Abstract:
  Recommender system recommends interesting items to users based on users' past
information history. Researchers have been paying attention to improvement of
algorithmic performance such as MAE and precision@K. Major techniques such as
matrix factorization and learning to rank are optimized based on such
evaluation metrics. However, the intrinsic Matthew Effect problem poses great
threat to the fairness of the recommender system, and the unfairness problem
cannot be resolved by optimization of traditional metrics. In this paper, we
propose a novel algorithm that incorporates Matthew Effect reduction with the
matrix factorization framework. We demonstrate that our approach can boost the
fairness of the algorithm and enhances performance evaluated by traditional
metrics.


Abstract:
  One of the key signatures of the Quark Gluon Plasma (QGP) is the energy loss
of high momentum particles as they traverse the strongly interacting medium.
The energy loss of these particles is governed by the jet transport coefficient
$\hat{q}/T^3$, wherein it has been thought that there is a large jump as the
system transitions between the hadron gas and Quark Gluon Plasma phases. Here
we calculate $\hat{q}/T^3$ within the Hadron Resonance Gas (HRG) model with the
particle list PDG16+ and find that, if one incorporates the experimental error
in the hadronic calculation of $\hat{q}/T^3$ and assumes a higher
pseudo-critical temperature, then a smooth transition from the hadron gas phase
into the Quark Gluon Plasma phase is possible. We also find a significant
enhancement in $\hat{q}/T^3$ at finite baryon chemical potential and find
issues with the relationship between the shear viscosity and the jet transport
coefficient within a hadron gas phase.


Abstract:
  In recent times, object detection and pose estimation have gained significant
attention in the context of robotic vision applications. Both the
identification of objects of interest as well as the estimation of their pose
remain important capabilities in order for robots to provide effective
assistance for numerous robotic applications ranging from household tasks to
industrial manipulation. This problem is particularly challenging because of
the heterogeneity of objects having different and potentially complex shapes,
and the difficulties arising due to background clutter and partial occlusions
between objects. As the main contribution of this work, we propose a system
that performs real-time object detection and pose estimation, for the purpose
of dynamic robot grasping. The robot has been pre-trained to perform a small
set of canonical grasps from a few fixed poses for each object. When presented
with an unknown object in an arbitrary pose, the proposed approach allows the
robot to detect the object identity and its actual pose, and then adapt a
canonical grasp in order to be used with the new pose. For training, the system
defines a canonical grasp by capturing the relative pose of an object with
respect to the gripper attached to the robot's wrist. During testing, once a
new pose is detected, a canonical grasp for the object is identified and then
dynamically adapted by adjusting the robot arm's joint angles, so that the
gripper can grasp the object in its new pose. We conducted experiments using a
humanoid PR2 robot and showed that the proposed framework can detect
well-textured objects, and provide accurate pose estimation in the presence of
tolerable amounts of out-of-plane rotation. The performance is also illustrated
by the robot successfully grasping objects from a wide range of arbitrary
poses.


Abstract:
  Modern deep learning applications require increasingly more compute to train
state-of-the-art models. To address this demand, large corporations and
institutions use dedicated High-Performance Computing clusters, whose
construction and maintenance are both environmentally costly and well beyond
the budget of most organizations. As a result, some research directions become
the exclusive domain of a few large industrial and even fewer academic actors.
To alleviate this disparity, smaller groups may pool their computational
resources and run collaborative experiments that benefit all participants. This
paradigm, known as grid- or volunteer computing, has seen successful
applications in numerous scientific areas. However, using this approach for
machine learning is difficult due to high latency, asymmetric bandwidth, and
several challenges unique to volunteer computing. In this work, we carefully
analyze these constraints and propose a novel algorithmic framework designed
specifically for collaborative training. We demonstrate the effectiveness of
our approach for SwAV and ALBERT pretraining in realistic conditions and
achieve performance comparable to traditional setups at a fraction of the cost.
Finally, we provide a detailed report of successful collaborative language
model pretraining with 40 participants.


Abstract:
  The boundary element method is an efficient algorithm for simulating acoustic
propagation through homogeneous objects embedded in free space. The
conditioning of the system matrix strongly depends on physical parameters such
as density, wavespeed and frequency. In particular, high contrast in density
and wavespeed across a material interface leads to an ill-conditioned
discretisation matrix. Therefore, the convergence of Krylov methods to solve
the linear system is slow. Here, specialised boundary integral formulations are
designed for the case of acoustic scattering at high-contrast media. The
eigenvalues of the resulting system matrix accumulate at two points in the
complex plane that depend on the density ratio and stay away from zero. The
spectral analysis of the Calder\'on preconditioned PMCHWT formulation yields a
single accumulation point. Benchmark simulations demonstrate the computational
efficiency of the high-contrast Neumann formulation for scattering at
high-contrast media.


Abstract:
  We theoretically investigated the Floquet states of bilayer graphene
heterostructure under the irradiation by horizontally incident circular
polarized light. The in-plane and out-of-plane electric field of the light
periodically perturbs the intra-layer and inter-layer hopping, respectively.
For circular polarized light, the two components of the electric field has
$\pi/2$ phase difference, so that the two types of hopping are periodically
perturbed with the $\pi/2$ phase difference, which modify the effective
inter-layer hopping. We focus on the model of bilayer graphene in the
heterostructure of antiferromagnetic van der Walls spin valve. The amplitude of
the irradiation can tune the band gap and topological properties of the bulk
state. The spin-polarized quantum anomalous Hall phase with Chern number being
one is predicted. The incident angle of the irradiation can tune the band gap
and dispersion of the edge states in zigzag nanoribbons.


Abstract:
  Tensor decomposition is a popular technique for tensor completion, However
most of the existing methods are based on linear or shallow model, when the
data tensor becomes large and the observation data is very small, it is prone
to over fitting and the performance decreases significantly. To address this
problem, the completion method for a tensor based on a Biased Deep Tensor
Factorization Network (BDTFN) is proposed. This method can not only overcome
the shortcomings of traditional tensor factorization, but also deal with
complex non-linear data. Firstly, the horizontal and lateral tensors
corresponding to the observed values of the input tensors are used as inputs
and projected to obtain their horizontal (lateral) potential feature tensors.
Secondly, the horizontal (lateral) potential feature tensors are respectively
constructed into a multilayer perceptron network. Finally, the horizontal and
lateral output tensors are fused by constructing a bilinear pooling layer.
Tensor forward-propagation is composed of those three step, and its parameters
are updated by tensor back-propagation using the multivariable chain rule. In
this paper, we consider the large-scale 5-minute traffic speed data set and use
it to address the missing data imputation problem for large-scale
spatiotemporal traffic data. In addition, we compare the numerical performance
of the proposed algorithm with those for state-of-the-art approaches on video
recovery and color image recovery. Numerical experimental results illustrate
that our approach is not only much more accurate than those state-of-the-art
methods, but it also has high speed.


Abstract:
  Explaining to users why some items are recommended is critical, as it can
help users to make better decisions, increase their satisfaction, and gain
their trust in recommender systems (RS). However, existing explainable RS
usually consider explanation as a side output of the recommendation model,
which has two problems: (1) it is difficult to evaluate the produced
explanations because they are usually model-dependent, and (2) as a result, how
the explanations impact the recommendation performance is less investigated.
  In this paper, explaining recommendations is formulated as a ranking task,
and learned from data, similar to item ranking for recommendation. This makes
it possible for standard evaluation of explanations via ranking metrics (e.g.,
NDCG). Furthermore, we extend traditional item ranking to an item-explanation
joint-ranking formalization to study if purposely selecting explanations could
achieve certain learning goals, e.g., improving recommendation performance. A
great challenge, however, is that the sparsity issue in the
user-item-explanation data would be inevitably severer than that in traditional
user-item interaction data, since not every user-item pair can be associated
with all explanations. To mitigate this issue, we propose to perform two sets
of matrix factorization by considering the ternary relationship as two groups
of binary relationships. Experiments on three large datasets verify our
solution's effectiveness on both item recommendation and explanation ranking.
To facilitate the development of explainable RS, we plan to make our code
publicly available.


Abstract:
  Short-lived radioactive nuclei (SLR) with mean lives below 100 Myr provide us
with unique insights into current galactic nucleosynthetic events, as well as
events that contributed to the material of our Solar System more that 4.6 Gyr
ago. Here we present a statistical analysis of the ratios of these radioactive
nuclei at the time of early Solar System (ESS) using both analytical
derivations and Monte Carlo methods. We aim to understand the interplay between
the production frequency and the mean lives of these isotopes, and its impact
on their theoretically predicted ratios in the interstellar medium (ISM). We
find that when the ratio of two SRLs, instead of the ratios of each single SLR
relative to its stable or long-lived isotope, is considered, not only the
uncertainties related to the galactic chemical evolution of the stable isotope
are completely eliminated, but also the statistical uncertainties are much
lower. We identify four ratios, 247Cm/129I, 107Pd/182Hf, 97Tc/98Tc, and
53Mn/97Tc, that have the potential to provide us with new insights into the r-,
s-, and p-process nucleosynthesis at the time of the formation of the Sun, and
need to be studied using variable stellar yields. Additionally, the latter two
ratios need to be better determined in the ESS to allow us to fully exploit
them to investigate the galactic sites of the p process.


Abstract:
  We consider the Gaussian free field $\varphi$ on $\mathbb{Z}^d$, for
$d\geq3$, and give sharp bounds on the probability that the radius of a finite
cluster in the excursion set $\{\varphi \geq h\}$ exceeds a large value $N$,
for any height $h \neq h_*$, where $h_*$ refers to the corresponding
percolation critical parameter. In dimension $d=3$, we prove that this
probability is sub-exponential in $N$ and decays as
$\exp\{-\frac{\pi}{6}(h-h_*)^2 \frac{N}{\log N} \}$ as $N \to \infty$ to
principal exponential order. When $d\geq 4$, we prove that these tails decay
exponentially in $N$. Our results extend to other quantities of interest, such
as truncated two-point functions and the two-arms probability for annuli
crossings at scale N.


Abstract:
  In this Letter, we introduce a paradigm to realize magneto-mechanical
metastructures inspired by multi-layer 2D materials, such as graphene bilayers.
The metastructures are intended to capture two aspects of their nanoscale
counterparts. One is the multi-layer geometry, which is implemented by stacking
hexagonal lattice sheets. The other is the landscape of weak inter-layer
forces, which is mimicked by the interactions between pairs of magnets located
at corresponding lattice sites on adjacent layers. We illustrate the potential
of this paradigm through a three-layer prototype. The two rigid outer lattices
serve as control layers, while the thin inner layer is free to experience
flexural motion under the confining action of the magnetic forces exchanged
with the outer ones, thus behaving as a lattice on elastic foundation. The
inner layer is free to rotate relatively to the others, giving rise to a rich
spectrum of inter-layer interaction patterns. Our objective is to determine how
the dynamical response can be tuned by changing the twist angle between the
layers. Specifically, we demonstrate experimentally that switching between
different stacking patterns has profound consequences on the phonon landscape,
opening and closing bandgaps in different frequency regimes.


Abstract:
  The infinite affine Lie algebras of type ABCD, also called
$\widehat{\mathfrak{gl}}(\infty)$, $\widehat{\mathfrak{o}}(\infty)$,
$\widehat{\mathfrak{sp}}(\infty)$, are equivalent to subalgebras of the quantum
$W_{1+\infty}$ algebras. They have well-known representations on the Fock space
of either a Dirac fermion ($\hat A_\infty$), a Majorana fermion ($\hat
B_\infty$ and $\hat D_\infty$) or a symplectic boson ($\hat C_\infty$).
Explicit formulas for the action of the quantum $W_{1+\infty}$ subalgebras on
the Fock states are proposed for each representation. These formulas are the
equivalent of the \textit{vertical presentation} of the quantum toroidal
$\mathfrak{gl}(1)$ algebra Fock representation. They provide an alternative to
the fermionic and bosonic expressions of the \textit{horizontal presentation}.
Furthermore, these algebras are known to have a deep connection with symmetric
polynomials. The action of the quantum $W_{1+\infty}$ generators leads to the
derivation of Pieri-like rules and q-difference equations for these
polynomials. In the specific case of $\hat B_\infty$, a q-difference equation
is obtained for $Q$-Schur polynomials indexed by strict partitions.


Abstract:
  Similar to the case of solar system planets, reflected starlight from
exoplanets is expected to be polarized due to atmospheric scattering and the
net disk integrated polarization should be non-zero owing to the asymmetrical
illumination of the planetary disk. The computation of the disk-integrated
reflected flux and its state of polarization involves techniques for the
calculation of the local reflection matrices as well as the numerical recipes
for integration over the planetary disks. In this paper, we present a novel
approach to calculate the azimuth-dependent reflected intensity vectors at each
location on the planetary disk divided into grids. We achieve this by solving
the vector radiative transfer equations that describe linear polarization. Our
calculations incorporate self-consistent atmospheric models of exoplanets over
a wide range of equilibrium temperature, surface gravity, atmospheric
composition, and cloud structure. A comparison of the flux and the amount of
polarization calculated by considering both single and multiple scattering
exhibits the effect of depolarization due to multiple scattering of light
depending on the scattering albedo of the atmosphere. We have benchmarked our
basic calculations against some of the existing models. We have also presented
our models for the hot Jupiter HD 189733 b, indicating the level of precision
required by future observations to detect the polarization of this planet in
the optical and near-infrared wavelength region. The generic nature and the
accuracy offered by our models make them an effective tool for modeling the
future observations of the polarized light reflected from exoplanets.


Abstract:
  The origins of thermodynamics from the microscopic properties of matter have
not been satisfactorily accounted for. This work presents a formulation that
connects Lagrangian mechanics to thermodynamics. By using such a formulation
and following similar steps to those performed in the laboratory, the heat
capacities, energetic and mechanical response coefficients, absorbed and
emitted heats, entropy changes, and thermodynamic energies of a prototype water
system, employed as an illustrative example, are found to be close to the
experimental results on water bulk. The present formulation is realistic. After
connecting Lagrangian mechanics to Langevin stochastic dynamics, the first law
of thermodynamics is derived. The formulation not only exhibits the
transformation of time-reversible equations onto time-irreversible equations,
and the molecular origins of heat as well, but also reveals new pathways to
investigate the thermodynamics of systems of nanometric sizes.


Abstract:
  Image-text matching plays a critical role in bridging the vision and
language, and great progress has been made by exploiting the global alignment
between image and sentence, or local alignments between regions and words.
However, how to make the most of these alignments to infer more accurate
matching scores is still underexplored. In this paper, we propose a novel
Similarity Graph Reasoning and Attention Filtration (SGRAF) network for
image-text matching. Specifically, the vector-based similarity representations
are firstly learned to characterize the local and global alignments in a more
comprehensive manner, and then the Similarity Graph Reasoning (SGR) module
relying on one graph convolutional neural network is introduced to infer
relation-aware similarities with both the local and global alignments. The
Similarity Attention Filtration (SAF) module is further developed to integrate
these alignments effectively by selectively attending on the significant and
representative alignments and meanwhile casting aside the interferences of
non-meaningful alignments. We demonstrate the superiority of the proposed
method with achieving state-of-the-art performances on the Flickr30K and MSCOCO
datasets, and the good interpretability of SGR and SAF modules with extensive
qualitative experiments and analyses.


Abstract:
  Lightning casualties cause tremendous loss to life and property. However,
very lately lightning has been considered as one of the major natural
calamities which is now studied or monitored with proper instrumentation. The
lightning characteristics over India have been studying by using daily data low
resolution time series and monthly data high resolution monthly climatology. We
have used ANN time series method (a neural network) to analyze the time series
and defined which one will be the best predictor of lightning over India. The
time series of lightning is output(dependent) and input (independent) are
k-index, AOD, Cape etc. The Gaussian process regression, support vector
machine, regression trees and linear regression defined the input variables.
Which show approximately linear relation.


Abstract:
  In this paper, we introduce a new concept, namely $\epsilon$-arithmetics, for
real vectors of any fixed dimension. The basic idea is to use vectors of
rational values (called rational vectors) to approximate vectors of real values
of the same dimension within $\epsilon$ range. For rational vectors of a fixed
dimension $m$, they can form a field that is an $m$th order extension
$\mathbf{Q}(\alpha)$ of the rational field $\mathbf{Q}$ where $\alpha$ has its
minimum polynomial of degree $m$ over $\mathbf{Q}$. Then, the arithmetics, such
as addition, subtraction, multiplication, and division, of real vectors can be
defined by using that of their approximated rational vectors within $\epsilon$
range. We also define complex conjugate of a real vector and then inner
products for two real vectors and two real vector sequences of the same length.


Abstract:
  An increasing share of captured images and videos are transmitted for storage
and remote analysis by computer vision algorithms, rather than to be viewed by
humans. Contrary to traditional standard codecs with engineered tools, neural
network based codecs can be trained end-to-end to optimally compress images
with respect to a target rate and any given differentiable performance metric.
Although it is possible to train such compression tools to achieve better
rate-accuracy performance for a particular computer vision task, it could be
practical and relevant to re-use the compressed bit-stream for multiple machine
tasks. For this purpose, we introduce 'Connectors' that are inserted between
the decoder and the task algorithms to enable a direct transformation of the
compressed content, which was previously optimized for a specific task, to
multiple other machine tasks. We demonstrate the effectiveness of the proposed
method by achieving significant rate-accuracy performance improvement for both
image classification and object segmentation, using the same bit-stream,
originally optimized for object detection.


Abstract:
  We prove upper bounds on the one-arm exponent $\eta_1$ for dependent
percolation models; while our main interest is level set percolation of smooth
Gaussian fields, the arguments apply to other models in the Bernoulli
percolation universality class, including Poisson-Voronoi and Poisson-Boolean
percolation. More precisely, in dimension $d=2$ we prove $\eta_1 \le 1/3$ for
Gaussian fields with rapid correlation decay (e.g.\ the Bargmann-Fock field),
and in general dimensions we prove $\eta_1 \le d/3$ for finite-range fields and
$\eta_1 \le d-2$ for fields with rapid correlation decay. Although these
results are classical for Bernoulli percolation (indeed they are best-known in
general), existing proofs do not extend to dependent percolation models, and we
develop a new approach based on exploration and relative entropy arguments. We
also establish a new Russo-type inequality for smooth Gaussian fields which we
use to prove the sharpness of the phase transition for finite-range fields.


Abstract:
  Due to real-time image semantic segmentation needs on power constrained edge
devices, there has been an increasing desire to design lightweight semantic
segmentation neural network, to simultaneously reduce computational cost and
increase inference speed. In this paper, we propose an efficient asymmetric
dilated semantic segmentation network, named EADNet, which consists of multiple
developed asymmetric convolution branches with different dilation rates to
capture the variable shapes and scales information of an image. Specially, a
multi-scale multi-shape receptive field convolution (MMRFC) block with only a
few parameters is designed to capture such information. Experimental results on
the Cityscapes dataset demonstrate that our proposed EADNet achieves
segmentation mIoU of 67.1 with smallest number of parameters (only 0.35M) among
mainstream lightweight semantic segmentation networks.


Abstract:
  We characterize the optical coherence and energy-level properties of the 795
nm $^3$H$_6$ to $^3$H$_4$ transition of Tm$^{3+}$ in a Ti$^{4+}$:LiNbO$_{3}$
waveguide at temperatures as low as 0.65 K. Coherence properties are measured
with varied temperature, magnetic field, optical excitation power and
wavelength, and measurement time-scale. We also investigate nuclear
spin-induced hyperfine structure and population dynamics with varying magnetic
field and laser excitation power. Except for accountable differences due to
difference Ti$^{4+}$ and Tm$^{3+}$-doping concentrations, we find that the
properties of Tm$^{3+}$:Ti$^{4+}$:LiNbO$_{3}$ produced by indiffusion doping
are consistent with those of a bulk-doped Tm$^{3+}$:LiNbO$_{3}$ crystal
measured under similar conditions. Our results, which complement previous work
in a narrower parameter space, support using rare-earth-ions for integrated
optical and quantum signal processing.


Abstract:
  We present a scattering model for nuclei with similar masses. In this
three-body model, the projectile has a core+valence structure, whereas the
target is identical to the core nucleus. The three-body wave functions must be
symmetrized for the exchange of the cores. This property gives rise to
non-local potentials, which are computed without approximation. The present
model is an extension of the Continuum Discretized Coupled Channel (CDCC)
formalism, with an additional treatment of core exchange. We solve the
coupled-channel system, including non-local terms, by the $R$-matrix method
using Lagrange functions. This model is applied to the $^{13}{\rm C}+^{12}$C,
$^{13}{\rm N}+^{12}$C and $^{16}{\rm O}+^{12}$C systems. Experimental
scattering cross sections are fairly well reproduced without any parameter
fitting. The backward-angle enhancement of the elastic cross sections is due to
the non-local potential. We discuss in more detail the various non-local
contributions and present effective local potentials.


Abstract:
  Privacy-constrained source coding problems have become increasingly important
recently, and the utility-privacy trade-off has been investigated for various
systems. As pioneering work, Yamamoto (1983) found theoretical limits of the
coding rate, privacy and utility in two cases; (i) both public and private
information is encoded and (ii) only public information is encoded. However,
the theoretical limit has not been characterized in a more general case; (iii)
encoded messages consist of public information and a part of private
information. Then in this paper, we characterize the trade-off relation in case
(iii). The obtained expression of the achievable region is a "unified
expression" because it includes the ones in case (i) and (ii) as special cases.
Through the numerical results, we clarify the significance of the choice of the
encoded set.


Abstract:
  We survey some recent developments in the analytic theory of multiple
Dirichlet series with arithmetical coefficients on the numerators.


Abstract:
  The non-equilibrium densities of nonlocal mass-energy are self-governed by
kinetic stresses toward quasi-equilibrium sub-configurations. System energy
integral of continuous matter-extension coordinates its adaptive densities on
each hierarchic level of structural sub-organizations. The logarithmic
potential for metric fields, kinetic densities, and local stresses in the
nonlocal distribution of mass-energy corresponds to the Shannon fundamental
limit for information rates. The phenomenological pulls of Newtonian gravity
can be quantitatively replaced by volume kinetic pushes from local stresses of
continuous space-matter with moving inertial densities. Thus, the observable
phenomenon of gravitation between volumetric parts of distributed mass-energy
is interpreted in this Cartesian nonlocality of self-governed material space
through correlated accelerations of continuous kinetic densities with local
inertia. The gravitational palliative of negative potentials is replaced with
the monistic approach to Cartesian matter through always positive kinetic
mass-energy.


Abstract:
  Understanding voluminous historical records provides clues on the past in
various aspects, such as social and political issues and even natural science
facts. However, it is generally difficult to fully utilize the historical
records, since most of the documents are not written in a modern language and
part of the contents are damaged over time. As a result, restoring the damaged
or unrecognizable parts as well as translating the records into modern
languages are crucial tasks. In response, we present a multi-task learning
approach to restore and translate historical documents based on a
self-attention mechanism, specifically utilizing two Korean historical records,
ones of the most voluminous historical records in the world. Experimental
results show that our approach significantly improves the accuracy of the
translation task than baselines without multi-task learning. In addition, we
present an in-depth exploratory analysis on our translated results via topic
modeling, uncovering several significant historical events.


Abstract:
  Unsupervised Domain adaptation is an effective method in addressing the
domain shift issue when transferring knowledge from an existing richly labeled
domain to a new domain. Existing manifold-based methods either are based on
traditional models or largely rely on Grassmannian manifold via minimizing
differences of single covariance matrices of two domains. In addition, existing
pseudo-labeling algorithms inadequately consider the quality of pseudo labels
in aligning the conditional distribution between two domains. In this work, a
deep spherical manifold Gaussian kernel (DSGK) framework is proposed to map the
source and target subspaces into a spherical manifold and reduce the
discrepancy between them by embedding both extracted features and a Gaussian
kernel. To align the conditional distributions, we further develop an
easy-to-hard pseudo label refinement process to improve the quality of the
pseudo labels and then reduce categorical spherical manifold Gaussian kernel
geodesic loss. Extensive experimental results show that DSGK outperforms
state-of-the-art methods, especially on challenging cross-domain learning
tasks.


Abstract:
  The stratified proportional hazards model represents a simple solution to
account for heterogeneity within the data while keeping the multiplicative
effect on the hazard function. Strata are typically defined a priori by
resorting to the values taken by a categorical covariate. A general framework
is proposed, which allows for the stratification of a generic accelerated life
time model, including as a special case the Weibull proportional hazard model.
The stratification is determined a posteriori by taking into account that
strata might be characterized by different baseline survivals as well as
different effects of the predictors. This is achieved by considering a Bayesian
nonparametric mixture model and the posterior distribution it induces on the
space of data partitions. The optimal stratification is then identified by
means of the variation of information criterion and, in turn, stratum-specific
inference is carried out. The performance of the proposed method and its
robustness to the presence of right-censored observations are investigated by
means of an extensive simulation study. A further illustration is provided by
the analysis of a data set extracted from the University of Massachusetts AIDS
Research Unit IMPACT Study.


Abstract:
  For a given K-polystable Fano manifold X and a natural number l, we show that
there exists a rational number 0 < c < 1 depending only on the dimension of X,
such that $D\in |-lK_X|$ is GIT-(semi/poly)stable under the action of Aut(X) if
and only if the pair $(X, \frac{\epsilon}{l} D)$ is K-(semi/poly)stable for
some rational $0 < {\epsilon} < c$.


Abstract:
  Neural network pruning is an essential approach for reducing the
computational complexity of deep models so that they can be well deployed on
resource-limited devices. Compared with conventional methods, the recently
developed dynamic pruning methods determine redundant filters variant to each
input instance which achieves higher acceleration. Most of the existing methods
discover effective sub-networks for each instance independently and do not
utilize the relationship between different inputs. To maximally excavate
redundancy in the given network architecture, this paper proposes a new
paradigm that dynamically removes redundant filters by embedding the manifold
information of all instances into the space of pruned networks (dubbed as
ManiDP). We first investigate the recognition complexity and feature similarity
between images in the training set. Then, the manifold relationship between
instances and the pruned sub-networks will be aligned in the training
procedure. The effectiveness of the proposed method is verified on several
benchmarks, which shows better performance in terms of both accuracy and
computational cost compared to the state-of-the-art methods. For example, our
method can reduce 55.3% FLOPs of ResNet-34 with only 0.57% top-1 accuracy
degradation on ImageNet.


Abstract:
  To realize autonomous shipping, autonomous berthing and unberthing are some
of the technical challenges. In the past, numerous research have been done on
the optimization of trajectory planning of berthing problems. However, these
studies assumed only a simple berth and did not consider obstacles.
Optimization of trajectory planning on berthing and unberthing in actual ports
must consider the spatial constraints and maintain sufficient distance to
obstacles. The main contributions of this study are as follows: (i) a collision
avoidance algorithm based on the ship domain which has variable size by the
ship speed is proposed, to include the spatial constraints to optimization;
(ii) the effect of wind disturbance is taken into account to the trajectory
planning to make a feasible trajectory based on the capacity limit of
actuators; (iii) showing that the optimization method for berthing is also
eligible for the unberthing, which has been almost neglected; (iv) waypoints
are included to the optimization process, to make optimization easier on
practical applications. The authors tested the proposed method on two existing
ports. The proposed method performed well on both the berthing and the
unberthing problem and optimized the control input and the trajectory while
avoiding collision with the complex obstacles.


Abstract:
  We report a systematic study of the superconducting (SC) and normal-state
anisotropy of Fe$_{1+y}$Te$_{0.6}$Se$_{0.4}$ single crystals with controlled
amounts of excess Fe ($y$ = 0, 0.07, and 0.14). The SC state anisotropy
$\gamma_{H}$ was obtained by measuring the upper critical fields under high
magnetic fields over 50 T for both $H\parallel ab$ and $H\parallel c$. On the
other hand, the normal state anisotropy $\gamma_{\rho}$ was obtained by
measuring the resistivity with current flowing in the $ab$ plane ($\rho_{ab}$)
and along the $c$ axis ($\rho_c$). To precisely measure $\rho_{ab}$ and
$\rho_c$ in the same part of a specimen avoiding the variation dependent on
pieces or parts, we adopt a new method using a micro-fabricated bridge with an
additional neck part along $c$ axis. The $\gamma_{H}$ decreases from a value
dependent on the amount of excess Fe at $T_{\rm{c}}$ to a common value $\sim$ 1
at 2 K. The different $\gamma_{H}$ at $T_{\rm{c}}$ ($\sim$1.5 for $y$ = 0, and
2.5 for $y$ = 0.14) suggests that the anisotropy of effective mass
$m_c^*/m_{ab}^*$ increases from $\sim$ 2.25 ($y$ = 0) to 6.25 ($y$ = 0.14) with
the excess Fe. The almost isotropic $\gamma_{H}$ at low temperatures is due to
the strong spin paramagnetic effect at $H\parallel ab$. By contrast, the
$\gamma_{\rho}$ shows a much larger value of $\sim$ 17 ($y$ = 0) to $\sim$ 50
($y$ = 0.14) at the temperature just above $T_{\rm{c}}$. Combined the results
of $\gamma_{H}$ and $\gamma_{\rho}$ near $T_{\rm{c}}$, we found out that the
discrepant anisotropies between the SC and normal states originates from a
large anisotropy of scattering time $\tau_{ab}$/$\tau_c$ $\sim$ 7.8. The
$\tau_{ab}$/$\tau_c$ is found to be independent of the excess Fe.


Abstract:
  We look for solutions to the Schr\"{o}dinger-Poisson-Slater equation $$-
\Delta u + \lambda u - \gamma (|x|^{-1} * |u|^2) u - a |u|^{p-2}u = 0 \quad
\text{in} \quad \mathbb{R}^3, $$ which satisfy \begin{equation*}
\int_{\mathbb{R}^3}|u|^2 \, dx = c \end{equation*} for some prescribed $c>0$.
Here $ u \in H^1(\mathbb{R}^3)$, $\gamma \in \mathbb{R},$ $ a \in \mathbb{R}$
and $p \in (\frac{10}{3}, 6]$. When $\gamma >0$ and $a > 0$, both in the
Sobolev subcritical case $p \in (\frac{10}{3}, 6)$ and in the Sobolev critical
case $p=6$, we show that there exists a $c_1>0$ such that, for any $c \in
(0,c_1)$, the equation admits two solutions $u_c^+$ and $u_c^-$ which can be
characterized respectively as a local minima and as a mountain pass critical
point of the associated {\it Energy} functional restricted to the norm
constraint. In the case $\gamma >0$ and $a < 0$, we show that, for any $p \in
(\frac{10}{3},6]$ and any $c>0$, the equation admits a solution which is a
global minimizer. Finally, in the case $\gamma <0$, $a >0$ and $p=6$ we show
that it does not admit positive solutions.


Abstract:
  Graph is an usual representation of relational data, which are ubiquitous in
manydomains such as molecules, biological and social networks. A popular
approach to learningwith graph structured data is to make use of graph kernels,
which measure the similaritybetween graphs and are plugged into a kernel
machine such as a support vector machine.Weisfeiler-Lehman (WL) based graph
kernels, which employ WL labeling scheme to extract subtree patterns and
perform node embedding, are demonstrated to achieve great performance while
being efficiently computable. However, one of the main drawbacks of ageneral
kernel is the decoupling of kernel construction and learning process. For
moleculargraphs, usual kernels such as WL subtree, based on substructures of
the molecules, consider all available substructures having the same importance,
which might not be suitable inpractice. In this paper, we propose a method to
learn the weights of subtree patterns in the framework of WWL kernels, the
state of the art method for graph classification task [14]. To overcome the
computational issue on large scale data sets, we present an efficient learning
algorithm and also derive a generalization gap bound to show its convergence.
Finally, through experiments on synthetic and real-world data sets, we
demonstrate the effectiveness of our proposed method for learning the weights
of subtree patterns.


Abstract:
  We study the entanglement islands and subsystem volume complexity
corresponding to the left/ right entanglement of a conformal defect in
$d$-dimensions in Randall-Sundrum (RS) braneworld model with subcritical
tension brane. The left and right modes of the defect mimic the eternal black
hole and radiation system respectively. Hence the entanglement entropy between
the two follows an eternal black hole Page curve which is unitarity compatible.
We compute the volumes corresponding to the left and right branes with
preferred Ryu-Takanayagi (RT) surfaces at different times, which provide a
probe of the subregion complexity of the black hole and the radiation states
respectively. An interesting jump in volume is found at Page time, where the
entanglement curve is saturated due to the inclusion of the island surfaces. We
explain various possibilities of this phase transition in complexity at Page
time and argue how these results match with a covariant proposal qualitatively.


Abstract:
  Frequency Response Functions (FRFs) are one of the cornerstones of musical
acoustic experimental research. They describe the way in which musical
instruments vibrate in a wide range of frequencies and are used to predict and
understand the acoustic differences between them. In the specific case of
stringed musical instruments such as violins, FRFs evaluated at the bridge are
known to capture the overall body vibration. These indicators, also called
bridge admittances, are widely used in the literature for comparative analyses.
However, due to their complex structure they are rather difficult to
quantitatively compare and study. In this manuscript we present a way to
quantify differences between FRFs, in particular violin bridge admittances,
that separates the effects in frequency, amplitude and quality factor of the
first resonance peaks characterizing the responses. This approach allows us to
define a distance between FRFs and clusterise measurements according to this
distance. We use two case studies, one based on Finite Element Analysis and
another exploiting measurements on real violins, to prove the effectiveness of
such representation. In particular, for simulated bridge admittances the
proposed distance is able to highlight the different impact of consecutive
simulation `steps' on specific vibrational properties and, for real violins,
gives a first insight on similar styles of making, as well as opposite ones.


Abstract:
  Deep reinforcement learning (DRL) algorithms have been demonstrated to be
effective in a wide range of challenging decision making and control tasks.
However, these methods typically suffer from severe action oscillations in
particular in discrete action setting, which means that agents select different
actions within consecutive steps even though states only slightly differ. This
issue is often neglected since the policy is usually evaluated by its
cumulative rewards only. Action oscillation strongly affects the user
experience and can even cause serious potential security menace especially in
real-world domains with the main concern of safety, such as autonomous driving.
To this end, we introduce Policy Inertia Controller (PIC) which serves as a
generic plug-in framework to off-the-shelf DRL algorithms, to enables adaptive
trade-off between the optimality and smoothness of the learned policy in a
formal way. We propose Nested Policy Iteration as a general training algorithm
for PIC-augmented policy which ensures monotonically non-decreasing updates
under some mild conditions. Further, we derive a practical DRL algorithm,
namely Nested Soft Actor-Critic. Experiments on a collection of autonomous
driving tasks and several Atari games suggest that our approach demonstrates
substantial oscillation reduction in comparison to a range of commonly adopted
baselines with almost no performance degradation.


Abstract:
  Building a dialogue system that can communicate naturally with humans is a
challenging yet interesting problem of agent-based computing. The rapid growth
in this area is usually hindered by the long-standing problem of data scarcity
as these systems are expected to learn syntax, grammar, decision making, and
reasoning from insufficient amounts of task-specific dataset. The recently
introduced pre-trained language models have the potential to address the issue
of data scarcity and bring considerable advantages by generating contextualized
word embeddings. These models are considered counterpart of ImageNet in NLP and
have demonstrated to capture different facets of language such as hierarchical
relations, long-term dependency, and sentiment. In this short survey paper, we
discuss the recent progress made in the field of pre-trained language models.
We also deliberate that how the strengths of these language models can be
leveraged in designing more engaging and more eloquent conversational agents.
This paper, therefore, intends to establish whether these pre-trained models
can overcome the challenges pertinent to dialogue systems, and how their
architecture could be exploited in order to overcome these challenges. Open
challenges in the field of dialogue systems have also been deliberated.


Abstract:
  Motivated by the recent successful formation of the MoSi2N4 monolayer [Hong
et al., Sci. 369, 670 (2020)], the structural, electronic and magnetic
properties of MoSi2N4 nanoribbons (NRs) is investigated for the first time .
The band structure calculations showed spin-polarization in zigzag edges and a
non-magnetic semiconducting character in armchair edges. For armchair-edges, we
identify an indirect to direct band gap shift compared to the MoSi2N4
monolayer, and its energy gap increases with increasing NR width. Anisotropic
electrical and magnetic behavior is observed via band structure calculations in
the zigzag and armchair edges, where, surprisingly, for the one type of
zigzag-edges configuration, we identify a Dirac-semimetal character. The
appearance of magnetism and Dirac-semimetal in MoSi2N4 ribbon can give rise to
novel physical properties, which could be useful in applications for
next-generation electronic devices.


Abstract:
  With the advent of next generation high resolution telescopes, our
understanding of how the magnetic field is organized in the internetwork (IN)
photosphere is likely to advance.We aim to evaluate the extent to which we can
retrieve information about the magnetic vector in the IN photosphere using
inversions. We use snapshots produced from high resolution 3D
magnetohydrodynamic (MHD) simulations and employ the Stokes Inversions based on
Response functions (SIR) code to produce synthetic observables in the near
infrared spectral window observed by the GREGOR Infrared Spectrograph (GRIS),
which contains the highly magnetically sensitive photospheric Fe I line pair at
15648.52 A and 15652.87 A. We perform nearly 14 million inversions to test how
well the true MHD atmospheric parameters can be constrained. Finally, we
degrade the synthetic Stokes vectors spectrally and spatially to GREGOR
resolutions and examine how this influences observations, considering the
impact of stray light, spatial resolution and signal-to-noise (S-to-N). We find
the depth-averaged parameters can be recovered by the inversions of the
undegraded profiles, and by adding gradients to magnetic field strength,
inclination and line of sight velocity we show an improvement in the chi
squared value is achieved. We evaluate the extent to which we can constrain
these parameters at various optical depths, with the kinematic and
thermodynamic parameters sensitive deeper in the atmosphere than the magnetic
parameters. We find the S-to-N and spatial resolution play a significant role
in determining how the atmosphere appears and the magnetic and kinematic
parameters are invariant upon inclusion of unpolarized stray light. We studied
a linear polarization feature which resembles those recently observed by GRIS,
appearing as loop-like structures with similar magnetic flux density.


Abstract:
  We deduce a microstructure inspired model for humidity and temperature
effects on the mechanical response of spider silks, modelled as a composite
material with a hard crystalline and a soft amorphous region. Water molecules
decrease the percentage of crosslinks in the softer region inducing a variation
of natural configuration of the macromolecules. The resulting kinematic
incompatibility between the regions crucially influences the final mechanical
response. We demonstrate the predictivity of the model by quantitatively
reproducing the experimentally observed behavior


Abstract:
  This paper investigates whether the power of the models pre-trained on text
data, such as BERT, can be transferred to general token sequence classification
applications. To verify pre-trained models' transferability, we test the
pre-trained models on text classification tasks with meanings of tokens
mismatches, and real-world non-text token sequence classification data,
including amino acid, DNA, and music. We find that even on non-text data, the
models pre-trained on text converge faster, perform better than the randomly
initialized models, and only slightly worse than the models using task-specific
knowledge. We also find that the representations of the text and non-text
pre-trained models share non-trivial similarities.


Abstract:
  The ability to train randomly initialised deep neural networks is known to
depend strongly on the variance of the weight matrices and biases as well as
the choice of nonlinear activation. Here we complement the existing geometric
analysis of this phenomenon with an information theoretic alternative. Lower
bounds are derived for the mutual information between an input and hidden layer
outputs. Using a mean field analysis we are able to provide analytic lower
bounds as functions of network weight and bias variances as well as the choice
of nonlinear activation. These results show that initialisations known to be
optimal from a training point of view are also superior from a mutual
information perspective.


Abstract:
  The levels of heavy elements in stars are the product of enhancement by
previous stellar generations, and the distribution of this metallicity among
the population contains clues to the process by which a galaxy formed. Most
famously, the "G-dwarf problem" highlighted the small number of low-metallicity
G-dwarf stars in the Milky Way, which is inconsistent with the simplest picture
of a galaxy formed from a "closed box" of gas. It can be resolved by treating
the Galaxy as an open system that accretes gas throughout its life. This
observation has classically only been made in the Milky Way, but the
availability of high-quality spectral data from SDSS-IV MaNGA and the
development of new analysis techniques mean that we can now make equivalent
measurements for a large sample of spiral galaxies. Our analysis shows that
high-mass spirals generically show a similar deficit of low-metallicity stars,
implying that the Milky Way's history of gas accretion is common. By contrast,
low-mass spirals show little sign of a G-dwarf problem, presenting the
metallicity distribution that would be expected if such systems evolved as
pretty much closed boxes. This distinction can be understood from the differing
timescales for star formation in galaxies of differing masses.


Abstract:
  Raychaudhuri equation is derived by assuming geometric flow in spacetime M of
n+1 dimensions. The equation turns into a harmonic oscillator form under
suitable transformations.Thereby a relation between geometrical entropy and
mean geodesic deviation is established. This has a connection to chaos theory
where the trajectories diverge exponentially. We discuss its application to
cosmology and black holes. Thus we present a connection between chaos theory
and general relativity.


Abstract:
  We identify a class of "semi-modular" forms invariant on special subgroups of
$GL_2(\mathbb Z)$, which includes classical modular forms together with
complementary classes of functions that are also nice in a specific sense. We
define an Eisenstein-like series summed over integer partitions, and use it to
construct families of semi-modular forms.


Abstract:
  A finite difference algorithm based on the integral Laguerre transform in
time for solving a three-dimensional one-way wave equation is proposed. This
allows achieving high accuracy of calculation results. In contrast to the
Fourier method, the approach does not need to solve systems of linear algebraic
equations with indefinite matrices. To filter the unstable components of a wave
field, Richardson extrapolation or spline approximation can be used. However,
these methods impose additional limitations on the integration step in depth.
This problem can be solved if the filtering is performed not in the direction
of extrapolation of the wave field, but in a horizontal plane. This approach
called for fast methods of converting the Laguerre series coefficients into the
Fourier series coefficients and vice versa. The high stability of the new
algorithm allows calculations with a large depth step without loss of accuracy
and, in combination with Marchuk-Strang splitting, this can significantly
reduce the calculation time. Computational experiments are performed. The
results have shown that this algorithm is highly accurate and efficient in
solving the problems of seismic migration.


Abstract:
  Permutation statistics $\wnm$ and $\rlm$ are both arising from permutation
tableaux. $\wnm$ was introduced by Chen and Zhou, which was proved equally
distributed with the number of unrestricted rows of a permutation tableau.
While $\rlm$ is showed by Nadeau equally distributed with the number of $1$'s
in the first row of a permutation tableau.
  In this paper, we investigate the joint distribution of $\wnm$ and $\rlm$.
Statistic $(\rlm,\wnm,\rlmin,\des,(\underline{321}))$ is shown equally
distributed with $(\rlm,\rlmin,\wnm,\des,(\underline{321}))$ on $S_n$. Then the
generating function of $(\rlm,\wnm)$ follows. An involution is constructed to
explain the symmetric property of the generating function. Also, we study the
triple statistic $(\wnm,\rlm,\asc)$, which is shown to be equally distributed
with $(\rlmax-1,\rlmin,\asc)$ as studied by Josuat-Verg$\grave{e}$s. The main
method we adopt throughout the paper is constructing bijections based on a
block decomposition of permutations.


Abstract:
  The paper presents a novel model-based method for intelligent tutoring, with
particular emphasis on the problem of selecting teaching interventions in
interaction with humans. Whereas previous work has focused on either
personalization of teaching or optimization of teaching intervention sequences,
the proposed individualized model-based planning approach represents
convergence of these two lines of research. Model-based planning picks the best
interventions via interactive learning of a user memory model's parameters. The
approach is novel in its use of a cognitive model that can account for several
key individual- and material-specific characteristics related to
recall/forgetting, along with a planning technique that considers users'
practice schedules. Taking a rule-based approach as a baseline, the authors
evaluated the method's benefits in a controlled study of artificial teaching in
second-language vocabulary learning (N=53).


Abstract:
  An indispensable component in task-oriented dialogue systems is the dialogue
state tracker, which keeps track of users' intentions in the course of
conversation. The typical approach towards this goal is to fill in multiple
pre-defined slots that are essential to complete the task. Although various
dialogue state tracking methods have been proposed in recent years, most of
them predict the value of each slot separately and fail to consider the
correlations among slots. In this paper, we propose a slot self-attention
mechanism that can learn the slot correlations automatically. Specifically, a
slot-token attention is first utilized to obtain slot-specific features from
the dialogue context. Then a stacked slot self-attention is applied on these
features to learn the correlations among slots. We conduct comprehensive
experiments on two multi-domain task-oriented dialogue datasets, including
MultiWOZ 2.0 and MultiWOZ 2.1. The experimental results demonstrate that our
approach achieves state-of-the-art performance on both datasets, verifying the
necessity and effectiveness of taking slot correlations into consideration.


Abstract:
  This paper is concerned with weak solutions {e,h} in L^2 x L^2 of the
time-dependent Maxwell equations. We show that these solutions obey an energy
equality. Our method of proof is based on the approximation of {e,h} by its
Steklov mean with respect to time t. This approximation technique is well-known
for establishing integral estimates for weak solutions of parabolic equations.
In addition we prove the uniqueness of {e,h}.