title: Transformer-based Streaming ASR with Cumulative Attention

abstract:
  In this paper, we propose an online attention mechanism, known as cumulative
attention (CA), for streaming Transformer-based automatic speech recognition
(ASR). Inspired by monotonic chunkwise attention (MoChA) and head-synchronous
decoder-end adaptive computation steps (HS-DACS) algorithms, CA triggers the
ASR outputs based on the acoustic information accumulated at each encoding
timestep, where the decisions are made using a trainable device, referred to as
halting selector. In CA, all the attention heads of the same decoder layer are
synchronised to have a unified halting position. This feature effectively
alleviates the problem caused by the distinct behaviour of individual heads,
which may otherwise give rise to severe latency issues as encountered by MoChA.
The ASR experiments conducted on AIShell-1 and Librispeech datasets demonstrate
that the proposed CA-based Transformer system can achieve on par or better
performance with significant reduction in latency during inference, when
compared to other streaming Transformer systems in literature.


title: Maximum confidence measurement for qubit states

abstract:
  In quantum state discrimination, one aims to identify unknown states from a
given ensemble by performing measurements. Different strategies such as
minimum-error discrimination or unambiguous state identification find different
optimal measurements. Maximum-confidence measurements (MCMs) maximise the
confidence with which inputs can be identified given the measurement outcomes.
This unifies a range of discrimination strategies including minimum-error and
unambiguous state identification, which can be understood as limiting cases of
MCM. In this work, we investigate MCMs for general ensembles of qubit states.
We present a method for finding MCMs for qubit-state ensembles by exploiting
their geometry, and apply it to several interesting cases, including ensembles
two and four mixed states and ensembles of an arbitrary number of pure states.
We also compare MCMs to minimum-error and unambiguous discrimination for
qubits. Our results provide interpretations to various qubit measurements in
terms of MCM and can be used to devise qubit protocols.


title: Learning Distinctive Margin toward Active Domain Adaptation

abstract:
  Despite plenty of efforts focusing on improving the domain adaptation ability
(DA) under unsupervised or few-shot semi-supervised settings, recently the
solution of active learning started to attract more attention due to its
suitability in transferring model in a more practical way with limited
annotation resource on target data. Nevertheless, most active learning methods
are not inherently designed to handle domain gap between data distribution, on
the other hand, some active domain adaptation methods (ADA) usually requires
complicated query functions, which is vulnerable to overfitting. In this work,
we propose a concise but effective ADA method called
Select-by-Distinctive-Margin (SDM), which consists of a maximum margin loss and
a margin sampling algorithm for data selection. We provide theoretical analysis
to show that SDM works like a Support Vector Machine, storing hard examples
around decision boundaries and exploiting them to find informative and
transferable data. In addition, we propose two variants of our method, one is
designed to adaptively adjust the gradient from margin loss, the other boosts
the selectivity of margin sampling by taking the gradient direction into
account. We benchmark SDM with standard active learning setting, demonstrating
our algorithm achieves competitive results with good data scalability. Code is
available at https://github.com/TencentYoutuResearch/ActiveLearning-SDM


title:
Safety-Aware and Data-Driven Predictive Control for Connected Automated
  Vehicles at a Mixed Traffic Signalized Intersection

abstract:
  A typical urban signalized intersection poses significant modeling and
control challenges in a mixed traffic environment with connected automated
vehicles (CAVs) and human-driven vehicles (HDVs). In this paper, we address the
problem of deriving safe trajectories for CAVs in a mixed traffic environment
that prioritize rear-end collision avoidance when the preceding HDVs approach
the yellow- and red signal phases of the intersection. We present a predictive
control framework for the CAVs that employs a recursive least squares algorithm
to approximate in real time the driving behavior of the preceding HDVs and then
uses this approximation to derive its safety-aware trajectory in a finite
horizon. We validate the effectiveness of our proposed framework through
numerical simulation and analyze the robustness of the control framework.


title:
QDrop: Randomly Dropping Quantization for Extremely Low-bit
  Post-Training Quantization

abstract:
  Recently, post-training quantization (PTQ) has driven much attention to
produce efficient neural networks without long-time retraining. Despite its low
cost, current PTQ works tend to fail under the extremely low-bit setting. In
this study, we pioneeringly confirm that properly incorporating activation
quantization into the PTQ reconstruction benefits the final accuracy. To deeply
understand the inherent reason, a theoretical framework is established,
indicating that the flatness of the optimized low-bit model on calibration and
test data is crucial. Based on the conclusion, a simple yet effective approach
dubbed as QDROP is proposed, which randomly drops the quantization of
activations during PTQ. Extensive experiments on various tasks including
computer vision (image classification, object detection) and natural language
processing (text classification and question answering) prove its superiority.
With QDROP, the limit of PTQ is pushed to the 2-bit activation for the first
time and the accuracy boost can be up to 51.49%. Without bells and whistles,
QDROP establishes a new state of the art for PTQ. Our code is available at
https://github.com/wimh966/QDrop and has been integrated into MQBench
(https://github.com/ModelTC/MQBench)


title:
Numerical package for QFT calculations of defect-induced phenomena in
  graphene

abstract:
  We introduce a computationally efficient method based on the path integral
formalism to describe defect-modified graphene. By taking into account the
entire Brillouin zone, our approach respects the lattice symmetry and can be
used to investigate both short-range and long-range effects. The proposed
method's key advantage is that the computational complexity does not increase
with the system size, scaling, instead, with the number of defects. As a
demonstration of our method, we explore the graphene-mediated RKKY interaction
between multiple magnetic impurities. Our results concur with earlier findings
by showing that the interaction strength and sign depend on various factors
like impurity separation, sublattice arrangement, and system doping. We
demonstrate that frustration can be introduced between the impurity spins by
controlling their relative positions and that this frustration can be switched
on and off by tuning the chemical potential of the system.


title: Bringing Source-Level Debugging Frameworks to Hardware Generators

abstract:
  High-level hardware generators have significantly increased the productivity
of design engineers. They use software engineering constructs to reduce the
repetition required to express complex designs and enable more composability.
However, these benefits are undermined by a lack of debugging infrastructure,
requiring hardware designers to debug generated, usually incomprehensible, RTL
code. This paper describes a framework that connects modern software
source-level debugging frameworks to RTL created from hardware generators. Our
working prototype offers an Integrated Development Environment (IDE) experience
for generators such as RocketChip (Chisel), allowing designers to set
breakpoints in complex source code, relate RTL simulation state back to
source-level variables, and do forward and backward debugging, with almost no
simulation overhead (less than 5%).


title: Effects of Hawking evaporation on PBH distributions

abstract:
  Primordial black holes (PBHs) lose mass by Hawking evaporation. For
sufficiently small PBHs, they may lose a large portion of their formation mass
by today, or even evaporate completely if they form with mass
$M<M_{\mathrm{crit}}\sim5\times10^{14}~\mathrm{g}$. We investigate the effect
of this mass loss on extended PBH distributions, showing that the shape of the
distribution is significantly changed between formation and today. We
reconsider the $\gamma$-ray constraints on PBH dark matter in the Milky Way
center with a correctly `evolved' lognormal distribution, and derive a
semi-analytic time-dependent distribution which can be used to accurately
project monochromatic constraints to extended distribution constraints. We also
derive the rate of black hole explosions in the Milky Way per year, finding
that although there is a significant number, it is extremely unlikely to find
one close enough to Earth to observe. Along with a more careful argument for
why monochromatic PBH distributions are unlikely to source an exploding PBH
population today, we (unfortunately) conclude that we are unlikely to witness
any PBH explosions.


title:
Semi-constraint Optimal Transport for Entity Alignment with Dangling
  Cases

abstract:
  Entity alignment (EA) merges knowledge graphs (KGs) by identifying the
equivalent entities in different graphs, which can effectively enrich knowledge
representations of KGs. However, in practice, different KGs often include
dangling entities whose counterparts cannot be found in the other graph, which
limits the performance of EA methods. To improve EA with dangling entities, we
propose an unsupervised method called Semi-constraint Optimal Transport for
Entity Alignment in Dangling cases (SoTead). Our main idea is to model the
entity alignment between two KGs as an optimal transport problem from one KG's
entities to the others. First, we set pseudo entity pairs between KGs based on
pretrained word embeddings. Then, we conduct contrastive metric learning to
obtain the transport cost between each entity pair. Finally, we introduce a
virtual entity for each KG to "align" the dangling entities from the other KGs,
which relaxes the optimization constraints and leads to a semi-constraint
optimal transport. In the experimental part, we first show the superiority of
SoTead on a commonly-used entity alignment dataset. Besides, to analyze the
ability for dangling entity detection with other baselines, we construct a
medical cross-lingual knowledge graph dataset, MedED, where our SoTead also
reaches state-of-the-art performance.


title:
Implementing Optimization-Based Control Tasks in Cyber-Physical Systems
  With Limited Computing Capacity

abstract:
  A common aspect of today's cyber-physical systems is that multiple
optimization-based control tasks may execute in a shared processor. Such
control tasks make use of online optimization and thus have large execution
times; hence, their sampling periods must be large as well to satisfy real-time
schedulability condition. However, larger sampling periods may cause worse
control performance. The goal of our work is to develop a robust to early
termination optimization approach that can be used to effectively solve onboard
optimization problems involved in controlling the system despite the presence
of unpredictable, variable, and limited computing capacity. The significance of
the developed approach is that the optimization iterations can be stopped at
any time instant with a guaranteed feasible solution; as a result,
optimization-based control tasks can be implemented with a small sampling
period (and consequently with a minimum degradation in the control
performance).


title:
Asymptotic dimension of Artin groups and a new upper bound for Coxeter
  groups

abstract:
  If $A_\Gamma$ ($W_\Gamma$) is the Artin (Coxeter) group with defining graph
$\Gamma$ we denote by $Sim(\Gamma)$ the number of vertices of the largest
clique in $\Gamma$. We show that $asdimA_\Gamma \leq Sim(\Gamma)$, if
$Sim(\Gamma)=2$. We conjecture that the inequality holds for every Artin group.
We prove that if for all free of infinity Artin (Coxeter) groups the conjecture
holds, then it holds for all Artin (Coxeter) groups. As a corollary, we show
that $asdimW_\Gamma \leq Sim(\Gamma)$ for all Coxeter groups, which is the best
known upper bound for the asymptotic dimension of Coxeter Groups. As a further
corollary, we show that the asymptotic dimension of any Artin group of large
type with $Sim(\Gamma)=3$ is exactly two.


title:
Hard ferromagnetism down to the thinnest limit of iron-intercalated
  tantalum disulfide

abstract:
  Two-dimensional (2D) magnetic crystals hold promise for miniaturized and
ultralow power electronic devices that exploit spin manipulation. In these
materials, large, controllable magnetocrystalline anisotropy is a prerequisite
for the stabilization and manipulation of long-range magnetic order. Owing to
their inherent structural and compositional diversity, 2D analogues of
transition metal-intercalated transition metal dichalcogenides could serve as
versatile platforms for the study of 2D magnetism and the exploration of new
device schemes. However, strong interlayer bonds in these intercalation
compounds preclude mechanical exfoliation of bulk crystals down to the
atomically thin limit. Here, we demonstrate the preparation of Fe-intercalated
TaS$_2$ by chemical intercalation of van der Waals layered TaS$_2$ crystals. We
find that Fe$_x$TaS$_2$ exhibits hard ferromagnetic behavior down to the
thinnest limit of this intercalation compound (Fe-intercalated bilayer
TaS$_2$), with giant coercive fields up to 3 T. Variable-temperature quantum
transport, transmission electron microscopy, and confocal Raman spectroscopy
measurements shed new light on the coupled effects of dimensionality, degree of
intercalation, and intercalant order/disorder on the magnetic behavior of
Fe$_x$TaS$_2$. More generally, we show that chemical intercalation gives access
to a rich synthetic parameter space for low-dimensional magnets, in which
magnetic properties can be tailored by the choice of the host material and
intercalant identity/amount, in addition to the manifold distinctive degrees of
freedom available in atomically thin, van der Waals crystals.


title:
Single quantum emitters with spin ground states based on Cl bound
  excitons in ZnSe

abstract:
  Defects in wide-bandgap semiconductors are promising qubit candidates for
quantum communication and computation. Epitaxially grown II-VI semiconductors
are particularly promising host materials due to their direct bandgap and
potential for isotopic purification to a spin-zero nuclear background. Here, we
show a new type of single photon emitter with potential electron spin qubit
based on Cl impurities in ZnSe. We utilize a quantum well to increase the
binding energies of donor emission and confirm single photon emission with
short radiative lifetimes of 192 ps. Furthermore, we verify that the ground
state of the Cl donor complex contains a single electron by observing
two-electron satellite emission, leaving the electron in higher orbital states.
We also characterize the Zeeman splitting of the exciton transition by
performing polarization-resolved magnetic spectroscopy on single emitters. Our
results suggest single Cl impurities are suitable as single photon source with
potential photonic interface.


title:
Classification from Positive and Biased Negative Data with Skewed
  Labeled Posterior Probability

abstract:
  The binary classification problem has a situation where only biased data are
observed in one of the classes. In this paper, we propose a new method to
approach the positive and biased negative (PbN) classification problem, which
is a weakly supervised learning method to learn a binary classifier from
positive data and negative data with biased observations. We incorporate a
method to correct the negative impact due to skewed confidence, which
represents the posterior probability that the observed data are positive. This
reduces the distortion of the posterior probability that the data are labeled,
which is necessary for the empirical risk minimization of the PbN
classification problem. We verified the effectiveness of the proposed method by
numerical experiments and real data analysis.


title:
Not so fuzzy: excluding FDM with sizes and stellar kinematics of
  ultra-faint dwarf galaxies

abstract:
  We use observations of ultra-faint dwarf (UFD) galaxies to constrain the
particle mass of ultra-light dark matter. Potential fluctuations created by
wave interference in virialized "fuzzy" dark matter (FDM) halos dynamically
heat stellar orbits in UFDs, some of which exhibit velocity dispersions of
$\lesssim$ 3 km/s and sizes $\lesssim$ 40 pc. Using simulations of FDM halos,
and existing measurements of sizes and stellar radial velocities in Segue 1 and
Segue 2 UFDs, we derive a lower limit on the dark matter particle mass of
$m_{fdm} > 3\times 10^{-19}$ eV at 99% confidence, marginalized over host halo
circular velocity. This constraint is conservative as it is derived under the
assumption that soliton heating is negligible, and that no other sources of
non-FDM dynamical heating of stars operate to increase velocity dispersion. It
can potentially be strengthened by future spectroscopic observations of
additional stars in ultra-faint galaxies and by tightening theoretical
constraints on the soliton size-halo mass relation. However, even the current
conservative lower limit on the FDM mass makes this model indistinguishable
from Cold Dark Matter at the scales probed by existing astronomical
observations.


title: Adjunctions between Eilenberg-Moore categories and a PBW-type theorem

abstract:
  Recently, Dotsenko and Tamaroff have shown that a morphism of
$T\longrightarrow S$ of monads over a category $\mathscr C$ satisfies the
PBW-property if and only if it makes $S$ into a free right $T$-module. We
consider an adjunction $\Psi=(G,F)$ between categories $\mathscr C$, $\mathscr
D$, a monad $S$ on $\mathscr C$ and a monad $T$ on $\mathscr D$. We show that a
morphism $\phi:(\mathscr C,S)\longrightarrow (\mathscr D,T)$ that is well
behaved with respect to the adjunction $\Psi$ has a PBW-property if and only if
it makes $S$ satisfy a certain freeness condition with respect to $T$-modules
with values in $\mathscr C$.


title:
Ultrafast intrinsic optical-to-electrical conversion dynamics in
  graphene photodetector

abstract:
  Optical-to-electrical (O-E) conversion in graphene is a central phenomenon
for realizing anticipated ultrafast and low-power-consumption information
technologies. However, revealing its mechanism and intrinsic time scale require
uncharted terahertz (THz) electronics and device architectures. Here, we
succeeded in resolving O-E conversion processes in high-quality graphene by
on-chip electrical readout of ultrafast photothermoelectric current. By
suppressing the RC time constant using a resistive zinc oxide top gate, we
constructed a gate-tunable graphene photodetector with a bandwidth of up to 220
GHz. By measuring nonlocal photocurrent dynamics, we found that the
photocurrent extraction from the electrode is instantaneous without a
measurable carrier transit time across several-micrometer-long graphene,
following the Shockley-Ramo theorem. The time for photocurrent generation is
exceptionally tunable from immediate to > 4 ps, and its origin is identified as
Fermi-level-dependent intraband carrier-carrier scattering. Our results bridge
the gap between ultrafast optical science and device engineering, accelerating
ultrafast graphene optoelectronic applications.

categories: cond-mat.mes-hall cond-mat.mtrl-sci physics.app-ph physics.optics


title: Two-zero textures based on $A_4$ symmetry and unimodular mixing matrix

abstract:
  Applying the $A_4$ symmetry in the scenario of unimodular second scheme of
trimaximal $TM_2$ mixing matrix, where the charged lepton mass matrix is
diagonal and the nature of neutrinos are Majorana, we investigate and analyze
feasible two zeros neutrino mass matrices. Among the seven possible two-zero
textures with $A_4$ symmetry, we have found that only two textures, namely the
texture with $(e, e)$ and $(e,\mu)$ vanishing element of mass matrix and its
permutation, are consistent with the experimental data in the non-perturbation
method. We also obtain new significant relations between phases of our model,
namely $\rho+\sigma=\phi\pm\pi$ and $\sin^2{\theta_{13}}=\frac{2}{3}R_\nu$
where $R_\nu=\frac{\delta m^2}{\Delta m^2}$. Subsequently, by admitting the
experimental ranges of $R_\nu$, we retrieve the allowed range of the unknown
phase $\phi$. Such a procedure assist us to determine the ranges of all the
neutrino observable parameters, the masses of neutrinos, the CP-violating
phases and $J$ parameter as well as to predict the normal hierarchy for the
neutrino mass. Finally, we show that our predictions with respect to our
herewith reported specific textures are consistent with the corresponding data
reported from neutrino oscillation, cosmic microwave background and
neutrinoless double beta decay experiments.


title:
General off-resonance error robust symmetric composite pulses with three
  elementary operations

abstract:
  Accurate quantum control is a key technology for realizing quantum
information processing, such as quantum communication and quantum computation.
In reality, a quantum state under control suffers from undesirable effects
caused by systematic errors. A composite pulse (CP) is used to eliminate the
effects of systematic errors during control. One qubit control, which is the
most fundamental in quantum control, is typically affected by two errors: pulse
length error (PLE) and off-resonance error (ORE). In this study, we focus on
ORE-robust CPs and systematically construct ORE-robust symmetric CPs with three
elementary operations. We find an infinitely large number of ORE-robust CPs and
evaluate their performance according to gate infidelity and operation time,
both of which are important for the realization of accurate quantum control.


title: Simulations for x-ray imaging of wave packet dynamics

abstract:
  Previous work on imaging wave packet dynamics with x-ray scattering revealed
that the scattering patterns deviate substantially from the notion of
instantaneous momentum density of the wave packet. Here we show that scattering
patterns can provide clear insights into the electron wave packet dynamics if
the final state of the scattered electron and the scattered photon momentum are
determined simultaneously. The scattering probability is shown to be
proportional to the modulus square of the Fourier transform of the
instantaneous spatial wave function weighted by the final state of the
electron. Several cases for the choice of final state of the electron are
explored. First, the case where the final state can be measured up to a given
principal quantum number $n$ and orbital angular momentum $l$ are presented.
Next, the case where the final states can only be determined up to a given
energy is discussed. Finally, the case of an initial wave packet consisting of
a large amount of a known stationary state and a small amount of an unknown
stationary state is examined. The scattering profile is used to determine the
properties of the unknown state in the wave packet.


title:
Active Phase-Encode Selection for Slice-Specific Fast MR Scanning Using
  a Transformer-Based Deep Reinforcement Learning Framework

abstract:
  Purpose: Long scan time in phase encoding for forming complete K-space
matrices is a critical drawback of MRI, making patients uncomfortable and
wasting important time for diagnosing emergent diseases. This paper aims to
reducing the scan time by actively and sequentially selecting partial phases in
a short time so that a slice can be accurately reconstructed from the resultant
slice-specific incomplete K-space matrix. Methods: A transformer based deep
reinforcement learning framework is proposed for actively determining a
sequence of partial phases according to reconstruction-quality based Q-value (a
function of reward), where the reward is the improvement degree of
reconstructed image quality. The Q-value is efficiently predicted from binary
phase-indicator vectors, incomplete K-space matrices and their corresponding
undersampled images with a light-weight transformer so that the sequential
information of phases and global relationship in images can be used. The
inverse Fourier transform is employed for efficiently computing the
undersampled images and hence gaining the rewards of selecting phases. Results:
Experimental results on the fastMRI dataset with original K-space data
accessible demonstrate the efficiency and accuracy superiorities of proposed
method. Compared with the state-of-the-art reinforcement learning based method
proposed by Pineda et al., the proposed method is roughly 150 times faster and
achieves significant improvement in reconstruction accuracy. Conclusions: We
have proposed a light-weight transformer based deep reinforcement learning
framework for generating high-quality slice-specific trajectory consisting of a
small number of phases. The proposed method, called TITLE (Transformer Involved
Trajectory LEarning), has remarkable superiority in phase-encode selection
efficiency and image reconstruction accuracy.


title:
A comparative study of non-deep learning, deep learning, and ensemble
  learning methods for sunspot number prediction

abstract:
  Solar activity has significant impacts on human activities and health. One
most commonly used measure of solar activity is the sunspot number. This paper
compares three important non-deep learning models, four popular deep learning
models, and their five ensemble models in forecasting sunspot numbers. Our
proposed ensemble model XGBoost-DL, which uses XGBoost as a two-level nonlinear
ensemble method to combine the deep learning models, achieves the best
forecasting performance among all considered models and the NASA's forecast.
Our XGBoost-DL forecasts a peak sunspot number of 133.47 in May 2025 for Solar
Cycle 25 and 164.62 in November 2035 for Solar Cycle 26, similar to but later
than the NASA's at 137.7 in October 2024 and 161.2 in December 2034.


title: Single-index models for extreme value index regression

abstract:
  Since the extreme value index (EVI) controls the tail behaviour of the
distribution function, the estimation of EVI is a very important topic in
extreme value theory. Recent developments in the estimation of EVI along with
covariates have been in the context of nonparametric regression. However, for
the large dimension of covariates, the fully nonparametric estimator faces the
problem of the curse of dimensionality. To avoid this, we apply the single
index model to EVI regression under Pareto-type tailed distribution. We study
the penalized maximum likelihood estimation of the single index model. The
asymptotic properties of the estimator are also developed. Numerical studies
are presented to show the efficiency of the proposed model.


title: Federated Remote Physiological Measurement with Imperfect Data

abstract:
  The growing need for technology that supports remote healthcare is being
acutely highlighted by an aging population and the COVID-19 pandemic. In
health-related machine learning applications the ability to learn predictive
models without data leaving a private device is attractive, especially when
these data might contain features (e.g., photographs or videos of the body)
that make identifying a subject trivial and/or the training data volume is
large (e.g., uncompressed video). Camera-based remote physiological sensing
facilitates scalable and low-cost measurement, but is a prime example of a task
that involves analysing high bit-rate videos containing identifiable images and
sensitive health information. Federated learning enables privacy-preserving
decentralized training which has several properties beneficial for camera-based
sensing. We develop the first mobile federated learning camera-based sensing
system and show that it can perform competitively with traditional
state-of-the-art supervised approaches. However, in the presence of corrupted
data (e.g., video or label noise) from a few devices the performance of weight
averaging quickly degrades. To address this, we leverage knowledge about the
expected noise profile within the video to intelligently adjust how the model
weights are averaged on the server. Our results show that this significantly
improves upon the robustness of models even when the signal-to-noise ratio is
low


title:
Facile synthesis of Cu2O nanorods in the presence of NaCl by SILAR
  method and its characterizations

abstract:
  Cu2O nanorods have been deposited on soda-lime glass (SLG) substrates by the
modified SILAR technique by varying the concentration of NaCl electrolyte into
the precursor complex solution. The structural, electrical, and optical
properties of synthesized Cu2O nanorod films have been studied by a variety of
characterization tools. Structural analyses by XRD confirmed the
polycrystalline Cu2O phase with (111) preferential growth. Raman scattering
spectroscopic measurements conducted at room temperature also showed
characteristic peaks of the pure Cu2O phase. The surface resistivity of the
Cu2O nanorod films decreased from 15,142 to 685 Ohm.cm with the addition of
NaCl from 0 to 4 mmol, and then exhibited an opposite trend with further
addition of NaCl. The optical bandgap of the synthesized Cu2O nanorod films was
observed as 1.88 - 2.36 eV, while the temperature-dependent activation energies
of the Cu2O films were measured as about 0.14 - 0.21 eV. SEM morphologies
demonstrated Cu2O nanorod as well as closely packed spherical grains with the
alteration of NaCl concentration. The Cu2O phase of nanorods was found stable
up to 230 0C corroborating the optical bandgap results of the same. The film
fabricated in presence of 4 mmol of NaCl showed the lowest resistivity and
activation energy as well comparatively uniform nanorod morphology. Our studies
demonstrate that the nominal presence of NaCl electrolytes in the pre-cursor
solutions has a significant impact on the physical properties of Cu2O nanorod
films which could be beneficial in optoelectronic research.


title:
Knee structure in the laser intensity dependence of high-order harmonic
  generations for graphene

abstract:
  We investigate the high-order harmonic generations (HHGs) of graphene
irradiated by linearly polarized lasers with intensities in a wide range from
$10^{8}$ W/cm$^2$ to $10^{13}$ W/cm$^2$. We find a striking knee structure in
the laser intensity dependence of HHGs, which consists of a linear growth
regime, followed by a plateau of the saturated HHGs, and then a transition to a
nonlinear growth. The knee structure is rather universal for the varied
harmonic orders and has been certificated by the calculations of two-band
density-matrix equations as well as the \textit{ab initio} calculations of
time-dependent density functional theory. Based on the two-band model, we
reveal the underlying mechanisms: The behavior of linear growth can be depicted
analytically by the perturbative theory of optical conductivity; While, the
plateau of saturated HHGs and the transition to a nonlinear growth are caused
by the quantum destructive interference and constructive interference of
harmonics generated by the electrons corresponding to the lattice momentums
around Dirac points and M points in Brillouin zone, respectively. In
particular, we find that tuning Fermi energy can effectively alter the knee
structure while the profile of the knee structure is not sensitive to the
temperature. Our calculations of the third-order harmonic vs. tuning Fermi
energy are compared with recent experiment showing a good agreement. Our
predicted knee structure and its associated properties are observable with the
current experimental techniques.


title: Structure of international trade hypergraphs

abstract:
  We study the structure of the international trade hypergraph consisting of
triangular hyperedges representing the exporter-importer-product relationship.
Measuring the mean hyperdegree of the adjacent vertices, we first find its
behaviors different from those in the pairwise networks and explain the origin
by tracing the relation between the hyperdegree and the pairwise degree. To
interpret the observed hyperdegree correlation properties in the context of
trade strategies, we decompose the correlation into two components by
identifying one with the background correlation remnant even in the exponential
random hypergraphs preserving the given empirical hyperdegree sequence. The
other component characterizes the net correlation and reveals the bias of the
exporters of low hyperdegree towards the importers of high hyperdegree and the
products of low hyperdegree, which information is not readily accessible in the
pairwise networks. Our study demonstrates the power of the hypergraph approach
in the study of real-world complex systems and offers a theoretical framework.


title:
An Efficient Accelerator for Deep Learning-based Point Cloud
  Registration on FPGAs

abstract:
  Point cloud registration is the basis for many robotic applications such as
odometry and Simultaneous Localization And Mapping (SLAM), which are
increasingly important for autonomous mobile robots. Computational resources
and power budgets are limited on these robots, thereby motivating the
development of resource-efficient registration method on low-cost FPGAs. In
this paper, we propose a novel approach for FPGA-based 3D point cloud
registration built upon a recent deep learning-based method, PointNetLK. A
highly-efficient FPGA accelerator for PointNet-based feature extraction is
designed and implemented on both low-cost and mid-range FPGAs (Avnet Ultra96v2
and Xilinx ZCU104). Our accelerator design is evaluated in terms of
registration speed, accuracy, resource usage, and power consumption.
Experimental results show that PointNetLK with our accelerator achieves up to
21.34x and 69.60x faster registration speed than the CPU counterpart and ICP,
respectively, while only consuming 722mW and maintaining the same level of
accuracy.


title: Compact stars in the Einstein dark energy model

abstract:
  We investigate the properties of high density compact objects in a vector
type theory, inspired by Einstein's 1919 theory of elementary particles, in
which Einstein assumed that elementary particles are held together by
gravitational as well as electromagnetic type forces. From a modern
perspective, Einstein's theory can be interpreted as a vector type model, with
the gravitational action constructed as a linear combination of the Ricci
scalar, of the trace of the matter energy-momentum tensor, and of a massive
self-interacting vector type field. To obtain the properties of stellar models
we consider the field equations for a static, spherically symmetric system, and
we investigate numerically their solutions for different equations of state of
quark and neutron matter, by assuming that the self-interaction potential of
the vector field either vanishes or is quadratic in the vector field potential.
We consider quark stars described by the MIT bag model equation of state and in
the Color Flavor Locked phase, as well as compact stars consisting of a
Bose-Einstein Condensate of neutron matter, with neutrons forming Cooper pairs.
Constant density stars, representing a generalization of the Interior
Schwarzschild solution of general relativity, are also analyzed. Also, we
consider the Douchin-Haensel (SLy) equation of state. The numerical solutions
are explicitly obtained in both standard general relativity, and the Einstein
dark energy model and an in depth comparison between the astrophysical
predictions of these two theories are performed. As a general conclusion of our
study, we find that for all the considered equations of state a much larger
variety of stellar structures can be obtained in the Einstein dark energy
model, including classes of stars that are more massive than their general
relativistic counterparts.


title: Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval

abstract:
  Recent rapid advancements in deep pre-trained language models and the
introductions of large datasets have powered research in embedding-based dense
retrieval. While several good research papers have emerged, many of them come
with their own software stacks. These stacks are typically optimized for some
particular research goals instead of efficiency or code structure. In this
paper, we present Tevatron, a dense retrieval toolkit optimized for efficiency,
flexibility, and code simplicity. Tevatron provides a standardized pipeline for
dense retrieval including text processing, model training, corpus/query
encoding, and search. This paper presents an overview of Tevatron and
demonstrates its effectiveness and efficiency across several IR and QA data
sets. We also show how Tevatron's flexible design enables easy generalization
across datasets, model architectures, and accelerator platforms(GPU/TPU). We
believe Tevatron can serve as an effective software foundation for dense
retrieval system research including design, modeling, and optimization.


title:
Dual reparametrized Variational Generative Model for Time-Series
  Forecasting

abstract:
  This paper propose DualVDT, a generative model for Time-series forecasting.
Introduced dual reparametrized variational mechanisms on variational
autoencoder (VAE) to tighter the evidence lower bound (ELBO) of the model,
prove the advance performance analytically. This mechanism leverage the latent
score based generative model (SGM), explicitly denoising the perturbation
accumulated on latent vector through reverse time stochastic differential
equation and variational ancestral sampling. The posterior of denoised latent
distribution fused with dual reparametrized variational density. The KL
divergence in ELBO will reduce to reach the better results of the model. This
paper also proposed a latent attention mechanisms to extract multivariate
dependency explicitly. Build the local temporal dependency simultaneously in
factor wised through constructed local topology and temporal wised. The proven
and experiment on multiple datasets illustrate, DualVDT, with a novel dual
reparametrized structure, which denoise the latent perturbation through the
reverse dynamics combining local-temporal inference, has the advanced
performance both analytically and experimentally.


title:
Effects of time-varying habitat connectivity on metacommunity
  persistence

abstract:
  Network structure or connectivity pattern is critical in determining
collective dynamics among interacting species in ecosystems. Conventional
research on species persistence in spatial populations has focused on static
network structure, though most real network structures change in time, forming
time-varying networks. This raises the question, in metacommunities, how does
the pattern of synchrony vary with temporal evolution in the network structure.
The synchronous dynamics among species are known to reduce metacommunity
persistence. Here, we consider a time-varying metacommunity small-world network
consisting of a chaotic three-species food chain oscillator in each patch/node.
The rate of change in the network connectivity is determined by the natural
frequency or its subharmonics of the constituent oscillator to allow sufficient
time for the evolution of species in between successive rewirings. We find that
over a range of coupling strengths and rewiring periods, even higher rewiring
probabilities drive a network from asynchrony towards synchrony. Moreover, in
networks with a small rewiring period, an increase in average degree (more
connected networks) pushes the asynchronous dynamics to synchrony. On the
contrary, in networks with a low average degree, a higher rewiring period
drives the synchronous dynamics to asynchrony resulting in increased species
persistence. Our results also follow the calculation of synchronization time
and robust across other ecosystem models. Overall, our study opens the
possibility of developing temporal connectivity strategies to increase species
persistence in ecological networks.


title: Research on Parallel SVM Algorithm Based on Cascade SVM

abstract:
  Cascade SVM (CSVM) can group datasets and train subsets in parallel, which
greatly reduces the training time and memory consumption. However, the model
accuracy obtained by using this method has some errors compared with direct
training. In order to reduce the error, we analyze the causes of error in
grouping training, and summarize the grouping without error under ideal
conditions. A Balanced Cascade SVM (BCSVM) algorithm is proposed, which
balances the sample proportion in the subset after grouping to ensure that the
sample proportion in the subset is the same as the original dataset. At the
same time, it proves that the accuracy of the model obtained by BCSVM algorithm
is higher than that of CSVM. Finally, two common datasets are used for
experimental verification, and the results show that the accuracy error
obtained by using BCSVM algorithm is reduced from 1% of CSVM to 0.1%, which is
reduced by an order of magnitude.


title:
DeTRM: Decentralised Trust and Reputation Management for
  Blockchain-based Supply Chains

abstract:
  Blockchain has the potential to enhance supply chain management systems by
providing stronger assurance in transparency and traceability of traded
commodities. However, blockchain does not overcome the inherent issues of data
trust in IoT enabled supply chains. Recent proposals attempt to tackle these
issues by incorporating generic trust and reputation management, which does not
entirely address the complex challenges of supply chain operations and suffers
from significant drawbacks. In this paper, we propose DeTRM, a decentralised
trust and reputation management solution for supply chains, which considers
complex supply chain operations, such as splitting or merging of product lots,
to provide a coherent trust management solution. We resolve data trust by
correlating empirical data from adjacent sensor nodes, using which the
authenticity of data can be assessed. We design a consortium blockchain, where
smart contracts play a significant role in quantifying trustworthiness as a
numerical score from different perspectives. A proof-of-concept implementation
in Hyperledger Fabric shows that DeTRM is feasible and only incurs relatively
small overheads compared to the baseline.


title:
Muon-spin relaxation study of the layered kagome superconductor
  CsV$_3$Sb$_5$

abstract:
  The ${\mathbb{Z}}_{2}$ topological metals $R$V$_3$Sb$_5$ ($R$ = K, Rb, Cs)
with a layered kagome structure provide a unique opportunity to investigate the
interplay between charge order, superconductivity and topology. Here, we report
muon-spin relaxation/rotation ($\mu$SR) measurements performed on CsV$_3$Sb$_5$
across a broad temperature range, in order to uncover the nature of the
charge-density wave order and superconductivity in this material. From
zero-field $\mu$SR, we find that spontaneous magnetic fields appear below 50 K
which is well below the charge-density wave transition ($T^* \sim 93$ K). We
show that these spontaneous fields are dynamic in nature making it difficult to
associate them with a hidden static order. The superconducting state of
CsV$_3$Sb$_5$ is found to preserve time-reversal symmetry and the
transverse-field $\mu$SR results are consistent with a superconducting state
that has two fully open gaps.


title:
Point sources and stability for an inverse problem for a hyperbolic PDE
  with space and time dependent coefficients

abstract:
  We study stability aspects for the determination of space and time-dependent
lower order perturbations of the wave operator in three space dimensions with
point sources. The problems under consideration here are formally determined
and we establish Lipschitz stability results for these problems. The main tool
in our analysis is a modified version of Bukgheim-Klibanov method based on
Carleman estimates.


title:
Sterile neutrino rates for general $M$, $T$, $\mu$, $k$: review of a
  theoretical framework

abstract:
  The temperature of sphaleron freeze-out, $T\approx 130$ GeV, sets a scale for
mechanisms for generating lepton asymmetries and converting them to baryon
asymmetry (leptogenesis). For Majorana masses $M \gg \pi T$, leptogenesis takes
place in the non-relativistic regime, while for $M \ll \pi T$, the dynamics is
ultrarelativistic. The intermediate case $M \sim \pi T$ is the most cumbersome,
as no effective theory methods are available. We review the definitions of and
provide integral representations for all rate coefficients and mass corrections
of $\mathcal{O}(h_\nu^2)$ in this regime, such that the expressions extrapolate
to known limits and are gauge independent to the order computed, both in the
symmetric and the Higgs phase, for any helicity, momentum, and set of chemical
potentials. Road signs towards a numerical evaluation are offered.


title: Discriminant indicator with generalized rotational symmetry

abstract:
  Discriminant indicators are recently introduced for systems with generalized
inversion symmetry. In this paper, we propose discriminant indicators for two-
and three-dimensional systems with generalized $n$-fold rotational symmetry
($n=4$, $6$). As is the case of generalized inversion symmetry, the indicator
taking a non-trivial value predicts the emergence of exceptional points and
exceptional loops without ambiguity of the reference energy. We also confirm
these properties by numerically analyzing toy models in two dimensions.


title:
Reinforcement Learning for Linear Quadratic Control is Vulnerable Under
  Cost Manipulation

abstract:
  In this work, we study the deception of a Linear-Quadratic-Gaussian (LQG)
agent by manipulating the cost signals. We show that a small falsification of
the cost parameters will only lead to a bounded change in the optimal policy.
The bound is linear on the amount of falsification the attacker can apply to
the cost parameters. We propose an attack model where the attacker aims to
mislead the agent into learning a `nefarious' policy by intentionally
falsifying the cost parameters. We formulate the attack's problem as a convex
optimization problem and develop necessary and sufficient conditions to check
the achievability of the attacker's goal.
  We showcase the adversarial manipulation on two types of LQG learners: the
batch RL learner and the other is the adaptive dynamic programming (ADP)
learner. Our results demonstrate that with only 2.296% of falsification on the
cost data, the attacker misleads the batch RL into learning the 'nefarious'
policy that leads the vehicle to a dangerous position. The attacker can also
gradually trick the ADP learner into learning the same `nefarious' policy by
consistently feeding the learner a falsified cost signal that stays close to
the actual cost signal. The paper aims to raise people's awareness of the
security threats faced by RL-enabled control systems.


title:
Physics-informed Reinforcement Learning for Perception and Reasoning
  about Fluids

abstract:
  Learning and reasoning about physical phenomena is still a challenge in
robotics development, and computational sciences play a capital role in the
search for accurate methods able to provide explanations for past events and
rigorous forecasts of future situations. We propose a physics-informed
reinforcement learning strategy for fluid perception and reasoning from
observations. As a model problem, we take the sloshing phenomena of different
fluids contained in a glass. Starting from full-field and high-resolution
synthetic data for a particular fluid, we develop a method for the tracking
(perception) and analysis (reasoning) of any previously unseen liquid whose
free surface is observed with a commodity camera. This approach demonstrates
the importance of physics and knowledge not only in data-driven (grey box)
modeling but also in the correction for real physics adaptation in low data
regimes and partial observations of the dynamics. The method here presented is
extensible to other domains such as the development of cognitive digital twins,
able to learn from observation of phenomena for which they have not been
trained explicitly.


title: Operadic approach to HNN-extensions of Leibniz algebras

abstract:
  We construct HNN-extensions of Lie di-algebras in the variety of di-algebras
and provide a presentation for the replicated HNN-extension of a Lie
di-algebras. Then, by applying the method of Gr\"obner-Shirshov bases for
replicated algebras, we obtain a linear basis. As an application of
HNN-extensions, we prove that Lie di-algebras are embedded in their
HNN-extension.


title:
Polarization-controlled dynamically switchable high-harmonic generation
  from all-dielectric metasurfaces governed by dual bound states in the
  continuum

abstract:
  Tailoring optical nonlinear effects (e.g. harmonic generation, sum-frequency
mixing, etc.) in the recently emerging all-dielectric platform is important for
both the fundamental science and industrial development of high-efficiency,
ultrafast, and miniaturized photonic devices. In this work, we propose a novel
paradigm for dynamically switchable high-harmonic generation in Silicon
nanodimer metasurfaces by exploiting polarization-controlled dual bound states
in the continuum (BIC). Owing to the high-quality factor of BIC resonances,
efficient harmonic signals including the third-harmonic generation and
fifth-harmonic generation from a direct process as well as a cascaded process
by degenerate four-wave mixing are obtained. Moreover, the BICs and their
resonantly enhanced harmonics can be switched on or off with high selectivity
respect to the fundamental pump polarization. Compared with previous reports,
our work provide a simple but effective tuning strategy by fully exploring the
structural symmetry and polarization degree of freedom rather than resorting to
additional external stimuli, which would have great advantages in smart
designing tunable and switchable nonlinear light source for chip-scale
applications.


title: Redistribution in Public Project Problems via Neural Networks

abstract:
  Many important problems in multiagent systems involve resource allocations.
Self-interested agents may lie about their valuations if doing so increases
their own utilities. Therefore, it is necessary to design mechanisms
(collective decision-making rules) with desired properties and objectives. The
VCG redistribution mechanisms are efficient (the agents who value the resources
the most will be allocated), strategy-proof (the agents have no incentives to
lie about their valuations), and weakly budget-balanced (no deficits).
  We focus on the VCG redistribution mechanisms for the classic public project
problem, where a group of agents needs to decide whether or not to build a
non-excludable public project. We design mechanisms via neural networks with
two welfare-maximizing objectives: optimal in the worst case and optimal in
expectation. Previous studies showed two worst-case optimal mechanisms for 3
agents, but worst-case optimal mechanisms have not been identified for more
than 3 agents. For maximizing expected welfare, there are no existing results.
  We use neural networks to design VCG redistribution mechanisms. Neural
networks have been used to design the redistribution mechanisms for multi-unit
auctions with unit demand. We show that for the public project problem, the
previously proposed neural networks, which led to optimal/near-optimal
mechanisms for multi-unit auctions with unit demand, perform abysmally for the
public project problem. We significantly improve the existing networks on
multiple fronts: We conduct a GAN network to generate worst-case type profiles
and feed prior distribution into loss function to provide quality gradients for
the optimal-in-expectation objective......


title:
An efficient multi-modes Monte Carlo homogenization method for random
  materials

abstract:
  In this paper, we propose and analyze a new stochastic homogenization method
for diffusion equations with random and fast oscillatory coefficients. In the
proposed method, the homogenized solutions are sought through a two-stage
procedure. In the first stage, the original oscillatory diffusion equation is
approximated, for each fixed random sample w, by a spatially homogenized
diffusion equation with piecewise constant coefficients, resulting a random
diffusion equation. In the second stage, the resulted random diffusion equation
is approximated and computed by using an efficient multi-modes Monte Carlo
method which only requires to solve a diffusion equation with a constant
diffusion coefficient and a random right-hand side. The main advantage of the
proposed method is that it separates the computational difficulty caused by the
spatial fast oscillation of the solution and that caused by the randomness of
the solution, so they can be overcome separately using different strategies.
The convergence of the solution of the spatially homogenized equation (from the
first stage) to the solution of the original random diffusion equation is
established and the optimal rate of convergence is also obtained for the
proposed multi-modes Monte Carlo method. Numerical experiments on some
benchmark test problems for random composite materials are also presented to
gauge the efficiency and accuracy of the proposed two-stage stochastic
homogenization method.


title:
Acoustic To Articulatory Speech Inversion Using Multi-Resolution
  Spectro-Temporal Representations Of Speech Signals

abstract:
  Multi-resolution spectro-temporal features of a speech signal represent how
the brain perceives sounds by tuning cortical cells to different spectral and
temporal modulations. These features produce a higher dimensional
representation of the speech signals. The purpose of this paper is to evaluate
how well the auditory cortex representation of speech signals contribute to
estimate articulatory features of those corresponding signals. Since obtaining
articulatory features from acoustic features of speech signals has been a
challenging topic of interest for different speech communities, we investigate
the possibility of using this multi-resolution representation of speech signals
as acoustic features. We used U. of Wisconsin X-ray Microbeam (XRMB) database
of clean speech signals to train a feed-forward deep neural network (DNN) to
estimate articulatory trajectories of six tract variables. The optimal set of
multi-resolution spectro-temporal features to train the model were chosen using
appropriate scale and rate vector parameters to obtain the best performing
model. Experiments achieved a correlation of 0.675 with ground-truth tract
variables. We compared the performance of this speech inversion system with
prior experiments conducted using Mel Frequency Cepstral Coefficients (MFCCs).


title: Pilot Aided Channel Estimation for AFDM in Doubly Dispersive Channels

abstract:
  Affine frequency division multiplexing (AFDM) is a multi-chirp waveform and a
promising solution for ultra-reliable communication under doubly dispersive
channels. In this paper, we propose two pilot aided channel estimation schemes
for AFDM, named single pilot aided (SPA) and multiple pilots aided (MPA)
respectively. Pilot, guard and data symbols in the discrete affine Fourier
transform (DAFT) domain are arranged appropriately at the transmitter. While at
the receiver, channel estimation is performed with the aid of an estimation
threshold and a mapping table. The bit error performance of AFDM applying the
proposed channel estimation schemes shows only marginal loss compared to AFDM
with ideally known channel state information. Moreover, extensions of the SPA
scheme to multiple-input multiple-output (MIMO) and multi-user uplink/downlink
are presented.


title: Overcoming Temptation: Incentive Design For Intertemporal Choice

abstract:
  Individuals are often faced with temptations that can lead them astray from
long-term goals. We're interested in developing interventions that steer
individuals toward making good initial decisions and then maintaining those
decisions over time. In the realm of financial decision making, a particularly
successful approach is the prize-linked savings account: individuals are
incentivized to make deposits by tying deposits to a periodic lottery that
awards bonuses to the savers. Although these lotteries have been very effective
in motivating savers across the globe, they are a one-size-fits-all solution.
We investigate whether customized bonuses can be more effective. We formalize a
delayed-gratification task as a Markov decision problem and characterize
individuals as rational agents subject to temporal discounting, a cost
associated with effort, and fluctuations in willpower. Our theory is able to
explain key behavioral findings in intertemporal choice. We created an online
delayed-gratification game in which the player scores points by selecting a
queue to wait in and then performing a series of actions to advance to the
front. Data collected from the game is fit to the model, and the instantiated
model is then used to optimize predicted player performance over a space of
incentives. We demonstrate that customized incentive structures can improve an
individual's goal-directed decision making.


title: The images of some simple derivations

abstract:
  In the paper, we study the relation between the images of polynomial
derivations and their simplicity. We prove that the images of simple Shamsuddin
derivations are not Mathieu-Zhao spaces. In addition, we also show that the
images of some simple derivations in dimension three are not Mathieu-Zhao
spaces. Thus, we conjecture that the images of simple derivations in dimension
greater than one are not Mathieu-Zhao spaces. We also prove that locally
nilpotent derivations are not simple in dimension greater than one.


title:
AI-enabled Automatic Multimodal Fusion of Cone-Beam CT and Intraoral
  Scans for Intelligent 3D Tooth-Bone Reconstruction and Clinical Applications

abstract:
  A critical step in virtual dental treatment planning is to accurately
delineate all tooth-bone structures from CBCT with high fidelity and accurate
anatomical information. Previous studies have established several methods for
CBCT segmentation using deep learning. However, the inherent resolution
discrepancy of CBCT and the loss of occlusal and dentition information largely
limited its clinical applicability. Here, we present a Deep Dental Multimodal
Analysis (DDMA) framework consisting of a CBCT segmentation model, an intraoral
scan (IOS) segmentation model (the most accurate digital dental model), and a
fusion model to generate 3D fused crown-root-bone structures with high fidelity
and accurate occlusal and dentition information. Our model was trained with a
large-scale dataset with 503 CBCT and 28,559 IOS meshes manually annotated by
experienced human experts. For CBCT segmentation, we use a five-fold cross
validation test, each with 50 CBCT, and our model achieves an average Dice
coefficient and IoU of 93.99% and 88.68%, respectively, significantly
outperforming the baselines. For IOS segmentations, our model achieves an mIoU
of 93.07% and 95.70% on the maxillary and mandible on a test set of 200 IOS
meshes, which are 1.77% and 3.52% higher than the state-of-art method. Our DDMA
framework takes about 20 to 25 minutes to generate the fused 3D mesh model
following the sequential processing order, compared to over 5 hours by human
experts. Notably, our framework has been incorporated into a software by a
clear aligner manufacturer, and real-world clinical cases demonstrate that our
model can visualize crown-root-bone structures during the entire orthodontic
treatment and can predict risks like dehiscence and fenestration. These
findings demonstrate the potential of multi-modal deep learning to improve the
quality of digital dental models and help dentists make better clinical
decisions.


title: Innovation Diffusion among Case-based Decision-makers

abstract:
  This paper analyzes a model of innovation diffusion with case-based
individuals a la Gilboa and Schmeidler (1995, 1996, 1997), who decide whether
to consume an incumbent's or an entrant's product based on their and their
social neighbors' previous consumption experience. I analyze how diffusion
pattern changes with individuals characteristics, innovation characteristics
and social network. In particular, radical innovation leads to higher initial
speed but lower acceleration compared to increment innovation. Social network
with lower degree of homophily or higher exposure of reviews from early
adopters speed up diffusion of innovation.


title:
Free energy landscape of two-state protein Acylphosphatase with large
  contact order revealed by force-dependent folding and unfolding dynamics

abstract:
  Acylphosphatase (AcP) is a small protein with 98 amino acid residues that
catalyzes the hydrolysis of carboxyl-phosphate bonds. AcP is a typical
two-state protein with slow folding rate due to its relatively large contact
order in the native structure. The mechanical properties and unfolding behavior
of AcP has been studied by atomic force microscope. But the folding and
unfolding dynamics at low forces has not been reported. Here using stable
magnetic tweezers, we measured the force-dependent folding rates within a force
range from 1 pN to 3 pN, and unfolding rates from 15 pN to 40 pN. The obtained
unfolding rates show different force sensitivities at forces below and above
~27 pN, which determines a free energy landscape with two energy barriers. Our
results indicate that the free energy landscape of small globule proteins have
general Bactrian camel shape, and large contact order of the native state
produces a high barrier dominate at low forces.


title:
Democracy Does Matter: Comprehensive Feature Mining for Co-Salient
  Object Detection

abstract:
  Co-salient object detection, with the target of detecting co-existed salient
objects among a group of images, is gaining popularity. Recent works use the
attention mechanism or extra information to aggregate common co-salient
features, leading to incomplete even incorrect responses for target objects. In
this paper, we aim to mine comprehensive co-salient features with democracy and
reduce background interference without introducing any extra information. To
achieve this, we design a democratic prototype generation module to generate
democratic response maps, covering sufficient co-salient regions and thereby
involving more shared attributes of co-salient objects. Then a comprehensive
prototype based on the response maps can be generated as a guide for final
prediction. To suppress the noisy background information in the prototype, we
propose a self-contrastive learning module, where both positive and negative
pairs are formed without relying on additional classification information.
Besides, we also design a democratic feature enhancement module to further
strengthen the co-salient features by readjusting attention values. Extensive
experiments show that our model obtains better performance than previous
state-of-the-art methods, especially on challenging real-world cases (e.g., for
CoCA, we obtain a gain of 2.0% for MAE, 5.4% for maximum F-measure, 2.3% for
maximum E-measure, and 3.7% for S-measure) under the same settings. Code will
be released soon.


title: CBlockSim: A Modular High-Performance Blockchain Simulator

abstract:
  Blockchain has attracted much attention from both academia and industry since
emerging in 2008. Due to the inconvenience of the deployment of large-scale
blockchains, blockchain simulators are used to facilitate blockchain design and
implementation. We evaluate state-of-the-art simulators applied to both Bitcoin
and Ethereum and find that they suffer from low performance and scalability
which are significant limitations. To build a more general and faster
blockchain simulator, we extend an existing blockchain simulator, i.e.
BlockSim. We add a network module integrated with a network topology generation
algorithm and a block propagation algorithm to generate a realistic blockchain
network and simulate the block propagation efficiently. We design a binary
transaction pool structure and migrate BlockSim from Python to C++ so that
bitwise operations can be used to accelerate the simulation and reduce memory
usage. Moreover, we modularize the simulator based on five primary blockchain
processes. Significant blockchain elements including consensus protocols (PoW
and PoS), information propagation algorithms (Gossip) and finalization rules
(Longest rule and GHOST rule) are implemented in individual modules and can be
combined flexibly to simulate different types of blockchains. Experiments
demonstrate that the new simulator reduces the simulation time by an order of
magnitude and improves scalability, enabling us to simulate more than ten
thousand nodes, roughly the size of the Bitcoin and Ethereum networks. Two
typical use cases are proposed to investigate network-related issues which are
not covered by most other simulators.


title: FLAG: Flow-based 3D Avatar Generation from Sparse Observations

abstract:
  To represent people in mixed reality applications for collaboration and
communication, we need to generate realistic and faithful avatar poses.
However, the signal streams that can be applied for this task from head-mounted
devices (HMDs) are typically limited to head pose and hand pose estimates.
While these signals are valuable, they are an incomplete representation of the
human body, making it challenging to generate a faithful full-body avatar. We
address this challenge by developing a flow-based generative model of the 3D
human body from sparse observations, wherein we learn not only a conditional
distribution of 3D human pose, but also a probabilistic mapping from
observations to the latent space from which we can generate a plausible pose
along with uncertainty estimates for the joints. We show that our approach is
not only a strong predictive model, but can also act as an efficient pose prior
in different optimization settings where a good initial latent code plays a
major role.


title: Surface acoustic wave lasing in a silicon optomechanical cavity

abstract:
  Integrated optomechanical cavities stand as a promising means to interface
mechanical motion and guided optical modes. State-of-the-art demonstrations
rely on optical and mechanical modes tightly confined of in micron-scale areas
to achieve strong optomechanical coupling. However, the need for tight
optomechanical confinement and the general use of suspended devices hinders
interaction with external devices, limiting the potential for the
implementation of complex circuits. Here, we propose and demonstrate a new
approach for optomechanical cavities coupling free-propagating surface acoustic
waves (SAWs) and guided optical modes. The cavity is formed by a periodic array
of silicon nanopillars with subwavelength separation, implemented in
silicon-on-insulator substrate. Optical pumping yields a strong radiation
pressure that drives the harmonic vibration of the pillars, periodically
deforming the silica under-cladding and exciting the SAW. The propagation of
the SAW deforms the cavity period, modulating the resonance wavelength to close
the optomechanical coupling loop. Based on this concept, we experimentally
demonstrate a phonon laser at room temperature and ambient conditions with
optical pump power as low as 1 mW. We also show the possibility to cascade this
process, achieving a frequency comb generation with more than 30 harmonic
lines. These results open a new path to achieve strong bidirectional coupling
between integrated waveguides and SAW, with a great potential for a wide range
of applications in quantum and classical domains.


title:
A study for recovering the cut-elimination property in cyclic proof
  systems by restricting the arity of inductive predicates

abstract:
  The framework of cyclic proof systems provides a reasonable proof system for
logics with inductive definitions. It also offers an effective automated proof
search procedure for such logics without finding induction hypotheses. Recent
researches have shown that the cut-elimination property, one of the most
fundamental properties in proof theory, of cyclic proof systems for several
logics does not hold. These results suggest that a naive proof search, which
avoids the Cut rule, is not enough.
  This paper shows that the cut-elimination property still fails in a simple
cyclic proof system even if we restrict languages to unary inductive predicates
and unary functions, aiming to clarify why the cut-elimination property fails
in the cyclic proof systems. The result in this paper is a sharper one than
that of the first authors' previous result, which gave a counterexample using
two ternary inductive predicates and a unary function symbol to show the
failure of the cut-elimination property in the cyclic proof system of the
first-order logic.


title: A nonuniform mesh method for wave scattered by periodic surfaces

abstract:
  In this paper, we propose a new nonuniform mesh method to simulate acoustic
scattering problems in two dimensional periodic structures with non-periodic
incident fields numerically. As existing methods are difficult to extend to
higher dimensions, we have designed the new method with such extensions in
mind. With the help of the Floquet-Bloch transform, the solution to the
original scattering problem is written as an integral of a family of
quasi-periodic problems. These are defined in bounded domains for each value of
the Floquet parameter which varies in a bounded interval. The key step in our
method is the numerical approximation of the integral by a quadrature rule
adapted to the regularity of the family of quasi-periodic solutions. We design
a nonuniform mesh with a Gaussian quadrature rule applied on each subinterval.
We prove that the numerical method converges exponentially with respect to both
the number of subintervals and the number of Gaussian quadrature points. Some
numerical experiments are provided to illustrate the results.


title:
PathSAGE: Spatial Graph Attention Neural Networks With Random Path
  Sampling

abstract:
  Graph Convolutional Networks (GCNs) achieve great success in non-Euclidean
structure data processing recently. In existing studies, deeper layers are used
in CCNs to extract deeper features of Euclidean structure data. However, for
non-Euclidean structure data, too deep GCNs will confront with problems like
"neighbor explosion" and "over-smoothing", it also cannot be applied to large
datasets. To address these problems, we propose a model called PathSAGE, which
can learn high-order topological information and improve the model's
performance by expanding the receptive field. The model randomly samples paths
starting from the central node and aggregates them by Transformer encoder.
PathSAGE has only one layer of structure to aggregate nodes which avoid those
problems above. The results of evaluation shows that our model achieves
comparable performance with the state-of-the-art models in inductive learning
tasks.


title: BERTopic: Neural topic modeling with a class-based TF-IDF procedure

abstract:
  Topic models can be useful tools to discover latent topics in collections of
documents. Recent studies have shown the feasibility of approach topic modeling
as a clustering task. We present BERTopic, a topic model that extends this
process by extracting coherent topic representation through the development of
a class-based variation of TF-IDF. More specifically, BERTopic generates
document embedding with pre-trained transformer-based language models, clusters
these embeddings, and finally, generates topic representations with the
class-based TF-IDF procedure. BERTopic generates coherent topics and remains
competitive across a variety of benchmarks involving classical models and those
that follow the more recent clustering approach of topic modeling.


title:
A High-Order-Accurate 3D Surface Integral Equation Solver for Uniaxial
  Anisotropic Media

abstract:
  This paper introduces a high-order accurate surface integral equation method
for solving 3D electromagnetic scattering for dielectric objects with
uniaxially anisotropic permittivity tensors. The N-M\"uller formulation is
leveraged resulting in a second-kind integral formulation, and a
finite-difference-based approach is used to deal with the strongly singular
terms resulting from the dyadic Green's functions for uniaxially anisotropic
media while maintaining the high-order accuracy of the discretization strategy.
The integral operators are discretized via a Nystr\"om-collocation approach,
which represents the unknown surface densities in terms of Chebyshev
polynomials on curvilinear quadrilateral surface patches. The convergence is
investigated for various geometries, including a sphere, cube, a complicated
NURBS geometry imported from a 3D CAD modeler software, and a nanophotonic
silicon waveguide, and results are compared against a commercial finite element
solver. To the best of our knowledge, this is the first demonstration of
high-order accuracy for objects with uniaxially anisotropic materials using
surface integral equations.


title:
Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark
  of Data, Model, and Supervision

abstract:
  Contrastive Language-Image Pretraining (CLIP) has emerged as a novel paradigm
to learn visual models from language supervision. While researchers continue to
push the frontier of CLIP, reproducing these works remains challenging. This is
because researchers do not choose consistent training recipes and even use
different data, hampering the fair comparison between different methods. In
this work, we propose CLIP-benchmark, a first attempt to evaluate, analyze, and
benchmark CLIP and its variants. We conduct a comprehensive analysis of three
key factors: data, supervision, and model architecture. We find considerable
intuitive or counter-intuitive insights: (1). Data quality has a significant
impact on performance. (2). Certain supervision has different effects for
Convolutional Networks (ConvNets) and Vision Transformers (ViT). Applying more
proper supervision can effectively improve the performance of CLIP. (3).
Curtailing the text encoder reduces the training cost but not much affect the
final performance. Moreover, we further combine DeCLIP with FILIP, bringing us
the strongest variant DeFILIP. The CLIP-benchmark would be released at:
https://github.com/Sense-GVT/DeCLIP for future CLIP research.


title: Long Time No See! Open-Domain Conversation with Long-Term Persona Memory

abstract:
  Most of the open-domain dialogue models tend to perform poorly in the setting
of long-term human-bot conversations. The possible reason is that they lack the
capability of understanding and memorizing long-term dialogue history
information. To address this issue, we present a novel task of Long-term Memory
Conversation (LeMon) and then build a new dialogue dataset DuLeMon and a
dialogue generation framework with Long-Term Memory (LTM) mechanism (called
PLATO-LTM). This LTM mechanism enables our system to accurately extract and
continuously update long-term persona memory without requiring multiple-session
dialogue datasets for model training. To our knowledge, this is the first
attempt to conduct real-time dynamic management of persona information of both
parties, including the user and the bot. Results on DuLeMon indicate that
PLATO-LTM can significantly outperform baselines in terms of long-term dialogue
consistency, leading to better dialogue engagingness.


title:
On the $p$-torsion of the Tate-Shafarevich group of abelian varieties
  over higher dimensional bases over finite fields

abstract:
  We prove a finiteness theorem for the first flat cohomology group of finite
flat group schemes over integral normal proper varieties over finite fields. As
a consequence, we can prove the invariance of the finiteness of the
Tate-Shafarevich group of Abelian schemes over higher dimensional bases under
isogenies and alterations over/of such bases for the $p$-part. Along the way,
we generalize previous results on the Tate-Shafarevich group in this situation.


title:
Almost global existence for some nonlinear Schr{\"o}dinger equations on
  $\mathbb{T}^d$ in low regularity

abstract:
  We are interested in the long time behavior of solutions of the nonlinear
Schr{\"o}dinger equation on the $d$-dimensional torus in low regularity, i.e.
for small initial data in the Sobolev space $H^{s_0}(\mathbb T^d)$ with
$s_0>d/2$. We prove that, even in this context of low regularity, the
$H^s$-norms, $s\geq 0$, remain under control during times, $T_\varepsilon= \exp
\big(-\frac{|\log\varepsilon|^2}{4\log|\log\varepsilon|} \big)$, exponential
with respect to the initial size of the initial datum in $H^{s_0}$,
$\|u(0)\|_{H^{s_0}}=\varepsilon$. For this, we add to the linear part of the
equation a random Fourier multiplier in $\ell^\infty(\mathbb Z^d)$ and show our
stability result for almost any realization of this multiplier. In particular,
with such Fourier multipliers, we obtain the almost global well posedness of
the nonlinear Schr{\"o}dinger equation on $H^{s_0}(\mathbb T^d)$ for any
$s_0>d/2$ and any $d\geq1$.


title:
An optimal feedback control that minimizes the epidemic peak in the SIR
  model under a budget constraint

abstract:
  We give the explicit solution of the optimal control problem which consists
in minimizing the epidemic peak in the SIR model when the control is an
attenuation factor of the infectious rate, subject to a L 1 budget constraint.
The optimal strategy is given as a feedback control which consists in an
singular arc maintaining the infected population at a constant level until the
immunity threshold is reached, and no intervention outside the singular arc.


title:
Moment properties for two-type continuous-state branching processes in
  random environments

abstract:
  We first derive the recurisions for integer moments of two-type
continuous-state branching processes in L\'{e}vy random environments. Result
shows that the $n$th moment of the process is a polynomial of the initial value
of the process with at most $n$ degree. Under some natural condition, the
criteria for the existence of $f$-moment of the process are also proved.


title:
Accurate conservative phase-field method for simulation of two-phase
  flows

abstract:
  In this work, we propose a novel phase-field model for the simulation of
two-phase flows that is accurate, conservative, bounded, and robust. The
proposed model conserves the mass of each of the phases, and results in bounded
transport of the volume fraction. We present results from the canonical test
cases of a drop advection and a drop in a shear flow, showing significant
improvement in the accuracy over the commonly used conservative phase-field
method.
  Moreover, the proposed model imposes a lesser restrictive
Courant-Friedrichs-Lewy condition, and hence, is less expensive compared to
other conservative phase-field models. We also propose improvements on
computation of surface tension forces and show that the proposed improvement
significantly reduces the magnitude of spurious velocities at the interface.
  We also derive a consistent and conservative momentum transport equation for
the proposed phase-field model and show that the proposed model when coupled
with the consistent momentum transport equation results in discrete
conservation of kinetic energy, which is a sufficient condition for the
numerical stability of incompressible flows, in the absence of dissipative
mechanisms. To illustrate the robustness of the method in simulating
high-density ratio turbulent two-phase flows, we present the numerical
simulations of high-density ratio droplet-laden isotropic turbulence at finite
and infinite Reynolds numbers.


title: Finite temperature quantum condensations in the space of states

abstract:
  If, for an isolated quantum system, we can find a partition of its space of
states $\mathbb{F}$ into two subspaces, $\mathbb{F}_\mathrm{cond}$ and
$\mathbb{F}_\mathrm{norm}$, such that, in the thermodynamic limit, $\dim
\mathbb{F}_\mathrm{cond}/ \dim \mathbb{F} \to 0$ and the ground state energies
of the system restricted to these subspaces cross each other for some value of
the Hamiltonian parameters, then, the system undergoes a first-order quantum
phase transition driven by that parameter. A proof of this general class of
phase transitions, which correspond to a condensation in the space of quantum
states, briefly, a quantum condensing, has been provided at zero temperature.
Here, we find its natural extension to finite temperature and suggest that
quantum condensing can have a pivotal role in establishing quantum supremacy
for optimization problems.

categories: quant-ph cond-mat.stat-mech math-ph math.MP


title:
Near-optimal Offline Reinforcement Learning with Linear Representation:
  Leveraging Variance Information with Pessimism

abstract:
  Offline reinforcement learning, which seeks to utilize offline/historical
data to optimize sequential decision-making strategies, has gained surging
prominence in recent studies. Due to the advantage that appropriate function
approximators can help mitigate the sample complexity burden in modern
reinforcement learning problems, existing endeavors usually enforce powerful
function representation models (e.g. neural networks) to learn the optimal
policies. However, a precise understanding of the statistical limits with
function representations, remains elusive, even when such a representation is
linear.
  Towards this goal, we study the statistical limits of offline reinforcement
learning with linear model representations. To derive the tight offline
learning bound, we design the variance-aware pessimistic value iteration
(VAPVI), which adopts the conditional variance information of the value
function for time-inhomogeneous episodic linear Markov decision processes
(MDPs). VAPVI leverages estimated variances of the value functions to reweight
the Bellman residuals in the least-square pessimistic value iteration and
provides improved offline learning bounds over the best-known existing results
(whereas the Bellman residuals are equally weighted by design). More
importantly, our learning bounds are expressed in terms of system quantities,
which provide natural instance-dependent characterizations that previous
results are short of. We hope our results draw a clearer picture of what
offline learning should look like when linear representations are provided.


title:
Incident Velocity-Recoil Angle Distribution and Angular Recoil-Energy
  Spectrum of 3-Dimensional WIMP-Nucleus Scattering Events

abstract:
  In this paper, as a supplementary of our study on the angular distribution of
the recoil flux of WIMP-scattered target nuclei and on that of the WIMP
effective scattering velocity distribution, we investigate the scattering
probability distribution of the WIMP incident velocity versus the nuclear
recoil angle in narrow recoil energy windows for different WIMP masses and
target nuclei. Our simulation results show that, not only the velocity
distribution of incident halo WIMPs, but also a factor of the recoil angle
could affect the scattering probability distribution of the available incident
velocity-recoil angle combination in a given recoil energy window.
Consequently, the 1-D WIMP "effective" velocity distribution corresponding to
the considered narrow energy window would not be consistent with that cut
simply from the (generating) velocity distribution of incident halo WIMPs. And
its contribution to the differential WIMP-nucleus scattering event rate in the
considered energy window could thus not be simply estimated by integrating over
the 1-D theoretical velocity distribution (of entire halo WIMPs).


title: Hippocampal and auditory contributions to speech segmentation

abstract:
  Statistical learning has been proposed as a mechanism to structure and
segment the continuous flow of information in several sensory modalities.
Previous studies proposed that the medial temporal lobe, and in particular the
hippocampus, may be crucial to parse the stream in the visual modality.
However, the involvement of the hippocampus in auditory statistical learning,
and specifically in speech segmentation is less clear. To explore the role of
the hippocampus in speech segmentation based on statistical learning, we
exposed seven pharmaco-resistant temporal lobe epilepsy patients to a
continuous stream of trisyllabic pseudowords and recorded intracranial
stereotaxic electro-encephalography (sEEG). We used frequency-tagging analysis
to quantify neuronal synchronization of the hippocampus and auditory regions to
the temporal structure of words and syllables of the stream. Results show that
while auditory regions highly respond to syllable frequency, the hippocampus
responds mostly to word frequency. These findings provide direct evidence of
the involvement of the hippocampus in speech segmentation process and suggest a
hierarchical organization of auditory information during speech processing.


title:
Improve Convolutional Neural Network Pruning by Maximizing Filter
  Variety

abstract:
  Neural network pruning is a widely used strategy for reducing model storage
and computing requirements. It allows to lower the complexity of the network by
introducing sparsity in the weights. Because taking advantage of sparse
matrices is still challenging, pruning is often performed in a structured way,
i.e. removing entire convolution filters in the case of ConvNets, according to
a chosen pruning criteria. Common pruning criteria, such as l1-norm or
movement, usually do not consider the individual utility of filters, which may
lead to: (1) the removal of filters exhibiting rare, thus important and
discriminative behaviour, and (2) the retaining of filters with redundant
information. In this paper, we present a technique solving those two issues,
and which can be appended to any pruning criteria. This technique ensures that
the criteria of selection focuses on redundant filters, while retaining the
rare ones, thus maximizing the variety of remaining filters. The experimental
results, carried out on different datasets (CIFAR-10, CIFAR-100 and
CALTECH-101) and using different architectures (VGG-16 and ResNet-18)
demonstrate that it is possible to achieve similar sparsity levels while
maintaining a higher performance when appending our filter selection technique
to pruning criteria. Moreover, we assess the quality of the found sparse
sub-networks by applying the Lottery Ticket Hypothesis and find that the
addition of our method allows to discover better performing tickets in most
cases


title: Font Shape-to-Impression Translation

abstract:
  Different fonts have different impressions, such as elegant, scary, and cool.
This paper tackles part-based shape-impression analysis based on the
Transformer architecture, which is able to handle the correlation among local
parts by its self-attention mechanism. This ability will reveal how
combinations of local parts realize a specific impression of a font. The
versatility of Transformer allows us to realize two very different approaches
for the analysis, i.e., multi-label classification and translation. A
quantitative evaluation shows that our Transformer-based approaches estimate
the font impressions from a set of local parts more accurately than other
approaches. A qualitative evaluation then indicates the important local parts
for a specific impression.


title:
Laser induced enhanced coupling between photons and squeezed magnons in
  antiferromagnets

abstract:
  In this paper we consider a honeycomb antiferromagnet subject to an external
laser field. Obtaining a time-independent effective Hamiltonian, we find that
the external laser renormalizes the exchange interaction between the in-plane
components of the spin-operators, and induces a synthetic Dzyaloshinskii-Moria
interaction (DMI) between second neighbors. The former allows the control of
the magnon dispersion's bandwidth and the latter breaks time-reversal symmetry
inducing non-reciprocity in momentum space. The eigen-excitations of the system
correspond to squeezed magnons whose squeezing parameters depend on the
properties of the laser. When studying how these spin excitations couple with
cavity photons, we obtain a coupling strength which can be enhanced by an order
of magnitude via careful tuning of the laser's intensity, when compared to the
case where the laser is absent. The transmission plots through the cavity are
presented, allowing the mapping of the magnons' dispersion relation.


title: Genus one half stacky curves violating the local-global principle

abstract:
  For any number field, we prove that there exists a stacky curve of genus one
half defined over the ring of its integers violating the local-global principle
for integral points.


title:
Reprogramming FairGANs with Variational Auto-Encoders: A New Transfer
  Learning Model

abstract:
  Fairness-aware GANs (FairGANs) exploit the mechanisms of Generative
Adversarial Networks (GANs) to impose fairness on the generated data, freeing
them from both disparate impact and disparate treatment. Given the model's
advantages and performance, we introduce a novel learning framework to transfer
a pre-trained FairGAN to other tasks. This reprogramming process has the goal
of maintaining the FairGAN's main targets of data utility, classification
utility, and data fairness, while widening its applicability and ease of use.
In this paper we present the technical extensions required to adapt the
original architecture to this new framework (and in particular the use of
Variational Auto-Encoders), and discuss the benefits, trade-offs, and
limitations of the new model.


title: Computing equivalence classes of discrete groups

abstract:
  In 2006 M. Conder published the list of orientably regular hypermaps up to
genus $101$ as well as the list of large groups acting on orientable surfaces
up to genus $101$. The regular hypermaps are closely related to actions of
triangle groups on closed orientable surfaces, whereas the large groups are
groups acting with either triangular or quadrangular signatures.
  This paper is aimed to at summarising the theoretical results needed to
obtain an algorithm classifying group actions on surfaces of small genera with
planar group signatures. From a theoretical point of view, the most difficult
part is to identify the equivalence classes of actions. A detailed analysis of
the action of the corresponding mapping class groups on the set of actions is
required. Using the algorithm implemented and employing a computer algebra
system, we were able to classify the actions of finite groups distinguished up
to equivalence for genera at most $7$.


title:
Averaging Spatio-temporal Signals using Optimal Transport and Soft
  Alignments

abstract:
  Several fields in science, from genomics to neuroimaging, require monitoring
populations (measures) that evolve with time. These complex datasets,
describing dynamics with both time and spatial components, pose new challenges
for data analysis. We propose in this work a new framework to carry out
averaging of these datasets, with the goal of synthesizing a representative
template trajectory from multiple trajectories. We show that this requires
addressing three sources of invariance: shifts in time, space, and total
population size (or mass/amplitude). Here we draw inspiration from dynamic time
warping (DTW), optimal transport (OT) theory and its unbalanced extension (UOT)
to propose a criterion that can address all three issues. This proposal
leverages a smooth formulation of DTW (Soft-DTW) that is shown to capture
temporal shifts, and UOT to handle both variations in space and size. Our
proposed loss can be used to define spatio-temporal barycenters as Fr\'echet
means. Using Fenchel duality, we show how these barycenters can be computed
efficiently, in parallel, via a novel variant of entropy-regularized debiased
UOT. Experiments on handwritten letters and brain imaging data confirm our
theoretical findings and illustrate the effectiveness of the proposed loss for
spatio-temporal data.


title: The Short Baseline Neutrino Program at Fermilab

abstract:
  The current status of the Short Baseline Neutrino (SBN) project at Fermilab
is reviewed. While the installation of SBND is still in progress, ICARUS has
taken its first neutrino data on beam: using both the Booster Neutrino Beam
(BNB) and the Neutrino at the Main Injector (NuMI) beam. MicroBooNE has
presently completed its data taking and is producing the world's first high
statistics results on $\nu$-Ar interactions, in both inclusive and exclusive
channels. In parallel, the unexpected MiniBooNE "low energy excess" is under
investigation, to search for sterile neutrinos. The physics potential for
sterile neutrino searches at SBN will be outlined, with emphasis on the
Neutrino-4 experiment and the possible ICARUS verification of this claim.


title:
Observation of resonance structures in $e^+e^-\to
  \pi^+\pi^-\psi_2(3823)$ and mass measurement of $\psi_2(3823)$

abstract:
  Using a data sample corresponding to an integrated luminosity of 11.3 $\rm
fb^{-1}$ collected at center-of-mass energies from
  $4.23$ to $4.70$~GeV with the BESIII detector, we measure the product of
  the $e^+e^-\to \pi^+\pi^-\psi_2(3823)$ cross section and the branching ratio
$\mathcal{B}[\psi_2(3823)\to \gamma\chi_{c1}]$.
  For the first time, resonance structures are observed in the cross section
line shape of $e^+e^-\to \pi^+\pi^-\psi_2(3823)$ with significance exceeding
five standard deviations. A fit to the $\sqrt{s}$-dependent cross section with
two coherent Breit-Wigner resonances yields
  $M(R_1)=4406.9\pm 17.2\pm 4.5$~MeV/$c^2$, $\Gamma(R_1)=128.1\pm 37.2\pm
2.3$~MeV,
  and $M(R_2)=4647.9\pm 8.6\pm 0.8$~MeV/$c^2$, $\Gamma(R_2)=33.1\pm 18.6\pm
4.1$~MeV. The mass of the $\psi_2(3823)$ state is measured as $(3823.12\pm
0.43\pm 0.13)$~MeV/$c^2$,
  which is the most precise measurement to date. Here, the first uncertainties
are statistical and the second systematic.
  An upper limit of $2.9$~MeV at the $90\%$ confidence level (C.L.) is
estimated for the width of $\psi_2(3823)$.
  The $\psi_2(3823)\to\gamma\chi_{c2}$ process is also investigated and an
upper limit for the relative branching ratio
  $R=\frac{\mathcal{B}[\psi_2(3823)\to\gamma\chi_{c2}]}{\mathcal{B}[\psi_2(3823)\to\gamma\chi_{c1}]}<0.51$
at the $90\%$ C.L.\ is given.


title: No Free Lunch Theorem for Security and Utility in Federated Learning

abstract:
  In a federated learning scenario where multiple parties jointly learn a model
from their respective data, there exist two conflicting goals for the choice of
appropriate algorithms. On one hand, private and sensitive training data must
be kept secure as much as possible in the presence of \textit{semi-honest}
partners, while on the other hand, a certain amount of information has to be
exchanged among different parties for the sake of learning utility. Such a
challenge calls for the privacy-preserving federated learning solution, which
maximizes the utility of the learned model and maintains a provable privacy
guarantee of participating parties' private data.
  This article illustrates a general framework that a) formulates the trade-off
between privacy loss and utility loss from a unified information-theoretic
point of view, and b) delineates quantitative bounds of privacy-utility
trade-off when different protection mechanisms including Randomization,
Sparsity, and Homomorphic Encryption are used. It was shown that in general
\textit{there is no free lunch for the privacy-utility trade-off} and one had
to trade the protection of privacy with a certain degree of degraded utility.
The quantitative analysis illustrated in this article may serve as the guidance
for the design of practical federated learning algorithms.


title:
Superconducting dome associated with the suppression and re-emergence of
  charge density wave states upon sulfur substitution in CuIr2Te4 chalcogenides

abstract:
  We report the path from the charge density wave (CDW)-bearing superconductor
CuIr2Te4 to the metal insulator transition (MIT)-bearing compound CuIr2S4 by
chemical alloying with the gradual substitution of S for Te. The evolution of
structural and physical properties of the CuIr2Te4-xSx polycrystalline system
with the doping range from 0 to 4 is systemically examined. The X-ray
diffraction (XRD) results imply CuIr2Te4-xSx in the range between 0 and 0.5
crystallizes in a NiAs defected trigonal structure, whereas it adapts to the
cubic spinel structure in the doping range from 3.6 to 4 and it is a mixed
phase in the doping range from 0.5 to 3.6. Unexpectedly, the resistivity and
magnetization measurements reveal that small-concentration S substitution for
Te can suppress the CDW transition, but it reappears around x = 0.2, and the
CDW transition temperature enhances clearly as x augments in the range from 0.2
to 0.5. Besides, the superconducting critical temperature (Tc) first increases
with S doping content and then decreases after reaching a maximum Tc = 2.82 K
for CuIr2Te3.85S0.15. MIT order has been observed in the spinel region from 3.6
to 4 associated with TMI increasing with x increasing. Finally, the rich
electronic phase diagram of temperature versus x for this CuIr2Te4-xSx system
is assembled, where the superconducting dome is associated with the suppression
and re-emergence of CDW as well as MIT states at the end upon sulfur
substitution in the CuIr2Te4-xSx chalcogenides.


title: ZIN: When and How to Learn Invariance by Environment Inference?

abstract:
  It is commonplace to encounter heterogeneous data, of which some aspects of
the data distribution may vary but the underlying causal mechanisms remain
constant. When data are divided into distinct environments according to the
heterogeneity, recent invariant learning methods have proposed to learn robust
and invariant models based on this environment partition. It is hence tempting
to utilize the inherent heterogeneity even when environment partition is not
provided. Unfortunately, in this work, we show that learning invariant features
under this circumstance is fundamentally impossible without further inductive
biases or additional information. Then, we propose a framework to jointly learn
environment partition and invariant representation, assisted by additional
auxiliary information. We derive sufficient and necessary conditions for our
framework to provably identify invariant features under a fairly general
setting. Experimental results on both synthetic and real world datasets
validate our analysis and demonstrate an improved performance of the proposed
framework over existing methods. Finally, our results also raise the need of
making the role of inductive biases more explicit in future works, when
considering learning invariant models without environment partition.