title: StandardSim: A Synthetic Dataset For Retail Environments

abstract:
  Autonomous checkout systems rely on visual and sensory inputs to carry out
fine-grained scene understanding in retail environments. Retail environments
present unique challenges compared to typical indoor scenes owing to the vast
number of densely packed, unique yet similar objects. The problem becomes even
more difficult when only RGB input is available, especially for data-hungry
tasks such as instance segmentation. To address the lack of datasets for
retail, we present StandardSim, a large-scale photorealistic synthetic dataset
featuring annotations for semantic segmentation, instance segmentation, depth
estimation, and object detection. Our dataset provides multiple views per
scene, enabling multi-view representation learning. Further, we introduce a
novel task central to autonomous checkout called change detection, requiring
pixel-level classification of takes, puts and shifts in objects over time. We
benchmark widely-used models for segmentation and depth estimation on our
dataset, show that our test set constitutes a difficult benchmark compared to
current smaller-scale datasets and that our training set provides models with
crucial information for autonomous checkout tasks.


title:
Learning a Discrete Set of Optimal Allocation Rules in Queueing Systems
  with Unknown Service Rates

abstract:
  We study learning-based admission control for a classical Erlang-B blocking
system with unknown service rate, i.e., an $M/M/k/k$ queueing system. At every
job arrival, a dispatcher decides to assign the job to an available server or
to block it. Every served job yields a fixed reward for the dispatcher, but it
also results in a cost per unit time of service. Our goal is to design a
dispatching policy that maximizes the long-term average reward for the
dispatcher based on observing the arrival times and the state of the system at
each arrival; critically, the dispatcher observes neither the service times nor
departure times.
  We develop our learning-based dispatch scheme as a parametric learning
problem a'la self-tuning adaptive control. In our problem, certainty equivalent
control switches between an always admit policy (always explore) and a never
admit policy (immediately terminate learning), which is distinct from the
adaptive control literature. Our learning scheme then uses maximum likelihood
estimation followed by certainty equivalent control but with judicious use of
the always admit policy so that learning doesn't stall. We prove that for all
service rates, the proposed policy asymptotically learns to take the optimal
action. Further, we also present finite-time regret guarantees for our scheme.
The extreme contrast in the certainty equivalent optimal control policies leads
to difficulties in learning that show up in our regret bounds for different
parameter regimes. We explore this aspect in our simulations and also follow-up
sampling related questions for our continuous-time system.


title: Spectral zeta function on discrete tori and Epstein-Riemann conjecture

abstract:
  We consider the combinatorial Laplacian on a sequence of discrete tori which
approximate the m-dimensional torus. In the special case m=1, Friedli and
Karlsson derived an asymptotic expansion of the corresponding spectral zeta
function in the critical strip, as the approximation parameter goes to
infinity. There, the authors have also formulated a conjecture on this
asymptotics, that is equivalent to the Riemann conjecture. In this paper,
inspired by the work of Friedli and Karlsson, we prove that a similar
asymptotic expansion holds for m=2. Similar argument applies to higher
dimensions as well. A conjecture on this asymptotics gives an equivalent
formulation of the Epstein-Riemann conjecture, if we replace the standard
discrete Laplacian with the $9$-point star discrete Laplacian.


title:
The rate of $\mathbb{F}$-convergence for Ricci flows with closed and
  smooth tangent flows

abstract:
  This article is a continuation of [CMZ21b], where we proved that a Ricci flow
with a closed and smooth tangent flow has unique tangent flow, and its
corresponding forward or backward modified Ricci flow converges in the rate of
$t^{-\beta}$ for some $\beta>0$. In this article, we calculate the
corresponding $\mathbb{F}$-convergence rate: after being scaled by a factor
$\lambda>0$, a Ricci flow with closed and smooth tangent flow is $|\log
\lambda|^{-\theta}$ close to its tangent flow in the $\mathbb{F}$-sense, where
$\theta$ is a positive number, $\lambda\gg 1$ in the blow-up case, and
$\lambda\ll 1$ in the blow-down case.


title: A Novel Criterion for Unpredictable Motions

abstract:
  We demonstrate the extension of unpredictable motions in coupled autonomous
systems with skew product structure in the case that generalized
synchronization takes place. Sufficient conditions for the existence of
unpredictable motions in the dynamics of the response system are provided. The
theoretical results are exemplified for coupled autonomous systems in which the
drive is a hybrid dynamical system and the response is a Lorenz system. The
auxiliary system approach and conditional Lyapunov exponents are utilized to
detect the presence of generalized synchronization.


title:
Improved Information Theoretic Generalization Bounds for Distributed and
  Federated Learning

abstract:
  We consider information-theoretic bounds on expected generalization error for
statistical learning problems in a networked setting. In this setting, there
are $K$ nodes, each with its own independent dataset, and the models from each
node have to be aggregated into a final centralized model. We consider both
simple averaging of the models as well as more complicated multi-round
algorithms. We give upper bounds on the expected generalization error for a
variety of problems, such as those with Bregman divergence or Lipschitz
continuous losses, that demonstrate an improved dependence of $1/K$ on the
number of nodes. These "per node" bounds are in terms of the mutual information
between the training dataset and the trained weights at each node, and are
therefore useful in describing the generalization properties inherent to having
communication or privacy constraints at each node.


title:
Prescribed mean curvature flow of non-compact space-like Cauchy
  hypersurfaces

abstract:
  In this paper we consider the prescribed mean curvature flow of a non-compact
space-like Cauchy hypersurface of bounded geometry in a generalized
Robertson-Walker space-time. We prove that the flow preserves the
space-likeness condition and exists for infinite time. We also prove
convergence in the setting of manifolds with boundary. Our discussion
generalizes previous work by Ecker, Huisken, Gerhardt and others with respect
to a crucial aspects: we consider any non-compact Cauchy hypersurface under the
assumption of bounded geometry. Moreover, we specialize the aforementioned
works by considering globally hyperbolic Lorentzian space-times equipped with a
specific class of warped product metrics.


title: Towards Non-archimedean Superstrings

abstract:
  An action for a prospect of a $p$-adic open superstring on a target Minkowski
space is proposed. The action is constructed for `worldsheet' fields taking
values in the $p$-adic field $\mathbb{Q}_p$, but it is assumed to be obtained
from a discrete action on the Bruhat-Tits tree. This action is proven to have
an analog of worldsheet supersymmetry and the superspace action is also
constructed in terms of superfields. The action does not have conformal
symmetry, however it is implemented in the definition of the amplitudes. The
tree-level amplitudes for this theory are obtained for $N$ vertex operators
corresponding to tachyon superfields and a Koba-Nielsen formula is obtained.
Finally, four-point amplitudes are computed explicitly and they are compared to
previous work on $p$-adic superstring amplitudes.


title:
The influence of labeling techniques in classifying human manipulation
  movement of different speed

abstract:
  In this work, we investigate the influence of labeling methods on the
classification of human movements on data recorded using a marker-based motion
capture system. The dataset is labeled using two different approaches, one
based on video data of the movements, the other based on the movement
trajectories recorded using the motion capture system. The dataset is labeled
using two different approaches, one based on video data of the movements, the
other based on the movement trajectories recorded using the motion capture
system. The data was recorded from one participant performing a stacking
scenario comprising simple arm movements at three different speeds (slow,
normal, fast). Machine learning algorithms that include k-Nearest Neighbor,
Random Forest, Extreme Gradient Boosting classifier, Convolutional Neural
networks (CNN), Long Short-Term Memory networks (LSTM), and a combination of
CNN-LSTM networks are compared on their performance in recognition of these arm
movements. The models were trained on actions performed on slow and normal
speed movements segments and generalized on actions consisting of fast-paced
human movement. It was observed that all the models trained on normal-paced
data labeled using trajectories have almost 20% improvement in accuracy on test
data in comparison to the models trained on data labeled using videos of the
performed experiments.


title:
Lightweight Compositional Embeddings for Incremental Streaming
  Recommendation

abstract:
  Most work in graph-based recommender systems considers a {\em static} setting
where all information about test nodes (i.e., users and items) is available
upfront at training time. However, this static setting makes little sense for
many real-world applications where data comes in continuously as a stream of
new edges and nodes, and one has to update model predictions incrementally to
reflect the latest state. To fully capitalize on the newly available data in
the stream, recent graph-based recommendation models would need to be
repeatedly retrained, which is infeasible in practice.
  In this paper, we study the graph-based streaming recommendation setting and
propose a compositional recommendation model -- Lightweight Compositional
Embedding (LCE) -- that supports incremental updates under low computational
cost. Instead of learning explicit embeddings for the full set of nodes, LCE
learns explicit embeddings for only a subset of nodes and represents the other
nodes {\em implicitly}, through a composition function based on their
interactions in the graph. This provides an effective, yet efficient, means to
leverage streaming graph data when one node type (e.g., items) is more amenable
to static representation. We conduct an extensive empirical study to compare
LCE to a set of competitive baselines on three large-scale user-item
recommendation datasets with interactions under a streaming setting. The
results demonstrate the superior performance of LCE, showing that it achieves
nearly skyline performance with significantly fewer parameters than alternative
graph-based models.


title:
Stratification of carotid atheromatous plaque using interpretable deep
  learning methods on B-mode ultrasound images

abstract:
  Carotid atherosclerosis is the major cause of ischemic stroke resulting in
significant rates of mortality and disability annually. Early diagnosis of such
cases is of great importance, since it enables clinicians to apply a more
effective treatment strategy. This paper introduces an interpretable
classification approach of carotid ultrasound images for the risk assessment
and stratification of patients with carotid atheromatous plaque. To address the
highly imbalanced distribution of patients between the symptomatic and
asymptomatic classes (16 vs 58, respectively), an ensemble learning scheme
based on a sub-sampling approach was applied along with a two-phase,
cost-sensitive strategy of learning, that uses the original and a resampled
data set. Convolutional Neural Networks (CNNs) were utilized for building the
primary models of the ensemble. A six-layer deep CNN was used to automatically
extract features from the images, followed by a classification stage of two
fully connected layers. The obtained results (Area Under the ROC Curve (AUC):
73%, sensitivity: 75%, specificity: 70%) indicate that the proposed approach
achieved acceptable discrimination performance. Finally, interpretability
methods were applied on the model's predictions in order to reveal insights on
the model's decision process as well as to enable the identification of novel
image biomarkers for the stratification of patients with carotid atheromatous
plaque.Clinical Relevance-The integration of interpretability methods with deep
learning strategies can facilitate the identification of novel ultrasound image
biomarkers for the stratification of patients with carotid atheromatous plaque.


title: Verifying Inverse Model Neural Networks

abstract:
  Inverse problems exist in a wide variety of physical domains from aerospace
engineering to medical imaging. The goal is to infer the underlying state from
a set of observations. When the forward model that produced the observations is
nonlinear and stochastic, solving the inverse problem is very challenging.
Neural networks are an appealing solution for solving inverse problems as they
can be trained from noisy data and once trained are computationally efficient
to run. However, inverse model neural networks do not have guarantees of
correctness built-in, which makes them unreliable for use in safety and
accuracy-critical contexts. In this work we introduce a method for verifying
the correctness of inverse model neural networks. Our approach is to
overapproximate a nonlinear, stochastic forward model with piecewise linear
constraints and encode both the overapproximate forward model and the neural
network inverse model as a mixed-integer program. We demonstrate this
verification procedure on a real-world airplane fuel gauge case study. The
ability to verify and consequently trust inverse model neural networks allows
their use in a wide variety of contexts, from aerospace to medicine.


title:
HENRI: High Efficiency Negotiation-based Robust Interface for
  Multi-party Multi-issue Negotiation over the Internet

abstract:
  This paper proposes a framework for a full fledged negotiation system that
allows multi party multi issue negotiation. It focuses on the negotiation
protocol to be observed and provides a platform for concurrent and independent
negotiation on individual issues using the concept of multi threading. It
depicts the architecture of an agent detailing its components. The paper sets
forth a hierarchical pattern for the multiple issues concerning every party.
The system also provides enhancements such as the time-to-live counters for
every advertisement, refinement of utility considering non-functional
attributes, prioritization of issues, by assigning weights to issues.


title: On Universal Portfolios with Continuous Side Information

abstract:
  A new portfolio selection strategy that adapts to a continuous
side-information sequence is presented, with a universal wealth guarantee
against a class of state-constant rebalanced portfolios with respect to a state
function that maps each side-information symbol to a finite set of states. In
particular, given that a state function belongs to a collection of functions of
finite Natarajan dimension, the proposed strategy is shown to achieve,
asymptotically to first order in the exponent, the same wealth as the best
state-constant rebalanced portfolio with respect to the best state function,
chosen in hindsight from observed market. This result can be viewed as an
extension of the seminal work of Cover and Ordentlich (1996) that assumes a
single state function.


title: Transformers and the representation of biomedical background knowledge

abstract:
  BioBERT and BioMegatron are Transformers models adapted for the biomedical
domain based on publicly available biomedical corpora. As such, they have the
potential to encode large-scale biological knowledge. We investigate the
encoding and representation of biological knowledge in these models, and its
potential utility to support inference in cancer precision medicine - namely,
the interpretation of the clinical significance of genomic alterations. We
compare the performance of different transformer baselines; we use probing to
determine the consistency of encodings for distinct entities; and we use
clustering methods to compare and contrast the internal properties of the
embeddings for genes, variants, drugs and diseases. We show that these models
do indeed encode biological knowledge, although some of this is lost in
fine-tuning for specific tasks. Finally, we analyse how the models behave with
regard to biases and imbalances in the dataset.


title:
SMODICE: Versatile Offline Imitation Learning via State Occupancy
  Matching

abstract:
  We propose State Matching Offline DIstribution Correction Estimation
(SMODICE), a novel and versatile algorithm for offline imitation learning (IL)
via state-occupancy matching. We show that the SMODICE objective admits a
simple optimization procedure through an application of Fenchel duality and an
analytic solution in tabular MDPs. Without requiring access to expert actions,
SMODICE can be effectively applied to three offline IL settings: (i) imitation
from observations (IfO), (ii) IfO with dynamics or morphologically mismatched
expert, and (iii) example-based reinforcement learning, which we show can be
formulated as a state-occupancy matching problem. We extensively evaluate
SMODICE on both gridworld environments as well as on high-dimensional offline
benchmarks. Our results demonstrate that SMODICE is effective for all three
problem settings and significantly outperforms prior state-of-art.


title:
Relationship between the ground-state wave function of a magnet and its
  static structure factor

abstract:
  We state and prove two theorems about the ground state of magnetic systems
described by very general Heisenberg-type models. The first theorem states that
the relationship between the Hamiltonian and the correlators is invertible. The
second theorem states that the relationship between the wave function and the
correlators is also invertible. We discuss the implications of these theorems
for neutron scattering. We propose, in particular, a variational approach to
quantum magnets where a representation of the wave function (held, for
instance, in a neural network or in the qubit register of a quantum processor)
is optimised to fit experimental neutron-scattering data directly, without the
involvement of a model Hamiltonian.


title: On Neural Differential Equations

abstract:
  The conjoining of dynamical systems and deep learning has become a topic of
great interest. In particular, neural differential equations (NDEs) demonstrate
that neural networks and differential equation are two sides of the same coin.
Traditional parameterised differential equations are a special case. Many
popular neural network architectures, such as residual networks and recurrent
networks, are discretisations.
  NDEs are suitable for tackling generative problems, dynamical systems, and
time series (particularly in physics, finance, ...) and are thus of interest to
both modern machine learning and traditional mathematical modelling. NDEs offer
high-capacity function approximation, strong priors on model space, the ability
to handle irregular data, memory efficiency, and a wealth of available theory
on both sides.
  This doctoral thesis provides an in-depth survey of the field.
  Topics include: neural ordinary differential equations (e.g. for hybrid
neural/mechanistic modelling of physical systems); neural controlled
differential equations (e.g. for learning functions of irregular time series);
and neural stochastic differential equations (e.g. to produce generative models
capable of representing complex stochastic dynamics, or sampling from complex
high-dimensional distributions).
  Further topics include: numerical methods for NDEs (e.g. reversible
differential equations solvers, backpropagation through differential equations,
Brownian reconstruction); symbolic regression for dynamical systems (e.g. via
regularised evolution); and deep implicit models (e.g. deep equilibrium models,
differentiable optimisation).
  We anticipate this thesis will be of interest to anyone interested in the
marriage of deep learning with dynamical systems, and hope it will provide a
useful reference for the current state of the art.

categories: cs.LG cs.NA math.CA math.DS math.NA stat.ML


title: Neural Logic Analogy Learning

abstract:
  Letter-string analogy is an important analogy learning task which seems to be
easy for humans but very challenging for machines. The main idea behind current
approaches to solving letter-string analogies is to design heuristic rules for
extracting analogy structures and constructing analogy mappings. However, one
key problem is that it is difficult to build a comprehensive and exhaustive set
of analogy structures which can fully describe the subtlety of analogies. This
problem makes current approaches unable to handle complicated letter-string
analogy problems. In this paper, we propose Neural logic analogy learning
(Noan), which is a dynamic neural architecture driven by differentiable logic
reasoning to solve analogy problems. Each analogy problem is converted into
logical expressions consisting of logical variables and basic logical
operations (AND, OR, and NOT). More specifically, Noan learns the logical
variables as vector embeddings and learns each logical operation as a neural
module. In this way, the model builds computational graph integrating neural
network with logical reasoning to capture the internal logical structure of the
input letter strings. The analogy learning problem then becomes a True/False
evaluation problem of the logical expressions. Experiments show that our
machine learning-based Noan approach outperforms state-of-the-art approaches on
standard letter-string analogy benchmark datasets.


title: Impact forces of water drops falling on superhydrophobic surfaces

abstract:
  A falling liquid drop, after impact on a rigid substrate, deforms and
spreads, owing to the normal reaction force. Subsequently, if the substrate is
non-wetting, the drop retracts and then jumps off. As we show here, not only is
the impact itself associated with a distinct peak in the temporal evolution of
the normal force, but also the jump-off, which was hitherto unknown. We
characterize both peaks and elucidate how they relate to the different stages
of the drop impact process. The time at which the second peak appears coincides
with the formation of a Worthington jet, emerging through flow-focusing, and it
is independent of the impact velocity. However, the magnitude of this peak is
dictated by the drop's inertia and surface tension. We show that even
low-velocity impacts can lead to a surprisingly high peak in the normal force,
namely when a more pronounced singular Worthington jet occurs due to the
collapse of an air cavity in the drop.


title:
Gender Inequities Throughout STEM: Compared to Men, Women with
  Significantly Higher Grades Drop STEM Majors

abstract:
  Efforts to promote equity and inclusion using evidence-based approaches are
vital to correcting long-standing societal inequities that have disadvantaged
women and discouraged them from pursuing studies, including in many STEM
disciplines. We used 10 years of institutional data from a large public
university to investigate the grade point average trends in different STEM
disciplines for men and women who declared a major and then either completed
the degree or dropped the major after declaring it. We found alarming trends,
such as that women who dropped majors on average earned higher grades than men,
and in some STEM majors, women who dropped the majors were earning comparable
grades to men who persisted in those majors. While these quantitative findings
call for a deeper understanding of the reasons women and men drop a major, we
provide suggestions for approaches to make learning environments more equitable
and inclusive so that traditionally excluded stereotyped groups can have a
higher sense of belonging and thrive.


title: Multistability and Paradoxes in Lossy Oscillator Networks

abstract:
  The analysis of dissipatively coupled oscillators is a challenging problem
with high stakes in actual applications, such as large scale physical systems.
Many standard mathematical methods are not applicable to such systems, due to
the lack of symmetry of the network induced by dissipative couplings. Here we
emphasize that the synchronization of coupled oscillators can be equivalently
interpreted as the problem of flow distribution over a network. Based on these
equivalent interpretations, we demonstrate a close correspondence between
multiple stable synchronous states and \emph{winding cells} in systems of
dissipatively coupled oscillators. The recently introduced notion of winding
cells, associated to a graph, forms a natural winding partition of the
$n$-torus and capture essential characteristics of synchronous states in
lossless systems. Leveraging the winding partition of the $n$-torus, we provide
algorithms to compute the synchronous solutions of general networks of coupled
oscillators. Furthermore, we identify three paradoxical behaviors of lossy
networked systems, to be contrasted with the behavior lossless systems. Namely,
we show that loop flows and dissipation can increase the system's transfer
capacity, and that dissipation can promote multistability.


title:
Zero Experience Required: Plug & Play Modular Transfer Learning for
  Semantic Visual Navigation

abstract:
  In reinforcement learning for visual navigation, it is common to develop a
model for each new task, and train that model from scratch with task-specific
interactions in 3D environments. However, this process is expensive; massive
amounts of interactions are needed for the model to generalize well. Moreover,
this process is repeated whenever there is a change in the task type or the
goal modality. We present a unified approach to visual navigation using a novel
modular transfer learning model. Our model can effectively leverage its
experience from one source task and apply it to multiple target tasks (e.g.,
ObjectNav, RoomNav, ViewNav) with various goal modalities (e.g., image, sketch,
audio, label). Furthermore, our model enables zero-shot experience learning,
whereby it can solve the target tasks without receiving any task-specific
interactive training. Our experiments on multiple photorealistic datasets and
challenging tasks show that our approach learns faster, generalizes better, and
outperforms SoTA models by a significant margin.


title: SEED: Sound Event Early Detection via Evidential Uncertainty

abstract:
  Sound Event Early Detection (SEED) is an essential task in recognizing the
acoustic environments and soundscapes. However, most of the existing methods
focus on the offline sound event detection, which suffers from the
over-confidence issue of early-stage event detection and usually yield
unreliable results. To solve the problem, we propose a novel Polyphonic
Evidential Neural Network (PENet) to model the evidential uncertainty of the
class probability with Beta distribution. Specifically, we use a Beta
distribution to model the distribution of class probabilities, and the
evidential uncertainty enriches uncertainty representation with evidence
information, which plays a central role in reliable prediction. To further
improve the event detection performance, we design the backtrack inference
method that utilizes both the forward and backward audio features of an ongoing
event. Experiments on the DESED database show that the proposed method can
simultaneously improve 13.0\% and 3.8\% in time delay and detection F1 score
compared to the state-of-the-art methods.


title:
Transfer Reinforcement Learning for Differing Action Spaces via
  Q-Network Representations

abstract:
  Transfer learning approaches in reinforcement learning aim to assist agents
in learning their target domains by leveraging the knowledge learned from other
agents that have been trained on similar source domains. For example, recent
research focus within this space has been placed on knowledge transfer between
tasks that have different transition dynamics and reward functions; however,
little focus has been placed on knowledge transfer between tasks that have
different action spaces. In this paper, we approach the task of transfer
learning between domains that differ in action spaces. We present a reward
shaping method based on source embedding similarity that is applicable to
domains with both discrete and continuous action spaces. The efficacy of our
approach is evaluated on transfer to restricted action spaces in the Acrobot-v1
and Pendulum-v0 domains. A comparison with two baselines shows that our method
does not outperform these baselines in these continuous action spaces but does
show an improvement in these discrete action spaces. We conclude our analysis
with future directions for this work.


title: Machine Learning Method for Functional Assessment of Retinal Models

abstract:
  Challenges in the field of retinal prostheses motivate the development of
retinal models to accurately simulate Retinal Ganglion Cells (RGCs) responses.
The goal of retinal prostheses is to enable blind individuals to solve complex,
reallife visual tasks. In this paper, we introduce the functional assessment
(FA) of retinal models, which describes the concept of evaluating the
performance of retinal models on visual understanding tasks. We present a
machine learning method for FA: we feed traditional machine learning
classifiers with RGC responses generated by retinal models, to solve object and
digit recognition tasks (CIFAR-10, MNIST, Fashion MNIST, Imagenette). We
examined critical FA aspects, including how the performance of FA depends on
the task, how to optimally feed RGC responses to the classifiers and how the
number of output neurons correlates with the model's accuracy. To increase the
number of output neurons, we manipulated input images - by splitting and then
feeding them to the retinal model and we found that image splitting does not
significantly improve the model's accuracy. We also show that differences in
the structure of datasets result in largely divergent performance of the
retinal model (MNIST and Fashion MNIST exceeded 80% accuracy, while CIFAR-10
and Imagenette achieved ~40%). Furthermore, retinal models which perform better
in standard evaluation, i.e. more accurately predict RGC response, perform
better in FA as well. However, unlike standard evaluation, FA results can be
straightforwardly interpreted in the context of comparing the quality of visual
perception.


title:
Spelunking the Deep: Guaranteed Queries for General Neural Implicit
  Surfaces

abstract:
  Neural implicit representations, which encode a surface as the level set of a
neural network applied to spatial coordinates, have proven to be remarkably
effective for optimizing, compressing, and generating 3D geometry. Although
these representations are easy to fit, it is not clear how to best evaluate
geometric queries on the shape, such as intersecting against a ray or finding a
closest point. The predominant approach is to encourage the network to have a
signed distance property. However, this property typically holds only
approximately, leading to robustness issues, and holds only at the conclusion
of training, inhibiting the use of queries in loss functions. Instead, this
work presents a new approach to perform queries directly on general neural
implicit functions for a wide range of existing architectures. Our key tool is
the application of range analysis to neural networks, using automatic
arithmetic rules to bound the output of a network over a region; we conduct a
study of range analysis on neural networks, and identify variants of affine
arithmetic which are highly effective. We use the resulting bounds to develop
geometric queries including ray casting, intersection testing, constructing
spatial hierarchies, fast mesh extraction, closest-point evaluation, evaluating
bulk properties, and more. Our queries can be efficiently evaluated on GPUs,
and offer concrete accuracy guarantees even on randomly-initialized networks,
enabling their use in training objectives and beyond. We also show a
preliminary application to inverse rendering.


title:
Spatial input-output analysis of large-scale structures in actuated
  turbulent boundary layers

abstract:
  This paper develops a spatial input-output approach to investigate the
dynamics of a turbulent boundary layer subject to a localized single frequency
excitation. This method uses one-way spatial integration to reformulate the
problem in terms of spatial evolution equations. The technique is used to
examine the effect of localized periodic actuation at a given temporal
frequency, based on an experimental set-up in which an active large-scale is
introduced into the outer layer of a turbulent boundary layer. First, the
large-scale structures associated with the phase-locked modal velocity field
obtained from spatial input-output analysis are shown to closely match those
computed based on hot-wire measurements. The approach is then used to further
investigate the response of the boundary layer to the synthetically generated
large-scale. A quadrant trajectory analysis indicates that the spatial
input-output response produces shear stress distributions consistent with those
in canonical wall-bounded turbulent flows in terms of both the order and types
of events observed. The expected correspondence between the dominance of
different quadrant behavior and actuation frequency is also observed. These
results highlight the promise of a spatial input-output framework for analyzing
the formation and streamwise evolution of structures in actuated wall-bounded
turbulent flows.


title: Adversarially Trained Actor Critic for Offline Reinforcement Learning

abstract:
  We propose Adversarially Trained Actor Critic (ATAC), a new model-free
algorithm for offline reinforcement learning under insufficient data coverage,
based on a two-player Stackelberg game framing of offline RL: A policy actor
competes against an adversarially trained value critic, who finds
data-consistent scenarios where the actor is inferior to the data-collection
behavior policy. We prove that, when the actor attains no regret in the
two-player game, running ATAC produces a policy that provably 1) outperforms
the behavior policy over a wide range of hyperparameters, and 2) competes with
the best policy covered by data with appropriately chosen hyperparameters.
Compared with existing works, notably our framework offers both theoretical
guarantees for general function approximation and a deep RL implementation
scalable to complex environments and large datasets. In the D4RL benchmark,
ATAC consistently outperforms state-of-the-art offline RL algorithms on a range
of continuous control tasks


title: Non-standard Null Lagrangians and Forces in Classical Mechanics

abstract:
  Non-standard Lagrangians are a family of Lagrangians with terms that do not
have clearly discernable energy-like forms. A special class among these
Lagrangians are non-standard null Lagrangians and their gauge functions, which
are used to introduce forces to the laws of dynamics. It is shown that the
non-standard Lagrangian for the law of inertia is Galilean invariant but the
forces resulting from the non-standard null Lagrangian are not Galilean
invariant, except special only time-dependent forces.


title: Linear Model with Local Differential Privacy

abstract:
  Scientific collaborations benefit from collaborative learning of distributed
sources, but remain difficult to achieve when data are sensitive. In recent
years, privacy preserving techniques have been widely studied to analyze
distributed data across different agencies while protecting sensitive
information. Secure multiparty computation has been widely studied for privacy
protection with high privacy level but intense computation cost. There are also
other security techniques sacrificing partial data utility to reduce disclosure
risk. A major challenge is to balance data utility and disclosure risk while
maintaining high computation efficiency. In this paper, matrix masking
technique is applied to encrypt data such that the secure schemes are against
malicious adversaries while achieving local differential privacy. The proposed
schemes are designed for linear models and can be implemented for both vertical
and horizontal partitioning scenarios. Moreover, cross validation is studied to
prevent overfitting and select optimal parameters without additional
communication cost. Simulation results present the efficiency of proposed
schemes to analyze dataset with millions of records and high-dimensional data
(n << p).


title: $k$-free lattice points in random walks

abstract:
  Let $\mathbb{Z}^2$ be the two-dimensional integer lattice. For an integer
$k\geq 1$, a non-zero lattice point is $k$-free if the greatest common divisor
of its coordinates is a $k$-free number. We consider the proportions of
$k$-free and twin $k$-free lattice points on a path of an $\alpha$-random
walker in $\mathbb{Z}^2$. Using the second-moment method and tools from
analytic number theory, we prove that these two proportions are $1/\zeta(2k)$
and $\prod_{p}(1-2p^{-2k})$, respectively, where $\zeta$ is the Riemann zeta
function and the infinite product takes over all primes.


title:
A Universal Graph Deep Learning Interatomic Potential for the Periodic
  Table

abstract:
  Interatomic potentials (IAPs), which describe the potential energy surface of
a collection of atoms, are a fundamental input for atomistic simulations.
However, existing IAPs are either fitted to narrow chemistries or too
inaccurate for general applications. Here, we report a universal IAP for
materials based on graph neural networks with three-body interactions (M3GNet).
The M3GNet IAP was trained on the massive database of structural relaxations
performed by the Materials Project over the past 10 years and has broad
applications in structural relaxation, dynamic simulations and property
prediction of materials across diverse chemical spaces. About 1.8 million
potentially stable materials were identified from a screening of 31 million
hypothetical crystal structures, demonstrating a machine learning-accelerated
pathway to the discovery of synthesizable materials with exceptional
properties.


title:
Age of Information-based Scheduling for Wireless D2D Systems with a Deep
  Learning Approach

abstract:
  Device-to-device (D2D) links scheduling for avoiding excessive interference
is critical to the success of wireless D2D communications. Most of the
traditional scheduling schemes only consider the maximum throughput or fairness
of the system and do not consider the freshness of information. In this paper,
we propose a novel D2D links scheduling scheme to optimize an age of
information (AoI) and throughput jointly scheduling problem when D2D links
transmit packets under the last-come-first-serve policy with packet-replacement
(LCFS-PR). It is motivated by the fact that the maximum throughput scheduling
may reduce the activation probability of links with poor channel conditions,
which results in terrible AoI performance. Specifically, We derive the
expression of the overall average AoI and throughput of the network under the
spatio-temporal interfering queue dynamics with the mean-field assumption.
Moreover, a neural network structure is proposed to learn the mapping from the
geographic location to the optimal scheduling parameters under a stationary
randomized policy, where the scheduling decision can be made without estimating
the channel state information(CSI) after the neural network is well-trained. To
overcome the problem that implicit loss functions cannot be back-propagated, we
derive a numerical solution of the gradient. Finally, numerical results reveal
that the performance of the deep learning approach is close to that of a local
optimal algorithm which has a higher computational complexity. The trade-off
curve of AoI and throughput is also obtained, where the AoI tends to infinity
when throughput is maximized.


title:
Security-Aware Virtual Network Embedding Algorithm based on
  Reinforcement Learning

abstract:
  Virtual network embedding (VNE) algorithm is always the key problem in
network virtualization (NV) technology. At present, the research in this field
still has the following problems. The traditional way to solve VNE problem is
to use heuristic algorithm. However, this method relies on manual embedding
rules, which does not accord with the actual situation of VNE. In addition, as
the use of intelligent learning algorithm to solve the problem of VNE has
become a trend, this method is gradually outdated. At the same time, there are
some security problems in VNE. However, there is no intelligent algorithm to
solve the security problem of VNE. For this reason, this paper proposes a
security-aware VNE algorithm based on reinforcement learning (RL). In the
training phase, we use a policy network as a learning agent and take the
extracted attributes of the substrate nodes to form a feature matrix as input.
The learning agent is trained in this environment to get the mapping
probability of each substrate node. In the test phase, we map nodes according
to the mapping probability and use the breadth-first strategy (BFS) to map
links. For the security problem, we add security requirements level constraint
for each virtual node and security level constraint for each substrate node.
Virtual nodes can only be embedded on substrate nodes that are not lower than
the level of security requirements. Experimental results show that the proposed
algorithm is superior to other typical algorithms in terms of long-term average
return, long-term revenue consumption ratio and virtual network request (VNR)
acceptance rate.


title: Vehicular Visible Light Communications for Automated Valet Parking

abstract:
  Visible light communication (VLC) is a promising Optical Wireless
Communications (OWC) scheme that is demonstrated to provide secure,
line-of-sight (LoS), and short-distance vehicle-to-vehicle (V2V) and
vehicle-to-infrastructure(V2I) communications. Recently, automated driving
applications, supported by V2I links are proposed to increase the reliability
of the autonomous vehicles. To this regard, we propose a VLCbased V2I scheme to
increase the V2I communication redundancy of autonomous valet parking (AVP)
applications, through jam-free and location-based characteristics of VLC. In
this paper, we demonstrate a novel architecture to support indoor
parking-garage online-map update with vehicle on-board data transmissions and
location-based map update dissemination through bidirectional VLC
communications. The proposed system yields error-free LoS transmissions with
Direct Current Biased Optical OFDM (DCO-OFDM) up to 33 m transmitter-receiver
distance enabling vehicle CAN Bus data, infrastructure camera video, and LIDAR
point cloud data sharing in an indoor parking garage.


title:
Supervised Learning based QoE Prediction of Video Streaming in Future
  Networks: A Tutorial with Comparative Study

abstract:
  The Quality of Experience (QoE) based service management remains key for
successful provisioning of multimedia services in next-generation networks such
as 5G/6G, which requires proper tools for quality monitoring, prediction and
resource management where machine learning (ML) can play a crucial role. In
this paper, we provide a tutorial on the development and deployment of the QoE
measurement and prediction solutions for video streaming services based on
supervised learning ML models. Firstly, we provide a detailed pipeline for
developing and deploying supervised learning-based video streaming QoE
prediction models which covers several stages including data collection,
feature engineering, model optimization and training, testing and prediction
and evaluation. Secondly, we discuss the deployment of the ML model for the QoE
prediction/measurement in the next generation networks (5G/6G) using network
enabling technologies such as Software-Defined Networking (SDN), Network
Function Virtualization (NFV) and Mobile Edge Computing (MEC) by proposing
reference architecture. Thirdly, we present a comparative study of the
state-of-the-art supervised learning ML models for QoE prediction of video
streaming applications based on multiple performance metrics.


title:
Using Stochastic local search in designing microstrip antenna for 5G
  communication

abstract:
  Well this paper defines methods to explore what is known as the local search
problem, this local search is what we are going to use in antenna to antenna
and antenna to device communication. The local search algorithm searches for
best next for search this is in turn used by us in antenna pairing. This is
prominently known as stochastic local search, We are going to design 5G
microstrip antenna operating between 2.4GHz to 24 Ghz of operation. This speaks
of a very novel idea which though was used in late sixties when microstrip was
in operation but the idea is having potential.


title:
Application of Machine Learning-Based Pattern Recognition in IoT
  Devices: Review

abstract:
  The Internet of things (IoT) is a rapidly advancing area of technology that
has quickly become more widespread in recent years. With greater numbers of
everyday objects being connected to the Internet, many different innovations
have been presented to make our everyday lives more straightforward. Pattern
recognition is extremely prevalent in IoT devices because of the many
applications and benefits that can come from it. A multitude of studies has
been conducted with the intention of improving speed and accuracy, decreasing
complexity, and reducing the overall required processing power of pattern
recognition algorithms in IoT devices. After reviewing the applications of
different machine learning algorithms, results vary from case to case, but a
general conclusion can be drawn that the optimal machine learning-based pattern
recognition algorithms to be used with IoT devices are support vector machine,
k-nearest neighbor, and random forest.


title:
Path Planning for the Dynamic UAV-Aided Wireless Systems using Monte
  Carlo Tree Search

abstract:
  For UAV-aided wireless systems, online path planning attracts much attention
recently. To better adapt to the real-time dynamic environment, we, for the
first time, propose a Monte Carlo Tree Search (MCTS)-based path planning
scheme. In details, we consider a single UAV acts as a mobile server to provide
computation tasks offloading services for a set of mobile users on the ground,
where the movement of ground users follows a Random Way Point model. Our model
aims at maximizing the average throughput under energy consumption and user
fairness constraints, and the proposed timesaving MCTS algorithm can further
improve the performance. Simulation results show that the proposed algorithm
achieves a larger average throughput and a faster convergence performance
compared with the baseline algorithms of Q-learning and Deep Q-Network.


title:
Advanced service data provisioning in ROF-based mobile
  backhauls/fronthauls

abstract:
  A new cost-efficient concept to realize a real-time monitoring of
quality-of-service metrics and other service data in 5G and beyond access
network using a separate return channel based on a vertical cavity surface
emitting laser in the optical injection locked mode that simultaneously
operates as an optical transmitter and as a resonant cavity enhanced
photodetector, is proposed and discussed. The feasibility and efficiency of the
proposed approach are confirmed by a proof-of-concept experiment when optically
transceiving high-speed digital signal with multi-position quadrature amplitude
modulation of a radio-frequency carrier.


title:
Space-Air-Ground Integrated Multi-domain Network Resource Orchestration
  based on Virtual Network Architecture: a DRL Method

abstract:
  Traditional ground wireless communication networks cannot provide
high-quality services for artificial intelligence (AI) applications such as
intelligent transportation systems (ITS) due to deployment, coverage and
capacity issues. The space-air-ground integrated network (SAGIN) has become a
research focus in the industry. Compared with traditional wireless
communication networks, SAGIN is more flexible and reliable, and it has wider
coverage and higher quality of seamless connection. However, due to its
inherent heterogeneity, time-varying and self-organizing characteristics, the
deployment and use of SAGIN still faces huge challenges, among which the
orchestration of heterogeneous resources is a key issue. Based on virtual
network architecture and deep reinforcement learning (DRL), we model SAGIN's
heterogeneous resource orchestration as a multi-domain virtual network
embedding (VNE) problem, and propose a SAGIN cross-domain VNE algorithm. We
model the different network segments of SAGIN, and set the network attributes
according to the actual situation of SAGIN and user needs. In DRL, the agent is
acted by a five-layer policy network. We build a feature matrix based on
network attributes extracted from SAGIN and use it as the agent training
environment. Through training, the probability of each underlying node being
embedded can be derived. In test phase, we complete the embedding process of
virtual nodes and links in turn based on this probability. Finally, we verify
the effectiveness of the algorithm from both training and testing.


title: J\'onsson J\'onsson-Tarski Algebras

abstract:
  By studying the variety of J\'{o}nsson-Tarski algebras, we demonstrate two
obstacles to the existence of large J\'{o}nsson algebras in certain varieties.
First, if an algebra $J$ in a language $L$ has cardinality greater than $|L|^+$
and a distributive subalgebra lattice, then it must have a proper subalgebra of
size $|J|$. Second, if an algebra $J$ in a language $L$ satisfies
$\text{cf}(|J|) > 2^{|L|^+}$ and lies in a residually small variety, then it
again must have a proper subalgebra of size $|J|$. We apply the first result to
show that J\'{o}nsson algebras in the variety of J\'{o}nsson-Tarski algebras
cannot have cardinality greater than $\aleph_1$. We also construct
$2^{\aleph_1}$ many pairwise nonisomorphic J\'{o}nsson algebras in this
variety, thus proving that for some varieties the maximum possible number of
J\'{o}nsson algebras can be achieved.


title: Gyromagnetic bifurcation in a levitated ferromagnetic particle

abstract:
  We examine mechanical rotation of a levitated magnetic particle that is
induced by ferromagnetic resonance under microwave irradiation. We show that
two stable solutions appear in a certain range of parameters by bifurcation
when the rotation frequency is comparable to the microwave frequency. This
phenomenon originates from the coexistence of the Barnett and the Einstein-de
Haas effects. We also reveal that this measurement is sensitive to the strength
of the spin-rotation coupling. Our work provides a platform for accessing a
microscopic relaxation process from spin to macroscopic rotation.


title: Sequential Veto Bargaining with Incomplete Information

abstract:
  We study sequential bargaining between a proposer and a veto player. Both
have single-peaked preferences, but the proposer is uncertain about the veto
player's ideal point. The proposer cannot commit to future proposals. When
players are patient, there can be equilibria with Coasian dynamics: the veto
player's private information can largely nullify proposer's bargaining power.
Our main result, however, is that there are also equilibria in which the
proposer obtains the high payoff that he would with commitment power. The
driving force is that the veto player's single-peaked preferences give the
proposer an option to "leapfrog", i.e., to secure agreement from only
low-surplus types early on to credibly extract surplus from high types later.
Methodologically, we exploit the connection between sequential bargaining and
static mechanism design.


title:
Saturated absorption spectroscopy of buffer-gas-cooled Barium
  monofluoride molecules

abstract:
  We report an experimental investigation on the Doppler-free saturated
absorption spectroscopy of buffer-gas-cooled Barium monofluoride (BaF)
molecules in a 4~K cryogenic cell. The obtained spectra with a resolution of
19~MHz, much smaller than previously observed in absorption spectroscopy,
clearly resolve the hyperfine transitions. Moreover, we use these
high-resolution spectra to fit the hyperfine splittings of excited $A(v=0)$
state and find the hyperfine splitting of the laser-cooling-relevant
$A^2\Pi_{1/2}(v=0, J=1/2, +)$ state is about 18 MHz, much higher than the
previous theoretically predicted value. This provides important missing
information for laser cooling of BaF molecules.


title:
One-Nearest-Neighbor Search is All You Need for Minimax Optimal
  Regression and Classification

abstract:
  Recently, Qiao, Duan, and Cheng~(2019) proposed a distributed
nearest-neighbor classification method, in which a massive dataset is split
into smaller groups, each processed with a $k$-nearest-neighbor classifier, and
the final class label is predicted by a majority vote among these groupwise
class labels. This paper shows that the distributed algorithm with $k=1$ over a
sufficiently large number of groups attains a minimax optimal error rate up to
a multiplicative logarithmic factor under some regularity conditions, for both
regression and classification problems. Roughly speaking, distributed
1-nearest-neighbor rules with $M$ groups has a performance comparable to
standard $\Theta(M)$-nearest-neighbor rules. In the analysis, alternative rules
with a refined aggregation method are proposed and shown to attain exact
minimax optimal rates.

categories: math.ST cs.DC cs.IT cs.LG math.IT stat.TH


title:
ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement
  Learning

abstract:
  Building assistive interfaces for controlling robots through arbitrary,
high-dimensional, noisy inputs (e.g., webcam images of eye gaze) can be
challenging, especially when it involves inferring the user's desired action in
the absence of a natural 'default' interface. Reinforcement learning from
online user feedback on the system's performance presents a natural solution to
this problem, and enables the interface to adapt to individual users. However,
this approach tends to require a large amount of human-in-the-loop training
data, especially when feedback is sparse. We propose a hierarchical solution
that learns efficiently from sparse user feedback: we use offline pre-training
to acquire a latent embedding space of useful, high-level robot behaviors,
which, in turn, enables the system to focus on using online user feedback to
learn a mapping from user inputs to desired high-level behaviors. The key
insight is that access to a pre-trained policy enables the system to learn more
from sparse rewards than a na\"ive RL algorithm: using the pre-trained policy,
the system can make use of successful task executions to relabel, in hindsight,
what the user actually meant to do during unsuccessful executions. We evaluate
our method primarily through a user study with 12 participants who perform
tasks in three simulated robotic manipulation domains using a webcam and their
eye gaze: flipping light switches, opening a shelf door to reach objects
inside, and rotating a valve. The results show that our method successfully
learns to map 128-dimensional gaze features to 7-dimensional joint torques from
sparse rewards in under 10 minutes of online training, and seamlessly helps
users who employ different gaze strategies, while adapting to distributional
shift in webcam inputs, tasks, and environments.


title: Handling Distribution Shifts on Graphs: An Invariance Perspective

abstract:
  There is increasing evidence suggesting neural networks' sensitivity to
distribution shifts, so that research on out-of-distribution (OOD)
generalization comes into the spotlight. Nonetheless, current endeavors mostly
focus on Euclidean data, and its formulation for graph-structured data is not
clear and remains under-explored, given two-fold fundamental challenges: 1) the
inter-connection among nodes in one graph, which induces non-IID generation of
data points even under the same environment, and 2) the structural information
in the input graph, which is also informative for prediction. In this paper, we
formulate the OOD problem on graphs and develop a new invariant learning
approach, Explore-to-Extrapolate Risk Minimization (EERM), that facilitates
graph neural networks to leverage invariance principles for prediction. EERM
resorts to multiple context explorers (specified as graph structure editers in
our case) that are adversarially trained to maximize the variance of risks from
multiple virtual environments. Such a design enables the model to extrapolate
from a single observed environment which is the common case for node-level
prediction. We prove the validity of our method by theoretically showing its
guarantee of a valid OOD solution and further demonstrate its power on various
real-world datasets for handling distribution shifts from artificial spurious
features, cross-domain transfers and dynamic graph evolution.


title: Group Testing with Correlation under Edge-Faulty Graphs

abstract:
  In applications of group testing in networks, e.g. identifying individuals
who are infected by a disease spread over a network, exploiting correlation
among network nodes provides fundamental opportunities in reducing the number
of tests needed. We model and analyze group testing on $n$ correlated nodes
whose interactions are specified by a graph $G$. We model correlation through
an edge-faulty random graph formed from $G$ in which each edge is dropped with
probability $1-r$, and all nodes in the same component have the same state. We
consider three classes of graphs: cycles and trees, $d$-regular graphs and
stochastic block models or SBM, and obtain lower and upper bounds on the number
of tests needed to identify the defective nodes. Our results are expressed in
terms of the number of tests needed when the nodes are independent and they are
in terms of $n$, $r$, and the target error. In particular, we quantify the
fundamental improvements that exploiting correlation offers by the ratio
between the total number of nodes $n$ and the equivalent number of independent
nodes in a classic group testing algorithm. The lower bounds are derived by
illustrating a strong dependence of the number of tests needed on the expected
number of components. In this regard, we establish a new approximation for the
distribution of component sizes in "$d$-regular trees" which may be of
independent interest and leads to a lower bound on the expected number of
components in $d$-regular graphs. The upper bounds are found by forming dense
subgraphs in which nodes are more likely to be in the same state. When $G$ is a
cycle or tree, we show an improvement by a factor of $log(1/r)$. For grid, a
graph with almost $2n$ edges, the improvement is by a factor of ${(1-r)
\log(1/r)}$, indicating drastic improvement compared to trees. When $G$ has a
larger number of edges, as in SBM, the improvement can scale in $n$.


title: Rethinking ValueDice: Does It Really Improve Performance?

abstract:
  Since the introduction of GAIL, adversarial imitation learning (AIL) methods
attract lots of research interests. Among these methods, ValueDice has achieved
significant improvements: it beats the classical approach Behavioral Cloning
(BC) under the offline setting, and it requires fewer interactions than GAIL
under the online setting. Are these improvements benefited from more advanced
algorithm designs? We answer this question with the following conclusions.
  First, we show that ValueDice could reduce to BC under the offline setting.
Second, we verify that overfitting exists and regularization matters.
Specifically, we demonstrate that with weight decay, BC also nearly matches the
expert performance as ValueDice does. The first two claims explain the superior
offline performance of ValueDice. Third, we establish that ValueDice does not
work at all when the expert trajectory is subsampled. Instead, the mentioned
success holds when the expert trajectory is complete, in which ValueDice is
closely related to BC that performs well as mentioned. Finally, we discuss the
implications of our research for imitation learning studies beyond ValueDice.


title: On-demand harnessing of photonic soliton molecules

abstract:
  Soliton molecules (SMs) are fundamentally important modes in nonlinear
optical systems. It is a challenge to experimentally produce SMs with a
required temporal separation in mode-locked fiber lasers. Here, we propose and
realize an experimental scenario for harnessing SM dynamics in a laser setup.
In particular, we tailor SMs in a mode-locked laser controlled by second-order
group-velocity dispersion and dispersion losses: the real part of dispersion
maintains the balance between the dispersion and nonlinearity, while the
dispersion loss determines the balance of gain and losses. The experimental
results demonstrate that the dispersion loss makes it possible to select
desired values of the temporal separation (TS) in bound pairs of SMs in the
system. Tunability of the SM's central wavelength and the corresponding
hysteresis are addressed too. The demonstrated regime allows us to create
multiple SMs with preselected values of the TS and central wavelength, which
shows the potential of our setup for the design of optical data-processing
schemes.


title: MarkovGNN: Graph Neural Networks on Markov Diffusion

abstract:
  Most real-world networks contain well-defined community structures where
nodes are densely connected internally within communities. To learn from these
networks, we develop MarkovGNN that captures the formation and evolution of
communities directly in different convolutional layers. Unlike most Graph
Neural Networks (GNNs) that consider a static graph at every layer, MarkovGNN
generates different stochastic matrices using a Markov process and then uses
these community-capturing matrices in different layers. MarkovGNN is a general
approach that could be used with most existing GNNs. We experimentally show
that MarkovGNN outperforms other GNNs for clustering, node classification, and
visualization tasks. The source code of MarkovGNN is publicly available at
\url{https://github.com/HipGraph/MarkovGNN}.


title:
Few-shot Learning as Cluster-induced Voronoi Diagrams: A Geometric
  Approach

abstract:
  Few-shot learning (FSL) is the process of rapid generalization from abundant
base samples to inadequate novel samples. Despite extensive research in recent
years, FSL is still not yet able to generate satisfactory solutions for a wide
range of real-world applications. To confront this challenge, we study the FSL
problem from a geometric point of view in this paper. One observation is that
the widely embraced ProtoNet model is essentially a Voronoi Diagram (VD) in the
feature space. We retrofit it by making use of a recent advance in
computational geometry called Cluster-induced Voronoi Diagram (CIVD). Starting
from the simplest nearest neighbor model, CIVD gradually incorporates
cluster-to-point and then cluster-to-cluster relationships for space
subdivision, which is used to improve the accuracy and robustness at multiple
stages of FSL. Specifically, we use CIVD (1) to integrate parametric and
nonparametric few-shot classifiers; (2) to combine feature representation and
surrogate representation; (3) and to leverage feature-level,
transformation-level, and geometry-level heterogeneities for a better ensemble.
Our CIVD-based workflow enables us to achieve new state-of-the-art results on
mini-ImageNet, CUB, and tiered-ImagenNet datasets, with ${\sim}2\%{-}5\%$
improvements upon the next best. To summarize, CIVD provides a mathematically
elegant and geometrically interpretable framework that compensates for extreme
data insufficiency, prevents overfitting, and allows for fast geometric
ensemble for thousands of individual VD. These together make FSL stronger.


title:
Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor
  Imagery Classification

abstract:
  Deep learning (DL) has been widely investigated in a vast majority of
applications in electroencephalography (EEG)-based brain-computer interfaces
(BCIs), especially for motor imagery (MI) classification in the past five
years. The mainstream DL methodology for the MI-EEG classification exploits the
temporospatial patterns of EEG signals using convolutional neural networks
(CNNs), which have been particularly successful in visual images. However,
since the statistical characteristics of visual images depart radically from
EEG signals, a natural question arises whether an alternative network
architecture exists apart from CNNs. To address this question, we propose a
novel geometric deep learning (GDL) framework called Tensor-CSPNet, which
characterizes spatial covariance matrices derived from EEG signals on symmetric
positive definite (SPD) manifolds and fully captures the temporospatiofrequency
patterns using existing deep neural networks on SPD manifolds, integrating with
experiences from many successful MI-EEG classifiers to optimize the framework.
In the experiments, Tensor-CSPNet attains or slightly outperforms the current
state-of-the-art performance on the cross-validation and holdout scenarios in
two commonly-used MI-EEG datasets. Moreover, the visualization and
interpretability analyses also exhibit the validity of Tensor-CSPNet for the
MI-EEG classification. To conclude, in this study, we provide a feasible answer
to the question by generalizing the DL methodologies on SPD manifolds, which
indicates the start of a specific GDL methodology for the MI-EEG
classification.


title: Categorical lifting of the Jones polynomial: a survey

abstract:
  This is a brief review of the categorification of the Jones polynomial and
its significance and ramifications in geometry, algebra, and low-dimensional
topology.


title: Importance Weighting Approach in Kernel Bayes' Rule

abstract:
  We study a nonparametric approach to Bayesian computation via feature means,
where the expectation of prior features is updated to yield expected posterior
features, based on regression from kernel or neural net features of the
observations. All quantities involved in the Bayesian update are learned from
observed data, making the method entirely model-free. The resulting algorithm
is a novel instance of a kernel Bayes' rule (KBR). Our approach is based on
importance weighting, which results in superior numerical stability to the
existing approach to KBR, which requires operator inversion. We show the
convergence of the estimator using a novel consistency analysis on the
importance weighting estimator in the infinity norm. We evaluate our KBR on
challenging synthetic benchmarks, including a filtering problem with a
state-space model involving high dimensional image observations. The proposed
method yields uniformly better empirical performance than the existing KBR, and
competitive performance with other competing methods.


title:
First-principles perspective on full-spectrum infrared photodetectors
  from doping an excitonic insulator

abstract:
  Innovations in imaging technology involves finding strategies and materials
suitable for detection applications over the entire infrared range. Herein, we
propose a new design concept based on the unique feature of an excitonic
insulator, namely, negative exciton transition energy ($E_t$). We demonstrate
this concept using first-principles $GW$-BSE calculations on one-dimensional
organometallic wire (CrBz)$_\infty$. The pristine (CrBz)$_\infty$ exhibits an
excitonic instability due to a negative $E_t$ for the lowest exciton.
Substitutional doping can continuously tune the $E_t$ from $\sim$0 to $\sim$0.6
eV, which shows the ability of photon detection from terahertz to
near-infrared. This type of detectors have advantages of outstanding wavelength
selectivity, reduced thermal disturbance and elevated working temperature. Our
work not only adds another member in the family of rare one-dimensional
excitonic insulators, but also opens a new avenue for the development of
high-performance infrared photodetectors in the future.


title:
Semantic Similarity Computing Model Based on Multi Model Fine-Grained
  Nonlinear Fusion

abstract:
  Natural language processing (NLP) task has achieved excellent performance in
many fields, including semantic understanding, automatic summarization, image
recognition and so on. However, most of the neural network models for NLP
extract the text in a fine-grained way, which is not conducive to grasp the
meaning of the text from a global perspective. To alleviate the problem, the
combination of the traditional statistical method and deep learning model as
well as a novel model based on multi model nonlinear fusion are proposed in
this paper. The model uses the Jaccard coefficient based on part of speech,
Term Frequency-Inverse Document Frequency (TF-IDF) and word2vec-CNN algorithm
to measure the similarity of sentences respectively. According to the
calculation accuracy of each model, the normalized weight coefficient is
obtained and the calculation results are compared. The weighted vector is input
into the fully connected neural network to give the final classification
results. As a result, the statistical sentence similarity evaluation algorithm
reduces the granularity of feature extraction, so it can grasp the sentence
features globally. Experimental results show that the matching of sentence
similarity calculation method based on multi model nonlinear fusion is 84%, and
the F1 value of the model is 75%.


title:
Retrieving the C and O Abundances of HR 7672~AB: a Solar-Type Primary
  Star with a Benchmark Brown Dwarf

abstract:
  A benchmark brown dwarf (BD) is a BD whose properties (e.g., mass and
chemical composition) are precisely and independently measured. Benchmark BDs
are valuable in testing theoretical evolutionary tracks, spectral synthesis,
and atmospheric retrievals for sub-stellar objects. Here, we report results of
atmospheric retrieval on a synthetic spectrum and a benchmark BD -- HR 7672~B
-- with \petit. First, we test the retrieval framework on a synthetic PHOENIX
BT-Settl spectrum with a solar composition. We show that the retrieved C and O
abundances are consistent with solar values, but the retrieved C/O is
overestimated by 0.13-0.18, which is $\sim$4 times higher than the formal error
bar. Second, we perform retrieval on HR 7672~B using high spectral resolution
data (R=35,000) from the Keck Planet Imager and Characterizer (KPIC) and near
infrared photometry. We retrieve [C/H], [O/H], and C/O to be $-0.24\pm0.05$,
$-0.19\pm0.04$, and $0.52\pm0.02$. These values are consistent with those of HR
7672~A within 1.5-$\sigma$. As such, HR 7672~B is among only a few benchmark
BDs (along with Gl 570~D and HD 3651~B) that have been demonstrated to have
consistent elemental abundances with their primary stars. Our work provides a
practical procedure of testing and performing atmospheric retrieval, and sheds
light on potential systematics of future retrievals using high- and
low-resolution data.


title:
Sensing Method for Two-Target Detection in Time-Constrained Vector
  Gaussian Channel

abstract:
  This paper considers a vector Gaussian channel of fixed identity covariance
matrix and binary input signalling as the mean of it. A linear transformation
is performed on the vector input signal. The objective is to find the optimal
scaling matrix, under the total time constraint, that would: i) maximize the
mutual information between the input and output random vectors, ii) maximize
the MAP detection. It was found that the two metrics lead to different optimal
solutions for our experimental design problem. We have used the Monte Carlo
method for our computational work.


title:
Algorithmic nudge to make better choices: Evaluating effectiveness of
  XAI frameworks to reveal biases in algorithmic decision making to users

abstract:
  In this position paper, we propose the use of existing XAI frameworks to
design interventions in scenarios where algorithms expose users to problematic
content (e.g. anti vaccine videos). Our intervention design includes facts (to
indicate algorithmic justification of what happened) accompanied with either
fore warnings or counterfactual explanations. While fore warnings indicate
potential risks of an action to users, the counterfactual explanations will
indicate what actions user should perform to change the algorithmic outcome. We
envision the use of such interventions as `decision aids' to users which will
help them make informed choices.


title: Numerical confirmations of joint spike transitions in $G_2$ cosmologies

abstract:
  We produce numerical evidence that the joint spike transitions between Kasner
eras of $G_2$ cosmologies are described by the non-orthogonally transitive
$G_2$ spike solution. A new matching procedure is developed for this purpose.


title: LotRec: A Recommender for Urban Vacant Lot Conversion

abstract:
  Vacant lots are neglected properties in a city that lead to environmental
hazards and poor standard of living for the community. Thus, reclaiming vacant
lots and putting them to productive use is an important consideration for many
cities. Given a large number of vacant lots and resource constraints for
conversion, two key questions for a city are (1) whether to convert a vacant
lot or not; and (2) what to convert a vacant lot as. We seek to provide
computational support to answer these questions. To this end, we identify the
determinants of a vacant lot conversion and build a recommender based on those
determinants. We evaluate our models on real-world vacant lot datasets from the
US cities of Philadelphia,PA and Baltimore, MD. Our results indicate that our
recommender yields mean F-measures of (1) 90% in predicting whether a vacant
lot should be converted or not within a single city, (2) 91% in predicting what
a vacant lot should be converted to, within a single city and, (3) 85% in
predicting whether a vacant lot should be converted or not across two cities.


title: Loss-Induced Quantum Revival

abstract:
  Conventional wisdom holds that quantum effects are fragile and can be
destroyed by loss. Here, contrary to general belief, we show how to realize
quantum revival of optical correlations at the single-photon level with the
help of loss. We find that, accompanying loss-induced transparency of light in
a nonlinear optical-molecule system, quantum suppression and revival of
photonic correlations can be achieved. Specifically, below critical values,
adding loss into the system leads to suppressions of both optical intensity and
its non-classical correlations; however, by further increasing the loss beyond
the critical values, quantum revival of photon blockade (PB) can emerge,
resulting in loss-induced switch between single-PB and two-PB or
super-Poissonian light. Our work provides a strategy to reverse the effect of
loss in fully quantum regime, opening up a counterintuitive route to explore
and utilize loss-tuned single-photon devices for quantum technology.


title:
Inside-Out Planet Formation. VII. Astrochemical Models of Protoplanetary
  Disks and Implications for Planetary Compositions

abstract:
  Inside-Out Planet Formation (IOPF) proposes that the abundant systems of
close-in Super-Earths and Mini-Neptunes form in situ at the pressure maximum
associated with the Dead Zone Inner Boundary (DZIB). We present a model of
physical and chemical evolution of protoplanetary disk midplanes that follows
gas advection, radial drift of pebbles and gas-grain chemistry to predict
abundances from 300~au down to the DZIB near 0.2 au. We consider typical disk
properties relevant for IOPF, i.e., accretion rates 1E-9 < dM/dt / (Msun/yr) <
1E-8 and viscosity parameter alpha = 1E-4, and evolve for fiducial duration of
t = 1E5 years. For outer, cool disk regions, we find that C and up to 90% of O
nuclei start locked in CO and O2 ice, which keeps abundances of CO2 and H2O one
order of magnitude lower. Radial drift of icy pebbles is influential, with
gas-phase abundances of volatiles enhanced up to two orders of magnitude at
ice-lines, while the outer disk becomes depleted of dust. Disks with decreasing
accretion rates gradually cool, which draws in icelines closer to the star. At
<~1 au, advective models yield water-rich gas with C/O ratios <~ 0.1, which may
be inherited by atmospheres of planets forming here via IOPF. For planetary
interiors built by pebble accretion, IOPF predicts volatile-poor compositions.
However, advectively-enhanced volatile mass fractions of ~10% can occur at the
water ice line.


title:
A "Distance Matters" Paradox: Facilitating Intra-Team Collaboration Can
  Harm Inter-Team Collaboration

abstract:
  By identifying the socio-technical conditions required for teams to work
effectively remotely, the Distance Matters framework has been influential in
CSCW since its introduction in 2000. Advances in collaboration technology and
practices have since brought teams increasingly closer to achieving these
conditions. This paper presents a ten-month ethnography in a remote
organization, where we observed that despite exhibiting excellent remote
collaboration, teams paradoxically struggled to collaborate across team
boundaries. We extend the Distance Matters framework to account for inter-team
collaboration, arguing that challenges analogous to those in the original
intra-team framework -- common ground, collaboration readiness, collaboration
technology readiness, and coupling of work -- persist but are actualized
differently at the inter-team scale. Finally, we identify a fundamental tension
between the intra- and inter-team layers: the collaboration technology and
practices that help individual teams thrive (e.g., adopting customized
collaboration software) can also prompt collaboration challenges in the
inter-team layer, and conversely the technology and practices that facilitate
inter-team collaboration (e.g., strong centralized IT organizations) can harm
practices at the intra-team layer. The addition of the inter-team layer to the
Distance Matters framework opens new opportunities for CSCW, where balancing
the tension between team and organizational collaboration needs will be a
critical technological, operational, and organizational challenge for remote
work in the coming decades.


title: Sharp regularity of the Hartman-Grobman theorem in $C^0$ linearization

abstract:
  The classical Hartman-Grobman theorem states the existence of a $C^0$
topological conjugacy between the nonlinear system and its linear part. It is
proved in the previous literature that the equivalent function $H$ and its
inverse $G=H^{-1}$ are both H\"{o}lder continuous.
  Questions: is it possible to improve the regularity? Is the regularity sharp?
  This paper gives a positive answer if the linear system is contractive (or
expansive) and the perturbation is nonlinear. We prove that $H$ is exactly
Lipschitzian, but the inverse $G=H^{-1}$ is merely H\"{o}lder continuous.
  Furthermore, we present an example to illustrates the sharpness of the
equivalent functions and we show that the
  regularity could not be improved anymore. It is the first time to observe
these new facts.


title: Discrete Gravity with Local Lorentz Invariance

abstract:
  A novel structure-preserving algorithm for general relativity in vacuum is
derived from a lattice gauge theoretic discretization of the tetradic Palatini
action. The resulting method is demonstrated to preserve local Lorentz
invariance and symplectic structure.


title:
An Olfactory EEG Signal Classification Network Based on Frequency Band
  Feature Extraction

abstract:
  Classification of olfactory-induced electroencephalogram (EEG) signals has
shown great potential in many fields. Since different frequency bands within
the EEG signals contain different information, extracting specific frequency
bands for classification performance is important. Moreover, due to the large
inter-subject variability of the EEG signals, extracting frequency bands with
subject-specific information rather than general information is crucial.
Considering these, the focus of this letter is to classify the olfactory EEG
signals by exploiting the spectral-domain information of specific frequency
bands. In this letter, we present an olfactory EEG signal classification
network based on frequency band feature extraction. A frequency band generator
is first designed to extract frequency bands via the sliding window technique.
Then, a frequency band attention mechanism is proposed to optimize frequency
bands for a specific subject adaptively. Last, a convolutional neural network
(CNN) is constructed to extract the spatio-spectral information and predict the
EEG category. Comparison experiment results reveal that the proposed method
outperforms a series of baseline methods in terms of both classification
quality and inter-subject robustness. Ablation experiment results demonstrate
the effectiveness of each component of the proposed method.


title:
A discussion of stochastic dominance and mean-CVaR optimal portfolio
  problems based on mean-variance-mixture models

abstract:
  The classical Markowitz mean-variance model uses variance as a risk measure
and calculates frontier portfolios in closed form by using standard
optimization techniques. For general mean-risk models such closed form optimal
portfolios are difficult to obtain. In this note, we obtain closed form
expressions for frontier portfolios under mean-CVaR criteria when return
vectors have normal mean-variance mixture (NMVM) distributions. To achieve this
goal, we first present necessary conditions for stochastic dominance within the
class of one dimensional NMVM models and then we apply them to portfolio
optimization problems. Our main result in this paper states that when return
vectors follow NMVM distributions the associated mean- CVaR frontier portfolios
can be obtained by optimizing a Markowitz mean-variance model with an
appropriately adjusted return vector


title:
Investigating the Challenges of Class Imbalance and Scale Variation in
  Object Detection in Aerial Images

abstract:
  While object detection is a common problem in computer vision, it is even
more challenging when dealing with aerial satellite images. The variety in
object scales and orientations can make them difficult to identify. In
addition, there can be large amounts of densely packed small objects such as
cars. In this project, we propose a few changes to the Faster-RCNN
architecture. First, we experiment with different backbones to extract better
features. We also modify the data augmentations and generated anchor sizes for
region proposals in order to better handle small objects. Finally, we
investigate the effects of different loss functions. Our proposed design
achieves an improvement of 4.7 mAP over the baseline which used a vanilla
Faster R-CNN with a ResNet-101 FPN backbone.


title: Heaps, crystals, and preprojective algebra modules

abstract:
  Fix a simply-laced semisimple Lie algebra. We study the crystal $
B(n\lambda)$, were $\lambda$ is a dominant minuscule weight and $n$ is a
natural number. On one hand, $B(n\lambda)$ can be realized combinatorially by
height $n$ reverse plane partitions on a heap associated to $\lambda$. On the
other hand, we use this heap to define a module over the preprojective algebra
of the underlying Dynkin quiver. Using the work of Saito and Savage-Tingley, we
realize $B(n\lambda)$ via irreducible components of the quiver Grassmannian of
$n$ copies of this module. In this paper, we describe an explicit bijection
between these two models for $B(n\lambda)$ and prove that our bijection yields
an isomorphism of crystals. Our main geometric tool is Nakajima's tensor
product quiver varieties.


title: Distributed Learning With Sparsified Gradient Differences

abstract:
  A very large number of communications are typically required to solve
distributed learning tasks, and this critically limits scalability and
convergence speed in wireless communications applications. In this paper, we
devise a Gradient Descent method with Sparsification and Error Correction
(GD-SEC) to improve the communications efficiency in a general worker-server
architecture. Motivated by a variety of wireless communications learning
scenarios, GD-SEC reduces the number of bits per communication from worker to
server with no degradation in the order of the convergence rate. This enables
larger-scale model learning without sacrificing convergence or accuracy. At
each iteration of GD-SEC, instead of directly transmitting the entire gradient
vector, each worker computes the difference between its current gradient and a
linear combination of its previously transmitted gradients, and then transmits
the sparsified gradient difference to the server. A key feature of GD-SEC is
that any given component of the gradient difference vector will not be
transmitted if its magnitude is not sufficiently large. An error correction
technique is used at each worker to compensate for the error resulting from
sparsification. We prove that GD-SEC is guaranteed to converge for strongly
convex, convex, and nonconvex optimization problems with the same order of
convergence rate as GD. Furthermore, if the objective function is strongly
convex, GD-SEC has a fast linear convergence rate. Numerical results not only
validate the convergence rate of GD-SEC but also explore the communication bit
savings it provides. Given a target accuracy, GD-SEC can significantly reduce
the communications load compared to the best existing algorithms without
slowing down the optimization process.


title: Predicting Future CSI Feedback For Highly-Mobile Massive MIMO Systems

abstract:
  Massive multiple-input multiple-output (MIMO) system is promising in
providing unprecedentedly high data rate. To achieve its full potential, the
transceiver needs complete channel state information (CSI) to perform
transmit/receive precoding/combining. This requirement, however, is challenging
in the practical systems due to the unavoidable processing and feedback delays,
which oftentimes degrades the performance to a great extent, especially in the
high mobility scenarios. In this paper, we develop a deep learning based
channel prediction framework that proactively predicts the downlink channel
state information based on the past observed channel sequence. In its core, the
model adopts a 3-D convolutional neural network (CNN) based architecture to
efficiently learn the temporal, spatial and frequency correlations of downlink
channel samples, based on which accurate channel prediction can be performed.
Simulation results highlight the potential of the developed learning model in
extracting information and predicting future downlink channels directly from
the observed past channel sequence, which significantly improves the
performance compared to the sample-and-hold approach, and mitigates the impact
of the dynamic communication environment.


title: Perfect pairing for monoidal adjunctions

abstract:
  We give another proof of the fact that there is a dual equivalence between
the $\infty$-category of monoidal $\infty$-categories with left adjoint oplax
monoidal functors and that with right adjoint lax monoidal functors by
constructing a perfect pairing between them.


title:
Neutrino Flavor Conversions in High-Density Astrophysical and
  Cosmological Environments

abstract:
  Despite being a well understood phenomenon in the context of current
terrestrial experiments, neutrino flavor conversions in dense astrophysical
environments probably represent one of the most challenging open problems in
neutrino physics. Apart from being theoretically interesting, such a problem
has several phenomenological implications in cosmology and in astrophysics,
including the primordial nucleosynthesis of light elements abundance and other
cosmological observables, nucleosynthesis of heavy nuclei and the explosion of
massive stars. In this review, we briefly summarize the state of the art on
this topic, focusing on three environments: early universe, core-collapse
supernovae and compact binary mergers.


title: Weisfeiler-Lehman meets Gromov-Wasserstein

abstract:
  The Weisfeiler-Lehman (WL) test is a classical procedure for graph
isomorphism testing. The WL test has also been widely used both for designing
graph kernels and for analyzing graph neural networks. In this paper, we
propose the Weisfeiler-Lehman (WL) distance, a notion of distance between
labeled measure Markov chains (LMMCs), of which labeled graphs are special
cases. The WL distance is polynomial time computable and is also compatible
with the WL test in the sense that the former is positive if and only if the WL
test can distinguish the two involved graphs. The WL distance captures and
compares subtle structures of the underlying LMMCs and, as a consequence of
this, it is more discriminating than the distance between graphs used for
defining the state-of-the-art Wasserstein Weisfeiler-Lehman graph kernel.
Inspired by the structure of the WL distance we identify a neural network
architecture on LMMCs which turns out to be universal w.r.t. continuous
functions defined on the space of all LMMCs (which includes all graphs) endowed
with the WL distance. Finally, the WL distance turns out to be stable w.r.t. a
natural variant of the Gromov-Wasserstein (GW) distance for comparing metric
Markov chains that we identify. Hence, the WL distance can also be construed as
a polynomial time lower bound for the GW distance which is in general NP-hard
to compute.


title: Knot reversal and rational concordance

abstract:
  We give an infinite family of knots that are not rationally concordant to
their reverses. More precisely, if R denotes the involution of the rational
knot concordance group QC induced by string reversal and Fix(R) denotes the
subgroup of knots fixed under R in QC, then QC/Fix(R) contains an infinite rank
subgroup. As a corollary, we show that there exists a knot K such that for
every pair of coprime integers p and q, the (p,q)-cable of K is not concordant
to the reverse of the (p,q)-cable of K.


title: Uniform Mordell-Lang Plus Bogomolov

abstract:
  In this paper, we prove a uniform version of Poonen's "Mordell-Lang Plus
Bogomolov" theorem for abelian varieties. We mainly generalize R\'emond's work
on large points to allow an extra $\epsilon$-neighborhood. The part on small
points follows from an earlier paper, joint with Gao and K\"uhne.


title: Spectroscopic Studies of Type Ia Supernovae Using LSTM Neural Networks

abstract:
  We present a data-driven method based on long short-term memory (LSTM) neural
networks to analyze spectral time series of Type Ia supernovae (SNe Ia). The
dataset includes 3091 spectra from 361 individual SNe Ia. The method allows for
accurate reconstruction of the spectral sequence of an SN Ia based on a single
observed spectrum around maximum light. The precision of the spectral
reconstruction increases with more spectral time coverages, but the significant
benefit of multiple epoch data at around optical maximum is only evident for
observations separated by more than a week. The method shows great power in
extracting the spectral information of SNe Ia, and suggests that the most
critical information of an SN Ia can be derived from a single spectrum around
the optical maximum. The algorithm we have developed is important for the
planning of spectroscopic follow-up observations of future SN surveys with the
LSST/Rubin and the WFIRST/Roman telescopes.


title: Asymptotic behavior of a stochastic particle system of 5 neighbors

abstract:
  We analyze a stochastic particle system of 5 neighbors. Considering
eigenvalue problem of transition matrix, we propose a conjecture that
asymptotic distribution of the system is determined by the number of specific
local patterns in the asymptotic solution. Based on the conjecture, mean flux
which depends of a pair of the conserved quantities is derived theoretically.
Moreover, we obtain mean flux in the deterministic case through the limit of
stochastic parameter.

categories: math-ph cond-mat.stat-mech math.MP nlin.CG


title: A Neural Beam Filter for Real-time Multi-channel Speech Enhancement

abstract:
  Most deep learning-based multi-channel speech enhancement methods focus on
designing a set of beamforming coefficients to directly filter the low
signal-to-noise ratio signals received by microphones, which hinders the
performance of these approaches. To handle these problems, this paper designs a
causal neural beam filter that fully exploits the spatial-spectral information
in the beam domain. Specifically, multiple beams are designed to steer towards
all directions using a parameterized super-directive beamformer in the first
stage. After that, the neural spatial filter is learned by simultaneously
modeling the spatial and spectral discriminability of the speech and the
interference, so as to extract the desired speech coarsely in the second stage.
Finally, to further suppress the interference components especially at low
frequencies, a residual estimation module is adopted to refine the output of
the second stage. Experimental results demonstrate that the proposed approach
outperforms many state-of-the-art multi-channel methods on the generated
multi-channel speech dataset based on the DNS-Challenge dataset.


title:
GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on
  Graph Attention Network

abstract:
  With the continuous extension of the Industrial Internet, cyber incidents
caused by software vulnerabilities have been increasing in recent years.
However, software vulnerabilities detection is still heavily relying on code
review done by experts, and how to automatedly detect software vulnerabilities
is an open problem so far. In this paper, we propose a novel solution named
GraphEye to identify whether a function of C/C++ code has vulnerabilities,
which can greatly alleviate the burden of code auditors. GraphEye is originated
from the observation that the code property graph of a non-vulnerable function
naturally differs from the code property graph of a vulnerable function with
the same functionality. Hence, detecting vulnerable functions is attributed to
the graph classification problem.GraphEye is comprised of VecCPG and GcGAT.
VecCPG is a vectorization for the code property graph, which is proposed to
characterize the key syntax and semantic features of the corresponding source
code. GcGAT is a deep learning model based on the graph attention graph, which
is proposed to solve the graph classification problem according to VecCPG.
Finally, GraphEye is verified by the SARD Stack-based Buffer Overflow,
Divide-Zero, Null Pointer Deference, Buffer Error, and Resource Error datasets,
the corresponding F1 scores are 95.6%, 95.6%,96.1%,92.6%, and 96.1%
respectively, which validate the effectiveness of the proposed solution.


title: A Coalition Formation Game Approach for Personalized Federated Learning

abstract:
  Facing the challenge of statistical diversity in client local data
distribution, personalized federated learning (PFL) has become a growing
research hotspot. Although the state-of-the-art methods with model
similarity-based pairwise collaboration have achieved promising performance,
they neglect the fact that model aggregation is essentially a collaboration
process within the coalition, where the complex multiwise influences take place
among clients. In this paper, we first apply Shapley value (SV) from coalition
game theory into the PFL scenario. To measure the multiwise collaboration among
a group of clients on the personalized learning performance, SV takes their
marginal contribution to the final result as a metric. We propose a novel
personalized algorithm: pFedSV, which can 1. identify each client's optimal
collaborator coalition and 2. perform personalized model aggregation based on
SV. Extensive experiments on various datasets (MNIST, Fashion-MNIST, and
CIFAR-10) are conducted with different Non-IID data settings (Pathological and
Dirichlet). The results show that pFedSV can achieve superior personalized
accuracy for each client, compared to the state-of-the-art benchmarks.


title: Adversarial Detector with Robust Classifier

abstract:
  Deep neural network (DNN) models are wellknown to easily misclassify
prediction results by using input images with small perturbations, called
adversarial examples. In this paper, we propose a novel adversarial detector,
which consists of a robust classifier and a plain one, to highly detect
adversarial examples. The proposed adversarial detector is carried out in
accordance with the logits of plain and robust classifiers. In an experiment,
the proposed detector is demonstrated to outperform a state-of-the-art detector
without any robust classifier.


title:
Partition of free energy for a Brownian quantum oscillator: Effect of
  dissipation and magnetic field

abstract:
  Recently, the quantum counterpart of energy equipartition theorem has drawn
considerable attention. Motivated by this, we formulate and investigate an
analogous statement for the free energy of a quantum oscillator linearly
coupled to a passive heat bath consisting of an infinite number of independent
harmonic oscillators. We explicitly demonstrate that the free energy of the
Brownian oscillator can be expressed in the form $F(T) = \langle f(\omega,T)
\rangle $ where $f(\omega,T)$ is the free energy of an individual bath
oscillator. The overall averaging process involves two distinct averages: the
first one is over the canonical ensemble for the bath oscillators, whereas the
second one signifies averaging over the entire bath spectrum of frequencies
from zero to infinity. The latter is performed over a relevant probability
distribution function $\mathcal{P}(\omega)$ which can be derived from the
knowledge of the generalized susceptibility encountered in linear response
theory. The effect of different dissipation mechanisms is also exhibited. We
find two remarkable consequences of our results. First, the quantum counterpart
of energy equipartition theorem follows naturally from our analysis. The second
corollary we obtain is a natural derivation of the third law of thermodynamics
for open quantum systems. Finally, we generalize the formalism to three spatial
dimensions in the presence of an external magnetic field.