May 2023 arXiv papers — page 123

Shuhao Li, Hu Wang, Jichao Yin, Daozhen Guo

In this study, an efficient reanalysis strategy for dynamic topology optimization is proposed. Compared with other related studies, an online successive dynamic reanalysis method and POD-based approximate dynamic displacement strategy are integrated. In dynamic reanalysis, the storage of the stiffness matrix decomposition can be avoided and the reduced basis

Ken Shiozaki, Niclas Heinsdorf, Shuhei Ohyama

The higher Berry curvature was introduced by Kapustin and Spodyneiko as an extension of the Berry curvature in quantum mechanical systems with finite degrees of freedom to quantum many-body systems in finite spatial dimensions. In this paper, we propose an alternative formulation of the higher Berry curvature using translationally invariant matrix product st

Yin Ching Lai

Hong Kong' senior geography curriculum has included GIS since the early 2000s. However, GIS in secondary schools does not play a significant role in Hong Kong secondary geography education. Analyzing GIS benefits by literature review, it is believed that GIS should be included in both the senior and junior geography curriculum. Moreover, the literature revie

Yumeki Goto, Tomoya Matsumoto, Hamada Rizk, Naoto Yanai

Taxi-demand prediction is an important application of machine learning that enables taxi-providing facilities to optimize their operations and city planners to improve transportation infrastructure and services. However, the use of sensitive data in these systems raises concerns about privacy and security. In this paper, we propose the use of federated learn

Kiryong Chung, Sanghyeon Lee

The smooth quintic del Pezzo variety $Y$ is well-known to be obtained as a linear sections of the Grassmannian variety $\mathrm{Gr}(2,5)$ under the Pl\"ucker embedding into $\mathbb{P}^{9}$. Through a local computation, we show the Hilbert scheme of conics in $Y$ for $\text{dim} Y \ge 3$ can be obtained from a certain Grassmannian bundle by a single blowing

Conall Butler, Martin Crane

Gas is the transaction-fee metering system of the Ethereum network. Users of the network are required to select a gas price for submission with their transaction, creating a risk of overpaying or delayed/unprocessed transactions in this selection. In this work, we investigate data in the aftermath of the London Hard Fork and shed insight into the transaction

Nicolò Dal Fabbro, Aritra Mitra, George J. Pappas

Federated learning (FL) has recently gained much attention due to its effectiveness in speeding up supervised learning tasks under communication and privacy constraints. However, whether similar speedups can be established for reinforcement learning remains much less understood theoretically. Towards this direction, we study a federated policy evaluation pro

Hans Harder, Simon Jantsch, Christel Baier, Clemens Dubslaff

Boolean functions and their representation through logics, circuits, machine learning classifiers, or binary decision diagrams (BDDs) play a central role in the design and analysis of computing systems. Quantifying the relative impact of variables on the truth value by means of importance values can provide useful insights to steer system design and debuggin

Betim Bahtiri, Behrouz Arash, Sven Scheffler, Maximilian Jux

In this work, we propose a deep learning (DL)-based constitutive model for investigating the cyclic viscoelastic-viscoplastic-damage behavior of nanoparticle/epoxy nanocomposites with moisture content. For this, a long short-term memory network is trained using a combined framework of a sampling technique and a perturbation method. The training framework, al

Genki Shibukawa, Satoshi Tsuchimi

By applying Slater's transformation formulas for the bilateral basic hypergeometric series ${}_2\psi_{2}$, we derive three type translation formulas for the generalized Zwegers' $\mu$-function (``continuous $q$-Hermite function'') which was introduced by Shibukawa--Tsuchimi (SIGMA, 2023). From some Bailey's transformation formula of ${}_2\psi_{2}$, we also g

Palle E. T. Jorgensen, Myung-Sin Song, James Tian

Motivated by applications, we consider here new operator theoretic approaches to Conditional mean embeddings (CME). Our present results combine a spectral analysis-based optimization scheme with the use of kernels, stochastic processes, and constructive learning algorithms. For initially given non-linear data, we consider optimization-based feature selection

Weiwei Lin, Chenhang He, Man-Wai Mak, Youzhi Tu

Self-supervised learning (SSL) speech models such as wav2vec and HuBERT have demonstrated state-of-the-art performance on automatic speech recognition (ASR) and proved to be extremely useful in low label-resource settings. However, the success of SSL models has yet to transfer to utterance-level tasks such as speaker, emotion, and language recognition, which

Linmi Tao, Ruiyang Liu, Donglai Tao, Wu Xia

Though a core element of the digital age, numerical difference algorithms struggle with noise susceptibility. This stems from a key disconnect between the infinitesimal quantities in continuous differentiation and the finite intervals in its discrete counterpart. This disconnect violates the fundamental definition of differentiation (Leibniz and Cauchy). To

Toshiya Yurikusa

In a categorification of skew-symmetric cluster algebras, each cluster variable corresponds with an indecomposable module over the associated Jacobian algebra. Buan, Marsh and Reiten studied when the denominator vector of each cluster variable in an acyclic cluster algebra coincides with the dimension vector of the corresponding module. In this paper, we giv

Songming Zhang, Yunlong Liang, Shuaibo Wang, Wenjuan Han

Knowledge distillation (KD) is a promising technique for model compression in neural machine translation. However, where the knowledge hides in KD is still not clear, which may hinder the development of KD. In this work, we first unravel this mystery from an empirical perspective and show that the knowledge comes from the top-1 predictions of teachers, which

Yuxuan He, Jia Liu, Xiaolin Ma, Xiao-Ping Wang

We present a scalar-driven sterile neutrino production model where the interaction with the ultralight scalar field modifies the oscillation production of sterile neutrinos in the early universe. The model effectively suppresses the production of sterile neutrinos at low temperatures due to the heavy scalar mass, resulting in a colder matter power spectrum t

Eslam Mostafa, Hussein A. Aly, Ahmed Elliethy

Genetic algorithm (GA) is typically used to solve nonlinear model predictive control's optimization problem. However, the size of the search space in which the GA searches for the optimal control inputs is crucial for its applicability to fast-response systems. This paper proposes accelerating the genetic optimization of NMPC by learning optimal search space

Beatriz Cabrero-Daniel

This study explores the benefits and challenges of integrating Artificial Intelligence with Agile software development methodologies, focusing on improving continuous integration and delivery. A systematic literature review and longitudinal meta-analysis of the retrieved studies was conducted to analyse the role of Artificial Intelligence and it's future app

Wentao Hu, Xiurong Jiang, Jiarun Liu, Yuqi Yang

In the field of few-shot learning (FSL), extensive research has focused on improving network structures and training strategies. However, the role of data processing modules has not been fully explored. Therefore, in this paper, we propose Meta-DM, a generalized data processing module for FSL problems based on diffusion models. Meta-DM is a simple yet effect

Yu-Mei Wu, Zu-Cheng Chen, Qing-Guo Huang

Ultralight boson fields, with a mass around $10^{-23}\text{eV}$, are promising candidates for the elusive cosmological dark matter. These fields induce a periodic oscillation of the spacetime metric in the nanohertz frequency band, which is detectable by pulsar timing arrays. In this paper, we investigate the gravitational effect of ultralight tensor dark ma

Martina Hofmanová, Umberto Pappalettera, Rongchan Zhu, Xiangchan Zhu

We propose a novel approach to induce anomalous dissipation through advection driven by turbulent fluid flows. Specifically, we establish the existence of a velocity field $v$ satisfying randomly forced Navier-Stokes equations, leading to total dissipation of kinetic energy in finite time when advecting a passive scalar. This dissipation phenomenon is unifor

Hamidreza Daniali, Davoud Kamani

We calculate the rate of the open string pair production from the interaction of two non-intersecting D-strings at angle. The D-strings have been equipped with the $U(1)$ gauge potentials in the presence of the Kalb-Ramond field. This rate will be discussed when the D-strings intersect each other. Our computations is in the framework of the bosonic string th

Xi Yang, Kejiang Chen, Weiming Zhang, Chang Liu

LLMs now exhibit human-like skills in various fields, leading to worries about misuse. Thus, detecting generated text is crucial. However, passive detection methods are stuck in domain specificity and limited adversarial robustness. To achieve reliable detection, a watermark-based method was proposed for white-box LLMs, allowing them to embed watermarks duri

Qiushi Sun, Chengcheng Han, Nuo Chen, Renyu Zhu

Large language models (LLMs) have shown increasing power on various natural language processing (NLP) tasks. However, tuning these models for downstream tasks usually needs exorbitant costs or is unavailable due to commercial considerations. Recently, black-box tuning has been proposed to address this problem by optimizing task-specific prompts without acces

Eric Ragoucy, Arim Song, Uhi Rinn Suh

We describe an $N=2$ supersymmetric Poisson vertex algebra structure of $N=1$ (resp. $N=0$) classical $W$-algebra associated with $\mathfrak{sl}(n+1|n)$ and the odd (resp. even) principal nilpotent element. This $N=2$ supersymmetric structure is connected to the principal $\mathfrak{sl}(2|1)$-embedding in $\mathfrak{sl}(n+1|n)$ superalgebras, which are the o

Anupam Mondal, Pritam Chandra Pramanik

Robin Forman's highly influential 2002 paper A User's Guide to Discrete Morse Theory presents an overview of the subject in a very readable manner. As a proof of concept, the author determines the topology (homotopy type) of the abstract simplicial complex of disconnected graphs of order $n$ (which was previously done by Victor Vassiliev using classical topo

Tommaso Ruggeri

We consider a general causal relativistic theory of divergence type in the framework of Rational Extended Thermodynamics (RET) for a compressible, possibly dense, gas. We require that the system converges in the Maxwellian iteration's first step to the parabolic Eckart equations. This requirement implies a constraint between the two coefficients present in t

A. Bondar, A. Buzulutskov, E. Frolov, E. Borisova

A recent discovery of additional mechanism of electroluminescence (EL) in noble gases due to the neutral bremsstrahlung (NBrS) effect led to a prediction that NBrS EL should be present in noble liquids as well. A theoretical model of NBrS EL in noble liquids was developed accordingly in the frameworks of Cohen-Lekner and Atrazhev. In this work, we confirm th

Xuebao Cai, Yuhang Tan, Ting Su, Dong Liang

The low-density imaging performance of a zone plate based nano-resolution hard X-ray computed tomography (CT) system can be significantly improved by incorporating a grating-based Lau interferometer. Due to the diffraction, however, the acquired nano-resolution phase signal may suffer splitting problem, which impedes the direct reconstruction of phase contra

A. Buzulutskov, E. Frolov, E. Borisova, V. Nosov

The effect of proportional electroluminescence (EL) is used to record the primary ionization signal (S2) in the gas phase of two-phase argon detectors for dark matter particle (WIMP) searches and low-energy neutrino experiments. Our previous studies of EL time properties revealed the presence of two unusual slow components in S2 signal of two-phase argon det

Dhruv Arora, Parth Bambhaniya, Dipanjan Dey, Pankaj S. Joshi

The concept of regular black holes has gained attention in recent years, especially in the context of quantum gravity theories. In these theories, the existence of singularities is paradoxical as they represent a breakdown of the laws of physics. Motivated by the recent developments in this area, we study the tidal force effects in one such family of regular

Ke Ma, Yiliang Sang, Yang Ming, Jin Lian

Deep learning based channel state information (CSI) feedback in frequency division duplex systems has drawn much attention in both academia and industry. In this paper, we focus on integrating the Type-II codebook in the beyond fifth-generation (B5G) wireless systems with deep learning to enhance the performance of CSI feedback. In contrast to its counterpar

Darshit Patel, Azim Eskandarian

Rapidly Exploring Random Trees (RRT) is one of the most widely used algorithms for motion planning in the field of robotics. To reduce the exploration time, RRT-Connect was introduced where two trees are simultaneously formed and eventually connected. Probabilistic RRT used the concept of position probability map to introduce goal biasing for faster converge

Jiancheng An, Chao Xu, Derrick Wing Kwan Ng, George C. Alexandropoulos

The revolutionary technology of \emph{Stacked Intelligent Metasurfaces (SIM)} has been recently shown to be capable of carrying out advanced signal processing directly in the native electromagnetic (EM) wave domain. An SIM is fabricated by a sophisticated amalgam of multiple stacked metasurface layers, which may outperform its single-layer metasurface counte

Qijie Wei, Jingyuan Yang, Bo Wang, Jinrui Wang

This paper addresses the emerging task of recognizing multiple retinal diseases from wide-field (WF) and ultra-wide-field (UWF) fundus images. For an effective use of existing large amount of labeled color fundus photo (CFP) data and the relatively small amount of WF and UWF data, we propose a supervised domain adaptation method named Cross-domain Collaborat

Reza Nematirad, M. M. Ardehali, Amir Khorsandi

Residential consumers can use the demand response program (DRP) if they can utilize the home energy management system (HEMS), which reduces consumer costs by automatically adjusting air conditioning (AC) setpoints and shifting some appliances to off-peak hours. If HEMS knows occupancy status, consumers can gain more economic benefits and thermal comfort. How

Maniratnam Mandal, Suna Gao

Adversarial attacks pose a significant threat to the security and safety of deep neural networks being applied to modern applications. More specifically, in computer vision-based tasks, experts can use the knowledge of model architecture to create adversarial samples imperceptible to the human eye. These attacks can lead to security problems in popular appli

Maniratnam Mandal, Imran Khan

Compressing deep networks is highly desirable for practical use-cases in computer vision applications. Several techniques have been explored in the literature, and research has been done in finding efficient strategies for combining them. For this project, we aimed to explore three different basic compression techniques - knowledge distillation, pruning, and

Caroline L. Wormell

Extended Dynamic Mode Decomposition (EDMD) is a data-driven tool for forecasting and model reduction of dynamics, which has been extensively taken up in the physical sciences. While the method is conceptually simple, in deterministic chaos it is unclear what its properties are or even what it converges to. In particular, it is not clear how EDMD's least-squa

Ryo Umagami, Yu Ono, Yusuke Mukuta, Tatsuya Harada

It is imperative to discern the relationships between multiple time series for accurate forecasting. In particular, for stock prices, components are often divided into groups with the same characteristics, and a model that extracts relationships consistent with this group structure should be effective. Thus, we propose the concept of hierarchical permutation

Hiroshi Teramoto, Takuya Saito, Masamitsu Aoki, Burai Murayama

This study develops an algorithm to reproduce reaction route maps (RRMs) in shape space from the outputs of potential search algorithms. To demonstrate this, GRRM is utilized as a potential search algorithm but the proposed algorithm should work with other potential search algorithms in principle. The proposed algorithm does not require any encoding of the m

Sirui Ning, Chon Man Sou, Yi Wang

It is well-known that the primordial scalar curvature and tensor perturbations, $\zeta$ and $\gamma_{ij}$, are conserved on super-horizon scales in minimal inflation models. However, their wave functional has a rapidly oscillating phase which is slow-roll unsuppressed, as can be seen either from boundary (total-derivative) terms of cosmological perturbations

Behnaz Soltani, Yipeng Zhou, Venus Haghighi, John C. S. Lui

In traditional machine learning, it is trivial to conduct model evaluation since all data samples are managed centrally by a server. However, model evaluation becomes a challenging problem in federated learning (FL), which is called federated evaluation in this work. This is because clients do not expose their original data to preserve data privacy. Federate

Burhaneddin Yaman, Tanvir Mahmud, Chun-Hao Liu

We propose an embarrassingly simple method -- instance-aware repeat factor sampling (IRFS) to address the problem of imbalanced data in long-tailed object detection. Imbalanced datasets in real-world object detection often suffer from a large disparity in the number of instances for each class. To improve the generalization performance of object detection mo

Fredric D. Ancel

Lebesgue measurable subsets A and B of parallel or identical k-dimensional affine subspaces of Euclidean n-space E^n satisfy The Product Formula for Volume: Vol_k(A)Vol_k(B) = \sum_{J \in S(n,k)} Vol_k({\pi}_J(A))Vol_k({\pi}_J(B)). Here Vol_k denotes k-dimensional Lebesgue measure; S(n,k) denotes the set of all k-element subsets of {1,2,..., n}; and for J \i

Shangeth Rajaa

Most End-to-End SLU methods depend on the pretrained ASR or language model features for intent prediction. However, other essential information in speech, such as prosody, is often ignored. Recent research has shown improved results in classifying dialogue acts by incorporating prosodic information. The margins of improvement in these methods are minimal as

Maniratnam Mandal, Deepti Ghadiyaram, Danna Gurari, Alan C. Bovik

Perception-based image analysis technologies can be used to help visually impaired people take better quality pictures by providing automated guidance, thereby empowering them to interact more confidently on social media. The photographs taken by visually impaired users often suffer from one or both of two kinds of quality issues: technical quality (distorti

Mauricio Bustamante, Manuel Krannich, Alexander Kupers, Bena Tshishiku

We determine for which exotic tori $\mathcal{T}$ of dimension $d\neq4$ the homomorphism from the group of isotopy classes of orientation-preserving diffeomorphisms of $\mathcal{T}$ to ${\rm SL}_d(\mathbb Z)$ given by the action on the first homology group is split surjective. As part of the proof we compute the mapping class group of all exotic tori $\mathca

Tim Stokes

We obtain an ESN theorem for a very general class of biunary semigroups with idempotent-valued domain and range operations, representing them in terms of small categories equipped with a suitable biaction of the identities on the category. Our results generalise the recent work of Fitzgerald and Kinyon connecting localisable semigroups to transcription categ

Jingbo Liu

Let $P_Z$ be a given distribution on $\mathbb{R}^n$. For any $y\in\mathbb{R}^n$, we may interpret $\rho(y):=\ln\mathbb{E}[e^{\left<y,Z\right>}]$ as a soft-max of $\left<y,Z\right>$. We explore lower bounds on $\mathbb{E}[\rho(Y)]$ in terms of the minimum mutual information $I(Z,\bar{Z})$ over $P_{Z\bar{Z}}$ which is a coupling of $P_Z$ and itself such that $

Yuta Saito, Qingyang Ren, Thorsten Joachims

We study off-policy evaluation (OPE) of contextual bandit policies for large discrete action spaces where conventional importance-weighting approaches suffer from excessive variance. To circumvent this variance issue, we propose a new estimator, called OffCEM, that is based on the conjunct effect model (CEM), a novel decomposition of the causal effect into a

Yuto Masamura

For given positive integers $d$ and $m$, consider the projective klt pairs $(X,B)$ of dimension $d$, of Cartier index $m$, and with semi-ample $K_X+B$ defining a contraction $\pi\colon X\to Z$. We prove that it is not possible in general to write $n(K_X+B)\sim\pi^*A_Z$ for some $n$ depending only on $d$ and $m$, and some Cartier divisor $A_Z$ on $Z$.

Matteo Biagiola, Andrea Stocco, Vincenzo Riccio, Paolo Tonella

Simulation-based testing represents an important step to ensure the reliability of autonomous driving software. In practice, when companies rely on third-party general-purpose simulators, either for in-house or outsourced testing, the generalizability of testing results to real autonomous vehicles is at stake. In this paper, we enhance simulation-based testi

Chenyang Lyu, Tianbo Ji, Yvette Graham, Jennifer Foster

Event-Level Video Question Answering (EVQA) requires complex reasoning across video events to obtain the visual information needed to provide optimal answers. However, despite significant progress in model performance, few studies have focused on using the explicit semantic connections between the question and visual information especially at the event level

Bin Li, Yeqian Yang, Yuxiang Fan, Cong Zhu

High-pressure structural search was performed on the hydrogen-rich compound LuBeH$_8$ at pressures up to 200 GPa. We found a $Fm\overline{3}m$ structure that exhibits stability and superconductivity above 100 GPa. Our phonon dispersion, electronic band structure, and superconductivity analyses in the 100-200 GPa pressure range reveal a strong electron-phonon

Colin A. Grambow, Hayley Weir, Christian N. Cunningham, Tommaso Biancalani

Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization. However, accurate, fast, and scalable methods for modeling macrocycle geometries remain elusive. Recent deep learning approaches have significantly accelerated protein structure prediction

Ke Wan, Yiwen Liu

When tackling binary optimization problems using quantum algorithms, the conventional Ising representation and Quantum Approximate Optimization Algorithm (QAOA) encounter difficulties in efficiently handling errors for large-scale problems involving multiple constraints. To address these challenges, this paper presents a hybrid framework that combines the us

Haitao Wang

We consider the problem of dynamically maintaining the convex hull of a set $S$ of points in the plane under the following special sequence of insertions and deletions (called {\em window-sliding updates}): insert a point to the right of all points of $S$ and delete the leftmost point of $S$. We propose an $O(|S|)$-space data structure that can handle each u

Xiaofeng Xue

In this paper, we give the moderate deviation principle from the hydrodynamic limit of the simple exclusion process on $1$-dimensional torus starting from a nonequilibrium state, which extends the result given in Gao and Quastel (2003) about the case where the process starts from an equilibrium state. The exponential tightness of the scaled density field of

Miao Zhang, Yiqing Shen, Shenghui Zhong

Images captured under low-light conditions are often plagued by several challenges, including diminished contrast, increased noise, loss of fine details, and unnatural color reproduction. These factors can significantly hinder the performance of computer vision tasks such as object detection and image segmentation. As a result, improving the quality of low-l

Ting Lan, Weijun Liu, Fu-Gang Yin

Let $\mathcal{D}$ be a non-trivial $G$-block-transitive $3$-$(v,k,1)$ design, where $T\leq G \leq \mathrm{Aut}(T)$ for some finite non-abelian simple group $T$. It is proved that if $T$ is a simple exceptional group of Lie type, then $T$ is either the Suzuki group ${}^2B_2(q)$ or $G_2(q)$. Furthermore, if $T={}^2B_2(q)$ then the design $\mathcal{D}$ has para

Xiao-Yu Sun, Fu-Wei Zhang, Yu-Ji Shi, Zhen-Xing Zhao

In this work, we perform a QCD sum rules analysis on the $\Xi_{Q}-\Xi_{Q}^{\prime}$ mixing. Contributions from up to dimension-6 four-quark operators are considered. However, it turns out that, only dimension-4 and dimension-5 operators contribute, which reveals the non-perturbative nature of mixing. Especially we notice that only the diagrams with the two l

Marcus Hoerger, Hanna Kurniawati, Dirk Kroese, Nan Ye

The Partially Observable Markov Decision Process (POMDP) provides a principled framework for decision making in stochastic partially observable environments. However, computing good solutions for problems with continuous action spaces remains challenging. To ease this challenge, we propose a simple online POMDP solver, called Lazy Cross-Entropy Search Over P

Huayi Tang, Yong Liu

Graph neural networks (GNNs) are the most widely adopted model in graph-structured data oriented learning and representation. Despite their extraordinary success in real-world applications, understanding their working mechanism by theory is still on primary stage. In this paper, we move towards this goal from the perspective of generalization. To be specific

Jinghua Xiong, Abhishek, Li Xu, Hrishikesh A. Chandanpurkar

The prevailing approaches for ET retrievals are either limited in spatiotemporal coverage or largely influenced by choice of input data or simplified model physics, or a combination thereof. Here, using an independent mass conservation approach, we develop water balance-based ET datasets (ET-WB) for the global land and the selected 168 major river basins. We

Wei Hong, Kang Jiao, Yu-Chen Wang, Tingting Zhang

Cosmology constraints serve as a crucial criterion in discriminating cosmological models. The traditional combined method to constrain the cosmological parameters designates the corresponding theoretical value and observational data as functions of redshift, however, sometimes the redshift cannot be measured directly, or the measurement error is large, or th

Valerio De Angelis, Dominic Marcello

In a Note in this Monthly, Klazar raised the question of whether the alternating sum of the Stirling numbers of the second kind $B^\pm(n)=\sum_{k=0}^n(-1)^kS(n,k)$ is ever zero for $n\neq 2$. In this article, we present an exposition of the history of this problem, and an economical account of a recent proof that there is at most one $n\neq 2$ for which $B^\

Qing-Kun Wan, Hai-Long Shi, Xi-Wen Guan

Magnons, as fundamental quasiparticles emerged in elementary spin excitations, hold a big promise for innovating quantum technologies in information coding and processing. Here we discover subtle roles of entanglement in a metrological scheme based on an experimentally feasible cavity magnomechanical system, where the magnons are responsible for sensing a we

Kuan-Jung Chiang, Steven Dong, Chung-Kuan Cheng, Tzyy-Ping Jung

Objective: The Electroencephalogram (EEG) is gaining popularity as a physiological measure for neuroergonomics in human factor studies because it is objective, less prone to bias, and capable of assessing the dynamics of cognitive states. This study investigated the associations between memory workload and EEG during participants' typical office tasks on a s

Peicheng Jiang

A search for an axion-like particle with 2.7 billion $\psi(3686)$ events collected by the BESIII detector is presented. No significant signal is observed, and the upper limits on the branching fraction of $J/\psi\rightarrow\gamma a$ and the ALP-photon coupling constant $g_{a\gamma\gamma}$ are set at the 95% confidence level in the mass range of $0.165\leq m_

Sheng Zhong, Nima Fazeli, Dmitry Berenson

This paper proposes a novel method for estimating the set of plausible poses of a rigid object from a set of points with volumetric information, such as whether each point is in free space or on the surface of the object. In particular, we study how pose can be estimated from force and tactile data arising from contact. Using data derived from contact is cha

Hector Reyes, Saeid Tizpaz-Niari, Vladik Kreinovich

In general, the more measurements we perform, the more information we gain about the system and thus, the more adequate decisions we will be able to make. However, in situations when we perform measurements to check for safety, the situation is sometimes opposite: the more additional measurements we perform beyond what is required, the worse the decisions wi

Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang

This paper considers a novel application of deep AUC maximization (DAM) for multi-instance learning (MIL), in which a single class label is assigned to a bag of instances (e.g., multiple 2D slices of a CT scan for a patient). We address a neglected yet non-negligible computational challenge of MIL in the context of DAM, i.e., bag size is too large to be load

Jingda Yang, Ying Wang, Yanjun Pan, Tuyen X. Tran

The virtualization and softwarization of 5G and NextG are critical enablers of the shift to flexibility, but they also present a potential attack surface for threats. However, current security research in communication systems focuses on specific aspects of security challenges and lacks a holistic perspective. To address this challenge, a novel systematic fu

Xinyu He, Ning Chen, Jingpei Chen, Xuezhou Wang

By using band structure calculations of quantum mechanical theory, some important peaks of DoS (Density of States) were obtained and classified based on crystal structure laws of cuprate superconductivity. In particular, the orbital interactions of the in-plane and out-of-plane ions of the copper-oxygen plane were investigated. The position, half-width, and

Ce Zhou, Qiben Yan, Zhiyuan Yu, Eshan Dixit

Electric Vehicle (EV) has become one of the promising solutions to the ever-evolving environmental and energy crisis. The key to the wide adoption of EVs is a pervasive charging infrastructure, composed of both private/home chargers and public/commercial charging stations. The security of EV charging, however, has not been thoroughly investigated. This paper

Andrey A. Popov, Renato Zanetti

Data-driven reduced order modeling of chaotic dynamics can result in systems that either dissipate or diverge catastrophically. Leveraging non-linear dimensionality reduction of autoencoders and the freedom of non-linear operator inference with neural-networks, we aim to solve this problem by imposing a synthetic constraint in the reduced order space. The sy

Kiseok Yeon

In this paper, we investigate the solubility of homogeneous polynomial equations. The work of Browning, Le boudec, Sawin [3] shows that almost all homogeneous equations of degree $d\geq 4$ in $d+1$ or more variables satisfy the Hasse principle, and in particular that a positive portion possess a non-trivial integral solution. Our main result, when combined w

Ranyang Zhou, Sabbir Ahmed, Adnan Siraj Rakin, Shaahin Angizi

With deep learning deployed in many security-sensitive areas, machine learning security is becoming progressively important. Recent studies demonstrate attackers can exploit system-level techniques exploiting the RowHammer vulnerability of DRAM to deterministically and precisely flip bits in Deep Neural Networks (DNN) model weights to affect inference accura

Chad McKell, Mohammad Sina Nabizadeh, Stephanie Wang, Albert Chern

Solving the wave equation on an infinite domain has been an ongoing challenge in scientific computing. Conventional approaches to this problem only generate numerical solutions on a small subset of the infinite domain. In this paper, we present a method for solving the wave equation on the entire infinite domain using only finite computation time and memory.

Xun-Jie Xu

Neutrinos as almost massless particles could mediate long-range forces, known as neutrino forces. In this talk, I will introduce some theoretical aspects of neutrino forces, including why the potential of a neutrino force has the $1/r^{5}$ form and how it may vary under different circumstances. Experimental probes and possible implications for cosmology are

Raza Imam, Muhammad Huzaifa, Mohammed El-Amine Azz

Privacy and confidentiality of medical data are of utmost importance in healthcare settings. ViTs, the SOTA vision model, rely on large amounts of patient data for training, which raises concerns about data security and the potential for unauthorized access. Adversaries may exploit vulnerabilities in ViTs to extract sensitive patient information and compromi

B. Ait Ouazghour, M. Chabab

We extend the work \cite{Ouazghour:2018mld} to a more general and detailed analysis through studying the naturalness problem and $B$ physics constraints within the context of two higgs doublets model augmented with a complex scalar triplet field ($2HDMcT$). We first derive the modified Vetman conditions and show that naturalness problem might be evaded at th

Zihao Wang, Le Ma, Chen Zhang, Bo Han

Music as an emotional intervention medium has important applications in scenarios such as music therapy, games, and movies. However, music needs real-time arrangement according to changing emotions, bringing challenges to balance emotion real-time fit and soft emotion transition due to the fine-grained and mutable nature of the target emotion. Existing studi

Zeinab Alizadeh, Felipe Parra Polanco, Afrooz Jalilzadeh

We consider a stochastic Inverse Variational Inequality (IVI) problem defined by a continuous and co-coercive map over a closed and convex set. Motivated by the absence of performance guarantees for stochastic IVI, we present a variance-reduced projection-based gradient method. Our proposed method ensures an almost sure convergence of the generated iterates

Sanghoon Lee

In this paper, we establish the existence of conformal deformations that uniformize fourth order curvature on 4-dimensional Riemannian manifolds with positive conformal invariants. Specifically, we prove that any closed, compact Riemannian manifold with positive Yamabe invariant and total $Q$-curvature can be conformally deformed into a metric with positive

Daniel W. Dudt, Alan G. Goodman, Rory Conlin, Dario Panici

Omnigenity is a desirable property of toroidal magnetic fields that ensures confinement of trapped particles. Confining charged particles is a basic requirement for any fusion power plant design, but it can be difficult to satisfy with the non-axisymmetric magnetic fields used by the stellarator approach. Every ideal magnetohydrodynamic equilibrium previousl

Tunde Kushimo, Beth Thacker

Quantum Computing is an exciting field that draws from information theory, computer science, mathematics, and quantum physics to process information in fundamentally new ways. There is an ongoing race to develop practical quantum computers and increase the quantum workforce. This needs to be accompanied by the development of quantum computing programs, cours

Jiajie Li, Amani Abusafia, Abdallah Lakhdari, Athman Bouguettaya

We propose a novel activity-based profiling framework to estimate IoT users' harvested energy based on their daily activities. Energy is harvested from natural sources such as the kinetic movement of IoT users. The profiling framework captures the users' physical activity data to define activity-based profiles. These profiles are utilized to estimate the har

Kai Fukami, Kunihiko Taira

Modern air vehicles perform a wide range of operations, including transportation, defense, surveillance, and rescue. These aircraft can fly in calm conditions but avoid operations in gusty environments, encountered in urban canyons, over mountainous terrains, and in ship wakes. With extreme weather becoming ever more frequent due to global warming, it is ant

S. E. Svyakhovskiy, N. I. Pyshkov

We present a method of creation of photonic structures whose optical spectrum of the reflection coefficient has an arbitrary shape and has predetermined features. We develop an algorithm for the construction of a photonic crystal structure, perform numerical simulation of its reflection spectra, and create an experimental sample of a photonic crystal that ha

P. S. Emelyantsev, N. I. Pyshkov, S. E. Svyakhovskiy

A method for solving the inverse problem of designing the structure of a one-dimensional photonic crystal is proposed and experimentally implemented. It is known that a one-dimensional photonic crystal with a spatial sinusoidal modulation of the refractive index, has a narrow photonic bandgap at a frequency related to the spatial frequency of this sinusoid.

Guihong Li, Kartikeya Bhardwaj, Yuedong Yang, Radu Marculescu

Anytime neural networks (AnytimeNNs) are a promising solution to adaptively adjust the model complexity at runtime under various hardware resource constraints. However, the manually-designed AnytimeNNs are biased by designers' prior experience and thus provide sub-optimal solutions. To address the limitations of existing hand-crafted approaches, we first mod

Rakesh R. Kamath, Hahn Choo, Kamel Fezzaa, Sudarsanam Suresh Babu

Understanding the spatio-temporal evolution of thermal gradient (G) at and velocity (R) of the solid-liquid and liquid-vapor interfaces is critical for the control of site-specific microstructures in additive manufacturing. In-situ dynamic x-ray radiography (DXR) has been used in recent years to probe the evolution of R with high spatial and temporal resolut

Benedikt Schroer, Carmelo Evoli, Pasquale Blasi

The recent detection of the Geminga PWN by HAWC in the multi-TeV band allows us to infer precious information about the transport of pairs in the immediate surroundings of the pulsar and on the spectrum of pairs contributed by a Geminga-like pulsar to the spectrum of pairs in the cosmic radiation. Moreover, this detection allows us to address the issue of ho

Benjamin Gutteridge, Xiaowen Dong, Michael Bronstein, Francesco Di Giovanni

Message passing neural networks (MPNNs) have been shown to suffer from the phenomenon of over-squashing that causes poor performance for tasks relying on long-range interactions. This can be largely attributed to message passing only occurring locally, over a node's immediate neighbours. Rewiring approaches attempting to make graphs 'more connected', and sup

Cara Van Uden, Jeremy Irvin, Mars Huang, Nathan Dean

Self-supervised learning (SSL) enables label efficient training for machine learning models. This is essential for domains such as medical imaging, where labels are costly and time-consuming to curate. However, the most effective supervised or SSL strategy for transferring models to different healthcare systems or novel tasks is not well understood. In this

Konstantin Asteriadis, Christian Brønnum-Hansen, Kirill Melnikov

We discuss the non-factorizable corrections to Higgs boson production in weak boson fusion at the Large Hadron Collider. Such corrections depend on the finite part of the two-loop virtual amplitude $q \, Q \rightarrow q^\prime \, Q^\prime + H$ which, up to now, has only been computed in the eikonal approximation. We combine this contribution with real-virtua

Richard Arnold, Stefanka Chukova, Yu Hayakawa

Hazard rate functions of natural and manufactured systems often show a bathtub shaped failure rate. A high early rate of failures is followed by an extended period of useful working life where failures are rare, and finally the failure rate increases as the system reaches the end of its life. Parametric modelling of such hazard rate functions can lead to unn

Md. Rabiul Islam, Daniel Massicotte, Philippe Y. Massicotte, Wei-Ping Zhu

Gesture recognition using low-resolution instantaneous HD-sEMG images opens up new avenues for the development of more fluid and natural muscle-computer interfaces. However, the data variability between inter-session and inter-subject scenarios presents a great challenge. The existing approaches employed very large and complex deep ConvNet or 2SRNN-based dom