October 2023 arXiv papers — page 63

Ataberk Olgun, Majd Osseiran, Abdullah Giray Yaglikci, Yahya Can Tugrul

We experimentally demonstrate the effects of read disturbance (RowHammer and RowPress) and uncover the inner workings of undocumented read disturbance defense mechanisms in High Bandwidth Memory (HBM). Detailed characterization of six real HBM2 DRAM chips in two different FPGA boards shows that (1) the read disturbance vulnerability significantly varies betw

Haoru Tan, Sitong Wu, Fei Du, Yukang Chen

In this paper, we propose a novel data-pruning approach called moving-one-sample-out (MoSo), which aims to identify and remove the least informative samples from the training set. The core insight behind MoSo is to determine the importance of each sample by assessing its impact on the optimal empirical risk. This is achieved by measuring the extent to which

Seongho Joo, Hyukhun Koh, Kyomin Jung

With the rapid advancement in deep generative models, recent neural Text-To-Speech(TTS) models have succeeded in synthesizing human-like speech. There have been some efforts to generate speech with various prosody beyond monotonous prosody patterns. However, previous works have several limitations. First, typical TTS models depend on the scaled sampling temp

David Morin, Milena Planells, Stéphane Mermoz, Florian Mouret

Mapping forest resources and carbon is important for improving forest management and meeting the objectives of storing carbon and preserving the environment. Spaceborne remote sensing approaches have considerable potential to support forest height monitoring by providing repeated observations at high spatial resolution over large areas. This study uses a mac

Yingyu Lin, Yi-An Ma, Yu-Xiang Wang, Rachel Redberg

Posterior sampling, i.e., exponential mechanism to sample from the posterior distribution, provides $\varepsilon$-pure differential privacy (DP) guarantees and does not suffer from potentially unbounded privacy breach introduced by $(\varepsilon,\delta)$-approximate DP. In practice, however, one needs to apply approximate sampling methods such as Markov chai

Matteo Tarocchi

We study a family of Thompson-like groups built as rearrangement groups of fractals from [BF19], each acting on a Wa\.zewski dendrite. Each of these is a finitely generated group that is dense in the full group of homeomorphisms of the dendrite (studied in [DM19]) and has infinite-index finitely generated simple commutator subgroup, with a single possible ex

Francesco Demelas, Joseph Le Roux, Mathieu Lacroix, Axel Parmentier

Lagrangian relaxation stands among the most efficient approaches for solving a Mixed Integer Linear Programs (MILP) with difficult constraints. Given any duals for these constraints, called Lagrangian Multipliers (LMs), it returns a bound on the optimal value of the MILP, and Lagrangian methods seek the LMs giving the best such bound. But these methods gener

Bruno Blanchet

This document presents the security protocol verifier CryptoVerif.CryptoVerif does not rely on the symbolic, Dolev-Yao model, but on the computational model. It can verify secrecy, correspondence (which include authentication), and indistinguishability properties. It produces proofs presented as sequences of games, like those manually written by cryptographe

Stefan F. Schouten, Peter Bloem, Ilia Markov, Piek Vossen

Natural language reasoning plays an increasingly important role in improving language models' ability to solve complex language understanding tasks. An interesting use case for reasoning is the resolution of context-dependent ambiguity. But no resources exist to evaluate how well Large Language Models can use explicit reasoning to resolve ambiguity in langua

Shigeki Matsutani

The periodic and quasi-periodic solutions of the integrable system have been studied for four decades based on the Riemann theta functions. However, there is a fundamental difficulty in representing the solutions graphically because the Riemann theta function requires several transcendental parameters. This paper presents a novel method for the graphical rep

Ricard Ravell Rodríguez, Mohammad Mehboudi, Michał Horodecki, Martí Perarnau-Llobet

We characterise the measurement sensitivity, quantified by the Quantum Fisher Information (QFI), of a single-fermionic thermometric probe strongly coupled to the sample of interest, a fermionic bath, at temperature $T$. For nonequilibrium protocols, in which the probe is measured before reaching equilibrium with the sample, we find new behaviour of the measu

Nithya R, Malavika S, Jordan F, Arjun Gangwar

India is home to a multitude of languages of which 22 languages are recognised by the Indian Constitution as official. Building speech based applications for the Indian population is a difficult problem owing to limited data and the number of languages and accents to accommodate. To encourage the language technology community to build speech based applicatio

Bo Liu, Rui-Hong Yue, De-Cheng Zou, Lina Zhang

In this paper, we focus on massive Einstein-dilaton gravity including the coupling of dilaton scalar field to massive graviton terms, and then derive static and spherically symmetric solutions of dilatonic black holes in four dimensional spacetime. We find that the dilatonic black hole could possess two horizons (event and cosmological), extreme (Nariai) and

Jiali Ma, Zhongqi Yue, Kagaya Tomoyuki, Suzuki Tomoki

Fair face recognition is all about learning invariant feature that generalizes to unseen faces in any demographic group. Unfortunately, face datasets inevitably capture the imbalanced demographic attributes that are ubiquitous in real-world observations, and the model learns biased feature that generalizes poorly in the minority group. We point out that the

Shoki Ohta, Takayuki Nishio

In the wake of the burgeoning expansion of generative artificial intelligence (AI) services, the computational demands inherent to these technologies frequently necessitate cloud-powered computational offloading, particularly for resource-constrained mobile devices. These services commonly employ prompts to steer the generative process, and both the prompts

Rafał Perczyński, Antoni Augustynowicz

Highly oscillatory differential equations present significant challenges in numerical treatments. The Modulated Fourier Expansion (MFE), used as an ansatz, is a commonly employed tool as a numerical approximation method. In this article, the Modulated Fourier Expansion is analytically derived for a linear partial differential equation with a multifrequency h

Sudhi Sharma, Pierre Jolivet, Victorita Dolean, Abhijit Sarkar

This article discusses the uncertainty quantification (UQ) for time-independent linear and nonlinear partial differential equation (PDE)-based systems with random model parameters carried out using sampling-free intrusive stochastic Galerkin method leveraging multilevel scalable solvers constructed combining two-grid Schwarz method and AMG. High-resolution s

Ankita Ram, Krishna Maity, Cédric Marchand, Aymen Mahmoudi

In this work, we demonstrate the suitability of Reconfigurable Ferroelectric Field-Effect- Transistors (Re-FeFET) for designing non-volatile reconfigurable logic-in-memory circuits with multifunctional capabilities. Modulation of the energy landscape within a homojunction of a 2D tungsten diselenide (WSe$_2$) layer is achieved by independently controlling tw

Boštjan Brešar, Csilla Bujtás, Vesna Iršič, Douglas F. Rall

Motivated by the success of domination games and by a variation of the coloring game called the indicated coloring game, we introduce a version of domination games called the indicated domination game. It is played on an arbitrary graph $G$ by two players, Dominator and Staller, where Dominator wants to finish the game in as few rounds as possible while Stal

Xuemei Li, Chenxi Liu, Xingdong Tang, Guixiang Xu

In this paper, we study long time dynamics of radial threshold solutions for the focusing, generalized energy-critical Hartree equation and classify all radial threshold solutions. The main arguments are the spectral theory of the linearized operator, the modulational analysis and the concentration compactness rigidity argument developed by T. Duyckaerts and

Ning Zhang, Si-Yuan Bai, Chong Chen

We investigate the temperature uncertainty relation in nonequilibrium probe-based temperature estimation process. We demonstrate that it is the fluctuation of heat that fundamentally determines temperature precision through the temperature-heat uncertainty relation. Specifically, we find that heat is divided into trajectory heat and correlation heat, which a

David Stap, Christof Monz

k-nearest-neighbor machine translation has demonstrated remarkable improvements in machine translation quality by creating a datastore of cached examples. However, these improvements have been limited to high-resource language pairs, with large datastores, and remain a challenge for low-resource languages. In this paper, we address this issue by combining re

Xingwei Gao, Hao He, Scott Sobolewski, Alexander Cerjan

Exceptional points (EPs) -- singularities in the parameter space of non-Hermitian systems where two nearby eigenmodes coalesce -- feature unique properties with applications such as sensitivity enhancement and chiral emission. Existing realizations of EP lasers operate with static populations in the gain medium. By analyzing the full-wave Maxwell--Bloch equa

Zhong Li, Liangchen Song, Zhang Chen, Xiangyu Du

In this paper, we address the problem of simultaneous relighting and novel view synthesis of a complex scene from multi-view images with a limited number of light sources. We propose an analysis-synthesis approach called Relit-NeuLF. Following the recent neural 4D light field network (NeuLF), Relit-NeuLF first leverages a two-plane light field representation

Yoshiki Kanazawa

QCD axion models have been proposed as a solution to the strong CP problem. QCD instanton effects explicitly violate the global U(1) Peccei-Quinn (PQ) symmetry, and the axion potential is minimized at the CP conserving points. However, it is expected that the global U(1) PQ symmetry is explicitly violated by quantum gravity. This gravitational violation may

Sebastien Galtier

Wave turbulence is the study of the long-time statistical behaviour of equations describing a set of weakly non-linear interacting waves. Such a theory, which has a natural asymptotic closure, allows us to probe the nature of turbulence more deeply than the exact Kolmogorov laws by rigorously proving the direction of the cascade and the existence of an inert

Nguyen Tuan Hung

Three-dimensional layered Mg$_3$Bi$_2$ has emerged as thermoelectric material due to its high cooling performance at ambient temperature, which benefits from its low lattice thermal conductivity and semimetal character. However, the semimetal character of Mg$_3$Bi$_2$ is sensitive to spin-orbit coupling (SOC). Thus, the underlying origin of low lattice therm

Chao Shen, Yu Liu, Tianquan Tang, Lixi Huang

The term `sub-wavelength' is commonly used to describe innovative sound-absorbing structures usually labeled as `metamaterials'. Such structures, however, inherently do not bring groundbreaking advancements. This study addresses the limitations imposed by the thickness criterion of Yang et al. by introducing the concept of equivalent mass-spring-damping para

Xu Yuan, Zheng Zhang, Xunguang Wang, Lin Wu

Deep hashing has been intensively studied and successfully applied in large-scale image retrieval systems due to its efficiency and effectiveness. Recent studies have recognized that the existence of adversarial examples poses a security threat to deep hashing models, that is, adversarial vulnerability. Notably, it is challenging to efficiently distill relia

Xing Yang, Jian Zhang, Qijian Chen, Li Wang

Automatic segmentation of breast tumors from the ultrasound images is essential for the subsequent clinical diagnosis and treatment plan. Although the existing deep learning-based methods have achieved significant progress in automatic segmentation of breast tumor, their performance on tumors with similar intensity to the normal tissues is still not pleasant

Hongyu Liu, Zhi-Qiang Miao, Guang-Hui Zheng

In this paper, we develop a general mathematical framework for enhanced hydrodynamic near-cloaking of electro-osmotic flow for more complex shapes, which is obtained by simultaneously perturbing the inner and outer boundaries of the perfect cloaking structure. We first derive the asymptotic expansions of perturbed fields and obtain a first-order coupled syst

Iulian D. Toader

This book concerns the metasemantics of quantum mechanics (QM). Roughly, it pursues an investigation at the intersection of philosophy of physics and philosophy of language, and it offers a critical analysis of rival explanations of the semantic facts of standard QM. Two problems for such explanations are discussed: categoricity and permanence. New results i

Petros Karypis, Julian McAuley, George Karypis

Effectively training language models on long inputs poses many technical challenges. As a cost consideration, languages models are pretrained on a fixed sequence length before being adapted to longer sequences. We explore various methods for adapting models to longer inputs by training on segmented sequences and an interpolation-based method for extending ab

Derek F. Holt, Sarah Rees

Our main theorem is that the word problem in the Artin group G = <a,b,c | aba=bab, ac=ca, {}_{n}(b,c) = {}_{n}(c,b) > for n >= 5 can be solved using a system R of length preserving rewrite rules that, together with free reduction, can be used to reduce any word over {a,b,c} to a geodesic word in $G$, in quadratic time. This result builds on work of Holt and

Guocong Quan, Atilla Eryilmaz, Ness Shroff

The ever-growing end user data demands, and the simultaneous reductions in memory costs are fueling edge-caching deployments. Caching at the edge is substantially different from that at the core and needs to take into account the nature of individual data demands. For example, an individual user may not be interested in requesting the same data item again, i

Kentaro Wada

We establish a Schur-Weyl duality between a shifted quantum affine algebra and an Ariki-Koike algebra. Then, we realize a cyclotomic $q$-Schur algebra in the context of the Schur-Weyl duality.

Revati M. Wahul, Aditya M. Rahalkar, Om M. Khare, Abhishek D. Patange

Tool Condition Monitoring (TCM) is vital for maintaining productivity and product quality in machining. This study leverages machine learning to analyze real-time force signals collected from experiments under various tool wear conditions. Statistical analysis and feature selection using decision trees were followed by classification using a K-Nearest Neighb

Tengxiao Liu, Qipeng Guo, Yuqing Yang, Xiangkun Hu

As large language models (LLMs) have shown effectiveness with different prompting methods, such as Chain of Thought, Program of Thought, we find that these methods have formed a great complementarity to each other on math reasoning tasks. In this work, we propose XoT, an integrated problem solving framework by prompting LLMs with diverse reasoning thoughts.

Henry Peng Zou, Yue Zhou, Cornelia Caragea, Doina Caragea

The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and response organizations. However, supervised learning models for monitoring disaster events require large amounts of annotated data, making them unrealistic for real-time use in dis

Yuanxing Liu, Wei-Nan Zhang, Yifan Chen, Yuchi Zhang

E-commerce pre-sales dialogue aims to understand and elicit user needs and preferences for the items they are seeking so as to provide appropriate recommendations. Conversational recommender systems (CRSs) learn user representation and provide accurate recommendations based on dialogue context, but rely on external knowledge. Large language models (LLMs) gen

Yong-Cheng Jiang, Toshikaze Kariyado, Xiao Hu

We unveil that the holey graphyne (HGY), a two-dimensional carbon allotrope where benzene rings are connected by two $-$C$\equiv$C$-$ bonds fabricated recently in a bottom-up way, exhibits topological electronic states. Using first-principles calculations and Wannier tight-binding modeling, we discover a higher-order topological invariant associated with $C_

Miroslav Mitev, Amitha Mayya, Arsenia Chorti

Joint communication and sensing is expected to be one of the features introduced by the sixth-generation (6G) wireless systems. This will enable a huge variety of new applications, hence, it is important to find suitable approaches to secure the exchanged information. Conventional security mechanisms may not be able to meet the stringent delay, power, and co

Hoang H. Nguyen, Ye Liu, Chenwei Zhang, Tao Zhang

While Chain-of-Thought prompting is popular in reasoning tasks, its application to Large Language Models (LLMs) in Natural Language Understanding (NLU) is under-explored. Motivated by multi-step reasoning of LLMs, we propose Coarse-to-Fine Chain-of-Thought (CoF-CoT) approach that breaks down NLU tasks into multiple reasoning steps where LLMs can learn to acq

Guanlan Huang, Sebastiano Boscarino, Tao Xiong

In this study, we investigate the Shallow Water Equations incorporating source terms accounting for Manning friction and a non-flat bottom topology. Our primary focus is on developing and validating numerical schemes that serve a dual purpose: firstly, preserving all steady states within the model, and secondly, maintaining the late-time asymptotic behavior

Zhanghan Lin, Haiping Huang

Spiking neural networks play an important role in brain-like neuromorphic computations and in studying working mechanisms of neural circuits. One drawback of training a large scale spiking neural network is that updating all weights is quite expensive. Furthermore, after training, all information related to the computational task is hidden into the weight ma

Rohit Kumar Shukla

Scrambling of quantum information in both integrable and nonintegrable Floquet spin systems is studied. Our study employs tripartite mutual information (TMI), with negative TMI serving as an indicator of scrambling, where a more negative value suggests a higher degree of scrambling. Both integrable and nonintegrable Floquet systems display scrambling behavio

Maxime Debiossac, Peng Pan, Philippe Roncin

Grazing incidence fast atom diffraction has mainly been investigated with helium atoms, considered as the best possible choice for surface analysis. This article presents experimental diffraction profiles recorded with neon projectile, between 300 eV and 4 keV kinetic energy with incidence angles $\theta_i$ between 0.3$^\circ$ and 1.5$^\circ$ along three dif

Jan Hamann, Yuqi Kang

Minkowski functionals are summary statistics that capture the geometric and morphological properties of fields. They are sensitive to all higher order correlations of the fields and can be used to complement more conventional statistics, such as the power spectrum of the field. We develop a Minkowski functional-based approach for a full likelihood analysis o

Christian Flores, Nagayoshi Ohashi, John J. Tobin, Jes K. Jørgensen

We present ALMA observations of the Class I source Oph IRS63 in the context of the Early Planet Formation in Embedded Disks (eDisk) large program. Our ALMA observations of Oph IRS63 show a myriad of protostellar features, such as a shell-like bipolar outflow (in $^{12}$CO), an extended rotating envelope structure (in $^{13}$CO), a streamer connecting the env

Abdelghani Maddi, Emmanuel Monneau, Catherine Gaspare, Floriana Gargiulo

The Streetlight Effect represents an observation bias that occurs when individuals search for something only where it is easiest to look. Despite the significant development of Post-Publication Peer Review (PPPR) in recent years, facilitated in part by platforms such as PubPeer, existing literature has not examined whether PPPR is affected by this type of bi

Tao Sun, Congliang Chen, Peng Qiao, Li Shen

Sign-based stochastic methods have gained attention due to their ability to achieve robust performance despite using only the sign information for parameter updates. However, the current convergence analysis of sign-based methods relies on the strong assumptions of first-order gradient Lipschitz and second-order gradient Lipschitz, which may not hold in prac

Frédéric Hélein

In this paper we present original variational formulations of Yang-Mills, Einstein's gravitation and Kaluza-Klein theories, where, in the spirit of General Relativity, the principal bundle structure over the space-time is not fixed a priori but is dynamical. In the Yang-Mills case only a topological fibration is given a priori. In the gravity and the Kaluza-

Yige Xu, Zhiwei Zeng, Zhiqi Shen

Emotion Recognition in Conversation (ERC) has been widely studied due to its importance in developing emotion-aware empathetic machines. The rise of pre-trained language models (PLMs) has further pushed the limit of ERC performance. However, most recent works on ERC using PLMs are heavily data-driven, and requires fine-tuning the entire PLMs. To improve both

Abdelghani Maddi, Emmanuel Monneau, Catherine Guaspare, Floriana Gargiulo

This article has a dual objective. Firstly, it aims to investigate whether gender diversity in publications reviewed on Pubpeer has an impact on the (non)retraction of those publications. Secondly, it seeks to analyze the reasons for retractions and examine if there are disparities in retractions based on male-female collaborations. To achieve this, the stud

Yingying Gao, Shilei Zhang, Zihao Cui, Chao Deng

Cascading multiple pre-trained models is an effective way to compose an end-to-end system. However, fine-tuning the full cascaded model is parameter and memory inefficient and our observations reveal that only applying adapter modules on cascaded model can not achieve considerable performance as fine-tuning. We propose an automatic and effective adaptive lea

Alex Borisevich

This paper formally shows that an exponentially rising current is optimal in terms of resistive ohmic loss for driving a semiconductor laser into the gain-switching mode. A metric to quantify the quality of laser operation that measures the similarity of a generated optical pulse to the delta function is proposed. Several circuit implementations to approxima

Yuya Nakamura, Hideki Asada

A triangular solution [Phys. Rev. D 107, 044005 (2023)] has recently been found to the planar circular three-body problem in the parametrized post-Newtonian (PPN) formalism, for which they focus on a class of fully conservative theories characterized by the Eddington-Robertson parameters $\beta$ and $\gamma$. The present paper extends the PPN triangular solu

Zhendong Ang, Umang Mathur

In this paper, we focus on the problem of dynamically analysing concurrent software against high-level temporal specifications. Existing techniques for runtime monitoring against such specifications are primarily designed for sequential software and remain inadequate in the presence of concurrency -- violations may be observed only in intricate thread interl

Jaechan Lee, Alisa Liu, Orevaoghene Ahia, Hila Gonen

The translation of ambiguous text presents a challenge for translation systems, as it requires using the surrounding context to disambiguate the intended meaning as much as possible. While prior work has studied ambiguities that result from different grammatical features of the source and target language, we study semantic ambiguities that exist in the sourc

Ghadi Nehme, Ishan Sabane, Tejas Y. Deo

At present, robots typically require extensive training to successfully accomplish a single task. However, to truly enhance their usefulness in real-world scenarios, robots should possess the capability to perform multiple tasks effectively. To address this need, various multi-task reinforcement learning (RL) algorithms have been developed, including multi-t

Xian Li, Hongguang Shi, Yunfei Wang, Yeqin Zhang

In Conversational Recommendation Systems (CRS), the central question is how the conversational agent can naturally ask for user preferences and provide suitable recommendations. Existing works mainly follow the hierarchical architecture, where a higher policy decides whether to invoke the conversation module (to ask questions) or the recommendation module (t

Vo Nguyen Le Duy, Hsuan-Tien Lin, Ichiro Takeuchi

We propose a novel statistical method for testing the results of anomaly detection (AD) under domain adaptation (DA), which we call CAD-DA -- controllable AD under DA. The distinct advantage of the CAD-DA lies in its ability to control the probability of misidentifying anomalies under a pre-specified level $\alpha$ (e.g., 0.05). The challenge within this DA

Yanchen Liu, Srishti Gautam, Jiaqi Ma, Himabindu Lakkaraju

Recent literature has suggested the potential of using large language models (LLMs) to make classifications for tabular tasks. However, LLMs have been shown to exhibit harmful social biases that reflect the stereotypes and inequalities present in society. To this end, as well as the widespread use of tabular data in many high-stake applications, it is import

Yan-Heng Yu, Sai Wang

We propose a new model to generate large anisotropies in the scalar-induced gravitational wave (SIGW) background via sound speed resonance in the inflaton-curvaton mixed scenario. Cosmological curvature perturbations are not only exponentially amplified at a resonant frequency, but also preserve significant non-Gaussianity of local type described by $f_{\mat

Fei Zhao, Chunhui Li, Zhen Wu, Yawen Ouyang

Multimodal Aspect-based Sentiment Analysis (MABSA) is a fine-grained Sentiment Analysis task, which has attracted growing research interests recently. Existing work mainly utilizes image information to improve the performance of MABSA task. However, most of the studies overestimate the importance of images since there are many noise images unrelated to the t

Amit Kumar Jha

This paper introduces a novel approach to financial risk assessment by incorporating topological data analysis (TDA), specifically cohomology groups, into the evaluation of equities portfolios. The study aims to go beyond traditional risk measures like Value at Risk (VaR) and Conditional Value at Risk (CVaR), offering a more nuanced understanding of market c

Sagiv Shiber, Emmanouil Chatzopoulos, Bradley Munson, Juhan Frank

We investigate the merger between a 16 solar mass star, on its way to becoming a red supergiant (RSG), and a 4 solar mass main-sequence companion. Our study employs three-dimensional hydrodynamic simulations using the state-of-the-art adaptive mesh refinement code Octo-Tiger. The initially corotating binary undergoes interaction and mass transfer, resulting

Tam Minh Vo, Khiem Vinh Tran

Recent studies have provided empirical evidence of the wide-ranging potential of Generative Pre-trained Transformer (GPT), a pretrained language model, in the field of natural language processing. GPT has been effectively employed as a decoder within state-of-the-art (SOTA) question answering systems, yielding exceptional performance across various tasks. Ho

Claudia Olaru, Mihail-Razvan Ioan, Mastaneh Zadehrafi

Optical detection of alpha particle emitters in the environment by air radioluminescence is a new technology that enables sensing a radiological threat at safe distances, without putting personnel at risk or contaminating equipment. Radioluminescence detection systems need to be fine-tuned to efficiently capture a substantial number of photons while minimizi

Martha N. Kamkuemah, J. W. Sanders

Non-fungible tokens, NFT, have been used to record ownership of real estate, art, digital assets, and more recently to serve legal notice. They provide an important and accessible non-financial use of cryptocurrency's blockchain but are peculiar because ownership by NFT confers no rights over the asset. This work shows that it is possible to specify that pec

Huiyu Mai, Wenhao Jiang, Zhihong Deng

Unsupervised text style transfer aims at training a generative model that can alter the style of the input sentence while preserving its content without using any parallel data. In this paper, we employ powerful pre-trained large language models and present a new prefix-tuning-based method for unsupervised text style transfer. We construct three different ki

S. Yamada, T. Suada, Y. Komiya, M. Aikawa

Carbon-enhanced metal-poor (CEMP) stars in the Galactic halo have a wide range of neutron-capture element abundance patterns. To identify their origin, we investigated three modes of $s$-process nucleosynthesis that have been proposed to operate in extremely metal-poor (EMP) Asymptotic Giant Branch (AGB) stars: the convective 13C burning, which occurs when h

Yan-Ke Chen, Wei-Lin Wu, Lu Meng, Shi-Lin Zhu

We calculate the mass spectrum of the $Qs\bar q \bar q$ $(Q=c, b)$ tetraquark states with $J^P=(0,1,2)^+$ using the AL1 quark potential model, which successfully describes the conventional hadron spectrum. We employ the Gaussian expansion method to solve the four-body Schr\"odinger equation, and use the complex scaling method to identify the resonances. With

Minghao Tang, Yongquan He, Yongxiu Xu, Hongbo Xu

Fine-grained entity typing (FET) is an essential task in natural language processing that aims to assign semantic types to entities in text. However, FET poses a major challenge known as the noise labeling problem, whereby current methods rely on estimating noise distribution to identify noisy labels but are confused by diverse noise distribution deviation.

Mor Oren-Loberman, Vered Azar, Wasim Huleihel

Modern social media platforms play an important role in facilitating rapid dissemination of information through their massive user networks. Fake news, misinformation, and unverifiable facts on social media platforms propagate disharmony and affect society. In this paper, we consider the problem of online auditing of information flow/propagation with the goa

Xue-Chen Gao, Xiao-Jie Wu, Cheng-Hua Bai, Shao-Xiong Wu

We propose a scheme to manipulate strong and nonreciprocal photon blockades in asymmetrical Fabry-Perot cavity with a $\Lambda$-type three-level atom. Utilizing the mechanisms of both conventional and unconventional blockade, the strong photon blockade is achieved by the anharmonic eigenenergy spectrum brought by $\Lambda$-type atom and the destructive quant

Shao-Xiong Wu, Cheng-Hua Bai, Gang Li, Chang-shui Yu

Quantum entanglement and Einstein-Podolsky-Rosen (EPR) steering are valuable resources in quantum information processing. How to enhance the quantum entanglement and EPR steering of coupled optomechanical systems with a weak squeezed vacuum field are studied when the displacement of detuning induced by the mechanical mode is considered. Compared with the con

Tai-Yu Pan, Chenyang Ma, Tianle Chen, Cheng Perng Phoo

Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train. In this work, we introduce an innovative pre-training approach, Grounded Point Colorization (GPC), to bridge the gap between data and labels by teaching the model to colorize LiDAR point clouds, equi

Mohamed Elwekeil, Lorenzo Galati Giordano, Paolo Baracca, Stefano Buzzi

In this paper, we consider the sixth generation (6G) sub-networks, where hyper reliable low latency communications (HRLLC) requirements are expected to be met. We focus on a scenario where multiple sub-networks are active in the service area and assess the feasibility of using the 6 GHz unlicensed spectrum to operate such deployment, evaluating the impact of

Kaixin Liu, Jiwei Zhou, Junda Wang

Using high-frequency donation records from a major medical crowdfunding site and careful difference-in-difference analysis, we demonstrate that the 2020 BLM surge decreased the fundraising gap between Black and non-Black beneficiaries by around 50\%. The reduction is largely attributed to non-Black donors. Those beneficiaries in counties with moderate BLM ac

Naoki Sasakura

As in random matrix theories, eigenvector/value distributions are important quantities of random tensors in their applications. Recently, real eigenvector/value distributions of Gaussian random tensors have been explicitly computed by expressing them as partition functions of quantum field theories with quartic interactions. This procedure to compute distrib

P. Marigo, P. Woitke, E. Tognelli, L. Girardi

In this study we compute the equation of state and Rosseland mean opacity from temperatures of T~30000 K down to T~400 K, pushing the capabilities of the AESOPUS code (Marigo et al., 2022; Marigo & Aringer, 2009) into the regime where solid grains can form. The GGchem code (Woitke et al. 2018) is used to solve the chemistry for temperatures less than ~3000 K

Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang

Modern search engines are built on a stack of different components, including query understanding, retrieval, multi-stage ranking, and question answering, among others. These components are often optimized and deployed independently. In this paper, we introduce a novel conceptual framework called large search model, which redefines the conventional search st

Xin Zheng, Miao Zhang, Chunyang Chen, Soheila Molaei

Evaluating the performance of graph neural networks (GNNs) is an essential task for practical GNN model deployment and serving, as deployed GNNs face significant performance uncertainty when inferring on unseen and unlabeled test graphs, due to mismatched training-test graph distributions. In this paper, we study a new problem, GNN model evaluation, that aim

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Based on $(27.08\pm 0.14)\times10^{8}$ $\psi(3686)$ events collected with the BESIII detector operating at the BEPCII collider, the $\psi(3686)\to\Sigma^{+}\bar{\Sigma}^{-}\omega$ and $\Sigma^{+}\bar{\Sigma}^{-}\phi$ decays are observed for the first time with statistical significances of 13.8$\sigma$ and 7.6$\sigma$, respectively. The corresponding branchin

Sam Armon

Demazure crystals give a combinatorial framework in which to study Demazure modules. They are extremal, in that they satisfy Kashiwara's string property, and they are Demazure atom-positive, in that they decompose naturally into subsets called crystal Demazure atoms. The properties of extremality and atom-positivity are further linked by a conjecture of Polo

Henry Peng Zou, Cornelia Caragea

Semi-supervised text classification (SSTC) has gained increasing attention due to its ability to leverage unlabeled data. However, existing approaches based on pseudo-labeling suffer from the issues of pseudo-label bias and error accumulation. In this paper, we propose JointMatch, a holistic approach for SSTC that addresses these challenges by unifying ideas

Katsunori Fujie, Takahiro Hasebe

Free probability of type B was invented by Biane--Goodman--Nica, and then it was generalized by Belinschi--Shlyakhtenko and F\'evrier--Nica to infinitesimal free probability. The latter found its applications to eigenvalues of perturbed random matrices in the work of Shlyakhtenko and C\'ebron--Dahlqvist--Gabriel. This paper offers a new framework, called ``f

Shuhei Yokoo, Peifei Zhu, Yuchi Ishikawa, Mikihiro Tanaka

Large web crawl datasets have already played an important role in learning multimodal features with high generalization capabilities. However, there are still very limited studies investigating the details or improvements of data design. Recently, a DataComp challenge has been designed to propose the best training data with the fixed models. This paper prese

Feiyu Shen, Yiwei Guo, Chenpeng Du, Xie Chen

Discrete audio tokens derived from self-supervised learning models have gained widespread usage in speech generation. However, current practice of directly utilizing audio tokens poses challenges for sequence modeling due to the length of the token sequence. Additionally, this approach places the burden on the model to establish correlations between tokens,

Jiyun Shin, Jinhyun Ahn, Honggu Kang, Joonhyuk Kang

Foundation models (FMs) have demonstrated remarkable performance in machine learning but demand extensive training data and computational resources. Federated learning (FL) addresses the challenges posed by FMs, especially related to data privacy and computational burdens. However, FL on FMs faces challenges in situations with heterogeneous clients possessin

Hamza Djelouat, Mikko J. Sillanpää, Markku Juntti

This paper provides a solution for the activity detection and channel estimation problem in grant-free access with correlated device activity patterns. In particular, we consider a machine-type communications (MTC) network operating in event-triggered traffic mode, where the devices are distributed over clusters with an activity behaviour that exhibits both

Keisuke Ozawa, Tomoya Itakura, Taisuke Ono

Smoothing is widely used approach for measurement noise reduction in spectral analysis. However, it suffers from signal distortion caused by peak suppression. A locally self-adjustive smoothing method is developed that retains sharp peaks and less distort signals. The proposed method uses only one parameter that determines global smoothness, while balancing

Henry Peng Zou, Yue Zhou, Weizhi Zhang, Cornelia Caragea

During crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support. Emergency relief organizations leverage such information to acquire timely crisis circumstances and expedite rescue operations. While existing works utilize such information to build models for crisis ev

Haoyu Deng, Ruijie Zhu, Xuerui Qiu, Yule Duan

The attention mechanism has been proven to be an effective way to improve spiking neural network (SNN). However, based on the fact that the current SNN input data flow is split into tensors to process on GPUs, none of the previous works consider the properties of tensors to implement an attention module. This inspires us to rethink current SNN from the persp

Ce Jin, Virginia Vassilevska Williams, Renfei Zhou

Listing copies of small subgraphs (such as triangles, $4$-cycles, small cliques) in the input graph is an important and well-studied problem in algorithmic graph theory. In this paper, we give a simple algorithm that lists $t$ (non-induced) $6$-cycles in an $n$-node undirected graph in $\tilde O(n^2+t)$ time. This nearly matches the fastest known algorithm f

Giulio Foggi Rota, Morie Koseki, Riya Agrawal, Stefano Olivieri

We characterize the dynamical behavior of a clamped flexible fiber immersed in wall turbulence over a wide range of natural frequencies by means of direct numerical simulations. Only two flapping states are possible: one where the fiber oscillates at the characteristic frequency of the largest turbulent eddies and another where the natural structural respons

Qianchu Liu, Stephanie Hyland, Shruthi Bannur, Kenza Bouzid

The recent success of general-domain large language models (LLMs) has significantly changed the natural language processing paradigm towards a unified foundation model across domains and applications. In this paper, we focus on assessing the performance of GPT-4, the most capable LLM so far, on the text-based applications for radiology reports, comparing aga

Pritam Kadasi, Mayank Singh

The NLP community has long advocated for the construction of multi-annotator datasets to better capture the nuances of language interpretation, subjectivity, and ambiguity. This paper conducts a retrospective study to show how performance scores can vary when a dataset expands from a single annotation per instance to multiple annotations. We propose a novel

J. N. H. S. Aditya, Hyein Yoon, James R. Allison, Tao An

We report an ASKAP search for associated HI 21-cm absorption against bright radio sources from the Molonglo Reference Catalogue (MRC) 1-Jy sample. The search uses pilot survey data from the ASKAP First Large Absorption Survey in \hi (FLASH) covering the redshift range $0.42 < z < 1.00$. From a sample of 62 MRC 1-Jy radio galaxies and quasars in this redshift