Abstract:
  Machine learning-based systems are rapidly gaining popularity and in-line
with that there has been a huge research surge in the field of explainability
to ensure that machine learning models are reliable, fair, and can be held
liable for their decision-making process. Explainable Artificial Intelligence
(XAI) methods are typically deployed to debug black-box machine learning models
but in comparison to tabular, text, and image data, explainability in time
series is still relatively unexplored. The aim of this study was to achieve and
evaluate model agnostic explainability in a time series forecasting problem.
This work focused on proving a solution for a digital consultancy company
aiming to find a data-driven approach in order to understand the effect of
their sales related activities on the sales deals closed. The solution involved
framing the problem as a time series forecasting problem to predict the sales
deals and the explainability was achieved using two novel model agnostic
explainability techniques, Local explainable model-agnostic explanations (LIME)
and Shapley additive explanations (SHAP) which were evaluated using human
evaluation of explainability. The results clearly indicate that the
explanations produced by LIME and SHAP greatly helped lay humans in
understanding the predictions made by the machine learning model. The presented
work can easily be extended to any time


Abstract:
  Single-image super-resolution (SR) and multi-frame SR are two ways to super
resolve low-resolution images. Single-Image SR generally handles each image
independently, but ignores the temporal information implied in continuing
frames. Multi-frame SR is able to model the temporal dependency via capturing
motion information. However, it relies on neighbouring frames which are not
always available in the real world. Meanwhile, slight camera shake easily
causes heavy motion blur on long-distance-shot low-resolution images. To
address these problems, a Blind Motion Deblurring Super-Reslution Networks,
BMDSRNet, is proposed to learn dynamic spatio-temporal information from single
static motion-blurred images. Motion-blurred images are the accumulation over
time during the exposure of cameras, while the proposed BMDSRNet learns the
reverse process and uses three-streams to learn Bidirectional spatio-temporal
information based on well designed reconstruction loss functions to recover
clean high-resolution images. Extensive experiments demonstrate that the
proposed BMDSRNet outperforms recent state-of-the-art methods, and has the
ability to simultaneously deal with image deblurring and SR.


Abstract:
  Analyzing political ideology and polarization is of critical importance in
advancing our understanding of the political context in society. Recent
research has made great strides towards understanding the ideological bias
(i.e., stance) of news media along a left-right spectrum. In this work, we take
a novel approach and study the ideology of the policy under discussion teasing
apart the nuanced co-existence of stance and ideology. Aligned with the
theoretical accounts in political science, we treat ideology as a
multi-dimensional construct, and introduce the first diachronic dataset of news
articles whose political ideology under discussion is annotated by trained
political scientists and linguists at the paragraph-level. We showcase that
this framework enables quantitative analysis of polarization, a temporal,
multifaceted measure of ideological distance. We further present baseline
models for ideology prediction.


Abstract:
  A single Majorana Kramers pair hosts only one component of the magnetic
multipole. This can be used to determine the bulk Cooper-pair symmetry through
surface-sensitive spectroscopic measurements, either by applying a magnetic
field or by using a ferromagnet/superconductor junction. This paper proposes
that the electric response, which is free from the Meissner effect, can be used
an alternative method to measure the bulk Cooper-pair symmetry in
time-reversal-invariant superconductors with double Majorana Kramers pairs. The
relationships among electric multipoles, strain tensors and superconducting
symmetries under a given wallpaper group on the surfaces of topological
crystalline superconductors are shown. This study also reveals that only a
specific irreducible representation of a uniform strain yields a gap in the
double Majorana Kramers pairs for the topological-crystalline-superconductor
candidate Sr$_3$SnO. This highlights the viability of electric detection
regarding Cooper-pair symmetry.


Abstract:
  The Higgs mode associated with amplitude fluctuations of the superconducting
gap in uniform superconductors usually is heavy, which makes its excitation and
detection difficult. We report on the existence of a gapless Higgs mode in the
Fulde-Ferrell-Larkin-Ovchinnikov states. This feature is originated from the
Goldstone mode associated with the translation symmetry breaking. The existence
of the gapless Higgs mode is demonstrated by using both a phenomenological
model and microscopic Bardeen-Cooper-Schrieffer (BCS) theory. The gapless Higgs
mode can avoid the decay into other low energy excitations, which renders it
stable and detectable.


Abstract:
  In this paper, we use a model of large random network game where the agents
plays selfishly and are affected by their neighbors, to explore the conditions
under which the Nash equilibrium (NE) of the game is affected by a perturbation
in the network. We use a phase transition phenomenon observed in finite rank
deformations of large random matrices, to study how the NE changes on crossing
critical threshold points. Our main contribution is as follows: when the
perturbation strength is greater than a critical point, it impacts the NE of
the game, whereas when this perturbation is below this critical point, the NE
remains independent of the perturbation parameter. This demonstrates a phase
transition in NE which alludes that perturbations can affect the behavior of
the society only if their strength is above a critical threshold. We provide
numerical examples for this result and present scenarios under which this
phenomenon could potentially occur in real world applications.


Abstract:
  Sequences and time-series often arise in robot tasks, e.g., in activity
recognition and imitation learning. In recent years, deep neural networks
(DNNs) have emerged as an effective data-driven methodology for processing
sequences given sufficient training data and compute resources. However, when
data is limited, simpler models such as logic/rule-based methods work
surprisingly well, especially when relevant prior knowledge is applied in their
construction. However, unlike DNNs, these "structured" models can be difficult
to extend, and do not work well with raw unstructured data. In this work, we
seek to learn flexible DNNs, yet leverage prior temporal knowledge when
available. Our approach is to embed symbolic knowledge expressed as linear
temporal logic (LTL) and use these embeddings to guide the training of deep
models. Specifically, we construct semantic-based embeddings of automata
generated from LTL formula via a Graph Neural Network. Experiments show that
these learnt embeddings can lead to improvements in downstream robot tasks such
as sequential action recognition and imitation learning.


Abstract:
  Stochastic gravitational wave backgrounds (SGWBs) receive increasing
attention and provide a new possibility to directly probe the early Universe.
In the preheating process at the end of inflation, parametric resonance can
generate large energy density perturbations and efficiently produce
gravitational waves (GWs) which carry unique information about inflation. Since
the peak frequency of such GWs is approximately proportional to the
inflationary energy scale, $\Lambda_{\mathrm{inf}}$, GWs from preheating are
expected to be observed by interferometer GW detectors in low-scale
inflationary models. We investigate the dependence of the amplitude of such GWs
on $\Lambda_{\mathrm{inf}}$, and find that the present energy spectrum of these
GWs does not depend on $\Lambda_{\mathrm{inf}}$ only in the case of
$\Lambda_{\mathrm{inf}}$ is above a critical value $\Lambda_{c}$, a parameter
depending on the resonance strength. We numerically obtain $\Lambda_{c}$ in
terms of the model parameters in linear approximation and then conduct lattice
simulations to verify this result. For
$\Lambda_{\mathrm{inf}}\lesssim\Lambda_{c}$, the amplitude of GWs quickly
decreases with $\Lambda_{\mathrm{inf}}$ and becomes challenging to observe. In
turn, observing such GWs in interferometer detectors also helps to determine
$\Lambda_{\mathrm{inf}}$ and the resonance strength during the preheating.


Abstract:
  We exhibit a non-hyperelliptic curve C of genus 3 such that the class of the
Ceresa cycle [C]-[-C] in the intermediate Jacobian of JC is torsion.


Abstract:
  Cascade prediction estimates the size or the state of a cascade from either
microscope or macroscope. It is of paramount importance for understanding the
information diffusion process such as the spread of rumors and the propagation
of new technologies in social networks. Recently, instead of extracting
hand-crafted features or embedding cascade sequences into feature vectors for
cascade prediction, graph neural networks (GNNs) are introduced to utilize the
network structure which governs the cascade effect. However, these models do
not take into account social factors such as personality traits which drive
human's participation in the information diffusion process. In this work, we
propose a novel multitask framework for enhancing cascade prediction with a
personality recognition task. Specially, we design a general plug-and-play GNN
gate, named PersonalityGate, to couple into existing GNN-based cascade
prediction models to enhance their effectiveness and extract individuals'
personality traits jointly. Experimental results on two real-world datasets
demonstrate the effectiveness of our proposed framework in enhancing GNN-based
cascade prediction models and in predicting individuals' personality traits as
well.


Abstract:
  We prove a new rigorous bound for the mean convective heat transport $\langle
w T \rangle$, where $w$ and $T$ are the nondimensional vertical velocity and
temperature, in internally heated convection between an insulating lower
boundary and an upper boundary with a fixed heat flux. The quantity $\langle wT
\rangle$ is equal to half the ratio of convective to conductive vertical heat
transport, and also to $\frac12$ plus the mean temperature difference between
the top and bottom boundaries. An analytical application of the background
method based on the construction of a quadratic auxiliary function yields
$\langle w T \rangle \leq \tfrac{1}{2}\big(\tfrac{1}{2}+ \tfrac{1}{\sqrt{3}}
\big) - 1.6552\, R^{-\frac13}$ uniformly in the Prandtl number, where $R$ is
the nondimensional control parameter measuring the strength of the internal
heating. Numerical optimisation of the auxiliary function suggests that the
asymptotic value of this bound and the $-1/3$ exponent are optimal within our
bounding framework. This new result halves the best existing (uniform in $R$)
bound (Goluskin 2016, Springer, Table 1.2) and its dependence on $R$ is
consistent with previous conjectures and heuristic scaling arguments. Contrary
to physical intuition, however, it does not rule out a mean heat transport
larger than $\frac12$ at high $R$, which corresponds to the top boundary being
hotter than the bottom one on average.


Abstract:
  Convolutional neural networks (CNN) have been frequently used to extract
subject-invariant features from electroencephalogram (EEG) for classification
tasks. This approach holds the underlying assumption that electrodes are
equidistant analogous to pixels of an image and hence fails to explore/exploit
the complex functional neural connectivity between different electrode sites.
We overcome this limitation by tailoring the concepts of convolution and
pooling applied to 2D grid-like inputs for the functional network of electrode
sites. Furthermore, we develop various graph neural network (GNN) models that
project electrodes onto the nodes of a graph, where the node features are
represented as EEG channel samples collected over a trial, and nodes can be
connected by weighted/unweighted edges according to a flexible policy
formulated by a neuroscientist. The empirical evaluations show that our
proposed GNN-based framework outperforms standard CNN classifiers across ErrP,
and RSVP datasets, as well as allowing neuroscientific interpretability and
explainability to deep learning methods tailored to EEG related classification
problems. Another practical advantage of our GNN-based framework is that it can
be used in EEG channel selection, which is critical for reducing computational
cost, and designing portable EEG headsets.


Abstract:
  We present a novel yet simple approach to produced multiple entangled photon
pairs through spontaneous parametric downconversion. We have developed Gaussian
masks to subdivide the pump beam before passing it through a nonlinear medium.
In this way, we are able to observe simultaneous separate down-converted
emission cones with spatial overlap. The technique we employ can be used to
greatly increase the dimensionality of entangled photonic systems generated
from spontaneous parametric down conversion, affording greater scalability to
optical quantum computing than previously explored.


Abstract:
  In this work, we investigate the asymptotic spectral density of the random
feature matrix $M = Y Y^\ast$ with $Y = f(WX)$ generated by a
single-hidden-layer neural network, where $W$ and $X$ are random rectangular
matrices with i.i.d. centred entries and $f$ is a non-linear smooth function
which is applied entry-wise. We prove that the Stieltjes transform of the
limiting spectral distribution approximately satisfies a quartic
self-consistent equation, which is exactly the equation obtained by
[Pennington, Worah] and [Benigni, P\'ech\'e] with the moment method. We extend
the previous results to the case of additive bias $Y=f(WX+B)$ with $B$ being an
independent rank-one Gaussian random matrix, closer modelling the neural
network infrastructures encountered in practice. Our key finding is that in the
case of additive bias it is impossible to choose an activation function
preserving the layer-to-layer singular value distribution, in sharp contrast to
the bias-free case where a simple integral constraint is sufficient to achieve
isospectrality. To obtain the asymptotics for the empirical spectral density we
follow the resolvent method from random matrix theory via the cumulant
expansion. We find that this approach is more robust and less combinatorial
than the moment method and expect that it will apply also for models where the
combinatorics of the former become intractable. The resolvent method has been
widely employed, but compared to previous works, it is applied here to
non-linear random matrices.


Abstract:
  We address inference for a partially observed nonlinear non-Gaussian latent
stochastic system comprised of interacting units. Each unit has a state, which
may be discrete or continuous, scalar or vector valued. In biological
applications, the state may represent a structured population or the abundances
of a collection of species at a single location. Units can have spatial
locations, allowing the description of spatially distributed interacting
populations arising in ecology, epidemiology and elsewhere. We consider models
where the collection of states is a latent Markov process, and a time series of
noisy or incomplete measurements is made on each unit. A model of this form is
called a spatiotemporal partially observed Markov process (SpatPOMP). The R
package spatPomp provides an environment for implementing SpatPOMP models,
analyzing data, and developing new inference approaches. We describe the
spatPomp implementations of some methods with scaling properties suited to
SpatPOMP models. We demonstrate the package on a simple Gaussian system and on
a nontrivial epidemiological model for measles transmission within and between
cities. We show how to construct user-specified SpatPOMP models within
spatPomp.


Abstract:
  This paper considers Bayesian revenue maximization in the $k$-unit setting,
where a monopolist seller has $k$ copies of an indivisible item and faces $n$
unit-demand buyers (whose value distributions can be non-identical). Four basic
mechanisms among others have been widely employed in practice and widely
studied in the literature: {\sf Myerson Auction}, {\sf Sequential
Posted-Pricing}, {\sf $(k + 1)$-th Price Auction with Anonymous Reserve}, and
{\sf Anonymous Pricing}. Regarding a pair of mechanisms, we investigate the
largest possible ratio between the two revenues (a.k.a.\ the revenue gap), over
all possible value distributions of the buyers.
  Divide these four mechanisms into two groups: (i)~the discriminating
mechanism group, {\sf Myerson Auction} and {\sf Sequential Posted-Pricing}, and
(ii)~the anonymous mechanism group, {\sf Anonymous Reserve} and {\sf Anonymous
Pricing}. Within one group, the involved two mechanisms have an asymptotically
tight revenue gap of $1 + \Theta(1 / \sqrt{k})$. In contrast, any two
mechanisms from the different groups have an asymptotically tight revenue gap
of $\Theta(\log k)$.


Abstract:
  We present the extension of a modeling technique for Lagrangian tracer
particles [B. Viggiano et al., J. Fluid Mech.(2020), vol. 900, A27] which
accounts for the effects of particle inertia. Thereby, the particle velocity
for several Stokes numbers is modeled directly by a multi-layered
Ornstein-Uhlenbeck process and a comparison of key statistical quantities
(second-order velocity structure function, acceleration correlation function,
and root mean square acceleration) to expressions derived from Batchelor's
model as well as to direct numerical simulations (DNS) is performed. In both
approaches, Stokes' drag is treated by an approximate ``linear filter'' which
replaces the particle position entering the fluid velocity field by the
corresponding ideal tracer position. Effects of preferential concentration of
inertial particles are taken into account in terms of an effective Stokes
number that is determined from the zero crossing of the acceleration
correlation function from DNS. This approximation thus allows the modeling of
inertial particle statistics through stochastic methods and models for the
Lagrangian velocity; the particle velocity is effectively decoupled from the
particle position. In contrast to the ordinary filtering technique [Cencini et
al., J. Turbul. (2006), 7, N36], our method captures the effects of
preferential concentration of particles at low Stokes numbers which manifest
themselves for instance by a sharp decrease of the acceleration variance for
increasing Stokes numbers.


Abstract:
  This paper addresses the challenge of understanding the waiting dependencies
between the threads and hardware resources required to complete a task. The
objective is to improve software performance by detecting the underlying
bottlenecks caused by system-level blocking dependencies. In this paper, we use
a system level tracing approach to extract a Waiting Dependency Graph that
shows the breakdown of a task execution among all the interleaving threads and
resources. The method allows developers and system administrators to quickly
discover how the total execution time is divided among its interacting threads
and resources. Ultimately, the method helps detecting bottlenecks and
highlighting their possible causes. Our experiments show the effectiveness of
the proposed approach in several industry-level use cases. Three performance
anomalies are analysed and explained using the proposed approach. Evaluating
the method efficiency reveals that the imposed overhead never exceeds 10.1%,
therefore making it suitable for in-production environments.


Abstract:
  In [6], D'Angelo introduced the notion of finite type for points $p$ of a
real hypersurface $M$ of $\mathbb C^n$ by defining the order of contact
$\Delta_q(M,p)$ of complex analytic $q$-dimensional varieties with $M$ at $p$.
Later, Catlin [4] defined $q$-type, $D_q(M,p)$ for points of hypersurfaces by
considering generic $(n-q+1)$-dimensional complex affine subspaces of $\mathbb
C^n$. We define a generalization of the Catlin's $q$-type for an arbitrary
subset $M$ of $\mathbb C^n$ in a similar way that D'Angelo's 1-type,
$\Delta_1(M,p)$, is generalized in [13]. Using recent results connecting the
D'Angelo and Catlin $q$-types in [1] and building on D'Angelo's work on the
openness of the set of points of finite $\Delta_q$-type, we prove the openness
of the set of points of finite Catlin $q$-type for an arbitrary subset
$M\subset \mathbb C^n$.


Abstract:
  Manipulation of light-induced magnetization has become a fundamentally hot
topic with a potentially high impact for atom trapping, confocal and magnetic
resonance microscopy, and data storage. The control of the magnetization
orientation mainly relies on the direct methods composed of amplitude, phase
and polarization modulations of the incident light under the tight focusing
condition, leaving the achievement of arbitrary desirable three-dimensional
(3D) magnetization orientation complicated, inflexible and inefficient. Here,
we propose a facile approach called machine learning inverse design to achieve
expected vectorial opto-magnetization orientation. This pathway is
time-efficient and accurate to produce the demanded incident beam for arbitrary
prescribed 3D magnetization orientation. It is highlighted that the machine
learning method is not only applied for magnetization orientations, but also
widely used in the control of magnetization structures.


Abstract:
  Measurement-based quantum correlation mimics several characteristics of
multipartite quantum correlations and at the same time, it reduces the parent
system to a smaller subsystem. On the other hand, genuine multipartite
entanglement measures can capture certain features of a multisite composite
system that are inaccessible via bipartite quantum correlation quantifiers. We
merge these two concepts by introducing localizable genuine multimode
entanglement for continuous variable systems, both for Gaussian and
non-Gaussian multimode parent states. We report a compact form of localizable
generalized geometric measure for multimode Gaussian states when Gaussian
measurements are performed in some of the modes. We show that non-Gaussian
measurements can concentrate more genuine multimode entanglement compared to
the Gaussian ones. For non-Gaussian states with non-Gaussian measurements, we
find that although four-mode squeezed vacuum state has permutation symmetry
with respect to the exchange of first and third modes as well as the second and
the fourth modes, the symmetry can be broken by performing measurements in one
of the modes in case of addition while for subtraction, such symmetry is
preserved, thereby providing a method for distinguishing multimode photon-added
and -subtracted states via localizations.


Abstract:
  Quantum sensors offer the capability to reach unprecedented precision by
operating at the standard quantum limit (SQL) or beyond by using quantum
entanglement. But an emerging class of quantum sensors that use Rydberg
electromagnetically-induced transparency (EIT) to detect rf electric fields
have yet to reach the SQL. In this work we prove that this discrepancy is due
to fundamental limitations in the EIT probing mechanism. We derive the optimum
sensitivity of a three-level quantum sensor based on EIT, or more generally
coherent spectroscopy, and compare this to the SQL. We apply a minimal set of
assumptions, while allowing strong probing fields, thermal broadening, and
large optical depth. We derive the optimal laser intensities and optical depth,
providing specific guidelines for sensitive operation under common experimental
conditions. Clear boundaries of performance are established, revealing that
ladder-EIT can not achieve the SQL due to unavoidable absorption loss. The
results may be applied to any EIT-based quantum sensor, but we particularly
emphasize our results' importance to the growing field of Rydberg quantum
sensing.


Abstract:
  Point cloud semantic segmentation is a crucial task in 3D scene
understanding. Existing methods mainly focus on employing a large number of
annotated labels for supervised semantic segmentation. Nonetheless, manually
labeling such large point clouds for the supervised segmentation task is
time-consuming. In order to reduce the number of annotated labels, we propose a
semi-supervised semantic point cloud segmentation network, named SSPC-Net,
where we train the semantic segmentation network by inferring the labels of
unlabeled points from the few annotated 3D points. In our method, we first
partition the whole point cloud into superpoints and build superpoint graphs to
mine the long-range dependencies in point clouds. Based on the constructed
superpoint graph, we then develop a dynamic label propagation method to
generate the pseudo labels for the unsupervised superpoints. Particularly, we
adopt a superpoint dropout strategy to dynamically select the generated pseudo
labels. In order to fully exploit the generated pseudo labels of the
unsupervised superpoints, we furthermore propose a coupled attention mechanism
for superpoint feature embedding. Finally, we employ the cross-entropy loss to
train the semantic segmentation network with the labels of the supervised
superpoints and the pseudo labels of the unsupervised superpoints. Experiments
on various datasets demonstrate that our semi-supervised segmentation method
can achieve better performance than the current semi-supervised segmentation
method with fewer annotated 3D points. Our code is available at
https://github.com/MMCheng/SSPC-Net.


Abstract:
  We study the Auslander ring of a basic left K\"othe ring $\Lambda$ and give a
characterization of basic left K\"othe rings in terms of their Auslander rings.
We also study the functor category Mod$((\Lambda$-mod$)^{\rm op})$ and
characterize basic left K\"othe rings $\Lambda$ by using functor categories
Mod$((\Lambda$-mod$)^{\rm op})$. As a consequence we show that there exists a
bijection between the Morita equivalence classes of left Kawada rings and the
Morita equivalence classes of Auslander generalized right QF-2 rings.


Abstract:
  Many tasks in robot-assisted surgery require planning and controlling
manipulators' motions that interact with highly deformable objects. This study
proposes a realistic, time-bounded simulator based on Position-based Dynamics
(PBD) simulation that mocks brain deformations due to catheter insertion for
pre-operative path planning and intra-operative guidance in keyhole surgical
procedures. It maximizes the probability of success by accounting for
uncertainty in deformation models, noisy sensing, and unpredictable actuation.
The PBD deformation parameters were initialized on a parallelepiped-shaped
simulated phantom to obtain a reasonable starting guess for the brain white
matter. They were calibrated by comparing the obtained displacements with
deformation data for catheter insertion in a composite hydrogel phantom.
Knowing the gray matter brain structures' different behaviors, the parameters
were fine-tuned to obtain a generalized human brain model. The brain
structures' average displacement was compared with values in the literature.
The simulator's numerical model uses a novel approach with respect to the
literature, and it has proved to be a close match with real brain deformations
through validation using recorded deformation data of in-vivo animal trials
with a mean mismatch of 4.73$\pm$2.15%. The stability, accuracy, and real-time
performance make this model suitable for creating a dynamic environment for KN
path planning, pre-operative path planning, and intra-operative guidance.


Abstract:
  Neural dialogue generation models trained with the one-hot target
distribution suffer from the over-confidence issue, which leads to poor
generation diversity as widely reported in the literature. Although existing
approaches such as label smoothing can alleviate this issue, they fail to adapt
to diverse dialog contexts. In this paper, we propose an Adaptive Label
Smoothing (AdaLabel) approach that can adaptively estimate a target label
distribution at each time step for different contexts. The maximum probability
in the predicted distribution is used to modify the soft target distribution
produced by a novel light-weight bi-directional decoder module. The resulting
target distribution is aware of both previous and future contexts and is
adjusted to avoid over-training the dialogue model. Our model can be trained in
an end-to-end manner. Extensive experiments on two benchmark datasets show that
our approach outperforms various competitive baselines in producing diverse
responses.


Abstract:
  The contact geometric structure of the thermodynamic phase space is used to
introduce a novel symplectic structure on the tangent bundle of the equilibrium
space. Moreover, it turns out that the equilibrium space can be interpreted as
a Lagrange submanifold of the corresponding tangent bundle, if the fundamental
equation is known explicitly. As a consequence, the Hamiltonian description of
thermodynamics is obtained in a natural way and a Hamilton-Jacobi-like equation
is introduced whose solutions determine entire families of thermodynamic
systems.


Abstract:
  We study the prospects of future gravitational wave (GW) detectors in probing
primordial black hole (PBH) binaries. We show that across a broad mass range
from $10^{-5}M_\odot$ to $10^7M_\odot$, future GW interferometers provide a
potential probe of the PBH abundance that is more sensitive than any currently
existing experiment. In particular, we find that galactic PBH binaries with
masses as low as $10^{-5}M_\odot$ may be probed with ET, AEDGE and LISA by
searching for nearly monochromatic continuous GW signals. Such searches could
independently test the PBH interpretation of the ultrashort microlensing events
observed by OGLE. We also consider the possibility of observing GWs from
asteroid mass PBH binaries through graviton-photon conversion.


Abstract:
  Let $K$ be a field and let $S=K[x_1,\dots,x_n]$ be a standard polynomial ring
over a field $K$. We characterize the extremal Betti numbers, values as well
positions, of a $t$-spread strongly stable ideal of $S$. Our approach is
constructive. Indeed, given some positive integers $a_1,\dots,a_r$ and some
pairs of positive integers $(k_1,\ell_1),\dots,(k_r,\ell_r)$, we are able to
determine under which conditions there exist a $t$-spread strongly stable ideal
$I$ of $S$ with $\beta_{k_i, k_i\ell_i}(I)=a_i$, $i=1, \ldots, r$, as extremal
Betti numbers, and then to construct it.


Abstract:
  Inferential (or soft) sensors are used in industry to infer the values of
imprecisely and rarely measured (or completely unmeasured) variables from
variables measured online (e.g., pressures, temperatures). The main challenge,
akin to classical model overfitting, in designing an effective inferential
sensor is the selection of a correct structure of the sensor. The sensor
structure is represented by the number of inputs to the sensor, which
correspond to the variables measured online and their (simple) combinations.
This work is focused on the design of inferential sensors for product
composition of an industrial distillation column in two oil refinery units, a
Fluid Catalytic Cracking unit and a Vacuum Gasoil Hydrogenation unit. As the
first design step, we use several well-known data pre-treatment (gross error
detection) methods and compare the ability of these approaches to indicate
systematic errors and outliers in the available industrial data. We then study
effectiveness of various methods for design of the inferential sensors taking
into account the complexity and accuracy of the resulting model. The
effectiveness analysis indicates that the improvements achieved over the
current inferential sensors are up to 19 %.


Abstract:
  How can we identify similar repositories and clusters among a large online
archive, such as GitHub? Determiningrepository similarity is an essential
building block in studying the dynamics and the evolution of such software
ecosystems. The key challenge is to determine the right representation for the
diverse repository features in a way that: (a) it captures all aspects of the
available information, and (b) it is readily usable by MLalgorithms. We propose
Repo2Vec, a comprehensive embedding approach to represent a repository as a
distributed vector by combining features from three types of information
sources. As our key novelty, we consider three types of information:
(a)metadata, (b) the structure of the repository, and (c) the source code. We
also introduce a series of embedding approaches to represent and combine these
information types into a single embedding. We evaluate our method with two real
datasets from GitHub for a combined 1013 repositories. First, we show that our
method outperforms previous methods in terms of precision (93%vs 78%), with
nearly twice as many Strongly Similar repositories and 30% fewer False
Positives. Second, we show how Repo2Vecprovides a solid basis for: (a)
distinguishing between malware and benign repositories, and (b) identifying a
meaningful hierarchical clustering. For example, we achieve 98% precision and
96%recall in distinguishing malware and benign repositories. Overall, our work
is a fundamental building block for enabling many repository analysis functions
such as repository categorization by target platform or intention, detecting
code-reuse and clones, and identifying lineage and evolution.


Abstract:
  Sponsored search ads appear next to search results when people look for
products and services on search engines. In recent years, they have become one
of the most lucrative channels for marketing. As the fundamental basis of
search ads, relevance modeling has attracted increasing attention due to the
significant research challenges and tremendous practical value. Most existing
approaches solely rely on the semantic information in the input query-ad pair,
while the pure semantic information in the short ads data is not sufficient to
fully identify user's search intents. Our motivation lies in incorporating the
tremendous amount of unsupervised user behavior data from the historical search
logs as the complementary graph to facilitate relevance modeling. In this
paper, we extensively investigate how to naturally fuse the semantic textual
information with the user behavior graph, and further propose three novel
AdsGNN models to aggregate topological neighborhood from the perspectives of
nodes, edges and tokens. Furthermore, two critical but rarely investigated
problems, domain-specific pre-training and long-tail ads matching, are studied
thoroughly. Empirically, we evaluate the AdsGNN models over the large industry
dataset, and the experimental results of online/offline tests consistently
demonstrate the superiority of our proposal.


Abstract:
  This paper highlights the trends in the field of predictive maintenance with
the use of machine learning. With the continuous development of the Fourth
Industrial Revolution, through IoT, the technologies that use artificial
intelligence are evolving. As a result, industries have been using these
technologies to optimize their production. Through scientific research
conducted for this paper, conclusions were drawn about the trends in Predictive
Maintenance applications with the use of machine learning bridging Artificial
Intelligence and IoT. These trends are related to the types of industries in
which Predictive Maintenance was applied, the models of artificial intelligence
were implemented, mainly of machine learning and the types of sensors that are
applied through the IoT to the applications. Six sectors were presented and the
production sector was dominant as it accounted for 54.54% of total
publications. In terms of artificial intelligence models, the most prevalent
among ten were the Artificial Neural Networks, Support Vector Machine and
Random Forest with 27.84%, 17.72% and 13.92% respectively. Finally, twelve
categories of sensors emerged, of which the most widely used were the sensors
of temperature and vibration with percentages of 60.71% and 46.42%
correspondingly.


Abstract:
  Reward decomposition is a critical problem in centralized training with
decentralized execution~(CTDE) paradigm for multi-agent reinforcement learning.
To take full advantage of global information, which exploits the states from
all agents and the related environment for decomposing Q values into individual
credits, we propose a general meta-learning-based Mixing Network with Meta
Policy Gradient~(MNMPG) framework to distill the global hierarchy for delicate
reward decomposition. The excitation signal for learning global hierarchy is
deduced from the episode reward difference between before and after "exercise
updates" through the utility network. Our method is generally applicable to the
CTDE method using a monotonic mixing network. Experiments on the StarCraft II
micromanagement benchmark demonstrate that our method just with a simple
utility network is able to outperform the current state-of-the-art MARL
algorithms on 4 of 5 super hard scenarios. Better performance can be further
achieved when combined with a role-based utility network.


Abstract:
  The high-frequency part of spectrum of electromagnetic waves propagating
parallel to the external magnetic field is considered for the macroscopically
motionless plasmas with the relativistic temperatures $T\sim m_{e}c^{2}$, where
$m_{e}$ is the mass of electron, $c$ is the speed of light. The analysis is
based on the novel hydrodynamic model based on four equations for the material
fields which can be combined in two four vectors. These material fields are the
concentration and the velocity field \emph{and} the average reverse
relativistic $\gamma$ functor and the flux of the reverse relativistic $\gamma$
functor. In the nonrelativistic regime we have three waves (the ions are
assumed to be motionless). Strong thermal effects lead to a coefficient in
front of cyclotron frequency which decreases the effective contribution of the
cyclotron frequency. At $T=0.1m_{e}c^{2}$ we have a decrease of area of
existence of fast magneto-sound wave from the area of the large frequencies.
While the area of existence of extraordinary waves becomes larger towards
smaller frequencies. The strong magnetic field limit $\mid\Omega_{e}\mid >
\omega_{Le}$ additional wave appears with frequency below thermally decreased
cyclotron frequency, where $\mid\Omega_{e}\mid$ is the electron cyclotron
frequency, and $\omega_{Le}$ is the Langmuir frequency. Further increase of
temperature leads to the disappearance of fast magneto-sound wave and to the
considerable increase of area of existence of extraordinary towards smaller
frequencies.


Abstract:
  Recently, spatiotemporal optical vortex pulses carrying a purely transverse
intrinsic orbital angular momentum were generated experimentally [{\it Optica}
{\bf 6}, 1547 (2019); {\it Nat. Photon.} {\bf 14}, 350 (2020)]. However, an
accurate theoretical analysis of such states and their angular-momentum
properties remains elusive. Here we provide such analysis, including scalar and
vector spatiotemporal Bessel-type solutions as well as descrption of their
propagational, polarization, and angular-momentum properties. Most importantly,
we calculate both local densities and integral values of the spin and orbital
angular momenta, and predict observable spin-orbit interaction phenomena
related to the coupling between the trasnverse spin and orbital angular
momentum. Our analysis is readily extended to spatiotemporal vortex pulses of
other natures (e.g., acoustic).


Abstract:
  Story visualization is an under-explored task that falls at the intersection
of many important research directions in both computer vision and natural
language processing. In this task, given a series of natural language captions
which compose a story, an agent must generate a sequence of images that
correspond to the captions. Prior work has introduced recurrent generative
models which outperform text-to-image synthesis models on this task. However,
there is room for improvement of generated images in terms of visual quality,
coherence and relevance. We present a number of improvements to prior modeling
approaches, including (1) the addition of a dual learning framework that
utilizes video captioning to reinforce the semantic alignment between the story
and generated images, (2) a copy-transform mechanism for
sequentially-consistent story visualization, and (3) MART-based transformers to
model complex interactions between frames. We present ablation studies to
demonstrate the effect of each of these techniques on the generative power of
the model for both individual images as well as the entire narrative.
Furthermore, due to the complexity and generative nature of the task, standard
evaluation metrics do not accurately reflect performance. Therefore, we also
provide an exploration of evaluation metrics for the model, focused on aspects
of the generated frames such as the presence/quality of generated characters,
the relevance to captions, and the diversity of the generated images. We also
present correlation experiments of our proposed automated metrics with human
evaluations. Code and data available at:
https://github.com/adymaharana/StoryViz


Abstract:
  Let $M$ be a topological space that admits a free involution $\tau$, and let
$N$ be a topological space. A homotopy class $\beta \in [ M,N ]$ is said to
have the Borsuk-Ulam property with respect to $\tau$ if for every
representative map $f: M \to N$ of $\beta$, there exists a point $x \in M$ such
that $f(\tau(x))= f(x)$. In this paper, we determine the homotopy class of maps
from the $2$-torus $T^2$ to the Klein bottle $K^2$ that possess the Borsuk-Ulam
property with respect to any free involution of $T^2$ for which the orbit space
is $K^2$. Our results are given in terms of a certain family of homomorphisms
involving the fundamental groups of $T^2$ and $K^2$. This completes the
analysis of the Borsuk-Ulam problem for the case $M=T^2$ and $N=K^2$, and for
any free involution $\tau$ of $T^2$.


Abstract:
  We explore the cosmological viability of a theory of gravity defined by the
Lagrangian $f(\mathcal{R})=\mathcal{R}^{n\left(\mathcal{R}\right)}$ in the
Palatini formalism, where $n\left(\mathcal{R}\right)$ is a dimensionless
function of the Palatini scalar curvature $\mathcal{R}$ that interpolates
between general relativity when $n\left(\mathcal{R}\right)=1$ and a locally
scale-invariant and superficially renormalizable theory when
$n\left(\mathcal{R}\right)=2$. We refer to this model as asymptotically
Weyl-invariant gravity (AWIG).
  We analyse perhaps the simplest possible implementation of AWIG. A phase
space analysis yields three fixed points with effective equation of states
corresponding to de Sitter, radiation and matter-dominated phases. An analysis
of the deceleration parameter suggests our model is consistent with an early
and late period of accelerated cosmic expansion, with an intermediate period of
decelerated expansion. We show that the model contains no obvious curvature
singularities. Therefore, AWIG appears to be cosmologically viable, at least
for the simple implementation explored.


Abstract:
  Generating personalized responses is one of the major challenges in natural
human-robot interaction. Current researches in this field mainly focus on
generating responses consistent with the robot's pre-assigned persona, while
ignoring the user's persona. Such responses may be inappropriate or even
offensive, which may lead to the bad user experience. Therefore, we propose a
Bilateral Personalized Dialogue Generation (BPDG) method for dyadic
conversation, which integrates user and robot personas into dialogue generation
via designing a dynamic persona-aware fusion method. To bridge the gap between
the learning objective function and evaluation metrics, the Conditional Mutual
Information Maximum (CMIM) criterion is adopted with contrastive learning to
select the proper response from the generated candidates. Moreover, a bilateral
persona accuracy metric is designed to measure the degree of bilateral
personalization. Experimental results demonstrate that, compared with several
state-of-the-art methods, the final results of the proposed method are more
personalized and consistent with bilateral personas in terms of both automatic
and manual evaluations.


Abstract:
  Multi-Pixel Photon Counter (MPPC) has been recently emerged and realized as a
great type of Silicon Photomultiplier to replace or compensate for the
conventional vacuum-based Photomultiplier tubes. MPPC provides many striking
features such as high electrical gain, outstanding photon detection efficiency,
fast timing response, immunity to the magnetic field, low-voltage operation,
compactness, portability, and cost-effectiveness. The report introduces and
examines the electrical and optical characteristics of the MPPC under loosely
controlled environmental conditions. Also, we report a measurement of the light
yield captured by the MPPC when the cosmic ray passes through the plastic
scintillator, demonstrating that such setup is suitable to build a simple,
cost-effective tabletop cosmic-ray detector for educational and research
purposes.


Abstract:
  Along with the development of modern computing technology and social
sciences, both theoretical research and practical applications of social
computing have been continuously extended. In particular with the boom of
artificial intelligence (AI), social computing is significantly influenced by
AI. However, the conventional technologies of AI have drawbacks in dealing with
more complicated and dynamic problems. Such deficiency can be rectified by
hybrid human-artificial intelligence (H-AI) which integrates both human
intelligence and AI into one unity, forming a new enhanced intelligence. H-AI
in dealing with social problems shows the advantages that AI can not surpass.
This paper firstly introduces the concept of H-AI. AI is the intelligence in
the transition stage of H-AI, so the latest research progresses of AI in social
computing are reviewed. Secondly, it summarizes typical challenges faced by AI
in social computing, and makes it possible to introduce H-AI to solve these
challenges. Finally, the paper proposes a holistic framework of social
computing combining with H-AI, which consists of four layers: object layer,
base layer, analysis layer, and application layer. It represents H-AI has
significant advantages over AI in solving social problems.


Abstract:
  The compressive learning framework reduces the computational cost of training
on large-scale datasets. In a sketching phase, the data is first compressed to
a lightweight sketch vector, obtained by mapping the data samples through a
well-chosen feature map, and averaging those contributions. In a learning
phase, the desired model parameters are then extracted from this sketch by
solving an optimization problem, which also involves a feature map. When the
feature map is identical during the sketching and learning phases, formal
statistical guarantees (excess risk bounds) have been proven.
  However, the desirable properties of the feature map are different during
sketching and learning (e.g. quantized outputs, and differentiability,
respectively). We thus study the relaxation where this map is allowed to be
different for each phase. First, we prove that the existing guarantees carry
over to this asymmetric scheme, up to a controlled error term, provided some
Limited Projected Distortion (LPD) property holds. We then instantiate this
framework to the setting of quantized sketches, by proving that the LPD indeed
holds for binary sketch contributions. Finally, we further validate the
approach with numerical simulations, including a large-scale application in
audio event classification.


Abstract:
  Recently, we proposed a novel speaker diarization method called
End-to-End-Neural-Diarization-vector clustering (EEND-vector clustering) that
integrates clustering-based and end-to-end neural network-based diarization
approaches into one framework. The proposed method combines advantages of both
frameworks, i.e. high diarization performance and handling of overlapped speech
based on EEND, and robust handling of long recordings with an arbitrary number
of speakers based on clustering-based approaches. However, the method was only
evaluated so far on simulated 2-speaker meeting-like data. This paper is to (1)
report recent advances we made to this framework, including newly introduced
robust constrained clustering algorithms, and (2) experimentally show that the
method can now significantly outperform competitive diarization methods such as
Encoder-Decoder Attractor (EDA)-EEND, on CALLHOME data which comprises real
conversational speech data including overlapped speech and an arbitrary number
of speakers. By further analyzing the experimental results, this paper also
discusses pros and cons of the proposed method and reveals potential for
further improvement. A set of the code to reproduce the results is available at
https://github.com/nttcslab-sp/EEND-vector-clustering.


Abstract:
  Regular sequences generalize the extensively studied automatic sequences. Let
$S$ be an abstract numeration system. When the numeration language $L$ is
prefix-closed and regular, a sequence is said to be $S$-regular if the module
generated by its $S$-kernel is finitely generated.
  In this paper, we give a new characterization of such sequences in terms of
the underlying numeration tree $T(L)$ whose nodes are words of $L$. We may
decorate these nodes by the sequence of interest following a breadth-first
enumeration. For a prefix-closed regular language $L$, we prove that a sequence
is $S$-regular if and only if the tree $T(L)$ decorated by the sequence is
linear, i.e., the decoration of a node depends linearly on the decorations of a
fixed number of ancestors.
  Next, we introduce and study regular sequences in a rational base numeration
system, whose numeration language is known to be highly non-regular. We
motivate and comment our definition that a sequence is $\frac{p}{q}$-regular if
the underlying numeration tree decorated by the sequence is linear. We give the
first few properties of such sequences, we provide a few examples of them, and
we propose a method for guessing $\frac{p}{q}$-regularity. Then we discuss the
relationship between $\frac{p}{q}$-automatic sequences and
$\frac{p}{q}$-regular sequences. We finally present a graph directed linear
representation of a $\frac{p}{q}$-regular sequence. Our study permits us to
highlight the places where the regularity of the numeration language plays a
predominant role.


Abstract:
  Mason and Remmel introduced a basis for quasisymmetric functions known as the
row-strict quasisymmetric Schur functions. This basis is generated
combinatorially by fillings of composition diagrams that are analogous to the
row-strict tableaux that generate Schur functions. We introduce a modification
known as Young row-strict quasisymmetric Schur functions, which are generated
by row-strict Young composition fillings. After discussing basic combinatorial
properties of these functions, we define a skew Young row-strict quasisymmetric
Schur function using the Hopf algebra of quasisymmetric functions and then
prove this is equivalent to a combinatorial description. We also provide a
decomposition of the skew Young row-strict quasisymmetric Schur functions into
a sum of Gessel's fundamental quasisymmetric functions and prove a
multiplication rule for the product of a Young row-strict quasisymmetric Schur
function and a Schur function.


Abstract:
  The paper is devoted to the analysis of relationships between principal
objects of the spectral theory of dynamical systems (transfer and weighted
shift operators) and basic characteristics of information theory and
thermodynamic formalism (entropy and topological pressure). We present explicit
formulae linking these objects with $t$-entropy and spectral potential.
Herewith we uncover the role of inverse rami-rate, forward entropy along with
essential set and the property of non-contractibility of a dynamical system.


Abstract:
  We reconstruct the history of reionization using Gaussian process regression.
Using the UV luminosity data compilation from Hubble Frontiers Fields we
reconstruct the redshift evolution of UV luminosity density and thereby the
evolution of the source term in the ionization equation. This model-independent
reconstruction rules out single power-law evolution of the luminosity density
but supports the logarithmic double power-law parametrization. We obtain
reionization history by integrating ionization equations with the reconstructed
source term. Using optical depth constraint from Planck Cosmic Microwave
Background observation, measurement of UV luminosity function integrated till
truncation magnitude of -17 and -15, and derived ionization fraction from high
redshift quasar, galaxies and gamma-ray burst observations, we constrain the
history of reionization. In the conservative case we find the constraint on the
optical depth as $\tau =0.052\pm0.001\pm0.002$ at 68% and 95% confidence
intervals. We find the redshift duration between 10% and 90% ionization to be
$2.05_{-0.21-0.30}^{+0.11+0.37}$. Longer duration of reionization is supported
if UV luminosity density data with truncation magnitude of -15 is used in the
joint analysis. Our results point out that even in a conservative
reconstruction, a combination of cosmological and astrophysical observations
can provide stringent constraints on the epoch of reionization.


Abstract:
  The meme stock phenomenon is yet to be explored. In this note, we provide
evidence that these stocks display common stylized facts on the dynamics of
price, trading volume, and social media activity. Using a regime-switching
cointegration model, we identify the meme stock "mementum" which exhibits a
different characterization with respect to other stocks with high volumes of
activity (persistent and not) on social media. Understanding these properties
helps the investors and market authorities in their decision.


Abstract:
  Existing speech enhancement methods mainly separate speech from noises at the
signal level or in the time-frequency domain. They seldom pay attention to the
semantic information of a corrupted signal. In this paper, we aim to bridge
this gap by extracting phoneme identities to help speech enhancement.
Specifically, we propose a phoneme-based distribution regularization (PbDr) for
speech enhancement, which incorporates frame-wise phoneme information into
speech enhancement network in a conditional manner. As different phonemes
always lead to different feature distributions in frequency, we propose to
learn a parameter pair, i.e. scale and bias, through a phoneme classification
vector to modulate the speech enhancement network. The modulation parameter
pair includes not only frame-wise but also frequency-wise conditions, which
effectively map features to phoneme-related distributions. In this way, we
explicitly regularize speech enhancement features by recognition vectors.
Experiments on public datasets demonstrate that the proposed PbDr module can
not only boost the perceptual quality for speech enhancement but also the
recognition accuracy of an ASR system on the enhanced speech. This PbDr module
could be readily incorporated into other speech enhancement networks as well.


Abstract:
  In this paper, we introduce a new distribution called Burr III-Weibull(BW)
distribution using the concept of competing risk. We derive moments,
conditional moments, mean deviation and quantiles of the proposed distribution.
Also the Renyi's entropy and order statistics of the distribution are obtained.
Estimation of parameters of the distribution is performed via maximum
likelihood method. A simulation study is performed to validate the maximum
likelihood estimator (MLE). A real practical data set is analyzed for
illustration.


Abstract:
  In this paper, we consider the problem of optimally allocating tasks,
expressed as global Linear Temporal Logic (LTL) specifications, to teams of
heterogeneous mobile robots. The robots are classified in different types that
capture their different capabilities, and each task may require robots of
multiple types. The specific robots assigned to each task are immaterial, as
long as they are of the desired type. Given a discrete workspace, our goal is
to design paths, i.e., sequences of discrete states, for the robots so that the
LTL specification is satisfied. To obtain a scalable solution to this complex
temporal logic task allocation problem, we propose a hierarchical approach that
first allocates specific robots to tasks using the information about the tasks
contained in the Nondeterministic Buchi Automaton (NBA) that captures the LTL
specification, and then designs low-level executable plans for the robots that
respect the high-level assignment. Specifically, we first prune and relax the
NBA by removing all negative atomic propositions. This step is motivated by
"lazy collision checking" methods in robotics and allows to simplify the
planning problem by checking constraint satisfaction only when needed. Then, we
extract sequences of subtasks from the relaxed NBA along with their temporal
orders, and formulate a Mixed Integer Linear Program (MILP) to allocate these
subtasks to the robots. Finally, we define generalized multi-robot path
planning problems to obtain low-level executable robot plans that satisfy both
the high-level task allocation and the temporal constraints captured by the
negative atomic propositions in the original NBA. We provide theoretical
results showing completeness and soundness of our proposed method and present
numerical simulations demonstrating that our method can generate robot paths
with lower cost, considerably faster than existing methods.


Abstract:
  Wikipedia is one of the main repositories of free knowledge available today,
with a central role in the Web ecosystem. For this reason, it can also be a
battleground for actors trying to impose specific points of view or even
spreading disinformation online. There is a growing need to monitor its
"health" but this is not an easy task. Wikipedia exists in over 300 language
editions and each project is maintained by a different community, with their
own strengths, weaknesses and limitations. In this paper, we introduce a
taxonomy of knowledge integrity risks across Wikipedia projects and a first set
of indicators to assess internal risks related to community and content issues,
as well as external threats such as the geopolitical and media landscape. On
top of this taxonomy, we offer a preliminary analysis illustrating how the lack
of editors' geographical diversity might represent a knowledge integrity risk.
These are the first steps of a research project to build a Wikipedia Knowledge
Integrity Risk Observatory.


Abstract:
  Keyphrase generation aims to summarize long documents with a collection of
salient phrases. Deep neural models have demonstrated a remarkable success in
this task, capable of predicting keyphrases that are even absent from a
document. However, such abstractiveness is acquired at the expense of a
substantial amount of annotated data. In this paper, we present a novel method
for keyphrase generation, AutoKeyGen, without the supervision of any human
annotation. Motivated by the observation that an absent keyphrase in one
document can appear in other places, in whole or in part, we first construct a
phrase bank by pooling all phrases in a corpus. With this phrase bank, we then
draw candidate absent keyphrases for each document through a partial matching
process. To rank both types of candidates, we combine their lexical- and
semantic-level similarities to the input document. Moreover, we utilize these
top-ranked candidates as to train a deep generative model for more absent
keyphrases. Extensive experiments demonstrate that AutoKeyGen outperforms all
unsupervised baselines and can even beat strong supervised methods in certain
cases.


Abstract:
  Although Convolutional Neural Networks (CNNs) are widely used, their
translation invariance (ability to deal with translated inputs) is still
subject to some controversy. We explore this question using
translation-sensitivity maps to quantify how sensitive a standard CNN is to a
translated input. We propose the use of Cosine Similarity as sensitivity metric
over Euclidean Distance, and discuss the importance of restricting the
dimensionality of either of these metrics when comparing architectures. Our
main focus is to investigate the effect of different architectural components
of a standard CNN on that network's sensitivity to translation. By varying
convolutional kernel sizes and amounts of zero padding, we control the size of
the feature maps produced, allowing us to quantify the extent to which these
elements influence translation invariance. We also measure translation
invariance at different locations within the CNN to determine the extent to
which convolutional and fully connected layers, respectively, contribute to the
translation invariance of a CNN as a whole. Our analysis indicates that both
convolutional kernel size and feature map size have a systematic influence on
translation invariance. We also see that convolutional layers contribute less
than expected to translation invariance, when not specifically forced to do so.


Abstract:
  We propose and investigate thermometry of a setup employing dual quantum dots
with staircase ground state configuration. The stair-case ground state
configuration actuates thermally controlled inelastic tunnelling, which
translates into a temperature sensitive conductance, thereby inducing
thermometry. The performance of the set-up is then analyzed employing quantum
master equation (QME) for such systems in the sequential tunnelling regime. In
particular, it is demonstrated that the system performance, in terms of
temperature sensitivity and efficiency, is maximum in the regime of low
temperature, making such system suitable for cryogenic thermometry. The
proposed set-up can pave the path towards realization of high performance
cryogenic nano temperature sensors.


Abstract:
  Global coordination is required to solve a wide variety of challenging
collective action problems from network colorings to the tragedy of the
commons. Recent empirical study shows that the presence of a few noisy
autonomous agents can greatly improve collective performance of humans in
solving networked color coordination games. To provide further analytical
insights into the role of behavioral randomness, here we study myopic
artificial agents attempt to solve similar network coloring problems using
decision update rules that are only based on local information but allow random
choices at various stages of their heuristic reasonings. We consider that
agents are distributed over a random bipartite network which is guaranteed to
be solvable with two colors. Using agent-based simulations and theoretical
analysis, we show that the resulting efficacy of resolving color conflicts is
dependent on the specific implementation of random behavior of agents,
including the fraction of noisy agents and at which decision stage noise is
introduced. Moreover, behavioral randomness can be finely tuned to the specific
underlying population structure such as network size and average network degree
in order to produce advantageous results in finding collective coloring
solutions. Our work demonstrates that distributed greedy optimization
algorithms exploiting local information should be deployed in combination with
occasional exploration via random choices in order to overcome local minima and
achieve global coordination.


Abstract:
  We present the ongoing NorLM initiative to support the creation and use of
very large contextualised language models for Norwegian (and in principle other
Nordic languages), including a ready-to-use software environment, as well as an
experience report for data preparation and training. This paper introduces the
first large-scale monolingual language models for Norwegian, based on both the
ELMo and BERT frameworks. In addition to detailing the training process, we
present contrastive benchmark results on a suite of NLP tasks for Norwegian.
For additional background and access to the data, models, and software, please
see http://norlm.nlpl.eu


Abstract:
  This paper presents a novel method, termed Bridge to Answer, to infer correct
answers for questions about a given video by leveraging adequate graph
interactions of heterogeneous crossmodal graphs. To realize this, we learn
question conditioned visual graphs by exploiting the relation between video and
question to enable each visual node using question-to-visual interactions to
encompass both visual and linguistic cues. In addition, we propose bridged
visual-to-visual interactions to incorporate two complementary visual
information on appearance and motion by placing the question graph as an
intermediate bridge. This bridged architecture allows reliable message passing
through compositional semantics of the question to generate an appropriate
answer. As a result, our method can learn the question conditioned visual
representations attributed to appearance and motion that show powerful
capability for video question answering. Extensive experiments prove that the
proposed method provides effective and superior performance than
state-of-the-art methods on several benchmarks.


Abstract:
  We show that every isometric action on a Cantor set is conjugate to an
inverse limit of actions on finite sets; and that every isometric action by a
finitely generated amenable group is residually finite.


Abstract:
  The Multiple Travelling Salesman Problem (MTSP) is among the most interesting
combinatorial optimization problems because it is widely adopted in real-life
applications, including robotics, transportation, networking, etc. Although the
importance of this optimization problem, there is no survey dedicated to
reviewing recent MTSP contributions. In this paper, we aim to fill this gap by
providing a comprehensive review of existing studies on MTSP. In this survey,
we focus on MTSP's recent contributions to both classical vehicles/robots and
unmanned aerial vehicles. We highlight the approaches applied to solve the MTSP
as well as its application domains. We analyze the MTSP variants and propose a
taxonomy and a classification of recent studies.


Abstract:
  Milner (1984) introduced a process semantics for regular expressions as
process graphs. Unlike for the language semantics, where every regular (that
is, DFA-accepted) language is the interpretation of some regular expression,
there are finite process graphs that are not bisimilar to the process
interpretation of any regular expression. For reasoning about graphs that are
expressible by regular expressions modulo bisimilarity it is desirable to have
structural representations of process graphs in the image of the
interpretation.
  For '1-free' regular expressions, their process interpretations satisfy the
structural property LEE (loop existence and elimination). But this is not in
general the case for all regular expressions, as we show by examples. Yet as a
remedy, we describe the possibility to recover the property LEE for a close
variant of the process interpretation. For this purpose we refine the process
semantics of regular expressions to yield process graphs with 1-transitions,
similar to silent moves for finite-state automata.


Abstract:
  The temperature distribution in the battery significantly impacts the
short-term and long-term performance of battery systems. Therefore, efficient,
safe, and reliable battery system operation requires an accurate estimation of
the temperature field. The current industry standard for sensors to battery
cell ratio is quite frugal. Thus, the problem of sensor placement for accurate
temperature estimation becomes non-trivial, especially for large-scale systems.
In this paper, we explore a greedy approach for sensor placement suitable for
large-scale battery systems. An observer to estimate the thermal field is
designed in an $\mathcal{H}_{\infty}$ framework while simultaneously minimizing
the sensor precisions, thus lowering the overall thermal management system's
economic cost.


Abstract:
  In this paper we study a 2-type linear-fractional branching process in
varying environment with asymptotically constant mean matrices. Let $\nu$ be
the extinction time and for $k\ge1$ let $M_k$ be the mean matrix of offspring
distribution of individuals of the $(k-1)$-th generation. Under certain
conditions, we show that $P(\nu=n)$ and $P(\nu>n)$ are asymptotically
equivalent to some functions of products of spectral radii of the mean
matrices. This paper complements a former result [arXiv: 2007.07840] which
requires in addition a condition $\forall k\ge1,\rm{det}(M_k)<-\varepsilon$ for
some $\varepsilon>0.$ Such a condition excludes a large class of mean matrices.
As byproducts, we also get some results on asymptotics of products of
nonhomogeneous matrices which have their own interests.


Abstract:
  When hexagonal boron nitride (hBN) and graphene are aligned at zero or small
twist angle, a moir\'e structure is formed due to the small lattice constant
mismatch between the two structures. In this work, we analyze magnetic ordering
tendencies, driven by onsite Coulomb interactions, of encapsulated bilayer
graphene (BG) forming a moir\'e structure with one (hBN-BG) or both hBN layers
(hBN-BG-hBN), using the random phase approximation. The calculations are
performed in a fully atomistic Hubbard model that takes into account all
$\pi$-electrons of the carbon atoms in one moir\'e unit cell. We analyze the
charge neutral case and find that the dominant magnetic ordering instability is
uniformly antiferromagnetic. Furthermore, at low temperatures, the critical
Hubbard interaction $U_c$ required to induce magnetic order is slightly larger
in those systems where the moir\'e structure has caused a band gap opening in
the non-interacting picture, although the difference is less than 6%.
Mean-field calculations are employed to estimate how such an
interaction-induced magnetic order may change the observable single-particle
gap sizes.


Abstract:
  High-resolution Fourier-transform spectroscopy using table-top sources in the
extreme ultraviolet (XUV) spectral range is still in its infancy. In this
contribution a significant advance is presented based on a Michelson-type
all-reflective split-and-delay autocorrelator operating in a quasi amplitude
splitting mode. The autocorrelator works under a grazing incidence angle in a
broad spectral range $\mathrm{(10\,nm - 1\,\mu m)}$ providing collinear
propagation of both pulse replicas and thus a constant phase difference across
the beam profile. The compact instrument allows for XUV pulse autocorrelation
measurements in the time domain with a single-digit attosecond precision
resulting in a resolution of $\mathrm{E/\Delta E=2000}$. Its performance for
spectroscopic applications is demonstrated by characterizing a very sharp
electronic transition at $\mathrm{26.6\,eV}$ in Ar gas induced by the
$\mathrm{11^{th}}$ harmonic of a frequency-doubled Yb-fiber laser leading to
the characteristic $\mathrm{3s3p^{6}4p^{1}P^{1}}$ Fano-resonance of Ar atoms.
We benchmark our time-domain interferometry results with a high-resolution XUV
grating spectrometer and find an excellent agreement. The common-path
interferometer opens up new opportunities for short-wavelength femtosecond and
attosecond pulse metrology and dynamic studies on extreme time scales in
various research fields.


Abstract:
  Existing neural ranking models follow the text matching paradigm, where
document-to-query relevance is estimated through predicting the matching score.
Drawing from the rich literature of classical generative retrieval models, we
introduce and formalize the paradigm of deep generative retrieval models
defined via the cumulative probabilities of generating query terms. This
paradigm offers a grounded probabilistic view on relevance estimation while
still enabling the use of modern neural architectures. In contrast to the
matching paradigm, the probabilistic nature of generative rankers readily
offers a fine-grained measure of uncertainty. We adopt several current neural
generative models in our framework and introduce a novel generative ranker
(T-PGN), which combines the encoding capacity of Transformers with the Pointer
Generator Network model. We conduct an extensive set of evaluation experiments
on passage retrieval, leveraging the MS MARCO Passage Re-ranking and TREC Deep
Learning 2019 Passage Re-ranking collections. Our results show the
significantly higher performance of the T-PGN model when compared with other
generative models. Lastly, we demonstrate that exploiting the uncertainty
information of deep generative rankers opens new perspectives to
query/collection understanding, and significantly improves the cut-off
prediction task.


Abstract:
  Interpreting mathematical expressions at runtime is a standard task in
scientific software engineering. There are different approaches to this problem
from creating an embedded domain-specific language (eDSL) with its own parser
and interpreter specifically for that task, to using a full-fledged embedded
compiler. This article is dedicated to a middle-ground solution implemented in
the KMath library, which uses the Kotlin object builder DSL and its own
algebraic abstractions to generate an AST for mathematical operations. This AST
is then compiled just-in-time to generate JVM bytecode. A similar approach is
tested on other Kotlin platforms, where its performance is compared across a
variety of supported platforms.


Abstract:
  We demonstrate that multiple-Higgs production at the LHC is the most
sensitive probe of first and second-generation quark flavor in the Higgs
sector. In models where new scalars couple to light quarks, gigantic di-Higgs
and even sizable tri-Higgs production rates can be obtained, which can be used
to either discover or severely constrain such theories. As an example, we show
that the most stringent bounds on enhanced interactions of the
$125\,\textrm{GeV}$ Higgs to the down quark in extended Higgs sectors are
obtained by looking for the extra Higgs bosons that provide for such
enhancements using the di-Higgs and $Zh$ topologies. In this context, we set
new limits on the 125 GeV Higgs coupling to the down quark as strong as
$\lambda_{hd\bar{d}} \lesssim 30 \lambda_{hd\bar{d}}^{\textrm{SM}}$ -- a
dramatic improvement over previously available bounds. Regarding
second-generation quark flavor, we obtain new limits in the coupling to strange
as strong as $\lambda_{hs\bar{s}} \lesssim 10
\lambda_{hs\bar{s}}^{\textrm{SM}}$. In addition, we show that the currently
unexplored triple-Higgs production topology could be a potential discovery
channel of a wide variety of extended Higgs sectors at the LHC, including not
only models where extra Higgses couple to light quarks, but also popular
theories where they have preferential couplings to the the top.


Abstract:
  Building on the deep learning based acoustic echo cancellation (AEC) in the
single-loudspeaker (single-channel) and single-microphone setup, this paper
investigates multi-channel AEC (MCAEC) and multi-microphone AEC (MMAEC). We
train a deep neural network (DNN) to predict the near-end speech from
microphone signals with far-end signals used as additional information. We find
that the deep learning approach avoids the non-uniqueness problem in
traditional MCAEC algorithms. For the AEC setup with multiple microphones,
rather than employing AEC for each microphone, a single DNN is trained to
achieve echo removal for all microphones. Also, combining deep learning based
AEC with deep learning based beamforming further improves the system
performance. Experimental results show the effectiveness of both bidirectional
long short-term memory (BLSTM) and convolutional recurrent network (CRN) based
methods for MCAEC and MMAEC. Furthermore, deep learning based methods are
capable of removing echo and noise simultaneously and work well in the presence
of nonlinear distortions.


Abstract:
  Semi-quantum inspired lightweight protocol is an important research issue in
realization of quantum protocols. However, the previous semi-quantum inspired
lightweight mediated quantum key distribution (SQIL-MQKD) protocols need to use
the Bell states or measure the Bell states. The generation and measurement of
Bell states are more difficult and expensive than those of single photons. To
solve this problem, a semi-quantum inspired lightweight mediated quantum key
distribution with limited resource protocol is proposed. In this protocol, an
untrusted third party (TP) only needs to perform the quantum operations related
to single photons and the participants only have to perform two quantum
operations: (1) reflecting qubits without disturbance (2) performing unitary
operations on single photons. In addition, this protocol is showed to be robust
under the collective attack.


Abstract:
  In this work we propose a topological valley phononic crystal plate and we
extensively investigate the refraction of valley modes into the surrounding
homogeneous medium. This phononic crystal includes two sublattices of
resonators (A and B) modeled by mass-spring systems. We show that two edge
states confined at the AB/BA and BA/AB type domain walls exhibit different
symmetries in physical space and energy peaks in the Fourier space. As a
result, distinct refraction behaviors, especially through an armchair cut edge,
are observed. On the other hand, the decay depth of these localized topological
modes, which is found to be solely determined by the relative resonant strength
between the scatterers, significantly affects the refraction patterns. More
interestingly, the outgoing traveling wave through a zigzag interface becomes
evanescent when operating at deep sub-wavelength scale. This is realized by
tuning the average resonant strength. We show that the evanescent modes only
exist along a particular type of outlet edge, and that they can couple with
both topological interface states. We also present two designs of topological
functional devices, including an elastic one-way transmission waveguide and a
near-ideal monopole/dipole emitter, both based on our phononic structure.


Abstract:
  As the role of algorithmic systems and processes increases in society, so
does the risk of bias, which can result in discrimination against individuals
and social groups. Research on algorithmic bias has exploded in recent years,
highlighting both the problems of bias, and the potential solutions, in terms
of algorithmic transparency (AT). Transparency is important for facilitating
fairness management as well as explainability in algorithms; however, the
concept of diversity, and its relationship to bias and transparency, has been
largely left out of the discussion. We reflect on the relationship between
diversity and bias, arguing that diversity drives the need for transparency.
Using a perspective-taking lens, which takes diversity as a given, we propose a
conceptual framework to characterize the problem and solution spaces of AT, to
aid its application in algorithmic systems. Example cases from three research
domains are described using our framework.


Abstract:
  Relocalization is a fundamental task in the field of robotics and computer
vision. There is considerable work in the field of deep camera relocalization,
which directly estimates poses from raw images. However, learning-based methods
have not yet been applied to the radar sensory data. In this work, we
investigate how to exploit deep learning to predict global poses from Emerging
Frequency-Modulated Continuous Wave (FMCW) radar scans. Specifically, we
propose a novel end-to-end neural network with self-attention, termed RadarLoc,
which is able to estimate 6-DoF global poses directly. We also propose to
improve the localization performance by utilizing geometric constraints between
radar scans. We validate our approach on the recently released challenging
outdoor dataset Oxford Radar RobotCar. Comprehensive experiments demonstrate
that the proposed method outperforms radar-based localization and deep camera
relocalization methods by a significant margin.


Abstract:
  The development of next generation perovskite-based optoelectronic devices
relies critically on the understanding of the interaction between charge
carriers and the polar lattice in out-of-equilibrium conditions. While it has
become increasingly evident for CsPbBr3 perovskites that the Pb-Br framework
flexibility plays a key role in their light-activated functionality, the
corresponding local structural rearrangement has not yet been unambiguously
identified. In this work, we demonstrate that the photoinduced lattice changes
in the system are due to a specific polaronic distortion, associated with the
activation of a longitudinal optical phonon mode at 18 meV by electron-phonon
coupling, and we quantify the associated structural changes with atomic-level
precision. Key to this achievement is the combination of time-resolved and
temperature-dependent studies at Br K-edge and Pb L3-edge X-ray absorption with
refined ab-initio simulations, which fully account for the screened core-hole
final state effects on the X-ray absorption spectra. From the temporal
kinetics, we show that carrier recombination reversibly unlocks the structural
deformation at both Br and Pb sites. The comparison with the
temperature-dependent XAS results rules out thermal effects as the primary
source of distortion of the Pb-Br bonding motif during photoexcitation. Our
work provides a comprehensive description of the CsPbBr3 perovskites
photophysics, offering novel insights on the light-induced response of the
system and its exceptional optoelectronic properties.


Abstract:
  NeRF synthesizes novel views of a scene with unprecedented quality by fitting
a neural radiance field to RGB images. However, NeRF requires querying a deep
Multi-Layer Perceptron (MLP) millions of times, leading to slow rendering
times, even on modern GPUs. In this paper, we demonstrate that real-time
rendering is possible by utilizing thousands of tiny MLPs instead of one single
large MLP. In our setting, each individual MLP only needs to represent parts of
the scene, thus smaller and faster-to-evaluate MLPs can be used. By combining
this divide-and-conquer strategy with further optimizations, rendering is
accelerated by three orders of magnitude compared to the original NeRF model
without incurring high storage costs. Further, using teacher-student
distillation for training, we show that this speed-up can be achieved without
sacrificing visual quality.


Abstract:
  We consider the category of Harish-Chandra modules for ${\rm SL}_2(\mathbb
R)$ as a module over the category of finite-dimensional representations of
${\rm SL}(2)$ with respect to the tensor product. In this note we use classical
results about principal series to obtain a classification of
$\otimes$-submodules of the category of Harish-Chandra modules of ${\rm
SL}_2(\mathbb R)$. The resulting classification recovers the classical
classification of irreducible Harish-Chandra modules. Our methods are expected
to generalize to arbitrary reductive pairs and more general base fields.


Abstract:
  The effectiveness of collective navigation of biological or artificial agents
requires to accommodate for contrasting requirements, such as staying in a
group while avoiding close encounters and at the same time limiting the energy
expenditure for manoeuvring. Here, we address this problem by considering a
system of active Brownian particles in a finite two-dimensional domain and ask
what is the control that realizes the optimal tradeoff between collision
avoidance and control expenditure. We couch this problem in the language of
optimal stochastic control theory and by means of a mean-field game approach we
derive an analytic mean-field solution, characterized by a second-order phase
transition in the alignment order parameter. We find that a mean-field version
of a classical model for collective motion based on alignment interactions
(Vicsek model) performs remarkably close to the optimal control. Our results
substantiate the view that observed group behaviors may be explained as the
result of optimizing multiple objectives and offer a theoretical ground for
biomimetic algorithms used for artificial agents.


Abstract:
  The observation of electromagnetic radiation emitted or absorbed by matter
was instrumental in revealing the quantum properties of atoms and molecules in
the early XX century, and constituted a turning-point in the development of the
quantum theory. Quantum mechanics changes dramatically the way radiation and
matter interact, making the probability of emission and absorption of light
strongly frequency dependent, as clearly manifested in atomic spectra. In this
essay, we advocate that gravitational radiation can play, for the quantum
aspects of black holes, a similar role as electromagnetic radiation did for
atoms, and that the advent of gravitational-wave astronomy can bring this
fascinating possibility to the realm of observations.


Abstract:
  This paper addresses the abstract dynamic programming (DP) in the online
scenario, where the abstract DP mapping is time-varying, instead of static. In
this case, optimal costs and policies at different time instants are not the
same in general, and the problem amounts to tracking time-varying optimal costs
and policies, which is of interest to many practical problems. It is thus
necessary to analyze the performance of classical value iteration (VI) and
policy iteration (PI) algorithms in the online case. In doing so, this paper
develops and provides the theoretical analysis for several online algorithms,
including approximate online VI, online PI, approximate online PI, online
optimistic PI, approximate online optimistic PI, and asynchronous online PI and
VI algorithms. It is proved that the tracking error bounds for all algorithms
critically depend upon the largest difference between any two consecutive
abstract mappings. Meanwhile, examples are presented to illustrate the
theoretical results.


Abstract:
  News recommendation is critical for personalized news access. Existing news
recommendation methods usually infer users' personal interest based on their
historical clicked news, and train the news recommendation models by predicting
future news clicks. A core assumption behind these methods is that news click
behaviors can indicate user interest. However, in practical scenarios, beyond
the relevance between user interest and news content, the news click behaviors
may also be affected by other factors, such as the bias of news presentation in
the online platform. For example, news with higher positions and larger sizes
are usually more likely to be clicked. The bias of clicked news may bring
noises to user interest modeling and model training, which may hurt the
performance of the news recommendation model.
  In this paper, we propose a bias-aware personalized news recommendation
method named DebiasRec, which can handle the bias information for more accurate
user interest inference and model training. The core of our method includes a
bias representation module, a bias-aware user modeling module, and a bias-aware
click prediction module. The bias representation module is used to model
different kinds of news bias and their interactions to capture their joint
effect on click behaviors. The bias-aware user modeling module aims to infer
users' debiased interest from the clicked news articles by using their bias
information to calibrate the interest model. The bias-aware click prediction
module is used to train a debiased news recommendation model from the biased
click behaviors, where the click score is decomposed into a preference score
indicating user's interest in the news content and a news bias score inferred
from its different bias features. Experiments on two real-world datasets show
that our method can effectively improve the performance of news recommendation.


Abstract:
  The management of the anthropogenic water cycle must ensure the preservation
of the quality and quantity of water resources and their careful allocation to
the different uses. Protection of water resources requires the control of
pollution sources that may deteriorate them. This is a challenging task in
multi-stressed catchments. This work presents an approach that combines
pesticide occurrence patterns and stable isotope analyses of nitrogen
(delta15N-NO3-, delta15N-NH4+), oxygen (delta18O-NO3-), and boron (delta11B) to
discriminate the origin of pesticides and nitrogenpollution to tackle this
challenge. The approach has been applied to a Mediterranean subcatchment
subject to a variety of natural and anthropogenic pressures. Combining the
results from both analytical approaches in selected locations of the basin, the
urban/industrial activity was identified as the main pressure on the quality of
the surface water resources, and to a large extent also on the groundwater
resources, although agriculture may play also an important role, mainly in
terms of nitrate and ammonium pollution. Total pesticide concentrations in
surface waters were one order of magnitude higher than in groundwaters and
believed to originate mainly from soil and/or sediments desorption processes
and urban and industrial use, as they were mainly associated with treated
wastewaters. These findings are supported by the stable isotope results, that
pointed to an organic origin of nitrate in surface waters and most groundwater
samples. Ammonium pollution observed in some aquifer locations is probably
generated by nitrate reduction. Overall, no significant attenuation processes
could be inferred for nitrate pollution. The approach presented here
exemplifies the investigative monitoring envisioned in the Water Framework
Directive.


Abstract:
  Nuclear magnetic resonance (NMR) schemes can be applied to micron-, and
nanometer-sized samples by the aid of quantum sensors such as nitrogen-vacancy
(NV) color centers in diamond. These minute devices allow for magnetometry of
nuclear spin ensembles with high spatial and frequency resolution at ambient
conditions, thus having a clear impact in different areas such as chemistry,
biology, medicine, and material sciences. In practice, NV quantum sensors are
driven by microwave (MW) control fields with a twofold objective: On the one
hand, MW fields bridge the energy gap between NV and nearby nuclei which
enables a coherent and selective coupling among them while, on the other hand,
MW fields remove environmental noise on the NV leading to enhanced
interrogation time. In this work we review distinct MW radiation patterns, or
dynamical decoupling techniques, for nanoscale NMR applications.


Abstract:
  Explaining the outcome of deep learning decisions based on affect is
challenging but necessary if we expect social companion robots to interact with
users on an emotional level. In this paper, we present a commonsense approach
that utilizes an interpretable hybrid neural-symbolic system to associate
extracted targets, noun chunks determined to be associated with the expressed
emotion, with affective labels from a natural language expression. We leverage
a pre-trained neural network that is well adapted to tree and sub-tree
processing, the Dependency Tree-LSTM, to learn the affect labels of dynamic
targets, determined through symbolic rules, in natural language. We find that
making use of the unique properties of the recursive network provides higher
accuracy and interpretability when compared to other unstructured and
sequential methods for determining target-affect associations in an
aspect-based sentiment analysis task.


Abstract:
  Modeling the Pauli energy, the contribution to the kinetic energy caused by
Pauli statistics, without using orbitals is the open problem of orbital-free
density functional theory. An important aspect of this problem is correctly
reproducing the Pauli potential, the response of the Pauli kinetic energy to a
change in density. We analyze the behavior of the Pauli potential of
non-relativistic neutral atoms under Lieb-Simon scaling -- the process of
taking nuclear charge and particle number to infinity, in which the kinetic
energy tends to the Thomas-Fermi limit. We do this by mathematical analysis of
the near-nuclear region and by calculating the exact orbital-dependent Pauli
potential using the approach of Ouyang and Levy for closed-shell atoms out to
element Z=976. In rough analogy to Lieb and Simon's own findings for the charge
density, we find that the potential does not converge smoothly to the
Thomas-Fermi limit on a point-by-point basis but separates into several
distinct regions of behavior. Near the nucleus, the potential approaches a
constant given by the difference in energy between the lowest and highest
occupied eigenvalues. We discover a transition region in the outer core where
the potential deviates unexpectedly and predictably from both the Thomas-Fermi
potential and the gradient expansion correction to it. These results may
provide insight into semi-classical description of Pauli statistics, and new
constraints to aid the improvement of orbital-free DFT functionals.


Abstract:
  The EDGES collaboration has reported the detection of a global 21-cm signal
with a plateau centered at 76 MHz (i.e., redshift 17.2), with an amplitude of
500^(+200)_(-500) mK. This anomalous measurement does not comport with standard
cosmology, which can only accommodate an amplitude < 230 mK. Nevertheless, the
line profile's redshift range (15 < z < 20) suggests a possible link to Pop III
star formation and an implied evolution out of the `dark ages.' Given this
tension with the standard model, we here examine whether the observed 21-cm
signal is instead consistent with the results of recent modeling based on the
alternative Friedmann-Lemaitre-Robertson-Walker cosmology known as the R_h=ct
universe, showing that--in this model--the CMB radiation might have been
rethermalized by dust ejected into the IGM by the first-generation stars at
redshift z < 16. We find that the requirements for this process to have
occurred would have self-consistently established an equilibrium spin
temperature T_s~3.4 K in the neutral hydrogen, via the irradiation of the IGM
by deep penetrating X-rays emitted at the termination shocks of Pop III
supernova remnants. Such a dust scenario has been strongly ruled out for the
standard model, so the spin temperature (~3.3 K) inferred from the 21-cm
absorption feature appears to be much more consistent with the R_h=ct profile
than that implied by LCDM, for which adiabatic cooling would have established a
spin temperature T_s(z=17.2)~6 K.


Abstract:
  Quantum key distribution (QKD) has been developed for decades and several
different QKD protocols have been proposed. But two difficulties limit the
implementation of most QKD protocols. First, the involved participants are
required to have heavy quantum capabilities, such as quantum joint operation,
quantum register, and so on. Second, a hypothetical authenticated classical
channel is used in most of the existing QKD protocols and this assumed channel
does not exist in reality. To solve both the above limitations at the same
time, this study proposes three lightweight authenticated QKD protocols with
key recycling and shows these proposed protocols are robust under the
collective attack.


Abstract:
  Let $\Lambda$ be a finite-dimensional algebra. A wide subcategory of
$\mathsf{mod}\Lambda$ is called left finite if the smallest torsion class
containing it is functorially finite. In this paper, we prove that the wide
subcategories of $\mathsf{mod}\Lambda$ arising from $\tau$-tilting reduction
are precisely the Serre subcategories of left finite wide subcategories. As a
consequence, we show that the class of such subcategories is closed under
further $\tau$-tilting reduction. This leads to a natural way to extend the
definition of the "$\tau$-cluster morphism category" of $\Lambda$ to arbitrary
finite-dimensional algebras. This category was recently constructed by
Buan-Marsh in the $\tau$-tilting finite case and by Igusa-Todorov in the
hereditary case.


Abstract:
  Despite considerable advancements with deep neural language models (LMs),
neural text generation still suffers from degeneration: the generated text is
repetitive, generic, self-contradictory, and often lacks commonsense. Our
analyses on sentence-level attention patterns in LMs reveal that neural
degeneration may be associated with insufficient learning of task-specific
characteristics by the attention mechanism. This finding motivates on-the-fly
attention modulation -- a simple but effective method that enables the
injection of priors into attention computation during inference. Automatic and
human evaluation results on three text generation benchmarks demonstrate that
attention modulation helps LMs generate text with enhanced fluency, creativity,
and commonsense reasoning, in addition to significantly reduce sentence-level
repetition.


Abstract:
  Community-level bans are a common tool against groups that enable online
harassment and harmful speech. Unfortunately, the efficacy of community bans
has only been partially studied and with mixed results. Here, we provide a
flexible unsupervised methodology to identify in-group language and track user
activity on Reddit both before and after the ban of a community (subreddit). We
use a simple word frequency divergence to identify uncommon words
overrepresented in a given community, not as a proxy for harmful speech but as
a linguistic signature of the community. We apply our method to 15 banned
subreddits, and find that community response is heterogeneous between
subreddits and between users of a subreddit. Top users were more likely to
become less active overall, while random users often reduced use of in-group
language without decreasing activity. Finally, we find some evidence that the
effectiveness of bans aligns with the content of a community. Users of dark
humor communities were largely unaffected by bans while users of communities
organized around white supremacy and fascism were the most affected.
Altogether, our results show that bans do not affect all groups or users
equally, and pave the way to understanding the effect of bans across
communities.