April 2023 arXiv papers — page 41

Jiahao Liu, Dongsheng Li, Hansu Gu, Tun Lu

In dynamic interaction graphs, user-item interactions usually follow heterogeneous patterns, represented by different structural information, such as user-item co-occurrence, sequential information of user interactions and the transition probabilities of item pairs. However, the existing methods cannot simultaneously leverage all three structural information

Jake Buzhardt, Prashanth Chivkula, Phanindra Tallapragada

In this paper, we present a novel rolling, jumping robot. The robot consists of a driven pendulum mounted to a wheel in a compact, lightweight, 3D printed design. We show that by driving the pendulum to shift the robot's weight distribution, the robot is able to obtain significant rolling speed, achieve jumps of up to 2.5 body lengths vertically, and clear h

Haodong Feng, Yue Wang, Hui Xiang, Zhiyang Jin

Deep reinforcement learning (DRL) for fluidic pinball, three individually rotating cylinders in the uniform flow arranged in an equilaterally triangular configuration, can learn the efficient flow control strategies due to the validity of self-learning and data-driven state estimation for complex fluid dynamic problems. In this work, we present a DRL-based r

Katsuhiko Inagaki, Keiji Nakatsugawa, Satoshi Tanda

We studied the lock-in transition of charge-density waves of one-dimensional conductors. Though this phenomenon has been known for decades, there are still discrepancies between the theories and the experiments. We focused on the pioneering study of this phenomenon by McMillan and revisited, in particular, his numerical calculations. We first reproduced his

Rongfeng Pan, Jianzong Wang, Lingwei Kong, Zhangcheng Huang

Text summarization is essential for information aggregation and demands large amounts of training data. However, concerns about data privacy and security limit data collection and model training. To eliminate this concern, we propose a federated learning text summarization scheme, which allows users to share the global model in a cooperative learning manner

Yawen Lu, Qifan Wang, Siqi Ma, Tong Geng

Optical flow is an indispensable building block for various important computer vision tasks, including motion estimation, object tracking, and disparity measurement. In this work, we propose TransFlow, a pure transformer architecture for optical flow estimation. Compared to dominant CNN-based methods, TransFlow demonstrates three advantages. First, it provid

Menglan Liao

In this paper, a class of variable-coefficient wave equations equipped with time-dependent damping and the nonlinear source is considered. We show that the total energy of the system decays to zero with an explicit and precise decay rate estimate under different assumptions on the feedback with the help of the method of weighted energy integral.

Xin Jin, Wu Zhou, Jinyu Wang, Duo Xu

Although computational aesthetics evaluation has made certain achievements in many fields, its research of music performance remains to be explored. At present, subjective evaluation is still a ultimate method of music aesthetics research, but it will consume a lot of human and material resources. In addition, the music performance generated by AI is still m

Souvika Sarkar, Mohammad Fakhruddin Babar, Md Mahadi Hassan, Monowar Hasan

This paper presents a performance study of transformer language models under different hardware configurations and accuracy requirements and derives empirical observations about these resource/accuracy trade-offs. In particular, we study how the most commonly used BERT-based language models (viz., BERT, RoBERTa, DistilBERT, and TinyBERT) perform on embedded

Xiaozhe Gu, Zixun Zhang, Yuncheng Jiang, Tao Luo

Despite the simplicity, stochastic gradient descent (SGD)-like algorithms are successful in training deep neural networks (DNNs). Among various attempts to improve SGD, weight averaging (WA), which averages the weights of multiple models, has recently received much attention in the literature. Broadly, WA falls into two categories: 1) online WA, which averag

Junjie Zhao

The study of US-China relations has always been a crucial topic in our economic development [4][5][7], and the US presidential election plays an integral role in shaping these relations. The presidential election is held every four years, and it is crucial to assess the impact of the 2020 election on China to prepare for the potential effects of the 2024 US

Yicheng Fan, Dana Alon, Jingyue Shen, Daiyi Peng

Neural Architecture Search (NAS) has become a popular method for discovering effective model architectures, especially for target hardware. As such, NAS methods that find optimal architectures under constraints are essential. In our paper, we propose LayerNAS to address the challenge of multi-objective NAS by transforming it into a combinatorial optimization

Alexander Blokh, Lex Oversteegen, Vladlen Timorin, Yimin Wang

We describe a locally connected model of the boundary of the cubic connectedness locus. The model is obtained by constructing a decomposition of the space of critical portraits and collapsing elements of the decomposition into points. This model is similar to a quotient of the quadratic combinatorial locus where all baby Mandelbrot sets are collapsed to poin

Richard Canary, Tengren Zhang, Andrew Zimmer

We study Patterson-Sullivan measures for a class of discrete subgroups of higher rank semisimple Lie groups, called transverse groups, whose limit set is well-defined and transverse in a partial flag variety. This class of groups includes both Anosov and relatively Anosov groups, as well as all discrete subgroups of rank one Lie groups. We prove an analogue

Rongen Dong, Hangjia He, Feng Shu, Qi Zhang

Recently, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have been introduced into wireless communication systems to enhance the performance of air-ground transmission. To make a good balance between performance, cost, and power consumption, a hybrid-IRS-and-UAV-assisted directional modulation (DM) network is investigated in this pape

Yue Hu, Yuhang Zhang, Yanbing Wang, Daniel Work

With the rapid development of Internet of Things technologies, the next generation traffic monitoring infrastructures are connected via the web, to aid traffic data collection and intelligent traffic management. One of the most important tasks in traffic is anomaly detection, since abnormal drivers can reduce traffic efficiency and cause safety issues. This

Xiaomeng Zhao, Ganghua Yuan

In this paper, we study the partial data inverse boundary value problem for the Schrodinger operator at a high frequency k>=1 in a bounded domain with smooth boundary in Rn, n>=3. Assuming that the potential is known in a neighborhood of the boundary, we obtain the logarithmic stability when both Dirichlet data and Neumann data are taken on arbitrary open su

Zhepeng Wang, Jinyang Li, Zhirui Hu, Blake Gage

Security has always been a critical issue in machine learning (ML) applications. Due to the high cost of model training -- such as collecting relevant samples, labeling data, and consuming computing power -- model-stealing attack is one of the most fundamental but vitally important issues. When it comes to quantum computing, such a quantum machine learning (

Yuhua Jiang, Feifei Gao, Yimin Liu, Shi Jin

Near field computational imaging has been recognized as a promising technique for non-destructive and highly accurate detection of the target. Meanwhile, reconfigurable intelligent surface (RIS) can flexibly control the scattered electromagnetic (EM) fields for sensing the target and can thus help computational imaging in the near field. In this paper, we pr

Jiayu Zheng, Tianhong Zhang, Yu Wenjing, Weiqin Zhou

In recent years, the end-to-end (E2E) scheme based on deep learning (DL) has been proposed as a potential scheme to jointly optimize the encoder and the decoder parameters of the optical communication system. Compared with conventional deep neural network (DNN) adopted in E2E design, center-oriented Gated Recurrent Unit (Co-GRU) network has the ability to le

Oliver Pechenik, Matthew Satriano

Symmetric function theory is a key ingredient in the Schubert calculus of Grassmannians. Quasisymmetric functions are analogues that are similarly central to algebraic combinatorics, but for which the associated geometry is poorly developed. Baker and Richter (2008) showed that $\textrm{QSym}$ manifests topologically as the cohomology ring of the loop suspen

Smrithi Ajit, Varsha R Mouli, Skylar Knickerbocker, Jonathan S. Wood

Traffic congestion caused by non-recurring incidents such as vehicle crashes and debris is a key issue for Traffic Management Centers (TMCs). Clearing incidents in a timely manner is essential for improving safety and reducing delays and emissions for the traveling public. However, TMCs and other responders face a challenge in predicting the duration of inci

Kiyokazu Nagatomo, Yuichi Sakai

We show that modular forms of fractional weights on principal congruence subgroups of odd levels, which are found by T. Ibukiyama, naturally appear as characters being multiplied $\eta^{c_{\text{eff}}}$ of the so-called minimal models of type $(2, p)$, where $c_{\text{eff}}$ is the effective central charge of a minimal model. Using this fact and modular inva

Sergey Fomin, Scott Neville

A mutation cycle is a cycle in a graph whose vertices are labeled by the quivers in a given mutation class and whose edges correspond to single mutations. For any fixed $n\ge 4$, we describe arbitrarily long mutation cycles involving $n$-vertex quivers. Each of these mutation cycles allows for an arbitrary choice of $n \choose 2$ positive integer parameters.

Ziwei Wang, Jiabin Wu

We study preference evolution when agents choose whom to interact with. In the short run, subjective preferences influence both partner choice and strategic behavior, which affect their material payoffs. These payoffs, in turn, determine how preferences evolve in the long run. To model this "match-to-interact" process, we combine stable matching with

David Hason Rudd, Huan Huo, Guandong Xu

Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Customer acquisition cost can be five to six times that of customer retention, hence investing in customers with churn risk is wise. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future

Hari Bhandari, Rebecca L. Dally, Peter E. Siegfried, Resham B. Regmi

Kagome lattice magnets are an interesting class of materials as they can host topological properties in their magnetic and electronic structures. YMn6Sn6 is one such compound in which a series of competing magnetic phases is stabilized by an applied magnetic field, and both an enigmatic topological Hall effect and a Dirac crossing close to the Fermi energy h

Shira Wein, Nathan Schneider

Translated texts bear several hallmarks distinct from texts originating in the language. Though individual translated texts are often fluent and preserve meaning, at a large scale, translated texts have statistical tendencies which distinguish them from text originally written in the language ("translationese") and can affect model performance. We frame the

Ehsan Malayjerdi, Gokhan Alcan, Eshagh Kargar, Hatem Darweesh

Autonomous vehicles are a growing technology that aims to enhance safety, accessibility, efficiency, and convenience through autonomous maneuvers ranging from lane change to overtaking. Overtaking is one of the most challenging maneuvers for autonomous vehicles, and current techniques for autonomous overtaking are limited to simple situations. This paper stu

Chao Zhang, Zebang Shen, Hui Qian, Tengfei Zhou

Alternating Direction Method of Multipliers (ADMM) is a popular method for solving large-scale Machine Learning problems. Stochastic ADMM was proposed to reduce the per iteration computational complexity, which is more suitable for big data problems. Recently, variance reduction techniques have been integrated with stochastic ADMM in order to get a faster co

Mohammed Nechba, Mustapha Ouyaaz, Abdellatif El Afia, Mohammed El Arrouchi

This paper presents a comprehensive introduction to the Hausdorff measure, a fundamental tool in fractal geometry and geometric measure theory. We begin by defining the Hausdorff outer measure on subsets of metric spaces, followed by a discussion of Caratheodory's criterion, which characterizes measurable sets. From this foundation, we construct the Hausdorf

Yaobin Xu, Hao Jia, Peiyuan Gao, Diego E. Galvez-Aranda

Solid electrolyte interphase (SEI), a thin layer that dynamically forms between active electrode and electrolyte during battery operation, critically governs the performance of rechargeable batteries1-5. An ideal SEI is expected to be electrically insulative to prevent persistently parasitic reactions between the electrode and the electrolyte, while ionicall

Alejandro Casallas Lagos, Javier M. Antelis, Claudia Moreno, Michele Zanolin

We present a methodology based on the implementation of a fully connected neural network to estimate the gravitational wave (GW) temporal evolution of the gmode fundamental resonant frequency for a Core Collapse Supernova (CCSN). To perform the estimation, we construct a training data set, using synthetic waveforms, that serves to train the ML algorithm, and

Sijun Niu, Venkatsai Bellala, Daanish A. Qureshi, Vikas Srivastava

High-density polyethylene (HDPE) is used in applications ranging from cooling water pipelines in nuclear power plants and distribution pipelines for natural gas and hydrogen to biomedical implants. Embedded crack-like flaws form within HDPE during fabrication or operations. Non-visible flaws can cause catastrophic failure if undetected. Large structures such

Shathushan Sivashangaran, Apoorva Khairnar, Azim Eskandarian

Deep Reinforcement Learning (DRL) enables cognitive Autonomous Ground Vehicle (AGV) navigation utilizing raw sensor data without a-priori maps or GPS, which is a necessity in hazardous, information poor environments such as regions where natural disasters occur, and extraterrestrial planets. The substantial training time required to learn an optimal DRL poli

Xin Li, Yan Zhong

In a recent work, Gryaznov, Pudl\'{a}k, and Talebanfard (CCC' 22) introduced a stronger version of affine extractors known as directional affine extractors, together with a generalization of $\mathsf{ROBP}$s where each node can make linear queries, and showed that the former implies strong lower bound for a certain type of the latter known as strongly read-o

Pinaki Mandal

In two-sided matching markets, ensuring both stability and strategy-proofness poses a significant challenge; it is impossible when agents' preferences are unrestricted. But what if agents' preferences have specific restricted structures? Such scenarios frequently arise in real-world applications. This study explores the possibility of achieving both stabilit

André Dosea, Rafael Holanda, Cleto B. Miranda-Neto

Our main goal in this paper is to answer new positive cases of the natural generalized version of Hartshorne's celebrated question on cofiniteness of local cohomology modules, and consequently of Huneke's conjecture on the finiteness of their sets of associated primes. Our approach, by means of which we extend several results from the literature, is essentia

Kimet Jusufi, Genly Leon, Alfredo D. Millano

We argue that the effect of cold dark matter in the cosmological setup can be explained by the coupling between the baryonic matter particles in terms of the long-range force having a graviton mass $m_g$ via the Yukawa gravitational potential. Such a quantum-corrected Yukawa-like gravitational potential is characterized by the coupling parameter $\alpha$, th

Takahiro Onizuka, Fumiya Iwashige, Shintaro Hashimoto

Estimating boundary curves has many applications such as economics, climate science, and medicine. Bayesian trend filtering has been developed as one of locally adaptive smoothing methods to estimate the non-stationary trend of data. This paper develops a Bayesian trend filtering for estimating the boundary trend. To this end, the truncated multivariate norm

Shima Rahimi Moghaddam, Christopher J. Honey

Large language models (LLMs) excel in many tasks in 2023, but they still face challenges in complex reasoning. Theory-of-mind (ToM) tasks, which require understanding agents' beliefs, goals, and mental states, are essential for common-sense reasoning involving humans, making it crucial to enhance LLM performance in this area. This study measures the ToM perf

Avani Dave, Nilanjan Banerjee, Chintan Patel

With the increased utilization, the small embedded and IoT devices have become an attractive target for sophisticated attacks that can exploit the devices security critical information and data in malevolent activities. Secure boot and Remote Attestation (RA) techniques verifies the integrity of the devices software state at boot-time and runtime. Correct im

Kazuo Yonekura

This short note describes the concept of guided training of deep neural networks (DNNs) to learn physically reasonable solutions. DNNs are being widely used to predict phenomena in physics and mechanics. One of the issues of DNNs is that their output does not always satisfy physical equations. One approach to consider physical equations is adding a residual

Ibrahim Fayad, Philippe Ciais, Martin Schwartz, Jean-Pierre Wigneron

Accurate and timely monitoring of forest canopy heights is critical for assessing forest dynamics, biodiversity, carbon sequestration as well as forest degradation and deforestation. Recent advances in deep learning techniques, coupled with the vast amount of spaceborne remote sensing data offer an unprecedented opportunity to map canopy height at high spati

A. Ghodousian, M. Mollakazemiha, N. Karimian

This paper proposes a novel population-based meta-heuristic optimization algorithm, called Perfectionism Search Algorithm (PSA), which is based on the psychological aspects of perfectionism. The PSA algorithm takes inspiration from one of the most popular model of perfectionism, which was proposed by Hewitt and Flett. During each iteration of the PSA algorit

Dhiraj Murthy, Constantine Caramanis, Koustav Rudra

Individuals involved in gang-related activity use mainstream social media including Facebook and Twitter to express taunts and threats as well as grief and memorializing. However, identifying the impact of gang-related activity in order to serve community member needs through social media sources has a unique set of challenges. This includes the difficulty o

Sara Kacmoli, Deborah L. Sivco, Claire F. Gmachl

Photonic molecules - particular systems composed of coupled optical resonators - emulate the behavior of complex physical systems exhibiting discrete energy levels. In this work, we present a novel photonic molecule composed of two strongly coupled, mid-infrared ring quantum cascade lasers. We explore both experimentally and numerically the key features of t

Dario Della Monica, Angelo Montanari, Gabriele Puppis, Pietro Sala

In this paper, we study the finite satisfiability problem for the logic BE under the homogeneity assumption. BE is the cornerstone of Halpern and Shoham's interval temporal logic, and features modal operators corresponding to the prefix (a.k.a. "Begins") and suffix (a.k.a. "Ends") relations on intervals. In terms of complexity, BE lies in between the "Chop l

Andrea Lama, Mario Di Bernardo

We present preliminary results on the problem of driving the dynamics of a group of agents, the herders, so as to steer the collective behaviour of another group of agents, the targets, interacting with them. We define this problem as the multiagent herding control problem and study the herdability of the target agents by relaxing some strong assumptions oft

Simon Kuang

Differentiation filters can be made proper by composition with a first-order low-pass filter at a desired bandwidth, resulting in a "dirty derivative." A stable closed-loop system that relies on output derivatives remains stable when the derivatives are replaced with dirty derivatives of sufficiently high bandwidth. I prove and generalize this fact by a freq

Victoria Arce Pistone, Martín Figallo

In order to develop efficient tools for automated reasoning with inconsistency (theorem provers), eventually making Logics of Formal inconsistency (LFI) a more appealing formalism for reasoning under uncertainty, it is important to develop the proof theory of the first-order versions of such LFI's. In our work, we intend make a first step in that direction.

Göran Fäldt

In two earlier papers it was demonstrated that Lorentzian and Galilean symmetries could both be useful in the analysis of the annihilation reaction $e^+ e^- \to \gamma \Lambda(\rightarrow p\pi^-) \bar{\Lambda}(\rightarrow \bar{p}\pi^+)$. It was also demonstrated that any pair of hyperon form factors would be acceptable, but that the $\{G_E, G_M\}$ pair would

Eunseop Lee, Inhan Kim, Daijin Kim

In computer vision, unsupervised domain adaptation (UDA) is an approach to transferring knowledge from a label-rich source domain to a fully-unlabeled target domain. Conventional UDA approaches have two problems. The first problem is that a class classifier can be biased to the source domain because it is trained using only source samples. The second is that

Sarah Azouvi, Guy Goren, Lioba Heimbach, Alexander Hicks

In 2021 Ethereum adjusted the transaction pricing mechanism by implementing EIP-1559, which introduces the base fee - a network fee that is burned and dynamically adjusts to the network demand. The authors of the Ethereum Improvement Proposal (EIP) noted that a miner with more than 50% of the mining power could be incentivized to deviate from the honest mini

Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu

Large language models (LLMs) have demonstrated remarkable zero-shot generalization abilities: state-of-the-art chatbots can provide plausible answers to many common questions that arise in daily life. However, so far, LLMs cannot reliably solve long-horizon planning problems. By contrast, classical planners, once a problem is given in a formatted way, can us

Alexandra G. Roberts, Dominick J. Romano, Mert Şişman, Alexey V. Dimov

To develop a tissue field filtering algorithm, called maximum Spherical Mean Value (mSMV), for reducing shadow artifacts in quantitative susceptibility mapping (QSM) of the brain without requiring brain tissue erosion. Residual background field is a major source of shadow artifacts in QSM. The mSMV algorithm filters large field values near the border, where

Fraser Binns, Hugo Zhou

We give a diagrammatic characterization of the $(1,1)$ knots in the three-sphere and lens spaces which admit large Dehn surgeries to manifolds with Heegaard Floer homology of next-to-minimal rank. This is inspired by a corresponding result for $(1,1)$ knots which admit large Dehn surgeries to manifolds with Heegaard Floer homology of minimal rank due to Gree

Eric W. Jones, Joshua Derrick, Roger M. Nisbet, Will Ludington

In exponential population growth, variability in the timing of individual division events and environmental factors (including stochastic inoculation) compound to produce variable growth trajectories. In several stochastic models of exponential growth we show power-law relationships that relate variability in the time required to reach a threshold population

Jacopo Tagliabue, Ciro Greco

As ecommerce continues growing, huge investments in ML and NLP for Information Retrieval are following. While the vector space model dominated retrieval modelling in product search - even as vectorization itself greatly changed with the advent of deep learning -, our position paper argues in a contrarian fashion that program synthesis provides significant ad

Orchid Chetia Phukan, Arun Balaji Buduru, Rajesh Sharma

Pre-trained models (PTMs) have shown great promise in the speech and audio domain. Embeddings leveraged from these models serve as inputs for learning algorithms with applications in various downstream tasks. One such crucial task is Speech Emotion Recognition (SER) which has a wide range of applications, including dynamic analysis of customer calls, mental

Christian Krattenthaler, Thomas W. Müller

In [Ramanujan J. 52 (2020), 275-290], Romik considered the Taylor expansion of Jacobi's theta function $\theta_3(q)$ at $q=e^{-\pi}$ and encoded it in an integer sequence $(d(n))_{n\ge0}$ for which he provided a recursive procedure to compute the terms of the sequence. He observed intriguing behaviour of $d(n)$ modulo primes and prime powers. Here we prove (

Haotian Xue, Antonio Torralba, Joshua B. Tenenbaum, Daniel LK Yamins

Given a visual scene, humans have strong intuitions about how a scene can evolve over time under given actions. The intuition, often termed visual intuitive physics, is a critical ability that allows us to make effective plans to manipulate the scene to achieve desired outcomes without relying on extensive trial and error. In this paper, we present a framewo

Y. Mohamed, A. Ahmedou, M. S. B. Elemine Vall

This study uses the Lyapunov method, the Poincar\'e-Bendixson theorem, and the Dulac criterion to analyze the stability of SIR and SIRS with non-monotone incidence and different mortality rates.

J. P. Boroński, Magdalena Foryś-Krawiec, Piotr Oprocha

A compact space $Y$ is called homeo-product-minimal if given any minimal system $(X,T)$, it admits a homeomorphism $S:Y\to Y$, such that the product system $(X\times Y,T\times S)$ is minimal. We show that a large class of cofrontiers is homeo-product-minimal. This class contains R. H. Bing's pseudo-circle, answering a question of Dirb\'{a}k, Snoha and \v{S}p

Xinhao Kong, Yibo Zhu, Huaping Zhou, Zhuo Jiang

High-speed RDMA networks are getting rapidly adopted in the industry for their low latency and reduced CPU overheads. To verify that RDMA can be used in production, system administrators need to understand the set of application workloads that can potentially trigger abnormal performance behaviors (e.g., unexpected low throughput, PFC pause frame storm). We

Alex Foote, Neel Nanda, Esben Kran, Ionnis Konstas

Understanding the function of individual neurons within language models is essential for mechanistic interpretability research. We propose $\textbf{Neuron to Graph (N2G)}$, a tool which takes a neuron and its dataset examples, and automatically distills the neuron's behaviour on those examples to an interpretable graph. This presents a less labour intensive

Xingyu Zhu

We prove several analogs of Gromov's macroscopic dimension conjecture with extra curvature assumptions. More explicitly, we show that for an open Riemannian $n$-manifold $(M,g)$ of nonnegative Ricci (resp. sectional) curvature, if it has uniformly positive scalar curvature and it is uniformly volume noncollapsed, then the essential (resp. Hausdorff) dimensio

Harnaik Dhami, Vishnu D. Sharma, Pratap Tokekar

Prediction-based active perception has shown the potential to improve the navigation efficiency and safety of the robot by anticipating the uncertainty in the unknown environment. The existing works for 3D shape prediction make an implicit assumption about the partial observations and therefore cannot be used for real-world planning and do not consider the c

Hassaan Hashmi, Spyridon Pougkakiotis, Dionysios S. Kalogerias

Electronically tunable metasurfaces, or Intelligent Reflective Surfaces (IRSs), are a popular technology for achieving high spectral efficiency in modern wireless systems by shaping channels using a multitude of tunable passive reflective elements. Capitalizing on key practical limitations of IRS-aided beamforming pertaining to system modeling and channel se

Samuel Schulter, Vijay Kumar B G, Yumin Suh, Konstantinos M. Dafnis

Language-based object detection is a promising direction towards building a natural interface to describe objects in images that goes far beyond plain category names. While recent methods show great progress in that direction, proper evaluation is lacking. With OmniLabel, we propose a novel task definition, dataset, and evaluation metric. The task subsumes s

Filip Turoboś, Oleksiy Dovgoshey

The celebrated Assouad embedding theorem has been known for over 40 years. It states that for any doubling metric space (with doubling constant $C_0$) there exists an integer $N$, such that for any $\alpha\in (0.5,1)$ there exists a positive constant $C(\alpha,C_0)$ and an injective function $F:X\to \mathbb{R}^N$ such that \[ \forall x,y\in X \quad C^{-1} d(

Benyamin Ghojogh, Ali Ghodsi

This is a tutorial paper on Recurrent Neural Network (RNN), Long Short-Term Memory Network (LSTM), and their variants. We start with a dynamical system and backpropagation through time for RNN. Then, we discuss the problems of gradient vanishing and explosion in long-term dependencies. We explain close-to-identity weight matrix, long delays, leaky units, and

Wuxia Chen, Taposh Banerjee, Jemin George, Carl Busart

The problem of reinforcement learning is considered where the environment or the model undergoes a change. An algorithm is proposed that an agent can apply in such a problem to achieve the optimal long-time discounted reward. The algorithm is model-free and learns the optimal policy by interacting with the environment. It is shown that the proposed algorithm

Ping Sun, Ze-Chun Hu, Wei Sun

Let $X$ be a random variable with finite second moment. We investigate the inequality: $P\{|X-E[X]|\le \sqrt{{\rm Var}(X)}\}\ge P\{|Z|\le 1\}$, where $Z$ is a standard normal random variable. We prove that this inequality holds for many familiar infinitely divisible continuous distributions including the Laplace, Gumbel, Logistic, Pareto, infinitely divisibl

Roberto Capuzzo-Dolcetta, Matteo Sadun Bordoni

The region around the center of our Galaxy is very dense of stars. The kinematics of inner moving stars in the Galaxy (the so called S-stars) has been deeply studied by different research groups leading to the conclusion of the existence of a very compact object (Sgr A$^*$, likely a supermassive black hole) responsible for their high speed. Here we start fro

Tapas Senapati, Ashwin Kumar, Kartik Senapati

Manifestation of orbital coupling of spin degree of freedom in condensed matter systems has opened up a new dimension for the field of spintronics. The most appealing aspect of the spin-orbit coupling is the apparent Magnus force sensed by a spin system which locks the Fermi momentum with electron spin in a fascinating manner. In the current carrying state,

Roberto Colombo

We study a one dimensional Lagrangian problem including the variational reformulation, derived in a recent work of Ambrosio-Baradat-Brenier, of the discrete Monge-Amp\`ere gravitational model, which describes the motion of interacting particles whose dynamics is ruled by the optimal transport problem. The more general action type functional we consider conta

Katarina Vuckovic, Farzam Hejazi, Nazanin Rahnavard

This work presents a date-driven user localization framework for single-site massive Multiple-Input-Multiple-Output (MIMO) systems. The framework is trained on a geo-tagged Channel State Information (CSI) dataset. Unlike the state-of-the-art Convolutional Neural Network (CNN) models, which require large training datasets to perform well, our method is specif

Nguyen Quang Hieu, Nguyen Le Quy Duong, Le Quang Hoa, Nguyen Quang Dat

In many Vietnamese schools, grades are still being inputted into the database manually, which is not only inefficient but also prone to human error. Thus, the automation of this process is highly necessary, which can only be achieved if we can extract information from academic transcripts. In this paper, we test our improved CRNN model in extracting informat

Gustavo J. R. Aroeira, Kyle Kairys, Raphael F. Ribeiro

We present a comprehensive study of exciton wave packet evolution in disordered lossless polaritonic wires. Our simulations reveal signatures of ballistic, diffusive, and subdiffusive exciton dynamics under strong light-matter coupling and identify the typical timescales associated with the transitions between these qualitatively distinct transport phenomena

Fırat Kıyak, A. C. Cem Say

The thermodynamical costs imposed by computational resource limitations like memory and time have been investigated before. We focus on a new computational limitation, namely, the machine being allowed to scan the input only once, and prove that it is associated with unavoidable thermodynamical cost, even in the presence of infinite time and memory resources

Zhengtian Qiu, Jianjun Liu, Guiyun Chen

Let $ H $ be a subgroup of a finite group $ G $. We say that $ H $ satisfies the partial $ \Pi $-property in $ G $ if if there exists a chief series $ \varGamma_{G}: 1 =G_{0} < G_{1} < \cdot\cdot\cdot < G_{n}= G $ of $ G $ such that for every $ G $-chief factor $ G_{i}/G_{i-1} $ $ (1\leq i\leq n) $ of $ \varGamma_{G} $, $ | G / G_{i-1} : N_{G/G_{i-1}} (HG_{i

Davoud Saadati, Omid Nejati Manzari, Sattar Mirzakuchaki

Medical image segmentation is crucial for the development of computer-aided diagnostic and therapeutic systems, but still faces numerous difficulties. In recent years, the commonly used encoder-decoder architecture based on CNNs has been applied effectively in medical image segmentation, but has limitations in terms of learning global context and spatial rel

Andrea Montanari, Yuchen Wu

Sampling from the posterior is a key technical problem in Bayesian statistics. Rigorous guarantees are difficult to obtain for Markov Chain Monte Carlo algorithms of common use. In this paper, we study an alternative class of algorithms based on diffusion processes and variational methods. The diffusion is constructed in such a way that, at its final time, i

Tian Li, LU Li, Wei Wang, Zhangchi Feng

Neural Radiance Field (NeRF) has received much attention in recent years due to the impressively high quality in 3D scene reconstruction and novel view synthesis. However, image degradation caused by the scattering of atmospheric light and object light by particles in the atmosphere can significantly decrease the reconstruction quality when shooting scenes i

D. Hoffman, F. Martin, B. White

In this paper, we describe new annular examples of complete translating solitons for the mean curvature flow and how they are related to a family of translating graphs, the $\Delta$-wings. In addition, we will prove several related results that answer questions that arise naturally in this investigation. These results apply to translators in general, not jus

Yansong Gao, Zhihong Pan, Xin Zhou, Le Kang

Denoising diffusion probabilistic models (DDPMs) are a class of powerful generative models. The past few years have witnessed the great success of DDPMs in generating high-fidelity samples. A significant limitation of the DDPMs is the slow sampling procedure. DDPMs generally need hundreds or thousands of sequential function evaluations (steps) of neural netw

Kudaibergen Abutalip, Numan Saeed, Mustaqeem Khan, Abdulmotaleb El Saddik

Variability in staining protocols, such as different slide preparation techniques, chemicals, and scanner configurations, can result in a diverse set of whole slide images (WSIs). This distribution shift can negatively impact the performance of deep learning models on unseen samples, presenting a significant challenge for developing new computational patholo

Mithun Goutham, Stephanie Stockar

The Multi-Commodity Pickup and Delivery Vehicle Routing Problem aims to optimize the pickup and delivery of multiple unique commodities using a fleet of several agents with limited payload capacities. This paper addresses the challenge of quickly recomputing the solution to this NP-hard problem when there are unexpected perturbations to the nominal task defi

Andrei Teimurazov, Sanjay Singh, Sylvie Su, Sven Eckert

In Rayleigh-B\'enard convection (RBC), the size of a flow domain and its aspect ratio $\varGamma$ (a ratio between the spatial length and height of the domain) affect the shape of the large-scale circulation (LSC). For some aspect ratios, the flow dynamics include a three-dimensional oscillatory mode known as a jump-rope vortex (JRV), however, the effects of

Guillaume Dauphinais, David W. Kribs, Michael Vasmer

We introduce a stabilizer formalism for the general quantum error correction framework called operator algebra quantum error correction (OAQEC), which generalizes Gottesman's formulation for traditional quantum error correcting codes (QEC) and Poulin's for operator quantum error correction and subsystem codes (OQEC). The construction generates hybrid classic

S. Flach, S. Parnovsky, A. A. Varlamov

Why do we need to pour less water in an egg steamer to prepare more eggs to the same degree of doneness? We discuss the physical processes at work in the electric egg steamer and resolve this seeming paradox. We demonstrate that the main heat transfer mechanism from steam to egg is due to latent heat through condensation. This not only explains the paradox,

Arunangshu Debnath, Robin Santra

We present a theoretical formulation for the multiphoton diffraction phenomenology in the nonrelativistic limit, suitable for interpreting high-energy x-ray diffraction measurements using synchrotron radiation sources. A hierarchy of approximations and the systematic analysis of limiting cases are presented. A convolutional representation of the diffraction

Thomas Biekötter

The Brout-Englert-Higgs mechanism describes the generation of masses of fundamental particles in the Standard Model (SM). It predicts the existence of one scalar particle with precisely predicted couplings to fermions and gauge bosons. Deviations from these predictions, such as the observation of additional scalar particles, would indicate non-minimal Higgs

Małgorzata Gutowska, Suzanne Little, Andrew McCarren

Unsupervised anomaly detection (AD) is critical for a wide range of practical applications, from network security to health and medical tools. Due to the diversity of problems, no single algorithm has been found to be superior for all AD tasks. Choosing an algorithm, otherwise known as the Algorithm Selection Problem (ASP), has been extensively examined in s

Ayaka Sakata, Kunihiko Kaneko

Biological systems must be robust for stable function against perturbations, but robustness alone is not sufficient. The ability to switch between appropriate states (phenotypes) in response to different conditions is essential for biological functions. How are robustness and plasticity simultaneously acquired through evolution? We examine the evolution of g

Hideaki Takahashi, Jingjing Liu, Yang Liu

Federated Learning with Model Distillation (FedMD) is a nascent collaborative learning paradigm, where only output logits of public datasets are transmitted as distilled knowledge, instead of passing on private model parameters that are susceptible to gradient inversion attacks, a known privacy risk in federated learning. In this paper, we found that even th

Zixiao Yu, Lele Fu, Zhiling Cai, Zhoumin Lu

Tensor-oriented multi-view subspace clustering has achieved significant strides in assessing high-order correlations and improving clustering analysis of multi-view data. Nevertheless, most of existing investigations are typically hampered by the two flaws. First, self-representation based tensor subspace learning usually induces high time and space complexi

Samruddhi Deode, Janhavi Gadre, Aditi Kajale, Ananya Joshi

The multilingual Sentence-BERT (SBERT) models map different languages to common representation space and are useful for cross-language similarity and mining tasks. We propose a simple yet effective approach to convert vanilla multilingual BERT models into multilingual sentence BERT models using synthetic corpus. We simply aggregate translated NLI or STS data

Yu Wang, Zhiwei Liu, Liangwei Yang, Philip S. Yu

Generative models have attracted significant interest due to their ability to handle uncertainty by learning the inherent data distributions. However, two prominent generative models, namely Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs), exhibit challenges that impede achieving optimal performance in sequential recommendation tas