May 2022 arXiv papers — page 25

Tingting Liang, Hongwei Xie, Kaicheng Yu, Zhongyu Xia

Fusing the camera and LiDAR information has become a de-facto standard for 3D object detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to leverage the feature from the image space. However, people discovered that this underlying assumption makes the current fusion framework infeasible to produce any prediction when there

Gianluigi Lopardo, Frederic Precioso, Damien Garreau

Anchors (Ribeiro et al., 2018) is a post-hoc, rule-based interpretability method. For text data, it proposes to explain a decision by highlighting a small set of words (an anchor) such that the model to explain has similar outputs when they are present in a document. In this paper, we present the first theoretical analysis of Anchors, considering that the se

Ding Pei, Binbin Wang, Zishu Zhou, Zhihai He

Twisted van der Waals heterostructures have recently been proposed as a condensed-matter platform for realizing controllable quantum models due to the low-energy moir\'e bands with specific charge distributions in moir\'e superlattices. Here, combining angle-resolved photoemission spectroscopy with sub-micron spatial resolution ({\mu}-ARPES) and scanning tun

Hoseung Song, Hao Chen

Testing the equality in distributions of multiple samples is a common task in many fields. However, this problem for high-dimensional or non-Euclidean data has not been well explored. In this paper, we propose new nonparametric tests based on a similarity graph constructed on the pooled observations from multiple samples, and make use of both within-sample e

Dionysios Gakis, Dimitra Atri

The surface of Mars is bombarded by energetic charged particles of solar and cosmic origin with little shielding compared to Earth. As space agencies are planning for crewed missions to the red planet, a major concern is the impact of ionizing radiation on astronaut health. Keeping exposure below acceptable radiation dose levels is crucial for the health of

Ankit Anand

In this paper, the information paradox on lower-dimensional Gauss-Bonnet gravity known as a three-dimensional EGB black hole is studied using the quantum extremal island approach. For that, we connect an auxiliary flat bath system to this timelike singularity spacetime and estimate the entropy of hawking radiation in its asymptotic regions when gravity is we

Francesco Giuli

We present fits to determine Parton Distribution Functions using a diverse set of measurements from the ATLAS experiment at the LHC, including inclusive $W$ and $Z$ boson production, $t\bar{t}$ production, $W$+jets and $Z$+jets production, inclusive jet production and direct photon production. These ATLAS measurements are used in combination with deep-inelas

Andrey V. Panov

Metasurfaces have attracted a great deal of attention from researchers due to their prominent optical properties. In particular, metasurfaces may consist of structures possessing optical anapole resonances with strong field confinement and substantially suppressed scattering. As a result, such nanostructures display enhanced nonlinear optical properties. In

Fangxuan Liu, L. -M. Duan

Ising model is a widely studied class of models in quantum computation. In this paper we investigate the computational characteristics of the random field Ising model (RFIM) with long-range interactions that decays as an inverse polynomial of distance, which can be achieved in current ion trap system. We prove that for an RFIM with long-range interaction emb

Francesco Giuli

In this proceeding, the xFitter project is presented. xFitter is an open-source package that provides a framework for the determination of the parton distribution and fragmentation functions for many different kinds of analyses in Quantum Chromodynamics. xFitter version 2.2.0 has recently been released and offers an expanded set of tools and options. xFitter

Majid Ramezani, Mohammad-Reza Feizi-Derakhshi, Mohammad-Ali Balafar

Nowadays, a tremendous amount of human communications occur on Internet-based communication infrastructures, like social networks, email, forums, organizational communication platforms, etc. Indeed, the automatic prediction or assessment of individuals' personalities through their written or exchanged text would be advantageous to ameliorate their relationsh

Yucheng Ye, Shuqi Xu, Manuel Sebastian Mariani, Linyuan Lü

Recent strides in economic complexity have shown that the future economic development of nations can be predicted with a single "economic fitness" variable, which captures countries' competitiveness in international trade. The predictions by this low-dimensional approach could match or even outperform predictions based on much more sophisticated methods, suc

Yu-Sheng Wang, Kai-Bo Li, Chao-Feng Chang, Tan-Wen Lin

We report the generation of biphotons, with a temporal full width at the half maximum (FWHM) of 13.4$\pm$0.3 $\mu$s and a spectral FWHM of 50$\pm$1 kHz, via the process of spontaneous four-wave mixing. The temporal width is the longest, and the spectral linewidth is the narrowest up to date. This is also the first biphoton result that obtains a linewidth bel

Kentaro Kojima, Yuri Okubo

The Hubble constant estimated from the CMB measurements shows large disagreement with the locally measured value. This inconsistency is called the Hubble tension and is vastly studied in recent years. Early Dark Energy (EDE) gives a few percent contribution to the total energy density of the universe only at an epoch before the recombination, and it is consi

Christian Burkert, Jonathan Balack, Hannes Federrath

Case studies of application software data models indicate that timestamps are excessively used in connection with user activity. This contradicts the principle of data minimisation which demands a limitation to data necessary for a given purpose. Prior work has also identified common purposes of timestamps that can be realised by more privacy-preserving alte

Lu Yang, He Jiang, Qing Song, Jun Guo

The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the

Mansi Gupta, Aman Swaraj, Karan Verma

Background and Objective: During pandemics, the use of artificial intelligence (AI) approaches combined with biomedical science play a significant role in reducing the burden on the healthcare systems and physicians. The rapid increment in cases of COVID-19 has led to an increase in demand for hospital beds and other medical equipment. However, since medical

Ramandeep Kaur Bagri, Yihsu Chen

In the past decade, summer wildfires have become the norm in California, and the United States of America. These wildfires are caused due to variety of reasons. The state collects wildfire funds to help the impacted customers. However, the funds are eligible only under certain conditions and are collected uniformly throughout California. Therefore, the overa

Davidson Cheng

We propose a finite automaton-style solution concept for supergames. In our model, we define an equilibrium to be a cycle of state switches and a supergame to be an infinite walk on states of a finite stage game. We show that if the stage game is locally non-cooperative, and the utility function is monotonously decreasing as the number of defective agents in

Julia Kiseleva, Alexey Skrynnik, Artem Zholus, Shrestha Mohanty

Human intelligence has the remarkable ability to adapt to new tasks and environments quickly. Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions. To facilitate research in this direction, we propose IGLU: Interactive Groun

Haoxin Wang, BaekGyu Kim, Jiang Xie, Zhu Han

Today very few deep learning-based mobile augmented reality (MAR) applications are applied in mobile devices because they are significantly energy-guzzling. In this paper, we design an edge-based energy-aware MAR system that enables MAR devices to dynamically change their configurations, such as CPU frequency, computation model size, and image offloading fre

Hao Chen, Wenyuan Li, Song Chen, Zhenwei Shi

Supervised deep learning models depend on massive labeled data. Unfortunately, it is time-consuming and labor-intensive to collect and annotate bitemporal samples containing desired changes. Transfer learning from pre-trained models is effective to alleviate label insufficiency in remote sensing (RS) change detection (CD). We explore the use of semantic info

Sota Arakawa, Hidekazu Tanaka, Eiichiro Kokubo

Understanding the collisional behavior of dust aggregates consisting of submicron-sized grains is essential to unveiling how planetesimals formed in protoplanetary disks. It is known that the collisional behavior of individual dust particles strongly depends on the strength of viscous dissipation force; however, impacts of viscous dissipation on the collisio

Y. Dai, Y. W. Zhao, L. Ma, M. Tang

Experimental measurements show that the angular dependence of the anisotropic magnetoresistance (AMR) in L1$_0$ ordered FePt epitaxial films on the current orientation and magnetization direction is a superposition of the corresponding dependences of twofold and fourfold symmetries. The twofold AMR exhibits a strong dependence on the current orientation, whe

Takumi Fukunaga, Hiroyuki Kasai

The optimal transport (OT) problem has been used widely for machine learning. It is necessary for computation of an OT problem to solve linear programming with tight mass-conservation constraints. These constraints prevent its application to large-scale problems. To address this issue, loosening such constraints enables us to propose the relaxed-OT method us

Nick Zhang

Moore's Law has been used by semiconductor industry as predicative indicators of the industry and it has become a self-fulfilling prophecy. Now more people tend to agree that the original Moore's Law started to falter. This paper proposes a possible quantitative modification to Moore's Law. It can cover other derivative laws of Moore's Law as well. It intend

Manabu Hirano, Ryotaro Kobayashi

Since modern anti-virus software mainly depends on a signature-based static analysis, they are not suitable for coping with the rapid increase in malware variants. Moreover, even worse, many vulnerabilities of operating systems enable attackers to evade such protection mechanisms. We, therefore, developed a thin and lightweight live-forensic hypervisor to cr

Zhi Tian, Xiangxiang Chu, Xiaoming Wang, Xiaolin Wei

We present a simple yet effective fully convolutional one-stage 3D object detector for LiDAR point clouds of autonomous driving scenes, termed FCOS-LiDAR. Unlike the dominant methods that use the bird-eye view (BEV), our proposed detector detects objects from the range view (RV, a.k.a. range image) of the LiDAR points. Due to the range view's compactness and

Seok Bin Son, Soohyun Park, Haemin Lee, Joongheon Kim

In the literature of modern network security research, deriving effective and efficient course-of-action (COA) attach search methods are of interests in industry and academia. As the network size grows, the traditional COA attack search methods can suffer from the limitations to computing and communication resources. Therefore, various methods have been deve

Debapriyo Chowdhury, Parthiv Haldar, Ahmadullah Zahed

This paper discusses the locality and analyticity of the crossing symmetric dispersion relation (CSDR). Imposing locality constraints on the CSDR gives rise to a local and fully crossing symmetric expansion of scattering amplitudes, dubbed as Feynman block expansion. A general formula is provided for the contact terms that emerge from the expansion. The anal

Q. Yu, Y. Zhang, Q. Kong, S. Kawata

All-optical Compton scattering is a remarkable method of generating high-quality $\gamma$ radiation source. It is easier achieved in experiment by employing a pulse based on laser wakefield accelerator. The driving laser is backward reflected when wakefield acceleration stage is over and thus it naturally collides with energetic electrons. To increase reflec

Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado

The ability to accurately model the fitness landscape of protein sequences is critical to a wide range of applications, from quantifying the effects of human variants on disease likelihood, to predicting immune-escape mutations in viruses and designing novel biotherapeutic proteins. Deep generative models of protein sequences trained on multiple sequence ali

S. A. Kadam, B. Mishra, S. K. Tripathy

In this paper, we have explored the field equations of $f(T,B)$ gravity and determined the dynamical parameters with the hyperbolic function of Hubble parameter. The accelerating behavior has been observed and the behavior of equation of state parameter indicates $\Lambda CDM$ model at late time. The role of model parameters in assessing the accelerating beh

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

The singly Cabibbo-suppressed decay $D_{s}^{+} \to K^+\pi^{+}\pi^{-}\pi^{0}$ is observed, using a data set corresponding to an integrated luminosity of 6.32 $\rm fb^{-1}$ recorded by the BESIII detector at the centre-of-mass energies between 4.178 and 4.226 GeV. The first amplitude analysis of $D_{s}^{+} \to K^+\pi^{+}\pi^{-}\pi^{0}$ reveals the sub-structur

Yasamin Kowsari, Sanaz Nakhodchi, Davoud Gholamiangonabadi

Cancer detection is one of the key research topics in the medical field. Accurate detection of different cancer types is valuable in providing better treatment facilities and risk minimization for patients. This paper deals with the classification problem of human cancer diseases by using gene expression data. It is presented a new methodology to analyze mic

Haruo Hosoya

Data of general object images have two most common structures: (1) each object of a given shape can be rendered in multiple different views, and (2) shapes of objects can be categorized in such a way that the diversity of shapes is much larger across categories than within a category. Existing deep generative models can typically capture either structure, bu

Evan R. Antoniuk, Peggy Li, Bhavya Kailkhura, Anna M. Hiszpanski

One of the grand challenges of utilizing machine learning for the discovery of innovative new polymers lies in the difficulty of accurately representing the complex structures of polymeric materials. Although a wide array of hand-designed polymer representations have been explored, there has yet to be an ideal solution for how to capture the periodicity of p

Chongjun Ouyang, Yuanwei Liu, Hongwen Yang

This paper analyzes the performance of a multiuser integrated sensing and communications (ISAC) system, where nonorthogonal multiple access (NOMA) is exploited to mitigate inter-user interference. Closed-form expressions are derived to evaluate the outage probability, ergodic communication rate, and sensing rate. Furthermore, asymptotic analyses are carried

Yashish M. Siriwardena, Ganesh Sivaraman, Carol Espy-Wilson

Multi-task learning (MTL) frameworks have proven to be effective in diverse speech related tasks like automatic speech recognition (ASR) and speech emotion recognition. This paper proposes a MTL framework to perform acoustic-to-articulatory speech inversion by simultaneously learning an acoustic to phoneme mapping as a shared task. We use the Haskins Product

Eda Okur, Saurav Sahay, Lama Nachman

Intelligent systems designed for play-based interactions should be contextually aware of the users and their surroundings. Spoken Dialogue Systems (SDS) are critical for these interactive agents to carry out effective goal-oriented communication with users in real-time. For the real-world (i.e., in-the-wild) deployment of such conversational agents, improvin

Dmitrii Avdiukhin, Grigory Yaroslavtsev

We give the first polynomial time algorithms for escaping from high-dimensional saddle points under a moderate number of constraints. Given gradient access to a smooth function $f \colon \mathbb R^d \to \mathbb R$ we show that (noisy) gradient descent methods can escape from saddle points under a logarithmic number of inequality constraints. This constitutes

Hossein Teimoori Faal

In this short note, we first associate a new simple undirected graph with a given word over an ordered alphabet of $n$-letters. We will call it the Lyndon graph of that word. Then, we introduce the concept of the Lyndon-word representable graph as a graph isomorphic to a Lyndon graph of some word. Then, we introduce the generalized Stirling cycle number $S(N

Tomoya Machide

The formal multiple zeta space we consider with a computer is an $\mathbb{F}_2$-vector space generated by $2^{k-2}$ formal symbols for a given weight $k$, where the symbols satisfy binary extended double shuffle relations. Up to weight $k=22$, we compute the dimensions of the formal multiple zeta spaces, and verify the dimension conjecture on original extend

Jingjun Yi, Beichen Zhou

Multiple instance learning is qualified for many pattern recognition tasks with weakly annotated data. The combination of artificial neural network and multiple instance learning offers an end-to-end solution and has been widely utilized. However, challenges remain in two-folds. Firstly, current MIL pooling operators are usually pre-defined and lack flexibil

Ying Li, Gang Xu

By means of ternary chemical potential phase diagram and phonon spectrum calculations, we propose that $M$Tl$_4$Te$_3$ ($M$ = Cd, Hg), the derivatives of Tl$_5$Te$_3$, are thermodynamically and dynamically stable in the body centered tetragonal crystal structure with $I$4/$mcm$ symmetry. Our electronic structures calculations confirm that a robust $s$-$p$ ba

Yicheng Wang, Xiaotian Han, Chia-Yuan Chang, Daochen Zha

Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bringing together the power of deep learning to bear on scientific computation. In forward modeling problems, PINNs are meshless partial differential equation (PDE) solvers that can handle irregular, high-dimensional physical domains. Naturally, the neural archit

Leonid Kitchatinov

Observations of long-term north-south asymmetry in solar activity demand the equator-symmetric (quadrupolar) mode be present in the solar magnetic field in line with the dominant antisymmetric (dipolar) mode. This paper proposes treating the sunspot area as a proxy for subsurface toroidal magnetic flux to infer the quadrupolar mode of the solar dynamo from s

Sihan Zeng, Thinh T. Doan, Justin Romberg

We study the problem of finding the Nash equilibrium in a two-player zero-sum Markov game. Due to its formulation as a minimax optimization program, a natural approach to solve the problem is to perform gradient descent/ascent with respect to each player in an alternating fashion. However, due to the non-convexity/non-concavity of the underlying objective fu

Min Li, Zhengyuan Shi, Qiuxia Lai, Sadaf Khan

We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. Unlike existing solutions trained on random SAT instances with relatively weak supervision, we propose applying the knowledge of the well-developed electronic design automation (EDA) field for SAT solving. Specifically, we first resort to logic synthesis a

Jingjun Yi, Beichen Zhou

Aerial scenes are more complicated in terms of object distribution and spatial arrangement than natural scenes due to the bird view, and thus remain challenging to learn discriminative scene representation. Recent solutions design \textit{local semantic descriptors} so that region of interests (RoIs) can be properly highlighted. However, each local descripto

Giovanni De Toni, Paolo Viappiani, Stefano Teso, Bruno Lepri

Algorithmic Recourse (AR) is the problem of computing a sequence of actions that -- once performed by a user -- overturns an undesirable machine decision. It is paramount that the sequence of actions does not require too much effort for users to implement. Yet, most approaches to AR assume that actions cost the same for all users, and thus may recommend unfa

Steven Blaber, David A. Sivak

Quadratic trapping potentials are widely used to experimentally probe biopolymers and molecular machines and drive transitions in steered molecular-dynamics simulations. Approximating energy landscapes as locally quadratic, we design multidimensional trapping protocols that minimize dissipation. The designed protocols are easily solvable and applicable to a

Ali Seyfi, Jean-Francois Rajotte, Raymond T. Ng

Generating multivariate time series is a promising approach for sharing sensitive data in many medical, financial, and IoT applications. A common type of multivariate time series originates from a single source such as the biometric measurements from a medical patient. This leads to complex dynamical patterns between individual time series that are hard to l

Christina Lu, Jackie Kay, Kevin R. McKee

Most machine learning systems that interact with humans construct some notion of a person's "identity," yet the default paradigm in AI research envisions identity with essential attributes that are discrete and static. In stark contrast, strands of thought within critical theory present a conception of identity as malleable and constructed entirely through i

Bin Wang

In this note, we prove that for every $0<\sigma<1$, there exists a smooth complete hypersurface $\Sigma$ in $\mathbb{H}^{n+1}$ with prescribed asymptotic boundary $\partial \Sigma=\Gamma$ at infinity, whose principal curvatures $\kappa=(\kappa_1,\ldots,\kappa_n)$ lie in a general cone $K$ and satisfy $f(\kappa)=\sigma$ at each point of $\Sigma$. Previously,

Yuxi Cai, Huicheng Lai

Image super-resolution reconstruction achieves better results than traditional methods with the help of the powerful nonlinear representation ability of convolution neural network. However, some existing algorithms also have some problems, such as insufficient utilization of phased features, ignoring the importance of early phased feature fusion to improve n

Tian Yu, Francesco Marmo, Pasquale Cesarano, Sigrid Adriaenssens

Creases are purposely introduced to thin structures for designing deployable origami, artistic geometries, and functional structures with tunable nonlinear mechanics. Modeling the mechanics of creased structures is challenging because creases introduce geometric discontinuity and often have complex mechanical responses due to the local material damage. In th

Feifan He, Baocheng Zhang

We investigate the response of a model gravitational wave detector consisting of two particles to the quantized cylindrical gravitational waves and obtain a relation between the standard deviation of the distance between two particles and the distance from the source to the detector. It is found that the quantum effect carried by the cylindrical gravitationa

Tengkuo Zhu, Stephen D. Boyles, Avinash Unnikrishnan

In (Zhu et al., 2022), it proposes an electric vehicle traveling salesman problem with drone while assuming that the electric vehicle (EV) is a battery-electric vehicle whose energy could be refreshed in a battery swap station in minutes. In this paper, we extend the work in (Zhu et al., 2022) by relaxing the fixed-time-full-charge assumption, assuming that

Yuefeng Si, Yingying Zhang, Yuxi Cai, Chunling Liu

Most currently used tensor regression models for high-dimensional data are based on Tucker decomposition, which has good properties but loses its efficiency in compressing tensors very quickly as the order of tensors increases, say greater than four or five. However, for the simplest tensor autoregression in handling time series data, its coefficient tensor

Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang

Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies upon for making predictions. Though various algorithms are proposed, most of them formalize this task by searchin

Daiki Ueno, Ryutaroh Matsumoto

In the previous research by Grassl, Huber and Winter, they proved a theorem which can make entanglement-assisted quantum error-correcting codes (EAQECC) from general quantum error-correcting codes (QECC). In this paper, we prove that the shortened EAQECC is a stabilizer code if the original EAQECC is a stabilizer code.

Wenhao Luo, Yee Hui Lee, Lai Fern Ow, Mohamed Lokman Mohd Yusof

Tree roots detection is a popular application of the Ground-penetrating radar (GPR). Normally, the ground surface above the tree roots is assumed to be flat, and standard processing methods based on hyperbolic fitting are applied to the hyperbolae reflection patterns of tree roots for detection purposes. When the surface of the land is undulating (not flat),

Tingting Liu, Chengyu Wang, Cen Chen, Ming Gao

Programming-based Pre-trained Language Models (PPLMs) such as CodeBERT have achieved great success in many downstream code-related tasks. Since the memory and computational complexity of self-attention in the Transformer grow quadratically with the sequence length, PPLMs typically limit the code length to 512. However, codes in real-world applications are ge

Greg Friedman, Efton Park

This paper continues the authors' work on the question of unitary equivalence of matrices with entries in the complex-valued functions of a topological space (matrices over spaces). Specifically, we here consider the question of unitary equivalence for pairs of normal matrices over a space that share a common characteristic polynomial that can be globally fa

Yunuo Xiong, Hongwei Xiong

By generalizing the recently developed path integral molecular dynamics for identical bosons and fermions, we consider the finite-temperature thermodynamic properties of fictitious identical particles with a real parameter $\xi$ interpolating continuously between bosons ($\xi=1$) and fermions ($\xi=-1$). Through general analysis and numerical experiments we

Yushi Cao, Zhiming Li, Tianpei Yang, Hao Zhang

Despite achieving superior performance in human-level control problems, unlike humans, deep reinforcement learning (DRL) lacks high-order intelligence (e.g., logic deduction and reuse), thus it behaves ineffectively than humans regarding learning and generalization in complex problems. Previous works attempt to directly synthesize a white-box logic program a

Zachary Ferguson, Pranav Jain, Denis Zorin, Teseo Schneider

High-order bases provide major advantages over linear ones in terms of efficiency, as they provide (for the same physical model) higher accuracy for the same running time, and reliability, as they are less affected by locking artifacts and mesh quality. Thus, we introduce a high-order finite element (FE) formulation (high-order bases) for elastodynamic simul

Wenceslao Shaw Cortez, Xiao Tan, Dimos V. Dimarogonas

We propose a novel (Type-II) zeroing control barrier function (ZCBF) for safety-critical control, which generalizes the original ZCBF approach. Our method allows for applications to a larger class of systems (e.g. passivity-based) while still ensuring robustness, for which the construction of conventional ZCBFs is difficult. We also propose a locally Lipschi

Omar Alrabiah, Eshan Chattopadhyay, Jesse Goodman, Xin Li

We continue a line of work on extracting random bits from weak sources that are generated by simple processes. We focus on the model of locally samplable sources, where each bit in the source depends on a small number of (hidden) uniformly random input bits. Also known as local sources, this model was introduced by De and Watson (TOCT 2012) and Viola (SICOMP

Yihao Ding, Zhe Huang, Runlin Wang, Yanhang Zhang

We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand the document visual question answering tasks. The V-Doc supports generating and using both extractive and abstractive question-answer pairs using documents images. The extractive

Hongzheng Yang, Cheng Chen, Meirui Jiang, Quande Liu

Test-time adaptation (TTA) has increasingly been an important topic to efficiently tackle the cross-domain distribution shift at test time for medical images from different institutions. Previous TTA methods have a common limitation of using a fixed learning rate for all the test samples. Such a practice would be sub-optimal for TTA, because test data may ar

Simran Arora, Christopher Ré

A key promise of machine learning is the ability to assist users with personal tasks. Because the personal context required to make accurate predictions is often sensitive, we require systems that protect privacy. A gold standard privacy-preserving system will satisfy perfect secrecy, meaning that interactions with the system provably reveal no private infor

Alessandra Costantini, Louiza Fouli, Jooyoun Hong

We introduce the notion of residual intersections of modules and prove their existence. We show that projective dimension one modules have Cohen-Macaulay residual intersections, namely they satisfy the relevant Artin-Nagata property. We then establish a formula for the core of orientable modules satisfying certain homological conditions, extending previous r

Tao Zhuo, Mohan Kankanhalli

As a step towards improving the abstract reasoning capability of machines, we aim to solve Raven's Progressive Matrices (RPM) with neural networks, since solving RPM puzzles is highly correlated with human intelligence. Unlike previous methods that use auxiliary annotations or assume hidden rules to produce appropriate feature representation, we only use the

Felix Schwock, Julien Bloch, Les Atlas, Shima Abadi

Correlating neural communication in brain networks with behavior and cognition can provide fundamental insights into the functionality of both healthy and diseased brains. We demonstrate how communication in the brain can be estimated from recorded neural activity using concepts from graph signal processing. The communication is modeled as a flow signals on

Wei Qiu, Weixun Wang, Rundong Wang, Bo An

We investigate model-free multi-agent reinforcement learning (MARL) in environments where off-beat actions are prevalent, i.e., all actions have pre-set execution durations. During execution durations, the environment changes are influenced by, but not synchronised with, action execution. Such a setting is ubiquitous in many real-world problems. However, mos

Seungjae Jung, Kyung-Min Kim

Survival analysis appears in various fields such as medicine, economics, engineering, and business. Recent studies showed that the Ordinary Differential Equation (ODE) modeling framework unifies many existing survival models while the framework is flexible and widely applicable. However, naively applying the ODE framework to survival analysis problems may mo

Chengqian Xian, Camila de Souza, John Jewell, Ronaldo Dias

Functional data analysis deals with data recorded densely over time (or any other continuum) with one or more observed curves per subject. Conceptually, functional data are continuously defined, but in practice, they are usually observed at discrete points. Among different kinds of functional data analyses, clustering analysis aims to determine underlying gr

Zachary Hartman, Sébastien Lépine, Ilija Medan

Over the past decade, the number of known wide binary systems has exponentially expanded thanks to the release of data from the Gaia Mission. Some of these wide binary systems are actually higher-order multiples, where one of the components is an unresolved binary itself. One way to search for these systems is by identifying overluminous components in the sy

Makoto Saito, Junya Yamauchi, Tesshu Fujinami, Marco Omainska

In this paper, we propose a control law for camera-equipped drone networks to pursue a target rigid body with unknown motion based on distributed Gaussian process. First, we consider the situation where each drone has its own dataset, and learned the unknown target motion in a distributed manner. Second, we propose a control law using the distributed Gaussia

Hehe Fan, Xin Yu, Yuhang Ding, Yi Yang

Point cloud sequences are irregular and unordered in the spatial dimension while exhibiting regularities and order in the temporal dimension. Therefore, existing grid based convolutions for conventional video processing cannot be directly applied to spatio-temporal modeling of raw point cloud sequences. In this paper, we propose a point spatio-temporal (PST)

H. Sano, H. Yamaguchi, M. Aruga, Y. Fukui

We report new HI observations of the Type Ia supernova remnant SN 1006 using the Australia Telescope Compact Array with an angular resolution of $4.5' \times 1.4'$ ($\sim$2 pc at the assumed SNR distance of 2.2 kpc). We find an expanding gas motion in position-velocity diagrams of HI with an expansion velocity of $\sim$4 km s$^{-1}$ and a mass of $\sim$1000

Antonio Robles-Kelly, Asef Nazari

In this paper, we incorporate the Barzilai-Borwein step size into gradient descent methods used to train deep networks. This allows us to adapt the learning rate using a two-point approximation to the secant equation which quasi-Newton methods are based upon. Moreover, the adaptive learning rate method presented here is quite general in nature and can be app

Michele Delli Veneri, Rafael S. de Souza, Alberto Krone-Martins, Emille E. O. Ishida

We present a citation pattern analysis between astronomical papers and 13 other disciplines, based on the arXiv database over the past decade ($2010 - 2020$). We analyze 12,600 astronomical papers citing over 14,531 unique publications outside astronomy. Two striking patterns are unraveled. First, general relativity recently became the most cited field by as

Jason M. Altschuler, Kunal Talwar

A central issue in machine learning is how to train models on sensitive user data. Industry has widely adopted a simple algorithm: Stochastic Gradient Descent with noise (a.k.a. Stochastic Gradient Langevin Dynamics). However, foundational theoretical questions about this algorithm's privacy loss remain open -- even in the seemingly simple setting of smooth

Xiyang Liu, Weihao Kong, Prateek Jain, Sewoong Oh

We study the canonical statistical task of computing the principal component from $n$ i.i.d.~data in $d$ dimensions under $(\varepsilon,\delta)$-differential privacy. Although extensively studied in literature, existing solutions fall short on two key aspects: ($i$) even for Gaussian data, existing private algorithms require the number of samples $n$ to scal

Minghuan Tan

This paper describes an approach to detect idiomaticity only from the contextualized representation of a MWE over multilingual pretrained language models. Our experiments find that larger models are usually more effective in idiomaticity detection. However, using a higher layer of the model may not guarantee a better performance. In multilingual scenarios, t

Shucheng Yang, Xiaoping Gao, Ruoting Yang, Jie Ren

The Single Flux Quantum (SFQ) logic family is a novel digital logic as it provides ultra-fast and energy-efficient circuits. For large-scale SFQ circuit design, specialized electronic design automation (EDA) tools are required due to the differences in logic type, timing constraints and circuit architecture, in contrast to the CMOS logic. In order to improve

Guodong Wang

In this paper, we consider a constrained maximization problem related to planar vortex pairs with prescribed impulse. We prove existence, stability and asymptotic behavior for the maximizers, hence obtain a family of stable traveling vortex pairs approaching a pair of point vortices with equal magnitude and opposite signs. As a corollary, we get fine asympto

Guanhua Ye, Tong Chen, Yawen Li, Lizhen Cui

In this paper, we propose a Similarity-Quality-based Messenger Distillation (SQMD) framework for heterogeneous asynchronous on-device healthcare analytics. By introducing a preloaded reference dataset, SQMD enables all participant devices to distill knowledge from peers via messengers (i.e., the soft labels of the reference dataset generated by clients) with

Yusheng Luo, Insung Park

This paper studies polynomials with core entropy zero. We give several characterizations of polynomials with core entropy zero. In particular, we show that a degree d post-critically finite polynomial f has core entropy zero if and only if f is in the degree d main molecule. The characterizations define several quantities which measure the complexities of po

Taishu Murakami, Shunsuke Sakuragi, Hiroshi Deguchi, Masaru Nakata

Analyzing the SARS-CoV-2 pandemic outbreak based on actual data while reflecting the characteristics of the real city provides beneficial information for taking reasonable infection control measures in the future. We demonstrate agent-based modeling for Tokyo based on GPS information and official national statistics and perform a spatiotemporal analysis of t

Seyed Kamyar Seyed Ghasemipour, Shixiang Shane Gu, Ofir Nachum

Motivated by the success of ensembles for uncertainty estimation in supervised learning, we take a renewed look at how ensembles of $Q$-functions can be leveraged as the primary source of pessimism for offline reinforcement learning (RL). We begin by identifying a critical flaw in a popular algorithmic choice used by many ensemble-based RL algorithms, namely

Kento Hasegawa, Seira Hidano, Kohei Nozawa, Shinsaku Kiyomoto

Hardware Trojans (HTs) have become a serious problem, and extermination of them is strongly required for enhancing the security and safety of integrated circuits. An effective solution is to identify HTs at the gate level via machine learning techniques. However, machine learning has specific vulnerabilities, such as adversarial examples. In reality, it has

Francisco Bento Lustosa, Nelson Pinto-Neto, Antony Valentini

In the context of de Broglie-Bohm pilot-wave theory, violations of the Born rule are allowed and can be considered as describing nonequilibrium distributions. We study the effects of interactions on quantum relaxation towards equilibrium for a system of one-dimensional coupled harmonic oscillators. We show by numerical simulations that interactions can delay

Jingwei Guo, Kaizhu Huang, Rui Zhang, Xinping Yi

While Graph Neural Networks (GNNs) have achieved enormous success in multiple graph analytical tasks, modern variants mostly rely on the strong inductive bias of homophily. However, real-world networks typically exhibit both homophilic and heterophilic linking patterns, wherein adjacent nodes may share dissimilar attributes and distinct labels. Therefore, GN

Shiping Cao, Hua Qiu, Yizhou Wang

We construct symmetric self-similar diffusions with sub-Gaussian heat kernel estimates on two types of polygon carpets, which are natural generalizations of planner Sierpinski carpets (SC). The first ones are called perfect polygon carpets that are natural analogs of SC in that any intersection cells are either side-to-side or point-to-point. The second ones

Dongjun Kim, Byeonghu Na, Se Jung Kwon, Dongsoo Lee

Whereas diverse variations of diffusion models exist, extending the linear diffusion into a nonlinear diffusion process is investigated by very few works. The nonlinearity effect has been hardly understood, but intuitively, there would be promising diffusion patterns to efficiently train the generative distribution towards the data distribution. This paper i

Sabina J. Sloman, Daniel M. Oppenheimer, Stephen B. Broomell, Cosma Rohilla Shalizi

Bayesian adaptive experimental design is a form of active learning, which chooses samples to maximize the information they give about uncertain parameters. Prior work has shown that other forms of active learning can suffer from active learning bias, where unrepresentative sampling leads to inconsistent parameter estimates. We show that active learning bias