December 2024 arXiv papers — page 46

J. Kluson

We analyze Born-Infeld inspired gravity in Palatini formulation in the framework of covariant canonical formalism. We determine covariant Hamiltonian and corresponding equations of motion.

Abel C. H. Chen

As quantum computing technology continues to advance, post-quantum cryptographic methods capable of resisting quantum attacks have emerged as a critical area of focus. Given the potential vulnerability of existing homomorphic encryption methods, such as RSA, ElGamal, and Paillier, to quantum computing attacks, this study proposes a lattice-based post-quantum

Sumit Nawathe, Ravi Panguluri, James Zhang, Sashwat Venkatesh

We propose a reinforcement learning (RL) framework that leverages multimodal data including historical stock prices, sentiment analysis, and topic embeddings from news articles, to optimize trading strategies for SP100 stocks. Building upon recent advancements in financial reinforcement learning, we aim to enhance the state space representation by integratin

Se Jin Park, Yeonju Kim, Hyeongseop Rha, Bella Godiva

In human communication, both verbal and non-verbal cues play a crucial role in conveying emotions, intentions, and meaning beyond words alone. These non-linguistic information, such as facial expressions, eye contact, voice tone, and pitch, are fundamental elements of effective interactions, enriching conversations by adding emotional and contextual depth. R

Takuya Kawada, Masashi Kawaguchi, Kei Yamamoto, Hiroki Matsumoto

We experimentally demonstrate that a spin current can be induced by the acousto-electric evanescent wave, an electric field associated with surface acoustic waves (SAWs) that decay along the surface normal. A previous study showed that a magnetic-field-dependent dc voltage (acoustic voltage) emerges in heavy metal (HM)/ferromagnet (FM) bilayers under excitat

Fa-Ting Hong, Zhan Xu, Haiyang Liu, Qinjie Lin

Diffusion-based human animation aims to animate a human character based on a source human image as well as driving signals such as a sequence of poses. Leveraging the generative capacity of diffusion model, existing approaches are able to generate high-fidelity poses, but struggle with significant viewpoint changes, especially in zoom-in/zoom-out scenarios w

Adam Byrne, William Kirby, Kirk M. Soodhalter, Sergiy Zhuk

The problem of estimating the ground-state energy of a quantum system is ubiquitous in chemistry and condensed matter physics. Krylov quantum diagonalization (KQD) has emerged as a promising approach for this task. However, many KQD methods rely on subroutines, particularly the Hadamard test, that are challenging to implement on near-term quantum computers.

Taewoong Kim, Byeonghwi Kim, Jonghyun Choi

Learning a perception and reasoning module for robotic assistants to plan steps to perform complex tasks based on natural language instructions often requires large free-form language annotations, especially for short high-level instructions. To reduce the cost of annotation, large language models (LLMs) are used as a planner with few data. However, when ela

Fei Liu, Rui Zhang, Zhuoliang Xie, Rui Sun

We introduce LLM4AD, a unified Python platform for algorithm design (AD) with large language models (LLMs). LLM4AD is a generic framework with modularized blocks for search methods, algorithm design tasks, and LLM interface. The platform integrates numerous key methods and supports a wide range of algorithm design tasks across various domains including optim

Bradley E. Schaefer

V445 Puppis is the only known example of a helium nova, where a layer of helium-rich gas accretes onto the surface of a white dwarf in a cataclysmic variable, with runaway helium burning making for the nova event. Speculatively, helium nova can provide one path to produce a Type Ia supernova (SNIa), within the larger framework of single-degenerate models. Re

Rui Liang, Yang Deng, Donghua Xie, Fang He

Advances in time-series forecasting are driving a shift from conventional machine learning models to foundation models (FMs) that are trained with generalized knowledge. However, existing FMs still perform poorly in the energy fields, such as building energy forecasting (BEF). This paper studies the adaptation of FM to BEF tasks. We demonstrate the shortcomi

Hengfu Yu, Jinhong Deng, Wen Li, Lixin Duan

Evaluating the performance of deep models in new scenarios has drawn increasing attention in recent years. However, while it is possible to collect data from new scenarios, the annotations are not always available. Existing DAOD methods often rely on validation or test sets on the target domain for model selection, which is impractical in real-world applicat

Hengrui Zhang, Alexandru B. Georgescu, Suraj Yerramilli, Christopher Karpovich

The increasing demands of sustainable energy, electronics, and biomedical applications call for next-generation functional materials with unprecedented properties. Of particular interest are emerging materials that display exceptional physical properties, making them promising candidates in energy-efficient microelectronic devices. As the conventional Edison

Riku Uemura, Kanji Tanaka, Kenta Tsukahara, Daiki Iwata

Point goal navigation (PGN) is a mapless navigation approach that trains robots to visually navigate to goal points without relying on pre-built maps. Despite significant progress in handling complex environments using deep reinforcement learning, current PGN methods are designed for single-robot systems, limiting their generalizability to multi-robot scenar

Tongle Wu, Ying Sun, Jicong Fan

Motivated by the settings where sensing the entire tensor is infeasible, this paper proposes a novel tensor compressed sensing model, where measurements are only obtained from sensing each lateral slice via mutually independent matrices. Leveraging the low tubal rank structure, we reparameterize the unknown tensor ${\boldsymbol {\mathcal X}}^\star$ using two

Osama A. Marzouk

This study presents a comprehensive mathematical framework for modeling the flight dynamics of a six-degree-of-freedom fixed-wing aircraft as a rigid body with three control surfaces: rudder, elevators, and ailerons. The framework consists of 35 differential-algebraic equations (DAEs) and requires 30 constants to be specified. It supports both direct and inv

Xuan Feng, Tianlong Gu, Xiaoli Liu, Liang Chang

Unnatural text correction aims to automatically detect and correct spelling errors or adversarial perturbation errors in sentences. Existing methods typically rely on fine-tuning or adversarial training to correct errors, which have achieved significant success. However, these methods exhibit poor generalization performance due to the difference in data dist

Jörn Kersten, Seong Chan Park, Yeji Park, Juhoon Son

We explore the production of gravitational waves (GW) resulting from a first-order phase transition (FOPT) in a non-minimally coupled `Dark Higgs Inflation' model. Utilizing a dark sector scalar field as the inflaton, we demonstrate how inflationary dynamics naturally set the stage for observable FOPT. These transitions, influenced by thermal and quantum eff

Francesco Vissani

In this contribution, which introduces the "Crossing the Portal" session of the NOW 2024 meeting, I discuss the value of the concepts of interdisciplinarity and innovation for neutrino physics. After some historical considerations, which provide an initial illustration of the significant role of these concepts, I review some well-known cases of neutrino scie

Bassam Nima, Mingyu Fan, Aleksandar Radak, Andrew M. Jayich

Searches for physics beyond the Standard Model using spin sensors are susceptible to spurious frequency shifts and noise due to magnetic fields. Therefore a comagnetometer -- an auxiliary sensor that allows mundane magnetic field effects to be differentiated from new physics -- is an essential feature of many precision searches. Here we demonstrate the opera

Shan-Shan Weng, Long Ji

Accreting X-ray pulsars, located in X-ray binaries, are neutron stars with magnetic fields as strong as $B\sim10^{12\text{--}13}$ G. This review offers a concise overview of the accretion and radiation processes of X-ray pulsars and summarizes their rich observational features, particularly focusing on complex and variable temporal phenomena, spectral proper

Rinto Tosa, Shingo Hattori, Yuichi Hiroi, Yuta Itoh

Visual guidance (VG) is critical for directing user attention in virtual and augmented reality applications. However, conventional methods using explicit visual annotations can obstruct visibility and increase cognitive load. To address this, we propose an unobtrusive VG technique based on color vibration, a phenomenon in which rapidly alternating colors at

James MacLaurin, Pedro Vilanova

The theory of `Balanced Neural Networks' is a very popular explanation for the high degree of variability and stochasticity in the brain's activity. We determine equations for the hydrodynamic limit of a balanced all-to-all network of 2n neurons for asymptotically large n. The neurons are divided into two classes (excitatory and inhibitory). Each excitatory

Xiangmei Ma, Grace Meijuan Yang, Qingyuan Zhuang, Yin Bun Cheung

Objectives: Prior event rate ratio (PERR) is a method shown to perform well in mitigating confounding in real-world evidence research but it depends on several model assumptions. We propose an analytic strategy to correct biases arising from violation of two model assumptions, namely, population homogeneity and event-independent treatment. Study Design and S

Alexander Alexandrov, Paul Norbury

Weil-Petersson volumes of the moduli space of curves are deeply related to the Kontsevich-Witten KdV tau function. They possess a Virasoro symmetry which comes out of recursion relations between the volumes due to Mirzakhani. Similarly, the super Weil-Petersson volumes of the moduli space of super curves with Neveu-Schwarz punctures are related to the Br\'ez

Jinming Xing, Dongwen Luo, Qisen Cheng, Chang Xue

Fuzzy Graph Attention Network (FGAT), which combines Fuzzy Rough Sets and Graph Attention Networks, has shown promise in tasks requiring robust graph-based learning. However, existing models struggle to effectively capture dependencies from multiple perspectives, limiting their ability to model complex data. To address this gap, we propose the Multi-view Fuz

Shen Wang, Zhengxue Cheng, Donghui Feng, Guo Lu

Learned image compression (LIC) methods often employ symmetrical encoder and decoder architectures, evitably increasing decoding time. However, practical scenarios demand an asymmetric design, where the decoder requires low complexity to cater to diverse low-end devices, while the encoder can accommodate higher complexity to improve coding performance. In th

Jin-Yi Cai, Ben Young

Recently, Cai showed that Shor's quantum factoring algorithm fails to factor large integers when the algorithm's quantum Fourier transform (QFT) is corrupted by a vanishing level of random noise on the QFT's precise controlled rotation gates. We show that under the same error model, Shor's quantum discrete log algorithm, and its various modifications, fail t

Guangyu Zhu, Xidong Mu, Li Guo, Ao Huang

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted simultaneous wireless information and power transfer (SWIPT) system is investigated. Both active and passive STAR-RISs are considered. Passive STAR-RISs can be cost-efficiently fabricated to large aperture sizes with significant near-field regions, but the des

Rami Oueslati

The pomeron topological cross-section is derived for the eikonal and the $U$-matrix unitarization schemes using a generalized expansion of the unitarized elastic amplitude in an effort to examine pomeron characteristics, namely the multiplicity distribution, fluctuation, and correlation, and to reveal the impact of pomeron weights on the $pp$ multiplicity di

Matthias J. Wurdack, Ivan Iorsh, Sarka Vavreckova, Tobias Bucher

Photonic bound states in the continuum (BICs) have emerged as a versatile tool for enhancing light-matter interactions by strongly confining light fields. Chiral BICs are photonic resonances with a high degree of circular polarisation, which hold great promise for spin-selective applications in quantum optics and nanophotonics. Here, we demonstrate a novel a

Ying Fang, Bo He, Zhi Liu, Sannyuya Liu

Xiaomai is an intelligent tutoring system (ITS) designed to help Chinese college students in learning advanced mathematics and preparing for the graduate school math entrance exam. This study investigates two distinctive features within Xiaomai: the incorporation of free-response questions with automatic feedback and the metacognitive element of reflecting o

Chengran Yang, Hong Jin Kang, Jieke Shi, David Lo

CodeLLMs have demonstrated remarkable advancements in software engineering tasks. However, while these models can generate functionally correct code, they often produce code that is inefficient in terms of runtime. This inefficiency is particularly problematic in resource-constrained environments, impacting software performance and sustainability. Existing a

Yunkang Cao, Haiming Yao, Wei Luo, Weiming Shen

This paper addresses a practical task: High-Resolution Image Anomaly Detection (HRIAD). In comparison to conventional image anomaly detection for low-resolution images, HRIAD imposes a heavier computational burden and necessitates superior global information capture capacity. To tackle HRIAD, this paper translates image anomaly detection into visual token pr

Yunfeng Shi, Li Wen, Dongfeng Yan

In this paper, we investigate random operators on $\mathbb{Z}^d$ with H\"older continuously distributed potentials and the long-range hopping. The hopping amplitude decays with the inter-particle distance $\|\bm x\|$ as $e^{-\log^{\rho}(\|\bm x\|+1)}$ with $\rho>1,\bm x\in\Z^d$. By employing the multi-scale analysis (MSA) technique, we prove that for large d

Sanu Bera

In this article, we study the multiparameter second quantum Weyl algebra at roots of unity. In this setting, the algebra is a polynomial identity (PI) algebra, and the dimension of its simple modules is bounded above by its PI degree. We explicitly determine the PI degree and provide a complete classification of simple modules. This classification offers a c

Xi-Er Du, Zhongjie Huang, Bo Wang, Ellis Ye Yuan

We study correlators of $\frac{1}{2}$-BPS mesons in two examples of 4d SQCDs with $\mathcal{N}=2$ superconformal symmetry in the planar limit. We focus on the weakly coupled regime and obtain one-loop corrections to $n$-point meson correlators with arbitrary operator dimensions. We show that these corrections can be resumed into generating functions which ex

Haitao Li, Junjie Chen, Jingli Yang, Qingyao Ai

With the increasing intelligence and autonomy of LLM agents, their potential applications in the legal domain are becoming increasingly apparent. However, existing general-domain benchmarks cannot fully capture the complexity and subtle nuances of real-world judicial cognition and decision-making. Therefore, we propose LegalAgentBench, a comprehensive benchm

Blanca Inigo, Yiqing Shen, Benjamin D. Killeen, Michelle Song

Vertebral compression fractures (VCFs) are a common and potentially serious consequence of osteoporosis. Yet, they often remain undiagnosed. Opportunistic screening, which involves automated analysis of medical imaging data acquired primarily for other purposes, is a cost-effective method to identify undiagnosed VCFs. In high-stakes scenarios like opportunis

Yue Lin, Daniel Zhuoyu Long, Viet Anh Nguyen, Jin Qi

Two-stage risk-averse distributionally robust optimization (DRO) problems are ubiquitous across many engineering and business applications. Despite their promising resilience, two-stage DRO problems are generally computationally intractable. To address this challenge, we propose a simple framework by decentralizing the decision-making process into two specia

Weihao Zeng, Yuzhen Huang, Lulu Zhao, Yijun Wang

In the absence of extensive human-annotated data for complex reasoning tasks, self-improvement -- where models are trained on their own outputs -- has emerged as a primary method for enhancing performance. However, the critical factors underlying the mechanism of these iterative self-improving methods remain poorly understood, such as under what conditions s

Rafid Ishrak Jahan, Heng Fan, Haihua Chen, Yunhe Feng

Emojis have become ubiquitous in online communication, serving as a universal medium to convey emotions and decorative elements. Their widespread use transcends language and cultural barriers, enhancing understanding and fostering more inclusive interactions. While existing work gained valuable insight into emojis understanding, exploring emojis' capability

Xingyao Li, Fengzhuo Zhang, Jiachun Pan, Yunlong Hou

Despite the considerable progress achieved in the long video generation problem, there is still significant room to improve the consistency of the generated videos, particularly in terms of their smoothness and transitions between scenes. We address these issues to enhance the consistency and coherence of videos generated with either single or multiple promp

Chao Song, Kai Wang, Yuanyuan Zhang, Guodong Zhou

This paper is the first in a series of works devoted to an operadic study of Nijenhuis structures, focusing on Nijenhuis associative algebras. We introduce the concept of homotopy Nijenhuis associative algebras and demonstrate that the differential graded (=dg) operad $\NjAoperad_{\infty}$ governing these structures serves as the minimal model of the operad

Farzan Moosavi, Bilal Farooq

We introduce a multi-modal autonomous delivery optimization framework as a coalition game for a fleet of UAVs and ADRs operating in two overlaying networks to address last-mile delivery in urban environments, including high-density areas and time-critical applications. The problem is defined as multiple depot pickup and delivery with time windows constrained

Teja Krishna Cherukuri, Nagur Shareef Shaik, Jyostna Devi Bodapati, Dong Hye Ye

Retinal image analysis is crucial for diagnosing and treating eye diseases, yet generating accurate medical reports from images remains challenging due to variability in image quality and pathology, especially with limited labeled data. Previous Transformer-based models struggled to integrate visual and textual information under limited supervision. In respo

Xiaopeng Li, Xiangyang Li, Hao Zhang, Zhaocheng Du

The performance of Dense retrieval (DR) is significantly influenced by the quality of negative sampling. Traditional DR methods primarily depend on naive negative sampling techniques or on mining hard negatives through external retriever and meticulously crafted strategies. However, naive negative sampling often fails to adequately capture the accurate bound

Zichen Song, Sitan Huang, Zhongfeng Kang

With the widespread application of large language models (LLM), concerns about the privacy leakage of model training data have increasingly become a focus. Membership Inference Attacks (MIAs) have emerged as a critical tool for evaluating the privacy risks associated with these models. Although existing attack methods, such as LOSS, Reference-based, min-k, a

Dibakar Gope, David Mansell, Danny Loh, Ian Bratt

Large language models (LLMs) have transformed the way we think about language understanding and generation, enthralling both researchers and developers. However, deploying LLMs for inference has been a significant challenge due to their unprecedented size and resource requirements. While quantizing model weights to sub-byte precision has emerged as a promisi

Yufeng Xin, Sajith Sasidharam, Cong Wang, Mert Cevik

The rapid expansion of global cloud infrastructures, coupled with the growing volume and complexity of network traffic, has fueled active research into scalable and resilient Traffic Engineering (TE) solutions for Wide Area Networks (WANs). Despite recent advancements, achieving an optimal balance between solution quality and computational complexity remains

Xiaowen Ma, Zhenkai Wu, Mengting Ma, Mengjiao Zhao

Convolutional neural networks and attention mechanisms have greatly benefited remote sensing change detection (RSCD) because of their outstanding discriminative ability. Existent RSCD methods often follow a paradigm of using a non-interactive Siamese neural network for multi-temporal feature extraction and change detection heads for feature fusion and change

Dingyan Zhang, Haotian Wang, Yang Liu, Xingda Wei

Model autoscaling is the key mechanism to achieve serverless model-as-a-service, but it faces a fundamental trade-off between scaling speed and storage/memory usage to cache parameters, and cannot meet frequent scaling requirements across multiple hosts. The key problem is that data plane performance is slow, and scaled instances remain stopped while paramet

Xinyi Wu, Donald Loveland, Runjin Chen, Yozen Liu

Deep recommender systems rely heavily on large embedding tables to handle high-cardinality categorical features such as user/item identifiers, and face significant memory constraints at scale. To tackle this challenge, hashing techniques are often employed to map multiple entities to the same embedding and thus reduce the size of the embedding tables. Concur

Toshizumi Fukui, Atsufumi Honda, Masaaki Umehara

We consider a surface embedded in the Euclidean 3-space and fix a tangential vector $v$ at a given point $p$ on the surface. In this paper, we first review a history of the formula obtained by Mannheim, d'Ocagne and Koenderink, which asserts that the Gaussian curvature of the surface at $p$ can be obtained if one knows "the normal curvature at $p$ with respe

Hanqi Li, Ruiwei Xiao, Hsuan Nieu, Ying-Jui Tseng

As artificial intelligence (AI) becomes increasingly central to various fields, there is a growing need to equip K-12 students with AI literacy skills that extend beyond computer science. This paper explores the integration of a Project-Based Learning (PBL) AI toolkit into diverse subject areas, aimed at helping educators teach AI concepts more effectively.

Yule Liu, Zhiyuan Zhong, Yifan Liao, Zhen Sun

The rising popularity of large language models (LLMs) has raised concerns about machine-generated text (MGT), particularly in academic settings, where issues like plagiarism and misinformation are prevalent. As a result, developing a highly generalizable and adaptable MGT detection system has become an urgent priority. Given that LLMs are most commonly misus

Phuong-Nam Tran, Nhat Truong Pham, Duc Ngoc Minh Dang, Eui-Nam Huh

Medical image segmentation plays a crucial role in assisting healthcare professionals with accurate diagnoses and enabling automated diagnostic processes. Traditional convolutional neural networks (CNNs) often struggle with capturing long-range dependencies, while transformer-based architectures, despite their effectiveness, come with increased computational

Yilong Zang, Lingfei Ren, Yue Li, Zhikang Wang

Graph neural networks (GNNs) have shown promise in integrating protein-protein interaction (PPI) networks for identifying cancer genes in recent studies. However, due to the insufficient modeling of the biological information in PPI networks, more faithfully depiction of complex protein interaction patterns for cancer genes within the graph structure remains

Yuhao Wang, Pingping Zhang, Xuehu Liu, Zhengzheng Tu

Person Re-identification (ReID) aims to retrieve the specific person across non-overlapping cameras, which greatly helps intelligent transportation systems. As we all know, Convolutional Neural Networks (CNNs) and Transformers have the unique strengths to extract local and global features, respectively. Considering this fact, we focus on the mutual fusion be

Jiawei Tan, Hongxing Wang, Kang Dang, Jiaxin Li

Video scene detection involves assessing whether each shot and its surroundings belong to the same scene. Achieving this requires meticulously correlating multi-modal cues, $\it{e.g.}$ visual entity and place modalities, among shots and comparing semantic changes around each shot. However, most methods treat multi-modal semantics equally and do not examine c

Lin Zhang, Bing Xie, Yuanhong Tao

Research on quantum states often focuses on the correlation between nonlocal effects and local unitary invariants, among which local unitary equivalence plays a significant role in quantum state classification and resource theories. This paper focuses on the local unitary equivalence of multipartite quantum states in quantum information theory, aiming to det

Hongyi Zhu, Qingying Deng

We investigate the combined occurrence of edge faults and vertex faults in the burnt pancake graph (\( BP_n \)). In this paper, we prove that \( BP_n - F \), where \( F \) includes pairs of end-vertices of matching edges and fault-tolerant edges, contains a Hamiltonian cycle when \( |F| \leq n-2 \) and a Hamiltonian path when \( |F| \leq n-3 \). This establi

Jie Xu, Guanyu Huang, Wenlu Yu, Xuanxuan Zhang

Research trends in SLAM systems are now focusing more on multi-sensor fusion to handle challenging and degenerative environments. However, most existing multi-sensor fusion SLAM methods mainly use all of the data from a range of sensors, a strategy we refer to as the all-in method. This method, while merging the benefits of different sensors, also brings in

Chengzhong Zhang, Hongyu Zhao, Wenjie Zhang

Effective early-stage detection of internal short circuit in lithium-ion batteries is crucial to preventing thermal runaway. This report proposes an effective approach to address this challenging issue, in which the current change, state of charge and resistance are considered simultaneously to depict the voltage differential envelope curve. The envelope nat

Jonathan Boretsky, Veronica Calvo Cortes, Yassine El Maazouz

A matrix is totally positive if all of its minors are positive. This notion of positivity coincides with the type A version of Lusztig's more general total positivity in reductive real-split algebraic groups. Since skew-symmetric matrices always have nonpositive entries, they are not totally positive in the classical sense. The space of skew-symmetric matric

Yunsong Liu, Yanlong Liu, Xuefei Wang, Nan Xia

The recent research interests in the non-relativistic spin splitting of electronic band structures have led to the exploration of altermagnets and other compensated magnets. Here, we show that various types of non-relativistic spin splitting can be robustly induced by constructing Van der Waals heterostructures consisting of materials with intra-plane anti-f

Qian Chen, Xianhao Chen, Kaibin Huang

To bridge the digital divide, space-ground integrated networks (SGINs) are expected to deliver artificial intelligence (AI) services to every corner of the world. One key mission of SGINs is to support federated learning (FL) at a global scale. However, existing space-ground integrated FL frameworks involve ground stations or costly inter-satellite links, en

Jing Guo, Xiushan Jiang, Weihai Zhang

In this article, we study a model-free design approach for stochastic linear quadratic (SLQ) controllers. Based on the convexity of the SLQ dual problem and the Karush-Kuhn-Tucker (KKT) conditions, we find the relationship between the optimal point of the dual problem and the Q-function, which can be used to develop a novel model-free semidefinite programmin

Yonglong Ding

As the particle count escalates, the computational demands of diverse simulation algorithms surge, paralleled by a marked enhancement in accuracy. The question arises whether this heightened precision asymptotically dwindles towards zero or plateaus at a finite constant. To address this, this work introduces an approach that translates infinite systems into

Robson Christie, Kyunghyun Baek, Jeongho Bang, Jaewoo Joo

Estimating transition rates in open quantum systems is hampered by computing-resource demands that grow rapidly with system size. We present a quantum-simulation framework that enables efficient estimation by recasting the transition rate, given as the time derivative of an equilibrium correlation function, into a set of independently measurable contribution

Jennifer Altreuter, Pavel Trukhanov, Morgan A. Paul, Michael J. Hassett

Background: Clinical trials are essential to advancing cancer treatments, but fewer than 10% of adults with cancer enroll in therapeutic trials. Open-source AI trial matching tools could democratize access to trial options. Methods: We created MatchMiner-AI, co-developed with practicing clinical oncologists and trained on synthetic electronic health record (

Francis R. Willett, Jingyuan Li, Trung Le, Chaofei Fan

Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with paralysis who have lost the ability to speak intelligibly. The Brain-to-Text Benchmark '24 and associated competition was created to foster the advancement of decoding algorithms that convert neural activity to te

Tianyi Yan, Junbo Yin, Xianpeng Lang, Ruigang Yang

To enhance autonomous driving safety in complex scenarios, various methods have been proposed to simulate LiDAR point cloud data. Nevertheless, these methods often face challenges in producing high-quality, diverse, and controllable foreground objects. To address the needs of object-aware tasks in 3D perception, we introduce OLiDM, a novel framework capable

Lichen Ma, Tiezhu Yue, Pei Fu, Yujie Zhong

Recently, significant advancements have been made in diffusion-based visual text generation models. Although the effectiveness of these methods in visual text rendering is rapidly improving, they still encounter challenges such as inaccurate characters and strokes when rendering complex visual text. In this paper, we propose CharGen, a highly accurate charac

Chenzhu Xie, Yong Zhang

To find a suitable framework for nonlinear crystal optics(NCO), we have revisited linear crystal optics(LCO). At the methodological level, three widely used plane wave bases are compared in terms of eigenanalysis in reciprocal space and light field propagation in real space. Inspired by complex ray tracing, we expand M.V. Berry \& M.R. Dennis's 2003 uniform

Yunkai Wang, Yujie Zhang, Virginia O. Lorenz

Previous work showed that thermal light with a blackbody spectrum cannot be decomposed into a mixture of independent localized pulses. However, we find that in the weak-source limit and under the assumption of a flat spectrum, the first non-vacuum term in the state expansion does form a mixture of such pulses. This decomposition is essential for quantum-enha

Yu Lu, Jiayi Zhang, Enyu Shi, Peng Zhang

Reconfigurable intelligent surfaces (RIS)-assisted cell-free massive multiple-input multiple-output (CF mMIMO) systems have emerged as a promising technology for sixth-generation communication systems. These systems capitalize on RIS to minimize power consumption, thereby achieving consistent performance and enhancing communication quality through the establ

Jaime Besprosvany, Rebeca Sánchez

The standard-model can be equivalently represented with its fields in a spin-extended basis, departing from fermion degrees of freedom. The common Higgs operator connects the electroweak and Yukawa sectors, restricting the top and bottom quark masses[Phys. Rev. D 99, 073001, 2019]. Using second quantization, within the heavy-particle sector, electroweak vect

Ada Masters, Adam Rennie

We extend unbounded Kasparov theory to encompass conformal group and quantum group equivariance. This new framework allows us to treat conformal actions on both manifolds and noncommutative spaces. As examples, we present unbounded representatives of Kasparov's $\gamma$-element for the real and complex Lorentz groups and display the conformal $SL_q(2)$-equiv

Dingjie Fu, Wenjin Hou, Shiming Chen, Shuhuang Chen

Generative Zero-Shot Learning (ZSL) methods synthesize class-related features based on predefined class semantic prototypes, showcasing superior performance. However, this feature generation paradigm falls short of providing interpretable insights. In addition, existing approaches rely on semantic prototypes annotated by human experts, which exhibit a signif

Rui Li

Based on the $6\times6$ Kane model in the spherical approximation, both the electron and the hole subband dispersions in a cylindrical HgTe nanowire are calculated using analytical method. The transcendental equations determining the subband energies in the nanowire are analytically derived by utilizing both the total angular momentum conservation and the ha

Nalin Arora, Sumit Bhagat, Riya Dhama, Ganesh Bagler

The dramatic increase in consumption of ultra-processed food has been associated with numerous adverse health effects. Given the public health consequences linked to ultra-processed food consumption, it is highly relevant to build computational models to predict the processing of food products. We created a range of machine learning, deep learning, and NLP m

Nathan London, Dil K. Limbu, Md Omar Faruque, Farnaz A. Shakib

DL_POLY Quantum 2.1 is introduced here as a highly modular, sustainable, and scalable general-purpose molecular dynamics (MD) simulation software for large-scale long-time MD simulations of condensed phase and interfacial systems with the essential nuclear quantum effects (NQEs) included. The new release improves upon version 2.0 through the introduction of

Anika Götz, Fakher F. Assaad, Natanael C. Costa

We consider a Su-Schrieffer-Heeger model in the assisted hopping limit, where direct electron hopping is subdominant. At fixed electron-phonon coupling and in the absence of Coulomb interactions, the model shows a deconfined quantum critical point (DQCP) between a $(\pi,0)$ valence bond solid in the adiabatic limit and a quantum antiferromagnetic (AFM) phase

Paolo Soffitta, Enrico Costa

X-ray Astrophysics, which addresses extreme physics in extreme conditions, is particularly well suited for answering questions related to known physics. Reversely tiny effects, but integrated along sidereal distances, allow to probe extensions of known physics or even new physics. The new window into polarimetry in this energy band, opened by the Imaging X-r

Jiangtong Li, Dungy Liu, Dawei Cheng, Changchun Jiang

\textbf{G}raph \textbf{N}eural \textbf{N}etworks~(GNNs) have achieved significant success in various real-world applications, including social networks, finance systems, and traffic management. Recent researches highlight their vulnerability to backdoor attacks in node classification, where GNNs trained on a poisoned graph misclassify a test node only when s

Noriyuki Tonami, Wataru Kohno, Keisuke Imoto, Yoshiyuki Yajima

Deep neural network (DNN)-based models for environmental sound classification are not robust against a domain to which training data do not belong, that is, out-of-distribution or unseen data. To utilize pretrained models for the unseen domain, adaptation methods, such as finetuning and transfer learning, are used with rich computing resources, e.g., the gra

Jiang Zhu, Menghuai Xu, Ruohai Guo, Fangyong Wang

Accurate targets detection and tracking with mmWave radar is a key sensing capability that will enable more intelligent systems, create smart, efficient, automated system. This paper proposes an end-to-end detection-estimation-track framework named MNOMP-SPA-KF consisting of the target detection and estimation module, the data association (DA) module and the

Hongsong Wang, Andi Xu, Pinle Ding, Jie Gui

Video Anomaly Detection (VAD) is essential for computer vision research. Existing VAD methods utilize either reconstruction-based or prediction-based frameworks. The former excels at detecting irregular patterns or structures, whereas the latter is capable of spotting abnormal deviations or trends. We address pose-based video anomaly detection and introduce

Zejun Liu, Bryan K. Clark

Determining the quantum-classical boundary between quantum circuits which can be efficiently simulated classically and those which cannot remains a fundamental question. One approach to classical simulation is to represent the output of a quantum circuit as a Clifford-augmented Matrix Product State (CAMPS) which, via a disentangling algorithm, decomposes the

Shuyu Zhang, Hiroki Sukeno, Kazuki Ikeda, Tzu-Chieh Wei

We study a $1$D supersymmetric (SUSY) hard-core fermion model first proposed by Fendley, Schoutens, and de Boer [Phys. Rev. Lett. 90, 120402 (2003)]. We focus on the full Hilbert space instead of a restricted subspace. Exact diagonalization shows the degeneracy of zero-energy states scales exponentially with size of the system, with a recurrence relation bet

W. Andreas Schroeder, L. A. Angeloni, I-J. Shan, L. B. Jones

A presented analytical formulation of (optical)phonon-mediated and momentum-resonant Franck-Condon emission of photoexcited electrons from polar semiconductors is shown to be very consistent with (i) the observed emission properties of a Cesiated GaAs(001) photocathode at 808nm [J. Phys. D: Appl. Phys. 54, 205301 (2021)] and (ii) the measured spectral emissi

Fumio Uchida, Koichi Miyamoto, Soichiro Yamazaki, Kotaro Fujisawa

Fault-tolerant quantum computing is a promising technology to solve linear partial differential equations that are classically demanding to integrate. It is still challenging to solve non-linear equations in fluid dynamics, such as the Burgers equation, using quantum computers. We propose a novel quantum algorithm to solve the Burgers equation. With the Cole

Hiroaki Nakajima, Ya Guo, Wenbin Lin

We study the wave equations with the various spins on the background of the Kerr metric deformed by a function of the radial coordinate, on which background we have studied the gravitational-wave equations previously. We obtain the unified expression of the Teukolsky-like master equations and the corresponding radial equations with various spins. We find tha

Sidong Chen, Frank Browne, Tomás R. Rodríguez, Volker Werner

Extensive gamma-ray spectroscopy of very neutron-rich nuclei of isotopes between the Ni and Sn isotopic chains was facilitated by the high luminosity LH2 target system, MINOS. Results show a persistence of deformation when going beyond the N = 60 threshold of the transition between spherical to deformed ground states at N < 60 and N > 60, respectively. Close

Benjamin X. Shi, Andrew S. Rosen, Tobias Schäfer, Andreas Grüneis

Quantum-mechanical simulations can offer atomic-level insights into chemical processes on surfaces. This understanding is crucial for the rational design of new solid catalysts as well as materials to store energy and mitigate greenhouse gases. However, achieving the accuracy needed for reliable predictions has proven challenging. Density functional theory (

Melkamu Mersha, Tsion Abay, Mingziem Bitewa, Gedare Bloom

Virtual-to-physical address translation is a critical performance bottleneck in paging-based virtual memory systems. The Translation Lookaside Buffer (TLB) accelerates address translation by caching frequently accessed mappings, but TLB misses lead to costly page walks. Hardware and software techniques address this challenge. Hardware approaches enhance TLB

O. Abla, M. J. Neves

Poisson electrodynamics is the semi-classical limit of $U(1)$ non-commutative gauge theory. It has been studied so far as a theoretical model, where an external field would be the source of the non-commutative effects in space-time. Being the Standard Model of fundamental interactions a local theory, the prediction of observables within it would be drastical

Rafael Alves Batista

Significant progress has been made over the past decades towards unveiling the sources of the most energetic particles in nature, the ultra-high-energy cosmic rays (UHECRs). Despite these advancements, the exact astrophysical sites capable of accelerating these particles to such extreme energies remain largely unknown. Moreover, the mechanisms by which they

Chong Wang, Beian Wang, Peng Liang, Jie Liang

In software engineering (SE) research and practice, UML is well known as an essential modeling methodology for requirements analysis and software modeling in both academia and industry. In particular, fundamental knowledge of UML modeling and practice in creating high-quality UML diagrams are included in SE-relevant courses in the undergraduate programs of m