title:
Partial volume correction improves theranostic 124I/131I-CLR1404 tumor
  dosimetry in xenograft models of head and neck cancer

abstract:
  Combination radiopharmaceutical and external beam radiotherapy offers the
potential to diminish locoregional toxicity that remains dose-limiting in the
conventional treatment paradigm for recurrent head and neck cancer (HNC). In
this study, we investigated the tumor targeting capacity of 131I-CLR1404 (CLR
131) in various HNC xenograft mouse models and the impact of partial volume
correction on theranostic dosimetry based on 124I-CLR1404 (CLR 124) PET/CT
imaging. Methods: Mice bearing flank tumor xenograft models of HNC (6 murine
cell line- and 6 human patient-derived) were intravenously administered 6.5-9.1
MBq of CLR 124 and imaged five times over the course of six days using
microPET/CT. In vivo tumor uptake of CLR 124 was assessed and partial volume
corrections (PVC) for 124I were applied using a novel preclinical phantom.
Using subject-specific theranostic dosimetry estimations for CLR 131 based on
CLR 124 imaging, a discrete radiation dose escalation study was performed to
evaluate tumor growth response to CLR 131 relative to a single fraction of XRT.
Results: PET imaging demonstrated consistent tumor selective uptake and
retention of CLR 124 across all HNC xenograft models. Peak uptake of 4.4 +/-
0.8% and 4.2 +/- 0.4% was observed in SCC-22B and UW-13, respectively. PVC
application increased uptake measures by 47-188% and reduced absolute
differences between in vivo and ex vivo uptake measurements from 3.3 to 1.0
%IA/g. Tumor dosimetry averaged over all HNC models was 0.85 +/- 0.27 Gy/MBq
(1.58 +/- 0.46 Gy/MBq with PVC). Therapeutic CLR 131 studies demonstrated a
variable, but linear relationship between CLR 131 radiation dose and tumor
growth delay (p < 0.05). Conclusion: CLR 131 demonstrated tumoricidal capacity
in preclinical HNC tumor models and the theranostic pairing of CLR 124/131
presents a promising new treatment approach for personalizing administration of
CLR 131.


title: Stable Ferromagnetism and High Curie Temperature in VGe2N4

abstract:
  The discovery of monolayer MA2Z4 (M = transition metals; A = IVA elements; Z
= VA elements, Science 369, 2020, 670-674) family has led another advance for
facilitating and harnessing magnetism in low-dimensional materials. However,
only Cr and V based MA2N4 compounds exhibit intrinsic magnetism yet with
unsatisfied magnetic ordering temperature. Herein, we identify a stable
ferromagnetic number of this family, i.e., VGe2Z4 monolayer, by means of
first-principles calculations. It is found that the magnetic configuration
sustains under both compression and tensile uniaxial in-plane strain, and the
former can act as a positive modulator to enhance magnetic ordering temperature
(Tc). Electronic structure calculations reveal a large band gap in the spin
down channel while band-gapless in the spin up channel, an impressive
near-half-metallic character, which is a favorable candidate for spintronic
device.


title: Observational Learning with Competitive Prices

abstract:
  Will people eventually learn the value of an asset through observable
information? This paper studies observational learning in a market with
competitive prices. Comparing a market with public signals and a market with
private signals in a sequential trading model, we find that Pairwise
Informativeness (PI) is the sufficient and necessary learning condition for a
market with public signals; and Avery and Zemsky Condition (AZC) is the
sufficient and necessary learning condition for a market with private signals.
Moreover, when the number of states is 2 or 3, PI and AZC are equivalent. And
when the number of states is greater than 3, PI and Monotonic Likelihood Ratio
Property (MLRP) together imply asymptotic learning in the private signal case.


title: Improved Stencil Selection for Meshless Finite Difference Methods in 3D

abstract:
  We introduce a geometric stencil selection algorithm for Laplacian in 3D that
significantly improves octant-based selection considered earlier. The goal of
the algorithm is to choose a small subset from a set of irregular points
surrounding a given point that admits an accurate numerical differentiation
formula. The subset serves as an influence set for the numerical approximation
of the Laplacian in meshless finite difference methods using either polynomial
or kernel-based techniques. Numerical experiments demonstrate a competitive
performance of this method in comparison to the finite element method and to
other selection methods for solving the Dirichlet problems for the Poisson
equation on several STL models. Discretization nodes for these domains are
obtained either by 3D triangulations or from Cartesian grids or Halton
quasi-random sequences.


title:
CellListMap.jl: Efficient and customizable cell list implementation for
  calculation of pairwise particle properties within a cutoff

abstract:
  Particle simulations and trajectory analysis rely on the calculation of
attributes that depend on pairwise particle distances within a cutoff.
Interparticle potential energies, forces, distribution functions, neighbor
lists, and distance-dependent distributions, for example, must all be
calculated. Cell lists are widely used to avoid computing distances outside the
cutoff, although efficient cell list implementations are difficult to
customize. Here, we provide a fast and parallel implementation of cell lists in
Julia that allows the mapping of custom functions to be computed from particle
positions in 2 or 3 dimensions. Arbitrary periodic boundary conditions are
supported. Automatic differentiation and unit propagation can be used. The
implementation provides a framework for the development of new analysis tools
and simulations with custom potentials. The performance of resulting
computations is comparable to state-of-the-art implementations of neighbor list
algorithms and cell list implementations available in specialized software.
Examples are provided for the computation of potential energies, forces,
distribution of pairwise velocities, neighbor lists and other typical
calculations in molecular and astrophysical simulations. The Julia package is
freely available at http://m3g.github.io/CellListMap.jl. Interfacing with
Python and R with minimal overhead is possible.


title: Beyond Worst-Case Analysis for Root Isolation Algorithms

abstract:
  Isolating the real roots of univariate polynomials is a fundamental problem
in symbolic computation and it is arguably one of the most important problems
in computational mathematics. The problem has a long history decorated with
numerous ingenious algorithms and furnishes an active area of research.
However, the worst-case analysis of root-finding algorithms does not correlate
with their practical performance. We develop a smoothed analysis framework for
polynomials with integer coefficients to bridge the gap between the complexity
estimates and the practical performance. In this setting, we derive that the
expected bit complexity of DESCARTES solver to isolate the real roots of a
polynomial, with coefficients uniformly distributed, is
$\widetilde{\mathcal{O}}_B(d^2 + d \tau)$, where $d$ is the degree of the
polynomial and $\tau$ the bitsize of the coefficients.


title:
FirstPersonScience: Quantifying Psychophysics for First Person Shooter
  Tasks

abstract:
  In the emerging field of esports research, there is an increasing demand for
quantitative results that can be used by players, coaches and analysts to make
decisions and present meaningful commentary for spectators. We present
FirstPersonScience, a software application intended to fill this need in the
esports community by allowing scientists to design carefully controlled
experiments and capture accurate results in the First Person Shooter esports
genre. An experiment designer can control a variety of parameters including
target motion, weapon configuration, 3D scene, frame rate, and latency.
Furthermore, we validate this application through careful end-to-end latency
analysis and provide a case study showing how it can be used to demonstrate the
training effect of one user given repeated task performance.


title:
Giving the Right Answer: a Brief Overview on How to Extend Ranking and
  Skyline Queries

abstract:
  To retrieve the best results in a database we use Top-K queries and Skyline
queries but some problems arise. The formers rely too much on user preferences,
which are difficult to quantify and may skew the fetching of the data, while
the latters tend to output too much data. In this paper, we explore three
different branches of research that seek to overcome such limitations:
Flexible/Restricted Skylines, Skyline Ordering/Ranking, and Regret
Minimization. We analyze how they work and we make comparisons among them to
guide the reader to choose the approach that best fits their use cases.


title:
AI can evolve without labels: self-evolving vision transformer for chest
  X-ray diagnosis through knowledge distillation

abstract:
  Although deep learning-based computer-aided diagnosis systems have recently
achieved expert-level performance, developing a robust deep learning model
requires large, high-quality data with manual annotation, which is expensive to
obtain. This situation poses the problem that the chest x-rays collected
annually in hospitals cannot be used due to the lack of manual labeling by
experts, especially in deprived areas. To address this, here we present a novel
deep learning framework that uses knowledge distillation through
self-supervised learning and self-training, which shows that the performance of
the original model trained with a small number of labels can be gradually
improved with more unlabeled data. Experimental results show that the proposed
framework maintains impressive robustness against a real-world environment and
has general applicability to several diagnostic tasks such as tuberculosis,
pneumothorax, and COVID-19. Notably, we demonstrated that our model performs
even better than those trained with the same amount of labeled data. The
proposed framework has a great potential for medical imaging, where plenty of
data is accumulated every year, but ground truth annotations are expensive to
obtain.


title: Robust Time-Varying Graph Signal Recovery Over Dynamic Topology

abstract:
  We propose a time-varying graph signal recovery method that estimates the
true graph signal from an observation corrupted by missing values, outliers of
unknown positions, and some random noise. Furthermore, we assume the underlying
graph to be time-varying like the signals, which we integrate into our
formulation for better performance. Conventional studies on time-varying graph
signal recovery have been focusing on online estimation and graph learning
under the assumption that entire dynamic graphs are unavailable. However, there
are many practical situations where the underlying graphs can be observed or
easily generated by simple algorithms like the k-nearest neighbor, especially
when targeting physical sensing data, where the graphs can be defined to
represent spatial correlations. To address such cases, in this paper, we tackle
a dynamic graph Laplacian-based recovery problem on given dynamic graphs. To
solve this, we formulate the recovery problem as a constrained convex
optimization problem to estimate both the time-varying graph signal and the
sparsely modeled outliers simultaneously. We integrate the graph dynamics into
the formulation by exploiting the given dynamic graph. In such a manner, we
succeed in separating the different types of corruption and achieving
state-of-the-art recovery performance in both synthetic and real-world problem
settings. We also conduct an extensive study to compare the contribution of
vertex and temporal domain regularization on the recovery performance.


title:
Analyticity, rank one perturbations and the invariance of the left
  spectrum

abstract:
  We address the question of the analyticity of a rank one perturbation of an
analytic operator. If $\mathscr M_z$ is the bounded operator of multiplication
by $z$ on a functional Hilbert space $\mathscr H_\kappa$ and $f \in \mathscr H$
with $f(0)=0,$ then $\mathscr M_z + f \otimes 1$ is always analytic. If $f(0)
\neq 0,$ then the analyticity of $\mathscr M_z + f \otimes 1$ is characterized
in terms of the membership to $\mathscr H_\kappa$ of the formal power series
obtained by multiplying $f(z)$ by $\frac{1}{f(0)-z}.$ As an application, we
discuss the problem of the invariance of the left spectrum under rank one
perturbation. In particular, we show that the left spectrum $\sigma_l(T + f
\otimes g)$ of the rank one perturbation $T + f \otimes g,$ $\,g \in
\ker(T^*),$ of a cyclic analytic left invertible bounded linear operator $T$
coincides with the left spectrum of $T$ except the point $\inp{f}{g}.$ In
general, the point $\inp{f}{g}$ may or may not belong to $\sigma_l(T + f
\otimes g).$ However, if it belongs to $\sigma_l(T + f \otimes g) \backslash
\{0\},$ then it is a simple eigenvalue of $T + f \otimes g.$


title: Perception-Aware Perching on Powerlines with Multirotors

abstract:
  Multirotor aerial robots are becoming widely used for the inspection of
powerlines. To enable continuous, robust inspection without human intervention,
the robots must be able to perch on the powerlines to recharge their batteries.
Highly versatile perching capabilities are necessary to adapt to the variety of
configurations and constraints that are present in real powerline systems. This
paper presents a novel perching trajectory generation framework that computes
perception-aware, collision-free, and dynamically-feasible maneuvers to guide
the robot to the desired final state. Trajectory generation is achieved via
solving a Nonlinear Programming problem using the Primal-Dual Interior Point
method. The problem considers the full dynamic model of the robot down to its
single rotor thrusts and minimizes the final pose and velocity errors while
avoiding collisions and maximizing the visibility of the powerline during the
maneuver. The generated maneuvers consider both the perching and the posterior
recovery trajectories. The framework adopts costs and constraints defined by
efficient mathematical representations of powerlines, enabling online onboard
execution in resource-constrained hardware. The method is validated on-board an
agile quadrotor conducting powerline inspection and various perching maneuvers
with final pitch values of up to 180 degrees. The developed code is available
online at: https://github.com/grvcPerception/pa_powerline_perching


title:
Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement
  Learning for URLLC and eMBB Services

abstract:
  Radio access network (RAN) slicing is a key technology that enables 5G
network to support heterogeneous requirements of generic services, namely
ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband
(eMBB). In this paper, we propose a two time-scales RAN slicing mechanism to
optimize the performance of URLLC and eMBB services. In a large time-scale, an
SDN controller allocates radio resources to gNodeBs according to the
requirements of the eMBB and URLLC services. In a short time-scale, each gNodeB
allocates its available resources to its end-users and requests, if needed,
additional resources from adjacent gNodeBs. We formulate this problem as a
non-linear binary program and prove its NP-hardness. Next, for each time-scale,
we model the problem as a Markov decision process (MDP), where the large-time
scale is modeled as a single agent MDP whereas the shorter time-scale is
modeled as a multi-agent MDP. We leverage the exponential-weight algorithm for
exploration and exploitation (EXP3) to solve the single-agent MDP of the large
time-scale MDP and the multi-agent deep Q-learning (DQL) algorithm to solve the
multi-agent MDP of the short time-scale resource allocation. Extensive
simulations show that our approach is efficient under different network
parameters configuration and it outperforms recent benchmark solutions.


title:
Signature and Toledo invariants for flat unitary bundles over surfaces
  with boundary

abstract:
  This paper deals with the representations of the fundamental groups of
compact surfaces with boundary into classical simple Lie groups of Hermitian
type. We relate work on the signature of the associated local systems, due to
Meyer and Atiyah, to Burger-Iozzi-Wienhard's Toledo invariant. To measure the
difference, we extend Atiyah-Patodi-Singer's rho invariant, initially defined
on $\mathrm{U}(p)$, to discontinuous class functions, first on
$\mathrm{U}(p,q)$, and then on other classical groups via embeddings into
$\mathrm{U}(p,q)$. As an application, we obtain a Milnor-Wood type inequality
which slightly differs from, and sometimes improves upon
Burger-Iozzi-Wienhard's version.


title: Coexisting Z-type charge and bond order in metallic NaRu$_2$O$_4$

abstract:
  How particular bonds form in quantum materials has been a long-standing
puzzle. Two key concepts dealing with charge degrees of freedom are
dimerization (forming metal-metal bonds) and charge ordering (CO). Since the
1930s, these two concepts have been frequently invoked to explain numerous
exciting quantum materials, typically insulators. Here we report dimerization
and CO within the dimers coexisting in metallic NaRu$_2$O$_4$. By combining
high-resolution x-ray diffraction studies and theoretical calculations, we
demonstrate that this unique phenomenon occurs through a new type of bonding,
which we call Z-type ordering. The low-temperature superstructure has strong
dimerization in legs of zigzag ladders, with short dimers in legs connected by
short zigzag bonds, forming Z-shape clusters: simultaneously, site-centered
charge ordering also appears. Our results demonstrate the yet unknown
flexibility of quantum materials with the intricate interplay among orbital,
charge, and lattice degrees of freedom.


title: Learning from Randomly Initialized Neural Network Features

abstract:
  We present the surprising result that randomly initialized neural networks
are good feature extractors in expectation. These random features correspond to
finite-sample realizations of what we call Neural Network Prior Kernel (NNPK),
which is inherently infinite-dimensional. We conduct ablations across multiple
architectures of varying sizes as well as initializations and activation
functions. Our analysis suggests that certain structures that manifest in a
trained model are already present at initialization. Therefore, NNPK may
provide further insight into why neural networks are so effective in learning
such structures.


title:
Communication and Computation O-RAN Resource Slicing for URLLC Services
  Using Deep Reinforcement Learning

abstract:
  The evolution of the future beyond-5G/6G networks towards a service-aware
network is based on network slicing technology. With network slicing,
communication service providers seek to meet all the requirements imposed by
the verticals, including ultra-reliable low-latency communication (URLLC)
services. In addition, the open radio access network (O-RAN) architecture paves
the way for flexible sharing of network resources by introducing more
programmability into the RAN. RAN slicing is an essential part of end-to-end
network slicing since it ensures efficient sharing of communication and
computation resources. However, due to the stringent requirements of URLLC
services and the dynamics of the RAN environment, RAN slicing is challenging.
In this article, we propose a two-level RAN slicing approach based on the O-RAN
architecture to allocate the communication and computation RAN resources among
URLLC end-devices. For each RAN slicing level, we model the resource slicing
problem as a single-agent Markov decision process and design a deep
reinforcement learning algorithm to solve it. Simulation results demonstrate
the efficiency of the proposed approach in meeting the desired quality of
service requirements.


title: Ultrasonic Backscatter Communication for Implantable Medical Devices

abstract:
  This paper proposes an ultrasonic backscatter communication (UsBC) system for
passive implantable medical devices (IMDs) that can operate without batteries,
enabling versatile revolutionary applications for future healthcare. The
proposed UsBC system consists of a reader and a tag. The reader sends
interrogation pulses to the tag. The tag backscatters the pulses based on the
piezoelectric effect of a piezo transducer. We present several basic modulation
schemes for UsBC by impedance matching of the piezo transducer. To mitigate the
interference of other scatters in the human body, the tag transmits information
bits by codeword mapping, and the reader performs codeword matching before
energy detection in the reader. We further derive the theoretical bit-error
rate (BER) expression. Monte Carlo simulations verify the theoretical analysis
and show that passive UsBC can achieve low BER and low complexity, which is
desirable for size- and energy-constrained IMDs.


title:
Experimental investigation of quantum uncertainty relations with
  classical shadows

abstract:
  The quantum component in uncertainty relation can be naturally characterized
by the quantum coherence of a quantum state, which is of paramount importance
in quantum information science. Here, we experimentally investigate quantum
uncertainty relations construed with relative entropy of coherence, $l_1$ norm
of coherence and coherence of formation. In stead of quantum state tomographic
technology, we employ the classical shadow algorithm for the detection of lower
bounds in quantum uncertainty relations. With an all optical setup, we prepare
a family of quantum states whose purity can be fully controlled. We
experimentally explore the tightness of various lower bounds in different
reference bases on the prepared states. Our results indicate the tightness of
quantum coherence lower bounds dependents on the reference bases as well as the
purity of quantum state.


title: Fast algorithm for overcomplete order-3 tensor decomposition

abstract:
  We develop the first fast spectral algorithm to decompose a random
third-order tensor over R^d of rank up to O(d^{3/2}/polylog(d)). Our algorithm
only involves simple linear algebra operations and can recover all components
in time O(d^{6.05}) under the current matrix multiplication time.
  Prior to this work, comparable guarantees could only be achieved via
sum-of-squares [Ma, Shi, Steurer 2016]. In contrast, fast algorithms [Hopkins,
Schramm, Shi, Steurer 2016] could only decompose tensors of rank at most
O(d^{4/3}/polylog(d)).
  Our algorithmic result rests on two key ingredients. A clean lifting of the
third-order tensor to a sixth-order tensor, which can be expressed in the
language of tensor networks. A careful decomposition of the tensor network into
a sequence of rectangular matrix multiplications, which allows us to have a
fast implementation of the algorithm.


title:
Learning Reward Models for Cooperative Trajectory Planning with Inverse
  Reinforcement Learning and Monte Carlo Tree Search

abstract:
  Cooperative trajectory planning methods for automated vehicles, are capable
to solve traffic scenarios that require a high degree of cooperation between
traffic participants. In order for cooperative systems to integrate in
human-centered traffic, it is important that the automated systems behave
human-like, so that humans can anticipate the system's decisions. While
Reinforcement Learning has made remarkable progress in solving the decision
making part, it is non-trivial to parameterize a reward model that yields
predictable actions. This work employs feature-based Maximum Entropy Inverse
Reinforcement Learning in combination with Monte Carlo Tree Search to learn
reward models that maximizes the likelihood of recorded multi-agent cooperative
expert trajectories. The evaluation demonstrates that the approach is capable
of recovering a reasonable reward model that mimics the expert and performs
similar to a manually tuned baseline reward model.


title:
Nonlinear Optical Joint Transform Correlator for Low Latency Convolution
  Operations

abstract:
  Convolutions are one of the most relevant operations in artificial
intelligence (AI) systems. High computational complexity scaling poses
significant challenges, especially in fast-responding network-edge AI
applications. Fortunately, the convolution theorem can be executed on-the-fly
in the optical domain via a joint transform correlator (JTC) offering to
fundamentally reduce the computational complexity. Nonetheless, the iterative
two-step process of a classical JTC renders them unpractical. Here we introduce
a novel implementation of an optical convolution-processor capable of near-zero
latency by utilizing all-optical nonlinearity inside a JTC, thus minimizing
electronic signal or conversion delay. Fundamentally we show how this nonlinear
auto-correlator enables reducing the high $O(n^4)$ scaling complexity of
processing two-dimensional data to only $O(n^2)$. Moreover, this optical JTC
processes millions of channels in time-parallel, ideal for large-matrix machine
learning tasks. Exemplary utilizing the nonlinear process of four-wave mixing,
we show light processing performing a full convolution that is temporally
limited only by geometric features of the lens and the nonlinear material's
response time. We further discuss that the all-optical nonlinearity exhibits
gain in excess of $>10^{3}$ when enhanced by slow-light effects such as
epsilon-near-zero. Such novel implementation for a machine learning accelerator
featuring low-latency and non-iterative massive data parallelism enabled by
fundamental reduced complexity scaling bears significant promise for
network-edge, and cloud AI systems.


title:
Existence of Large-Data Global Weak Solutions to Kinetic Models of
  Nonhomogeneous Dilute Polymeric Fluids

abstract:
  We prove the existence of large-data global-in-time weak solutions to a
general class of coupled bead-spring chain models with finitely extensible
nonlinear elastic (FENE) type spring potentials for nonhomogeneous
incompressible dilute polymeric fluids in a bounded domain in $\mathbb{R}^d$,
$d=2$ or $3$. The class of models under consideration involves the
Navier--Stokes system with variable density, where the viscosity coefficient
depends on both the density and the polymer number density, coupled to a
Fokker--Planck equation with a density-dependent drag coefficient. The proof is
based on combining a truncation of the probability density function with a
two-stage Galerkin approximation and weak compactness and compensated
compactness techniques to pass to the limits in the sequence of Galerkin
approximations and in the truncation level.


title: Some Consequences of Restrictions on Digitally Continuous Functions

abstract:
  We study the consequences of some restrictions on digitally continuous
functions. One of our results modifies easily to yield an analogous result for
topological spaces.


title:
Measurement of $e^{+}e^{-} \to K^{+}K^{-}\pi^{0}$ cross section and
  observation of a resonant structure

abstract:
  Based on $e^{+}e^{-}$ collision data collected by the BESIII detector at the
BEPCII collider at center-of-mass energies from 2.000 to 3.080 GeV, a
partial-wave analysis is performed for the process $e^{+}e^{-} \to
K^{+}K^{-}\pi^{0}$. The Born cross section of the process $e^{+}e^{-} \to
K^{+}K^{-}\pi^{0}$ and its subprocesses $e^{+}e^{-} \to \phi \pi^{0}$,
$K^{*}(892)K$ and $K^{*}_{2}(1430)K$ are measured. The results for $e^{+}e^{-}
\to K^{+}K^{-}\pi^{0}$ and $\phi \pi^{0}$ are consistent with the BaBar
measurements and with improved precision. By analyzing the cross section, of
the subprocesses $e^{+}e^{-} \to$ $K^{*}(892)K$ and $K^{*}_{2}(1430)K$, a
structure with mass $M_R$ = (2208 $\pm$ 19 $\pm$ 24) MeV/$c^{2}$ and width
$\Gamma_R$ = (168 $\pm$ 24 $\pm$ 39) MeV is observed with a combined
statistical significance of 7.6$\sigma$. The measured resonance parameters
suggest it can be identified as the $\phi(2170)$, thus the results provide
valuable input to understand the internal nature of this state.


title:
Effect of orientational ordering of water dipoles on stratification of
  counterions of different size in multicomponent electrolyte solution near
  charged surface -- a mean field approach

abstract:
  We theoretically studied electric double layer by using the mean-field
approach including the non-uniform size effect and the orientational ordering
of water dipoles in electrolyte solution. Performing the minimization of the
free energy, the resulting ion and water distribution functions are determined
numerically in the process of solving the Poisson's equation. The effect of
non-uniform ionic size and orientational ordering of water dipoles on
stratification of counterions of different size in multicomponent electrolyte
solution near the charged surface are presented and discussed. Spatial
dependence of relative permittivity and differential capacitance of electric
double layer are also calculated for binary electrolyte solution and then
compared with the predictions of the previous models. Effect of bulk
concentration of counterions and ion size effects on surface charge density,
excess portion of counterions, relative permittivity and differential
capacitance are studied for multicomponent electrolyte solution in detail.


title:
Notes on BIM and BFM Optimal Power Flow With Parallel Lines and Total
  Current Limits

abstract:
  The second-order cone relaxation of the branch flow model (BFM) and bus
injection model (BIM) variants of optimal power flow are well-known to be
equivalent for radial networks. In this work we show that in meshed networks
with parallel lines, BIM dominates BFM, and propose novel constraints to make
them equivalent in general. Furthermore, we develop an improvement to the
second-order cone relaxations of optimal power flow, adding novel and valid
linear constraints on the lifted current expressions. We develop two simple
test cases to highlight the advantages of the proposed constraints. These novel
constraints tighten the second-order cone relaxation gap on test cases in the
`PG Lib' optimal power flow benchmark library, albeit generally in limited
fashion.


title:
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and
  Optimality

abstract:
  Deployment efficiency is an important criterion for many real-world
applications of reinforcement learning (RL). Despite the community's increasing
interest, there lacks a formal theoretical formulation for the problem. In this
paper, we propose such a formulation for deployment-efficient RL (DE-RL) from
an "optimization with constraints" perspective: we are interested in exploring
an MDP and obtaining a near-optimal policy within minimal \emph{deployment
complexity}, whereas in each deployment the policy can sample a large batch of
data. Using finite-horizon linear MDPs as a concrete structural model, we
reveal the fundamental limit in achieving deployment efficiency by establishing
information-theoretic lower bounds, and provide algorithms that achieve the
optimal deployment efficiency. Moreover, our formulation for DE-RL is flexible
and can serve as a building block for other practically relevant settings; we
give "Safe DE-RL" and "Sample-Efficient DE-RL" as two examples, which may be
worth future investigation.


title:
Influence of solvent polarization and non-uniform ionic size on
  electrokinetic transport in a nanochannel

abstract:
  In this paper, we study the electroosmotic transport in a nanofluidic channel
by using a mean-field theory accounting for non-uniform size effect and solvent
polarization effect. We witness that in the presence of the given zeta
potential, an enhancement of ion size invariably lowers the electroosmotic
velocity, thereby increasing the magnitude of electrostatic potential,
irrespective of considering solvent polarization. It is also proved that
solvent polarization allows both the magnitude of electrostatic potential and
the electroosmotic velocities to decrease. In addition, we find that increasing
zeta potential augments not only ion size effect but also solvent polarization
effect. Furthermore, we demonstrate that decreasing bulk ion number density
causes an increase in electroosmotic velocity at the centerline. We compare the
properties of aqueous electrolytes with those of the electrolytes where solvent
is ethylalcohol. Finally, we study how solvent polarization and ionic size
affect streaming potential and electroviscous effect. It is emphasized that the
present study can provide a good way to control the nanofluidic transport for a
plethora of biological and industrial applications.


title: Multi-user Beam Alignment in Presence of Multi-path

abstract:
  To overcome the high path-loss and the intense shadowing in millimeter-wave
(mmWave) communications, effective beamforming schemes are required which
incorporate narrow beams with high beamforming gains. The mmWave channel
consists of a few spatial clusters each associated with an angle of departure
(AoD). The narrow beams must be aligned with the channel AoDs to increase the
beamforming gain. This is achieved through a procedure called beam alignment
(BA). Most of the BA schemes in the literature consider channels with a single
dominant path while in practice the channel has a few resolvable paths with
different AoDs, hence, such BA schemes may not work correctly in the presence
of multi-path or at the least do not exploit such multipath to achieve
diversity or increase robustness.
  In this paper, we propose an efficient BA scheme in presence of multi-path.
The proposed BA scheme transmits probing packets using a set of scanning beams
and receives feedback for all the scanning beams at the end of the probing
phase from each user. We formulate the BA scheme as minimizing the expected
value of the average transmission beamwidth under different policies. The
policy is defined as a function from the set of received feedback to the set of
transmission beams (TB). In order to maximize the number of possible feedback
sequences, we prove that the set of scanning beams (SB) has a special form,
namely, Tulip Design. Consequently, we rewrite the minimization problem with a
set of linear constraints and a reduced number of variables which is solved by
using an efficient greedy algorithm.


title:
Input-to-State Stable Neural Ordinary Differential Equations with
  Applications to Transient Modeling of Circuits

abstract:
  This paper proposes a class of neural ordinary differential equations
parametrized by provably input-to-state stable continuous-time recurrent neural
networks. The model dynamics are defined by construction to be input-to-state
stable (ISS) with respect to an ISS-Lyapunov function that is learned jointly
with the dynamics. We use the proposed method to learn cheap-to-simulate
behavioral models for electronic circuits that can accurately reproduce the
behavior of various digital and analog circuits when simulated by a commercial
circuit simulator, even when interconnected with circuit components not
encountered during training. We also demonstrate the feasibility of learning
ISS-preserving perturbations to the dynamics for modeling degradation effects
due to circuit aging.


title:
Reply to Comment on "Phase-space consideration on barrier transmission
  in a time-dependent variational approach with superposed wave packets"

abstract:
  In the paper by Hasegawa, Hagino and Tanimura (HHT) [Phys. Lett. B 808 (2020)
135693, arXiv:2006.06944], they concluded that quantum tunneling was simulated
by a time-dependent generator coordinate method (TDGCM). In contrast,
difficulties of TDGCM in describing quantum tunneling were pointed out by Ono
in the paper [Phys. Lett. B 826 (2022) 136931, arXiv:2201.02966]. Recently, HHT
submitted a comment [arXiv:2202.00513v1], by which they appear to give some
counterarguments to Ono's paper. Here I examine their arguments, reviewing the
main points of Ono's paper. The confusion in HHT's arguments may be mostly
resolved by recognizing how the wave packets interfere with each other when
they are coherently superposed.


title:
On boundedness of singularities and minimal log discrepancies of
  Koll\'ar components

abstract:
  Recent study in K-stability suggests that klt singularities whose local
volumes are bounded away from zero should be bounded up to special
degeneration. We show that this is true in dimension three, or when the minimal
log discrepancies of Koll\'ar components are bounded from above. We conjecture
that the minimal log discrepancies of Koll\'ar components are always bounded
from above, and verify it in dimension three when the local volumes are bounded
away from zero. We also answer a question of Han, Liu and Qi on the relation
between log canonical thresholds and local volumes.


title:
Discrete orthogonality of hypergeometric polynomial sequences on linear
  and quadratic lattices

abstract:
  We present a method to obtain weight functions associated with linear and
quadratic lattices that yield discrete orthogonality with respect to a
quasi-definite moment functional of the orthogonal polynomial sequences in the
Askey scheme, with the exception of the Jacobi, Bessel, Laguerre, and Hermite
polynomials.


title:
Developing high-performance DIRC detector for the Future Electron Ion
  Collider Experiment

abstract:
  The Electron-Ion Collider (EIC) will be the next frontier project of nuclear
physics in the United States. It is planned to be built in the Brookhaven
National Laboratory (BNL) in close collaboration with Jefferson Lab. One of the
key requirement for the EIC central detector is excellent particle
identification (PID). A detector using the Detection of Internally Reflected
Cherenkov light (DIRC) principle, with a radial size of only 7-8~cm, is a very
attractive solution for identification of the hadrons in the final state. The
R$\&$D program performed by the EIC PID collaboration (eRD14) is focused on
designing a high-performance DIRC (hpDIRC) detector that would extend the
momentum coverage well beyond the state-of-the-art 3 standard deviations or
more separation of $\pi/K$ up to 6~GeV/$c$, $e/\pi$ up to 1.8~GeV/$c$, and
$p/K$ up to 10~GeV/$c$. Key components of the hpDIRC detector are a 3-layer
compound lens and small pixel-size photo-sensors. This article describes the
status of the high-performance DIRC R$\&$D for the EIC detector, with a focus
on efforts towards developing and validating the radiation hard 3-layer lens.


title:
Faster hyperspectral image classification based on selective kernel
  mechanism using deep convolutional networks

abstract:
  Hyperspectral imagery is rich in spatial and spectral information. Using
3D-CNN can simultaneously acquire features of spatial and spectral dimensions
to facilitate classification of features, but hyperspectral image information
spectral dimensional information redundancy. The use of continuous 3D-CNN will
result in a high amount of parameters, and the computational power requirements
of the device are high, and the training takes too long. This letter designed
the Faster selective kernel mechanism network (FSKNet), FSKNet can balance this
problem. It designs 3D-CNN and 2D-CNN conversion modules, using 3D-CNN to
complete feature extraction while reducing the dimensionality of spatial and
spectrum. However, such a model is not lightweight enough. In the converted
2D-CNN, a selective kernel mechanism is proposed, which allows each neuron to
adjust the receptive field size based on the two-way input information scale.
Under the Selective kernel mechanism, it mainly includes two components, se
module and variable convolution. Se acquires channel dimensional attention and
variable convolution to obtain spatial dimension deformation information of
ground objects. The model is more accurate, faster, and less computationally
intensive. FSKNet achieves high accuracy on the IN, UP, Salinas, and Botswana
data sets with very small parameters.


title:
Work in progress: Identifying Two-Factor Authentication Support in
  Banking Sites

abstract:
  Two-factor authentication (2FA) offers several security benefits that
security-conscious users might expect from high-value services such as online
banks. In this work, we present our preliminary study to develop a scoring
scheme to automatically recognize when bank sites mention support for
two-factor authentication. We extract information related to security features
(primarily 2FA) offered by 379 bank domains from 93 countries. We use a subset
of these sites to refine our scoring scheme to include several heuristics for
identifying whether sites offer 2FA. For each bank domain in our dataset, we
use our algorithm based on text-analysis to calculate whether the domain offers
2FA to the users of the domain's online banking platform. Our preliminary
findings suggest that 2FA is yet to be widely adopted by banking domains.


title: Simultaneous Transport Evolution for Minimax Equilibria on Measures

abstract:
  Min-max optimization problems arise in several key machine learning setups,
including adversarial learning and generative modeling. In their general form,
in absence of convexity/concavity assumptions, finding pure equilibria of the
underlying two-player zero-sum game is computationally hard [Daskalakis et al.,
2021]. In this work we focus instead in finding mixed equilibria, and consider
the associated lifted problem in the space of probability measures. By adding
entropic regularization, our main result establishes global convergence towards
the global equilibrium by using simultaneous gradient ascent-descent with
respect to the Wasserstein metric -- a dynamics that admits efficient particle
discretization in high-dimensions, as opposed to entropic mirror descent. We
complement this positive result with a related entropy-regularized loss which
is not bilinear but still convex-concave in the Wasserstein geometry, and for
which simultaneous dynamics do not converge yet timescale separation does.
Taken together, these results showcase the benign geometry of bilinear games in
the space of measures, enabling particle dynamics with global qualitative
convergence guarantees.


title:
Asymptotic behavior of solutions to the Monge--Amp\`ere equations with
  slow convergence rate at infinity

abstract:
  We consider the asymptotic behavior of solutions to the Monge--Amp\`ere
equations with slow convergence rate at infinity and fulfill previous results
under faster convergence rate by Bao--Li--Zhang [Calc. Var PDE. 52(2015). pp.
39-63]. Different from known results, we obtain the limit of Hessian and/or
gradient of solution at infinity relying on the convergence rate. The basic
idea is to use a revised level set method, the spherical harmonic expansion and
the iteration method.


title: Causal Structural Learning on MPHIA Individual Dataset

abstract:
  The Population-based HIV Impact Assessment (PHIA) is an ongoing project that
conducts nationally representative HIV-focused surveys for measuring national
and regional progress toward UNAIDS' 90-90-90 targets, the primary strategy to
end the HIV epidemic. We believe the PHIA survey offers a unique opportunity to
better understand the key factors that drive the HIV epidemics in the most
affected countries in sub-Saharan Africa. In this article, we propose a novel
causal structural learning algorithm to discover important covariates and
potential causal pathways for 90-90-90 targets. Existing constrained-based
causal structural learning algorithms are quite aggressive in edge removal. The
proposed algorithm preserves more information about important features and
potential causal pathways. It is applied to the Malawi PHIA (MPHIA) data set
and leads to interesting results. For example, it discovers age and condom
usage to be important for female HIV awareness; the number of sexual partners
to be important for male HIV awareness; and knowing the travel time to HIV care
facilities leads to a higher chance of being treated for both females and
males. We further compare and validate the proposed algorithm using BIC and
using Monte Carlo simulations, and show that the proposed algorithm achieves
improvement in true positive rates in important feature discovery over existing
algorithms.


title:
Shear-induced mixing of granular materials featuring broad granule size
  distributions

abstract:
  Granular flows during a shear-induced mixing process are studied using
Discrete Element Methods. The aim is to understand the underlying elementary
mechanisms of transition from unmixed to mixed phases for a granular material
featuring a broad distribution of particles, which we investigate
systematically by varying the strain rate and system size. Here the strain rate
varies over four orders of magnitude and the system size varies from ten
thousand to more than a million granules. A strain rate-dependent transition
from quasistatic to purely inertial flow is observed. At the macroscopic scale,
the contact stresses drop due to the formation of shear-induced instabilities
that serves as an onset of granular flows and initiates mixing between the
granules. The stress-drop displays a profound system size dependence. At the
granular scale, mixing dynamics are correlated with the formation of shear
bands, which result in significantly different timescales of mixing, especially
for those regions that are close to the system walls and the bulk. Overall, our
results reveal that although the transient dynamics display a generic behavior
these have a significant finite-size effect. In contrast, macroscopic behaviors
at steady states have negligible system size dependence.


title:
Learn by Challenging Yourself: Contrastive Visual Representation
  Learning with Hard Sample Generation

abstract:
  Contrastive learning (CL), a self-supervised learning approach, can
effectively learn visual representations from unlabeled data. However, CL
requires learning on vast quantities of diverse data to achieve good
performance, without which the performance of CL will greatly degrade. To
tackle this problem, we propose a framework with two approaches to improve the
data efficiency of CL training by generating beneficial samples and joint
learning. The first approach generates hard samples for the main model. The
generator is jointly learned with the main model to dynamically customize hard
samples based on the training state of the main model. With the progressively
growing knowledge of the main model, the generated samples also become harder
to constantly encourage the main model to learn better representations.
Besides, a pair of data generators are proposed to generate similar but
distinct samples as positive pairs. In joint learning, the hardness of a
positive pair is progressively increased by decreasing their similarity. In
this way, the main model learns to cluster hard positives by pulling the
representations of similar yet distinct samples together, by which the
representations of similar samples are well-clustered and better
representations can be learned. Comprehensive experiments show superior
accuracy and data efficiency of the proposed methods over the state-of-the-art
on multiple datasets. For example, about 5% accuracy improvement on
ImageNet-100 and CIFAR-10, and more than 6% accuracy improvement on CIFAR-100
are achieved for linear classification. Besides, up to 2x data efficiency for
linear classification and up to 5x data efficiency for transfer learning are
achieved.


title:
A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from
  Simple Usage to Structure Mortification

abstract:
  Image analysis technology is used to solve the inadvertences of artificial
traditional methods in disease, wastewater treatment, environmental change
monitoring analysis and convolutional neural networks (CNN) play an important
role in microscopic image analysis. An important step in detection, tracking,
monitoring, feature extraction, modeling and analysis is image segmentation, in
which U-Net has increasingly applied in microscopic image segmentation. This
paper comprehensively reviews the development history of U-Net, and analyzes
various research results of various segmentation methods since the emergence of
U-Net and conducts a comprehensive review of related papers. First, this paper
has summarized the improved methods of U-Net and then listed the existing
significance of image segmentation techniques and their improvements that has
introduced over the years. Finally, focusing on the different improvement
strategies of U-Net in different papers, the related work of each application
target is reviewed according to detailed technical categories to facilitate
future research. Researchers can clearly see the dynamics of transmission of
technological development and keep up with future trends in this
interdisciplinary field.


title:
Measuring "Why" in Recommender Systems: a Comprehensive Survey on the
  Evaluation of Explainable Recommendation

abstract:
  Explainable recommendation has shown its great advantages for improving
recommendation persuasiveness, user satisfaction, system transparency, among
others. A fundamental problem of explainable recommendation is how to evaluate
the explanations. In the past few years, various evaluation strategies have
been proposed. However, they are scattered in different papers, and there lacks
a systematic and detailed comparison between them. To bridge this gap, in this
paper, we comprehensively review the previous work, and provide different
taxonomies for them according to the evaluation perspectives and evaluation
methods. Beyond summarizing the previous work, we also analyze the
(dis)advantages of existing evaluation methods and provide a series of
guidelines on how to select them. The contents of this survey are based on more
than 100 papers from top-tier conferences like IJCAI, AAAI, TheWebConf, Recsys,
UMAP, and IUI, and their complete summarization are presented at
https://shimo.im/sheets/VKrpYTcwVH6KXgdy/MODOC/. With this survey, we finally
aim to provide a clear and comprehensive review on the evaluation of
explainable recommendation.


title: Optimizing Random Mixup with Gaussian Differential Privacy

abstract:
  Differentially private data release receives rising attention in machine
learning community. Recently, an algorithm called DPMix is proposed to release
high-dimensional data after a random mixup of degree $m$ with differential
privacy. However, limited theoretical justifications are given about the "sweet
spot $m$" phenomenon, and directly applying DPMix to image data suffers from
severe loss of utility. In this paper, we revisit random mixup with recent
progress on differential privacy. In theory, equipped with Gaussian
Differential Privacy with Poisson subsampling, a tight closed form analysis is
presented that enables a quantitative characterization of optimal mixup $m^*$
based on linear regression models. In practice, mixup of features, extracted by
handcraft or pre-trained neural networks such as self-supervised learning
without labels, is adopted to significantly boost the performance with privacy
protection. We name it as Differentially Private Feature Mixup (DPFMix).
Experiments on MNIST, CIFAR10/100 are conducted to demonstrate its remarkable
utility improvement and protection against attacks.


title:
Asymmetric nuclear matter in relativistic mean-field models with
  isoscalar- and isovector-meson mixing

abstract:
  Using the relativistic mean-field model with nonlinear couplings between the
isoscalar and isovector mesons, we study the properties of isospin-asymmetric
nuclear matter. Not only the vector mixing,
$\omega_{\mu}\omega^{\mu}\mathbf{\rho}_{\nu}\mathbf{\rho}^{\nu}$, but also the
quartic interaction due to the scalar mesons, $\sigma^{2}\mathbf{\delta}^{2}$,
is taken into account to investigate the density dependence of nuclear symmetry
energy, $E_{\rm sym}$, and the neutron-star properties. It is found that the
$\delta$ meson increases $E_{\rm sym}$ at high densities, whereas the
$\sigma$-$\delta$ mixing makes $E_{\rm sym}$ soft above the saturation density.
Furthermore, the $\delta$ meson and its mixing have a large influence on the
radius and tidal deformability of a neutron star. In particular, the
$\sigma$-$\delta$ mixing reduces the neutron-star radius, and, thus, the
present calculation can simultaneously reproduce the dimensionless tidal
deformabilities of a canonical $1.4M_{\odot}$ neutron star observed from the
binary neutron star merger, GW170817, and from the compact binary coalescence,
GW190814.


title:
Survey for Distant Solar Twins (SDST) -- I. EPIC method for stellar
  parameter measurement

abstract:
  Solar twins are stars of key importance to the field of astronomy and offer a
multitude of science applications. Only a small number ($\lesssim200$) of solar
twins are known today, all of which are relatively close to our Sun
($\lesssim800\,pc$). The goal of our Survey for Distant Solar Twins (SDST) is
to identify many more solar twin and solar analogue stars out to much larger
distances ($\sim4\,kpc$). In this paper, we present a new method to identify
solar twins using relatively low $S/N$, medium resolving power ($R\sim
28{,}000$) spectra that will be typical of such distant targets observed with
HERMES on the $3.9\,m$ Anglo-Australian Telescope (AAT). We developed a novel
approach, namely EPIC, to measure stellar parameters which we use to identify
stars similar to our Sun. EPIC determines the stellar atmospheric parameters
(effective temperature $T_{\mathrm{eff}}$, surface gravity $\log g$ and
metallicity [Fe/H]) using differential equivalent width (EW) measurements of
selected spectroscopic absorption features and a simple model, trained on
previously analysed spectra, that connects these EWs to the stellar parameters.
The reference for the EW measurements is a high $S/N$ solar spectrum which is
used to minimise several systematic effects. EPIC is fast, optimised for
Sun-like stars and yields stellar parameter measurements with small enough
uncertainties to enable spectroscopic identification of solar twin and analogue
stars up to $\sim4\,kpc$ away using AAT/HERMES, i.e.\
$\sigma\left(T_{\mathrm{eff}}, \log g, \textrm{[Fe/H]}\right) = \left(50\,K,
0.08\,dex, 0.03\,dex\right)$ on average at $S/N=25$.


title:
Tightly Coupled Learning Strategy for Weakly Supervised Hierarchical
  Place Recognition

abstract:
  Visual place recognition (VPR) is a key issue for robotics and autonomous
systems. For the trade-off between time and performance, most of methods use
the coarse-to-fine hierarchical architecture, which consists of retrieving
top-N candidates using global features, and re-ranking top-N with local
features. However, since the two types of features are usually processed
independently, re-ranking may harm global retrieval, termed re-ranking
confusion. Moreover, re-ranking is limited by global retrieval. In this paper,
we propose a tightly coupled learning (TCL) strategy to train triplet models.
Different from original triplet learning (OTL) strategy, it combines global and
local descriptors for joint optimization. In addition, a bidirectional search
dynamic time warping (BS-DTW) algorithm is also proposed to mine locally
spatial information tailored to VPR in re-ranking. The experimental results on
public benchmarks show that the models using TCL outperform the models using
OTL, and TCL can be used as a general strategy to improve performance for
weakly supervised ranking tasks. Further, our lightweight unified model is
better than several state-of-the-art methods and has over an order of magnitude
of computational efficiency to meet the real-time requirements of robots.


title: Semantic Communication Meets Edge Intelligence

abstract:
  The development of emerging applications, such as autonomous transportation
systems, are expected to result in an explosive growth in mobile data traffic.
As the available spectrum resource becomes more and more scarce, there is a
growing need for a paradigm shift from Shannon's Classical Information Theory
(CIT) to semantic communication (SemCom). Specifically, the former adopts a
"transmit-before-understanding" approach while the latter leverages artificial
intelligence (AI) techniques to "understand-before-transmit", thereby
alleviating bandwidth pressure by reducing the amount of data to be exchanged
without negating the semantic effectiveness of the transmitted symbols.
However, the semantic extraction (SE) procedure incurs costly computation and
storage overheads. In this article, we introduce an edge-driven training,
maintenance, and execution of SE. We further investigate how edge intelligence
can be enhanced with SemCom through improving the generalization capabilities
of intelligent agents at lower computation overheads and reducing the
communication overhead of information exchange. Finally, we present a case
study involving semantic-aware resource optimization for the wireless powered
Internet of Things (IoT).


title:
Asymptotically Unbiased Estimation for Delayed Feedback Modeling via
  Label Correction

abstract:
  Alleviating the delayed feedback problem is of crucial importance for the
conversion rate(CVR) prediction in online advertising. Previous delayed
feedback modeling methods using an observation window to balance the trade-off
between waiting for accurate labels and consuming fresh feedback. Moreover, to
estimate CVR upon the freshly observed but biased distribution with fake
negatives, the importance sampling is widely used to reduce the distribution
bias. While effective, we argue that previous approaches falsely treat fake
negative samples as real negative during the importance weighting and have not
fully utilized the observed positive samples, leading to suboptimal
performance.
  In this work, we propose a new method, DElayed Feedback modeling with
UnbiaSed Estimation, (DEFUSE), which aim to respectively correct the importance
weights of the immediate positive, the fake negative, the real negative, and
the delay positive samples at finer granularity. Specifically, we propose a
two-step optimization approach that first infers the probability of fake
negatives among observed negatives before applying importance sampling. To
fully exploit the ground-truth immediate positives from the observed
distribution, we further develop a bi-distribution modeling framework to
jointly model the unbiased immediate positives and the biased delay
conversions. Experimental results on both public and our industrial datasets
validate the superiority of DEFUSE. Codes are available at
https://github.com/ychen216/DEFUSE.git.


title: Gain-loss ratio of storing intermediate data from workflows

abstract:
  Sequentially, the systematic processing of a significant amount of data can
be necessary for input datasets to get desired outputs. In a workflow
management system(WMS), usually, users build workflows by manually selecting
and interconnecting various modules concerning some particular tasks. Thus, a
system of automatically suggesting the appropriate intermediate datasets for
modules and a suggestion for the decision of saving intermediate states will be
helpful in a pipeline building process. This work investigates a technique for
both automatically suggesting the intermediate datasets to use and store
through mining and analyzing association rules from the previously developed
workflows. Investigation on workflows shows that the association rule mining
technique can help us to suggest subsequent modules for retrieving and storing
data and explain them with gain-loss ratios.


title:
Extracting Label-specific Key Input Features for Neural Code
  Intelligence Models

abstract:
  The code intelligence (CI) models are often black-box and do not offer any
insights on the input features that they learn for making correct predictions.
This opacity may lead to distrust in their prediction and hamper their wider
adoption in safety-critical applications. In recent, the program reduction
technique is widely being used to identify key input features in order to
explain the prediction of CI models. The approach removes irrelevant parts from
an input program and keeps the minimal snippets that a CI model needs to
maintain its prediction. However, the state-of-the-art approaches mainly use a
syntax-unaware program reduction technique that does not follow the syntax of
programs, which adds significant overhead to the reduction of input programs
and explainability of models. In this paper, we apply a syntax-guided program
reduction technique that follows the syntax of input programs during reduction.
Our experiments on multiple models across different types of input programs
show that the syntax-guided program reduction technique significantly
outperforms the syntax-unaware program reduction technique in reducing the size
of input programs. Extracting key input features from reduced programs reveals
that the syntax-guided reduced programs contain more label-specific key input
features and are more vulnerable to adversarial transformation when renaming
the key tokens in programs. These label-specific key input features may help to
understand the reasoning of models' prediction from different perspectives and
increase the trustworthiness to correct classification given by CI models.


title: Parameterization of Fullerenes Via Singularity-Combinations

abstract:
  Fullerene can be embedded in a piecewise linear 2-manifold with singularities
corresponding to the non-hexagonal faces. Adjacent two or three singularities
can be combined as a whole cut out from a parent cone led by a parent
singularity. With proper combinations, fullerene can be simplified to a parent
structure easily to be parameterized. In this paper, singularity-combinations
of fullerenes were studied, and the symmetries of the combining singularities
on the parent cone were revealed. Then, fullerenes with different shapes were
studied for parameterization with singularity-combinations, and parameters
characterizing its shape were obtained for each type. These parameters are
practical for fullerenes, especially those far from icosahedrons.


title: Modeling Intention, Emotion and External World in Dialogue Systems

abstract:
  Intention, emotion and action are important elements in human activities.
Modeling the interaction process between individuals by analyzing the
relationships between these elements is a challenging task. However, previous
work mainly focused on modeling intention and emotion independently, and
neglected of exploring the mutual relationships between intention and emotion.
In this paper, we propose a RelAtion Interaction Network (RAIN), consisting of
Intention Relation Module and Emotion Relation Module, to jointly model mutual
relationships and explicitly integrate historical intention information. The
experiments on the dataset show that our model can take full advantage of the
intention, emotion and action between individuals and achieve a remarkable
improvement over BERT-style baselines. Qualitative analysis verifies the
importance of the mutual interaction between the intention and emotion.


title:
Characterizing Differentially-Private Techniques in the Era of
  Internet-of-Vehicles

abstract:
  Recent developments of advanced Human-Vehicle Interactions rely on the
concept Internet-of-Vehicles (IoV), to achieve large-scale communications and
synchronizations of data in practice. The concept of IoV is highly similar to a
distributed system, where each vehicle is considered as a node and all nodes
are grouped with a centralized server. In this manner, the concerns of data
privacy are significant since all vehicles collect, process and share personal
statistics (e.g. multi-modal, driving statuses and etc.). Therefore, it's
important to understand how modern privacy-preserving techniques suit for IoV.
  We present the most comprehensive study to characterize modern
privacy-preserving techniques for IoV to date. We focus on Differential Privacy
(DP), a representative set of mathematically-guaranteed mechanisms for both
privacy-preserving processing and sharing on sensitive data. The purpose of our
study is to demystify the tradeoffs of deploying DP techniques, in terms of
service quality. We first characterize representative privacy-preserving
processing mechanisms, enabled by advanced DP approaches. Then we perform a
detailed study of an emerging in-vehicle, Deep-Neural-Network-driven
application, and study the upsides and downsides of DP for diverse types of
data streams. Our study obtains 11 key findings and we highlight FIVE most
significant observations from our detailed characterizations. We conclude that
there are a large volume of challenges and opportunities for future studies, by
enabling privacy-preserving IoV with low overheads for service quality.


title: Homogenous and Heterogenous Parallel Clustering: An Overview

abstract:
  Recent advances in computer architecture and networking opened the
opportunity for parallelizing the clustering algorithms. This
divide-and-conquer strategy often results in better results to centralized
clustering with a much-improved time performance. This paper reviews key
parallel clustering and provides insight into their strategy. The review brings
together disparate attempts in parallel clustering to provide a comprehensive
account of advances in this emerging field


title:
Order of Commitments in Bayesian Persuasion with Partial-informed
  Senders

abstract:
  The commitment power of senders distinguishes Bayesian persuasion problems
from other games with (strategic) communication. Persuasion games with multiple
senders have largely studied simultaneous commitment and signalling settings.
However, many real-world instances with multiple senders have sequential
signalling. In such contexts, commitments can also be made sequentially, and
then the order of commitment by the senders -- the sender signalling last
committing first or last -- could significantly impact the equilibrium payoffs
and strategies. For a two-sender persuasion game where the senders are
partially aware of the state of the world, we find necessary and sufficient
conditions to determine when different commitment orders yield different payoff
profiles. In particular, for the two-sender setting, we show that different
payoff profiles arise if two properties hold: 1) the two senders are willing to
collaborate in persuading the receiver in some state(s); and 2) the sender
signalling second can carry out a credible threat when committing first such
that the other sender's room to design signals gets constrained.


title: Neutrinos from Gamma-ray Bursts

abstract:
  Gamma-ray bursts (GRBs) are the most luminous electromagnetic burst in the
Universe. They occur when a rapidly rotating massive star collapses or a binary
neutron star merges. These events leave a newborn central compact object,
either a black hole or neutron star, which launches relativistic jets that emit
the luminous gamma-ray signals. These jets can accelerate non-thermal protons,
which are expected to produce high-energy neutrinos via photohadronic
interactions. This Chapter briefly summarizes the current physical picture of
GRBs, and discusses neutrino emissions from GRBs, including both prompt and
afterglow phases. Neutrinos from sub-classes of GRBs, including low-luminosity
GRBs and short GRBs, are also discussed.


title: A Survey on Machine Learning Approaches for Modelling Intuitive Physics

abstract:
  Research in cognitive science has provided extensive evidence of human
cognitive ability in performing physical reasoning of objects from noisy
perceptual inputs. Such a cognitive ability is commonly known as intuitive
physics. With advancements in deep learning, there is an increasing interest in
building intelligent systems that are capable of performing physical reasoning
from a given scene for the purpose of building better AI systems. As a result,
many contemporary approaches in modelling intuitive physics for machine
cognition have been inspired by literature from cognitive science. Despite the
wide range of work in physical reasoning for machine cognition, there is a
scarcity of reviews that organize and group these deep learning approaches.
Especially at the intersection of intuitive physics and artificial
intelligence, there is a need to make sense of the diverse range of ideas and
approaches. Therefore, this paper presents a comprehensive survey of recent
advances and techniques in intuitive physics-inspired deep learning approaches
for physical reasoning. The survey will first categorize existing deep learning
approaches into three facets of physical reasoning before organizing them into
three general technical approaches and propose six categorical tasks of the
field. Finally, we highlight the challenges of the current field and present
some future research directions.


title: Splitting numerical integration for matrix completion

abstract:
  Low rank matrix approximation is a popular topic in machine learning. In this
paper, we propose a new algorithm for this topic by minimizing the
least-squares estimation over the Riemannian manifold of fixed-rank matrices.
The algorithm is an adaptation of classical gradient descent within the
framework of optimization on manifolds. In particular, we reformulate an
unconstrained optimization problem on a low-rank manifold into a differential
dynamic system. We develop a splitting numerical integration method by applying
a splitting integration scheme to the dynamic system. We conduct the
convergence analysis of our splitting numerical integration algorithm. It can
be guaranteed that the error between the recovered matrix and true result is
monotonically decreasing in the Frobenius norm. Moreover, our splitting
numerical integration can be adapted into matrix completion scenarios.
Experimental results show that our approach has good scalability for
large-scale problems with satisfactory accuracy


title: BiFSMN: Binary Neural Network for Keyword Spotting

abstract:
  The deep neural networks, such as the Deep-FSMN, have been widely studied for
keyword spotting (KWS) applications. However, computational resources for these
networks are significantly constrained since they usually run on-call on edge
devices. In this paper, we present BiFSMN, an accurate and extreme-efficient
binary neural network for KWS. We first construct a High-frequency Enhancement
Distillation scheme for the binarization-aware training, which emphasizes the
high-frequency information from the full-precision network's representation
that is more crucial for the optimization of the binarized network. Then, to
allow the instant and adaptive accuracy-efficiency trade-offs at runtime, we
also propose a Thinnable Binarization Architecture to further liberate the
acceleration potential of the binarized network from the topology perspective.
Moreover, we implement a Fast Bitwise Computation Kernel for BiFSMN on ARMv8
devices which fully utilizes registers and increases instruction throughput to
push the limit of deployment efficiency. Extensive experiments show that BiFSMN
outperforms existing binarization methods by convincing margins on various
datasets and is even comparable with the full-precision counterpart (e.g., less
than 3% drop on Speech Commands V1-12). We highlight that benefiting from the
thinnable architecture and the optimized 1-bit implementation, BiFSMN can
achieve an impressive 22.3x speedup and 15.5x storage-saving on real-world edge
hardware.


title:
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic
  Segmentation

abstract:
  In the field of domain adaptation, a trade-off exists between the model
performance and the number of target domain annotations. Active learning,
maximizing model performance with few informative labeled data, comes in handy
for such a scenario. In this work, we present D2ADA, a general active domain
adaptation framework for semantic segmentation. To adapt the model to the
target domain with minimum queried labels, we propose acquiring labels of the
samples with high probability density in the target domain yet with low
probability density in the source domain, complementary to the existing source
domain labeled data. To further facilitate labeling efficiency, we design a
dynamic scheduling policy to adjust the labeling budgets between domain
exploration and model uncertainty over time. Extensive experiments show that
our method outperforms existing active learning and domain adaptation baselines
on two benchmarks, GTA5 -> Cityscapes and SYNTHIA -> Cityscapes. With less than
5% target domain annotations, our method reaches comparable results with that
of full supervision.


title:
Model-Based Neural Network and Its Application to Line Spectral
  Estimation

abstract:
  This paper presents the concept of "model-based neural network"(MNN), which
is inspired by the classic artificial neural network (ANN) but for different
usages. Instead of being used as a data-driven classifier, a MNN serves as a
modeling tool with artfully defined inputs, outputs, and activation functions
which have explicit physical meanings. Owing to the same layered form as an
ANN, a MNN can also be optimized using the back-propagation (BP) algorithm. As
an interesting application, the classic problem of line spectral estimation can
be modeled by a MNN. We propose to first initialize the MNN by the fast Fourier
transform (FFT) based spectral estimation, and then optimize the MNN by the BP
algorithm, which automatically yields the maximum likelihood (ML) parameter
estimation of the frequency spectrum. We also design a method of merging and
pruning the hidden-layer nodes of the MNN, which can be used for model-order
selection, i.e., to estimate the number of sinusoids. Numerical simulations
verify the effectiveness of the proposed method.


title:
Giant Impact onto a Vesta-Like Asteroid and Formation of Mesosiderites
  through Mixing of Metallic Core and Surface Crust

abstract:
  Mesosiderites are a type of stony-iron meteorites composed of a mixture of
silicates and Fe-Ni metals. The mesosiderite silicates and metals are
considered to have originated from the crust and metal core, respectively, of a
differentiated asteroid. In contrast, mesosiderites rarely contain the olivine
that is mainly included in a mantle. Although a giant impact onto a
differentiated asteroid is considered to be a probable mechanism to mix crust
and metal materials to form mesosiderites, it is not obvious how such a giant
impact can form mesosiderite-like materials without including mantle materials.
We conducted numerical simulations of giant impacts onto differentiated
asteroids, using the smoothed particle hydrodynamics method, to investigate the
detailed distribution of mixed materials on the resultant bodies. For the
internal structure of a target body, we used a thin-crust model derived from
the magma ocean crystallization model of the asteroid Vesta and a thick-crust
and a large-core model suggested from the proximity observation of Vesta by the
Dawn probe. In the simulations with the former model, excavation of the metal
core requires nearly catastrophic impacts and mantle is exposed over large
surface areas. Thus, stony-iron materials produced on its surface are likely to
include mantle materials and it is difficult to produce mesosiderite-like
materials. Conversely, in the simulations with the latter model, mantle
materials are exposed only at impact sites, even when the impacts excavate the
metal core, and the formation of a surface with little mantle material and the
formation of mesosiderite-like materials are possible. Therefore, our
simulations suggest that an internal structure with a thick crust and a large
core is more likely as a mesosiderite parent body rather than the thin-crust
internal structure inferred from the conventional magma ocean model.


title:
Combinatorial aspects of sandpile models on wheel and fan graphs:
  subgraphs of cycles and lattice paths

abstract:
  We study combinatorial aspects of the sandpile model on wheel and fan graphs,
seeking bijective characterisations of the model's recurrent configurations on
these families. For wheel graphs, we exhibit a bijection between these
recurrent configuations and the set of subgraphs of the cycle graph which maps
the level of the configuration to the number of edges of the subgraph. This
bijection relies on two key ingredients. The first consists in considering a
stochastic variant of the standard Abelian sandpile model (ASM), rather than
the ASM itself. The second ingredient is a mapping from a given recurrent state
to a canonical minimal recurrent state, exploiting similar ideas to previous
studies of the ASM on complete bipartite graphs and Ferrers graphs. We also
show that on the wheel graph with $2n$ vertices, the number of recurrent states
with level $n$ is given by the first differences of the central Delannoy
numbers. Finally, we prove using similar tools that the recurrent
configurations of the ASM on fan graphs are in bijection with certain lattice
paths, which we name Kimberling paths after the author of the corresponding
entry in the Online Encyclopedia of Integer Sequences.


title: Finding Dynamics Preserving Adversarial Winning Tickets

abstract:
  Modern deep neural networks (DNNs) are vulnerable to adversarial attacks and
adversarial training has been shown to be a promising method for improving the
adversarial robustness of DNNs. Pruning methods have been considered in
adversarial context to reduce model capacity and improve adversarial robustness
simultaneously in training. Existing adversarial pruning methods generally
mimic the classical pruning methods for natural training, which follow the
three-stage 'training-pruning-fine-tuning' pipelines. We observe that such
pruning methods do not necessarily preserve the dynamics of dense networks,
making it potentially hard to be fine-tuned to compensate the accuracy
degradation in pruning. Based on recent works of \textit{Neural Tangent Kernel}
(NTK), we systematically study the dynamics of adversarial training and prove
the existence of trainable sparse sub-network at initialization which can be
trained to be adversarial robust from scratch. This theoretically verifies the
\textit{lottery ticket hypothesis} in adversarial context and we refer such
sub-network structure as \textit{Adversarial Winning Ticket} (AWT). We also
show empirical evidences that AWT preserves the dynamics of adversarial
training and achieve equal performance as dense adversarial training.


title:
Design and Optimization of Gregorian-based Reflector Systems for THz
  Imaging System Optics

abstract:
  Application of reflector antenna configurations in designing the optical
section of terahertz imaging systems has attracted great attention during
recent years. Advantages of reflective optics in comparison with refractive
optics is one of the main reasons for this trend. Specular reflection in broad
spectral range, no or little reflection and transmission loss, no chromatic
aberration, high reliability in high power applications and even cancellation
of off-axis aberrations as a result of precise optimized configuration are just
some of these advantages. Moreover, utilizing novel satellite communication
reflector antenna systems in an optical section of imaging systems has improved
many imaging performance factors and provided a reliable platform for different
imaging objectives. In this paper, we will design a Gregorian-based reflector
antenna system for the optical section of terahertz imaging systems and using
optical techniques, we optimize the structure for optimum imaging performance.


title:
Temperature-dependent thermal transport of single molecular junctions
  from semi-classical Langevin molecular dynamics

abstract:
  Thermal conductance of single molecular junctions at room temperature has
been measured recently using picowatt-resolution scanning probes. However,
fully understanding thermal transport in a much wider temperature range is
needed for the exploration of energy transfer at single-molecular limit and the
development of single-molecular devices. Here, employing a semiclassical
Langevin molecular dynamics method, a comparative study is performed on the
thermal transport of an alkane chain between Au and graphene electrodes,
respectively. We illustrate the different roles of quantum statistics and
anharmonic interaction in the two types of junctions. For a graphene junction,
quantum statistics is essential at room temperature, while the anharmonic
interaction is negligible. For a Au junction, it is the other way. Our study
paves the way for theoretically understanding thermal transport of realistic
single-molecular junctions in the full temperature range by including both
quantum statistics and anharmonic interaction within one theoretical framework.


title: Adversarial Graph Contrastive Learning with Information Regularization

abstract:
  Contrastive learning is an effective unsupervised method in graph
representation learning. Recently, the data augmentation based contrastive
learning method has been extended from images to graphs. However, most prior
works are directly adapted from the models designed for images. Unlike the data
augmentation on images, the data augmentation on graphs is far less intuitive
and much harder to provide high-quality contrastive samples, which are the key
to the performance of contrastive learning models. This leaves much space for
improvement over the existing graph contrastive learning frameworks. In this
work, by introducing an adversarial graph view and an information regularizer,
we propose a simple but effective method, Adversarial Graph Contrastive
Learning (ARIEL), to extract informative contrastive samples within a
reasonable constraint. It consistently outperforms the current graph
contrastive learning methods in the node classification task over various
real-world datasets and further improves the robustness of graph contrastive
learning.


title: Polynomial $\alpha$-attractors

abstract:
  Inflationary $\alpha$-attractor models can be naturally implemented in
supergravity with hyperbolic geometry. They have stable predictions for
observables, such as $n_s=1-{2/ N_e} $, assuming that the potential in terms of
the original geometric variables, as well as its derivatives, are not singular
at the boundary of the hyperbolic disk, or half-plane. In these models, the
potential in the canonically normalized inflaton field $\varphi$ has a plateau,
which is approached exponentially fast at large $\varphi$. We call them
exponential $\alpha$-attractors. We present a closely related class of models,
where the potential is not singular, but its derivative is singular at the
boundary. The resulting inflaton potential is also a plateau potential, but it
approaches the plateau polynomially. We call them polynomial
$\alpha$-attractors. Predictions of these two families of attractors completely
cover the sweet spot of the Planck/BICEP/Keck data. The exponential ones are on
the left, the polynomial are on the right.


title: FLHub: a Federated Learning model sharing service

abstract:
  As easy-to-use deep learning libraries such as Tensorflow and Pytorch are
popular, it has become convenient to develop machine learning models. Due to
privacy issues with centralized machine learning, recently, federated learning
in the distributed computing framework is attracting attention. The central
server does not collect sensitive and personal data from clients in federated
learning, but it only aggregates the model parameters. Though federated
learning helps protect privacy, it is difficult for machine learning developers
to share the models that they could utilize for different-domain applications.
In this paper, we propose a federated learning model sharing service named
Federated Learning Hub (FLHub). Users can upload, download, and contribute the
model developed by other developers similarly to GitHub. We demonstrate that a
forked model can finish training faster than the existing model and that
learning progressed more quickly for each federated round.


title:
BROOK Dataset: A Playground for Exploiting Data-Driven Techniques in
  Human-Vehicle Interactive Designs

abstract:
  Emerging Autonomous Vehicles (AV) breed great potentials to exploit
data-driven techniques for adaptive and personalized Human-Vehicle
Interactions. However, the lack of high-quality and rich data supports limits
the opportunities to explore the design space of data-driven techniques, and
validate the effectiveness of concrete mechanisms. Our goal is to initialize
the efforts to deliver the building block for exploring data-driven
Human-Vehicle Interaction designs. To this end, we present BROOK dataset, a
multi-modal dataset with facial video records. We first brief our rationales to
build BROOK dataset. Then, we elaborate how to build the current version of
BROOK dataset via a year-long study, and give an overview of the dataset. Next,
we present three example studies using BROOK to justify the applicability of
BROOK dataset. We also identify key learning lessons from building BROOK
dataset, and discuss about how BROOK dataset can foster an extensive amount of
follow-up studies.


title:
HUT: Enabling High-UTility, Batched Queries under Differential Privacy
  Protection for Internet-of-Vehicles

abstract:
  The emerging trends of Internet-of-Vehicles (IoV) demand centralized servers
to collect/process sensitive data with limited computational resources on a
single vehicle. Such centralizations of sensitive data demand practical privacy
protections. One widely-applied paradigm, Differential Privacy, can provide
strong guarantees over sensitive data by adding noises. However, directly
applying DP for IoV incurs significant challenges for data utility and
effective protection. We observe that the key issue about DP-enabled protection
in IoV lies in how to synergistically combine DP with special characteristics
of IoV, whose query sequences are usually formed as unbalanced batches due to
frequent interactions between centralized servers and edge vehicles.
  To this end, we propose HUT, a new algorithm to enable High UTility for
DP-enabled protection in IoV. Our key insight is to leverage the inherent
characteristics in IoV: the unbalanced batches. Our key idea is to aggregate
local batches and apply Order Constraints, so that information loss from DP
protection can be mitigated. We evaluate the effectiveness of HUT against the
state-of-the-art DP protection mechanisms. The results show that HUT can
provide much lower information loss by 95.69\% and simultaneously enable strong
mathematically-guaranteed protection over sensitive data.


title: NEDMP: Neural Enhanced Dynamic Message Passing

abstract:
  Predicting stochastic spreading processes on complex networks is critical in
epidemic control, opinion propagation, and viral marketing. We focus on the
problem of inferring the time-dependent marginal probabilities of states for
each node which collectively quantifies the spreading results. Dynamic Message
Passing (DMP) has been developed as an efficient inference algorithm for
several spreading models, and it is asymptotically exact on locally tree-like
networks. However, DMP can struggle in diffusion networks with lots of local
loops. We address this limitation by using Graph Neural Networks (GNN) to learn
the dependency amongst messages implicitly. Specifically, we propose a hybrid
model in which the GNN module runs jointly with DMP equations. The GNN module
refines the aggregated messages in DMP iterations by learning from simulation
data. We demonstrate numerically that after training, our model's inference
accuracy substantially outperforms DMP in conditions of various network
structure and dynamics parameters. Moreover, compared to pure data-driven
models, the proposed hybrid model has a better generalization ability for
out-of-training cases, profiting from the explicitly utilized dynamics priors
in the hybrid model. A PyTorch implementation of our model is at
https://github.com/FeiGSSS/NEDMP.


title:
Universal and Efficient p-Doping of Organic Semiconductors by
  Electrophilic Attack of Cations

abstract:
  Doping is of great importance to tailor the electrical properties of
semiconductors. However, the present doping methodologies for organic
semiconductors (OSCs) are either inefficient or can only apply to a small
number of OSCs, seriously limiting their general application. Herein, we reveal
a novel p-doping mechanism by investigating the interactions between the dopant
trityl cation and poly(3-hexylthiophene) (P3HT). It is found that electrophilic
attack of the trityl cations on thiophenes results in the formation of
alkylated ions that induce electron transfer from neighboring P3HT chains,
resulting in p-doping. This unique p-doping mechanism can be employed to dope
various OSCs including those with high ionization energy (IE=5.8 eV). Moreover,
this doping mechanism endows trityl cation with strong doping ability, leading
to polaron yielding efficiency of 100 % and doping efficiency of over 80 % in
P3HT. The discovery and elucidation of this novel doping mechanism not only
points out that strong electrophiles are a class of efficient p-dopants for
OSCs, but also provides new opportunities towards highly efficient doping of
OSCs.


title:
Task-Adaptive Feature Transformer with Semantic Enrichment for Few-Shot
  Segmentation

abstract:
  Few-shot learning allows machines to classify novel classes using only a few
labeled samples. Recently, few-shot segmentation aiming at semantic
segmentation on low sample data has also seen great interest. In this paper, we
propose a learnable module that can be placed on top of existing segmentation
networks for performing few-shot segmentation. This module, called the
task-adaptive feature transformer (TAFT), linearly transforms task-specific
high-level features to a set of task agnostic features well-suited to
conducting few-shot segmentation. The task-conditioned feature transformation
allows an effective utilization of the semantic information in novel classes to
generate tight segmentation masks. We also propose a semantic enrichment (SE)
module that utilizes a pixel-wise attention module for high-level feature and
an auxiliary loss from an auxiliary segmentation network conducting the
semantic segmentation for all training classes. Experiments on PASCAL-$5^i$ and
COCO-$20^i$ datasets confirm that the added modules successfully extend the
capability of existing segmentators to yield highly competitive few-shot
segmentation performances.


title:
Real World Large Scale Recommendation Systems Reproducibility and Smooth
  Activations

abstract:
  Real world recommendation systems influence a constantly growing set of
domains. With deep networks, that now drive such systems, recommendations have
been more relevant to the user's interests and tasks. However, they may not
always be reproducible even if produced by the same system for the same user,
recommendation sequence, request, or query. This problem received almost no
attention in academic publications, but is, in fact, very realistic and
critical in real production systems. We consider reproducibility of real large
scale deep models, whose predictions determine such recommendations. We
demonstrate that the celebrated Rectified Linear Unit (ReLU) activation, used
in deep models, can be a major contributor to irreproducibility. We propose the
use of smooth activations to improve recommendation reproducibility. We
describe a novel family of smooth activations; Smooth ReLU (SmeLU), designed to
improve reproducibility with mathematical simplicity, with potentially cheaper
implementation. SmeLU is a member of a wider family of smooth activations.
While other techniques that improve reproducibility in real systems usually
come at accuracy costs, smooth activations not only improve reproducibility,
but can even give accuracy gains. We report metrics from real systems in which
we were able to productionalize SmeLU with substantial reproducibility gains
and better accuracy-reproducibility trade-offs. These include
click-through-rate (CTR) prediction systems, content, and application
recommendation systems.


title:
A note on the topological stability theorem from RCD spaces to
  Riemannian manifolds

abstract:
  Inspired by a recent work of Wang-Zhao, in this note we prove that for a
fixed $n$-dimensional closed Riemannian manifold $(M^n, g)$, if an
$\mathrm{RCD}(K, n)$ space $(X, \mathsf{d}, \mathfrak{m})$ is Gromov-Hausdorff
close to $M^n$, then there exists a regular homeomorphism $F$ from $X$ to $M^n$
such that $F$ is Lipschitz continuous and that $F^{-1}$ is H\"older continuous,
where the Lipschitz constant of $F$, the H\"older exponent and the H\"older
constant of $F^{-1}$ can be chosen arbitrary close to $1$. This is sharp in the
sense that in general such a map cannot be improved to being bi-Lipschitz.
Moreover if $X$ is smooth, then such a homeomorphism can be chosen as a
diffeomorphism, which improves the intrinsic Reifenberg theorem established by
Cheeger-Colding. The Nash embedding theorem plays a key role in the proof.


title:
Gradient models of moving heat sources for powder bed fusion
  applications

abstract:
  In this paper, we derive closed form solutions for the quasi-stationary
problems of moving heat sources within the gradient theory of heat transfer.
This theory can be formally deduced from the two-temperature model and it can
be treated as a generalized variant of the Guyer-Krumhansl model with the
fourth order governing equation. We show that considered variant of the
gradient theory allows to obtain a useful singularity-free solutions for the
moving point and line heat sources that can be used for the refined analysis of
the melt pool shape in the laser powder bed fusion processes. Derived solutions
contain single additional length scale parameter that can be related to the
mean particles size of the powder bed. Namely, we show that developed gradient
models allow to describe the decrease of the melt pool depth with the increase
of the powder's particles size that was observed previously in the experiments.
We also derive the dimensionless relations that can be used for the
experimental identification of the model's length scale parameter for different
materials. Semi-analytical solution for the Gaussian laser beam is also derived
and studied based on the Green function method within the considered theory.


title:
Joint Modeling and Prediction of Massive Spatio-Temporal Wildfire Count
  and Burnt Area Data with the INLA-SPDE Approach

abstract:
  This paper describes the methodology used by the team RedSea in the data
competition organized for EVA 2021 conference. We develop a novel two-part
model to jointly describe the wildfire count data and burnt area data provided
by the competition organizers with covariates. Our proposed methodology relies
on the integrated nested Laplace approximation combined with the stochastic
partial differential equation (INLA-SPDE) approach. In the first part, a binary
non-stationary spatio-temporal model is used to describe the underlying process
that determines whether or not there is wildfire at a specific time and
location. In the second part, we consider a non-stationary model that is based
on log-Gaussian Cox processes for positive wildfire count data, and a
non-stationary log-Gaussian model for positive burnt area data. Dependence
between the positive count data and positive burnt area data is captured by a
shared spatio-temporal random effect. Our two-part modeling approach performs
well in terms of the prediction score criterion chosen by the data competition
organizers. Moreover, our model results show that surface pressure is the most
influential driver for the occurrence of a wildfire, whilst surface net solar
radiation and surface pressure are the key drivers for large numbers of
wildfires, and temperature and evaporation are the key drivers of large burnt
areas.


title:
Adaptive graph convolutional networks for weakly supervised anomaly
  detection in videos

abstract:
  For the weakly supervised anomaly detection task, most existing work is
limited to the problem of inadequate video representation due to the inability
to model long-time contextual information. We propose a weakly supervised
adaptive graph convolutional network (WAGCN) to model the contextual
relationships among video segments. And we fully consider the influence of
other video segments on the current segment when generating the anomaly
probability score for each segment. Firstly, we combine the temporal
consistency as well as feature similarity of video segments for composition,
which makes full use of the association information among spatial-temporal
features of anomalous events in videos. Secondly, we propose a graph learning
layer in order to break the limitation of setting topology manually, which
adaptively extracts sparse graph adjacency matrix based on data. Extensive
experiments on two public datasets (i.e., UCF-Crime dataset and ShanghaiTech
dataset) demonstrate the effectiveness of our approach.


title:
Analytic Learning of Convolutional Neural Network For Pattern
  Recognition

abstract:
  Training convolutional neural networks (CNNs) with back-propagation (BP) is
time-consuming and resource-intensive particularly in view of the need to visit
the dataset multiple times. In contrast, analytic learning attempts to obtain
the weights in one epoch. However, existing attempts to analytic learning
considered only the multilayer perceptron (MLP). In this article, we propose an
analytic convolutional neural network learning (ACnnL). Theoretically we show
that ACnnL builds a closed-form solution similar to its MLP counterpart, but
differs in their regularization constraints. Consequently, we are able to
answer to a certain extent why CNNs usually generalize better than MLPs from
the implicit regularization point of view. The ACnnL is validated by conducting
classification tasks on several benchmark datasets. It is encouraging that the
ACnnL trains CNNs in a significantly fast manner with reasonably close
prediction accuracies to those using BP. Moreover, our experiments disclose a
unique advantage of ACnnL under the small-sample scenario when training data
are scarce or expensive.