February 2024 arXiv papers — page 41

Zhitong Guan, Zeyu Xiong, Mingming Fan

Parcel lockers have become an increasingly prevalent last-mile delivery method. Yet, a recent study revealed its accessibility challenges to blind and low-vision people (BLV). Informed by the study, we designed FetchAid, a standalone intelligent mobile app assisting BLV in using a parcel locker in real-time by integrating computer vision and augmented realit

Xianjun Wang, Yufu Liu, Yunlin Li, Langlang Xiong

Although photon spin-orbit interaction (SOI) has been extensively studied, the vortex-conversion efficiency and the enhancement of spin Hall effect in abnormal modes in SOI remain to be investigated. Using an one-dimensional (1D) photonic crystal (PhC) system with the anisotropic defect layer(ADL), we firstly find that the generation efficiency of the vortex

Chaoya Jiang, Hongrui Jia, Wei Ye, Mengfan Dong

Large Vision Language Models exhibit remarkable capabilities but struggle with hallucinations inconsistencies between images and their descriptions. Previous hallucination evaluation studies on LVLMs have identified hallucinations in terms of objects, attributes, and relations but overlooked complex hallucinations that create an entire narrative around a fic

R. R. S. Oliveira

In this paper, we analyze the relativistic and nonrelativistic energy spectra (fermionic Landau levels) for the noncommutative quantum Hall effect with anomalous magnetic moment in the conical G\"odel-type spacetime in (2+1)-dimensions, where such spacetime is the combination of the flat G\"odel-type spacetime with a cosmic string (conical gravitational topo

Zeyu Xiong, Shihan Fu, Yanying Zhu, Chenqing Zhu

Chinese traditional opera (Xiqu) performers often experience skin problems due to the long-term use of heavy-metal-laden face paints. To explore the current skincare challenges encountered by Xiqu performers, we conducted an online survey (N=136) and semi-structured interviews (N=15) as a formative study. We found that incomplete makeup removal is the leadin

Jihao Long, Xiaojun Peng, Lei Wu

Kernel ridge regression (KRR), also known as the least-squares support vector machine, is a fundamental method for learning functions from finite samples. While most existing analyses focus on the noisy setting with constant-level label noise, we present a comprehensive study of KRR in the noiseless regime -- a critical setting in scientific computing where

M. Kosmakov, V. Tarasov

We give new combinatorial formulae for vector-valued weight functions (off-shell nested Bethe vectors) for the evaluation modules over the Yangian Y(gl_n). This paper extends the result for the Yangian Y(gl_4) established earlier in arXiv:2312.00980.

Hongjian Yang

We study the instanton Floer homology for links in $\mathbb{RP}^3$ and compute the second page of Kronheimer--Mrowka's spectral sequence. As an application, we show that Khovanov homology detects the unknot and the projective unknot in $\mathbb{RP}^3$.

Nikola B. Kovachki, Samuel Lanthaler, Andrew M. Stuart

Operator learning refers to the application of ideas from machine learning to approximate (typically nonlinear) operators mapping between Banach spaces of functions. Such operators often arise from physical models expressed in terms of partial differential equations (PDEs). In this context, such approximate operators hold great potential as efficient surroga

So Yamagata

Discrete homotopy theory or A-homotopy theory is a combinatorial homotopy theory defined on graphs, simplicial complexes, and metric spaces, reflecting information about their connectivity. The present paper aims to further understand the (non-)similarities between the A-homotopy and ordinary homotopy theories through explicit constructions. More precisely,

Xia Liang, Xingjian Du, Jiaju Lin, Pei Zou

Large Language Models (LLM) have shown encouraging progress in multimodal understanding and generation tasks. However, how to design a human-aligned and interpretable melody composition system is still under-explored. To solve this problem, we propose ByteComposer, an agent framework emulating a human's creative pipeline in four separate steps : "Conception

Shengkun Ma, Jiale Han, Yi Liang, Bo Cheng

Continual Few-shot Relation Extraction (CFRE) is a practical problem that requires the model to continuously learn novel relations while avoiding forgetting old ones with few labeled training data. The primary challenges are catastrophic forgetting and overfitting. This paper harnesses prompt learning to explore the implicit capabilities of pre-trained langu

Moreshwar Tayde, Zinnat Hassan, P. K. Sahoo

This study investigates the possible existence of wormhole solutions with dark matter galactic halo profiles in the background of $f(Q,T)$ gravity. The primary focus of the current study is to find the significance of dark matter (DM) in the search for traversable wormhole solutions within galactic halos. Various dark matter profiles, such as Universal Rotat

Ziyong Wu, Yu Luo, Wei Wang, Xi Kang

In the regime of cosmology and large-scale structure formation, filaments are vital components of the cosmic web. This study employs statistical methods to examine the formation, evolution, and cosmological constraints of filaments identified by DisPerSe. We run large-sample of N-body simulations to study the filament length and its evolution. In general, th

L. M. Seaton, M. L. McCall, N. T. McCall

The kinematics of isolated dwarf galaxies in the Local Sheet have been studied to ascertain how the Council of Giants has affected flows. Peculiar velocities parallel to the Sheet in the frame of reference of the Council ascend steeply from negative to positive values on the near side of the Council at a heliocentric radius of $2.4 \pm 0.2$ Mpc. They descend

Seffi Cohen, Lior Rokach

This paper introduces BagStacking, a novel ensemble learning method designed to enhance the detection of Freezing of Gait (FOG) in Parkinson's Disease (PD) by using a lower-back sensor to track acceleration. Building on the principles of bagging and stacking, BagStacking aims to achieve the variance reduction benefit of bagging's bootstrap sampling while als

Saptarshi Chakraborty, Peter L. Bartlett

Variational Autoencoders (VAEs) have gained significant popularity among researchers as a powerful tool for understanding unknown distributions based on limited samples. This popularity stems partly from their impressive performance and partly from their ability to provide meaningful feature representations in the latent space. Wasserstein Autoencoders (WAEs

Kyle Gannon, James E. Hanson

We develop a notion of sampling, called \emph{generic sampling}, for the context of global Keisler measures where the standard product is replaced by the Morley product. Choosing a point randomly in this space with respect to our distribution yields a \emph{random generic type} in infinitely many variables. We investigate several natural model-theoretic even

Ziyi Ye, Jingtao Zhan, Qingyao Ai, Yiqun Liu

Query augmentation is a crucial technique for refining semantically imprecise queries. Traditionally, query augmentation relies on extracting information from initially retrieved, potentially relevant documents. If the quality of the initially retrieved documents is low, then the effectiveness of query augmentation would be limited as well. We propose Brain-

Rohit Singh, Roshan M. Bodile

This article provides a quick overview of quantum communication, bringing together several innovative aspects of quantum enabled transmission. We first take a neutral look at the role of quantum communication, presenting its importance for the forthcoming wireless. Then, we summarise the principles and basic mechanisms involved in quantum communication, incl

Qianrui Liu, Mohan Chen

Accurate prediction of electron transport coefficients is crucial for understanding warm dense matter. Utilizing the density functional theory (DFT) with the Kubo-Greenwood formula is widely used to evaluate the electrical and thermal conductivities of electrons. By adding the non-local potential correction term that appears in the dynamic Onsager coefficien

Nguyen Do, Tanmoy Chowdhury, Chen Ling, Liang Zhao

Multiplex influence maximization (MIM) asks us to identify a set of seed users such as to maximize the expected number of influenced users in a multiplex network. MIM has been one of central research topics, especially in nowadays social networking landscape where users participate in multiple online social networks (OSNs) and their influences can propagate

Cai Xu, Jiajun Si, Ziyu Guan, Wei Zhao

Multi-view learning aims to combine multiple features to achieve more comprehensive descriptions of data. Most previous works assume that multiple views are strictly aligned. However, real-world multi-view data may contain low-quality conflictive instances, which show conflictive information in different views. Previous methods for this problem mainly focus

Jin Hyung Lee, Ben Seiyon Lee

Gaussian and discrete non-Gaussian spatial datasets are common across fields like public health, ecology, geosciences, and social sciences. Bayesian spatial generalized linear mixed models (SGLMMs) are a flexible class of models for analyzing such data, but they struggle to scale to large datasets. Many scalable Bayesian methods, built upon basis representat

Ziang Wu, Jinwei Xie, Xuanyu Zhang, Tao Wang

Convolutional neural networks can automatically learn features via deep network architectures and given input samples. However, the robustness of obtained models may face challenges in varying scenes. Bigger differences in network architecture are beneficial to extract more diversified structural information to strengthen the robustness of an obtained super-

Ruiqi Zhang, Yuexiang Zhai, Andrea Zanette

What can an agent learn in a stochastic Multi-Armed Bandit (MAB) problem from a dataset that contains just a single sample for each arm? Surprisingly, in this work, we demonstrate that even in such a data-starved setting it may still be possible to find a policy competitive with the optimal one. This paves the way to reliable decision-making in settings wher

Ying Cao, Yi Zhu

The existence of edge states is one of the most vital properties of topological insulators. Although tremendous success has been accomplished in describing and explaining edge states associated with PT symmetry breaking, little work has been done on PT symmetry preserving cases. Two-dimensional Schroedinger operators with super honeycomb lattice potentials a

Parth S. Shah, Frank Yang, Chaitali Joshi, Mohammad Mirhosseini

Distributing entanglement between remote sites is integral to quantum networks. Here, we demonstrate the autonomous stabilization of remote entanglement between a pair of non-interacting superconducting qubits connected by an open waveguide on a chip. In this setting, the interplay between a classical continuous drive - supplied through the waveguide - and d

Junyu Luo, Xiaochen Wang, Jiaqi Wang, Aofei Chang

Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing. One of the most important directions for boosting the performance of automatic ICD coding is modeling ICD code relations. However, current methods insufficiently model the intrica

Zixuan Hu, Sabre Kais

We develop a "qubit information logic" (QIL) theory that uses the "qubit information equation" (QIE) and logic to describe the correlation behaviors of multi-qubit entanglement. Introducing the "global information status" and "local information availability", the QIL gives an alternative and natural interpretation of the "spooky action" and the quantum no-co

M. M. Hedman, M. S. Tiscareno, M. R. Showalter, L. N. Fletcher

JWST measured the infrared spectra of Saturn's rings and several of its small moons (Epimetheus, Pandora, Telesto and Pallene) as part of Guaranteed Time Observation program 1247. The NIRSpec instrument obtained near-infrared spectra of the small moons between 0.6 and 5.3 microns, which are all dominated by water-ice absorption bands. The shapes of the water

Ruth Johnson, Bogdan Pasaniuc

Precision medicine aims to create biomedical solutions tailored to specific factors that affect disease risk and treatment responses within the population. The success of the genomics era and recent widespread availability of electronic health records (EHR) has ushered in a new wave of genomic biobanks connected to EHR databases (EHR-linked biobanks). This p

Leonie Bensch, Andrea Casini, Aidan Cowley, Florian Dufresne

This chapter explores the practice of conducting user research studies and design assessments in virtual reality (VR). An overview of key VR hardware and software tools is provided, including game engines, such as Unity and Unreal Engine. Qualitative and quantitative research methods, along with their various synergies with VR, are likewise discussed, and so

Florian Dufresne, Tommy Nilsson, Geoffrey Gorisse, Enrico Guerra

Space agencies are in the process of drawing up carefully thought-out Concepts of Operations (ConOps) for future human missions on the Moon. These are typically assessed and validated through costly and logistically demanding analogue field studies. While interactive simulations in Virtual Reality (VR) offer a comparatively cost-effective alternative, they h

Youbao Zhang, Huijie Huang

Photolithography control system (PCS) is an extremely complex distributed control system, which is composed of dozens of networked microprocessors, hundreds of actuators, hundreds of thousands of sensors, and millions of lines of code. Cyber-physical system (CPS), which deeply merges computation with physical processes together, copes with complex system fro

Leonie Bensch, Tommy Nilsson, Jan Wulkop, Paul de Medeiros

Future crewed missions to the Moon will face significant environmental and operational challenges, posing risks to the safety and performance of astronauts navigating its inhospitable surface. Whilst head-up displays (HUDs) have proven effective in providing intuitive navigational support on Earth, the design of novel human-spaceflight solutions typically re

Fan Yang, Pierre Le Bodic, Michael Kamp, Mario Boley

Gradient boosting of prediction rules is an efficient approach to learn potentially interpretable yet accurate probabilistic models. However, actual interpretability requires to limit the number and size of the generated rules, and existing boosting variants are not designed for this purpose. Though corrective boosting refits all rule weights in each iterati

Zhenhua Wang, Wei Xie, Baosheng Wang, Enze Wang

Large Language Models (LLMs) have gradually become the gateway for people to acquire new knowledge. However, attackers can break the model's security protection ("jail") to access restricted information, which is called "jailbreaking." Previous studies have shown the weakness of current LLMs when confronted with such jailbreaking attacks. Nevertheless, compr

Molla Basir Ahamed, Partha Pratim Roy

In this paper, we study some improved and refined versions of the classical Bohr inequality applicable to the class $\mathcal{B}$, which consists of self-analytic mappings defined on the unit disk $\mathbb{D}$. First, we improve the Bohr inequality for the class $\mathcal{B}$ of analytic self-maps, incorporating the area measurements of sub-disks $\mathbb{D}

Shikun Mei, Fangfang Li, Quanxue Gao, Ming Yang

Anchor-based methods are a pivotal approach in handling clustering of large-scale data. However, these methods typically entail two distinct stages: selecting anchor points and constructing an anchor graph. This bifurcation, along with the initialization of anchor points, significantly influences the overall performance of the algorithm. To mitigate these is

Xinrui Song, Xuanang Xu, Pingkun Yan

Existing medical image registration algorithms rely on either dataset specific training or local texture-based features to align images. The former cannot be reliably implemented without large modality-specific training datasets, while the latter lacks global semantics thus could be easily trapped at local minima. In this paper, we present a training-free de

Nikhil S. Mande, Changpeng Shao

Quantum-inspired classical algorithms provide us with a new way to understand the computational power of quantum computers for practically-relevant problems, especially in machine learning. In the past several years, numerous efficient algorithms for various tasks have been found, while an analysis of lower bounds is still missing. Using communication comple

Yujiro Kawamata

We will develop a formal non-commutative (NC) deformation theory of smooth algebraic varieties $X$ defined over a field $k$, and describe a semi-universal deformation where the tangent space $T^1$ and the obstruction space $T^2$ are given as subspaces of Hochschild cohomology groups.

Yichao Lin, Hui Yang, Jeremy Hare, Igor Volkov

We present a collection of $\sim10,000$ X-ray sources from the 4th XMM-Newton Serendipitous Source Catalog (4XMM-DR13) with literature-verified classifications and multi-wavelength (MW) counterparts. We describe the process by which MW properties are obtained and an interactive online visualization tool we developed.

David Gamba, Yulin Yu, Yuan Yuan, Grant Schoenebeck

Amidst growing uncertainty and frequent restructurings, the impacts of employee exits are becoming one of the central concerns for organizations. Using rich communication data from a large holding company, we examine the effects of employee departures on socialization networks among the remaining coworkers. Specifically, we investigate how network metrics ch

B. Acharya, J. Alexandre, S. C. Behera, P. Benes

We report on a search for magnetic monopoles (MMs) produced in ultraperipheral Pb--Pb collisions during Run-1 of the LHC. The beam pipe surrounding the interaction region of the CMS experiment was exposed to 184.07 \textmu b$^{-1}$ of Pb--Pb collisions at 2.76 TeV center-of-mass energy per collision in December 2011, before being removed in 2013. It was scan

W. Leite, R. C. de Lamare

This paper investigates the problem of noncoherent direction-of-arrival (DOA) estimation using different sparse subarrays. In particular, we present a Multiple Measurements Vector (MMV) model for noncoherent DOA estimation based on a low-rank and sparse recovery optimization problem. Moreover, we develop two different practical strategies to obtain sparse ar

Qian Ma, Hongliang Chi, Hengrui Zhang, Kay Liu

The rise of self-supervised learning, which operates without the need for labeled data, has garnered significant interest within the graph learning community. This enthusiasm has led to the development of numerous Graph Contrastive Learning (GCL) techniques, all aiming to create a versatile graph encoder that leverages the wealth of unlabeled data for variou

Haochuan Xu, Ninh Pham

We present sDBSCAN, a scalable density-based clustering algorithm in high dimensions with cosine distance. Utilizing the neighborhood-preserving property of random projections, sDBSCAN can quickly identify core points and their neighborhoods, the primary hurdle of density-based clustering. Theoretically, sDBSCAN outputs a clustering structure similar to DBSC

Siqi Wang, Hailong Yang, Xuezhu Wang, Tongxuan Liu

Large language models (LLM) have recently attracted surging interest due to their outstanding capabilities across various domains. However, enabling efficient LLM inference is challenging due to its autoregressive decoding that generates tokens only one at a time. Although research works apply pruning or quantization to speed up LLM inference, they typically

Hoang Huy Vu, Quyen Ngoc Nguyen, Tuynh Van Pham, Chuong Van Nguyen

This paper studies a consensus problem in multidimensional networks having the same agent-to-agent interaction pattern under both intra- and cross-layer time delays. Several conditions for the agents to asymptotically reach a consensus are derived, which involve the overall network's structure, the local interacting pattern, and the assumptions specified on

Laurent W. Marcoux, Heydar Radjavi, Yuanhang Zhang

Let $\mathcal{H}$ be a complex, separable Hilbert space, and set $\mathfrak{c}($NIL$_2)=\{ MN - NM : N, M \in \mathcal{B}(\mathcal{H}), M^2 = 0 = N^2 \}$. When $\dim\, \mathcal{H}$ is finite, we characterise the set $\mathfrak{c}($NIL$_2)$ and its norm-closure CLOS$(\mathfrak{c}($NIL$_2))$. In the infinite-dimensional setting, we characterise the intersectio

Máximo Bañados

We derive a Weyl invariant equation for Gravity by gauging the global Weyl invariance of vacuum Einstein equations. The equation is linear in the curvature and a natural generalization of Einstein equations to Weyl geometry. The system has 5 physical polarizations, two tensor modes, two vectors modes and one scalar, associated to the cosmological constant. A

Shuling Wang, Zekun Ji, Bohua Zhan, Xiong Xu

Hybrid Communicating Sequential Processes (HCSP) is a formal model for hybrid systems, including primitives for evolution along an ordinary differential equation (ODE), communication, and parallel composition. Code generation is needed to convert HCSP models into code that can be executed in practice, and the correctness of this conversion is essential to en

Xingyu Guo, Jinfeng Liao, Hongxi Xing

Studies of exotic hadrons such as the $\chi_{cl}(3872)$ state provide crucial insights into the fundamental force governing the strong interaction dynamics, with an emerging new frontier to investigate their production in high energy nuclear collisions where a partonic medium is present. This contribution discusses the production mechanisms of exotic hadrons

Max Goldberg, Konstantin Batygin

The dynamics of the outer regular satellites of Saturn are driven primarily by the outward migration of Titan, but several independent constraints on Titan's migration are difficult to reconcile with the current resonant orbit of the small satellite Hyperion. We argue that Hyperion's rapid irregular tumbling greatly increases tidal dissipation with a steep d

Raj Korpan

Many measures of human-robot trust have proliferated across the HRI research literature because each attempts to capture the factors that impact trust despite its many dimensions. None of the previous trust measures, however, address the systems of inequity and structures of power present in HRI research or attempt to counteract the systematic biases and pot

Maziyar Khadivi, Mostafa Abbasi, Todd Charter, Homayoun Najjaran

This paper addresses a production scheduling problem derived from an industrial use case, focusing on unrelated parallel machine scheduling with the personnel availability constraint. The proposed model optimizes the production plan over a multi-period scheduling horizon, accommodating variations in personnel shift hours within each time period. It assumes s

T. Wu

Let G be a graph. The Laplacian ratio of G is the permanent of the Laplacian matrix of G divided by the product of degrees of all vertices. The computational complexity of Laplacian ratio is #P-complete. Brualdi and Goldwasser studied systematicly the properties of Laplacian ratios of graphs. And they proposed an open problem: what is the minimum value of th

Mina Dalirrooyfard, Konstantin Makarychev, Slobodan Mitrović

Given a graph with positive and negative edge labels, the correlation clustering problem aims to cluster the nodes so to minimize the total number of between-cluster positive and within-cluster negative edges. This problem has many applications in data mining, particularly in unsupervised learning. Inspired by the prevalence of large graphs and constantly ch

Andrew J. Soto Levins

Let $R$ be a regular local ring containing a field, let $I$ be an ideal with $d=\text{ht}{I}$, and assume $\text{ht}{p}=d$ for every minimal prime $p$ of $I$. We compute the Bass numbers $\mu^{0}(q,H_{I}^{d}(R))$ and $\mu^{1}(q,H_{I}^{d}(R))$ for all primes $q$. We then study $\mu^{2}(q,H_{I}^{d}(R))$ by considering the associated primes of $H_{I}^{d+1}(R)$.

Shu-Ting Pi, Cheng-Ping Hsieh, Qun Liu, Yuying Zhu

Building machine learning models can be a time-consuming process that often takes several months to implement in typical business scenarios. To ensure consistent model performance and account for variations in data distribution, regular retraining is necessary. This paper introduces a solution for improving online customer service in e-commerce by presenting

Shu-Ting Pi, Michael Yang, Yuying Zhu, Qun Liu

Customer service is often the most time-consuming aspect for e-commerce websites, with each contact typically taking 10-15 minutes. Effectively routing customers to appropriate agents without transfers is therefore crucial for e-commerce success. To this end, we have developed a machine learning framework that predicts the complexity of customer contacts and

Yufeng Ye, Jeremy B. Kline, Sean Chen, Kevin P. O'Brien

Fast, high-fidelity, and quantum nondemolition (QND) qubit readout is an essential element of quantum information processing. For superconducting qubits, state-of-the-art readout is based on a dispersive cross-Kerr coupling between a qubit and its readout resonator. The resulting readout can be high-fidelity and QND, but readout times are currently limited t

Zhaoyue Sun, Gabriele Pergola, Byron C. Wallace, Yulan He

With the advent of large language models (LLMs), there has been growing interest in exploring their potential for medical applications. This research aims to investigate the ability of LLMs, specifically ChatGPT, in the context of pharmacovigilance event extraction, of which the main goal is to identify and extract adverse events or potential therapeutic eve

Jiawen Wang, Leah Kawka

Deep convolutional neural networks have been shown to successfully recognize facial emotions for the past years in the realm of computer vision. However, the existing detection approaches are not always reliable or explainable, we here propose our model GiMeFive with interpretations, i.e., via layer activations and gradient-weighted class activation mapping.

Tim Dalby

Property($M$) in separable Banach spaces has played an important role in metric fixed point theory. This paper explores some of the Banach space properties that can be associated with Property($M$) and Property($M^*$).

Sachiko Saito

We consider toric resolutions of some strongly mixed weighted homogeneous polynomials of type $J_{10}^-$. We show that the strongly mixed weighted homogeneous polynomial $f := f_{2,2,1,2,1,4}\ (k=3)$ (see \S 3) has no mixed critical points on ${\mathbb{C}^*}^2$ (Lemma 14), and moreover, show that the strict transform $\tilde V$ of the mixed hypersurface sing

Lixian Zhang, Runmin Dong, Shuai Yuan, Jinxiao Zhang

Nighttime light (NTL) remote sensing observation serves as a unique proxy for quantitatively assessing progress toward meeting a series of Sustainable Development Goals (SDGs), such as poverty estimation, urban sustainable development, and carbon emission. However, existing NTL observations often suffer from pervasive degradation and inconsistency, limiting

Haritha Sindhu Rajeev, Xiao Hu, Wei-Liang Chen, Depei Zhang

Two-dimensional hybrid organic-inorganic perovskites (HOIPs) have emerged as promising materials for light-emitting diode applications. In this study, by using time-of-flight neutron spectroscopy we identified and quantitatively separated the lattice vibrational and molecular rotational dynamics of two perovskites, butylammonium lead iodide (BA)$_{2}$PbI$_{4

Mitchell C. Begelman

We present an analytic, quasi-local model for accretion discs threaded by net, vertical magnetic flux. In a simple slab geometry and ignoring stochastic mean-field dynamo effects, we calculate the large-scale field resulting from the balance between kinematic field amplification and turbulent diffusion. The ability of the disc to accumulate magnetic flux is

Shu-Ting Pi, Sidarth Srinivasan, Yuying Zhu, Michael Yang

E-commerce companies deal with a high volume of customer service requests daily. While a simple annotation system is often used to summarize the topics of customer contacts, thoroughly exploring each specific issue can be challenging. This presents a critical concern, especially during an emerging outbreak where companies must quickly identify and address sp

Ashutosh Singh, Ricardo Augusto Borsoi, Deniz Erdogmus, Tales Imbiriba

Recent advances in the theory of Neural Operators (NOs) have enabled fast and accurate computation of the solutions to complex systems described by partial differential equations (PDEs). Despite their great success, current NO-based solutions face important challenges when dealing with spatio-temporal PDEs over long time scales. Specifically, the current the

Shu-Ting Pi, Michael Yang, Qun Liu

Customers who reach out for customer service support may face a range of issues that vary in complexity. Routing high-complexity contacts to junior agents can lead to multiple transfers or repeated contacts, while directing low-complexity contacts to senior agents can strain their capacity to assist customers who need professional help. To tackle this, a mac

Sadaf Ghaffari, Nikhil Krishnaswamy

In this paper, we present an exploration of LLMs' abilities to problem solve with physical reasoning in situated environments. We construct a simple simulated environment and demonstrate examples of where, in a zero-shot setting, both text and multimodal LLMs display atomic world knowledge about various objects but fail to compose this knowledge in correct s

Wenzhi Peng, Zheng Liu, Haolin Pan, Peng Wang

The Anomalous Hall Effect (AHE) manifests as a transverse voltage proportional to magnetization in ferromagnetic materials under the application of a charge current, being an indispensable tool for probing magnetism, especially in nanoscale devices. However, the AHE primarily sensitizes to out-of-plane magnetization, thereby hindering its capacity to discern

Yanqi Qiao, Dazhuang Liu, Rui Wang, Kaitai Liang

While convolutional neural networks (CNNs) have achieved success in computer vision tasks, it is vulnerable to backdoor attacks. Such attacks could mislead the victim model to make attacker-chosen prediction with a specific trigger pattern. Until now, the trigger injection of existing attacks is mainly limited to spatial domain. Recent works take advantage o

Premysl Jedlicka, Agata Pilitowska

We study the diagonal mappings in non-involutive set-theoretic solutions of the Yang-Baxter equation. We show that, for non-degenerate solutions, they are commuting bijections. This gives the positive answer to the question: ``Is every non-degenerate solution bijective?'' of Ced\'{o}, Jespers and Verwimp. Additionally, we show that for a subclass of solution

Natalia L. Rubio, Luca Pegolotti, Martin R. Pfaller, Eric F. Darve

Reduced-order models (ROMs) allow for the simulation of blood flow in patient-specific vasculatures without the high computational cost and wait time associated with traditional computational fluid dynamics (CFD) models. Unfortunately, due to the simplifications made in their formulations, ROMs can suffer from significantly reduced accuracy. One common simpl

Zihan Zhou, Jonathan Booher, Khashayar Rohanimanesh, Wei Liu

Safe reinforcement learning tasks are a challenging domain despite being very common in the real world. The widely adopted CMDP model constrains the risks in expectation, which makes room for dangerous behaviors in long-tail states. In safety-critical domains, such behaviors could lead to disastrous outcomes. To address this issue, we first describe the prob

Chris La Valle, Josué Tonelli-Cueto

Separation bounds are a fundamental measure of the complexity of solving a zero-dimensional system as it measures how difficult it is to separate its zeroes. In the positive dimensional case, the notion of reach takes its place. In this paper, we provide bounds on the reach of a smooth algebraic variety in terms of several invariants of interest: the conditi

Hang Guo, Jinmin Li, Tao Dai, Zhihao Ouyang

Recent years have seen significant advancements in image restoration, largely attributed to the development of modern deep neural networks, such as CNNs and Transformers. However, existing restoration backbones often face the dilemma between global receptive fields and efficient computation, hindering their application in practice. Recently, the Selective St

Krishnendu Mandal, Alexander G. Kosovichev, Valery V. Pipin

Solar differential rotation exhibits a prominent feature: its cyclic variations over the solar cycle, referred to as zonal flows or torsional oscillations, are observed throughout the convection zone. Given the challenge of measuring magnetic fields in subsurface layers, understanding deep torsional oscillations becomes pivotal in deciphering the underlying

Lam M. Nguyen, Katya Scheinberg, Trang H. Tran

We develop and analyze stochastic variants of ISTA and a full backtracking FISTA algorithms [Beck and Teboulle, 2009, Scheinberg et al., 2014] for composite optimization without the assumption that stochastic gradient is an unbiased estimator. This work extends analysis of inexact fixed step ISTA/FISTA in [Schmidt et al., 2011] to the case of stochastic grad

Chen Liu, Gregery T. Buzzard, Xiangxiong Zhang

We consider an optimization based limiter for enforcing positivity of internal energy in a semi-implicit scheme for solving gas dynamics equations. With Strang splitting, the compressible Navier-Stokes system is splitted into the compressible Euler equations, solved by the positivity-preserving Runge-Kutta discontinuous Galerkin (DG) method, and the paraboli

Matt McEwen, Kevin C. Miao, Juan Atalaya, Alex Bilmes

Quantum error correction (QEC) provides a practical path to fault-tolerant quantum computing through scaling to large qubit numbers, assuming that physical errors are sufficiently uncorrelated in time and space. In superconducting qubit arrays, high-energy impact events produce correlated errors, violating this key assumption. Following such an event, phonon

Bereket A. Yilma, Chan Mi Kim, Gerald C. Cupchik, Luis A. Leiva

Staying in the intensive care unit (ICU) is often traumatic, leading to post-intensive care syndrome (PICS), which encompasses physical, psychological, and cognitive impairments. Currently, there are limited interventions available for PICS. Studies indicate that exposure to visual art may help address the psychological aspects of PICS and be more effective

Mirmukhsin Makhmudov, Evgeny Verbitskiy, Qian Xiao

It has often been observed that the Multifractal Formalism and the Large Deviation Principles are intimately related. In fact, Multifractal Formalism was heuristically derived using the Large Deviations ideas. In numerous examples in which the multifractal results have been rigorously established, the corresponding Large Deviation results are valid as well.

Luke Lozenski, Refik Mert Cam, Mark A. Anastasio, Umberto Villa

The spherical Radon transform (SRT) is an integral transform that maps a function to its integrals over concentric spherical shells centered at specified sensor locations. It has several imaging applications, including synthetic aperture radar and photoacoustic computed tomography. However, computation of the SRT can be expensive. Efficient implementation of

Martin Tajmar, Matthias Kößling, Oliver Neunzig

Any means to control gravity like electromagnetism is currently out of reach by many orders of magnitude even under extreme laboratory conditions. Some often poorly executed experiments or pseudoscience theories appear from time to time claiming for example anomalous forces from capacitors that suggest a connection between the two fields. We developed novel

Lahcen El Bourkhissi, Ion Necoara

In this paper we consider a nonconvex optimization problem with nonlinear equality constraints. We assume that both, the objective function and the functional constraints, are locally smooth. For solving this problem, we propose a linearized quadratic penalty method, i.e., we linearize the objective function and the functional constraints in the penalty form

Aftab Hussain, Md Rafiqul Islam Rabin, Mohammad Amin Alipour

Trojan signatures, as described by Fields et al. (2021), are noticeable differences in the distribution of the trojaned class parameters (weights) and the non-trojaned class parameters of the trojaned model, that can be used to detect the trojaned model. Fields et al. (2021) found trojan signatures in computer vision classification tasks with image models, s

Hao Ban, Kaiyi Ji

By jointly learning multiple tasks, multi-task learning (MTL) can leverage the shared knowledge across tasks, resulting in improved data efficiency and generalization performance. However, a major challenge in MTL lies in the presence of conflicting gradients, which can hinder the fair optimization of some tasks and subsequently impede MTL's ability to achie

Yanzheng Xiang, Hanqi Yan, Lin Gui, Yulan He

In-context learning has become a popular paradigm in natural language processing. However, its performance can be significantly influenced by the order of in-context demonstration examples. In this paper, we found that causal language models (CausalLMs) are more sensitive to this order compared to prefix language models (PrefixLMs). We attribute this phenome

Xiaoyu Xie, Saviz Mowlavi, Mouhacine Benosman

Spatiotemporal modeling is critical for understanding complex systems across various scientific and engineering disciplines, but governing equations are often not fully known or computationally intractable due to inherent system complexity. Data-driven reduced-order models (ROMs) offer a promising approach for fast and accurate spatiotemporal forecasting by

Xi Chen, Zhewen Hou, Christopher A. Metzler, Arian Maleki

We investigate both the theoretical and algorithmic aspects of likelihood-based methods for recovering a complex-valued signal from multiple sets of measurements, referred to as looks, affected by speckle (multiplicative) noise. Our theoretical contributions include establishing the first existing theoretical upper bound on the Mean Squared Error (MSE) of th

Hao Jiang, Zhaolin Wang, Yuanwei Liu

An active-sensing-based sense-then-train (STT) scheme is proposed for beam training in near-field multiple-input multiple-output (MIMO) systems. Compared to conventional codebook-based schemes, the proposed STT scheme is capable of not only addressing the complex spherical-wave propagation but also effectively exploiting the additional degrees-of-freedoms (D

Stefano Scali, Chukwudubem Umeano, Oleksandr Kyriienko

We provide a quantum protocol to perform topological data analysis (TDA) via the distillation of quantum thermal states. Recent developments of quantum thermal state preparation algorithms reveal their characteristic scaling defined by properties of dissipative Lindbladians. This contrasts with protocols based on unitary evolution which have a scaling depend

Feitong Qiao, Aryana Mohammadi, Jürgen Cito, Mark Santolucito

Infrastructure as Code (IaC) has enabled cloud customers to have more agility in creating and modifying complex deployments of cloud-provisioned resources. By writing a configuration in IaC languages such as CloudFormation, users can declaratively specify their infrastructure and CloudFormation will handle the creation of the resources. However, understandin

Ante Wang, Linfeng Song, Baolin Peng, Ye Tian

This work studies improving large language model (LLM) generations at inference time by mitigating fact-conflicting hallucinations. Particularly, we propose a self-endorsement framework that leverages the fine-grained fact-level comparisons across multiple sampled responses. Compared with prior ensemble methods (Wang et al., 2022;Chen et al., 2023)) that per