high-resolution images by
applying adversarial training.


Abstract:
  Topological phases exhibit unconventional order that cannot be detected by
any local order parameter. In the framework of Projected Entangled Pair
States(PEPS), topological order is characterized by an entanglement symmetry of
the local tensor which describes the model. This symmetry can take the form of
a tensor product of group representations, or in the more general case a
correlated symmetry action in the form of a Matrix Product Operator(MPO), which
encompasses all string-net models. Among other things, these entanglement
symmetries allow for the description of ground states and anyon excitations.
Recently, the idea has been put forward to use those symmetries and the anyonic
objects they describe as order parameters for probing topological phase
transitions, and the applicability of this idea has been demonstrated for
Abelian groups. In this paper, we extend this construction to the domain of
non-Abelian models with MPO symmetries, and use it to study the breakdown of
topological order in the double Fibonacci (DFib) string-net and its Galois
conjugate, the non-hermitian double Yang-Lee (DYL) string-net. We start by
showing how to construct topological order parameters for condensation and
deconfinement of anyons using the MPO symmetries. Subsequently, we set up
interpolations from the DFib and the DYL model to the trivial phase, and show
that these can be mapped to certain restricted solid on solid(RSOS) models,
which are equivalent to the $((5\pm\sqrt{5})/2)$-state Potts model,
respectively. The known exact solutions of the statistical models allow us to
locate the critical points, and to predict the critical exponents for the order
parameters. We complement this by numerical study of the phase transitions,
which fully confirms our theoretical predictions; remarkably, we find that both
models exhibit a duality between the order parameters for condensation and
deconfinement.


Abstract:
  In this paper we propose a novel class of methods for high order accurate
integration of multirate systems of ordinary differential equation
initial-value problems. The proposed methods construct multirate schemes by
approximating the action of matrix $\varphi$-functions within explicit
exponential Rosenbrock (ExpRB) methods, thereby called Multirate Exponential
Rosenbrock (MERB) methods. They consist of the solution to a sequence of
modified "fast" initial-value problems, that may themselves be approximated
through subcycling any desired IVP solver. In addition to proving how to
construct MERB methods from certain classes of ExpRB methods, we provide
rigorous convergence analysis of these methods and derive efficient MERB
schemes of orders two through six (the highest order ever constructed
infinitesimal multirate methods). We then present numerical simulations to
confirm these theoretical convergence rates, and to compare the efficiency of
MERB methods against other recently-introduced high order multirate methods.


Abstract:
  When the inflaton couples to photons and amplifies electric fields, charged
particles produced via the Schwinger effect can dominate the universe after
inflation, which is dubbed as the Schwinger preheating. Using the hydrodynamic
approach for the Boltzmann equation, we numerically study two cases, the
Starobinsky inflation model with the kinetic coupling and the anisotropic
inflation model. The Schwinger preheating is not observed in the latter model
but occurs for a sufficiently large inflaton-photon coupling in the first
model. We analytically address its condition and derive a general attractor
solution of the electric fields. The occurrence of the Schwinger preheating in
the first model is determined by whether the electric fields enter the
attractor solution during inflation or not.


Abstract:
  In this paper we have presented the mechanism of the barrier crossing
dynamics of a Brownian particle which is coupled to a thermal bath in the
presence of both time independent and fluctuating magnetic fields. Here the
following three aspects are important in addition to the role of the thermal
bath on the barrier crossing dynamics. Magnetic field induced coupling may
introduce a resonance like effect. Another role of the field is that
enhancement of its strength reduces the frequency factor of the barrier
crossing rate constant. Finally, the fluctuating magnetic field introduces an
induced electric field which activates the Brownian particle to cross the
energy barrier. As a result of interplay among these aspects versatile
non-monotonic behavior may appear in the variation of the rate constant as a
function of the strength of the time independent magnetic field.


Abstract:
  The space radiation environment is a complex combination of fast-moving ions
derived from all atomic species found in the periodic table. The energy
spectrum of each ion species varies widely but is prominently in the range of
400 - 600 MeV/n. The large dynamic range in ion energy is difficult to simulate
in ground-based radiobiology experiments. Most ground-based irradiations with
mono-energetic beams of a single one ion species are delivered at comparatively
high dose rates. In some cases, sequences of such beams are delivered with
various ion species and energies to crudely approximate the complex space
radiation environment. This approximation may cause profound experimental bias
in processes such as biologic repair of radiation damage, which are known to
have strong temporal dependancies. It is possible that this experimental bias
leads to an overprediction of risks of radiation effects that have not been
observed in the astronaut cohort. None of the primary health risks presumely
attributed to space radiation exposure, such as radiation carciogenesis,
cardiovascular disease, cognitive deficits, etc., have been observed in
astronaut or cosmonaut crews. This fundamentally and profoundly limits our
understanding of the effects of GCR on humans and limits the development of
effective radiation countermeasures.


Abstract:
  Time series forecasting methods play critical role in estimating the spread
of an epidemic. The coronavirus outbreak of December 2019 has already infected
millions all over the world and continues to spread on. Just when the curve of
the outbreak had started to flatten, many countries have again started to
witness a rise in cases which is now being referred as the 2nd wave of the
pandemic. A thorough analysis of time-series forecasting models is therefore
required to equip state authorities and health officials with immediate
strategies for future times. This aims of the study are three-fold: (a) To
model the overall trend of the spread; (b) To generate a short-term forecast of
10 days in countries with the highest incidence of confirmed cases (USA, India
and Brazil); (c) To quantitatively determine the algorithm that is best suited
for precise modelling of the linear and non-linear features of the time series.
The comparison of forecasting models for the total cumulative cases of each
country is carried out by comparing the reported data and the predicted value,
and then ranking the algorithms (Prophet, Holt-Winters, LSTM, ARIMA, and
ARIMA-NARNN) based on their RMSE, MAE and MAPE values. The hybrid combination
of ARIMA and NARNN (Nonlinear Auto-Regression Neural Network) gave the best
result among the selected models with a reduced RMSE, which proved to be almost
35.3% better than one of the most prevalent method of time-series prediction
(ARIMA). The results demonstrated the efficacy of the hybrid implementation of
the ARIMA-NARNN model over other forecasting methods such as Prophet, Holt
Winters, LSTM, and the ARIMA model in encapsulating the linear as well as
non-linear patterns of the epidemical datasets.


Abstract:
  We present mid infrared imaging of two young clusters, the Coronet in the CrA
cloud core and B59 in the Pipe Nebula, using the FORCAST camera on the
Stratospheric Observatory for Infrared Astronomy. We also analyze Herschel
Space Observatory PACS and SPIRE images of the associated clouds. The two
clusters are at similar, and very close, distances. Star formation is ongoing
in the Coronet, which hosts at least one Class 0 source and several pre-stellar
cores, which may collapse and form stars. The B59 cluster is older, although it
still has a few Class I sources, and is less compact. The CrA cloud has a
diameter of about 0.16 pc, and we determine a dust temperature of 15.7 K and a
star formation efficiency of about 27 %, while the B59 core is approximately
twice as large, has a dust temperature of about 11.4 K and a star formation
efficiency of about 14 %. We infer that the gas densities are much higher in
the Coronet, which has also formed intermediate mass stars, while B59 has only
formed low-mass stars.


Abstract:
  This paper presents approximation methods for time-dependent thermal
radiative transfer problems in high energy density physics. It is based on the
multilevel quasidiffusion method defined by the high-order radiative transfer
equation (RTE) and the low-order quasidiffusion (aka VEF) equations for the
moments of the specific intensity. A large part of data storage in TRT problems
between time steps is determined by the dimensionality of grid functions of the
radiation intensity. The approximate implicit methods with reduced memory for
the time-dependent Boltzmann equation are applied to the high-order RTE,
discretized in time with the backward Euler (BE) scheme. The high-dimensional
intensity from the previous time level in the BE scheme is approximated by
means of the low-rank proper orthogonal decomposition (POD). Another version of
the presented method applies the POD to the remainder term of P2 expansion of
the intensity. The accuracy of the solution of the approximate implicit methods
depends of the rank of the POD. The proposed methods enable one to reduce
storage requirements in time dependent problems. Numerical results of a
Fleck-Cummings TRT test problem are presented.


Abstract:
  In this paper we study nonlinear interpolation problems for interpolation and
peak-interpolation sets of function algebras. The subject goes back to the
classical Rudin-Carleson interpolation theorem. In particular, we prove the
following nonlinear version of this theorem:
  Let $\bar{\mathbb D}\subset \mathbb C$ be the closed unit disk, $\mathbb
T\subset\bar{\mathbb D}$ the unit circle, $S\subset\mathbb T$ a closed subset
of Lebesgue measure zero and $M$ a connected complex manifold.
  Then for every continuous $M$-valued map $f$ on $S$ there exists a continuous
$M$-valued map $g$ on $\bar{\mathbb D}$ holomorphic on its interior such that
$g|_S=f$. We also consider similar interpolation problems for continuous maps
  $f: S\rightarrow\bar M$, where $\bar M$ is a complex manifold with boundary
$\partial M$ and interior $M$. Assuming that $f(S)\cap\partial M\ne\emptyset$
we are looking for holomorphic extensions $g$ of $f$ such that $g(\bar{\mathbb
D}\setminus S)\subset M$.


Abstract:
  In this paper, we investigate Riesz energy problems on unbounded conductors
in $\R^d$ in the presence of general external fields $Q$, not necessarily
satisfying the growth condition $Q(x)\to\infty$ as $x\to\infty$ assumed in
several previous studies. We provide sufficient conditions on $Q$ for the
existence of an equilibrium measure and the compactness of its support.
Particular attention is paid to the case of the hyperplanar conductor $\R^{d}$,
embedded in $\R^{d+1}$, when the external field is created by the potential of
a signed measure $\nu$ outside of $\R^{d}$. Simple cases where $\nu$ is a
discrete measure are analyzed in detail. New theoretic results for Riesz
potentials, in particular an extension of a classical theorem by de La
Vall\'ee-Poussin, are established. These results are of independent interest.


Abstract:
  DNA sequencing is becoming increasingly commonplace, both in medical and
direct-to-consumer settings. To promote discovery, collected genomic data is
often de-identified and shared, either in public repositories, such as OpenSNP,
or with researchers through access-controlled repositories. However, recent
studies have suggested that genomic data can be effectively matched to
high-resolution three-dimensional face images, which raises a concern that the
increasingly ubiquitous public face images can be linked to shared genomic
data, thereby re-identifying individuals in the genomic data. While these
investigations illustrate the possibility of such an attack, they assume that
those performing the linkage have access to extremely well-curated data. Given
that this is unlikely to be the case in practice, it calls into question the
pragmatic nature of the attack. As such, we systematically study this
re-identification risk from two perspectives: first, we investigate how
successful such linkage attacks can be when real face images are used, and
second, we consider how we can empower individuals to have better control over
the associated re-identification risk. We observe that the true risk of
re-identification is likely substantially smaller for most individuals than
prior literature suggests. In addition, we demonstrate that the addition of a
small amount of carefully crafted noise to images can enable a controlled
trade-off between re-identification success and the quality of shared images,
with risk typically significantly lowered even with noise that is imperceptible
to humans.


Abstract:
  Recent works on Binary Neural Networks (BNNs) have made promising progress in
narrowing the accuracy gap of BNNs to their 32-bit counterparts. However, the
accuracy gains are often based on specialized model designs using additional
32-bit components. Furthermore, almost all previous BNNs use 32-bit for feature
maps and the shortcuts enclosing the corresponding binary convolution blocks,
which helps to effectively maintain the accuracy, but is not friendly to
hardware accelerators with limited memory, energy, and computing resources.
Thus, we raise the following question: How can accuracy and energy consumption
be balanced in a BNN network design? We extensively study this fundamental
problem in this work and propose a novel BNN architecture without most commonly
used 32-bit components: \textit{BoolNet}. Experimental results on ImageNet
demonstrate that BoolNet can achieve 4.6x energy reduction coupled with 1.2\%
higher accuracy than the commonly used BNN architecture Bi-RealNet. Code and
trained models are available at: https://github.com/hpi-xnor/BoolNet.


Abstract:
  Many task-oriented dialogue systems use deep reinforcement learning (DRL) to
learn policies that respond to the user appropriately and complete the tasks
successfully. Training DRL agents with diverse dialogue trajectories prepare
them well for rare user requests and unseen situations. One effective
diversification method is to let the agent interact with a diverse set of
learned user models. However, trajectories created by these artificial user
models may contain generation errors, which can quickly propagate into the
agent's policy. It is thus important to control the quality of the
diversification and resist the noise. In this paper, we propose a novel
dialogue diversification method for task-oriented dialogue systems trained in
simulators. Our method, Intermittent Short Extension Ensemble (I-SEE),
constrains the intensity to interact with an ensemble of diverse user models
and effectively controls the quality of the diversification. Evaluations on the
Multiwoz dataset show that I-SEE successfully boosts the performance of several
state-of-the-art DRL dialogue agents.


Abstract:
  When manipulating three-dimensional data, it is possible to ensure that
rotational and translational symmetries are respected by applying so-called
SE(3)-equivariant models. Protein structure prediction is a prominent example
of a task which displays these symmetries. Recent work in this area has
successfully made use of an SE(3)-equivariant model, applying an iterative
SE(3)-equivariant attention mechanism. Motivated by this application, we
implement an iterative version of the SE(3)-Transformer, an SE(3)-equivariant
attention-based model for graph data. We address the additional complications
which arise when applying the SE(3)-Transformer in an iterative fashion,
compare the iterative and single-pass versions on a toy problem, and consider
why an iterative model may be beneficial in some problem settings. We make the
code for our implementation available to the community.


Abstract:
  In the framework of the Standard Model (SM) a theoretical description of the
neutron beta decay is given at the level of 10^{-5}. The neutron lifetime and
correlation coefficients of the neutron beta decay for a polarized neutron, a
polarized electron and an unpolarized proton are calculated at the account for
i) the radiative corrections of order O(\alpha E_e/m_N) ~ 10^{-5} to Sirlin's
outer and inner radiative corrections of order O(\alpha/\pi), ii) the
corrections of order O(E^2_e/m^2_N) ~ 10^{-5}, caused by weak magnetism and
proton recoil, and iii) Wilkinson's corrections of order 10^{-5} (Wilkinson,
Nucl. Phys. A377, 474 (1982)). These corrections define the SM background of
the theoretical description of the neutron beta decay at the level of 10^{-5},
which is required by experimental searches of interactions beyond the SM with
experimental uncertainties of a few parts of 10^{-5}.


Abstract:
  Face swapping has both positive applications such as entertainment,
human-computer interaction, etc., and negative applications such as DeepFake
threats to politics, economics, etc. Nevertheless, it is necessary to
understand the scheme of advanced methods for high-quality face swapping and
generate enough and representative face swapping images to train DeepFake
detection algorithms. This paper proposes the first Megapixel level method for
one shot Face Swapping (or MegaFS for short). Firstly, MegaFS organizes face
representation hierarchically by the proposed Hierarchical Representation Face
Encoder (HieRFE) in an extended latent space to maintain more facial details,
rather than compressed representation in previous face swapping methods.
Secondly, a carefully designed Face Transfer Module (FTM) is proposed to
transfer the identity from a source image to the target by a non-linear
trajectory without explicit feature disentanglement. Finally, the swapped faces
can be synthesized by StyleGAN2 with the benefits of its training stability and
powerful generative capability. Each part of MegaFS can be trained separately
so the requirement of our model for GPU memory can be satisfied for megapixel
face swapping. In summary, complete face representation, stable training, and
limited memory usage are the three novel contributions to the success of our
method. Extensive experiments demonstrate the superiority of MegaFS and the
first megapixel level face swapping database is released for research on
DeepFake detection and face image editing in the public domain. The dataset is
at this link.


Abstract:
  In this paper, we study the problem of mobile user profiling, which is a
critical component for quantifying users' characteristics in the human mobility
modeling pipeline. Human mobility is a sequential decision-making process
dependent on the users' dynamic interests. With accurate user profiles, the
predictive model can perfectly reproduce users' mobility trajectories. In the
reverse direction, once the predictive model can imitate users' mobility
patterns, the learned user profiles are also optimal. Such intuition motivates
us to propose an imitation-based mobile user profiling framework by exploiting
reinforcement learning, in which the agent is trained to precisely imitate
users' mobility patterns for optimal user profiles. Specifically, the proposed
framework includes two modules: (1) representation module, which produces state
combining user profiles and spatio-temporal context in real-time; (2) imitation
module, where Deep Q-network (DQN) imitates the user behavior (action) based on
the state that is produced by the representation module. However, there are two
challenges in running the framework effectively. First, epsilon-greedy strategy
in DQN makes use of the exploration-exploitation trade-off by randomly pick
actions with the epsilon probability. Such randomness feeds back to the
representation module, causing the learned user profiles unstable. To solve the
problem, we propose an adversarial training strategy to guarantee the
robustness of the representation module. Second, the representation module
updates users' profiles in an incremental manner, requiring integrating the
temporal effects of user profiles. Inspired by Long-short Term Memory (LSTM),
we introduce a gated mechanism to incorporate new and old user characteristics
into the user profile.


Abstract:
  We study the polarization dynamics of ultrafast solitons in mode-locked fiber
lasers. We find that when a stable soliton is generated, it's
state-of-polarization shifts toward a stable state and when the soliton is
generated with excess power levels it experiences relaxation oscillations in
its intensity and timing. On the other hand, when a soliton is generated in an
unstable state-of-polarization, it either decays in intensity until it
disappears, or its temporal width decreases until it explodes into several
solitons and then it disappears. We also found that when two solitons are
simultaneously generated close to each other, they attract each other until
they collide and merge into a single soliton. Although, these two solitons are
generated with different states-of-polarization, they shift their
state-of-polarization closer to each other until the polarization coincides
when they collide. We support our findings by numerical calculations of a
non-Lagrangian approach by simulating the Ginzburg-Landau equation governing
the dynamics of solitons in a laser cavity. Our model also predicts the
relaxation oscillations of stable solitons and the two types of unstable
solitons observed in the experimental measurements.


Abstract:
  This paper presents Favalon, a functional programming language built on the
premise of a lambda calculus for use as an interactive shell replacement.
Favalon seamlessly integrates with typed versions of existing libraries and
commands using type inference, flexible runtime type metadata, and the same
techniques employed by shells to link commands together. Much of Favalon's
syntax is customizable via user-defined functions, allowing it to be extended
by anyone who is familiar with a command-line shell. Furthermore, Favalon's
type inference engine can be separated from its runtime library and easily
repurposed for other applications.


Abstract:
  Recently, asymmetric plasmonic nanojunctions [Karnetzky et. al., Nature Comm.
2471, 9 (2018)] have shown promise as on-chip electronic devices to convert
femtosecond optical pulses to current bursts, with a bandwidth of
multi-terahertz scale, although yet at low temperatures and pressures. Such
nanoscale devices are of great interest for novel ultrafast electronics and
opto-electronic applications. Here, we operate the device in air and at room
temperature, revealing the mechanisms of photoemission from plasmonic
nanojunctions, and the fundamental limitations on the speed of
optical-to-electronic conversion. Inter-cycle interference of coherent
electronic wavepackets results in a complex energy electron distribution and
birth of multiphoton effects. This energy structure, as well as reshaping of
the wavepackets during their propagation from one tip to the other, determine
the ultrafast dynamics of the current. We show that, up to some level of
approximation, the electron flight time is well-determined by the mean
ponderomotive velocity in the driving field.


Abstract:
  The necessary and sufficient conditions are given for a sequence of complex
numbers to be the periodic (or antiperiodic) spectrum of non-self-adjoint Dirac
operator.


Abstract:
  In this paper, we consider a type of image quality assessment as a
task-specific measurement, which can be used to select images that are more
amenable to a given target task, such as image classification or segmentation.
We propose to train simultaneously two neural networks for image selection and
a target task using reinforcement learning. A controller network learns an
image selection policy by maximising an accumulated reward based on the target
task performance on the controller-selected validation set, whilst the target
task predictor is optimised using the training set. The trained controller is
therefore able to reject those images that lead to poor accuracy in the target
task. In this work, we show that the controller-predicted image quality can be
significantly different from the task-specific image quality labels that are
manually defined by humans. Furthermore, we demonstrate that it is possible to
learn effective image quality assessment without using a ``clean'' validation
set, thereby avoiding the requirement for human labelling of images with
respect to their amenability for the task. Using $6712$, labelled and
segmented, clinical ultrasound images from $259$ patients, experimental results
on holdout data show that the proposed image quality assessment achieved a mean
classification accuracy of $0.94\pm0.01$ and a mean segmentation Dice of
$0.89\pm0.02$, by discarding $5\%$ and $15\%$ of the acquired images,
respectively. The significantly improved performance was observed for both
tested tasks, compared with the respective $0.90\pm0.01$ and $0.82\pm0.02$ from
networks without considering task amenability. This enables image quality
feedback during real-time ultrasound acquisition among many other medical
imaging applications.


Abstract:
  We show that a novel, general phase space mapping Hamiltonian for
nonadiabatic systems, which is reminiscent of the renowned Meyer-Miller mapping
Hamiltonian, involves a commutator variable matrix rather than the conventional
zero-point-energy parameter. In the exact mapping formulation on constraint
space for phase space approaches for nonadiabatic dynamics, the general mapping
Hamiltonian with commutator variables can be employed to generate approximate
trajectory-based dynamics. Various benchmark model tests, which range from gas
phase to condensed phase systems, suggest that the overall performance of the
general mapping Hamiltonian is better than that of the conventional
Meyer-Miller Hamiltonian.


Abstract:
  Many man-made objects are characterised by a shape that is symmetric along
one or more planar directions. Estimating the location and orientation of such
symmetry planes can aid many tasks such as estimating the overall orientation
of an object of interest or performing shape completion, where a partial scan
of an object is reflected across the estimated symmetry plane in order to
obtain a more detailed shape. Many methods processing 3D data rely on expensive
3D convolutions. In this paper we present an alternative novel encoding that
instead slices the data along the height dimension and passes it sequentially
to a 2D convolutional recurrent regression scheme. The method also comprises a
differentiable least squares step, allowing for end-to-end accurate and fast
processing of both full and partial scans of symmetric objects. We use this
approach to efficiently handle 3D inputs to design a method to estimate planar
reflective symmetries. We show that our approach has an accuracy comparable to
state-of-the-art techniques on the task of planar reflective symmetry
estimation on full synthetic objects. Additionally, we show that it can be
deployed on partial scans of objects in a real-world pipeline to improve the
outputs of a 3D object detector.


Abstract:
  In this paper we present a novel mechanism for producing the observed Dark
Matter(DM) relic abundance during the First Order Phase Transition (FOPT) in
the early universe. We show that the bubble expansion with ultra-relativistic
velocities can lead to the abundance of DM particles with masses much larger
than the scale of the transition. We study this non-thermal production
mechanism in the context of a generic phase transition and the electroweak
phase transition. The application of the mechanism to the Higgs portal DM as
well as the signal in the Stochastic Gravitational Background are discussed.


Abstract:
  Introduction. Can the infection due to the human immunodeficiency virus type
1 induce a change in the differentiation status or process in T cells?.
Methods. We will consider two stochastic Markov chain models, one which will
describe the T-helper cell differentiation process, and another one describing
that process of infection of the T-helper cell by the virus; in these Markov
chains, we will consider a set of states $\{X_t \}$ comprised of those proteins
involved in each of the processes and their interactions (either
differentiation or infection of the cell), such that we will obtain two
stochastic transition matrices ($A,B$), one for each process; afterwards, the
computation of their eigenvalues shall be performed, in which, should the
eigenvalue $\lambda_i=1$ exist, the computation for the equilibrium
distribution $\pi^n$ will be obtained for each of the matrices, which will
inform us on the trends of interactions amongst the proteins in the long-term.
Results. The stochastic processes considered possess an equilibrium
distribution, when reaching their equilibrium distribution, there exists an
increase in their informational entropy, and their log-rank distributions can
be modeled as discrete beta generalized distributions (DGBD). Discussion. The
equilibrium distributions of both process can be regarded as states in which
the cell is well-differentiated, ergo there exists an induction of a novel
HIV-dependent differentiated state in the T-cell; these processes due to their
DGBD distribution can be considered complex processes; due to the increasing
entropy, the equilibrium states are stable ones. Conclusion. The HIV virus can
promote a novel differentiated state in the T-cell, which can give account for
clinical features seen in patients; this model, notwithstanding does not give
account of YES/NO logical switches involved in the regulatory networks.


Abstract:
  We propose an optimal MMSE precoding technique using quantized signals with
constant envelope. Unlike the existing MMSE design that relies on 1-bit
resolution, the proposed approach employs uniform phase quantization and the
bounding step in the branch-and-bound method is different in terms of
considering the most restrictive relaxation of the nonconvex problem, which is
then utilized for a suboptimal design also. Moreover, unlike prior studies, we
propose three different soft detection methods and an iterative detection and
decoding scheme that allow the utilization of channel coding in conjunction
with low-resolution precoding. Besides an exact approach for computing the
extrinsic information, we propose two approximations with reduced computational
complexity. Numerical simulations show that utilizing the MMSE criterion
instead of the established maximum-minimum distance to the decision threshold
yields a lower bit-error-rate in many scenarios. Furthermore, when using the
MMSE criterion, a smaller number of bound evaluations in the branch-and-bound
method is required for low and medium SNR. Finally, results based on an LDPC
block code indicate that the receive processing schemes yield a lower
bit-error-rate compared to the conventional design.


Abstract:
  We consider the geodesic of the directed last passage percolation with iid
exponential weights. We find the explicit one point distribution of the
geodesic location joint with the last passage times, and its limit when the
size goes to infinity.


Abstract:
  We consider the problem of minimizing age of information in general
single-hop and multihop wireless networks. First, we formulate a way to convert
AoI optimization problems into equivalent network stability problems. Then, we
propose a heuristic low complexity approach for achieving stability that can
handle general network topologies; unicast, multicast and broadcast flows;
interference constraints; link reliabilities; and AoI cost functions. We
provide numerical results to show that our proposed algorithms behave as well
as the best known scheduling and routing schemes available in the literature
for a wide variety of network settings.


Abstract:
  We develop a theory for the non-equilibrium screening of a charged impurity
in a two-dimensional electron system under a strong time-periodic drive. Our
analysis of the time-averaged polarization function and dielectric function
reveals that Floquet driving modifies the screened impurity potential in two
main regimes. In the weak drive regime, the time-averaged screened potential
exhibits unconventional Friedel oscillations with multiple spatial periods
contributed by a principal period modulated by higher-order periods, which are
due to the emergence of additional Kohn anomalies in the polarization function.
In the strong drive regime, the time-averaged impurity potential becomes almost
unscreened and does not exhibit Friedel oscillations. This tunable Friedel
oscillations is a result of the dynamic gating effect of the time-dependent
driving field on the two-dimensional electron system.


Abstract:
  In this paper, based on the idea of self-adjusting steepness based
schemes[5], a two-dimensional calculation method of steepness parameter is
proposed, and thus a two-dimensional self-adjusting steepness based limiter is
constructed. With the application of such limiter to the over-intersection
based remapping framework, a low dissipation remapping method has been proposed
that can be applied to the existing ALE method.


Abstract:
  We derive the Laws of Cosines and Sines in the super hyperbolic plane using
Minkowski supergeometry and find the identical formulae to the classical case,
but remarkably involving different expressions for cosines and sines of angles
which include substantial fermionic corrections. In further analogy to the
classical case, we apply these results to show that two parallel supergeodesics
which are not ultraparallel admit a unique common orthogonal supergeodesic, and
we briefly describe aspects of elementary supernumber theory, leading to a
prospective analogue of the Gauss product of quadratic forms.


Abstract:
  We present an analysis of the galaxy environment and physical properties of a
partial Lyman limit system at z = 0.83718 with HI and metal line components
closely separated in redshift space ($|\Delta v| \approx 400$ km/s) towards the
background quasar HE1003+0149. The HST/COS far-ultraviolet spectrum provides
coverage of lines of oxygen ions from OI to OV. Comparison of observed spectral
lines with synthetic profiles generated from Bayesian ionization modeling
reveals the presence of two distinct gas phases in the absorbing medium. The
low-ionization phase of the absorber has sub-solar metallicities (1/10-th
solar) with indications of [C/O] < 0 in each of the components. The OIV and OV
trace a more diffuse higher-ionization medium with predicted HI column
densities that are $\approx 2$ dex lower. The quasar field observed with
VLT/MUSE reveals three dwarf galaxies with stellar masses of $M^* \sim 10^{8} -
10^{9}$ M$_\odot$, and with star formation rates of $\approx 0.5 - 1$ M$_\odot$
yr$^{-1}$, at projected separations of $\rho/R_{\mathrm{vir}} \approx 1.8 -
3.0$ from the absorber. Over a wider field with projected proper separation of
$\leq 5$ Mpc and radial velocity offset of $|\Delta v| \leq 1000$ km/s from the
absorber, 21 more galaxies are identified in the $VLT$/VIMOS and Magellan deep
galaxy redshift surveys, with 8 of them within $1$ Mpc and $500$ km/s,
consistent with the line of sight penetrating a group of galaxies. The absorber
presumably traces multiple phases of cool ($T \sim 10^4$ K) photoionized
intragroup medium. The inferred [C/O] < 0 hints at preferential enrichment from
core-collapse supernovae, with such gas displaced from one or more of the
nearby galaxies, and confined to the group medium.


Abstract:
  Transition metal dichalcogenides (TMDs) combine interesting optical and
spintronic properties in an atomically-thin material, where the light
polarization can be used to control the spin and valley degrees-of-freedom for
the development of novel opto-spintronic devices. These promising properties
emerge due to their large spin-orbit coupling in combination with their crystal
symmetries. Here, we provide simple symmetry arguments in a group-theory
approach to unveil the symmetry-allowed spin scattering mechanisms, and
indicate how one can use these concepts towards an external control of the spin
lifetime. We perform this analysis for both monolayer (inversion asymmetric)
and bilayer (inversion symmetric) crystals, indicating the different mechanisms
that play a role in these systems. We show that, in monolayer TMDs, electrons
and holes transform fundamentally differently -- leading to distinct
spin-scattering processes. We find that one of the electronic states in the
conduction band is partially protected by time-reversal symmetry, indicating a
longer spin lifetime for that state. In bilayer and bulk TMDs, a hidden
spin-polarization can exist within each layer despite the presence of global
inversion symmetry. We show that this feature enables control of the interlayer
spin-flipping scattering processes via an out-of-plane electric field,
providing a mechanism for electrical control of the spin lifetime.


Abstract:
  We study the dynamics of a one-dimensional Rydberg lattice gas under
facilitation (anti-blockade) conditions which implements a so-called
kinetically constrained spin system. Here an atom can only be excited to a
Rydberg state when one of its neighbors is already excited. Once two or more
atoms are simultaneously excited mechanical forces emerge, which couple the
internal electronic dynamics of this many-body system to external vibrational
degrees of freedom in the lattice. This electron-phonon coupling results in a
so-called phonon dressing of many-body states which in turn impacts on the
facilitation dynamics. In our theoretical study we focus on a scenario in which
all energy scales are sufficiently separated such that a perturbative treatment
of the coupling between electronic and vibrational states is possible. This
allows to analytically derive an effective Hamiltonian for the evolution of
consecutive clusters of Rydberg excitations in the presence of phonon dressing.
We analyze the spectrum of this Hamiltonian and show -- by employing Fano
resonance theory -- that the interaction between Rydberg excitations and
lattice vibrations leads to the emergence of slowly decaying bound states that
inhibit fast relaxation of certain initial states.


Abstract:
  Drafting as a process to reduce drag and to benefit from the presence of
other competitors is applied in various sports with several recent examples of
competitive running in formations. In this study, the aerodynamics of a
realistic model of a female runner is calculated by computational fluid
dynamics (CFD) simulations at four running speeds of 15 km/h, 18 km/h, 21 km/h,
and 36 km/h. Aerodynamic power fractions of the total energy expenditure are
found to be in the range of 2.6-8.5%. Additionally, four exemplary formations
are analysed with respect to their drafting potential and resulting drag values
are compared for the main runner and her pacers. The best of the formations
achieves a total drag reduction on the main runner of 75.6%. Moreover, there
are large variations in the drag reduction between the considered formations of
up to 42% with respect to the baseline single-runner case. We conclude that
major drag reduction of more than 70% can already be achieved with fairly
simple formations, while certain factors, such as runners on the sides, can
have a detrimental effect on drag reduction due to local acceleration of the
passing flow. Using an empirical model for mechanical power output during
running, gains of metabolic power and performance predictions are evaluated for
all considered formations. Improvements in running economy are up to 3.5% for
the best formation, leading to velocity gains of 2.3%. This translates to 154 s
(~2.6 min) saved over a marathon distance. Consequently, direct conclusions are
drawn from the obtained data for ideal drafting of long-distance running in
highly packed formations.


Abstract:
  Turbulence in the upper ocean in the submesoscale range (scales smaller than
the deformation radius) plays an important role for the heat exchange with the
atmosphere and for oceanic biogeochemistry. Its dynamics should strongly depend
on the seasonal cycle and the associated mixed-layer instabilities. The latter
are particularly relevant in winter and are responsible for the formation of
energetic small scales that extend over the whole depth of the mixed layer. The
knowledge of the transport properties of oceanic flows at depth, which is
essential to understand the coupling between surface and interior dynamics,
however, is still limited. By means of numerical simulations, we explore the
Lagrangian dispersion properties of turbulent flows in a quasi-geostrophic
model system allowing for both thermocline and mixed-layer instabilities. The
results indicate that, when mixed-layer instabilities are present, the
dispersion regime is local from the surface down to depths comparable with that
of the interface with the thermocline, while in their absence dispersion
quickly becomes nonlocal versus depth. We then identify the origin of such
behavior in the existence of fine-scale energetic structures due to mixed-layer
instabilities. We further discuss the effect of vertical shear on the
Lagrangian particle spreading and address the correlation between the
dispersion properties at the surface and at depth, which is relevant to assess
the possibility of inferring the dynamical features of deeper flows from the
more accessible surface ones.


Abstract:
  Electrochemically mediated selective adsorption is an emerging
electrosorption technique that utilizes Faradaically enhanced redox active
electrodes, which can adsorb ions not only electrostatically, but also
electrochemically. The superb selectivity (>100) of this technique enables
selective removal of toxic or high-value target ions under low energy
consumption. Here, we develop a general theoretical framework to describe the
competitive electrosorption phenomena involving multiple ions and surface-bound
redox species. The model couples diffusion, convection and electromigration
with competitive surface adsorption reaction kinetics, consistently derived
from non-equilibrium thermodynamics. To optimize the selective removal of the
target ions, design criteria were derived analytically from physically relevant
dimensionless groups and time scales, where the propagation of the target
anions concentration front is the limiting step. Detailed computational studies
are reported for three case studies that cover a wide range of inlet
concentration ratios between the competing ions. And in all three cases, target
anions in the electrosorption cell forms a self-sharpening reaction-diffusion
wave front. Based on the model, a three-step stop-flow operation scheme with a
pure stripping solution of target anions is proposed that optimizes the ion
adsorption performance and increases the purity of the regeneration stream to
almost 100%, which is beneficial for downstream processing.


Abstract:
  Quality of website design is one of the influential factors of website
success. How the design helps the users using effectively and efficiently
website and satisfied at the end of the use. However, it is a common tendency
that websites are designed based on the developer's perspectives and lack
considering user importance. Thus, the degree of website usability tends to be
low according to user perceptions. This study purposed to understand the user
experiences using an institutional repository (IR) website in a public
university in Indonesia. The research was performed based on usability testing
framework as the usability testing method. About 12 participants were purposely
involved concerning their key informant characteristics. Following three
empirical data collection techniques (i.e., query technique, formal experiment,
and thinking aloud), both descriptive analysis using usability scale matric and
content analysis using qualitative data analysis (QDA) Miner Lite software were
used in the data analysis stage. Lastly, several visual design recommendations
were then proposed at the end of the study. In terms of a case study, besides
the practical recommendations which may contextually useful for the next
website development; the clarity of the research design may also help scholars
how to combine more than one usability testing technique within a
multi-technique study design.


Abstract:
  The SIMT execution model is commonly used for general GPU development. CUDA
and OpenCL developers write scalar code that is implicitly parallelized by
compiler and hardware. On Intel GPUs, however, this abstraction has profound
performance implications as the underlying ISA is SIMD and important hardware
capabilities cannot be fully utilized. To close this performance gap we
introduce C-For-Metal (CM), an explicit SIMD programming framework designed to
deliver close-to-the-metal performance on Intel GPUs. The CM programming
language and its vector/matrix types provide an intuitive interface to exploit
the underlying hardware features, allowing fine-grained register management,
SIMD size control and cross-lane data sharing. Experimental results show that
CM applications from different domains outperform the best-known SIMT-based
OpenCL implementations, achieving up to 2.7x speedup on the latest Intel GPU.


Abstract:
  We systematically investigate axisymmetric extremal isolated horizons (EIHs)
defined by vanishing surface gravity, corresponding to zero temperature. In the
first part, using the Newman-Penrose and GHP formalism we derive the most
general metric function for such EIHs in the Einstein-Maxwell theory, which
complements the previous result of Lewandowski and Pawlowski. We prove that it
depends on 5 independent parameters, namely deficit angles on the north and
south poles of a spherical-like section of the horizon, its radius (area), and
total electric and magnetic charges of the black hole. The deficit angles and
both charges can be separately set to zero. In the second part of our paper, we
identify this general axially symmetric solution for EIH with extremal horizons
in exact electrovacuum Plebanski-Demianski spacetimes, using the convenient
parametrization of this family by Griffiths and Podolsky. They represent all
(double aligned) black holes of algebraic type D without a cosmological
constant. Apart from a conicity, they depend on 6 physical parameters (mass,
Kerr-like rotation, NUT parameter, acceleration, electric and magnetic charges)
constrained by the extremality condition. We were able to determine their
relation to the EIH geometrical parameters. This explicit identification of
type D extremal black holes with a unique form of EIH includes several
interesting subclasses, such as accelerating extremely charged
Reissner-Nordstrom black hole (C-metric), extremal accelerating Kerr-Newman,
accelerating Kerr-NUT, or non-accelerating Kerr-Newman-NUT black holes.


Abstract:
  Multimode nonlinear optics offers to overcome a long-standing limitation of
fiber optics, tightly phase locking several spatial modes and enabling the
coherent transport of a wavepacket through a multimode fiber. A similar problem
is encountered in the temporal compression of multi-mJ pulses to few-cycle
duration in hollow gas-filled fibers. Scaling the fiber length to up to six
meters, hollow fibers have recently reached 1 TW of peak power. Despite the
remarkable utility of the hollow fiber compressor and its widespread
application, however, no analytical model exists to enable insight into the
scaling behavior of maximum compressibility and peak power. Here we extend a
recently introduced formalism for describing mode-locking to the spatially
analogue scenario of locking spatial fiber modes together. Our formalism
unveils the coexistence of two soliton branches for anomalous modal dispersion
and indicates the formation of stable spatio-temporal light bullets that would
be unstable in free space, similar to the temporal cage solitons in
mode-locking theory. Our model enables deeper understanding of the physical
processes behind the formation of such light bullets and predict the existence
of multimode solitons in a much wider range of fiber types than previously
considered possible.


Abstract:
  Let $f : X \to S$ be a family of smooth projective algebraic varieties over a
smooth connected base $S$, with everything defined over
$\overline{\mathbb{Q}}$. Denote by $\mathbb{V} = R^{2i} f_{*} \mathbb{Z}(i)$
the associated integral variation of Hodge structure on the degree $2i$
cohomology. We consider the following question: when can a fibre
$\mathbb{V}_{s}$ above an algebraic point $s \in S(\overline{\mathbb{Q}})$ be
isomorphic to a transcendental fibre $\mathbb{V}_{s'}$ with $s' \in
S(\mathbb{C}) \setminus S(\overline{\mathbb{Q}})$? When $\mathbb{V}$ induces a
quasi-finite period map $\varphi : S \to \Gamma \backslash D$, conjectures in
Hodge theory predict that such isomorphisms cannot exist. We introduce new
differential-algebraic techniques to show this is true for all points $s \in
S(\overline{\mathbb{Q}})$ outside of an explicit proper closed algebraic subset
of $S$. As a corollary we establish the existence of a canonical
$\overline{\mathbb{Q}}$-algebraic model for normalizations of period images.


Abstract:
  We study co-dimension two monodromy defects in theories of conformally
coupled scalars and free Dirac fermions in arbitrary $d$ dimensions. We
characterise this family of conformal defects by computing the one-point
functions of the stress-tensor and conserved current for Abelian flavour
symmetries as well as two-point functions of the displacement operator. In the
case of $d=4$, the normalisation of these correlation functions are related to
defect Weyl anomaly coefficients, and thus provide crucial information about
the defect conformal field theory. We provide explicit checks on the values of
the defect central charges by calculating the universal part of the defect
contribution to entanglement entropy, and further, we use our results to
extract the universal part of the vacuum R\'enyi entropy. Moreover, we leverage
the non-supersymmetric free field results to compute a novel defect Weyl
anomaly coefficient in a $d=4$ theory of free $\mathcal{N}=2$ hypermultiplets.
Including singular modes in the defect operator product expansion of
fundamental fields, we identify notable relevant deformations in the singular
defect theories and show that they trigger a renormalisation group flow towards
an IR fixed point with the most regular defect OPE. We also study Gukov-Witten
defects in free $d=4$ Maxwell theory and show that their central charges
vanish.


Abstract:
  Loneliness (i.e., the distressing feeling that often accompanies the
subjective sense of social disconnection) is detrimental to mental and physical
health, and deficits in self-reported feelings of being understood by others is
a risk factor for loneliness. What contributes to these deficits in lonely
people? We used functional magnetic resonance imaging (fMRI) to unobtrusively
measure the relative alignment of various aspects of people's mental processing
of naturalistic stimuli (specifically, videos) as they unfold over time. We
thereby tested whether lonely people actually process the world in
idiosyncratic ways, rather than only exaggerating or misperceiving how
dissimilar others' views are to their own (which could lead them to feel
misunderstood, even if they actually see the world similarly to those around
them). We found evidence for such idiosyncrasy: lonely individuals' neural
responses during free viewing of the videos were dissimilar to peers in their
communities, particularly in brain regions (e.g., regions of the default-mode
network) in which similar responses have been associated with shared
psychological perspectives and subjective understanding. Our findings were
robust even after controlling for demographic similarities, participants'
overall levels of objective social isolation, and their friendships with each
other. These results suggest that being surrounded predominantly by people who
see the world differently from oneself may be a risk factor for loneliness,
even if one is friends with them.


Abstract:
  The Traditional Approximation of Rotation (TAR) is a treatment of the
hydrodynamic equations of rotating and stably stratified fluids in which the
action of the Coriolis acceleration along the direction of the entropy and
chemical stratifications is neglected because it is weak in comparison with the
buoyancy force. The dependent variables in the equations for the dynamics of
gravito-inertial waves (GIWs) then become separable into radial and horizontal
parts as in the non-rotating case. The TAR is built on the assumptions that the
star is spherical (i.e. its centrifugal deformation is neglected) and uniformly
rotating. We study the feasibility of carrying out a generalisation of the TAR
to account for the centrifugal acceleration in the case of strongly deformed
uniformly and rapidly rotating stars (and planets), and to identify the
validity domain of this approximation. We built analytically a complete
formalism that allows the study of the dynamics of GIWs in spheroidal
coordinates which take into account the flattening of rapidly rotating stars by
assuming the hierarchies of frequencies adopted within the TAR in the spherical
case and by deriving a generalised Laplace tidal equation for the horizontal
eigenfunctions of the GIWs and their asymptotic wave periods, which can be used
to probe the structure and dynamics of rotating deformed stars with
asteroseismology. Using 2D ESTER stellar models, we determine the validity
domain of the generalised TAR as a function of the rotation rate of the star
normalised by its critical angular velocity and its pseudo-radius. This
generalisation allows us to study the signature of the centrifugal effects on
GIWs in rapidly rotating deformed stars. We found that the effects of the
centrifugal acceleration in rapidly rotating early-type stars on GIWs are
theoretically detectable in modern space photometry using observations from
Kepler.


Abstract:
  Artificial intelligence is applied in a range of sectors, and is relied upon
for decisions requiring a high level of trust. For regression methods, trust is
increased if they approximate the true input-output relationships and perform
accurately outside the bounds of the training data. But often performance
off-test-set is poor, especially when data is sparse. This is because the
conditional average, which in many scenarios is a good approximation of the
`ground truth', is only modelled with conventional Minkowski-r error measures
when the data set adheres to restrictive assumptions, with many real data sets
violating these. To combat this there are several methods that use prior
knowledge to approximate the `ground truth'. However, prior knowledge is not
always available, and this paper investigates how error measures affect the
ability for a regression method to model the `ground truth' in these scenarios.
Current error measures are shown to create an unhelpful bias and a new error
measure is derived which does not exhibit this behaviour. This is tested on 36
representative data sets with different characteristics, showing that it is
more consistent in determining the `ground truth' and in giving improved
predictions in regions beyond the range of the training data.


Abstract:
  This paper addresses the task of (complex) conversational question answering
over a knowledge graph. For this task, we propose LASAGNE (muLti-task semAntic
parSing with trAnsformer and Graph atteNtion nEtworks). It is the first
approach, which employs a transformer architecture extended with Graph
Attention Networks for multi-task neural semantic parsing. LASAGNE uses a
transformer model for generating the base logical forms, while the Graph
Attention model is used to exploit correlations between (entity) types and
predicates to produce node representations. LASAGNE also includes a novel
entity recognition module which detects, links, and ranks all relevant entities
in the question context. We evaluate LASAGNE on a standard dataset for complex
sequential question answering, on which it outperforms existing baseline
averages on all question types. Specifically, we show that LASAGNE improves the
F1-score on eight out of ten question types; in some cases, the increase in
F1-score is more than 20% compared to the state of the art.


Abstract:
  Calculation of conductivity in the Hubbard model is a challenging task.
Recent years have seen much progress in this respect and numerically exact
solutions are now possible in certain regimes. In this paper we discuss the
calculation of conductivity for the square lattice Hubbard model in the
presence of a perpendicular magnetic field, focusing on orbital effects. We
present the relevant formalism in all detail and in full generality, and then
discuss the simplifications that arise at the level of the dynamical mean field
theory (DMFT). We prove that the Kubo bubble preserves gauge and translational
invariance, and that in the DMFT the vertex corrections cancel regardless of
the magnetic field. We present the DMFT results for the spectral function and
both the longitudinal and Hall conductivity in several regimes of parameters.
We analyze thoroughly the quantum oscillations of the longitudinal conductivity
and identify a high-frequency oscillation component, arising as a combined
effect of scattering and temperature, in line with recent experimental
observations in moir\'e systems.


Abstract:
  Millions of people use platforms such as YouTube, Facebook, Twitter, and
other mass media. Due to the accessibility of these platforms, they are often
used to establish a narrative, conduct propaganda, and disseminate
misinformation. This work proposes an approach that uses state-of-the-art NLP
techniques to extract features from video captions (subtitles). To evaluate our
approach, we utilize a publicly accessible and labeled dataset for classifying
videos as misinformation or not. The motivation behind exploring video captions
stems from our analysis of videos metadata. Attributes such as the number of
views, likes, dislikes, and comments are ineffective as videos are hard to
differentiate using this information. Using caption dataset, the proposed
models can classify videos among three classes (Misinformation, Debunking
Misinformation, and Neutral) with 0.85 to 0.90 F1-score. To emphasize the
relevance of the misinformation class, we re-formulate our classification
problem as a two-class classification - Misinformation vs. others (Debunking
Misinformation and Neutral). In our experiments, the proposed models can
classify videos with 0.92 to 0.95 F1-score and 0.78 to 0.90 AUC ROC.


Abstract:
  The 2D TI edge states are considered within the Volkov-Pankratov (VP)
Hamiltonian. A smooth transition between TI and OI is assumed. The edge states
are formed in the total gap of homogeneous 2D material. A pair of these states
are of linear dispersion, others have gapped Dirac spectra. The optical
selection rules are found. The optical transitions between the neighboring edge
states appear in the global 2D gap for the in-plane light electric field
directed across the edge.
  The electrons in linear edge states have no backscattering, that is
indicative of the fact of topological protection. However, when linear edge
states get to the energy domain of Dirac edge states, the backscattering
becomes permitted. The elastic backscattering rate is found. The Drude-like
conductivity is found when the Fermi level gets into the energy domain of the
coexistence of linear and Dirac edge states. The localization edge conductance
of a finite sample at zero temperature is determined.


Abstract:
  This paper compares the advantages, limitations, and computational
considerations of using Finite-Time Lyapunov Exponents (FTLEs) and Lagrangian
Descriptors (LDs) as tools for identifying barriers and mechanisms of fluid
transport in two-dimensional time-periodic flows. These barriers and mechanisms
of transport are often referred to as "Lagrangian Coherent Structures," though
this term often changes meaning depending on the author or context. This paper
will specifically focus on using FTLEs and LDs to identify stable and unstable
manifolds of hyperbolic stagnation points, and the Kolmogorov-Arnold-Moser
(KAM) tori associated with elliptic stagnation points. The background and
theory behind both methods and their associated phase space structures will be
presented, and then examples of FTLEs and LDs will be shown based on a simple,
periodic, time-dependent double-gyre toy model with varying parameters.


Abstract:
  Railway systems provide pivotal support to modern societies, making their
efficiency and robustness important to ensure. However, these systems are
susceptible to disruptions and delays, leading to accumulating economic damage.
The large spatial scale of delay spreading typically make it difficult to
distinguish which regions will ultimately affected from an initial disruption,
creating uncertainty for risk assessment. In this paper, we identify
geographical structures that reflect how delay spreads through railway
networks. We do so by proposing a graph-based, hybrid schedule and
empirical-based model for delay propagation and apply spectral clustering. We
apply the model to four European railway systems: the Netherlands, Germany,
Switzerland and Italy. We characterize geographical structures in the railway
systems of these countries and interpret these regions in terms of delay
severity and how dynamically disconnected they are from the rest. The method
also allows us to point out important differences between these countries'
railway systems. For practitioners, this geographical characterization of
railways provide natural boundaries for local decision-making structures and a
first-order prioritization on which regions are at risk, given an initial
disruption.


Abstract:
  Brain parcellations play a ubiquitous role in the analysis of magnetic
resonance imaging (MRI) datasets. Over 100 years of research has been conducted
in pursuit of an ideal brain parcellation. Different methods have been
developed and studied for constructing brain parcellations using different
imaging modalities. More recently, several data-driven parcellation methods
have been adopted from data mining, machine learning, and statistics
communities. With contributions from different scientific fields, there is a
rich body of literature that needs to be examined to appreciate the breadth of
existing research and the gaps that need to be investigated. In this work, we
review the large body of in vivo brain parcellation research spanning different
neuroimaging modalities and methods. A key contribution of this work is a
semantic organization of this large body of work into different taxonomies,
making it easy to understand the breadth and depth of the brain parcellation
literature. Specifically, we categorized the existing parcellations into three
groups: Anatomical parcellations, functional parcellations, and structural
parcellations which are constructed using T1-weighted MRI, functional MRI
(fMRI), and diffusion-weighted imaging (DWI) datasets, respectively. We provide
a multi-level taxonomy of different methods studied in each of these
categories, compare their relative strengths and weaknesses, and highlight the
challenges currently faced for the development of brain parcellations.


Abstract:
  Effective molecular representation learning is of great importance to
facilitate molecular property prediction, which is a fundamental task for the
drug and material industry. Recent advances in graph neural networks (GNNs)
have shown great promise in applying GNNs for molecular representation
learning. Moreover, a few recent studies have also demonstrated successful
applications of self-supervised learning methods to pre-train the GNNs to
overcome the problem of insufficient labeled molecules. However, existing GNNs
and pre-training strategies usually treat molecules as topological graph data
without fully utilizing the molecular geometry information. Whereas, the
three-dimensional (3D) spatial structure of a molecule, a.k.a molecular
geometry, is one of the most critical factors for determining molecular
physical, chemical, and biological properties. To this end, we propose a novel
Geometry Enhanced Molecular representation learning method (GEM) for Chemical
Representation Learning (ChemRL). At first, we design a geometry-based GNN
architecture that simultaneously models atoms, bonds, and bond angles in a
molecule. To be specific, we devised double graphs for a molecule: The first
one encodes the atom-bond relations; The second one encodes bond-angle
relations. Moreover, on top of the devised GNN architecture, we propose several
novel geometry-level self-supervised learning strategies to learn spatial
knowledge by utilizing the local and global molecular 3D structures. We compare
ChemRL-GEM with various state-of-the-art (SOTA) baselines on different
molecular benchmarks and exhibit that ChemRL-GEM can significantly outperform
all baselines in both regression and classification tasks. For example, the
experimental results show an overall improvement of 8.8% on average compared to
SOTA baselines on the regression tasks, demonstrating the superiority of the
proposed method.


Abstract:
  We investigate program equivalence for linear higher-order(sequential)
languages endowed with primitives for computational effects. More specifically,
we study operationally-based notions of program equivalence for a linear
$\lambda$-calculus with explicit copying and algebraic effects \emph{\`a la}
Plotkin and Power. Such a calculus makes explicit the interaction between
copying and linearity, which are intensional aspects of computation, with
effects, which are, instead, \emph{extensional}. We review some of the notions
of equivalences for linear calculi proposed in the literature and show their
limitations when applied to effectful calculi where copying is a first-class
citizen. We then introduce resource transition systems, namely transition
systems whose states are built over tuples of programs representing the
available resources, as an operational semantics accounting for both
intensional and extensional interactive behaviors of programs. Our main result
is a sound and complete characterization of contextual equivalence as trace
equivalence defined on top of resource transition systems.


Abstract:
  One of the exciting recent developments in decentralized finance (DeFi) has
been the development of decentralized cryptocurrency exchanges that can
autonomously handle conversion between different cryptocurrencies.
Decentralized exchange protocols such as Uniswap, Curve and other types of
Automated Market Makers (AMMs) maintain a liquidity pool (LP) of two or more
assets constrained to maintain at all times a mathematical relation to each
other, defined by a given function or curve. Examples of such functions are the
constant-sum and constant-product AMMs. Existing systems however suffer from
several challenges. They require external arbitrageurs to restore the price of
tokens in the pool to match the market price. Such activities can potentially
drain resources from the liquidity pool. In particular, dramatic market price
changes can result in low liquidity with respect to one or more of the assets
and reduce the total value of the LP. We propose in this work a new approach to
constructing the AMM by proposing the idea of dynamic curves. It utilizes input
from a market price oracle to modify the mathematical relationship between the
assets so that the pool price continuously and automatically adjusts to be
identical to the market price. This approach eliminates arbitrage opportunities
and, as we show through simulations, maintains liquidity in the LP for all
assets and the total value of the LP over a wide range of market prices.


Abstract:
  We develop a model of interacting zwitterionic membranes with rotating
surface dipoles immersed in a monovalent salt, and implement it in a field
theoretic formalism. In the mean-field regime of monovalent salt, the
electrostatic forces between the membranes are characterized by a non-uniform
trend: at large membrane separations, the interfacial dipoles on the opposing
sides behave as like-charge cations and give rise to repulsive membrane
interactions; at short membrane separations, the anionic field induced by the
dipolar phosphate groups sets the behavior in the intermembrane region. The
attraction of the cationic nitrogens in the dipolar lipid headgroups leads to
the adhesion of the membrane surfaces via dipolar bridging. The underlying
competition between the opposing field components of the individual dipolar
charges leads to the non-uniform salt ion affinity of the zwitterionic membrane
with respect to the separation distance; large inter-membrane separations imply
anionic excess while small, nanometer size separations, favor cationic excess.
This complex ionic selectivity of zwitterionic membranes may have relevant
repercussions on nanofiltration and nanofluidic transport techniques.


Abstract:
  Recently the leading order of the correlation energy of a Fermi gas in a
coupled mean-field and semiclassical scaling regime has been derived, under the
assumption of an interaction potential with a small norm and with compact
support in Fourier space. We generalize this result to large interaction
potentials, requiring only $|\cdot| \hat{V} \in \ell^1 (\mathbb{Z}^3)$. Our
proof is based on approximate, collective bosonization in three dimensions.
Significant improvements compared to recent work include stronger bounds on
non-bosonizable terms and more efficient control on the bosonization of the
kinetic energy.


Abstract:
  The localization spread gives a criterion to decide between metallic versus
insulating behaviour of a material. It is defined as the second moment cumulant
of the many-body position operator, divided by the number of electrons.
Different operators are used for systems treated with Open or Periodic Boundary
Conditions. In particular, in the case of periodic systems, we use the
complex-position definition, that was already used in similar contexts for the
treatment of both classical and quantum situations. In this study, we show that
the localization spread evaluated on a finite ring system of radius $R$ with
Open Boundary Conditions leads, in the large $R$ limit, to the same formula
derived by Resta et al. for 1D systems with periodic Born-von K\'arm\'an
boundary conditions. A second formula, alternative to the Resta's one, is also
given, based on the sum-over-state formalism, allowing for an interesting
generalization to polarizability and other similar quantities.


Abstract:
  In the recent years, there has been a shift in facial behavior analysis from
the laboratory-controlled conditions to the challenging in-the-wild conditions
due to the superior performance of deep learning based approaches for many real
world applications.However, the performance of deep learning approaches relies
on the amount of training data. One of the major problems with data acquisition
is the requirement of annotations for large amount of training data. Labeling
process of huge training data demands lot of human support with strong domain
expertise for facial expressions or action units, which is difficult to obtain
in real-time environments.Moreover, labeling process is highly vulnerable to
ambiguity of expressions or action units, especially for intensities due to the
bias induced by the domain experts. Therefore, there is an imperative need to
address the problem of facial behavior analysis with weak annotations. In this
paper, we provide a comprehensive review of weakly supervised learning (WSL)
approaches for facial behavior analysis with both categorical as well as
dimensional labels along with the challenges and potential research directions
associated with it. First, we introduce various types of weak annotations in
the context of facial behavior analysis and the corresponding challenges
associated with it. We then systematically review the existing state-of-the-art
approaches and provide a taxonomy of these approaches along with their insights
and limitations. In addition, widely used data-sets in the reviewed literature
and the performance of these approaches along with evaluation principles are
summarized. Finally, we discuss the remaining challenges and opportunities
along with the potential research directions in order to apply facial behavior
analysis with weak labels in real life situations.


Abstract:
  We show that the action of the mapping class group on the space of closed
curves of a closed surface effectively tracks the corresponding action on
Teichm\"uller space in the following sense: for all but quantitatively few
mapping classes, the information of how a mapping class moves a given point of
Teichm\"uller space determines, up to a power saving error term, how it changes
the geometric intersection numbers of a given closed curve with respect to
arbitrary geodesic currents. Applications include an effective estimate
describing the speed of convergence of Teichm\"uller geodesic rays to the
boundary at infinity of Teichm\"uller space, an effective estimate comparing
the Teichm\"uller and Thurston metrics along mapping class group orbits of
Teichm\"uller space, and, in the sequel, effective estimates for countings of
filling closed geodesics on closed, negatively curved surfaces.


Abstract:
  We discuss a model based on dark sector described by non-Abelian $SU(2)_D$
gauge symmetry where we introduce $SU(2)_L \times SU(2)_D$ bi-doublet
vector-like leptons to generate active neutrino masses and kinetic mixing
between $SU(2)_D$ and $U(1)_Y$ gauge fields at one-loop level. After
spontaneous symmetry breaking of $SU(2)_D$, we have remnant $Z_4$ symmetry
guaranteeing stability of dark matter candidates. We formulate neutrino mass
matrix and related lepton flavor violating processes and discus dark matter
physics estimating relic density. It is found that our model realize
multicomponent dark matter scenario due to the $Z_4$ symmetry and relic density
can be explained by gauge interactions with kinetic mixing effect.


Abstract:
  In this article we continue with the research initiated in our previous work
on singular Liouville equations with quantized singularity. The main goal of
this article is to prove that as long as the bubbling solutions violate the
spherical Harnack inequality near a singular source, the first derivatives of
coefficient functions must tend to zero.


Abstract:
  We compute explicit solutions $\Lambda^\pm_m$ of the Painleve VI (PVI)
differential equation from equivariant instanton bundles $E_m$ corresponding to
Yang-Mills instantons with "quadrupole symmetry." This is based on a
generalization of Hitchin's logarithmic connection to vector bundles with an
$SL_2({\mathbb C})$ action. We then identify explicit Okamoto transformation
which play the role of "creation operators" for construction $\Lambda^\pm_m$
from the "ground state" $\Lambda^\pm_0$, suggesting that the equivariant
instanton bundles $E_m$ might similarly be related to the trivial "ground
state" $E_0$.


Abstract:
  Machine unlearning has great significance in guaranteeing model security and
protecting user privacy. Additionally, many legal provisions clearly stipulate
that users have the right to demand model providers to delete their own data
from training set, that is, the right to be forgotten. The naive way of
unlearning data is to retrain the model without it from scratch, which becomes
extremely time and resource consuming at the modern scale of deep neural
networks. Other unlearning approaches by refactoring model or training data
struggle to gain a balance between overhead and model usability.
  In this paper, we propose an approach, dubbed as DeepObliviate, to implement
machine unlearning efficiently, without modifying the normal training mode. Our
approach improves the original training process by storing intermediate models
on the hard disk. Given a data point to unlearn, we first quantify its temporal
residual memory left in stored models. The influenced models will be retrained
and we decide when to terminate the retraining based on the trend of residual
memory on-the-fly. Last, we stitch an unlearned model by combining the
retrained models and uninfluenced models. We extensively evaluate our approach
on five datasets and deep learning models. Compared to the method of retraining
from scratch, our approach can achieve 99.0%, 95.0%, 91.9%, 96.7%, 74.1%
accuracy rates and 66.7$\times$, 75.0$\times$, 33.3$\times$, 29.4$\times$,
13.7$\times$ speedups on the MNIST, SVHN, CIFAR-10, Purchase, and ImageNet
datasets, respectively. Compared to the state-of-the-art unlearning approach,
we improve 5.8% accuracy, 32.5$\times$ prediction speedup, and reach a
comparable retrain speedup under identical settings on average on these
datasets. Additionally, DeepObliviate can also pass the backdoor-based
unlearning verification.


Abstract:
  Theoretical studies of superradiant lasing on optical clock transitions
predict a superb frequency accuracy and precision closely tied to the bare
atomic linewidth. Such a superradiant laser is also robust against cavity
fluctuations when the spectral width of the lasing mode is much larger than
that of the atomic medium. Recent predictions suggest that this unique feature
persists even for a hot and thus strongly broadened ensemble, provided the
effective atom number is large enough. Here we use a second-order cumulant
expansion approach to study the power, linewidth and lineshifts of such a
superradiant laser as a function of the inhomogeneous width of the ensemble
including variations of the spatial atom-field coupling within the resonator.
We present conditions on the atom numbers, the pump and coupling strengths
required to reach the buildup of collective atomic coherence as well as scaling
and limitations for the achievable laser linewidth.


Abstract:
  Many sequence-to-sequence tasks in natural language processing are roughly
monotonic in the alignment between source and target sequence, and previous
work has facilitated or enforced learning of monotonic attention behavior via
specialized attention functions or pretraining. In this work, we introduce a
monotonicity loss function that is compatible with standard attention
mechanisms and test it on several sequence-to-sequence tasks:
grapheme-to-phoneme conversion, morphological inflection, transliteration, and
dialect normalization. Experiments show that we can achieve largely monotonic
behavior. Performance is mixed, with larger gains on top of RNN baselines.
General monotonicity does not benefit transformer multihead attention, however,
we see isolated improvements when only a subset of heads is biased towards
monotonic behavior.


Abstract:
  For hidden Markov models one of the most popular estimates of the hidden
chain is the Viterbi path -- the path maximising the posterior probability. We
consider a more general setting, called the pairwise Markov model (PMM), where
the joint process consisting of finite-state hidden process and observation
process is assumed to be a Markov chain. It has been recently proven that under
some conditions the Viterbi path of the PMM can almost surely be extended to
infinity, thereby defining the infinite Viterbi decoding of the observation
sequence, called the Viterbi process. This was done by constructing a block of
observations, called a barrier, which ensures that the Viterbi path goes trough
a given state whenever this block occurs in the observation sequence. In this
paper we prove that the joint process consisting of Viterbi process and PMM is
regenerative. The proof involves a delicate construction of regeneration times
which coincide with the occurrences of barriers. As one possible application of
our theory, some results on the asymptotics of the Viterbi training algorithm
are derived.