October 2024 arXiv papers — page 76

Diane Soussan, Ali Tahrioui, R R de la Haba, Adrien Forge

Antiterminators are essential components of bacterial transcriptional regulation, allowing the control of gene expression in response to fluctuating environmental conditions. RNA-binding antiterminators are particularly important regulatory proteins that play a significant role in preventing transcription termination by binding to specific RNA sequences. The

Jonathan Gail, Alisa Schmidt, Markus H. Thoma

The study investigated the effects of atmospheric plasma on various dielectric materials (FR-4, PTFE, Al$_2$O$_3$) and electrode materials (copper, silver, gold-plated copper) used in surface micro-discharge plasma sources. XPS and laser microscopy were used to analyze changes in surface properties and chemical composition after 10 hours of plasma exposure.

Sobihan Surendran, Antoine Godichon-Baggioni, Sylvain Le Corff

Variational Autoencoders (VAE) are popular generative models used to sample from complex data distributions. Despite their empirical success in various machine learning tasks, significant gaps remain in understanding their theoretical properties, particularly regarding convergence guarantees. This paper aims to bridge that gap by providing non-asymptotic con

Abdelghani Maddi, Marion Maisonobe, Chérifa Boukacem-Zeghmouri

This study aims to compare the geographical and disciplinary coverage of OA journals in three databases: OpenAlex, Scopus and the WoS. We used the ROAD database, managed by the ISSN International Centre, as a reference database which indexes 62,701 OA active resources (as of May 2024). Among the 62,701 active resources indexed in the ROAD database, the Web o

Samy Skander Bahoura

We give a uniform estimate and an inequality for solutions of an equation with Dirichlet boundary condition.

Jayden Dongwoo Lee, Donghoon Seo, Jongho Shin, Hyochoong Bang

Lithium-ion batteries (LIBs) are utilized as a major energy source in various fields because of their high energy density and long lifespan. During repeated charging and discharging, the degradation of LIBs, which reduces their maximum power output and operating time, is a pivotal issue. This degradation can affect not only battery performance but also safet

Panos Fitsilis, Paraskevi Tsoutsa, Vyron Damasiotis, Vasileios Kyriatzis

This article analyzes around 200 online articles to identify trends within Industry 5.0 using artificial intelligence techniques. Specifically, it applies algorithms such as LDA, BERTopic, LSA, and K-means, in various configurations, to extract and compare the central themes present in the literature. The results reveal a convergence around a core set of the

Zhe Wang

We quantize Hamiltonian structures with hydrodynamic leading terms using the Heisenberg vertex algebra. As an application, we construct the quantum dispersionless KdV hierarchy via a non-associative Weyl quantization procedure and compute the corresponding eigenvalue problem.

Shriram Srinivasan, Kaarthik Sundar

The solution of potential-driven steady-state flow in large networks is a task which manifests in various engineering applications, such as transport of natural gas or water through pipeline networks. The resultant system of nonlinear equations depends on the network topology and in general there is no numerical algorithm that offers guaranteed convergence t

Jiaqi Chen, Yan Yang, Shizhuo Deng, Da Teng

Human action recognition (HAR) plays a key role in various applications such as video analysis, surveillance, autonomous driving, robotics, and healthcare. Most HAR algorithms are developed from RGB images, which capture detailed visual information. However, these algorithms raise concerns in privacy-sensitive environments due to the recording of identifiabl

Bettina Klaus

We consider a housing market model with limited externalities where agents care both about their own consumption via demand preferences and about the agent who receives their endowment via supply preferences (we extend the associated lexicographic preference domains introduced in Klaus and Meo, 2023). If preferences are demand lexicographic, then our model e

Aleksi Suonsivu, Lauri Salmela, Edoardo Peretti, Leevi Uosukainen

Time-resolved single photon imaging is a promising imaging modality characterized by the unique capability of timestamping the arrivals of single photons. Single-Photon Avalanche Diodes (SPADs) are the leading technology for implementing modern time-resolved pixels, suitable for passive imaging with asynchronous readout. However, they are currently limited t

Mattis van 't Schip

Many modern consumer devices rely on network connections and cloud services to perform their core functions. This dependency is especially present in Internet of Things (IoT) devices, which combine hardware and software with network connections (e.g., a 'smart' doorbell with a camera). This paper argues that current European product legislation, which aims t

Shyam Sundar Ghoshal, Parasuram Venkatesh, Emil Wiedemann

The analysis of non-local regularisations of scalar conservation laws is an active research program. Applications of such equations are found in the modelling of physical phenomena such as traffic flow. In this paper, we propose a novel inviscid, non-local regularisation in non-divergence form. The salient feature of our approach is that we can obtain sharp

Silin Chen, Ziqian Bi, Junyu Liu, Benji Peng

This book provides a comprehensive introduction to the foundational concepts of machine learning (ML) and deep learning (DL). It bridges the gap between theoretical mathematics and practical application, focusing on Python as the primary programming language for implementing key algorithms and data structures. The book covers a wide range of topics, includin

Andrea Ercolino

The light curves and spectra of many Type I and Type II supernovae (SNe) are heavily influenced by the interaction of the SN ejecta with circumstellar material (CSM) surrounding the progenitor star. The observed diversity shows that many progenitors have undergone some level of stripping and polluted their CSM shortly before the explosion. The presence of a

Benjamin Osafo Agyare, Alec Chu, Blessing Oloyede

Metastasis is a leading cause of cancer-related mortality and remains challenging to detect during early stages. Accurate identification of cancers likely to metastasize can improve treatment strategies and patient outcomes. This study leverages publicly available gene expression profiles from primary cancers, with and without distal metastasis, to build pre

Koichiro Nakashima, Atsushi J. Nishizawa, Kentaro Nagamine, Yuri Oku

The Ly$\alpha$ forest, a series of HI absorption lines in the quasar spectra, is a powerful tool for probing the large-scale structure of the intergalactic medium. Its three-dimensional (3D) correlation and cross-correlations with quasars allow precise measurements of the baryon acoustic oscillation feature and redshift space distortions at redshifts $z>2$.

Yanjun Chen, Xinming Zhang, Xianghui Wang, Zhiqiang Xu

Soft Actor-Critic algorithm is widely recognized for its robust performance across a range of deep reinforcement learning tasks, where it leverages the tanh transformation to constrain actions within bounded limits. However, this transformation induces a distribution shift, distorting the original Gaussian action distribution and potentially leading the poli

Som Sagar, Aditya Taparia, Ransalu Senanayake

In large deep neural networks that seem to perform surprisingly well on many tasks, we also observe a few failures related to accuracy, social biases, and alignment with human values, among others. Therefore, before deploying these models, it is crucial to characterize this failure landscape for engineers to debug or audit models. Nevertheless, it is infeasi

Gecheng Chen

The partial domain adaptation (PDA) challenge is a prevalent issue in industrial fault diagnosis. Drawing inspiration from traditional classification settings where such partial challenge is not a concern, we propose a novel PDA framework called Interactive Residual Domain Adaptation Networks (IRDAN), which introduces domain-wise models for each domain to pr

Qintong Li, Jiahui Gao, Sheng Wang, Renjie Pi

Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data, leading to impressive performance across a range of downstream applications. Current methods often rely on human-annotated data or predefined task templates to direct powerful LLMs in synthesizing task-relevant data for effective model trainin

Shrey Goel, Peregrine M. Schray, Yinuo Zhang, Sophia Vincoff

Reparameterized diffusion models (RDMs) have recently matched autoregressive methods in protein generation, motivating their use for challenging tasks such as designing membrane proteins, which possess interleaved soluble and transmembrane (TM) regions. We introduce the Membrane Diffusion Language Model (MemDLM), a fine-tuned RDM-based protein language model

Shengbo Wang, Xuemeng Li, Jialin Ding, Weihao Ma

Memristive associative learning has gained significant attention for its ability to mimic fundamental biological learning mechanisms while maintaining system simplicity. In this work, we introduce a high-order memristive associative learning framework with a biologically realistic structure. By utilizing memristors as synaptic modules and their state informa

Francisco Florez-Revuelta, Alin Ake-Kob, Pau Climent-Perez, Paulo Coelho

This booklet on Active Assisted Living (AAL) technologies has been created as part of the GoodBrother COST Action, which has run from 2020 to 2024. COST Actions are European research programs that promote collaboration across borders, uniting researchers, professionals, and institutions to address key societal challenges. GoodBrother focused on ethical and p

Runpu Wei, Zijin Yin, Kongming Liang, Min Min

Automatic polyp segmentation is helpful to assist clinical diagnosis and treatment. In daily clinical practice, clinicians exhibit robustness in identifying polyps with both location and size variations. It is uncertain if deep segmentation models can achieve comparable robustness in automated colonoscopic analysis. To benchmark the model robustness, we focu

Sushil Kumar, Soumya P. Dash

A reconfigurable intelligent surface (RIS)-aided multiple-input multiple-output (MIMO) wireless communication system is considered in this paper wherein the transmitter, Alice modulates secret keys, by using a continuous variable quantum key distribution technique to be transmitted to the receiver, Bob, which employs homodyne detection for data decoding. The

Bing Du, A-Li Luo, Song Wang, Yinbi Li

The discrepancies between theoretical and observed spectra, and the systematic differences between various spectroscopic parameter estimates, complicate the determination of atmospheric parameters of M-type stars. In this work, we present an empirical sample of 5105 M-type star spectra with homogeneous atmospheric parameter labels through stellar-label trans

Yuchang Liu, Wei Guo, Yan Jiang, Mengping Zhang

In this paper, we propose a class of non-oscillatory, entropy-stable discontinuous Galerkin (NOES-DG) schemes for solving hyperbolic conservation laws. By incorporating a specific form of artificial viscosity, our new scheme directly controls entropy production and suppresses spurious oscillations. To address the stiffness introduced by the artificial terms,

Yuming Fu, Jun Hu, Oleg Muzician

Under conjugation by affine transformations, the dynamical moduli space of cubic polynomials $f$ with a $2$-cycle of Siegel disks is parameterized by a three-punctured complex plane as a degree-$2$ cover. Assuming the rotation number of $f^2$ on the Siegel disk is of bounded type, we show that on the three-punctured complex plane, the locus of the cubic poly

Huang Huang, Balakumar Sundaralingam, Arsalan Mousavian, Adithyavairavan Murali

Running optimization across many parallel seeds leveraging GPU compute have relaxed the need for a good initialization, but this can fail if the problem is highly non-convex as all seeds could get stuck in local minima. One such setting is collision-free motion optimization for robot manipulation, where optimization converges quickly on easy problems but str

Guanrou Yang, Fan Yu, Ziyang Ma, Zhihao Du

While automatic speech recognition (ASR) systems have achieved remarkable performance with large-scale datasets, their efficacy remains inadequate in low-resource settings, encompassing dialects, accents, minority languages, and long-tail hotwords, domains with significant practical relevance. With the advent of versatile and powerful text-to-speech (TTS) mo

Rytis Jursenas

It is a classical result that, if a maximal symmetric operator $T$ in a Krein space $\mathcal{H}=\mathcal{H}^-[\oplus]\mathcal{H}^+$ has the property $\mathcal{H}^-\subseteq\mathcal{D}_T$, then the imaginary part of its eigenvalue $\lambda$ from upper or lower half-plane is bounded by $\lvert \mathrm{Im}\,\lambda\rvert\leq2\lVert TP^- \rVert$. We prove that

Suvarthi Sarkar, Abinash Kumar Ray, Aryabartta Sahu

The autonomous vehicle industry is rapidly expanding, requiring significant computational resources for tasks like perception and decision-making. Vehicular edge computing has emerged to meet this need, utilizing roadside computational units (roadside edge servers) to support autonomous vehicles. Aligning with the trend of green cloud computing, these roadsi

Chetna Singhal, Yashuo Wu, Francesco Malandrino, Sharon Ladron de Guevara Contreras

Mobile systems will have to support multiple AI-based applications, each leveraging heterogeneous data sources through DNN architectures collaboratively executed within the network. To minimize the cost of the AI inference task subject to requirements on latency, quality, and - crucially - reliability of the inference process, it is vital to optimize (i) the

Zhenhao Zhang, Yunquan Song

In our paper, we focus on robust variable selection for missing data and measurement error. Missing data and measurement errors can lead to confusing data distribution. We propose an exponential loss function with a tuning parameter to apply to Missing and measurement errors data. By adjusting the parameter, the loss function can be better and more robust un

Parth Kumar, Charles A. Stafford

Several prior attempts to formulate the Laws of Thermodynamics for a small region within a larger quantum system have led to inconsistencies and unexplained infinities. The entropy and external work, in particular, require careful analysis when partitioning over the various subsystems. In this work, we analyze the thermodynamics of a quantum subsystem driven

Angela Tsang, Jiankai Sun, Boo Xie, Azeem Khan

We present NodeOP, a novel framework designed to optimize the management of General Node Operators in decentralized networks. By integrating Agent-Based Modeling (ABM) with a Tendermint Byzantine Fault Tolerance (BFT)-based consensus mechanism, NodeOP addresses key challenges in task allocation, consensus formation, and system stability. Through rigorous mat

Evans Xu Han, Linghao Jin, Xiaofeng Liu, Paul Pu Liang

Despite the impressive text-to-image (T2I) synthesis capabilities of diffusion models, they often struggle to understand compositional relationships between objects and attributes, especially in complex settings. Existing solutions have tackled these challenges by optimizing the cross-attention mechanism or learning from the caption pairs with minimal semant

Gathika Ratnayaka, James Nichols, Qing Wang

Partial graph matching extends traditional graph matching by allowing some nodes to remain unmatched, enabling applications in more complex scenarios. However, this flexibility introduces additional complexity, as both the subset of nodes to match and the optimal mapping must be determined. While recent studies have explored deep learning techniques for part

Jianju Tang, Songlei Wang, Yuhang Hou, Hongyi Yu

We numerically solve the electron-hole relative wave function of interlayer excitons in bilayer transition metal dichalcogenides, taking into account the screening effects from both the constituent transition metal dichalcogenides layers and the surrounding dielectric environment. We find that the wave function of the 1s ground state is close to the gaussian

Federico Blasi, Reinhard Furrer

Paradoxically, while the assumptions of second-order stationarity and isotropy appear outdated in light of modern spatial data, they remain remarkably robust in practice, as nonstationary methods often provide marginal improvements in predictive performance. This limitation reflects a fundamental trade-off: nonparametric approaches, while offering extreme fl

Ben Wang, Tomohiro Fukushima, Hiro Minamimoto, Andrey Lyalin

A comprehensive understanding of the electrode-electrolyte interface in energy conversion systems remains challenging due to the complex and multifaceted nature of interfacial processes. This complexity hinders the development of more efficient electrocatalysts. In this work, we propose a hybrid approach to the theoretical description of the OER process on n

Mingzhi Wang, Chengdong Ma, Qizhi Chen, Linjian Meng

Self-play methods have demonstrated remarkable success in enhancing model capabilities across various domains. In the context of Reinforcement Learning from Human Feedback (RLHF), self-play not only boosts Large Language Model (LLM) performance but also overcomes the limitations of traditional Bradley-Terry (BT) model assumptions by finding the Nash equilibr

Joshua Kazdan, Rylan Schaeffer, Apratim Dey, Matthias Gerstgrasser

What happens when generative machine learning models are pretrained on web-scale datasets containing data generated by earlier models? Some prior work warns of "model collapse" as the web is overwhelmed by synthetic data; other work suggests the problem can be contained (i.e. collapse can be avoided) by managing how available data are used in pretraining. In

Guo-Ping Li, He-Bin Zheng, Ke-Jian He, Qing-Quan Jiang

By considering the celestial light source and the thin disk source, we employ the backward ray-tracing method to carefully study the shadow, inner shadow and observational images of the non-singular rotating black holes in loop quantum gravity. The results show that the increase of quantum parameter $\lambda$ causes the shadow to shrink, while increases the

Anand Kumar Rai, Siddharth D Jaiswal, Shubham Prakash, Bendi Pragnya Sree

Automatic Speech Recognition (ASR) systems have been examined and shown to exhibit biases toward particular groups of individuals, influenced by factors such as demographic traits, accents, and speech styles. Noise can disproportionately impact speakers with certain accents, dialects, or speaking styles, leading to biased error rates. In this work, we introd

Ganga Prasad Basyal, David Zeng, Bhaskar Pm Rimal

The application of deep learning-based architecture has seen a tremendous rise in recent years. For example, medical image classification using deep learning achieved breakthrough results. Convolutional Neural Networks (CNNs) are implemented predominantly in medical image classification and segmentation. On the other hand, transfer learning has emerged as a

Zhiwei Deng, Tao Li, Yang Li

Curating a desirable dataset for training has been the core of building highly capable large language models (Touvron et al., 2023; Achiam et al., 2023; Team et al.,2024). Gradient influence scores (Pruthi et al., 2020; Xia et al., 2024) are shown to be correlated with model performance and are commonly used as the criterion for data selection. However, exis

Masato Kimura, Kazunori Matsui, Yosuke Mizuno

We study a universal approximation property of ODENet and ResNet. The ODENet is a map from an initial value to the final value of an ODE system in a finite interval. It is considered a mathematical model of a ResNet-type deep learning system. We consider dynamical systems with vector fields given by a single composition of the activation function and an affi

Zhichao Yan, Jiapu Wang, Jiaoyan Chen, Xiaoli Li

Attributed Question Answering (AQA) aims to provide both a trustworthy answer and a reliable attribution report for a given question. Retrieval is a widely adopted approach, including two general paradigms: Retrieval-Then-Read (RTR) and post-hoc retrieval. Recently, Large Language Models (LLMs) have shown remarkable proficiency, prompting growing interest in

Zhixiong Nan, Xianghong Li, Tao Xiang, Jifeng Dai

This paper is motivated by an interesting phenomenon: the performance of object detection lags behind that of instance segmentation (i.e., performance imbalance) when investigating the intermediate results from the beginning transformer decoder layer of MaskDINO (i.e., the SOTA model for joint detection and segmentation). This phenomenon inspires us to think

Daniel Hothem, Jordan Hines, Charles Baldwin, Dan Gresh

High-fidelity mid-circuit measurements, which read out the state of specific qubits in a multiqubit processor without destroying them or disrupting their neighbors, are a critical component for useful quantum computing. They enable fault-tolerant quantum error correction, dynamic circuits, and other paths to solving classically intractable problems. But ther

Kikun Park, Hyerim Bae

In ports, a variety of tasks are carried out, and scheduling these tasks is crucial due to its significant impact on productivity, making the generation of precise plans essential. This study proposes a method to solve the Quay Crane Scheduling Problem (QCSP), a representative task scheduling problem in ports known to be NP-Hard, more quickly and accurately.

Mark A. Burgess, Brendan Hosking, Roc Reguant, Anubhav Kaphle

Machine-generated data is a valuable resource for training Artificial Intelligence algorithms, evaluating rare workflows, and sharing data under stricter data legislations. The challenge is to generate data that is accurate and private. Current statistical and deep learning methods struggle with large data volumes, are prone to hallucinating scenarios incomp

Masahito Hayashi, Hao-Chung Cheng, Li Gao

Channel resolvability concerns the minimum resolution for approximating the channel output. We study the resolvability of classical-quantum channels in two settings, for the channel output generated from the worst input, and form the fixed independent and identically distributed (i.i.d.) input. The direct part of the worst-input setting is derived from seque

Burc Gokden

We present the Large Language Model from Power Law Decoder Representations (PLDR-LLM), a language model that leverages non-linear and linear transformations through Power Law Graph Attention mechanism to generate well-defined deductive and inductive outputs. We pretrain the PLDR-LLMs of varying layer sizes with a small batch size of 32 and $\sim$8B tokens fr

Pengfei Wang, Tianming Zhu, Jin-Ting Zhang

The challenge of location testing for high-dimensional data in statistical inference is notable. Existing literature suggests various methods, many of which impose strong regularity conditions on underlying covariance matrices to ensure asymptotic normal distribution of test statistics, leading to difficulties in size control. To address this, a recent set o

Veeramakali Vignesh Manivannan, Yasaman Jafari, Srikar Eranky, Spencer Ho

The use of Large Language Models (LLMs) in climate science has recently gained significant attention. However, a critical issue remains: the lack of a comprehensive evaluation framework capable of assessing the quality and scientific validity of model outputs. To address this issue, we develop ClimaGen (Climate QA Generator), an adaptive learning framework t

Anuradha Wickramarachchi, Shakila Tonni, Sonali Majumdar, Sarvnaz Karimi

Enabling clinicians and researchers to directly interact with global genomic data resources by removing technological barriers is vital for medical genomics. AskBeacon enables Large Language Models to be applied to securely shared cohorts via the GA4GH Beacon protocol. By simply "asking" Beacon, actionable insights can be gained, analyzed and made publicatio

Xiang Cheng, Lawrence Carin, Suvrit Sra

We show theoretically and empirically that the linear Transformer, when applied to graph data, can implement algorithms that solve canonical problems such as electric flow and eigenvector decomposition. The Transformer has access to information on the input graph only via the graph's incidence matrix. We present explicit weight configurations for implementin

Koshi Watanabe, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama

Dimensionality reduction (DR) offers a useful representation of complex high-dimensional data. Recent DR methods focus on hyperbolic geometry to derive a faithful low-dimensional representation of hierarchical data. However, existing methods are based on neighbor embedding, frequently ruining the continual relation of the hierarchies. This paper presents hyp

Dwaipayan Ray, Robert Collister, Hussain Rasiwala, Lucas Backes

Neutrinoless double beta decay {($0\nu\beta\beta$)} provides a way to probe physics beyond the Standard Model of particle physics. The upcoming nEXO experiment will search for $0\nu\beta\beta$ decay in $^{136}$Xe with a projected half-life sensitivity exceeding $10^{28}$ years at the 90\% confidence level using a liquid xenon (LXe) Time Projection Chamber (T

Ziran Yin, Xiaoyu Chen, Jihong Zhang

This paper aims to provide a series of characterizations of the robust isolated calmness of the Karush-Kuhn-Tucker (KKT) mapping for spectral norm regularized convex optimization problems. By establishing the variational properties of the spectral norm function, we directly prove that the KKT mapping is isolated calm if and only if the strict Robinson constr

Anatoli S. Kheifets

Resonances leave prominent signatures in atomic and molecular ionization triggered by the absorption of single or multiple photons. These signatures reveal various aspects of the ionization process, characterizing both the initial and final states of the target. Resonant spectral features are typically associated with sharp variations in the photoionization

Yang Yu, Yuezun Li, Xin Sun, Junyu Dong

Phytoplankton are a crucial component of aquatic ecosystems, and effective monitoring of them can provide valuable insights into ocean environments and ecosystem changes. Traditional phytoplankton monitoring methods are often complex and lack timely analysis. Therefore, deep learning algorithms offer a promising approach for automated phytoplankton monitorin

Qunxi Zhu, Bolin Zhao, Jingdong Zhang, Peiyang Li

Complex systems in physics, chemistry, and biology that evolve over time with inherent randomness are typically described by stochastic differential equations (SDEs). A fundamental challenge in science and engineering is to determine the governing equations of a complex system from snapshot data. Traditional equation discovery methods often rely on stringent

Francisca Vasconcelos, Hsin-Yuan Huang

We present the first computationally-efficient algorithm for average-case learning of shallow quantum circuits with many-qubit gates. Specifically, we provide a quasi-polynomial time and sample complexity algorithm for learning unknown QAC$^0$ circuits -- constant-depth circuits with arbitrary single-qubit gates and polynomially many $CZ$ gates of unbounded

Xu Cai, Jonathan Scarlett

The optimization of black-box functions with noisy observations is a fundamental problem with widespread applications, and has been widely studied under the assumption that the function lies in a reproducing kernel Hilbert space (RKHS). This problem has been studied extensively in the stationary setting, and near-optimal regret bounds are known via developme

Iasson Karafyllis, Miroslav Krstic

Stability theory plays a crucial role in feedback control. However, adaptive control theory requires advanced and specialized stability notions that are not frequently used in standard feedback control theory. The present document is a set of notes for a graduate course. It describes the global stability notions needed in (robust) adaptive control and develo

Vedanth Padmaraman, Sasank Chilamkurthy

Llama$.$lisp is a compiler framework intended to target offload processor backends such as GPUs, using intermediate representation languages (IRs) that are device-agnostic. The Llama$.$lisp IRs are formulated as S-expressions. This makes them easy to generate using higher level programming languages, which is one of the primary goals for Llama$.$lisp. The hi

Sailesh Ranjan Mohanty, Utkarsh Mali, H. C. Das, Bharat Kumar

We constrain the fundamental-mode ($f$-mode) oscillation frequencies of nonrotating neutron stars using a phenomenological Gaussian process model for the unknown dense-matter equation of state conditioned on a suite of gravitational-wave, radio and X-ray observations. We infer the quadrupolar $f$-mode frequency preferred by the astronomical data as a functio

Jiwon Kong, Jieun Jeon, Jinn-Ouk Gong

We calculate the scalar-induced one-loop correction to the power spectrum of tensor perturbations produced during single-field slow-roll inflation. We find that the correction is given by the square of the product of the slow-roll parameter and the tree-level scalar power spectrum. We also discuss the implications of the logarithmic contribution.

Yuhong Cao, Jeric Lew, Jingsong Liang, Jin Cheng

Autonomous robot exploration requires a robot to efficiently explore and map unknown environments. Compared to conventional methods that can only optimize paths based on the current robot belief, learning-based methods show the potential to achieve improved performance by drawing on past experiences to reason about unknown areas. In this paper, we propose DA

Jumman Hossain, Emon Dey, Snehalraj Chugh, Masud Ahmed

Cross reality integration of simulation and physical robots is a promising approach for multi-robot operations in contested environments, where communication may be intermittent, interference may be present, and observability may be degraded. We present SERN (Simulation-Enhanced Realistic Navigation), a framework that tightly couples a high-fidelity virtual

Kazuki Yamamoto, Takuto Kawakami, Mikito Koshino

Among the most significant macroscopic quantum phenomena in condensed matter physics is the Meissner effect observed in superconductivity, which arises from the unique interaction between superfluids of charged particles and electromagnetic fields. However, superfluids can also emerge from particles possessing distinct electromagnetic properties. In particul

Kosuke Yuchi, Daisuke Nishio-Hamane, Keita Kojima, Kodai Moriyama

We report the synthesis and electronic properties of polycrystalline samples of Zr6MBi2 (M = Ru and Fe) crystallizing in the hexagonal Zr6CoAl2-type structure. Based on their electrical resistivity, magnetization, and heat capacity data, Zr6RuBi2 and Zr6FeBi2 are found to exhibit bulk superconductivity below Tc = 4.9 and 1.4 K, respectively. Although Zr6RuBi

Shin-ichi Yasutomi

J. Hurwitz introduced an algorithm that generates a continued fraction expansion for complex numbers $\alpha \in \mathbb{C}$, where the partial quotients belong to $(1+i)\mathbb{Z}[i]$. J. Hurwitz's work also provides a result analogous to Lagrange's theorem on periodic continued fractions, describing purely periodic points using the dual continued fraction

Oleg Rybakov, Mike Chrzanowski, Peter Dykas, Jinze Xue

Training stability of large language models(LLMs) is an important research topic. Reproducing training instabilities can be costly, so we use a small language model with 830M parameters and experiment with higher learning rates to force models to diverge. One of the sources of training instability is the growth of logits in attention layers. We extend the fo

Jake Horder, Dominic Scognamiglio, Nathan Coste, Angus Gale

Coherent quantum emitters are a central resource for advanced quantum technologies. Hexagonal boron nitride (hBN) hosts a range of quantum emitters that can be engineered using techniques such as high-temperature annealing, optical doping, and irradiation with electrons or ions. Here, we demonstrate that such processes can degrade the coherence, and hence th

Shunsuke Kitou, Kentaro Ueda, Yuiga Nakamura, Kunihisa Sugimoto

Lanthanide 4f electrons are strongly influenced by spin-orbit coupling, resulting in well-defined J multiplets, which are further split by the crystalline electric field in condensed matter. While the anisotropy of 4f electrons is closely linked to material properties, direct experimental observation of the 4f electron distribution in real space remains a si

Son Cao, Megan Friend

Intense and well-characterized neutrino sources, coupled with large and high-performance detectors, are essential for elucidating the unknowns in leptonic mixing through neutrino oscillation measurements. The J-PARC accelerator and neutrino extraction beamline have recently undergone upgrades, successfully delivering an 800-kW beam. We discuss the developmen

Shamaila Rani, Muhammad Adeel, M. Zeeshan Gul, Abdul Jawad

This research paper examines the feasibility and stability of compact stars in the context of $f(\mathcal{Q})$ theory, where $\mathcal{Q}$ represents the non-metricity scalar. To achieve this objective, a static spherical line element is assumed in the interior region and the Schwarzschild spacetime is used in the exterior region of the star. The unknown con

Shavika Rastogi, Nik Dennler, Michael Schmuker, André van Schaik

Rapid detection of gas concentration is important in different domains like gas leakage monitoring, pollution control, and so on, for the prevention of health hazards. Out of different types of gas sensors, Metal oxide (MOx) sensors are extensively used in such applications because of their portability, low cost, and high sensitivity for specific gases. Howe

Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu

Causal reasoning (CR) is a crucial aspect of intelligence, essential for problem-solving, decision-making, and understanding the world. While language models (LMs) can generate rationales for their outputs, their ability to reliably perform causal reasoning remains uncertain, often falling short in tasks requiring a deep understanding of causality. In this p

Oluwafemi Odu, Daniel Méndez Beltran, Emiliano Berrones Gutiérrez, Alvine B. Belle

Developing industry-wide standards and ensuring producers of mission-critical systems comply with them is crucial to fostering consumer acceptance. Producers of such systems can rely on assurance cases to demonstrate to regulatory authorities how they have complied with such standards to help prevent system failure, which could result in fatalities and envir

Lin Yang, Riyi Zheng, Sheng Zhang, Wenshuai Zhang

We demonstrate that bound states in the continuum (BICs) form continuous lines along high-symmetry directions of momentum space in a simple phononic crystal slab. Contrary to common sense, these BICs are symmetry-protected (SP) BICs not only at the center of the Brillouin zone (gamma point) but also off the gamma point. We utilize numerical simulations, a gr

Jiying Zhang, Zijing Liu, Shengyuan Bai, He Cao

Antibodies are proteins produced by the immune system that recognize and bind to specific antigens, and their 3D structures are crucial for understanding their binding mechanism and designing therapeutic interventions. The specificity of antibody-antigen binding predominantly depends on the complementarity-determining regions (CDR) within antibodies. Despite

Chen Qian, Dongrui Liu, Jie Zhang, Yong Liu

Ensuring awareness of fairness and privacy in Large Language Models (LLMs) is critical. Interestingly, we discover a counter-intuitive trade-off phenomenon that enhancing an LLM's privacy awareness through Supervised Fine-Tuning (SFT) methods significantly decreases its fairness awareness with thousands of samples. To address this issue, inspired by the info

Jiamu Wang, Jin Tae Kwak

Nuclei instance segmentation is an essential task in pathology image analysis, serving as the foundation for many downstream applications. The release of several public datasets has significantly advanced research in this area, yet many existing methods struggle with data imbalance issues. To address this challenge, this study introduces a data augmentation

Chen Yang, Chenyang Zhao, Quanquan Gu, Dongruo Zhou

Sequential reasoning in agent systems has been significantly advanced by large language models (LLMs), yet existing approaches face limitations. Reflection-driven reasoning relies solely on knowledge in pretrained models, limiting performance in novel scenarios, while experience-assisted reasoning often depends on external experiences and lacks clear princip

Yikun Bai, Abihith Kothapalli, Hengrong Du, Rocio Diaz Martin

The Gromov-Wasserstein (GW) problem, a variant of the classical optimal transport (OT) problem, has attracted growing interest in the machine learning and data science communities due to its ability to quantify similarity between measures in different metric spaces. However, like the classical OT problem, GW imposes an equal mass constraint between measures,

Chenyi Li, Guande Wu, Gromit Yeuk-Yin Chan, Dishita G Turakhia

Augmented Reality (AR) assistance is increasingly used for supporting users with physical tasks like assembly and cooking. However, most systems rely on reactive responses triggered by user input, overlooking rich contextual and user-specific information. To address this, we present Satori, a novel AR system that proactively guides users by modeling both --

José Luis Abreu, Javier Bracho

The purpose of this paper is to present projective geometry in a synthetic, visual and intuitive style through the central notion of harmonicity which leads to harmonic curves. This presentation includes new results, unpublished proofs of some classic theorems and a slight reformulation of its axiomatics.

Beka Modrekiladze

Generative Adversarial Networks (GANs) have demonstrated remarkable advancements in generative modeling; however, their training is often resource-intensive, requiring extensive computational time and hundreds of thousands of epochs. This paper proposes a novel optimization approach that transforms the training process by operating within a dual space of the

Jumman Hossain, Abu-Zaher Faridee, Derrik Asher, Jade Freeman

Autonomous navigation in unstructured outdoor environments is inherently challenging due to the presence of asymmetric traversal costs, such as varying energy expenditures for uphill versus downhill movement. Traditional reinforcement learning methods often assume symmetric costs, which can lead to suboptimal navigation paths and increased safety risks in re

Jing-Jing Li, Valentina Pyatkin, Max Kleiman-Weiner, Liwei Jiang

The ideal AI safety moderation system would be both structurally interpretable (so its decisions can be reliably explained) and steerable (to align to safety standards and reflect a community's values), which current systems fall short on. To address this gap, we present SafetyAnalyst, a novel AI safety moderation framework. Given an AI behavior, SafetyAnaly

Mahmudul Bari Hridoy

Seasonal variations in the incidence of infectious diseases are a well-established phenomenon, driven by factors such as climate changes, social behaviors, and ecological interactions that influence host susceptibility and transmission rates. While seasonality plays a significant role in shaping epidemiological dynamics, it is often overlooked in both empiri

Haoran Lin, Xianzhi Yu, Kang Zhao, Lu Hou

FlashAttention series has been widely applied in the inference of large language models (LLMs). However, FlashAttention series only supports the high-level GPU architectures, e.g., Ampere and Hopper. At present, FlashAttention series is not easily transferrable to NPUs and low-resource GPUs. Moreover, FlashAttention series is inefficient for multi- NPUs or G

Xiaolan Chen, Ruoyu Chen, Pusheng Xu, Weiyi Zhang

Accurate diagnosis of ophthalmic diseases relies heavily on the interpretation of multimodal ophthalmic images, a process often time-consuming and expertise-dependent. Visual Question Answering (VQA) presents a potential interdisciplinary solution by merging computer vision and natural language processing to comprehend and respond to queries about medical im