Abstract:
  Reinforcement learning techniques successfully generate convincing agent
behaviors, but it is still difficult to tailor the behavior to align with a
user's specific preferences. What is missing is a communication method for the
system to explain the behavior and for the user to repair it. In this paper, we
present a novel interaction method that uses interactive explanations using
templates of natural language as a communication method. The main advantage of
this interaction method is that it enables a two-way communication channel
between users and the agent; the bot can explain its thinking procedure to the
users, and the users can communicate their behavior preferences to the bot
using the same interactive explanations. In this manner, the thinking procedure
of the bot is transparent, and users can provide corrections to the bot that
include a suggested action to take, a goal to achieve, and the reasons behind
these decisions. We tested our proposed method in a clone of the video game
named \textit{Super Mario Bros.}, and the results demonstrate that our
interactive explanation approach is effective at diagnosing and repairing bot
behaviors.


Abstract:
  Many real-world data comes in the form of graphs, such as social networks and
protein structure. To fully utilize the information contained in graph data, a
new family of machine learning (ML) models, namely graph neural networks
(GNNs), has been introduced. Previous studies have shown that machine learning
models are vulnerable to privacy attacks. However, most of the current efforts
concentrate on ML models trained on data from the Euclidean space, like images
and texts. On the other hand, privacy risks stemming from GNNs remain largely
unstudied.
  In this paper, we fill the gap by performing the first comprehensive analysis
of node-level membership inference attacks against GNNs. We systematically
define the threat models and propose three node-level membership inference
attacks based on an adversary's background knowledge. Our evaluation on three
GNN structures and four benchmark datasets shows that GNNs are vulnerable to
node-level membership inference even when the adversary has minimal background
knowledge. Besides, we show that graph density and feature similarity have a
major impact on the attack's success. We further investigate two defense
mechanisms and the empirical results indicate that these defenses can reduce
the attack performance but with moderate utility loss.


Abstract:
  We discuss and illustrate the properties of several parton-shower algorithms
available in Pythia and Vincia, in the context of Higgs production via vector
boson fusion (VBF). In particular, the distinctive colour topology of VBF
processes allows to define observables sensitive to the coherent radiation
pattern of additional jets. We study a set of such observables, using the
Vincia sector-antenna shower as our main reference, and contrast it to Pythia's
transverse-momentum-ordered DGLAP shower as well as Pythia's dipole-improved
shower. We then investigate the robustness of these predictions as successive
levels of higher-order perturbative matrix elements are incorporated, including
next-to-leading-order matched and tree-level merged calculations, using Powheg
Box and Sherpa respectively to generate the hard events.


Abstract:
  This paper develops change-point methods for the spectrum of a locally
stationary time series. We focus on series with a bounded spectral density that
change smoothly under the null hypothesis but exhibits change-points or becomes
less smooth under the alternative. We address two local problems. The first is
the detection of discontinuities (or breaks) in the spectrum at unknown dates
and frequencies. The second involves abrupt yet continuous changes in the
spectrum over a short time period at an unknown frequency without signifying a
break. Both problems can be cast into changes in the degree of smoothness of
the spectral density over time. We consider estimation and minimax-optimal
testing. We determine the optimal rate for the minimax distinguishable
boundary, i.e., the minimum break magnitude such that we are able to uniformly
control type I and type II errors. We propose a novel procedure for the
estimation of the change-points based on a wild sequential top-down algorithm
and show its consistency under shrinking shifts and possibly growing number of
change-points. Our method can be used across many fields and a companion
program is made available in popular software packages.


Abstract:
  Notoriously, the two main problems of the standard $\Lambda$CDM model of
cosmology are the cosmological constant $\Lambda$ and the cold dark matter,
CDM. This essay shows that both the $\Lambda$ and the CDM arise as integration
constants in a careful derivation of Einstein's equations from first principles
in a Lorentz gauge theory. The dark sector of the universe might only reflect
the geometry of a spontaneous symmetry breaking that is necessary for the
existence of a spacetime and an observer therein.


Abstract:
  Consider simple random walk $(S_n)_{n\geq0}$ on a transitive graph with
spectral radius $\rho$. Let $u_n=\mathbb{P}[S_n=S_0]$ be the $n$-step return
probability. It is a folklore conjecture that on transient, transitive graphs
$u_n/\rho^n$ is at most of the order $n^{-3/2}$. We prove this conjecture for
graphs with a closed, transitive, amenable and nonunimodular subgroup of
automorphisms.
  We also study the first return probability $f_n$. For a graph $G$ with a
closed, transitive, nonunimodular subgroup of automorphisms, we show that there
is a positive constant $c$ such that $f_n\geq \frac{u_n}{cn^c}$. We also make
some conjectures related to $f_n$ and $u_n$ for transient, transitive graphs.


Abstract:
  In a material prone to a nematic instability, anisotropic strain in principle
provides a preferred symmetry-breaking direction for the electronic nematic
state to follow. This is consistent with experimental observations, where
electronic nematicity and structural anisotropy typically appear hand-in-hand.
In this work, we discover that electronic nematicity can be locally decoupled
from the underlying structural anisotropy in strain-engineered iron-selenide
(FeSe) thin films. We use heteroepitaxial molecular beam epitaxy to grow FeSe
with a nanoscale network of modulations that give rise to spatially varying
strain. We map local anisotropic strain by analyzing scanning tunneling
microscopy topographs, and visualize electronic nematic domains from
concomitant spectroscopic maps. While the domains form so that the energy of
nemato-elastic coupling is minimized, we observe distinct regions where
electronic nematic ordering fails to flip direction, even though the underlying
structural anisotropy is locally reversed. The findings point towards a
nanometer-scale stiffness of the nematic order parameter.


Abstract:
  Frei et al. [6] showed that the problem to decide whether a graph is stable
with respect to some graph parameter under adding or removing either edges or
vertices is $\Theta_2^{\text{P}}$-complete. They studied the common graph
parameters $\alpha$ (independence number), $\beta$ (vertex cover number),
$\omega$ (clique number), and $\chi$ (chromatic number) for certain variants of
the stability problem. We follow their approach and provide a large number of
polynomial-time algorithms solving these problems for special graph classes,
namely for graphs without edges, complete graphs, paths, trees, forests,
bipartite graphs, and co-graphs.


Abstract:
  We present a graph neural network model for solving graph-to-graph learning
problems. Most deep learning on graphs considers ``simple'' problems such as
graph classification or regressing real-valued graph properties. For such
tasks, the main requirement for intermediate representations of the data is to
maintain the structure needed for output, i.e., keeping classes separated or
maintaining the order indicated by the regressor. However, a number of learning
tasks, such as regressing graph-valued output, generative models, or graph
autoencoders, aim to predict a graph-structured output. In order to
successfully do this, the learned representations need to preserve far more
structure. We present a conditional auto-regressive model for graph-to-graph
learning and illustrate its representational capabilities via experiments on
challenging subgraph predictions from graph algorithmics; as a graph
autoencoder for reconstruction and visualization; and on pretraining
representations that allow graph classification with limited labeled data.


Abstract:
  In this paper, we propose a multi-target image tracking algorithm based on
continuously apative mean-shift (Cam-shift) and unscented Kalman filter. We
improved the single-lamp tracking algorithm proposed in our previous work to
multi-target tracking, and achieved better robustness in the case of occlusion,
the real-time performance to complete one positioning and relatively high
accuracy by dynamically adjusting the weights of the multi-target motion
states. Our previous algorithm is limited to the analysis of tracking error. In
this paper, the results of the tracking algorithm are evaluated with the
tracking error we defined. Then combined with the double-lamp positioning
algorithm, the real position of the terminal is calculated and evaluated with
the positioning error we defined. Experiments show that the defined tracking
error is 0.61cm and the defined positioning error for 3-D positioning is 3.29cm
with the average processing time of 91.63ms per frame. Even if nearly half of
the LED area is occluded, the tracking error remains at 5.25cm. All of this
shows that the proposed visible light positioning (VLP) method can track
multiple targets for positioning at the same time with good robustness,
real-time performance and accuracy. In addition, the definition and analysis of
tracking errors and positioning errors indicates the direction for future
efforts to reduce errors.


Abstract:
  We present a novel, fast differentiable simulator for soft-body learning and
control applications. Existing differentiable soft-body simulators can be
classified into two categories based on their time integration methods:
Simulators using explicit time-stepping schemes require tiny time steps to
avoid numerical instabilities in gradient computation, and simulators using
implicit time integration typically compute gradients by employing the adjoint
method and solving the expensive linearized dynamics. Inspired by Projective
Dynamics (PD), we present Differentiable Projective Dynamics (DiffPD), an
efficient differentiable soft-body simulator based on PD with implicit time
integration. The key idea in DiffPD is to speed up backpropagation by
exploiting the prefactorized Cholesky decomposition in forward PD simulation.
In terms of contact handling, DiffPD supports two types of contacts: a
penalty-based model describing contact and friction forces and a
complementarity-based model enforcing non-penetration conditions and static
friction. We evaluate the performance of DiffPD and observe it is 4-19 times
faster compared with the standard Newton's method in various applications
including system identification, inverse design problems, trajectory
optimization, and closed-loop control. We also apply DiffPD in a
reality-to-simulation (real-to-sim) example with contact and collisions and
show its capability of reconstructing a digital twin of real-world scenes.


Abstract:
  Let $G$ be a finite group and $\sigma_1(G)=\frac{1}{|G|}\sum_{H\leq G}\,|H|$.
In this paper, we prove that if $\sigma_1(G)<2+\frac{11}{|G|}$\,, then $G$ is
supersolvable. In particular, some new characterizations of the well-known
groups $\mathbb{Z}_2\times\mathbb{Z}_4$ and $A_4$ are obtained. We also show
that $\sigma_1(G)<c$ does not imply the supersolvability of $G$ for no constant
$c\in(2,\infty)$.


Abstract:
  Port-based teleportation (PBT) is a protocol of quantum teleportation in
which a receiver does not have to apply correction to the transmitted state. In
this protocol two spatially separated parties can teleport an unknown quantum
state only by exploiting joint measurements on shared $d-$dimensional maximally
entangled states (resource state) together with a state to be teleported and
one way classical communication. In this paper we analyse degradation of the
resource state after one round of PBT and implications for the recycling
protocol for deterministic PBT introduced earlier. In the recycling protocol
the main idea is to re-use the remaining resource state after one or many
rounds of PBT for further processes of teleportation. It was stated by other
authors that the recycling protocol is by arguing that the resource state does
not degrade too much after each round of teleportation process. In particular,
there is a claim that the fidelity between ideal resource state and its real
version, each of them after one round of PBT, reaches asymptotically 1 when the
number of shared entangled pairs tends to infinity. Here, considering original
setup for the recycling protocol, we disprove these claims. We show the
resource state is heavily distorted after even one round of PBT with fidelity
not exceeding the value $1/d$. This bound was obtained by referring only to
Schwarz inequality and general properties of measurements exploited in the
protocol. As additional results we present explicit formula for the mentioned
fidelity involving group-theoretic parameters describing irreducible
representations in the Schur-Weyl duality. For the first time, we also analyse
the degradation of the resource state for the optimal PBT scheme and show its
substantial distortion for all $d\geq 2$. (...)


Abstract:
  Interstellar clouds can act as target material for hadronic cosmic rays;
gamma rays subsequently produced through inelastic proton-proton collisions and
spatially associated with such clouds can provide a key indicator of efficient
particle acceleration. However, even in the case that particle acceleration
proceeds up to PeV energies, the system of accelerator and nearby target
material must fulfil a specific set of conditions in order to produce a
detectable gamma-ray flux. In this study, we rigorously characterise the
necessary properties of both cloud and accelerator. By using available
Supernova Remnant (SNR) and interstellar cloud catalogues, we produce a ranked
shortlist of the most promising target systems, those for which a detectable
gamma-ray flux is predicted, in the case that particles are accelerated to PeV
energies in a nearby SNR. We discuss detection prospects for future facilities
including CTA, LHAASO and SWGO; and compare our predictions with known
gamma-ray sources. The four interstellar clouds with the brightest predicted
fluxes >100 TeV identified by this model are located at (l,b) = (330.05, 0.13),
(15.82, -0.46), (271.09, -1.26), and (21.97, -0.29). These clouds are
consistently bright under a range of model scenarios, including variation in
the diffusion coefficient and particle spectrum. On average, a detectable
gamma-ray flux is more likely for more massive clouds; systems with lower
separation distance between the SNR and cloud; and for slightly older SNRs.


Abstract:
  Recently, the application of machine learning models has gained momentum in
natural sciences and engineering, which is a natural fit due to the abundance
of data in these fields. However, the modeling of physical processes from
simulation data without first principle solutions remains difficult. Here, we
present a Graph Neural Networks approach towards accurate modeling of complex
3D granular flow simulation processes created by the discrete element method
LIGGGHTS and concentrate on simulations of physical systems found in real world
applications like rotating drums and hoppers. We discuss how to implement Graph
Neural Networks that deal with 3D objects, boundary conditions, particle -
particle, and particle - boundary interactions such that an accurate modeling
of relevant physical quantities is made possible. Finally, we compare the
machine learning based trajectories to LIGGGHTS trajectories in terms of
particle flows and mixing entropies.


Abstract:
  We propose NeRF-VAE, a 3D scene generative model that incorporates geometric
structure via NeRF and differentiable volume rendering. In contrast to NeRF,
our model takes into account shared structure across scenes, and is able to
infer the structure of a novel scene -- without the need to re-train -- using
amortized inference. NeRF-VAE's explicit 3D rendering process further contrasts
previous generative models with convolution-based rendering which lacks
geometric structure. Our model is a VAE that learns a distribution over
radiance fields by conditioning them on a latent scene representation. We show
that, once trained, NeRF-VAE is able to infer and render
geometrically-consistent scenes from previously unseen 3D environments using
very few input images. We further demonstrate that NeRF-VAE generalizes well to
out-of-distribution cameras, while convolutional models do not. Finally, we
introduce and study an attention-based conditioning mechanism of NeRF-VAE's
decoder, which improves model performance.


Abstract:
  Our interest in this work is in group extensions of minimal flows with
compact abelian groups in the fibres. We study their structure from categorical
and algebraic points of view, and describe relations of their dynamics to the
one-dimensional algebraic-topological invariants. We determine the first
cohomology groups of flows with simply connected acting groups and those of
topologically free flows possessing a free cycle. As an application we show
that minimal extensions of these flows not only do exist, but they have a rich
algebraic structure.


Abstract:
  Transformer encoding networks have been proved to be a powerful tool of
understanding natural languages. They are playing a critical role in native ads
service, which facilitates the recommendation of appropriate ads based on
user's web browsing history. For the sake of efficient recommendation,
conventional methods would generate user and advertisement embeddings
independently with a siamese transformer encoder, such that approximate nearest
neighbour search (ANN) can be leveraged. Given that the underlying semantic
about user and ad can be complicated, such independently generated embeddings
are prone to information loss, which leads to inferior recommendation quality.
Although another encoding strategy, the cross encoder, can be much more
accurate, it will lead to huge running cost and become infeasible for realtime
services, like native ads recommendation. In this work, we propose hybrid
encoder, which makes efficient and precise native ads recommendation through
two consecutive steps: retrieval and ranking. In the retrieval step, user and
ad are encoded with a siamese component, which enables relevant candidates to
be retrieved via ANN search. In the ranking step, it further represents each ad
with disentangled embeddings and each user with ad-related embeddings, which
contributes to the fine-grained selection of high-quality ads from the
candidate set. Both steps are light-weighted, thanks to the pre-computed and
cached intermedia results. To optimize the hybrid encoder's performance in this
two-stage workflow, a progressive training pipeline is developed, which builds
up the model's capability in the retrieval and ranking task step-by-step. The
hybrid encoder's effectiveness is experimentally verified: with very little
additional cost, it outperforms the siamese encoder significantly and achieves
comparable recommendation quality as the cross encoder.


Abstract:
  Affective Computing has recently attracted the attention of the research
community, due to its numerous applications in diverse areas. In this context,
the emergence of video-based data allows to enrich the widely used spatial
features with the inclusion of temporal information. However, such
spatio-temporal modelling often results in very high-dimensional feature spaces
and large volumes of data, making training difficult and time consuming. This
paper addresses these shortcomings by proposing a novel model that efficiently
extracts both spatial and temporal features of the data by means of its
enhanced temporal modelling based on latent features. Our proposed model
consists of three major networks, coined Generator, Discriminator, and
Combiner, which are trained in an adversarial setting combined with curriculum
learning to enable our adaptive attention modules. In our experiments, we show
the effectiveness of our approach by reporting our competitive results on both
the AFEW-VA and SEWA datasets, suggesting that temporal modelling improves the
affect estimates both in qualitative and quantitative terms. Furthermore, we
find that the inclusion of attention mechanisms leads to the highest accuracy
improvements, as its weights seem to correlate well with the appearance of
facial movements, both in terms of temporal localisation and intensity.
Finally, we observe the sequence length of around 160\,ms to be the optimum one
for temporal modelling, which is consistent with other relevant findings
utilising similar lengths.


Abstract:
  We show that a general but shear-free perturbation of homogeneous and
isotropic universes are necessarily silent, without any gravitational waves. We
prove this in two steps. First we establish that a shear free perturbation of
these universes are acceleration-free and the fluid flow geodesics of the
background universe maps onto themselves in the perturbed universe. This effect
then decouples the evolution equations of the electric and magnetic parts of
the Weyl tensor in the perturbed spacetimes and the magnetic part no longer
contains any tensor modes. Although the electric part, that drives the tidal
forces, do have tensor modes sourced by the anisotropic stress, these modes
have homogeneous oscillations at every point on a time slice without any wave
propagation. We also show the presence of vorticity vector waves that are
sourced by the curl of heat flux. This analysis shows the critical role of the
shear tensor in generating cosmological gravitational waves in an expanding
universe.


Abstract:
  Recently developed concept of dissipative measure-valued solution for
compressible flows is a suitable tool to describe oscillations and
singularities possibly developed in solutions of multidimensional Euler
equations. In this paper we study the convergence of the first-order finite
volume method based on the exact Riemann solver for the complete compressible
Euler equations. Specifically, we derive entropy inequality and prove the
consistency of numerical method. Passing to the limit, we show the weak and
strong convergence of numerical solutions and identify their limit. The
numerical results presented for the spiral, Kelvin-Helmholtz and the
Richtmyer-Meshkov problem are consistent with our theoretical analysis.


Abstract:
  We propose an inflationary primordial feature model that can explain both the
large and small-scale anomalies in the currently measured cosmic microwave
background (CMB) anisotropy spectra, revealing a clip of adventurous history of
the Universe during its primordial epoch. Although the model is currently
statistically indistinguishable from the Standard Model, we show that future
observations such as the Simons Observatory and LiteBIRD will complement each
other in distinguishing the model differences due to their accurate E-mode
polarization measurements, and the PICO mission, if funded, can put stringent
constraints on all characteristic properties. The model predicts a signal of
classical primordial standard clock, which can also be used to distinguish the
inflation and alternative scenarios in a model-independent fashion.


Abstract:
  While quantum accelerometers sense with extremely low drift and low bias,
their practical sensing capabilities face two limitations compared with
classical accelerometers: a lower sample rate due to cold atom interrogation
time, and a reduced dynamic range due to signal phase wrapping. In this paper,
we propose a maximum likelihood probabilistic data fusion method, under which
the actual phase of the quantum accelerometer can be unwrapped by fusing it
with the output of a classical accelerometer on the platform. Consequently, the
proposed method enables quantum accelerometers to be applied in practical
inertial navigation scenarios with enhanced performance. The recovered
measurement from the quantum accelerometer is also used to re-calibrate the
classical accelerometer. We demonstrate the enhanced error performance achieved
by the proposed fusion method using a simulated 1D inertial navigation
scenario. We conclude with a discussion on fusion error and potential
solutions.


Abstract:
  The attractor equations for an arbitrary one-parameter family of Calabi-Yau
manifolds are studied in the large complex structure region. These equations
are solved iteratively, generating what we term an N-expansion, which is a
power series in the Gromov-Witten invariants of the manifold. The coefficients
of this series are associated with integer partitions. In important cases we
are able to find closed-form expressions for the general term of this
expansion. To our knowledge, these are the first generic solutions to attractor
equations that incorporate instanton contributions. In particular, we find a
simple closed-form formula for the entropy associated to rank two attractor
points, including those recently discovered. The applications of our solutions
are briefly discussed. Most importantly, we are able to give an expression for
the Wald entropy of black holes that includes all genus 0 instanton
corrections.


Abstract:
  We have investigated the stellar and interstellar content of the distant star
formation region IRAS 17591-2228 (WISE HII region GAL 007.47+0.06). It is
associated to a water maser, whose parallax distance is d=20.4^{+2.8} {-2.2}
kpc, supported by independent measurements of proper motion and radial
velocity. It is projected in the same direction as an extremely red (J-Ks ~ 6
mag) group of stars, and a shell of mid-infrared emission. We qualify the group
of stars as a cluster candidate, VVV CL177. Its radius spans between 0.45' and
1' and contains at least two young stellar objects with an extreme extinction
near Av ~ 40 mag. Yet more analysis will be required to determine is it is a
real single cluster associated with the water maser. The 13CO emissions at the
radial velocity of the maser corresponds to the mid-infrared emission.


Abstract:
  We study modulational instability (MI) in optical fibers with random group
velocity dispersion (GVD) generated by sharply localized perturbations of a
normal GVD fiber that are either randomly or periodically placed along the
fiber and that have random strength. This perturbation leads to the appearance
of low frequency MI side lobes that grow with the strength of the
perturbations, whereas they are faded by randomness in their position. If the
random perturbations exhibit a finite average value, they can be compared with
periodically perturbed fibers, where Arnold tongues appear. In that case,
increased randomness in the strengths of the variations tends to affect the
Arnold tongues less than increased randomness in their positions.


Abstract:
  With $T_c \sim 9.6~\mathrm{K}$, Be$_{22}$Re exhibits one of the highest
critical temperatures among Be-rich compounds. We have carried out a series of
high-pressure electrical resistivity measurements on this compound to 30 GPa.
The data show that the critical temperature $T_c$ is suppressed gradually at a
rate of $dT_c/dP = -0.05~\mathrm{K/GPa}$. Using density functional theory (DFT)
calculations of the electronic and phonon density of states (DOS) and the
measured critical temperature, we estimate that the rapid increase in lattice
stiffening in Be$_{22}$Re overwhelms a moderate increase in the electron-ion
interaction with pressure, resulting in the decrease in $T_c$. High pressure
x-ray diffraction measurements show that the ambient pressure crystal structure
of Be$_{22}$Re persists to at least 154 GPa. We discuss the relationship
between low-Z Be-rich superconductors and the high-$T_c$ superhydrides.


Abstract:
  Most Video Super-Resolution (VSR) methods enhance a video reference frame by
aligning its neighboring frames and mining information on these frames.
Recently, deformable alignment has drawn extensive attention in VSR community
for its remarkable performance, which can adaptively align neighboring frames
with the reference one. However, we experimentally find that deformable
alignment methods still suffer from fast motion due to locally loss-driven
offset prediction and lack explicit motion constraints. Hence, we propose a
Matching-based Flow Estimation (MFE) module to conduct global semantic feature
matching and estimate optical flow as coarse offset for each location. And a
Flow-guided Deformable Module (FDM) is proposed to integrate optical flow into
deformable convolution. The FDM uses the optical flow to warp the neighboring
frames at first. And then, the warped neighboring frames and the reference one
are used to predict a set of fine offsets for each coarse offset. In general,
we propose an end-to-end deep network called Flow-guided Deformable Alignment
Network (FDAN), which reaches the state-of-the-art performance on two benchmark
datasets while is still competitive in computation and memory consumption.


Abstract:
  In \cite{J}, Jab\l o\'{n}ski proved that a piecewise expanding $C^{2}$
multidimensional Jab\l o\'{n}ski map admits an absolutely continuous invariant
probability measure (ACIP). In \cite{BL}, Boyarsky and Lou extended this result
to the case of i.i.d. compositions of the above maps, with an on average
expanding condition. We generalize these results to the (quenched) setting of
random Jab\l o\'{n}ski maps, where the randomness is governed by an ergodic,
invertible and measure preserving transformation. We prove that the skew
product associated to this random dynamical system admits a finite number of
ergodic ACIPs. Furthermore, we provide two different upper bounds on the number
of mutually singular ergodic ACIP's, motivated by the works of Buzzi \cite{B}
in one dimension and Gora, Boyarsky and Proppe \cite{GBP} in higher dimensions.


Abstract:
  Assistive robot arms enable people with disabilities to conduct everyday
tasks on their own. These arms are dexterous and high-dimensional; however, the
interfaces people must use to control their robots are low-dimensional.
Consider teleoperating a 7-DoF robot arm with a 2-DoF joystick. The robot is
helping you eat dinner, and currently you want to cut a piece of tofu. Today's
robots assume a pre-defined mapping between joystick inputs and robot actions:
in one mode the joystick controls the robot's motion in the x-y plane, in
another mode the joystick controls the robot's z-yaw motion, and so on. But
this mapping misses out on the task you are trying to perform! Ideally, one
joystick axis should control how the robot stabs the tofu and the other axis
should control different cutting motions. Our insight is that we can achieve
intuitive, user-friendly control of assistive robots by embedding the robot's
high-dimensional actions into low-dimensional and human-controllable latent
actions. We divide this process into three parts. First, we explore models for
learning latent actions from offline task demonstrations, and formalize the
properties that latent actions should satisfy. Next, we combine learned latent
actions with autonomous robot assistance to help the user reach and maintain
their high-level goals. Finally, we learn a personalized alignment model
between joystick inputs and latent actions. We evaluate our resulting approach
in four user studies where non-disabled participants reach marshmallows, cook
apple pie, cut tofu, and assemble dessert. We then test our approach with two
disabled adults who leverage assistive devices on a daily basis.


Abstract:
  We showcase applications of nonlinear algebra in the sciences and
engineering. Our review is organized into eight themes: polynomial
optimization, partial differential equations, algebraic statistics, integrable
systems, configuration spaces of frameworks, biochemical reaction networks,
algebraic vision, and tensor decompositions. Conversely, developments on these
topics inspire new questions and algorithms for algebraic geometry.


Abstract:
  We present the results of a study of the two-time correlation functions of a
dichotomously driven two-level system in contact with a thermal bath by using
corrections beyond the quantum regression theorem. In the strong
system-environment coupling regime, it is found that the noise parameters could
be tuned to control the magnitude of corrections at low environmental
temperatures. The motional averaging and narrowing effect of the external noise
was observed on the absorption and emission spectra of the two-state system.
Furthermore, effects similar to the destruction of tunneling and noise
enhancement of transport are observed in the dynamics of the two-time
correlations.


Abstract:
  Intelligent agents powered by AI planning assist people in complex scenarios,
such as managing teams of semi-autonomous vehicles. However, AI planning models
may be incomplete, leading to plans that do not adequately meet the stated
objectives, especially in unpredicted situations. Humans, who are apt at
identifying and adapting to unusual situations, may be able to assist planning
agents in these situations by encoding their knowledge into a planner at
run-time. We investigate whether people can collaborate with agents by
providing their knowledge to an agent using linear temporal logic (LTL) at
run-time without changing the agent's domain model. We presented 24
participants with baseline plans for situations in which a planner had
limitations, and asked the participants for workarounds for these limitations.
We encoded these workarounds as LTL constraints. Results show that
participants' constraints improved the expected return of the plans by 10% ($p
< 0.05$) relative to baseline plans, demonstrating that human insight can be
used in collaborative planning for resilience. However, participants used more
declarative than control constraints over time, but declarative constraints
produced plans less similar to the expectation of the participants, which could
lead to potential trust issues.


Abstract:
  Violence against women occurs predominantly in the family and domestic
context. The COVID-19 pandemic led Brazil to recommend and, at times, impose
social distancing, with the partial closure of economic activities, schools,
and restrictions on events and public services. Preliminary evidence shows that
intense coexistence increases domestic violence, while social distancing
measures may have prevented access to public services and networks,
information, and help. We propose an agent-based model (ABM), called VIDA, to
illustrate and examine multi-causal factors that influence events that generate
violence. A central part of the model is the multi-causal stress indicator,
created as a probability trigger of domestic violence occurring within the
family environment. Two experimental design tests were performed: (a) absence
or presence of the deterrence system of domestic violence against women and (b)
measures to increase social distancing. VIDA presents comparative results for
metropolitan regions and neighbourhoods considered in the experiments. Results
suggest that social distancing measures, particularly those encouraging staying
at home, may have increased domestic violence against women by about 10%. VIDA
suggests further that more populated areas have comparatively fewer cases per
hundred thousand women than less populous capitals or rural areas of urban
concentrations. This paper contributes to the literature by formalising, to the
best of our knowledge, the first model of domestic violence through agent-based
modelling, using empirical detailed socioeconomic, demographic, educational,
gender, and race data at the intraurban level (census sectors).


Abstract:
  We report here our finding of two new blueberry galaxies using optical IFU
spectroscopic data from the MaNGA survey. Both the blueberries are found to be
compact ($\leq$ $1 - 2$ kpc) starburst systems located at the outskirts of Low
Surface Brightness (LSB) galaxies. Our blueberries have the lowest stellar
masses $\sim$ 10$^{5}$ M$_{\odot}$ amongst the locally known blueberry
galaxies. We find a significantly large mean metallicity difference ($\sim$ 0.5
dex) between the blueberry sources and their associated LSBs. Moreover, the
radial metallicity gradients in our blueberries are also different than their
respective LSBs - suggesting that these had significantly different metallicity
histories. The likelihood of survival of these blueberries as TDGs is analyzed
based on their structural and kinematic properties. Our analysis shows that
although the two blueberries are stable against internal motions, they would
not have survived against the tidal force of the host galaxy. Based on the
velocity difference between the host LSBs and the blueberries, it appears that
they are compact, starburst systems in their advanced stage of merger with
these LSBs situated in a dense environment. Implications of our findings are
discussed.


Abstract:
  The Higgs bosons and the top quark decay into rich and diverse final states,
containing both light and heavy quarks, gluons, photons as well as W and Z
bosons. This article reviews the challenges involved in reconstructing Higgs
and top events at the FCC-ee and identifies the areas where novel developments
are needed. The precise identification and reconstruction of these final states
at the FCC-ee rely on the capability of the detector to provide excellent
flavour tagging, jet energy and angular resolution, and global kinematic event
reconstruction. Excellent flavour tagging performance requires low-material
vertex and tracking detectors, and advanced machine learning techniques as
successfully employed in LHC experiments. In addition, the Z pole run will
provide abundant samples of heavy flavour partons that can be used for
calibration of the tagging algorithms. For the reconstruction of jets, leptons,
and missing energy, particle-flow algorithms are crucial to explore the full
potential of the highly granular tracking and calorimeter systems, and give
access to excellent energy-momentum resolution and precise identification of
heavy bosons in their hadronic decays. This enables, among many other key
elements, the reconstruction of Higgsstrahlung processes with leptonically and
hadronically decaying Z bosons, and an almost background-free identification of
top quark pair events. Exploiting the full available kinematic constraints
together with exclusive jet clustering algorithms will allow for the
optimisation of global event reconstruction with kinematic fitting techniques.


Abstract:
  Accurate characterization of the inner surface of X-ray monocapillary optics
(XMCO) is of great significance in X-ray optics research. Compared with other
characterization methods, the micro computed tomography (micro-CT) method has
its unique advantages but also has some disadvantages, such as a long scanning
time, long image reconstruction time, and inconvenient scanning process. In
this paper, sparse sampling was proposed to shorten the scanning time, GPU
acceleration technology was used to improve the speed of image reconstruction,
and a simple geometric calibration algorithm was proposed to avoid the
calibration phantom and simplify the scanning process. These methodologies will
popularize the use of the micro-CT method in XMCO characterization.


Abstract:
  We evaluate the entanglement wedge cross section (EWCS) in asymptotically AdS
geometries which are dual to boundary excited states. We carry out a
perturbative analysis for calculating EWCS between the vacuum and other states
for a symmetric configuration consisting of two disjoint strips and obtain
analytical results in the specific regimes of the parameter space. In
particular, when the states described by purely gravitational excitations in
the bulk we find that the leading correction to EWCS is negative and hence the
correlation between the boundary subregions decreases. We also study other
types of excitations upon adding the extra matter fields including current and
scalar condensate. Our study reveals some generic properties of boundary
information measures dual to EWCS, e.g., entanglement of purification,
logarithmic negativity and reflected entropy. Finally, we discuss how these
results are consistent with the behavior of other correlation measures
including the holographic mutual information.


Abstract:
  Conversational passage retrieval relies on question rewriting to modify the
original question so that it no longer depends on the conversation history.
Several methods for question rewriting have recently been proposed, but they
were compared under different retrieval pipelines. We bridge this gap by
thoroughly evaluating those question rewriting methods on the TREC CAsT 2019
and 2020 datasets under the same retrieval pipeline. We analyze the effect of
different types of question rewriting methods on retrieval performance and show
that by combining question rewriting methods of different types we can achieve
state-of-the-art performance on both datasets.


Abstract:
  We formulate and prove a local arithmetic Siegel--Weil formula for GSpin
Rapoport--Zink spaces, which is a precise identity between the arithmetic
intersection numbers of special cycles on GSpin Rapoport--Zink spaces and the
derivatives of local representation densities of quadratic forms. As a first
application, we prove a semi-global arithmetic Siegel--Weil formula as
conjectured by Kudla, which relates the arithmetic intersection numbers of
special cycles on GSpin Shimura varieties at a place of good reduction and the
central derivatives of nonsingular Fourier coefficients of incoherent Siegel
Eisenstein series.


Abstract:
  Hyers-Ulam stability of the sequence $ \{z_n\}_{n \in \mathbb{N}} $ generated
by linear maps is studied. The difference equation $ z_{n+1} = g_n(z_n) $ where
$ g_n(z) = a_nz + b_n $ and $ a_n \neq 0 $. Hyers-Ulam stability is determined
by the the limit of the geometric average of $ a_n $ rather than the type of
each linear maps. In particular, if $ \prod_{j=1}^{n-1}|a_j| $ is
subexponentially increasing sequence, then $ \{z_n\}_{n \in \mathbb{N}} $ has
no Hyers-Ulam stability. Other complementary results are also established with
properties of $ \prod_{j=1}^{n-1}|a_j| $. This result contains Hyers-Ulam
stability of the first order difference equation with periodic coefficients
also.


Abstract:
  This volume of EPTCS contains the proceedings of the Seventh Workshop on
Proof Exchange for Theorem Proving (PxTP 2021), held on 11 July 2021 as part of
the CADE-28 online conference in Pittsburgh, USA. The PxTP workshop series
brings together researchers working on various aspects of communication,
integration, and cooperation between reasoning systems and formalisms, with a
special focus on proofs. The progress in computer-aided reasoning, both
automated and interactive, during the past decades, made it possible to build
deduction tools that are increasingly more applicable to a wider range of
problems and are able to tackle larger problems progressively faster. In recent
years, cooperation between such tools in larger systems has demonstrated the
potential to reduce the amount of manual intervention. Cooperation between
reasoning systems relies on availability of theoretical formalisms and
practical tools to exchange problems, proofs, and models. The PxTP workshop
series strives to encourage such cooperation by inviting contributions on all
aspects of cooperation between reasoning tools, whether automatic or
interactive.


Abstract:
  Recently, representation learning for text and speech has successfully
improved many language related tasks. However, all existing methods suffer from
two limitations: (a) they only learn from one input modality, while a unified
representation for both speech and text is needed by tasks such as end-to-end
speech translation, and as a result,(b) they can not exploit various
large-scale text and speech data and their performance is limited by the
scarcity of parallel speech translation data.To address these problems, we
propose a Fused Acoustic and Text Masked Language Model (FAT-MLM) which jointly
learns a unified representation for both acoustic and text input from various
types of corpora including parallel data for speech recognition and machine
translation, and even pure speech and text data. Within this cross-modal
representation learning framework, we further present an end-to-end model for
Fused Acoustic and Text Speech Translation (FAT-ST). Experiments on three
translation directions show that by fine-tuning from FAT-MLM, our proposed
speech translation models substantially improve translation quality by up to
+5.9 BLEU.


Abstract:
  In this paper, we propose a novel learnable image encryption method for
privacy-preserving deep neural networks (DNNs). The proposed method is carried
out on the basis of block scrambling used in combination with data augmentation
techniques such as random cropping, horizontal flip and grid mask. The use of
block scrambling enhances robustness against various attacks, and in contrast,
the combination with data augmentation enables us to maintain a high
classification accuracy even when using encrypted images. In an image
classification experiment, the proposed method is demonstrated to be effective
in privacy-preserving DNNs.


Abstract:
  The Reynolds-averaged Navier-Stokes (RANS) equations are widely used in
turbulence applications. They require accurately modeling the anisotropic
Reynolds stress tensor, for which traditional Reynolds stress closure models
only yield reliable results in some flow configurations. In the last few years,
there has been a surge of work aiming at using data-driven approaches to tackle
this problem. The majority of previous work has focused on the development of
fully-connected networks for modeling the anisotropic Reynolds stress tensor.
In this paper, we expand upon recent work for turbulent channel flow and
develop new convolutional neural network (CNN) models that are able to
accurately predict the normalized anisotropic Reynolds stress tensor. We apply
the new CNN model to a number of one-dimensional turbulent flows. Additionally,
we present interpretability techniques that help drive the model design and
provide guidance on the model behavior in relation to the underlying physics.


Abstract:
  A standard pipeline of current face recognition frameworks consists of four
individual steps: locating a face with a rough bounding box and several
fiducial landmarks, aligning the face image using a pre-defined template,
extracting representations and comparing. Among them, face detection, landmark
detection and representation learning have long been studied and a lot of works
have been proposed. As an essential step with a significant impact on
recognition performance, the alignment step has attracted little attention. In
this paper, we first explore and highlight the effects of different alignment
templates on face recognition. Then, for the first time, we try to search for
the optimal template automatically. We construct a well-defined searching space
by decomposing the template searching into the crop size and vertical shift,
and propose an efficient method Face Alignment Policy Search (FAPS). Besides, a
well-designed benchmark is proposed to evaluate the searched policy.
Experiments on our proposed benchmark validate the effectiveness of our method
to improve face recognition performance.


Abstract:
  Deep learning approaches are nowadays ubiquitously used to tackle computer
vision tasks such as semantic segmentation, requiring large datasets and
substantial computational power. Continual learning for semantic segmentation
(CSS) is an emerging trend that consists in updating an old model by
sequentially adding new classes. However, continual learning methods are
usually prone to catastrophic forgetting. This issue is further aggravated in
CSS where, at each step, old classes from previous iterations are collapsed
into the background. In this paper, we propose Local POD, a multi-scale pooling
distillation scheme that preserves long- and short-range spatial relationships
at feature level. Furthermore, we design an entropy-based pseudo-labelling of
the background w.r.t. classes predicted by the old model to deal with
background shift and avoid catastrophic forgetting of the old classes. Finally,
we introduce a novel rehearsal method that is particularly suited for
segmentation. Our approach, called PLOP, significantly outperforms
state-of-the-art methods in existing CSS scenarios, as well as in newly
proposed challenging benchmarks.


Abstract:
  We consider the Klein-Gordon operator on an $n$-dimensional asymptotically
anti-de Sitter spacetime $(M,g)$ together with arbitrary boundary conditions
encoded by a self-adjoint pseudodifferential operator on $\partial M$ of order
up to $2$. Using techniques from $b$-calculus and a propagation of
singularities theorem, we prove that there exist advanced and retarded
fundamental solutions, characterizing in addition their structural and
microlocal properties. We apply this result to the problem of constructing
Hadamard two-point distributions. These are bi-distributions which are weak
bi-solutions of the underlying equations of motion with a prescribed form of
their wavefront set and whose anti-symmetric part is proportional to the
difference between the advanced and the retarded fundamental solutions. In
particular, under a suitable restriction of the class of admissible boundary
conditions and setting to zero the mass, we prove their existence extending to
the case under scrutiny a deformation argument which is typically used on
globally hyperbolic spacetimes with empty boundary.


Abstract:
  We investigate strong field gravitational lensing by rotating Simpson-Visser
black hole, which has an additional parameter ($0\leq l/2M \leq1$), apart from
mass ($M$) and rotation parameter ($a$). A rotating Simpson-Visser metric
correspond to (i) a Schwarzschild metric for $l/2M=a/2M=0$ and $ M \neq 0 $,
(ii) a Kerr metric for $l/2M=0$, $|a/2M|< 0.5$ and $ M \neq 0 $ (iii) a
rotating regular black hole metric for $|a/2M|< 0.5$, $ M \neq 0 $ and $l/2M$
in the range $0<l/2M<0.5 + \sqrt{\left(0.5\right)^2-(a/2M)^2}$, and (iv) a
traversable wormhole for a $|a/2M| >0.5$ and $l/2M\neq 0$. We find a decrease
in the deflection angle $\alpha_D$ and also in the ratio of the flux of the
first image and all other images $ r_{mag}$. On the other hand, angular
position $\theta_{1}$ increases more slowly and photon sphere radius $x_{m}$
decreases more quickly, but angular separation $s$ increases more rapidly, and
their behaviour is similar to that of the Kerr black hole. The formalism is
applied to discuss the astrophysical consequences in the supermassive black
holes and find that the rotating Simpson-Visser black holes can be
distinguished from the Kerr black hole via gravitational lensing. The deviation
of the lensing observables $\Delta\theta_1$ and $\Delta s$ of rotating Simpson
Visser black holes from Kerr black hole for $0<l/2M <0.6$ ($a/2M=0.45$), for
supermassive black holes Sgr A* and M87, respectively, are in the range
$0.0422-0.11658~\mu$as and $0.031709-0.08758~\mu$as while $|\Delta r_{mag}|$ is
in the range $0.2037 - 0.95668$. It is difficult to distinguish the two black
holes because the departure are in $\mathcal{O}(\mu$as), which are unlikely to
get resolved by the current EHT observations. We also derive a two-dimensional
lens equation and formula for deflection angle in the strong field limit by
focusing on trajectories close to the equatorial plane.


Abstract:
  We provide a Liouville principle for integration in terms of elliptic
integrals. Our methods are essentially those of Abel and Liouville changed to
modern notation. We expose Lie theoretic aspect of Liouville's work.


Abstract:
  The Bangla language is the seventh most spoken language, with 265 million
native and non-native speakers worldwide. However, English is the predominant
language for online resources and technical knowledge, journals, and
documentation. Consequently, many Bangla-speaking people, who have limited
command of English, face hurdles to utilize English resources. To bridge the
gap between limited support and increasing demand, researchers conducted many
experiments and developed valuable tools and techniques to create and process
Bangla language materials. Many efforts are also ongoing to make it easy to use
the Bangla language in the online and technical domains. There are some review
papers to understand the past, previous, and future Bangla Natural Language
Processing (BNLP) trends. The studies are mainly concentrated on the specific
domains of BNLP, such as sentiment analysis, speech recognition, optical
character recognition, and text summarization. There is an apparent scarcity of
resources that contain a comprehensive study of the recent BNLP tools and
methods. Therefore, in this paper, we present a thorough review of 71 BNLP
research papers and categorize them into 11 categories, namely Information
Extraction, Machine Translation, Named Entity Recognition, Parsing, Parts of
Speech Tagging, Question Answering System, Sentiment Analysis, Spam and Fake
Detection, Text Summarization, Word Sense Disambiguation, and Speech Processing
and Recognition. We study articles published between 1999 to 2021, and 50% of
the papers were published after 2015. We discuss Classical, Machine Learning
and Deep Learning approaches with different datasets while addressing the
limitations and current and future trends of the BNLP.


Abstract:
  Recently there has been an increased interest to apply the sensitive
$\beta$-decay asymmetry detected nuclear magnetic resonance ($\beta$-NMR)
technique to biological studies. A liquid-sample $\beta$-NMR setup was build at
ISOLDE to allow such investigations and to use the resolution gain of
liquid-state NMR in nuclear physics. As part of this setup a magnetic field
locking system, a set of printed circuit board shimming coils, a sample
exchange system, a set of compact $\beta$-detectors and a custom experimental
vacuum chamber were developed. The main magnetic field was stabilized down to
the ppm level by the locking system while allowing the direct determination of
the absolute magnetic field. The homogeneity of the magnetic field was improved
to $\leq$~5~ppm over the sample volume by the shimming coils. Time spent on
changing samples was reduced by a factor of five by the liquid sample exchange
system. During experiments it was possible to continuously observe the liquid
sample thanks to the custom chamber and compact $\beta$-detectors. The absolute
field determination allows for a novel way to reference $\beta$-NMR
measurements, removing the need for time consuming reference measurements. The
improved accuracy and resolution resulting from these innovations allows the
study of the distribution of nuclear magnetization and (bio)chemicals using
high-accuracy liquid-sample $\beta$-NMR.


Abstract:
  The distribution of hot interstellar medium in early-type galaxies bears the
imprint of the various astrophysical processes it underwent during its
evolution. The X-ray observations of these galaxies have identified various
structural features related to AGN and stellar feedback and environmental
effects such as merging and sloshing. In our XMM-Newton Galaxy Atlas (NGA)
project, we analyze archival observations of 38 ETGs, utilizing the high
sensitivity and large field of view of XMM-Newton to construct spatially
resolved 2D spectral maps of the hot gas halos. To illustrate our NGA data
products in conjunction with the Chandra Galaxy Atlas (Kim et al. 2019), we
describe two distinct galaxies - NGC 4636 and NGC 1550, in detail. We discuss
their evolutionary history with a particular focus on the asymmetric
distribution of metal-enriched, low-entropy gas caused by sloshing and AGN-
driven uplift. We will release the NGA data products to a dedicated website,
which users can download to perform further analyses.


Abstract:
  This work presents twelve fine-tuned deep learning architectures to solve the
bacterial classification problem over the Digital Image of Bacterial Species
Dataset. The base architectures were mainly published as mobile or efficient
solutions to the ImageNet challenge, and all experiments presented in this work
consisted of making several modifications to the original designs, in order to
make them able to solve the bacterial classification problem by using
fine-tuning and transfer learning techniques. This work also proposes a novel
data augmentation technique for this dataset, which is based on the idea of
artificial zooming, strongly increasing the performance of every tested
architecture, even doubling it in some cases. In order to get robust and
complete evaluations, all experiments were performed with 10-fold
cross-validation and evaluated with five different metrics: top-1 and top-5
accuracy, precision, recall, and F1 score. This paper presents a complete
comparison of the twelve different architectures, cross-validated with the
original and the augmented version of the dataset, the results are also
compared with several literature methods. Overall, eight of the eleven
architectures surpassed the 0.95 scores in top-1 accuracy with our data
augmentation method, being 0.9738 the highest top-1 accuracy. The impact of the
data augmentation technique is reported with relative improvement scores.


Abstract:
  The notion of Cyber-Physical-Social System (CPSS) is an emerging concept
developed as a result of the need to understand the impact of Cyber-Physical
Systems (CPS) on humans and vice versa. This paradigm shift from CPS to CPSS
was mainly attributed to the increasing use of sensor-enabled smart devices and
the tight link with the users. The concept of CPSS has been around for over a
decade and it has gained increasing attention over the past few years. The
evolution to incorporate human aspects in the CPS research has unlocked a
number of research challenges. Particularly human dynamics brings additional
complexity that is yet to be explored. The exploration to conceptualise the
notion of CPSS has been partially addressed in few scientific literatures.
Although its conceptualisation has always been use-case dependent. Thus, there
is a lack of generic view as most works focus on specific domains. Furthermore,
the systemic core and design principles linking it with the theory of systems
are loose. This work aims at addressing these issues by first exploring and
analysing scientific literature to understand the complete spectrum of CPSS
through a Systematic Literature Review (SLR). Thereby identifying the
state-of-the-art perspectives on CPSS regarding definitions, underlining
principles and application areas. Subsequently, based on the findings of the
SLR, we propose a domain-independent definition and a meta-model for CPSS,
grounded in the Theory of Systems. Finally, a discussion on feasible future
research directions is presented based on the systemic notion and the proposed
meta-models.


Abstract:
  Robust estimation of a mean vector, a topic regarded as obsolete in the
traditional robust statistics community, has recently surged in machine
learning literature in the last decade. The latest focus is on the sub-Gaussian
performance and computability of the estimators in a non-asymptotic setting.
Numerous traditional robust estimators are computationally intractable, which
partly contributes to the renewal of the interest in the robust mean
estimation.
  Robust centrality estimators, however, include the trimmed mean and the
sample median. The latter has the best robustness but suffers a low-efficiency
drawback. Trimmed mean and median of means, %as robust alternatives to the
sample mean, and achieving sub-Gaussian performance have been proposed and
studied in the literature.
  This article investigates the robustness of leading sub-Gaussian estimators
of mean and reveals that none of them can resist greater than $25\%$
contamination in data and consequently introduces an outlyingness induced
winsorized mean which has the best possible robustness (can resist up to $50\%$
contamination without breakdown) meanwhile achieving high efficiency.
Furthermore, it has a sub-Gaussian performance for uncontaminated samples and a
bounded estimation error for contaminated samples at a given confidence level
in a finite sample setting. It can be computed in linear time.


Abstract:
  In this note we prove a spectral gap for various Markov chains on various
functional spaces. While proving that a spectral gap exists is relatively
common, explicit estimates seems somewhat rare.These estimates are then used to
apply the concentration inequalities of "Effective limit theorems for Markov
chains with a spectral gap" (most of the present material was part of Section 3
of that article, which has been reduced to its core in the published version).


Abstract:
  This paper generalizes the method of deducing Dirac's equation to
constructing a family of equations that represent the $N$-th root of the basic
Energy-Momentum relation for a free particle \cite{Dattoli1, Dattoli2,
Dattoli3, Dattoli4}. Then these equations are recast in a form that allows one
to interpret them as the fundamental dynamical equations for particles with
fractional spin, which we study in detail for the case of $N=4$ and $N=3$. We
explicitly prove that the equation is invariant to rotations and boosts and
indeed represents spin-$\frac{3}{8}$ and spin-$\frac{1}{8}$ particles for $N=4$
and spin-$\frac{1}{6}$ and $0$ for $N=3$.


Abstract:
  In this paper, we solve the multiple product price optimization problem under
interval uncertainties of the price sensitivity parameters in the demand
function. The objective of the price optimization problem is to maximize the
overall revenue of the firm where the decision variables are the prices of the
products supplied by the firm. We propose an approach that yields optimal
solutions under different variations of the estimated price sensitivity
parameters. We adopt a robust optimization approach by building a data-driven
uncertainty set for the parameters, and then construct a deterministic
counterpart for the robust optimization model. The numerical results show that
two objectives are fulfilled: the method reflects the uncertainty embedded in
parameter estimations, and also an interval is obtained for optimal prices. We
also conducted a simulation study to which we compared the results of our
approach. The comparisons show that although robust optimization is deemed to
be conservative, the results of the proposed approach show little loss compared
to those from the simulation.


Abstract:
  This manual describes bch, an efficient program written in the C programming
language for the fast computation of the Baker-Campbell-Hausdorff (BCH) and
similar Lie series. The Lie series can be represented in the Lyndon basis, in
the classical Hall basis, or in the right-normed basis of E.S. Chibrikov. In
the Lyndon basis, which proves to be particularly efficient for this purpose,
the computation of 111013 coefficients for the BCH series up to terms of degree
20 takes less than half a second on an ordinary personal computer and requires
negligible 11MB of memory. Up to terms of degree 30, which is the maximum
degree the program can handle, the computation of 74248451 coefficients takes
55 hours but still requires only a modest 5.5GB of memory.


Abstract:
  Deep fakes became extremely popular in the last years, also thanks to their
increasing realism. Therefore, there is the need to measures human's ability to
distinguish between real and synthetic face images when confronted with
cutting-edge creation technologies. We describe the design and results of a
perceptual experiment we have conducted, where a wide and diverse group of
volunteers has been exposed to synthetic face images produced by
state-of-the-art Generative Adversarial Networks (namely, PG-GAN, StyleGAN,
StyleGAN2). The experiment outcomes reveal how strongly we should call into
question our human ability to discriminate real faces from synthetic ones
generated through modern AI.


Abstract:
  Recently Marcus, Spielman and Srivastava proved Weaver's ${\rm{KS}}_r$
conjecture, which gives a positive solution to the Kadison-Singer problem.
Cohen and Br\"and\'en independently extended this result to obtain the
arbitrary-rank version of Weaver's ${\rm{KS}}_r$ conjecture. In this paper, we
present a new bound in Weaver's ${\rm{KS}}_r$ conjecture for the arbitrary-rank
case. To do that, we introduce the definition of $(k,m)$-characteristic
polynomials and employ it to improve the previous estimate on the largest root
of the mixed characteristic polynomials. For the rank-one case, our bound
agrees with the Bownik-Casazza-Marcus-Speegle's bound when $r=2$ and with the
Ravichandran-Leake's bound when $r>2$. For the higher-rank case, we sharpen the
previous bounds from Cohen and from Br\"and\'en .


Abstract:
  A CCD photometry of the dwarf nova MASTER OT J172758.09 +380021.5 was carried
out in 2019 during 134 nights. Observations covered three superoutbursts, five
normal outbursts and quiescence between them. The available ASASSN and ZTF data
for 2014-2020 were also examined. Spectral observations were done in 2020 when
the object was in quiescence. Spectra and photometry revealed that the star is
an H-rich active ER UMa-type dwarf nova with a highly variable supercycle of
~50-100 d that implies a high and variable mass-transfer rate. This object
demonstrated peculiar behaviour: short-lasted superoutbursts (a week); a slow
superoutburst decline and cases of rebrightenings; low frequency (from none to
a few) of the normal outbursts during the supercycle. In 2019 a mean period of
positive superhumps was found to be 0.05829 d during the superoutbursts. Late
superhumps with a mean period of 0.057915 d which lasted about ~20 d after the
end of superoutburst and were replaced by an orbital period of 0.057026 d or
its orbital-negative superhump beat period were detected. An absence of eclipse
in the orbital light curve and its moderate amplitude are consistent with the
orbital inclination of about 40 degr found from spectroscopy. The blue peaks of
the V-Ic and B-Rc of superhumps during the superoutburst coincided with minima
of the light curves, while B-Rc of the late superhumps coincided with a rising
branch of the light curves. We found that a low mass ratio q=0.08 could explain
most of the peculiarities of this dwarf nova. The mass-transfer rate should be
accordingly higher than what is expected from gravitational radiation only,
this assumes the object is in a post-nova state and underwent a nova eruption
relatively recently -- hundreds of years ago. This object would provide
probably the first observational evidence that a nova eruption can occur even
in CVs near the period minimum.


Abstract:
  We establish the theory for pretty good state transfer in discrete-time
quantum walks. For a class of walks, we show that pretty good state transfer is
characterized by the spectrum of certain Hermitian adjacency matrix of the
graph; more specifically, the vertices involved in pretty good state transfer
must be $m$-strongly cospectral relative to this matrix, and the arccosines of
its eigenvalues must satisfy some number theoretic conditions. Using normalized
adjacency matrices, cyclic covers, and the theory on linear relations between
geodetic angles, we construct several infinite families of walks that exhibits
this phenomenon.


Abstract:
  We study ternary sequences associated with a multidimensional continued
fraction algorithm introduced by the first author. The algorithm is defined by
two matrices and we show that it is measurably isomorphic to the shift on the
set $\{1,2\}^\mathbb{N}$ of directive sequences. For a given set $\mathcal{C}$
of two substitutions, we show that there exists a $\mathcal{C}$-adic sequence
for every vector of letter frequencies or, equivalently, for every directive
sequence. We show that their factor complexity is at most $2n+1$ and is $2n+1$
if and only if the letter frequencies are rationally independent if and only if
the $\mathcal{C}$-adic representation is primitive. It turns out that in this
case, the sequences are dendric. We also prove that $\mu$-almost every
$\mathcal{C}$-adic sequence is balanced, where $\mu$ is any shift-invariant
ergodic Borel probability measure on $\{1,2\}^\mathbb{N}$ giving a positive
measure to the cylinder $[12121212]$. We also prove that the second Lyapunov
exponent of the matrix cocycle associated with the measure $\mu$ is negative.


Abstract:
  We classify which local problems with inputs on oriented paths have so-called
Borel solution and show that this class of problems remains the same if we
instead require a measurable solution, a factor of iid solution, or a solution
with the property of Baire.
  Together with the work from the field of distributed computing [Balliu et al.
PODC 2019], the work from the field of descriptive combinatorics [Gao et al.
arXiv:1803.03872, Bernshteyn arXiv:2004.04905] and the work from the field of
random processes [Holroyd et al. Annals of Prob. 2017, Greb\'ik, Rozho\v{n}
arXiv:2103.08394], this finishes the classification of local problems with
inputs on oriented paths using complexity classes from these three fields.
  A simple picture emerges: there are four classes of local problems and most
classes have natural definitions in all three fields.
  Moreover, we now know that randomness does \emph{not} help with solving local
problems on oriented paths.


Abstract:
  Dense depth estimation is essential to scene-understanding for autonomous
driving. However, recent self-supervised approaches on monocular videos suffer
from scale-inconsistency across long sequences. Utilizing data from the
ubiquitously copresent global positioning systems (GPS), we tackle this
challenge by proposing a dynamically-weighted GPS-to-Scale (g2s) loss to
complement the appearance-based losses. We emphasize that the GPS is needed
only during the multimodal training, and not at inference. The relative
distance between frames captured through the GPS provides a scale signal that
is independent of the camera setup and scene distribution, resulting in richer
learned feature representations. Through extensive evaluation on multiple
datasets, we demonstrate scale-consistent and -aware depth estimation during
inference, improving the performance even when training with low-frequency GPS
data.


Abstract:
  Animal behavior and neural recordings show that the brain is able to measure
both the intensity of an odor and the timing of odor encounters. However,
whether intensity or timing of odor detections is more informative for
olfactory-driven behavior is not understood. To tackle this question, we
consider the problem of locating a target using the odor it releases. We ask
whether the position of a target is best predicted by measures of timing
intensity of its odor, sampled for a short period of time. To answer this
question, we feed data from accurate numerical simulations of odor transport to
machine learning algorithms that learn how to connect odor to target location.
We find that both intensity and timing can separately predict target location
even from a distance of several meters; however their efficacy varies with the
dilution of the odor in space. Thus organisms that use olfaction from different
ranges may have to switch among different modalities. This has implications on
how the brain should represent odors as the target is approached. We
demonstrate simple strategies to improve accuracy and robustness of the
prediction by modifying odor sampling and appropriately combining distinct
measures together. To test the predictions, animal behavior and odor
representation should be monitored as the animal moves relative to the target,
or in virtual conditions that mimic concentrated dilute environments.


Abstract:
  We report on a flexible 300 mm process that optimally combines optical and
electron beam lithography to fabricate silicon spin qubits. It enables
on-the-fly layout design modifications while allowing devices with either n- or
p-type ohmic implants, a pitch smaller than 100 nm, and uniform critical
dimensions down to 30 nm with a standard deviation ~ 1.6 nm. Various n- and
p-type qubits are characterized in a dilution refrigerator at temperatures ~ 10
mK. Electrical measurements demonstrate well-defined quantum dots, tunable
tunnel couplings, and coherent spin control, which are essential requirements
for the implementation of a large-scale quantum processor.


Abstract:
  Distant supervision tackles the data bottleneck in NER by automatically
generating training instances via dictionary matching. Unfortunately, the
learning of DS-NER is severely dictionary-biased, which suffers from spurious
correlations and therefore undermines the effectiveness and the robustness of
the learned models. In this paper, we fundamentally explain the dictionary bias
via a Structural Causal Model (SCM), categorize the bias into intra-dictionary
and inter-dictionary biases, and identify their causes. Based on the SCM, we
learn de-biased DS-NER via causal interventions. For intra-dictionary bias, we
conduct backdoor adjustment to remove the spurious correlations introduced by
the dictionary confounder. For inter-dictionary bias, we propose a causal
invariance regularizer which will make DS-NER models more robust to the
perturbation of dictionaries. Experiments on four datasets and three DS-NER
models show that our method can significantly improve the performance of
DS-NER.


Abstract:
  This chapter presents some novel information theoretic results for the
analysis of stationary time series in the frequency domain. In particular, the
spectral distribution that corresponds to the most uncertain or unpredictable
time series with some values of the autocovariance function fixed, is the
generalized von Mises spectral distribution. It is thus a maximum entropy
spectral distribution and the corresponding stationary time series is called
the generalized von Mises time series. The generalized von Mises distribution
is used in directional statistics for modelling planar directions that follow a
multimodal distribution. Furthermore, the Gaussian-generalized von Mises times
series is presented as the stationary time series that maximizes entropies in
frequency and time domains, respectively referred to as spectral and temporal
entropies. Parameter estimation and some computational aspects with this time
series are briefly analyzed.


Abstract:
  We study the convergence towards a unique equilibrium distribution of the
solutions to a time-discrete model with non-overlapping generations arising in
quantitative genetics. The model describes the dynamics of a phenotypic
distribution with respect to a multi-dimensional trait, which is shaped by
selection and Fisher's infinitesimal model of sexual reproduction. We extend
some previous works devoted to the time-continuous analogues, that followed a
perturbative approach in the regime of weak selection, by exploiting the
contractivity of the infinitesimal model operator in the Wasserstein metric.
Here, we tackle the case of quadratic selection by a global approach. We
establish uniqueness of the equilibrium distribution and exponential
convergence of the renormalized profile. Our technique relies on an accurate
control of the propagation of information across the large binary trees of
ancestors (the pedigree chart), and reveals an ergodicity property, meaning
that the shape of the initial datum is quickly forgotten across generations. We
combine this information with appropriate estimates for the emergence of
Gaussian tails and propagation of quadratic and exponential moments to derive
quantitative convergence rates. Our result can be interpreted as a
generalization of the Krein-Rutman theorem in a genuinely non-linear, and
non-monotone setting.


Abstract:
  In this paper, we generalize text infilling (e.g., masked language models) by
proposing Sequence Span Rewriting (SSR) as a self-supervised
sequence-to-sequence (seq2seq) pre-training objective. SSR provides more
fine-grained learning signals for text representations by supervising the model
to rewrite imperfect spans to ground truth, and it is more consistent than text
infilling with many downstream seq2seq tasks that rewrite a source sentences
into a target sentence. Our experiments with T5 models on various seq2seq tasks
show that SSR can substantially improve seq2seq pre-training. Moreover, we
observe SSR is especially helpful to improve pre-training a small-size seq2seq
model with a powerful imperfect span generator, which indicates a new
perspective of transferring knowledge from a large model to a smaller model for
seq2seq pre-training.


Abstract:
  We explore the effects on nuclear bulk properties of using regularization
cutoffs larger than the nucleon mass within the chiral effective field theory
using a power counting that ensures order-by-order renormalization in the
two-nucleon system. To do so we calculate ground-state properties of the
$^{16}$O nucleus in the Hartree--Fock approach in a basis made up of plane
waves confined in a cube. We find a strong sensitivity to the regularization
cutoff through the counter-terms in attractive singular partial waves and to
the correction for spurious deeply bound states. This questions the possibility
of testing in nuclei the renormalization-group invariance of renormalizable
potentials from chiral effective field theory at leading order. A possible way
out of this problem is proposed.


Abstract:
  The bond fluctuation method is used to simulate both non-concatenated
entangled and interpenetrating melts of ring polymers. We find that the
swelling of interpenetrating rings upon dilution follows the same laws as for
linear chains. Knotting and linking probabilities of ring polymers in
semi-dilute solution are analyzed using the HOMFLY polynomial. We find an
exponential decay of the knotting probability of rings. The correlation length
of the semi-dilute solution can be used to superimpose knotting data at
different concentrations. A power law dependence $f_{n}\sim\phi
R^{2}\sim\phi^{0.77}N$ for the average number $f_{n}$ of linked rings per ring
at concentrations larger than the overlap volume fraction of rings $\phi^{*}$
is determined from the simulation data. The fraction of non-concatenated rings
displays an exponential decay $P_{OO}\sim\exp(-f_{n})$, which indicates $f_{n}$
to provide the entropic effort for not forming concatenated conformations.
Based upon this results we find four different regimes for the conformations of
rings in melts that are separated by a critical lengths $N_{OO}$, $N_{C}$ and
$N^{*}$. $N_{OO}$ describes the onset of the effect of non-concatenation below
which topological effects are not important, $N_{C}$ is the cross-over between
weak and strong compression of rings, and $N^{*}$ is defined by the cross-over
from a non-concatenation contribution $f_{n}\sim\phi R^{2}$ to an overlap
dominated concatenation contribution $f_{n}\sim\phi N^{1/2}$ at $N>N^{*}$. For
$N_{OO}<N<N_{C}$, the scaling of ring sizes $R\sim N^{2/5}$ results from
balancing non-concatenation with weak compression of rings. For
$N_{C}<N<N^{*}$, non-concatenation and strong compression imply $R\sim
N^{3/8}$. Our simulation data for non-interpenetrating rings up to $N=1024$ are
in good agreement with the prediction for weakly compressed rings.


Abstract:
  Some large freight railroads ship a number of shipments over the rail network
annually. To reduce unnecessary reclassifications of shipments on their routes,
each railroad is willing to operate the entire train for an individual
shipment. In other words, the motivation for providing the entire train service
lies in a simple realization that door to door transportation (directly from
origin to destination) can reduce operating costs by decreasing classification.
However, this mode will increase inventory costs for customers when commodities
are transported by low frequency entire train services. Thus, this study
proposes the trade-off strategy to keep a balance between saving operating
costs of railroads and increasing inventory costs of customers. We analyze the
revenue and losses after a shipment shifting from the transfer transportation
which contains a series of train services to the direct transportation by
entire train service.


Abstract:
  We propose that the optomechanical systems can be potential platforms to
implement the Fr\"{o}hlich condensate of phonons. We consider a one-dimensional
array of membranes coupled to the cavity field via a quadratic interaction, and
the cavity is pumped by an external laser. Analytical and numerical results
predict that the high phonon occupancy of the lowest or highest mechanical mode
is achievable depending on the detuning of the driving laser, the optomechnical
strength, and the temperature. The decoherence of the Fr\"{o}hlich condensate
can be largely suppressed by the large number of membranes. Our results shed
light on narrow-linewidth phonon laser, energy conversion/transfer, and
efficient multimode cooling.


Abstract:
  Cryptic crosswords, the dominant crossword variety in the UK, are a promising
target for advancing NLP systems that seek to process semantically complex,
highly compositional language. Cryptic clues read like fluent natural language
but are adversarially composed of two parts: a definition and a wordplay cipher
requiring character-level manipulations. Expert humans use creative
intelligence to solve cryptics, flexibly combining linguistic, world, and
domain knowledge. In this paper, we make two main contributions. First, we
present a dataset of cryptic clues as a challenging new benchmark for NLP
systems that seek to process compositional language in more creative,
human-like ways. After showing that three non-neural approaches and T5, a
state-of-the-art neural language model, do not achieve good performance, we
make our second main contribution: a novel curriculum approach, in which the
model is first fine-tuned on related tasks such as unscrambling words.We also
introduce a challenging data split, examine the meta-linguistic capabilities of
subword-tokenized models, and investigate model systematicity by perturbing the
wordplay part of clues, showing that T5 exhibits behavior partially consistent
with human solving strategies. Although our curricular approach considerably
improves on the T5 baseline, our best-performing model still fails to
generalize to the extent that humans can. Thus, cryptic crosswords remain an
unsolved challenge for NLP systems and a potential source of future innovation.


Abstract:
  China has made great achievements in electric power industry during the
long-term deepening of reform and opening up. However, the complex regional
economic, social and natural conditions, electricity resources are not evenly
distributed, which accounts for the electricity deficiency in some regions of
China. It is desirable to develop a robust electricity forecasting model.
Motivated by which, we propose a Panel Semiparametric Quantile Regression
Neural Network (PSQRNN) by utilizing the artificial neural network and
semiparametric quantile regression. The PSQRNN can explore a potential linear
and nonlinear relationships among the variables, interpret the unobserved
provincial heterogeneity, and maintain the interpretability of parametric
models simultaneously. And the PSQRNN is trained by combining the penalized
quantile regression with LASSO, ridge regression and backpropagation algorithm.
To evaluate the prediction accuracy, an empirical analysis is conducted to
analyze the provincial electricity consumption from 1999 to 2018 in China based
on three scenarios. From which, one finds that the PSQRNN model performs better
for electricity consumption forecasting by considering the economic and
climatic factors. Finally, the provincial electricity consumptions of the next
$5$ years (2019-2023) in China are reported by forecasting.


Abstract:
  We propose an extension of the Schwinger parametric representation for
Feynman amplitudes in $D$ euclidean dimensions to a scenario where $d$
dimensions are compactified ($d<D$) through the introduction of periodic
boundary conditions in space. We obtain two valid representations, one useful
near the bulk (large compactification length) and another useful near the
dimensional reduction (small compactification length). Also, to illustrate, we
exhibit some Feynman amplitudes up to three loops in a compactified scalar
field theory.


Abstract:
  Double Q-learning is a popular reinforcement learning algorithm in Markov
decision process (MDP) problems. Clipped Double Q-learning, as an effective
variant of Double Q-learning, employs the clipped double estimator to
approximate the maximum expected action value. Due to the underestimation bias
of the clipped double estimator, performance of clipped Double Q-learning may
be degraded in some stochastic environments. In this paper, in order to reduce
the underestimation bias, we propose an action candidate based clipped double
estimator for Double Q-learning. Specifically, we first select a set of elite
action candidates with the high action values from one set of estimators. Then,
among these candidates, we choose the highest valued action from the other set
of estimators. Finally, we use the maximum value in the second set of
estimators to clip the action value of the chosen action in the first set of
estimators and the clipped value is used for approximating the maximum expected
action value. Theoretically, the underestimation bias in our clipped Double
Q-learning decays monotonically as the number of the action candidates
decreases. Moreover, the number of action candidates controls the trade-off
between the overestimation and underestimation biases. In addition, we also
extend our clipped Double Q-learning to continuous action tasks via
approximating the elite continuous action candidates. We empirically verify
that our algorithm can more accurately estimate the maximum expected action
value on some toy environments and yield good performance on several benchmark
problems.


Abstract:
  Matching and recommending products is beneficial for both customers and
companies. With the rapid increase in home goods e-commerce, there is an
increasing demand for quantitative methods for providing such recommendations
for millions of products. This approach is facilitated largely by online stores
such as Amazon and Wayfair, in which the goal is to maximize overall sales.
Instead of focusing on overall sales, we take a product design perspective, by
employing big-data analysis for determining the design qualities of a highly
recommended product. Specifically, we focus on the visual style compatibility
of such products. We build off previous work which implemented a style-based
similarity metric for thousands of furniture products. Using analysis and
visualization, we extract attributes of furniture products that are highly
compatible style-wise. We propose a designer in-the-loop workflow that mirrors
methods of displaying similar products to consumers browsing e-commerce
websites. Our findings are useful when designing new products, since they
provide insight regarding what furniture will be strongly compatible across
multiple styles, and hence, more likely to be recommended.


Abstract:
  In background dynamics of a spatially flat FLRW model of the universe, we
investigate an interacting scenario of quintessence scalar field as dark energy
which interacts with pressure-less dust as dark matter, mass of which varies
with time through scalar field in context of Lyra's geometry. Mass of dark
matter particles and the potential of the quintessence field are considered to
be functions depending exponentially on scalar field. Cosmological evolution
equations in this context are converted into an autonomous system of ordinary
differential equations by suitable transformation of the cosmological variables
into dimensionless dynamical variables and the nature of critical points are
found by applying linear stability theory. We have obtained cosmologically
interested critical points which describe the attractor solutions at late
times. Some of which representing the accelerated scalar field-dark matter
scaling solutions in attractor regime with similar order of energy densities
can provide the possible solution of the coincidence problem.


Abstract:
  In order to explore the practical potential and needs of interdisciplinary
knowledge and competence requirements of Blockchain technology, the project
activity "Development of Interdisciplinary Blockchain Skills Concept" starts
with the literature review identifying the state of the art of Blockchain in
Supply Chain Management and Logistics, Business and Finance, as well as
Computer Science and IT-Security. The project activity further explores the
academic and industry landscape of existing initiatives in education which
offer Blockchain courses. Moreover, job descriptions and adverts are analyzed
in order to specify today's competence requirements from enterprises. To
discuss and define the future required competence, expert workshops are
organized to validate the findings by academic experts. Based on the research
outcome and validation, an interdisciplinary approach for Blockchain competence
is developed.
  A second part focuses on the development of the Blockchain Best Practices
activity while conducting qualitative empirical research based on case studies
with industry representatives. Therefore, company interviews, based on the
theoretical basis of Output 1, explore existing Blockchain use cases in
different sectors. Due to the interdisciplinary importance of Blockchain
technology, these skills will be defined by different perspectives of
Blockchain from across multiple mentioned disciplines. The use cases and
companies for the interviews will be selected based on various sampling
criteria to gain results valid for a broad scale. The analysis of the various
use cases will be conducted and defined in a standardized format to identify
the key drivers and competence requirements for Blockchain technology
applications and their adoption. On the one hand, this approach ensures
comparability, on the other hand, it facilitates the development of a
structured and systematic framework.


Abstract:
  Mean-Field Ring Polymer Molecular Dynamics (MF-RPMD) is a powerful,
efficient, and accurate method for approximate quantum dynamic simulations of
multi-level system dynamics. Initial efforts to compute nonadiabatic reaction
rates using MF-RPMD were not successful; recent work showed that this can be
remedied by including a simple, if adhoc, correction term that accounts for the
formation of `kinked' or mixed electronic state ring polymer configurations.
Here, we build on this idea, introducing a electronic state population based
reaction coordinate and novel skew dividing surface that constrains nuclear
positions to configurations where the reactant and product state potentials are
near-degenerate and that samples kinked electronic state configurations. We
then demonstrate the numerical accuracy of this method in computing rates for a
series of nonadiabatic model systems.


Abstract:
  Intravascular ultrasound and optical coherence tomography are widely
available for characterizing coronary stenoses and provide critical vessel
parameters to optimize percutaneous intervention. Intravascular
polarization-sensitive optical coherence tomography (PS-OCT) simultaneously
provides high-resolution cross-sectional images of vascular structures while
also revealing preponderant tissue components such as collagen and smooth
muscle and thereby enhances plaque characterization. Automated interpretation
of these features promises to facilitate the objective clinical investigation
of the natural history and significance of coronary atheromas. Here, we propose
a convolutional neural network model, optimized using a new multi-term loss
function, to classify the lumen, intima, and media layers in addition to the
guidewire and plaque shadows. We demonstrate that our multi-class
classification model outperforms state-of-the-art methods in detecting the
coronary anatomical layers. Furthermore, the proposed model segments two
classes of common imaging artifacts and detects the anatomical layers within
the thickened vessel wall regions that were excluded from analysis by other
studies. The source code and the trained model are publicly available at
https://github.com/mhaft/OCTseg


Abstract:
  This work presents a small-signal high-frequency (HF) equivalent circuit (EC)
to model AC performances of black-phosphorous field-effect transistors
(BPFETs). The proposed EC is able to describe correctly both the experimental
HF intrinsic and extrinsic figures of merit, as well as S-parameters, from
different BPFET technologies. Single- and double-stage radio frequency gain
amplifiers, are designed at 2.4 GHz using the experimentally-calibrated
small-signal BPFET EC. Results show high-gain high-selective BPFET-based
amplifiers.


Abstract:
  We consider a combination of local and nonlocal $p$-Laplace equations and
discuss several regularity properties of weak solutions. More precisely, we
establish local boundedness of weak subsolutions, local H\"older continuity of
weak solutions, Harnack inequality for weak solutions and weak Harnack
inequality for weak supersolutions. We also discuss lower semicontinuity of
weak supersolutions as well as upper semicontinuity of weak subsolutions. Our
approach is purely analytic and it is based on the De Giorgi-Nash-Moser theory,
the expansion of positivity and estimates involving a tail term. The main
results apply to sign changing solutions and capture both local and nonlocal
features of the equation.


Abstract:
  Modeling realistic fluid and plasma flows is computationally intensive,
motivating the use of reduced-order models for a variety of scientific and
engineering tasks. However, it is challenging to characterize, much less
guarantee, the global stability (i.e., long-time boundedness) of these models.
The seminal work of Schlegel and Noack (JFM, 2015) provided a theorem outlining
necessary and sufficient conditions to ensure global stability in systems with
energy-preserving, quadratic nonlinearities, with the goal of evaluating the
stability of projection-based models. In this work, we incorporate this theorem
into modern data-driven models obtained via machine learning. First, we propose
that this theorem should be a standard diagnostic for the stability of
projection-based and data-driven models, examining the conditions under which
it holds. Second, we illustrate how to modify the objective function in machine
learning algorithms to promote globally stable models, with implications for
the modeling of fluid and plasma flows. Specifically, we introduce a modified
"trapping SINDy" algorithm based on the sparse identification of nonlinear
dynamics (SINDy) method. This method enables the identification of models that,
by construction, only produce bounded trajectories. The effectiveness and
accuracy of this approach are demonstrated on a broad set of examples of
varying model complexity and physical origin, including the vortex shedding in
the wake of a circular cylinder.


Abstract:
  We present the confirmation of the eccentric warm giant planet TOI-201 b,
first identified as a candidate in \textit{TESS} photometry (Sectors 1-8,
10-13, and 27-28) and confirmed using ground-based photometry from NGTS and
radial velocities from FEROS, HARPS, CORALIE, and \textsc{Minerva}-Australis.
TOI-201 b orbits a young ($\mathrm{0.87^{+0.46}_{-0.49} \, Gyr}$) and
bright(V=9.07 mag) F-type star with a $\mathrm{52.9781 \, d}$ period. The
planet has a mass of $\mathrm{0.42^{+0.05}_{-0.03}\, M_J}$, a radius of
$\mathrm{1.008^{+0.012}_{-0.015}\, R_J}$, and an orbital eccentricity of
$0.28^{+0.06}_{-0.09}$; it appears to still be undergoing fairly rapid cooling,
as expected given the youth of the host star. The star also shows long-term
variability in both the radial velocities and several activity indicators,
which we attribute to stellar activity. The discovery and characterization of
warm giant planets such as TOI-201 b is important for constraining formation
and evolution theories for giant planets.


Abstract:
  Data augmentation has been an indispensable tool to improve the performance
of deep neural networks, however the augmentation can hardly transfer among
different tasks and datasets. Consequently, a recent trend is to adopt AutoML
technique to learn proper augmentation policy without extensive hand-crafted
tuning. In this paper, we propose an efficient differentiable search algorithm
called Direct Differentiable Augmentation Search (DDAS). It exploits
meta-learning with one-step gradient update and continuous relaxation to the
expected training loss for efficient search. Our DDAS can achieve efficient
augmentation search without relying on approximations such as Gumbel Softmax or
second order gradient approximation. To further reduce the adverse effect of
improper augmentations, we organize the search space into a two level
hierarchy, in which we first decide whether to apply augmentation, and then
determine the specific augmentation policy. On standard image classification
benchmarks, our DDAS achieves state-of-the-art performance and efficiency
tradeoff while reducing the search cost dramatically, e.g. 0.15 GPU hours for
CIFAR-10. In addition, we also use DDAS to search augmentation for object
detection task and achieve comparable performance with AutoAugment, while being
1000x faster.