May 2023 arXiv papers — page 90

Pablo Villanueva-Perez, Valerio Bellucci, Yuhe Zhang, Sarlota Birnsteinova

X-ray time-resolved tomography is one of the most popular X-ray techniques to probe dynamics in three dimensions (3D). Recent developments in time-resolved tomography opened the possibility of recording kilohertz-rate 3D movies. However, tomography requires rotating the sample with respect to the X-ray beam, which prevents characterization of faster structur

Nicholas S. Yama, I-Tung Chen, Srivatsa Chakravarthi, Bingzhao Li

The compact size, scalability, and strongly confined fields in integrated photonic devices enable new functionalities in photonic networking and information processing, both classical and quantum. Gallium phosphide (GaP) is a promising material for active integrated photonics due to its high refractive index, wide band gap, strong nonlinear properties, and l

Puyuan Peng, Shang-Wen Li, Okko Räsänen, Abdelrahman Mohamed

In this paper, we show that representations capturing syllabic units emerge when training a self-supervised speech model with a visually-grounded training objective. We demonstrate that a nearly identical model architecture (HuBERT) trained with a masked language modeling loss does not exhibit this same ability, suggesting that the visual grounding objective

Qing Huo Liu, Siwei Wan, Chunhui Zhu

We present an accurate spectral integral method (SIM) for the analyses of scattering from multiple circular perfect electric conductor (PEC) cylinders. It solves the coupled surface integral equations by using the Fourier series and addition theorem to decouple the system. The SIM has exponential convergence so that the error decreases exponentially with the

Brandon Keller, Andrew Meneely, Benjamin Meyers

BACKGROUND: Software engineers must be vigilant in preventing and correcting vulnerabilities and other critical bugs. In servicing this need, numerous tools and techniques have been developed to assist developers. Fuzzers, by autonomously generating inputs to test programs, promise to save time by detecting memory corruption, input handling, exception cases,

Kenji Fukushima, Yoshimasa Hidaka, Takuya Shimazaki, Hidetoshi Taya

We investigate the chiral anomaly in a Floquet system under a time-periodic electric field in (1+1) dimensions. Using the van~Vleck high-frequency expansion, we analytically calculate the chiral current and the pseudo-scalar condensate for massless/massive fermions and how they are balanced with the topological charge. In the high-frequency limit, we find th

Zitong Wang, Feng Shi, Xiaohu Yang, Qingyang Li

Herein, we present a deep-learning technique for reconstructing the dark-matter density field from the redshift-space distribution of dark-matter halos. We built a UNet-architecture neural network and trained it using the COmoving Lagrangian Acceleration fast simulation, which is an approximation of the N-body simulation with $512^3$ particles in a box size

Shubhra Kanti Karmaker Santu, Dongji Feng

While LLMs have shown great success in understanding and generating text in traditional conversational settings, their potential for performing ill-defined complex tasks is largely under-studied. Indeed, we are yet to conduct comprehensive benchmarking studies with multiple LLMs that are exclusively focused on a complex task. However, conducting such benchma

Rodrigo Nemmen, Artur Vemado, Ivan Almeida, Javier Garcia

Stellar mass black holes in X-ray binaries (XRBs) are known to display different states characterized by different spectral and timing properties, understood in the framework of a hot corona coexisting with a thin accretion disk whose inner edge is truncated. There are several open questions related to the nature and properties of the corona, the thin disk,

Elette Boyle, Ran Cohen, Deepesh Data, Pavel Hubáček

Secure multiparty computation (MPC) on incomplete communication networks has been studied within two primary models: (1) Where a partial network is fixed a priori, and thus corruptions can occur dependent on its structure, and (2) Where edges in the communication graph are determined dynamically as part of the protocol. Whereas a rich literature has succeede

Martin Brenner, Napoleon H. Reyes, Teo Susnjak, Andre L. C. Barczak

In the last decade, the computer vision field has seen significant progress in multimodal data fusion and learning, where multiple sensors, including depth, infrared, and visual, are used to capture the environment across diverse spectral ranges. Despite these advancements, there has been no systematic and comprehensive evaluation of fusing RGB-D and thermal

Movahhed Sadeghi, Soheil Khadirsharbiyani, Mostafa Eghbali Zarch, Mahmut Taylan Kandemir

The growing prevalence of near-term intermediate-scale quantum (NISQ) systems has brought forth a heightened focus on the issue of circuit reliability. Several quantum computing activities, such as circuit design and multi-qubit mapping, are focused on enhancing reliability via the use of different optimization techniques. The optimization of quantum classic

Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun

Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of-Thought prompting) during in-context learning can significantly enhance the performance of these models, particularly on tasks that require reasoning capabilities

Zhe Chen, Hao Tan, Tao Wang, Tianrun Shen

This paper presents a novel transformer architecture for graph representation learning. The core insight of our method is to fully consider the information propagation among nodes and edges in a graph when building the attention module in the transformer blocks. Specifically, we propose a new attention mechanism called Graph Propagation Attention (GPA). It e

Adiwena Putra, Prasetiyo, Yi Chen, John Kim

Homomorphic encryption (HE) enables computations on encrypted data by concealing information under noise for security. However, the process of bootstrapping, which resets the noise level in the ciphertext, is computationally expensive and requires a large bootstrapping key. The TFHE scheme offers a faster and programmable bootstrapping algorithm called PBS,

Kaptsov Oleg

In this paper we consider mappings of jet spaces that preserve the module of canonical Pfaffian forms, but are not generally invertible. These mappings are called contact. A lemma on the prolongation of contact mappings is proved. Conditions are found for which mappings transform solutions of some partial differential equations into ones of other equations.

Hao Wu, Fan Xu, Chong Chen, Xian-Sheng Hua

In this paper, we investigate the challenge of spatio-temporal video prediction task, which involves generating future video frames based on historical spatio-temporal observation streams. Existing approaches typically utilize external information such as semantic maps to improve video prediction accuracy, which often neglect the inherent physical knowledge

Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa

Decentralized learning has recently been attracting increasing attention for its applications in parallel computation and privacy preservation. Many recent studies stated that the underlying network topology with a faster consensus rate (a.k.a. spectral gap) leads to a better convergence rate and accuracy for decentralized learning. However, a topology with

Miguel Lopez-Montiel, Daniel Alejandro Lopez, Oscar Montiel

Real-time semantic segmentation is a challenging task that requires high-accuracy models with low-inference times. Implementing these models on embedded systems is limited by hardware capability and memory usage, which produces bottlenecks. We propose an efficient model for real-time semantic segmentation called JetSeg, consisting of an encoder called JetNet

Jiayi Fu, Haoying Han, Xing Su, Chao Fan

Pre-trained large language models (PLMs) have the potential to support urban science research through content creation, information extraction, assisted programming, text classification, and other technical advances. In this research, we explored the opportunities, challenges, and prospects of PLMs in urban science research. Specifically, we discussed potent

Guohao Shen

We investigate the complexity of deep neural networks through the lens of functional equivalence, which posits that different parameterizations can yield the same network function. Leveraging the equivalence property, we present a novel bound on the covering number for deep neural networks, which reveals that the complexity of neural networks can be reduced.

A. Y. Shaikh, D. P. Tadas, S. D. Katore

In this article, we investigated a Locally Rotationally Symmetric (LRS) Bianchi-II cosmological model with matter and Holographic Dark Energy (HDE) in the context of $f(R)$ theory of gravity. In order to find exact solutions to the field equations, we assumed that the Shear scalar $(\sigma)$ is proportional to Expansion scalar $(\theta)$. For HDE, it is obse

Yihuan Di, Yuan Li, Feng Yuan, Fangzheng Shi

We perform high-resolution hydrodynamical simulations using the framework of {\it MACER} to investigate supermassive black hole (SMBH) feeding and feedback in a massive compact galaxy, which has a small effective radius but a large stellar mass, with a simulation duration of 10 Gyr. We compare the results with a reference galaxy with a similar stellar mass b

Sixing Yu, J. Pablo Muñoz, Ali Jannesari

Foundation Models (FMs), such as LLaMA, BERT, GPT, ViT, and CLIP, have demonstrated remarkable success in a wide range of applications, driven by their ability to leverage vast amounts of data for pre-training. However, optimizing FMs often requires access to sensitive data, raising privacy concerns and limiting their applicability in many domains. In this p

Ibrahim Malik, Siddique Latif, Raja Jurdak, Björn Schuller

In this paper, we propose to utilise diffusion models for data augmentation in speech emotion recognition (SER). In particular, we present an effective approach to utilise improved denoising diffusion probabilistic models (IDDPM) to generate synthetic emotional data. We condition the IDDPM with the textual embedding from bidirectional encoder representations

Rosemary C. Dorsey, Michele T. Bannister, Samantha M. Lawler, Alex H. Parker

We calculate the upper bounds of the population of theoretically stable Centaur orbits between Uranus and Neptune. These small bodies are on low-eccentricity, low-inclination orbits in two specific bands of semi-major axis, centred at $\sim$24.6 au and $\sim$25.6 au. They exhibit unusually long Gyr-stable lifetimes in previously published numerical integrati

Dong Zhang, Rong Ye, Tom Ko, Mingxuan Wang

How can speech-to-text translation (ST) perform as well as machine translation (MT)? The key point is to bridge the modality gap between speech and text so that useful MT techniques can be applied to ST. Recently, the approach of representing speech with unsupervised discrete units yields a new way to ease the modality problem. This motivates us to propose D

Xia Liu, Huan Yang, Li Yang

Since the elliptic curve discrete logarithms problem (ECDLP) was proposed, it has been widely used in cryptosystem because of its strong security. Although the proposal of the extended Shor's algorithm offers hope for cracking ECDLP, it is debatable whether the algorithm can actually pose a threat in practice. From the perspective of the quantum circuit of t

Shuming Yang, Peng Xie, Enwei Liang, Zhao Wang

Using quantum chemical calculations, we model the pathways for synthesizing two purine nucleobases, adenine and guanine, in the gas-phase interstellar environment, surrounded by neutral atomic hydrogen (HI). HI is found active in facilitating a series of fundamental proton transfer processes of organic synthesis, including bond formation, cyclization, dehydr

Sara Papi, Marco Turchi, Matteo Negri

Attention is the core mechanism of today's most used architectures for natural language processing and has been analyzed from many perspectives, including its effectiveness for machine translation-related tasks. Among these studies, attention resulted to be a useful source of information to get insights about word alignment also when the input text is substi

Jun Wen, Jue Hou, Clara-Lea Bonzel, Yihan Zhao

Electronic health record (EHR) data are increasingly used to support real-world evidence (RWE) studies. Yet its ability to generate reliable RWE is limited by the lack of readily available precise information on the timing of clinical events such as the onset time of heart failure. We propose a LAbel-efficienT incidenT phEnotyping (LATTE) algorithm to accura

Eric Budish, Ruiquan Gao, Abraham Othman, Aviad Rubinstein

Approximate Competitive Equilibrium from Equal Incomes (A-CEEI) is an equilibrium-based solution concept for fair division of discrete items to agents with combinatorial demands. In theory, it is known that in asymptotically large markets: 1. For incentives, the A-CEEI mechanism is Envy-Free-but-for-Tie-Breaking (EF-TB), which implies that it is Strategyproo

Belle Collaboration, H. K. Moon, E. Won, I. Adachi

We search for $CP$ violation by measuring a $T$-odd asymmetry in the Cabibbo-suppressed $D^{+}\rightarrow K^{+}K^{0}_{S}\pi^{+}\pi^{-} $ decay, and in the Cabibbo-favored $D^{+}_{s}\rightarrow K^{+}K^{0}_{S}\pi^{+}\pi^{-}$ and $D^{+}\rightarrow K^{+}K^{-}K^{0}_{S}\pi^{+}$ decays. We use 980 ${\rm fb}^{-1}$ of data collected by the Belle detector running at t

Indranil Biswas, S. Senthamarai Kannan, Pinakinath Saha

Let $G$ be a semi-simple simply connected algebraic group over the field $\mathbb{C}$ of complex numbers. Let $T$ be a maximal torus of $G,$ and let $W$ be the Weyl group of $G$ with respect to $T$. Let $Z(w,\, \underline{i})$ be the Bott-Samelson-Demazure-Hansen variety corresponding to a tuple $\underline{i}$ associated to a reduced expression of an elemen

Ling Zheng, Jinchen Zhu, Jinpeng Shi, Shizhuang Weng

Recently, Transformer-based methods have achieved impressive results in single image super-resolution (SISR). However, the lack of locality mechanism and high complexity limit their application in the field of super-resolution (SR). To solve these problems, we propose a new method, Efficient Mixed Transformer (EMT) in this study. Specifically, we propose the

Simone Giombi, Bowei Liu

We study a surface defect in the free and critical $O(N)$ vector models, defined by adding a quadratic perturbation localized on a two-dimensional subspace of the $d$-dimensional CFT. We compute the beta function for the corresponding defect renormalization group (RG) flow, and provide evidence that at long distances the system flows to a nontrivial defect c

Shigeru Takata

The thermal relaxation of a dense gas described by the modified Enskog equation is studied for a closed system in contact with a heat bath. As in the case of the Boltzmann equation, the Helmholtz free energy $\mathcal{F}$ that decreases monotonically in time is found under the conventional kinetic boundary condition that satisfies the Darrozes--Guiraud inequ

Cat P. Le, Juncheng Dong, Ahmed Aloui, Vahid Tarokh

The main challenge in continual learning for generative models is to effectively learn new target modes with limited samples while preserving previously learned ones. To this end, we introduce a new continual learning approach for conditional generative adversarial networks by leveraging a mode-affinity score specifically designed for generative modeling. Fi

Fuwang Dong, Fan Liu, Shihang Lu, Weijie Yuan

The integrated sensing and communication (ISAC) technique has the potential to achieve coordination gain by exploiting the mutual assistance between sensing and communication (S&C) functions. While the sensing-assisted communications (SAC) technology has been extensively studied for high-mobility scenarios, the communication-assisted sensing (CAS) counterpar

Yelena Mejova, Lydia Manikonda

Unprecedented lockdowns at the start of the COVID-19 pandemic have drastically changed the routines of millions of people, potentially impacting important health-related behaviors. In this study, we use YouTube videos embedded in tweets about diet, exercise and fitness posted before and during COVID-19 to investigate the influence of the pandemic lockdowns o

Faxian Cao, Yongqiang Cheng, Adil Mehmood Khan, Zhijing Yang

In an era where asynchronous environments pose challenges to traditional self-positioning methods, we propose a new transformation to the existing paradigm. Traditionally, time of arrival (TOA) measurements require both microphone and source signals, limiting their applicability in environments with unknown emission time of human voices or sources and unknow

J. Alberto Vázquez, David Tamayo, Gabriela Garcia-Arroyo, Isidro Gómez-Vargas

The main aim of this paper is to present the multi scalar field components as candidates to be the dark energy of the universe and their observational constraints. We start with the canonical Quintessence and Phantom fields with quadratic potentials and show that a more complex model should bear in mind to satisfy current cosmological observations. Then we p

Zengqian Ding, Boyi zhou, Tao Wang, Lina Yang

The presence of amorphous oxide layers can significantly affect the coherent time of superconducting qubits due to their high dielectric loss. Typically, the surface oxides of superconductor films exhibit lossy and unstable behavior when exposed to air. To increase the coherence time, it is essential for qubits to have stable and low dielectric loss oxides,

Tharindu Fernando, Harshala Gammulle, Sridha Sridharan, Simon Denman

Humans exhibit complex motions that vary depending on the task that they are performing, the interactions they engage in, as well as subject-specific preferences. Therefore, forecasting future poses based on the history of the previous motions is a challenging task. This paper presents an innovative auxiliary-memory-powered deep neural network framework for

Mingliang Zhai, Yulin Li, Xiameng Qin, Chen Yi

Transformers achieve promising performance in document understanding because of their high effectiveness and still suffer from quadratic computational complexity dependency on the sequence length. General efficient transformers are challenging to be directly adapted to model document. They are unable to handle the layout representation in documents, e.g. wor

Xiaowei Huang, Wenjie Ruan, Wei Huang, Gaojie Jin

Large Language Models (LLMs) have exploded a new heatwave of AI for their ability to engage end-users in human-level conversations with detailed and articulate answers across many knowledge domains. In response to their fast adoption in many industrial applications, this survey concerns their safety and trustworthiness. First, we review known vulnerabilities

Takuto Isoyama, Shunsuke Kidani, Masashi Unoki

Sound-quality metrics (SQMs), such as sharpness, roughness, and fluctuation strength, are calculated using a standard method for calculating loudness (Zwicker method, ISO532B, 1975). Since ISO 532 had been revised to contain the Zwicker method (ISO 5321) and Moore-Glasberg method (ISO 532-2) in 2017, the classical computational SQM model should also be revis

Ya-Lin Zhang, Jun Zhou, Yankun Ren, Yue Zhang

In this paper, we consider the problem of long tail scenario modeling with budget limitation, i.e., insufficient human resources for model training stage and limited time and computing resources for model inference stage. This problem is widely encountered in various applications, yet has received deficient attention so far. We present an automatic system na

Shiyu Wang, Guangji Bai, Qingyang Zhu, Zhaohui Qin

Graph transformation that predicts graph transition from one mode to another is an important and common problem. Despite much progress in developing advanced graph transformation techniques in recent years, the fundamental assumption typically required in machine-learning models that the testing and training data preserve the same distribution does not alway

Liuyi Wang, Chengju Liu, Zongtao He, Shu Li

Vision-and-language navigation (VLN) is a crucial but challenging cross-modal navigation task. One powerful technique to enhance the generalization performance in VLN is the use of an independent speaker model to provide pseudo instructions for data augmentation. However, current speaker models based on Long-Short Term Memory (LSTM) lack the ability to atten

Rudrajit Banerjee, Max Niedermaier

States of Low Energy are a class of exact Hadamard states for free quantum fields on cosmological spacetimes whose structure is fixed at {\it all} scales by a minimization principle. The original construction was for Friedmann-Lema\^{i}tre geometries and is here generalized to anisotropic Bianchi I geometries relevant to primordial cosmology. In addition to

Anaelia Ovalle, Mehrab Beikzadeh, Parshan Teimouri, Kai-Wei Chang

Large language models have been useful in expanding mental health care delivery. ChatGPT, in particular, has gained popularity for its ability to generate human-like dialogue. However, data-sensitive domains -- including but not limited to healthcare -- face challenges in using ChatGPT due to privacy and data-ownership concerns. To enable its utilization, we

Faxian Cao, Yongqiang Cheng, Adil Mehmood Khan, Zhijing Yang

This study comes as a timely response to mounting criticism of the information bottleneck (IB) theory, injecting fresh perspectives to rectify misconceptions and reaffirm its validity. Firstly, we introduce an auxiliary function to reinterpret the maximal coding rate reduction method as a special yet local optimal case of IB theory. Through this auxiliary fu

Raphael Poulain, Mirza Farhan Bin Tarek, Rahmatollah Beheshti

Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes applications such as those in healthcare. However, health AI models' overall prediction performance is often prioritized over the possible biases such models could have. In this study, we show one possible approach to mitigate bias concerns by having healthcare

Zhiyinan Huang, Jinfeng Liu, Biao Huang

We propose a robust nonlinear model predictive control design with generalized zone tracking (ZMPC) in this work. The proposed ZMPC has guaranteed convergence into the target zone in the presence of bounded disturbance. The proposed approach achieves this by modifying the actual target zone such that the effect of disturbances is rejected. A control invarian

Haram Kim, Ji Oon Lee

We consider the spherical Sherrington-Kirkpatrick model of spin glass with sparse interaction, where the interactions between most of the pairs of the spin variables are possibly zero. With suitable normalization, we prove that the limiting free energy does not depend on the sparsity whereas the fluctuation of the free energy does. We also prove that both in

Po-Nien Kung, Nanyun Peng

Recent works on instruction tuning (IT) have achieved great performance with zero-shot generalizability to unseen tasks. With additional context (e.g., task definition, examples) provided to models for fine-tuning, they achieved much higher performance than untuned models. Despite impressive performance gains, what models learn from IT remains understudied.

Brian Pinsky

Frucht's theorem is the statement that "every group is the automorphism group of a graph". This was shown over ZFC independently by Sabidussi and deGroot, by induction using a well ordered generating set for the group. Sabidussi's proof is easily modified to use induction on the rank of a generating set, and thus holds over ZF. We show that Frucht's theorem

Tatiana Likhomanenko, Loren Lugosch, Ronan Collobert

Recent work has shown that it is possible to train an $\textit{unsupervised}$ automatic speech recognition (ASR) system using only unpaired audio and text. Existing unsupervised ASR methods assume that no labeled data can be used for training. We argue that even if one does not have any labeled audio for a given language, there is $\textit{always}$ labeled d

Banghua Zhu, Sai Praneeth Karimireddy, Jiantao Jiao, Michael I. Jordan

The creator economy has revolutionized the way individuals can profit through online platforms. In this paper, we initiate the study of online learning in the creator economy by modeling the creator economy as a three-party game between the users, platform, and content creators, with the platform interacting with the content creator under a principal-agent m

X. F. Dong, Z. B. Zhang, Q. M. Li, Y. F. Huang

The relation between the event rate of long Gamma-Ray Bursts at low redshift and the star formation rate is still controversial, especially in the low-redshift end. Dong et al. confirmed that the Gamma-Ray Burst rate always exceeds the star formation rate at low-redshift of z < 1 in despite of the sample completeness. However, the reason of low-redshift exce

Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang

A Markov network characterizes the conditional independence structure, or Markov property, among a set of random variables. Existing work focuses on specific families of distributions (e.g., exponential families) and/or certain structures of graphs, and most of them can only handle variables of a single data type (continuous or discrete). In this work, we ch

Wei Liu, Meng Li, Houqiang Teng, Heshan Liu

Current single-molecule imaging techniques are incapable of providing stochastic information of individual chemical reactions. We have developed an optical imaging method for stochastic analysis of individual electrochemical reactions. The method bypasses diffraction limits and allows for the direct imaging of the electronic states of single molecules in ele

Karandeep Singh, Yu-Che Tsai, Cheng-Te Li, Meeyoung Cha

Custom officials across the world encounter huge volumes of transactions. With increased connectivity and globalization, the customs transactions continue to grow every year. Associated with customs transactions is the customs fraud - the intentional manipulation of goods declarations to avoid the taxes and duties. With limited manpower, the custom offices c

Shengjie Hu, Zhenlei Yang, Sergio Torres, Zipeng Wang

Power law size distribution is found to associate with fractal, self-organized behaviors and patterns of complex systems. Such distribution also emerges from natural lakes, with potentially important links to the dynamics of lake systems. But the driving mechanism that generates and shapes this feature in lake systems remains unclear. Moreover, the power law

Mahdi Mollakazemiha, Hassan Fatzade

There are two approaches for simulating memory as well as learning in artificial intelligence; the functionalistic approach and the cognitive approach. The necessary condition to put the second approach into account is to provide a model of brain activity that contains a quite good congruence with observational facts such as mistakes and forgotten experience

Benedict Guttman-Kenney, Jesse Leary, Neil Stewart

We find it is common for consumers who are not in financial distress to make credit card payments at or close to the minimum. This pattern is difficult to reconcile with economic factors but can be explained by minimum payment information presented to consumers acting as an anchor that weighs payments down. Building on Stewart (2009), we conduct a hypothetic

Dhara Yu, Noah D. Goodman, Jesse Mu

Humans teach others about the world through language and demonstration. When might one of these modalities be more effective than the other? In this work, we study the factors that modulate the effectiveness of language vs. demonstration using multi-agent systems to model human communication. Specifically, we train neural network agents to teach via language

Shohei Uchigasaki, Tomo Miyazaki, Shinichiro Omachi

Image compression is a fundamental technology for Internet communication engineering. However, a high compression rate with general methods may degrade images, resulting in unreadable texts. In this paper, we propose an image compression method for maintaining text quality. We developed a scene text image quality assessment model to assess text quality in co

Benjamin M. Alessio, Ankur Gupta

Turing patterns are fundamental in biophysics, emerging from short-range activation and long-range inhibition processes. However, their paradigm is based on diffusive transport processes, which yields Turing patters that are less sharp than the ones observed in nature. A complete physical description of why the Turing patterns observed in nature are signific

Yoshihiro Honda, Eri Igarashi, Yuya Shoji, Tomohiro Amemiya

Optical waveguide theory is essential to the development of various optical devices. Although there are reports on the theory of optical waveguides with magneto-optical (MO) and magnetoelectric (ME) effects, a comprehensive theoretical analysis of waveguides considering these two effects has not yet been published. In this study, the conventional waveguide t

Avia Raviv-Moshe, Siwei Zhong

We study mass-type surface defects in a free scalar and Wilson-Fisher (WF) $O(N)$ theories. We obtain exact results for the free scalar defect, including its RG flow and defect Weyl anomaly. We classify phases of such defects at the WF fixed point near four dimensions, whose perturbative RG flow is investigated. We propose an IR effective action for the non-

David F. Renteria-Estrada, Roger J. Hernandez-Pinto, G. F. R. Sborlini, Pia Zurita

High Energy collider experiments are moving to the highest precision frontier quickly. The predictions of observables are based on the factorization formula which helps to connect small to large distances. These predictions can be contrasted with experimental measurements and the success of this phenomenological approach is based on the correct description o

Wentai Deng, Zhi-Cheng Yang

We propose a scheme that generates period-doubled responses via periodically driving certain Hamiltonians hosting quantum many-body scars, akin to recent experimental observations in driven Rydberg atom arrays. Our construction takes advantage of an su(2) spectrum generating algebra associated with the static quantum-scarred Hamiltonian, which enacts a $\pi$

Liangqi Yuan, Yuan Wei, Jia Li

With the emphasis on healthcare, early childhood education, and fitness, non-invasive measurement and recognition methods have received more attention. Pressure sensing has been extensively studied because of its advantages of simple structure, easy access, visualization application, and harmlessness. This paper introduces a Smart Pressure e-Mat (SPeM) syste

Zifeng Wang, Cao Xiao, Jimeng Sun

Clinical trials are critical for drug development. Constructing the appropriate eligibility criteria (i.e., the inclusion/exclusion criteria for patient recruitment) is essential for the trial's success. Proper design of clinical trial protocols should consider similar precedent trials and their eligibility criteria to ensure sufficient patient coverage. In

Sakib Reza, Balaji Sundareshan, Mohsen Moghaddam, Octavia Camps

Egocentric temporal action segmentation in videos is a crucial task in computer vision with applications in various fields such as mixed reality, human behavior analysis, and robotics. Although recent research has utilized advanced visual-language frameworks, transformers remain the backbone of action segmentation models. Therefore, it is necessary to improv

Emily Reif, Minsuk Kahng, Savvas Petridis

Large language models (LLMs) can be used to generate smaller, more refined datasets via few-shot prompting for benchmarking, fine-tuning or other use cases. However, understanding and evaluating these datasets is difficult, and the failure modes of LLM-generated data are still not well understood. Specifically, the data can be repetitive in surprising ways,

J. C. del Valle, A. V. Turbiner

Following our previous study of the Bohr-Sommerfeld (B-S) quantization condition for one-dimensional case (del Valle \& Turbiner (2021) \cite{First}), we extend it to $d$-dimensional power-like radial potentials. The B-S quantization condition for $S$-states of the $d$-dimensional radial Schr\"odinger equation is proposed. Based on numerical results obtained

Joon Suk Huh

This work addresses the problem of revenue maximization in a repeated, unlimited supply item-pricing auction while preserving buyer privacy. We present a novel algorithm that provides differential privacy with respect to the buyer's input pair: item selection and bid. Notably, our algorithm is the first to offer a sublinear $O(\sqrt{T}\log{T})$ regret with a

David Liu, Virginie Do, Nicolas Usunier, Maximilian Nickel

Group fairness is a popular approach to prevent unfavorable treatment of individuals based on sensitive attributes such as race, gender, and disability. However, the reliance of group fairness on access to discrete group information raises several limitations and concerns, especially with regard to privacy, intersectionality, and unforeseen biases. In this w

Junfeng Jiao, Ryan Hardesty Lewis, Kijin Seong, Arya Farahi

Fires and burning are the chief causes of particulate matter (PM2.5), a key measurement of air quality in communities and cities worldwide. This work develops a live fire tracking platform to show active reported fires from over twenty cities in the U.S., as well as predict their smoke paths and impacts on the air quality of regions within their range. Speci

Chun-Wei Ho, Chao-Han Huck Yang, Sabato Marco Siniscalchi

In this work, we devise a parameter-efficient solution to bring differential privacy (DP) guarantees into adaptation of a cross-lingual speech classifier. We investigate a new frozen pre-trained adaptation framework for DP-preserving speech modeling without full model fine-tuning. First, we introduce a noisy teacher-student ensemble into a conventional adapt

Maryame El Moutamid, Kevin B. Stevenson, Billy Quarles, Nikole K. Lewis

While various indirect methods are used to detect exoplanets, one of the most effective and accurate methods is the transit method, which measures the brightness of a given star for periodic dips when an exoplanet is passing in front of the parent star. For systems with multiple transiting planets, the gravitational perturbations between planets affect their

Manuel Rios, Nicanor Quijano, Luis Felipe Giraldo

Social dilemmas are situations where groups of individuals can benefit from mutual cooperation but conflicting interests impede them from doing so. This type of situations resembles many of humanity's most critical challenges, and discovering mechanisms that facilitate the emergence of cooperative behaviors is still an open problem. In this paper, we study t

Takuma Akimoto, Eiji Yamamoto, Takashi Uneyama

Complex systems such as protein conformational fluctuations and supercooled liquids exhibit a long relaxation time and are considered to posses multiple relaxation times. We analytically obtain the exact correlation function for stochastic processes with multiple relaxation times. We show that the time-averaged correlation function calculated by a trajectory

Long Chen, Xuehai Huang

A new $H(\textrm{divdiv})$-conforming finite element is presented, which avoids the need for super-smoothness by redistributing the degrees of freedom to edges and faces. This leads to a hybridizable mixed method with superconvergence for the biharmonic equation. Moreover, new finite element divdiv complexes are established. Finally, new weak Galerkin and $C

Jacob Eisenstein, Vinodkumar Prabhakaran, Clara Rivera, Dorottya Demszky

We introduce a new dataset of conversational speech representing English from India, Nigeria, and the United States. The Multi-Dialect Dataset of Dialogues (MD3) strikes a new balance between open-ended conversational speech and task-oriented dialogue by prompting participants to perform a series of short information-sharing tasks. This facilitates quantitat

Robert Stanton, Dhara J. Trivedi

The increased computational and experimental interest in perovskite systems comprising novel phases and reduced dimensionality has greatly expanded the search space for this class of materials. In similar fields, unified frameworks exist for the procedural generation and subsequent analysis of these complex condensed matter systems. Given the relatively rece

Tomoharu Iwata, Yoichi Chikahara

This article proposes a meta-learning method for estimating the conditional average treatment effect (CATE) from a few observational data. The proposed method learns how to estimate CATEs from multiple tasks and uses the knowledge for unseen tasks. In the proposed method, based on the meta-learner framework, we decompose the CATE estimation problem into sub-

David Jennings, Matteo Lostaglio, Sam Pallister, Andrew T Sornborger

Solving linear systems of equations is a fundamental problem with a wide variety of applications across many fields of science, and there is increasing effort to develop quantum linear solver algorithms. [Suba\c{s}i et al., Phys. Rev. Lett. (2019)] proposed a randomized algorithm inspired by adiabatic quantum computing, based on a sequence of random Hamilton

Jill Walker Rettberg

This commentary tests a methodology proposed by Munk et al. (2022) for using failed predictions in machine learning as a method to identify ambiguous and rich cases for qualitative analysis. Using a dataset describing actions performed by fictional characters interacting with machine vision technologies in 500 artworks, movies, novels and videogames, I train

Marco Sutti, Bart Vandereycken

We propose two implicit numerical schemes for the low-rank time integration of stiff nonlinear partial differential equations. Our approach uses the preconditioned Riemannian trust-region method of Absil, Baker, and Gallivan, 2007. We demonstrate the efficiency of our method for solving the Allen-Cahn and the Fisher-KPP equation on the manifold of fixed-rank

Farid Labib, Shintaro Suzuki, Asuka Ishikawa, Takenori Fujii

This study reports the first observation of ferromagnetic (FM) order in the non-Au-based approximant crystals (ACs) using a novel approach whereby a total electron-per-atom (e/a) ratio of the spin-glass Ga50Pd36Gd14 1/1 AC is lowered by simultaneously substituting certain ratios of a tri-valent Ga and a zero-valent Pd by a mono-valent Au. The emergence of FM

Zhifeng Kong, Kamalika Chaudhuri

Deep generative models are known to produce undesirable samples such as harmful content. Traditional mitigation methods include re-training from scratch, filtering, or editing; however, these are either computationally expensive or can be circumvented by third parties. In this paper, we take a different approach and study how to post-edit an already-trained

Maxwell Allman, Itai Ashlagi

In many centralized labor markets candidates interview with potential employers before matches are formed through a clearinghouse One prominent example is the market for medical residencies and fellowships, which in recent years has had a large increase in the number of interviews. There have been numerous efforts to reduce the cost of interviewing in these

Zijun Zhang, Adam R. Lamson, Michael Shelley, Olga Troyanskaya

Finely-tuned enzymatic pathways control cellular processes, and their dysregulation can lead to disease. Creating predictive and interpretable models for these pathways is challenging because of the complexity of the pathways and of the cellular and genomic contexts. Here we introduce Elektrum, a deep learning framework which addresses these challenges with

Amila Silva, Ling Luo, Shanika Karunasekera, Christopher Leckie

The emergence of social media as one of the main platforms for people to access news has enabled the wide dissemination of fake news. This has motivated numerous studies on automating fake news detection. Although there have been limited attempts at unsupervised fake news detection, their performance suffers due to not exploiting the knowledge from various m

Yuxin Xiao, Shulammite Lim, Tom Joseph Pollard, Marzyeh Ghassemi

Data sharing is crucial for open science and reproducible research, but the legal sharing of clinical data requires the removal of protected health information from electronic health records. This process, known as de-identification, is often achieved through the use of machine learning algorithms by many commercial and open-source systems. While these syste

Elise Bishoff, Charles Godfrey, Myles McKay, Eleanor Byler

In overhead image segmentation tasks, including additional spectral bands beyond the traditional RGB channels can improve model performance. However, it is still unclear how incorporating this additional data impacts model robustness to adversarial attacks and natural perturbations. For adversarial robustness, the additional information could improve the mod