July 2023 arXiv papers — page 66

Manqing Dong, Zhanxiang Zhao, Yitong Geng, Wentao Li

Time series anomaly detection is crucial for industrial monitoring services that handle a large volume of data, aiming to ensure reliability and optimize system performance. Existing methods often require extensive labeled resources and manual parameter selection, highlighting the need for automation. This paper proposes a comprehensive framework for automat

Tim Schopf, Karim Arabi, Florian Matthes

As an efficient approach to understand, generate, and process natural language texts, research in natural language processing (NLP) has exhibited a rapid spread and wide adoption in recent years. Given the increasing research work in this area, several NLP-related approaches have been surveyed in the research community. However, a comprehensive study that ca

Removed by arXiv

The content of this page is no longer available

Zhipeng Zhang, Piao Tong, Yingwei Ma, Qiao Liu

Session-based recommendation techniques aim to capture dynamic user behavior by analyzing past interactions. However, existing methods heavily rely on historical item ID sequences to extract user preferences, leading to challenges such as popular bias and cold-start problems. In this paper, we propose a hybrid multimodal approach for session-based recommenda

Soohan Kim, Jimyeong Kim, Hong Kee Sul, Youngjoon Hong

The purpose of this research is to devise a tactic that can closely track the daily cumulative volume-weighted average price (VWAP) using reinforcement learning. Previous studies often choose a relatively short trading horizon to implement their models, making it difficult to accurately track the daily cumulative VWAP since the variations of financial data a

Samie Mostafavi, Gourav Prateek Sharma, James Gross

With the emergence of new application areas, such as cyber-physical systems and human-in-the-loop applications, there is a need to guarantee a certain level of end-to-end network latency with extremely high reliability, e.g., 99.999%. While mechanisms specified under IEEE 802.1as time-sensitive networking (TSN) can be used to achieve these requirements for s

Geunsu Choi, Mingu Jung

We introduce two new notions called the Daugavet constant and $\Delta$-constant of a point, which measure quantitatively how far the point is from being Daugavet point and $\Delta$-point and allow us to study Daugavet and $\Delta$-points in Banach spaces from a quantitative viewpoint. We show that these notions can be viewed as a localized version of certain

Mikko Majamaa, Henrik Martikainen, Jani Puttonen, Timo Hämälainen

Non-Terrestrial Networks (NTNs) can be used to provide ubiquitous 5G and beyond services to un(der)served areas. To ensure reliable communication in such networks, packet duplication (PD) through multi-connectivity is a promising solution. However, the existing PD schemes developed for terrestrial environments may not be reactive enough for the NTN environme

Wolfdieter Lang

Cantor gave in his fundamental article an elegant proof of the countability of real algebraic numbers based on a positive integer height, denoted by him as N, of integer and irreducible polynomials of given degree (denoted by him as n) with relative prime coefficients. The finite number of real algebraic numbers with given height he called phi(N), and gave t

Frank Nielsen

Data sets of multivariate normal distributions abound in many scientific areas like diffusion tensor imaging, structure tensor computer vision, radar signal processing, machine learning, just to name a few. In order to process those normal data sets for downstream tasks like filtering, classification or clustering, one needs to define proper notions of dissi

Zhixuan He, Wenjuan Bu, Chaoyuan Xiao, Meng Li

The transition between isobaric analog states (IAS) in the (3He, t) charge exchange reaction presents a unique opportunity to access the isospin structure of the nuclei. In this study not only the Fermi transition but also the Gamow-Teller (G-T) transition of the IAS reaction were investigated for the 13,14C(3He, t) and 17,18,19,20O(3He, t) reactions, in ord

Qichao Ying, Jiaxin Liu, Sheng Li, Haisheng Xu

The widespread use of face retouching filters on short-video platforms has raised concerns about the authenticity of digital appearances and the impact of deceptive advertising. To address these issues, there is a pressing need to develop advanced face retouching techniques. However, the lack of large-scale and fine-grained face retouching datasets has been

Mehdi Kiani, Mahsa Zolfaghari, Jalal Kiani

This paper outlines a new approach to designing tunable electromagnetic (EM) graphene-based metasurfaces using convolutional neural networks (CNNs). EM metasurfaces have previously been used to manipulate EM waves by adjusting the local phase of subwavelength elements within the wavelength scale, resulting in a variety of intriguing devices. However, the maj

Francesco Serafin, Mark J. Bowick

Using the proof of Willmore's conjecture by Marques and Neves, we conjecture that the free energy of tensionless fluid membranes of arbitrary genus has an upper bound. This implies that the average genus of such a membrane, in equilibrium, is finite, regardless of external constraints. We propose that the Gaussian rigidity may be determined by measuring the

Ngoc Luyen Le, Marie-Hélène Abel, Philippe Gouspillou

In today's era of information explosion, more users are becoming more reliant upon recommender systems to have better advice, suggestions, or inspire them. The measure of the semantic relatedness or likeness between terms, words, or text data plays an important role in different applications dealing with textual data, as in a recommender system. Over the pas

Ke Zhu, Yin-Yin He, Jianxin Wu

Neural network quantization aims to accelerate and trim full-precision neural network models by using low bit approximations. Methods adopting the quantization aware training (QAT) paradigm have recently seen a rapid growth, but are often conceptually complicated. This paper proposes a novel and highly effective QAT method, quantized feature distillation (QF

Michael Sarrazin, Coraline Stasser

A model of baryogenesis is introduced where our usual visible Universe is a 3-brane coevolving with a hidden 3-brane in a multidimensional bulk. The visible matter and antimatter sectors are naturally coupled with the hidden matter and antimatter sectors, breaking the C/CP invariance and leading to baryogenesis occurring after the quark-gluon era. The issue

Mohan Zhou, Yalong Bai, Wei Zhang, Ting Yao

A reliable and comprehensive evaluation metric that aligns with manual preference assessments is crucial for conversational head video synthesis methods development. Existing quantitative evaluations often fail to capture the full complexity of human preference, as they only consider limited evaluation dimensions. Qualitative evaluations and user studies off

Xiaoxuan Wang, Ziniu Hu, Pan Lu, Yanqiao Zhu

Most of the existing Large Language Model (LLM) benchmarks on scientific problem reasoning focus on problems grounded in high-school subjects and are confined to elementary algebraic operations. To systematically examine the reasoning capabilities required for solving complex scientific problems, we introduce an expansive benchmark suite SciBench for LLMs. S

Musa Nuri Ihtiyar, Arzucan Ozgur

Language models, especially transformer-based ones, have achieved colossal success in NLP. To be precise, studies like BERT for NLU and works like GPT-3 for NLG are very important. If we consider DNA sequences as a text written with an alphabet of four letters representing the nucleotides, they are similar in structure to natural languages. This similarity h

Shriyash K. Upadhyay, Etan J. Ginsberg

Large Language Models have many methods for solving the same problem. This introduces novel strengths (different methods may work well for different problems) and weaknesses (it may be difficult for users to know which method to use). In this paper, we introduce Multi-Method Self-Training (MMST), where one method is trained on the filtered outputs of another

Mathuran Kandeepan, Clara Ciocan, Adrien Cassagne, Lionel Lacassagne

This article presents the methods used to parallelize a new computer vision application. The system is able to automatically detect meteor from non-stabilized cameras and noisy video sequences. The application is designed to be embedded in weather balloons or for airborne observation campaigns. Thus, the final target is a low power system-on-chip (< 10 Watts

Zhiwei Fu, Steven H. H. Ding, Furkan Alaca, Benjamin C. M. Fung

The practice of code reuse is crucial in software development for a faster and more efficient development lifecycle. In reality, however, code reuse practices lack proper control, resulting in issues such as vulnerability propagation and intellectual property infringements. Assembly clone search, a critical shift-right defence mechanism, has been effective i

Lorenzo Brandolese, Cilon F Perusato, Paulo R Zingano

In 1987, Michael Wiegner in his seminal paper [17] provided an important result regarding the energy decay of Leray solutions $\boldsymbol u(\cdot,t)$ to the incompressible Navier-Stokes in $\mathbb{R}^{n}$: if the associated Stokes flows had their $\hspace{-0.020cm}L^{2}\hspace{-0.050cm}$ norms bounded by $O(1 + t)^{-\;\!\alpha} $ for some $ 0 < \alpha \leq

Arnaud Plagnol

Underlying the theory of inferences, a primary task of logic is language analysis. Such a task can be understood as depending on a general theory of representation, taking as a starting point the idea that some entities (`` representations '') can present some entites (`` contents ''). We outline a theory of representation accounting for the capacity of repr

Wonbin Kim, Hyun-seo Shin, Ju-ho Kim, Jungwoo Heo

Background noise reduces speech intelligibility and quality, making speaker verification (SV) in noisy environments a challenging task. To improve the noise robustness of SV systems, additive noise data augmentation method has been commonly used. In this paper, we propose a new additive noise method, partial additive speech (PAS), which aims to train SV syst

Philippe Laurençot, Christoph Walker

Well-posedness in $L_\infty$ of the nonlocal Gray-Scott model is studied for integrable kernels, along with the stability of the semi-trivial spatially homogeneous steady state. In addition, it is shown that the solutions to the nonlocal Gray-Scott system converge to those to the classical Gray-Scott system in the diffusive limit.

Roeland ter Hoeven, Benjamin E. Niehoff, Sagar Sudhir Kale, Wolfgang Lechner

Parity compilation is the challenge of laying out the required constraints for the parity mapping in a local way. We present the first constructive compilation algorithm for the parity architecture using plaquettes for arbitrary higher-order optimization problems. This enables adiabatic protocols, where the plaquette layout can natively be implemented, as we

Suncheng Xiang, Cang Liu, Sijia Du, Dahong Qian

Colonoscopic Polyp Re-Identification aims to match a specific polyp in a large gallery with different cameras and views, which plays a key role for the prevention and treatment of colorectal cancer in the computer-aided diagnosis. However, traditional methods mainly focus on the visual representation learning, while neglect to explore the potential of semant

Kun Li, Dan Guo, Guoliang Chen, Xinge Peng

In this paper, we briefly introduce the solution of our team HFUT-VUT for the Micros-gesture Classification in the MiGA challenge at IJCAI 2023. The micro-gesture classification task aims at recognizing the action category of a given video based on the skeleton data. For this task, we propose a 3D-CNNs-based micro-gesture recognition network, which incorpora

Carlos A. Pires, David Docquier, Stéphane Vannitsem

A general theory for computing information transfers in nonlinear stochastic systems driven by deterministic forcing and additive and/or multiplicative noises, is presented. It extends the Liang-Kleeman framework of causality inference to nonlinear cases based on information transfer across system variables (Liang, 2016). We present an effective method of co

Phan Thành Nam, Simone Rademacher

We consider N bosons on the unit torus $\Lambda = [0,1]^3$ in the Gross-Pitaevski regime where the interaction potential scales as $N^2 V (N(x -y))$. We prove that the thermal equilibrium at low temperatures exhibits the Bose-Einstein condensation in a strong sense, namely the probability of having $n$ particles outside of the condensation decays exponential

Ingemar Bengtsson

This is a brief account of the life and work of G\"oran Lindblad.

Jifei Miao, Kit Ian Kou, Hongmin Cai, Lizhi Liu

In recent years, tensor networks have emerged as powerful tools for solving large-scale optimization problems. One of the most promising tensor networks is the tensor ring (TR) decomposition, which achieves circular dimensional permutation invariance in the model through the utilization of the trace operation and equitable treatment of the latent cores. On t

Aofei Sang, Jie Jiang, Ming Zhang

We investigate the collisional Penrose process of extended test particles near extremal Kerr black holes using the pole-dipole-quadrupole approximation. We analyze the motion of the test particles and examine the dynamics and maximum efficiency of energy extraction in this process. Our results demonstrate that the maximum extracted energy in the collisional

Chuandong Li, Sai Sha, Yangqing Zeng, Xiran Yang

As more data-intensive tasks with large footprints are deployed in virtual machines (VMs), huge pages are widely used to eliminate the increasing address translation overhead. However, once the huge page mapping is established, all the base page regions in the huge page share a single extended page table (EPT) entry, so that the hypervisor loses awareness of

Anusuya Baby Hari Krishnan

In the contemporary digital landscape, online reviews have become an indispensable tool for promoting products and services across various businesses. Marketers, advertisers, and online businesses have found incentives to create deceptive positive reviews for their products and negative reviews for their competitors' offerings. As a result, the writing of de

Mang Ye, Xiuwen Fang, Bo Du, Pong C. Yuen

Federated learning (FL) has drawn increasing attention owing to its potential use in large-scale industrial applications. Existing federated learning works mainly focus on model homogeneous settings. However, practical federated learning typically faces the heterogeneity of data distributions, model architectures, network environments, and hardware devices a

Kshitiz Verma

Indian Judiciary is suffering from burden of millions of cases that are lying pending in its courts at all the levels. In this paper, we analyze the data that we have collected on the pendency of 24 high courts in the Republic of India as they were made available on High Court NJDG (HC-NJDG). We collected data on 73 days beginning August 31, 2017 to December

Ehsan Latif, Ramviyas Parasuraman

Relative localization is crucial for multi-robot systems to perform cooperative tasks, especially in GPS-denied environments. Current techniques for multi-robot relative localization rely on expensive or short-range sensors such as cameras and LIDARs. As a result, these algorithms face challenges such as high computational complexity (e.g., map merging), dep

Haichen Jia, Bowen Ma, Rui Leonard Luo, Gang Chen

Motivated by the recent experiments and the wide tunability on the MoTe$_2$/WSe$_2$ moir\'{e} heterobilayer, we consider a physical model to explore the underlying physics for the interplay between the itinerant carriers and the local magnetic moments. In the regime where the MoTe$_2$ is tuned to a triangular lattice Mott insulator and the WSe$_2$ layer is d

Xi Chen

We consider the following $p$ order nonlinear half wave Schr{\"o}dinger equations$$\left(i \partial\_{t}+\partial\_{x }^2-\left|D\_{y}\right|\right) u=\pm|u|^{p-1} u$$on the plane $\mathbb{R}^2$ with $1<p\leq 2$. This equation is considered as a toy model motivated by the study of solutions to weakly dispersive equations. In particular, the global well-posed

Ross G. Pinsky, Tomer Zilca

In the classical secretary problem, $n$ ranked items arrive one by one, and each item's rank relative to its predecessors is noted. The observer must select or reject each item as it arrives, with the object of selecting the item of highest rank. For $M_n\in\{0,1,\cdots, n-1\}$, let $\mathcal{S}(n,M_n)$ denote the strategy whereby the observer rejects the fi

Joachim Gudmundsson, Zijin Huang, André van Renssen, Sampson Wong

We present a near-linear time approximation algorithm for the subtrajectory cluster problem of $c$-packed trajectories. The problem involves finding $m$ subtrajectories within a given trajectory $T$ such that their Fr\'echet distances are at most $(1 + \varepsilon)d$, and at least one subtrajectory must be of length~$l$ or longer. A trajectory $T$ is $c$-pac

Weihang Ran, Wei Yuan, Xiaodan Shi, Zipei Fan

Building outline extracted from high-resolution aerial images can be used in various application fields such as change detection and disaster assessment. However, traditional CNN model cannot recognize contours very precisely from original images. In this paper, we proposed a CNN and Transformer based model together with active contour model to deal with thi

Sombuddha Bhattacharyya, Pranav Kumar

In this article, we study an inverse problem with local data for a linear polyharmonic operator with several lower order tensorial perturbations. We consider our domain to have an inaccessible portion of the boundary where neither the input can be prescribed nor the output can be measured. We prove the unique determination of all the tensorial coefficients o

R. Krithika, V. K. Kutty Malu, Roohani Sharma, Prafullkumar Tale

In this work, we initiate the complexity study of Biclique Contraction and Balanced Biclique Contraction. In these problems, given as input a graph G and an integer k, the objective is to determine whether one can contract at most k edges in G to obtain a biclique and a balanced biclique, respectively. We first prove that these problems are NP-complete even

Martin Lopez-Corredoira

Since the beginning of the 20th century, a continuous evolution and perfection of what we today call the standard cosmological model has been produced, although some authors like to distinguish separate periods within this evolution. A possible historical division of the development of cosmology into six periods is: (1) the initial period (1917-1927); (2) th

Nicholas Mueller, Santiago Badia

This work proposes novel techniques for the efficient numerical simulation of parameterized, unsteady partial differential equations. Projection-based reduced order models (ROMs) such as the reduced basis method employ a (Petrov-)Galerkin projection onto a linear low-dimensional subspace. In unsteady applications, space-time reduced basis (ST-RB) methods hav

Adrian Valadkhani, Belén Zúñiga Céspedes, Salony Mandloi, Mingyu Xu

Lattice strains of appropriate symmetry have served as an excellent tool to explore the interaction of superconductivity in the iron-based superconductors with nematic and stripe spin-density wave (SSDW) order, which are both closely tied to an orthorhombic distortion. In this work, we contribute to a broader understanding of the coupling of strain to superc

Ajay Jaiswal, Xingguang Zhang, Stanley H. Chan, Zhangyang Wang

Image distortion by atmospheric turbulence is a stochastic degradation, which is a critical problem in long-range optical imaging systems. A number of research has been conducted during the past decades, including model-based and emerging deep-learning solutions with the help of synthetic data. Although fast and physics-grounded simulation tools have been in

Xingxuan Zhang, Renzhe Xu, Han Yu, Yancheng Dong

Domain generalization (DG) seeks to learn robust models that generalize well under unknown distribution shifts. As a critical aspect of DG, optimizer selection has not been explored in depth. Currently, most DG methods follow the widely used benchmark, DomainBed, and utilize Adam as the default optimizer for all datasets. However, we reveal that Adam is not

Yuan Da Liao

Researchers in physical science aim to uncover universal features in strongly interacting many-body systems, often hidden in complicated observables like entanglement entropy (EE). The non-local nature of EE makes it challenging to compute numerically, necessitating the development of an unbiased and convenient algorithm. In this paper, we use quantum Monte

Dongyun Lin, Yi Cheng, Aiyuan Guo, Shangbo Mao

To address 3D object retrieval, substantial efforts have been made to generate highly discriminative descriptors of 3D objects represented by a single modality, e.g., voxels, point clouds or multi-view images. It is promising to leverage the complementary information from multi-modality representations of 3D objects to further improve retrieval performance.

Rifat Ara Shams, Didar Zowghi, Muneera Bano

Artificial Intelligence (AI)'s pervasive presence and variety necessitate diversity and inclusivity (D&I) principles in its design for fairness, trust, and transparency. Yet, these considerations are often overlooked, leading to issues of bias, discrimination, and perceived untrustworthiness. In response, we conducted a Systematic Review to unearth challenge

Divyang G. Bhimani, Saikatul Haque

We have established (a weak form of) ill-posedness for the KdV-Burgers equation on a real line in Fourier amalgam spaces $\widehat{w}_s^{p,q}$ with $s<-1$. The particular case $p=q=2$ recovers the result of L. Molinet and F. Ribaud [Int. Math. Res. Not., (2002), pp. 1979-2005]. The result is new even in Fourier Lebesgue space $\mathcal{F}L_s^q$ which corresp

Paulo Montero-Camacho, Yao Zhang, Yi Mao

X-ray photons can penetrate deep into the intergalactic medium (IGM), leading to preheating of the IGM prior to cosmic reionization. X-ray preheating wipes out some of the small-scale structures that would otherwise be present prior to the passage of an ionization front. Accurate modeling of the small-scale structure is vital to the post-reionization IGM sin

Hossein Ehsani

This study introduces a novel approach for deriving the governing equations of the musculoskeletal system in the human body. The proposed formalism offers a framework to effectively incorporate the kinematic characteristics of biological joints and the complexities of kinematic chains into the differential equations of motion. This approach, known as "Empiri

Tin Lai, Farnaz Farid, Abubakar Bello, Fariza Sabrina

The Internet of Things (IoT) integrates more than billions of intelligent devices over the globe with the capability of communicating with other connected devices with little to no human intervention. IoT enables data aggregation and analysis on a large scale to improve life quality in many domains. In particular, data collected by IoT contain a tremendous a

Tirthankar Bhattacharyya, Abhay Jindal

The Sz.-Nagy Foias characteristic function for a contraction has had a rejuvenation in recent times due to a number of authors. Such a classical object relates to an object of very contemporary interest, viz., the complete Nevanlinna-Pick kernels. Indeed, a unitarily invariant kernel on the unit ball {\em admits} a characteristic function if and only if it i

Christopher Funk, Ofer Dagan, Benjamin Noack, Nisar R. Ahmed

A key challenge in Bayesian decentralized data fusion is the `rumor propagation' or `double counting' phenomenon, where previously sent data circulates back to its sender. It is often addressed by approximate methods like covariance intersection (CI) which takes a weighted average of the estimates to compute the bound. The problem is that this bound is not t

Ziwei Wang, Timothy Molloy, Pieter van Goor, Robert Mahony

Event-based cameras are popular for tracking fast-moving objects due to their high temporal resolution, low latency, and high dynamic range. In this paper, we propose a novel algorithm for tracking event blobs using raw events asynchronously in real time. We introduce the concept of an event blob as a spatio-temporal likelihood of event occurrence where the

Jiyue He, Arkady Pertsov, John Bullinga, Rahul Mangharam

One of the challenges in the development of patient-specific models of cardiac arrhythmias for clinical applications has been accounting for myocardial fiber organization. The fiber varies significantly from heart to heart, but cannot be directly measured in live tissue. The goal of this paper is to evaluate in-silico the accuracy of left atrium activation m

Subhrajyoty Roy, Abhik Ghosh, Ayanendranath Basu

The traditional method of computing singular value decomposition (SVD) of a data matrix is based on a least squares principle, thus, is very sensitive to the presence of outliers. Hence the resulting inferences across different applications using the classical SVD are extremely degraded in the presence of data contamination (e.g., video surveillance backgrou

Matteo Biagiola, Paolo Tonella

Recent advances in Deep Neural Networks (DNNs) and sensor technologies are enabling autonomous driving systems (ADSs) with an ever-increasing level of autonomy. However, assessing their dependability remains a critical concern. State-of-the-art ADS testing approaches modify the controllable attributes of a simulated driving environment until the ADS misbehav

Takeshi Ishida

The Turing pattern model is one type of reaction-diffusion (RD) model. The first identification of pattern formation by the Turing pattern model in an actual animal was made in the 1990s with the observation of patterns in the sea anemone. But can we assume that all epidermal patterns in animals can be explained by the Turing pattern model? Even for fish, th

Hanif Tayarani, Trisha V. Ramadoss, Vaishnavi Karanam, Gil Tal

Energy systems, climate change, and public health are among the primary reasons for moving toward electrification in transportation. Transportation electrification is being promoted worldwide to reduce emissions. As a result, many automakers will soon start making only battery electric vehicles (BEVs). BEV adoption rates are rising in California, mainly due

Anand Kumar Rai, Siddharth D Jaiswal, Animesh Mukherjee

Automatic speech recognition (ASR) systems are designed to transcribe spoken language into written text and find utility in a variety of applications including voice assistants and transcription services. However, it has been observed that state-of-the-art ASR systems which deliver impressive benchmark results, struggle with speakers of certain regions or de

Anthony Corso, David Karamadian, Romeo Valentin, Mary Cooper

As machine learning (ML) systems increasingly permeate high-stakes settings such as healthcare, transportation, military, and national security, concerns regarding their reliability have emerged. Despite notable progress, the performance of these systems can significantly diminish due to adversarial attacks or environmental changes, leading to overconfident

Yonatan Ashenafi, Peter R. Kramer

We study the stochastic hydrodynamics of colonies of flagellated swimming cells, typified by multicellular choanoflagellates, which can form both rosette and chainlike shapes. The objective is to link cell-scale dynamics to colony-scale dynamics for various colonial morphologies. Via autoregressive stochastic models for the cycle-averaged flagellar force dyn

Jiang Wu, Dongyu Liu, Ziyang Guo, Yingcai Wu

Modeling each hit as a multivariate event in racket sports and conducting sequential analysis aids in assessing player/team performance and identifying successful tactics for coaches and analysts. However, the complex correlations among multiple event attributes require pattern mining algorithms to be highly effective and efficient. This paper proposes Beep

Dejia Xu, Xingqian Xu, Wenyan Cong, Humphrey Shi

Have you ever imagined how it would look if we placed new objects into paintings? For example, what would it look like if we placed a basketball into Claude Monet's ``Water Lilies, Evening Effect''? We propose Reference-based Painterly Inpainting, a novel task that crosses the wild reference domain gap and implants novel objects into artworks. Although previ

Meng Xiaoyuan, Lang bo, Yanxi Liu, Yuhao Yan

Nowadays, botnets have become one of the major threats to cyber security. The characteristics of botnets are mainly reflected in bots network behavior and their intercommunication relationships. Existing botnet detection methods use flow features or topology features individually, which overlook the other type of feature. This affects model performance. In t

Kristina Brown Nuttall, Christiana Z. Suggs, Henry E. Fischer, Mitchell M. Bordelon

We present a neutron diffraction study of NaYbO$_2$, a candidate quantum spin liquid compound hosting a geometrically frustrated triangular lattice of magnetic Yb$^{3+}$ ions. We observe diffuse magnetic scattering that persists to at least 20 K, demonstrating the presence of short-range magnetic correlations in this system up to a relatively high energy sca

Yasin Ferdous Alam, Kohki Kawabata, Tatsuma Nishioka, Takuya Okuda

We generalize the construction of Narain conformal field theories (CFTs) from qudit stabilizer codes to the construction from quantum stabilizer codes over the finite field of prime power order ($\mathbb{F}_{p^m}$ with $p$ prime and $m\geq 1$) or over the ring $\mathbb{Z}_k$ with $k>1$. Our construction results in rational CFTs, which cover a larger set of p

Yanfei Xiang, Qinghong Zhang, Mingqing Wang, Ruixue Xia

Accurate and timely prediction of sea fog is very important for effectively managing maritime and coastal economic activities. Given the intricate nature and inherent variability of sea fog, traditional numerical and statistical forecasting methods are often proven inadequate. This study aims to develop an advanced sea fog forecasting method embedded in a nu

Ziyao Ren, Yan Kang, Lixin Fan, Linghua Yang

SecureBoost is a tree-boosting algorithm leveraging homomorphic encryption to protect data privacy in vertical federated learning setting. It is widely used in fields such as finance and healthcare due to its interpretability, effectiveness, and privacy-preserving capability. However, SecureBoost suffers from high computational complexity and risk of label l

Manuel Grumet, Clara von Scarpatetti, Tomáš Bučko, David A. Egger

Raman spectroscopy is an important characterization tool with diverse applications in many areas of research. We propose a machine learning method for predicting polarizabilities with the goal of providing Raman spectra from molecular dynamics trajectories at reduced computational cost. A linear-response model is used as a first step and symmetry-adapted mac

Hugo Latapie, Shan Yu, Patrick Hammer, Kristinn R. Thorisson

Traditional computer vision models often necessitate extensive data acquisition, annotation, and validation. These models frequently struggle in real-world applications, resulting in high false positive and negative rates, and exhibit poor adaptability to new scenarios, often requiring costly retraining. To address these issues, we present Ethosight, a flexi

Yo Kusafuka, Katsuaki Asano, Takumi Ohmura, Tomohisa Kawashima

To demonstrate the magnetic energy dissipation via relativistic shocks, we carry out spherically symmetrical one-dimensional special relativistic magneto-hydrodynamic simulations of highly magnetised outflows with an adaptive mesh refinement method. We first investigate the detail of the dynamical energy dissipation via interaction between a single ejecta an

Yu Qiao, Huy Q. Le, Choong Seon Hong

As a distributed machine learning technique, federated learning (FL) requires clients to collaboratively train a shared model with an edge server without leaking their local data. However, the heterogeneous data distribution among clients often leads to a decrease in model performance. To tackle this issue, this paper introduces a prototype-based regularizat

Can Jiang, Xin Li, Jia-Rui Lin, Ming Liu

Due to complexity and dynamics of construction work, resource, and cash flows, poor management of them usually leads to time and cost overruns, bankruptcy, even project failure. Existing approaches in construction failed to achieve optimal control of resource flow in a dynamic environment with uncertainty. Therefore, this paper introducess a model and method

Rylan Schaeffer, Kateryna Pistunova, Samar Khanna, Sarthak Consul

Language models can be prompted to reason through problems in a manner that significantly improves performance. However, \textit{why} such prompting improves performance is unclear. Recent work showed that using logically \textit{invalid} Chain-of-Thought (CoT) prompting improves performance almost as much as logically \textit{valid} CoT prompting, and that

Wenqing Su, Xiao Guo, Xiangyu Chang, Ying Yang

Modern network analysis often involves multi-layer network data in which the nodes are aligned, and the edges on each layer represent one of the multiple relations among the nodes. Current literature on multi-layer network data is mostly limited to undirected relations. However, direct relations are more common and may introduce extra information. This study

Anjana Arunkumar, Lace Padilla, Gi-Yeul Bae, Chris Bryan

How do people internalize visualizations: as images or information? In this study, we investigate the nature of internalization for visualizations (i.e., how the mind encodes visualizations in memory) and how memory encoding affects its retrieval. This exploratory work examines the influence of various design elements on a user's perception of a chart. Speci

Yang Li, Kimball A. Milton, Iver Brevik

Multi-particle thermal Casimir interactions are investigated, mostly in terms of the Casimir entropy, from the point of view based on multiple-scattering processes. The geometry of the scattering path is depicted in detail, and the contributions from different types of channels, namely the transverse, longitudinal and mixing channels, are demonstrated. The g

Andres Carranza, Dhruv Pai, Rylan Schaeffer, Arnuv Tandon

As the capabilities of large machine learning models continue to grow, and as the autonomy afforded to such models continues to expand, the spectre of a new adversary looms: the models themselves. The threat that a model might behave in a seemingly reasonable manner, while secretly and subtly modifying its behavior for ulterior reasons is often referred to a

H. Yao, M. Schnaubelt, A. Szalay, T. Zaki

Two common definitions of the spatially local rate of kinetic energy cascade at some scale $\ell$ in turbulent flows are (i) the cubic velocity difference term appearing in the generalized Kolmogorov-Hill equation (GKHE) (structure function approach), and (ii) the subfilter-scale energy flux term in the transport equation for subgrid-scale kinetic energy (fi

Qi Zhang, Sipeng Zheng, Qin Jin

Temporal video grounding (TVG) aims to retrieve the time interval of a language query from an untrimmed video. A significant challenge in TVG is the low "Semantic Noise Ratio (SNR)", which results in worse performance with lower SNR. Prior works have addressed this challenge using sophisticated techniques. In this paper, we propose a no-frills TVG model that

Wenjie Deng, Zhaonan Luo, Zhaoyang Yin

This paper is devoted to global existence and optimal decay rate of weak solutions to some inviscid Oldroyd-B models with center diffusion. By virtue of the properties of Calderon-Zygmund operator and the Littlewood-Paley decomposition theory, we firstly prove that the 2-D co-rotation inviscid Oldroyd-B model admits global weak solutions with some large data

Binhua Zhang, Junyi Ji, Changsong Xu, Hongjun Xiang

Kagome materials provide a promising platform to explore intriguing correlated phenomena including magnetism, charge density wave (CDW), and nontrivial band topology. Recently, a CDW order was observed in antiferromagnetic kagome metal FeGe, sparking enormous research interests in intertwining physics of CDW and magnetism. Two of the core questions are (i) w

Haruyoshi Tanaka

We consider infinite graph-directed iterated function systems (GIFSs) whose contraction mappings are nonconformal. As our main result, we formulate asymptotic perturbations from conformal GIFSs to nonconformal GIFSs, and give the asymptotic behaviour of the Hausdorff dimension of the limit set of the perturbed system. We also investigate perturbed self-affin

Dhruv Pai, Andres Carranza, Rylan Schaeffer, Arnuv Tandon

We present FACADE, a novel probabilistic and geometric framework designed for unsupervised mechanistic anomaly detection in deep neural networks. Its primary goal is advancing the understanding and mitigation of adversarial attacks. FACADE aims to generate probabilistic distributions over circuits, which provide critical insights to their contribution to cha

Shaokui Wei, Mingda Zhang, Hongyuan Zha, Baoyuan Wu

Backdoor attacks are serious security threats to machine learning models where an adversary can inject poisoned samples into the training set, causing a backdoored model which predicts poisoned samples with particular triggers to particular target classes, while behaving normally on benign samples. In this paper, we explore the task of purifying a backdoored

Fernando Meneses, Rongrong Qi, Alexander J. Healey, Yi You

Magnetic van der Waals materials are often proposed for use in future spintronic devices, aiming to leverage the combination of long-range magnetic order and near-atomic thinness to produce energy-efficient components. One class of material that has been discussed in this context are the iron halides FeCl$_2$ and FeBr$_2$, which are A-type antiferromagnets w

Po-Wei Huang, Patrick Rebentrost

Hybrid quantum-classical computing in the noisy intermediate-scale quantum (NISQ) era with variational algorithms can exhibit barren plateau issues, causing difficult convergence of gradient-based optimization techniques. In this paper, we discuss "post-variational strategies", which shift tunable parameters from the quantum computer to the classical compute

Yutian Pang, Jueming Hu, Christopher S. Lieber, Nancy J. Cooke

Air traffic control (ATC) is a safety-critical service system that demands constant attention from ground air traffic controllers (ATCos) to maintain daily aviation operations. The workload of the ATCos can have negative effects on operational safety and airspace usage. To avoid overloading and ensure an acceptable workload level for the ATCos, it is importa

Shiyang Li, Jun Yan, Hai Wang, Zheng Tang

While instruction-tuned models have shown remarkable success in various natural language processing tasks, accurately evaluating their ability to follow instructions remains challenging. Existing benchmarks primarily focus on common instructions that align well with what the model learned during training. However, proficiency in responding to these instructi

Chenghuai Wu, Wei Wang, Run Long, Hong Li

Due to the properties of high temperature superconducting (HTS) materials, current attenuation is inevitable during the closed-loop operation of HTS magnets. When a contact DC power supply is used to supplement this attenuation, it inevitably creates a huge thermal burden on the cryogenic system. The flux pump is a revolutionary new power source that can cha

Mike Rainey

In this short paper, we explore a new way to refactor a simple but tricky-to-parallelize tree-traversal algorithm to harness multicore parallelism. Crucially, the refactoring draws from some classic techniques from programming-languages research, such as the continuation-passing-style transform and defunctionalization. The algorithm we consider faces a parti