March 2024 arXiv papers — page 145

Jiachen Li, Weitao Yang

We develop a functional derivative approach to calculate the chemical potentials of the second-order perturbation theory (MP2). In the functional derivative approach, the correlation part of the MP2 chemical potential, which is the derivative of the MP2 correlation energy with respect to the occupation number of frontier orbitals, is obtained from the chain

Zihao Tang, Zheqi Lv, Shengyu Zhang, Yifan Zhou

Due to privacy or patent concerns, a growing number of large models are released without granting access to their training data, making transferring their knowledge inefficient and problematic. In response, Data-Free Knowledge Distillation (DFKD) methods have emerged as direct solutions. However, simply adopting models derived from DFKD for real-world applic

Qiongqiong Wang, Kong Aik Lee

Uncertainty modeling in speaker representation aims to learn the variability present in speech utterances. While the conventional cosine-scoring is computationally efficient and prevalent in speaker recognition, it lacks the capability to handle uncertainty. To address this challenge, this paper proposes an approach for estimating uncertainty at the speaker

Qingdong He, Jinlong Peng, Zhengkai Jiang, Xiaobin Hu

Recent success of vision foundation models have shown promising performance for the 2D perception tasks. However, it is difficult to train a 3D foundation network directly due to the limited dataset and it remains under explored whether existing foundation models can be lifted to 3D space seamlessly. In this paper, we present PointSeg, a novel training-free

Manish Chandra, Debasis Ganguly, Iadh Ounis

In-context learning (ICL) refers to the process of adding a small number of localized examples from a training set of labelled data to an LLM's prompt with an objective to effectively control the generative process seeking to improve the downstream task performance. Existing ICL approaches use an identical number of examples (a pre-configured hyper-parameter

Peng Zhang, Ting Wu, Jinsheng Sun, Weiqing Li

Existing interactive point cloud segmentation approaches primarily focus on the object segmentation, which aim to determine which points belong to the object of interest guided by user interactions. This paper concentrates on an unexplored yet meaningful task, i.e., interactive point cloud semantic segmentation, which assigns high-quality semantic labels to

Yuhao Jia, Wenhan Tan

Diffusion-driven text-to-image (T2I) generation has achieved remarkable advancements in recent years. To further improve T2I models' capability in numerical and spatial reasoning, layout is employed as an intermedium to bridge large language models and layout-based diffusion models. However, these methods often rely on closed-source, large-scale LLMs for lay

Michael Ginn, Lindia Tjuatja, Taiqi He, Enora Rice

Language documentation projects often involve the creation of annotated text in a format such as interlinear glossed text (IGT), which captures fine-grained morphosyntactic analyses in a morpheme-by-morpheme format. However, there are few existing resources providing large amounts of standardized, easily accessible IGT data, limiting their applicability to l

Etash Guha, Vihan Lakshman

While deep neural networks have demonstrated groundbreaking performance in various settings, these models often suffer from \emph{catastrophic forgetting} when trained on new tasks in sequence. Several works have empirically demonstrated that increasing the width of a neural network leads to a decrease in catastrophic forgetting but have yet to characterize

Xuefeng Wang, Henglin Pu, Hyung Jun Kim, Husheng Li

Safe Multi-agent reinforcement learning (safe MARL) has increasingly gained attention in recent years, emphasizing the need for agents to not only optimize the global return but also adhere to safety requirements through behavioral constraints. Some recent work has integrated control theory with multi-agent reinforcement learning to address the challenge of

Jianhao Xie, Ziang Zhang, Guibo Luo, Yuesheng Zhu

Large pre-trained models with their numerous model parameters and extensive training datasets have shown excellent performance in various tasks. Many publicly available medical image datasets do not have a sufficient amount of data so there are few large-scale models in medical imaging. We propose a large-scale Tumor Segmentation Foundation Model (TSFM) with

Luis Verde-Star

We use linear algebraic methods to obtain general results about linear operators on a space of polynomials that we apply to the operators associated with a polynomial sequence by the monomiality property. We show that all such operators are differential operators with polynomial coefficients of finite of infinite order. We consider the monomiality operators

Rukhshanda Hussain, Hui Xian Grace Lim, Borchun Chen, Mubarak Shah

Novel view synthesis has observed tremendous developments since the arrival of NeRFs. However, Nerf models overfit on a single scene, lacking generalization to out of distribution objects. Recently, diffusion models have exhibited remarkable performance on introducing generalization in view synthesis. Inspired by these advancements, we explore the capabiliti

Jielin Yang, Suchuan Dong

We present the general forms of piece-wise functions on partitioned domains satisfying an intrinsic $C^0$ or $C^1$ continuity across the sub-domain boundaries. These general forms are constructed based on a strategy stemming from the theory of functional connections, and we refer to partitioned domains endowed with these general forms as functionally connect

Yingtian Zou, Kenji Kawaguchi, Yingnan Liu, Jiashuo Liu

Generalizing to out-of-distribution (OOD) data or unseen domain, termed OOD generalization, still lacks appropriate theoretical guarantees. Canonical OOD bounds focus on different distance measurements between source and target domains but fail to consider the optimization property of the learned model. As empirically shown in recent work, the sharpness of l

Ryu Sasaki

Krylov complexity is considered to provide a measure of the growth of operators evolving under Hamiltonian dynamics. The main strategy is the analysis of the structure of Krylov subspace $\mathcal{K}_M(\mathcal{H},\eta)$ spanned by the multiple applications of the Liouville operator $\mathcal{L}$ defined by the commutator in terms of a Hamiltonian $\mathcal{

Apriadi Salim Adam, Firdaus, Mirza Satriawan

We investigate sphaleron solutions of the field equations in the modified mirror model. This model is based on SU(3)$_1$ $\otimes$ SU(3)$_2$ $\otimes$ SU(2)$_L$ $\otimes$ SU(2)$_R$ $\otimes$ U(1)$_{Y}$ $\otimes$ U(1)$_{X}$ gauge group. Different from the usual Weinberg-Salam theory, we will have two types of fields, the ordinary (standard model) and its mirr

Cun Xue, Kai-Wei Cao, Tian He, Chong Wei

Flux jumps observed in high-$J_c$ Nb$_3$Sn conductors are urgent problems to construct high field superconducting magnets. The low-field instabilities usually reduce the current-carrying capability and thus cause the premature quench of Nb$_3$Sn coils at low magnetic field. In this paper, we explore suppressing the flux jumps by ferromagnetic (FM) layer. Fir

Md. Shirajum Munir, Sravanthi Proddatoori, Manjushree Muralidhara, Walid Saad

Understanding the potential of generative AI (GenAI)-based attacks on the power grid is a fundamental challenge that must be addressed in order to protect the power grid by realizing and validating risk in new attack vectors. In this paper, a novel zero trust framework for a power grid supply chain (PGSC) is proposed. This framework facilitates early detecti

Yufeng Yang, Ashutosh Pandey, DeLiang Wang

It has been shown that the intelligibility of noisy speech can be improved by speech enhancement (SE) algorithms. However, monaural SE has not been established as an effective frontend for automatic speech recognition (ASR) in noisy conditions compared to an ASR model trained on noisy speech directly. The divide between SE and ASR impedes the progress of rob

Matthias Maier, Dennis Corraliza-Rodriguez, Dionisios Margetis

We study numerically and analytically how the Dyakonov-Shur instability for a two-dimensional (2D) inviscid electronic fluid in a long channel can be affected by an external, out-of-plane static magnetic field. By linear stability analysis for a model based on the shallow-water equations, we describe the discrete spectrum of frequencies. When the fluid syste

Xinyu Li, Yutong Guo, Jixuan He, Jiacheng Zhao

The rapid development of social networks has a wide range of social effects, which facilitates the study of social issues. Accurately forecasting the information propagation process within social networks is crucial for promptly understanding the event direction and effectively addressing social problems in a scientific manner. The relationships between non-

Hua Guan, Xiao-Qiu Qi, Jian-Guo Li, Peng-Peng Zhou

Accurately describing nuclear interactions within atomic nuclei remains a challenge, which hinders our exploration of new physics beyond the Standard Model. However, these nuclear interactions can be characterized by nuclear parameters such as the Zemach radius and the electric quadrupole moment, which are reflected in atomic spectra. Our work has achieved h

Weilun Xu, An Chang

Given a graph $F$, let $SPEX_P(n,F)$ be the set of graphs with the maximum spectral radius among all $F$-free $n$-vertex planner graph. In 2017, Tait and Tobin proved that for sufficiently $n$, $K_2+P_{n-2}$ is the unique graph with the maximum spectral radius over all $n$-vertex planner graphs. In this paper, focusing on $SPEX_P(n,K_2+H)$ in which $H$ is a

Jiameng Bai, Sai Wu, Jie Song, Junbo Zhao

As a fundamental problem in transfer learning, model selection aims to rank off-the-shelf pre-trained models and select the most suitable one for the new target task. Existing model selection techniques are often constrained in their scope and tend to overlook the nuanced relationships between models and tasks. In this paper, we present a pragmatic framework

Xinyu Li, Yu Gu, Chenwei Wang, Peng Zhao

With the continuous development of computer technology and network technology, the scale of the network continues to expand, the network space tends to be complex, and the application of computers and networks has been deeply into politics, the military, finance, electricity, and other important fields. When security events do not occur, the vulnerability as

Yang Zhang, Teoh Tze Tzun, Lim Wei Hern, Tiviatis Sim

Recent advancements in diffusion models have notably improved the perceptual quality of generated images in text-to-image synthesis tasks. However, diffusion models often struggle to produce images that accurately reflect the intended semantics of the associated text prompts. We examine cross-attention layers in diffusion models and observe a propensity for

Runze Guo, Feng Xue, Anlong Ming, Nicu Sebe

Recently, neural networks (NN) have made great strides in combinatorial optimization. However, they face challenges when solving the capacitated arc routing problem (CARP) which is to find the minimum-cost tour covering all required edges on a graph, while within capacity constraints. In tackling CARP, NN-based approaches tend to lag behind advanced metaheur

Yue Shang, Yifan Han, Wenhui Wan, Yong Liu

In the present work, the three stable MXenes M n+1 C n O 2 (M=Nb,Ta) are explored based onfirst-principles calculations. These materials are important derivatives of 2D materials and exhib-it distinctive properties, holding vast potential in nanodevices. All these M n+1 C n O 2 (M=Nb,Ta)materials exhibit outstanding superconducting performance, with correspo

Bo Zhang, Wenhui Wan, Yong Liu, Yanfeng Ge

Compounds from groups IV and V have been the focus of recent research due to their impressive physical characteristics and structural stability. In this study, the MX monolayers (M=Sn, Pb; N=P, As) are investigated with first-principles calculations based on Boltzmann transport theory. The results show that SnP, SnAs, and PbAs all exhibit indirect band gaps,

Linyi Li, Shijie Geng, Zhenwen Li, Yibo He

Large Language Models for code (code LLMs) have witnessed tremendous progress in recent years. With the rapid development of code LLMs, many popular evaluation benchmarks, such as HumanEval, DS-1000, and MBPP, have emerged to measure the performance of code LLMs with a particular focus on code generation tasks. However, they are insufficient to cover the ful

Lang Nie, Chunyu Lin, Kang Liao, Yun Zhang

In this paper, we retarget video stitching to an emerging issue, named warping shake, when extending image stitching to video stitching. It unveils the temporal instability of warped content in non-overlapping regions, despite image stitching having endeavored to preserve the natural structures. Therefore, in most cases, even if the input videos to be stitch

Viktor V. Dodonov, Alexandre V. Dodonov

We have obtained explicit analytical formulas for the mean energy and its variance (characterizing the energy fluctuations) of a quantum harmonic oscillator with time-dependent frequency in the adiabatic regimes after the frequency passes through zero. The behavior of energy turns out to be quite different in two cases: when the frequency remains real and wh

Bernard Chazelle, Kritkorn Karntikoon, Jakob Nogler

We investigate the emergence of periodic behavior in opinion dynamics and its underlying geometry. For this, we use a bounded-confidence model with contrarian agents in a convolution social network. This means that agents adapt their opinions by interacting with their neighbors in a time-varying social network. Being contrarian, the agents are kept from reac

Shuai Tan, Bin Ji, Ye Pan

Generating emotional talking faces is a practical yet challenging endeavor. To create a lifelike avatar, we draw upon two critical insights from a human perspective: 1) The connection between audio and the non-deterministic facial dynamics, encompassing expressions, blinks, poses, should exhibit synchronous and one-to-many mapping. 2) Vibrant expressions are

Zelin Tan, Jianfa Zhang, Zhihong Zhu, Wei Chen

Compared with well-developed free space polarization converters, polarization conversion between TE and TM modes in waveguide is generally considered to be caused by shape birefringence, like curvature, morphology of waveguide cross section and scattering. Here, we reveal a hidden polarization conversion mechanism in X-cut lithium niobate microrings, that is

V. Ryzhii, C. Tang, T. Otsuji, M. Ryzhii

We analyze the operation of the hot-electron FET bolometers with the graphene channels (GCs) and the gate barrier layers (BLs). Such bolometers use the thermionic emission of the hot electrons heated by incident modulated THz radiation. The hot electron transfer from the GC into the metal gate. As the THz detectors, these bolometers can operate at room tempe

Yizhou Dang, Yuting Liu, Enneng Yang, Guibing Guo

Sequential recommendation aims to provide users with personalized suggestions based on their historical interactions. When training sequential models, padding is a widely adopted technique for two main reasons: 1) The vast majority of models can only handle fixed-length sequences; 2) Batching-based training needs to ensure that the sequences in each batch ha

Yohei Sawada

Although data assimilation originates from control theory, the relationship between modern data assimilation methods in geoscience and model predictive control has not been extensively explored. In the present paper, I discuss that the modern data assimilation methods in geoscience and model predictive control essentially minimize the similar quadratic cost

Vida Dujmović, Gwenaël Joret, Piotr Micek, Pat Morin

Let $T$ be a tree on $t$ vertices. We prove that for every positive integer $k$ and every graph $G$, either $G$ contains $k$ pairwise vertex-disjoint subgraphs each having a $T$ minor, or there exists a set $X$ of at most $t(k-1)$ vertices of $G$ such that $G-X$ has no $T$ minor. The bound on the size of $X$ is best possible and improves on an earlier $f(t)k

Ronaldo Laishram, Tadayuki Kodama, Takahiro Morishita, Andreas Faisst

We explore the morphological features and star formation activities of [OII] emitters in the COSMOS UltraDeep field at $z \sim 1.5$ using JWST NIRCam data from the COSMOS-Web survey and Subaru Hyper Suprime-Cam. We also report the discovery of large filamentary structures traced by [OII] emitters, surrounding an extremely overdense core with a galaxy number

Jiayi Wen, Zixi Ye, Xuan Zhang

Justification bias, wherein retirees may report poorer health to rationalize their retirement, poses a major concern to the widely-used measure of self-assessed health in retirement studies. This paper introduces a novel method for testing the presence of this bias in the spirit of regression discontinuity. The underlying idea is that any sudden shift in sel

Danrui Qi, Weiling Zheng, Jiannan Wang

Feature augmentation from one-to-many relationship tables is a critical but challenging problem in ML model development. To augment good features, data scientists need to come up with SQL queries manually, which is time-consuming. Featuretools [1] is a widely used tool by the data science community to automatically augment the training data by extracting new

Narim Jeong, Donghwan Lee

Soft Q-learning is a variation of Q-learning designed to solve entropy regularized Markov decision problems where an agent aims to maximize the entropy regularized value function. Despite its empirical success, there have been limited theoretical studies of soft Q-learning to date. This paper aims to offer a novel and unified finite-time, control-theoretic a

Shuai Tan, Bin Ji, Ye Pan

Although automatically animating audio-driven talking heads has recently received growing interest, previous efforts have mainly concentrated on achieving lip synchronization with the audio, neglecting two crucial elements for generating expressive videos: emotion style and art style. In this paper, we present an innovative audio-driven talking face generati

Anton Morgunov, Henry K. Tran, Oinam Romesh Meitei, Yu-Che Chien

X-ray photoelectron spectroscopy (XPS) measures core-electron binding energies (CEBEs) to reveal element-specific insights into chemical environment and bonding. Accurate theoretical CEBE prediction aids XPS interpretation but requires proper modeling of orbital relaxation and electron correlation upon core-ionization. This work systematically investigates b

Shuai Tan, Bin Ji, Yu Ding, Ye Pan

Generating stylized talking head with diverse head motions is crucial for achieving natural-looking videos but still remains challenging. Previous works either adopt a regressive method to capture the speaking style, resulting in a coarse style that is averaged across all training data, or employ a universal network to synthesize videos with different styles

Braxton S. Cuneo, Ilham Variansyah

The techniques used to generate pseudo-random numbers for Monte Carlo (MC) applications bear many implications on the quality and speed of that programs work. As a random number generator (RNG) slows, the production of random numbers begins to dominate runtime. As RNG output grows in correlation, the final product becomes less reliable. These difficulties ar

Yulong Liu, Yongqiang Ma, Guibo Zhu, Haodong Jing

Deciphering visual content from functional Magnetic Resonance Imaging (fMRI) helps illuminate the human vision system. However, the scarcity of fMRI data and noise hamper brain decoding model performance. Previous approaches primarily employ subject-specific models, sensitive to training sample size. In this paper, we explore a straightforward but overlooked

Ioana Marinescu, Christiane Fellbaum

Determining the intended, context-dependent meanings of noun compounds like "shoe sale" and "fire sale" remains a challenge for NLP. Previous work has relied on inventories of semantic relations that capture the different meanings between compound members. Focusing on Romanian compounds, whose morphosyntax differs from that of their English counterparts, we

Pedram Yousefian, Betul Akkopru-Akgun, Clive A. Randall, Susan Trolier-McKinstry

The properties of dielectric and piezoelectric oxides are determined by their processing history, crystal structure, chemical composition, microstructure, dopants (or defect) distribution, and defect kinetics. These materials are essential in a diverse range of applications including aerospace, medical, military, transportation, power engineering, and commun

Zhenwei Li, Xuefei Chen

Gravitational Waves (GWs) provide a unique way to explore our Universe. The ongoing ground-based detectors, e.g., LIGO, Virgo, and KAGRA, and the upcoming next-generation detectors, e.g., Cosmic Explorer and Einstein Telescope, as well as the future space-borne GW antennas, e.g., LISA, TianQin, and TaiJi, cover a wide range of GW frequencies {from $\sim 10^{

Chaoyi Wang, Yaozhe Song, Yafeng Zhang, Jun Pei

Currently, various studies have been exploring generation of long videos. However, the generated frames in these videos often exhibit jitter and noise. Therefore, in order to generate the videos without these noise, we propose a novel framework composed of four modules: separate tuning module, average fusion module, combined tuning module, and inter-frame co

Ming Zhang, Ke Chang, Yunfang Wu

Multi-modal semantic understanding requires integrating information from different modalities to extract users' real intention behind words. Most previous work applies a dual-encoder structure to separately encode image and text, but fails to learn cross-modal feature alignment, making it hard to achieve cross-modal deep information interaction. This paper p

Michael Andersland

Large Language Models (LLMs) like GPT-4 and LLaMA have shown incredible proficiency at natural language processing tasks and have even begun to excel at tasks across other modalities such as vision and audio. Despite their success, LLMs often struggle to perform well on low-resource languages because there is so little training data available. This shortcomi

Yen Q. Do

We consider random polynomials $p_n(x)=\xi_0+\xi_1+\dots+\xi_n x^n$ whose coefficients are independent and identically distributed with zero mean, unit variance, and bounded $(2+\epsilon)^{th}$ moment (for some $\epsilon>0$), also known as the Kac polynomials. Let $N_n$ denote the number of real roots of $p_n$. In this paper, motivated by a question from Igo

Xing Lei, Longjun Liu, Zhiheng Zhou, Hongbin Sun

In this paper, we explore how to design lightweight CNN architecture for embedded computing systems. We propose L-Mobilenet model for ZYNQ based hardware platform. L-Mobilenet can adapt well to the hardware computing and accelerating, and its network structure is inspired by the state-of-the-art work of Inception-ResnetV1 and MobilenetV2, which can effective

Mi Luo, Zihui Xue, Alex Dimakis, Kristen Grauman

We investigate exocentric-to-egocentric cross-view translation, which aims to generate a first-person (egocentric) view of an actor based on a video recording that captures the actor from a third-person (exocentric) perspective. To this end, we propose a generative framework called Exo2Ego that decouples the translation process into two stages: high-level st

Mohammed Safi Ur Rahman Khan, Priyam Mehta, Ananth Sankar, Umashankar Kumaravelan

Despite the considerable advancements in English LLMs, the progress in building comparable models for other languages has been hindered due to the scarcity of tailored resources. Our work aims to bridge this divide by introducing an expansive suite of resources specifically designed for the development of Indic LLMs, covering 22 languages, containing a total

Omnia Alwazzan, Abbas Khan, Ioannis Patras, Gregory Slabaugh

Brain tumors are an abnormal growth of cells in the brain. They can be classified into distinct grades based on their growth. Often grading is performed based on a histological image and is one of the most significant predictors of a patients prognosis, the higher the grade, the more aggressive the tumor. Correct diagnosis of a tumor grade remains challengin

Jan Laukemann, Ahmed E. Helal, S. Isaac Geronimo Anderson, Fabio Checconi

High-dimensional sparse data emerge in many critical application domains such as healthcare and cybersecurity. To extract meaningful insights from massive volumes of these multi-dimensional data, scientists employ unsupervised analysis tools based on tensor decomposition (TD) methods. However, real-world sparse tensors exhibit highly irregular shapes and dat

Raza Imam, Faisal Anwer

With recent elevated adaptation of cloud services in almost every major public sector, the health sector emerges as a vulnerable segment, particularly in data exchange of sensitive Health records, as determining the retention, exchange, and efficient use of patient records without jeopardizing patient privacy, particularly on mobile-applications remains an a

Luca Candelori, Vladimir Y. Chernyak, John R. Klein

For an $n$-qubit system, a rational function on the space of mixed states which is invariant with respect to the action of the group of local symmetries may be viewed as a detailed measure of entanglement. We show that the field of all such invariant rational functions is purely transcendental over the complex numbers and has transcendence degree $4^n - 2n-1

Fedor V. Kovalev, Andrey E. Miroshnichenko, Alexey A. Basharin, Hannes Toepfer

The remarkable properties of toroidal metasurfaces, featuring ultrahigh-Q bound states in the continuum (BIC) resonances and nonradiating anapole modes, have garnered significant attention. The active manipulation of quasi-BIC resonance characteristics offers substantial potential for advancing tunable metasurfaces. Our study explores explicitly the applicat

E. Garrido, A. S. Jensen

The squeezing process of a three-dimensional quantum system by use of an external deformed one-body oscillator potential can also be described by the $d$-method, without external field and where the dimension can take non-integer values. In this work we first generalize both methods to $N$ particles and any transition between dimensions below $3$. Once this

Sefa Kayraklik, Ibrahim Yildirim, Ertugrul Basar, Ibrahim Hokelek

This paper presents reconfigurable intelligent surface (RIS)-aided deep learning (DL)-based spectrum sensing for next-generation cognitive radios. To that end, the secondary user (SU) monitors the primary transmitter (PT) signal, where the RIS plays a pivotal role in increasing the strength of the PT signal at the SU. The spectrograms of the synthesized data

Mark Whitmeyer

We explore the connection between an agent's decision problem and her ranking of information structures. We find that a finite amount of ordinal data on the agent's ranking of experiments is enough to identify her (finite) set of undominated actions (up to relabeling and duplication) and the beliefs rendering each such action optimal. An additional smatterin

Edward Valachovic, Ekaterina Shishova

Since the emergence of the SARS-CoV-2 virus, research into the existence, extent, and pattern of seasonality has been of the highest importance for public health preparation. This study uses a novel bandpass bootstrap approach called the Variable Bandpass Periodic Block Bootstrap (VBPBB) to investigate the periodically correlated (PC) components including se

Jaemin Oh, Seung Yeon Cho, Seok-Bae Yun, Eunbyung Park

In this study, we introduce a method based on Separable Physics-Informed Neural Networks (SPINNs) for effectively solving the BGK model of the Boltzmann equation. While the mesh-free nature of PINNs offers significant advantages in handling high-dimensional partial differential equations (PDEs), challenges arise when applying quadrature rules for accurate in

Mathieu Labbé, François Michaud

Distributed as an open source library since 2013, RTAB-Map started as an appearance-based loop closure detection approach with memory management to deal with large-scale and long-term online operation. It then grew to implement Simultaneous Localization and Mapping (SLAM) on various robots and mobile platforms. As each application brings its own set of contr

Wenbo Shi, Neel Kanth Kundu, Matthew R. McKay, Robert Malaney

As an alternative to quantum error correction, quantum error mitigation methods, including Zero-Noise Extrapolation (ZNE), have been proposed to alleviate run-time errors in current noisy quantum devices. In this work, we propose a modified version of ZNE that provides for a significant performance enhancement on current noisy devices. Our modified ZNE metho

Omnia Alwazzan, Ioannis Patras, Gregory Slabaugh

Fusion of multimodal healthcare data holds great promise to provide a holistic view of a patient's health, taking advantage of the complementarity of different modalities while leveraging their correlation. This paper proposes a simple and effective approach, inspired by attention, to fuse discriminative features from different modalities. We propose a novel

Kaspar Märtens, Christopher Yau

Generative models for multimodal data permit the identification of latent factors that may be associated with important determinants of observed data heterogeneity. Common or shared factors could be important for explaining variation across modalities whereas other factors may be private and important only for the explanation of a single modality. Multimodal

Yinyan Bu, Robin Rajamäki, Anand Dabak, Rajan Narasimha

This paper addresses the problem of single snapshot Direction-of-Arrival (DOA) estimation, which is of great importance in a wide-range of applications including automotive radar. A popular approach to achieving high angular resolution when only one temporal snapshot is available is via subspace methods using spatial smoothing. This involves leveraging spati

Lydia Beresford, Savannah Clawson, Jesse Liu

Measuring the tau-lepton ($\tau$) anomalous magnetic moment $a_\tau = (g_\tau -2)/2$ in photon fusion production ($\gamma\gamma\to\tau\tau$) tests foundational Standard Model principles. However, $\gamma\gamma\to\tau\tau$ eludes observation in LHC proton collisions (pp) despite enhanced new physics sensitivity from higher-mass reach than existing probes. We

Pasin Manurangsi

In the Max $k$-Weight SAT (aka Max SAT with Cardinality Constraint) problem, we are given a CNF formula with $n$ variables and $m$ clauses together with a positive integer $k$. The goal is to find an assignment where at most $k$ variables are set to one that satisfies as many constraints as possible. Recently, Jain et al. [SODA'23] gave an FPT approximation

Andrey Kudinov

In this paper we consider the topological products of modal logics of S4.1 and S4. We prove that it is equal to the fusion of logics S4.1 and S4 plus one additional axiom. We also show that this product is decidable. This is an example of a topological product of logics that is greater than the fusion but less than the expanding product of the corresponding

Takumi Aizawa, Motoya Shinozaki, Yoshihiro Fujiwara, Takeshi Kumasaka

The quantum cellular automata (QCA) effect is a transition in which multiple electron move coordinately by Coulomb interactions and observed in multiple quantum dots. This effect will be useful for realizing and improving quantum cellular automata and information transfer using multiple electron transfer. In this paper, we investigate the real-time dynamics

Nelson Colón Vargas

This paper explores the intricate relationship between capitalism, racial injustice, and artificial intelligence (AI), arguing that AI acts as a contemporary vehicle for age-old forms of exploitation. By linking historical patterns of racial and economic oppression with current AI practices, this study illustrates how modern technology perpetuates and deepen

Pablo Zambrano

Addressing Alzheimer's disease (AD) requires innovative strategies beyond current single-target drugs. This Letter to the Editor suggests that multitarget molecules, especially those targeting neuronal membrane protection, could offer a comprehensive approach to AD therapy, advocating for further research into their mechanisms and therapeutic potential.

Christian Genest, Frédéric Ouimet, Donald Richards

In 1934, the American statistician Samuel S. Wilks derived remarkable formulas for the joint moments of embedded principal minors of sample covariance matrices in multivariate Gaussian populations, and he used them to compute the moments of sample statistics in various applications related to multivariate linear regression. These important but little-known m

Nathan Johnson, Aashwin Ananda Mishra, Apurva Mehta

The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a beginner but still struggles to consistently achieve precision when synth

Chuning Zhu, Xinqi Wang, Tyler Han, Simon S. Du

Intelligent agents must be generalists, capable of quickly adapting to various tasks. In reinforcement learning (RL), model-based RL learns a dynamics model of the world, in principle enabling transfer to arbitrary reward functions through planning. However, autoregressive model rollouts suffer from compounding error, making model-based RL ineffective for lo

Ryo Kanno, Pham H. Nguyen, Joshua Pinskier, David Howard

One of the trendsetting themes in soft robotics has been the goal of developing the ultimate universal soft robotic gripper. One that is capable of manipulating items of various shapes, sizes, thicknesses, textures, and weights. All the while still being lightweight and scalable in order to adapt to use cases. In this work, we report a soft gripper that enab

Fei Wang, Chao Shang, Sarthak Jain, Shuai Wang

User alignment is crucial for adapting general-purpose language models (LMs) to downstream tasks, but human annotations are often not available for all types of instructions, especially those with customized constraints. We observe that user instructions typically contain constraints. While assessing response quality in terms of the whole instruction is ofte

K. Thompson, U. Zülicke, J. Schmalian, M. Governale

We investigate odd-in-time - or odd-frequency - pairing of fermions in equilibrium systems within the particle-number-conserving framework of Penrose, Onsager and Yang, where superfluid order is defined by macroscopic eigenvalues of reduced density matrices. We show that odd-frequency pair correlations are synonymous with even fermion-exchange symmetry in a

Sami Khairy, Gabriel Mittag, Vishak Gopal, Francis Y. Yan

The quality of experience (QoE) delivered by video conferencing systems to end users depends in part on correctly estimating the capacity of the bottleneck link between the sender and the receiver over time. Bandwidth estimation for real-time communications (RTC) remains a significant challenge, primarily due to the continuously evolving heterogeneous networ

Kaiwen Wang, Dawen Liang, Nathan Kallus, Wen Sun

We study risk-sensitive RL where the goal is learn a history-dependent policy that optimizes some risk measure of cumulative rewards. We consider a family of risks called the optimized certainty equivalents (OCE), which captures important risk measures such as conditional value-at-risk (CVaR), entropic risk and Markowitz's mean-variance. In this setting, we

Scott Siegel, Jiaqing Zhang, Sabyasachi Bandyopadhyay, Subhash Nerella

Despite the importance of closely monitoring patients in the Intensive Care Unit (ICU), many aspects are still assessed in a limited manner due to the time constraints imposed on healthcare providers. For example, although excessive visitations during rest hours can potentially exacerbate the risk of circadian rhythm disruption and delirium, it is not captur

Anka He Chen, Ziheng Liu, Yin Yang, Cem Yuksel

We introduce vertex block descent, a block coordinate descent solution for the variational form of implicit Euler through vertex-level Gauss-Seidel iterations. It operates with local vertex position updates that achieve reductions in global variational energy with maximized parallelism. This forms a physics solver that can achieve numerical convergence with

Jacob Carruth, Maximilian F. Eggl, Charles Fefferman, Clarence W. Rowley

We consider a simple control problem in which the underlying dynamics depend on a parameter $a$ that is unknown and must be learned. We study three variants of the control problem: Bayesian control, in which we have a prior belief about $a$; bounded agnostic control, in which we have no prior belief about $a$ but we assume that $a$ belongs to a bounded set;

Hamid Mozaffari, Sunav Choudhary, Amir Houmansadr

Federated learning (FL) is a distributed machine learning paradigm that enables training models on decentralized data. The field of FL security against poisoning attacks is plagued with confusion due to the proliferation of research that makes different assumptions about the capabilities of adversaries and the adversary models they operate under. Our work ai

Drew Armstrong

For each pair of coprime integers $a$ and $b$ we have a rational $q$-Catalan number $\operatorname{Cat}(a,b)_q=\binom{a+b}{a}_q/[a+b]_q$. It is known that this is a polynomial in $q$ with nonnegative integer coefficients, but the nature of these coefficients is still mysterious. Our current understanding is based on the rational shuffle conjecture that was c

Soodeh Kalaie, Andy Bulpitt, Alejandro F. Frangi, Ali Gooya

Generative modelling for shapes is a prerequisite for In-Silico Clinical Trials (ISCTs), which aim to cost-effectively validate medical device interventions using synthetic anatomical shapes, often represented as 3D surface meshes. However, constructing AI models to generate shapes closely resembling the real mesh samples is challenging due to variable verte

K. Altenmüller, J. F. Castel, S. Cebrián, T. Dafni

In this paper we present measurements performed with a Micromegas X-ray detector setup. The detector is a prototype in the context of the BabyIAXO helioscope, which is under construction to search for an emission of the hypothetical axion particle from the sun. An important component of such a helioscope is a low background X-ray detector with a high efficie

Maria Teresa Parreira, Sukruth Gowdru Lingaraju, Adolfo Ramirez-Aristizabal, Manaswi Saha

Machine learning models are commonly tested in-distribution (same dataset); performance almost always drops in out-of-distribution settings. For HRI research, the goal is often to develop generalized models. This makes domain generalization - retaining performance in different settings - a critical issue. In this study, we present a concise analysis of domai

Yuxin Zhao, Antonios Kyriazis, Pierre Sikivie

We investigate the effect of a dark matter caustic passing through the Solar System. We find, confirming a previous result, that the Sun tracks the caustic surface for some time. We integrate numerically the equations of motion of the Sun and a comet for a large number of initial conditions and of caustic passage properties. We calculate the probability for

Vikram Goddla

Deep reinforcement learning(DRL) has shown significant promise in a wide range of applications including computer games and robotics. Yet, training DRL policies consume extraordinary computing resources resulting in dense policies which are prone to overfitting. Moreover, inference with dense DRL policies limit their practical applications, especially in edg

Ruzanna Mat Jusoh, Konstantinos Ampountolas

A control scheme for the multi-gated perimeter traffic flow control problem of cities is presented. The proposed scheme determines feasible and optimally distributed input flows for the various gates located at the periphery of a protected network. A parsimonious model is employed to describe the traffic dynamics of the protected network. To describe traffic

Thomas Mühlenstädt, Jelena Frtunikj

This paper targets the question of predicting machine learning classification model performance, when taking into account the number of training examples per class and not just the overall number of training examples. This leads to the a combinatorial question, which combinations of number of training examples per class should be considered, given a fixed ov