February 2024 arXiv papers — page 54

Rex Ying, Tianyu Fu, Andrew Wang, Jiaxuan You

Identifying frequent subgraphs, also called network motifs, is crucial in analyzing and predicting properties of real-world networks. However, finding large commonly-occurring motifs remains a challenging problem not only due to its NP-hard subroutine of subgraph counting, but also the exponential growth of the number of possible subgraphs patterns. Here we

Huaien Zhang, Yu Pei, Shuyun Liang, Shin Hwei Tan

Static analyzers can reason about the properties and behaviors of programs and detect various issues without executing them. Hence, they should extract the necessary information to understand the analyzed program well. Annotation has been a widely used feature for different purposes in Java since the introduction of Java 5. Annotations can change program str

Carsten Pitsch, Dominik Walter, Leonardo Gasparini, Helge Bürsing

Single photon detection and timing gathered increasing interest in the last few years due to both its necessity in the field of quantum sensing and the advantages of single quanta detection in the field of low level light imaging. While simple bucket detectors are mature enough for commercial applications, more complex imaging detectors are still a field of

Takumi Ogawa, Shuji Koyama, Toshihiro Omori, Kenji Kikuchi

Sponges, the basalmost members of the animal kingdom, exhibit a range of complex architectures in which microfluidic channels connect multitudes of spherical chambers lined with choanocytes, flagellated filter-feeding cells. Choanocyte chambers can possess scores or even hundreds of such cells, which drive complex flows entering through porous walls and exit

E. Alhassan, D. Rochman, G. Schnabel, A. J. Koning

To ensure agreement between theoretical calculations and experimental data, parameters to selected nuclear physics models, are perturbed, and fine-tuned in nuclear data evaluations. This approach assumes that the chosen set of models accurately represents the `true' distribution. Furthermore, the models are chosen globally, indicating their applicability acr

Dang-Zheng Liu, Lu Zhang

For an additive perturbation of the complex Ginibre ensemble under a deterministic matrix $X_0$, under certain assumption on $X_0$, we observe that there are only two kinds of local statistical patterns at the spectral edge: GinUE statistics and critical statistics, which corresponds to regular or quadratic vanishing spectral points. As a continuation of our

Kezhi Kong, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan

Large Language Models (LLMs) trained on large volumes of data excel at various natural language tasks, but they cannot handle tasks requiring knowledge that has not been trained on previously. One solution is to use a retriever that fetches relevant information to expand LLM's knowledge scope. However, existing textual-oriented retrieval-based LLMs are not i

Hansol Hong, Hyeongjun Jin, Sangwook Lee

When a Weinstein manifold admits an action of a finite abelian group, we propose its mirror construction following the equivariant TQFT-type construction, and obtain as a mirror the orbifolding of the mirror of the quotient with respect to the induced dual group action. As an application, we construct an orbifold Landau-Ginzburg mirror of a punctured Riemann

Xiuying Chen, Tairan Wang, Qingqing Zhu, Taicheng Guo

The summarization capabilities of pretrained and large language models (LLMs) have been widely validated in general areas, but their use in scientific corpus, which involves complex sentences and specialized knowledge, has been less assessed. This paper presents conceptual and experimental analyses of scientific summarization, highlighting the inadequacies o

Takahiko Nobukawa

We give a Jackson integral representation for Kajihara's $q$-hypergeometric series $W^{M,2}$. We construct a $q$-difference system that corresponds to this integral. This system is an extension of the variant of $q$-hypergeometric equation of degree three, defined by Hatano-Matsunawa-Sato-Takemura. We show that this system includes the $q$-Appell-Lauricella

Z. Y. Liu, P. Y. Jiang, S. Y. Liu, L. L. Zhang

We have developed a hybrid code GMEC: Gyro-kinetic Magnetohydrodynamics (MHD) Energetic-particle Code that can numerically simulate energetic particle-driven Alfv\'en eigenmodes and energetic particle transport in tokamak plasmas. In order to resolve the Alfv\'en eigenmodes with high toroidal numbers effectively, the field-aligned coordinates and meshes are

Martin Willame, Charles Wiame, Jérôme Louveaux, Claude Oestges

This study proposes a novel stochastic geometry framework analyzing power control strategies in spatially correlated network topologies. Heterogeneous networks are studied, with users modeled via the superposition of homogeneous and Poisson cluster processes. First, a new expression approaching the distribution of the number of users per base station is prov

Ning Bian, Xianpei Han, Hongyu Lin, Yaojie Lu

Building machines with commonsense has been a longstanding challenge in NLP due to the reporting bias of commonsense rules and the exposure bias of rule-based commonsense reasoning. In contrast, humans convey and pass down commonsense implicitly through stories. This paper investigates the inherent commonsense ability of large language models (LLMs) expresse

Anqi Cheng, Zhiyuan Yang, Haiyue Zhu, Kezhi Mao

Self-supervised depth estimation has evolved into an image reconstruction task that minimizes a photometric loss. While recent methods have made strides in indoor depth estimation, they often produce inconsistent depth estimation in textureless areas and unsatisfactory depth discrepancies at object boundaries. To address these issues, in this work, we propos

Zihao Wang, Kar Wai Fok, Vrizlynn L. L. Thing

The popularity of 5G networks poses a huge challenge for malicious traffic detection technology. The reason for this is that as the use of 5G technology increases, so does the risk of malicious traffic activity on 5G networks. Malicious traffic activity in 5G networks not only has the potential to disrupt communication services, but also to compromise sensit

Maciej Dunajski, Timothy Moy

In \cite{B3}, Bridgeland defined a geometric structure, named a Joyce structure, conjectured to exist on the space $M$ of stability conditions of a $CY_3$ triangulated category. Given a non-degeneracy assumption, a feature of this structure is a complex hyper-K\"ahler metric with homothetic symmetry on the total space $X = TM$ of the holomorphic tangent bund

Tsvetana Stoyanova

In this paper we study the integrability of the Sasano system of type $A^{(2)}_4$ from the point of view of the Hamiltonian dynamics. We prove rigorously that for these values of the parameters for which the Sasano system of type $A^{(2)}_4$ has a particular rational solution it is not integrable by rational first integrals. By an explicit computation we sho

Chu Guo, Ruofan Chen

The time-evolving matrix product operator (TEMPO) method has become a very competitive numerical method for studying the real-time dynamics of quantum impurity problems. For small impurities, the most challenging calculation in TEMPO is to construct the matrix product state representation of the Feynman-Vernon influence functional. In this work we propose an

Kai Zhao, Zhiming Liu, Jiaqi Liu, Jingbiao Zhou

Epicardial adipose tissue (EAT) is a type of visceral fat that can secrete large amounts of adipokines to affect the myocardium and coronary arteries. EAT volume and density can be used as independent risk markers measurement of volume by noninvasive magnetic resonance images is the best method of assessing EAT. However, segmenting EAT is challenging due to

Seungah Son, Juhee Jin

Manual optimization of traffic light cycles is a complex and time-consuming task, necessitating the development of automated solutions. In this paper, we propose the application of reinforcement learning to optimize traffic light cycles in real-time. We present a case study using the Simulation Urban Mobility simulator to train a Deep Q-Network algorithm. Th

Z. Rajabi Najjar, K. Azizi, H. R. Moshfegh

We study the triply heavy spin-1/2 baryons with quark contents $ ccb $ and $ bbc $, and calculate their mass and residue using QCD sum rules. In the calculations, we consider the ground (1S), first orbitally excited (1P) and first radially excited (2S) states. Aiming to achieve higher accuracies in the results, we perform the computations by taking into acco

Zijia Li, Hans-Peter Schröcker, Johannes Siegele, Daren A. Thimm

Spinor polynomials are polynomials with coefficients in the even sub-algebra of conformal geometric algebra whose norm polynomial is real. They describe rational conformal motions. Factorizations of spinor polynomial corresponds to the decomposition of the rational motion into elementary motions. Generic spinor polynomials allow for a finite number of factor

Francesco Malandrino, Giuseppe Di Giacomo, Marco Levorato, Carla Fabiana Chiasserini

The existing work on the distributed training of machine learning (ML) models has consistently overlooked the distribution of the achieved learning quality, focusing instead on its average value. This leads to a poor dependability}of the resulting ML models, whose performance may be much worse than expected. We fill this gap by proposing DepL, a framework fo

Shaojie Zhang, Yinghui Wang, Jiaxing Ma, Wei Li

In Visual SLAM, achieving accurate feature matching consumes a significant amount of time, severely impacting the real-time performance of the system. This paper proposes an accelerated method for Visual SLAM by integrating GMS (Grid-based Motion Statistics) with RANSAC (Random Sample Consensus) for the removal of mismatched features. The approach first util

Emma Chiaramello, Carla Fabiana Chiasserini, Francesco Malandrino, Alessandro Nordio

In next-generation networks, cells will be replaced by a collection of points-of-access (PoAs), with overlapping coverage areas and/or different technologies. Along with a promise for greater performance and flexibility, this creates further pressure on network management algorithms, which must make joint decisions on (i) PoA-to-user association and (ii) PoA

Jean Cardinal, Lionel Pournin

The expansion of a polytope is an important parameter for the analysis of the random walks on its graph. A conjecture of Mihai and Vazirani states that all $0/1$-polytopes have expansion at least 1. We show that the generalization to half-integral polytopes does not hold by constructing $d$-dimensional half-integral polytopes whose expansion decreases expone

Ankush Sharma, Alka Upadhyay

By taking into light the discovery of pentaquark structures like $P_{\psi s}^\Lambda(4338)^0$, $P_c(4380)$ and $P_c(4450)$, we performed the spectroscopy of hidden-bottom pentaquarks. By utilizing special unitary representations, we systematically classified the hidden bottom pentaquarks into two distinct configurations within the SU(3) flavor representation

Wael Othman, Wadha Al Falasi, Tanveer Hussain, Nacir Tit

Exceptionally high energy density by mass, natural abundance, widespread applications, and environmental friendliness make hydrogen (H2) a front-runner among clean energy options. However, the transition toward clean and renewable energy applications and the actualization of H2 economy require an efficient H2 storage medium. Material-based H2 storage is a vi

S. I. Manaenkov

Advantages of the amplitude method for the vector-meson-production description in respect of the spin-density-matrix-element (SDME) method are discussed. It is shown that for any nonzero amplitudes the angular distribution of final particles is non-negative. The exact formula for $R = \frac{d \sigma_L}{d t}/\frac{d \sigma_T}{d t}$ in terms of the SDMEs for s

Sangwon Choi, Daejune Choi, Duksu Kim

Monocular depth estimation (MDE) is essential for numerous applications yet is impeded by the substantial computational demands of accurate deep learning models. To mitigate this, we introduce a novel Teacher-Independent Explainable Knowledge Distillation (TIE-KD) framework that streamlines the knowledge transfer from complex teacher models to compact studen

Weiyang Liu, Hu Zhan, Yan Gong, Xin Wang

The analysis of the Cosmic Microwave Background (CMB) data acquired by the Atacama Cosmology Telescope (ACT) and the large-scale ($\ell\lesssim1300$) Planck Telescope show a preference for the Early Dark Energy (EDE) theory, which was set to alleviate the Hubble tension of the $\Lambda$ Cold Dark Matter ($\Lambda$CDM) model by decreasing the sound horizon $r

Byoung S. Ham

Recently, the delayed-choice quantum eraser has been applied for coherently excited superresolution using phase-controlled projection measurements of laser light to overcome the diffraction limit in classical physics as well as to solve the limited photon number of the N00N state in quantum physics. Unlike other methods of phase-controlled superresolution in

Hazel H. Kim

The quality of rationales is essential in the reasoning capabilities of language models. Rationales not only enhance reasoning performance in complex natural language tasks but also justify model decisions. However, obtaining impeccable rationales is often impossible. Our study aims to investigate how ambiguous rationales play in model performances of natura

Jan Grießer, Lars Pastewka

Amorphous solids are viscoelastic. They dissipate energy when deformed at finite rate and finite temperature. We here use analytic theory and molecular simulations to demonstrate that linear viscoelastic dissipation can be directly related to the static and dynamic properties of the fundamental vibrational excitations of an amorphous system. We study ultrast

David Bonet, Daniel Mas Montserrat, Xavier Giró-i-Nieto, Alexander G. Ioannidis

Training deep learning models and performing hyperparameter tuning can be computationally demanding and time-consuming. Meanwhile, traditional machine learning methods like gradient-boosting algorithms remain the preferred choice for most tabular data applications, while neural network alternatives require extensive hyperparameter tuning or work only in toy

Hanseok Oh, Hyunji Lee, Seonghyeon Ye, Haebin Shin

Despite the critical need to align search targets with users' intention, retrievers often only prioritize query information without delving into the users' intended search context. Enhancing the capability of retrievers to understand intentions and preferences of users, akin to language model instructions, has the potential to yield more aligned search targe

Kanji Mori, Tomoya Takiwaki, Kei Kotake, Shunsaku Horiuchi

Core-collapse supernovae can be a copious source of sterile neutrinos, hypothetical particles that mix with active neutrinos. We develop two-dimensional stellar core-collapse models that incorporate the mixing between tau neutrinos and heavy sterile neutrinos -- those with the mass of 150--200 MeV -- to investigate signatures of sterile neutrinos in supernov

Vaggos Chatziafratis, Ishani Karmarkar, Yingxi Li, Ellen Vitercik

Data-driven algorithm selection is a powerful approach for choosing effective heuristics for computational problems. It operates by evaluating a set of candidate algorithms on a collection of representative training instances and selecting the one with the best empirical performance. However, running each algorithm on every training instance is computational

Syota Esaki, Daisuke Kazukawa, Ayato Mitsuishi

We prove that the sequence of cones of metric measure spaces converges if the sequence of base spaces converges in Gromov's box, concentration, and weak topologies. As an application, we show that the generalized Cauchy distribution with suitable scaling converges to a half line in the concentration topology as the dimension diverges to infinity. This is a n

Victor P. Ruban

Collisions of left- and right-polarized spatiotemporal optical solitons have been numerically simulated for a locally isotropic focusing Kerr medium with anomalous chromatic dispersion. The stable propagation of such ``light bullets'' in a moderate nonlinear regime is ensured by a transverse parabolic profile of the refraction index in a multimode waveguide.

Hagen Papenburg

We prove unconditional local well-posedness in a space of quasi-periodic functions for dispersive equations of the form $$\partial_tu + Lu + \partial_x(u^{p+1})=0,$$ where $L$ is a multiplier operator with purely imaginary symbol which grows at most exponentially. The class of equations to which our method applies includes the generalized Korteweg-de Vries e

Zhaoyi Li, Gangwei Jiang, Hong Xie, Linqi Song

LLMs have marked a revolutonary shift, yet they falter when faced with compositional reasoning tasks. Our research embarks on a quest to uncover the root causes of compositional reasoning failures of LLMs, uncovering that most of them stem from the improperly generated or leveraged implicit reasoning results. Inspired by our empirical findings, we resort to

Delong Chen, Samuel Cahyawijaya, Jianfeng Liu, Baoyuan Wang

Patch-based image tokenization ignores the morphology of the visual world, limiting effective and efficient learning of image understanding. Inspired by subword tokenization, we introduce subobject-level adaptive token segmentation and explore several approaches, including superpixel, SAM, and a proposed Efficient and PanOptiC (EPOC) image tokenizer. Our EPO

Mingxuan Yan, Yi Wang, Xuedou Xiao, Zhiqing Luo

Offloading computing to edge servers is a promising solution to support growing video understanding applications at resource-constrained IoT devices. Recent efforts have been made to enhance the scalability of such systems by reducing inference costs on edge servers. However, existing research is not directly applicable to pixel-level vision tasks such as vi

Ankush Bhaskar, Jayadev Pradeep, Shyama Narendranath, Dibyendu Nandy

In the present-day context, small satellites and their constellations consisting of varying sizes (nano, micro, pico satellites) are being favored for remote sensing and in situ probing of the heliosphere and terrestrial magnetosphere-ionosphere system. We introduce a mission concept aimed at concurrently observing Earth's northern and southern auroral ovals

Jaume Calvo-de la Rosa, Antoni García-Santiago, Joan Manel Hernàndez, Marc Vazquez-Aige

This work reports experimental evidence of random magnetic behavior observed in modified barium hexagonal ferrites. We observe a significant transition in the magnetic properties of this system when divalent cations (Ni2+, Cu2+, Mn2+) are incorporated into the structure and give rise to a magnetic nanocomposite. Such introduction randomly occurs throughout e

Ming Liang, Xiaoheng Xie, Gehao Zhang, Xunjin Zheng

The success of language models in code assistance has spurred the proposal of repository-level code completion as a means to enhance prediction accuracy, utilizing the context from the entire codebase. However, this amplified context can inadvertently increase inference latency, potentially undermining the developer experience and deterring tool adoption - a

Suparna Biswas, Rituparna Sen

Left truncated and right censored data are encountered frequently in insurance loss data due to deductibles and policy limits. Risk estimation is an important task in insurance as it is a necessary step for determining premiums under various policy terms. Spectral risk measures are inherently coherent and have the benefit of connecting the risk measure to th

Lin-Ding Yuan, Alexandru B. Georgescu, James M. Rondinelli

The non-relativistic spin-splitting (NRSS) of electronic bands in "altermagnets" has sparked renewed interest in antiferromagnets (AFMs) that have no net magnetization. However, altermagnets with collinear and compensated magnetism are not the only type of NRSS AFMs. In this study, we identify the symmetry conditions and characteristic signatures of a distin

Chang Zong, Yuchen Yan, Weiming Lu, Jian Shao

Recent progress with LLM-based agents has shown promising results across various tasks. However, their use in answering questions from knowledge bases remains largely unexplored. Implementing a KBQA system using traditional methods is challenging due to the shortage of task-specific training data and the complexity of creating task-focused model structures.

Norisuke Ioku, Kazuhiro Ishige, Tatsuki Kawakami

In this paper we introduce uniformly local weak Zygmund type spaces, and obtain an optimal sufficient condition for the existence of solutions to the critical fractional semilinear heat equation.

Sławomir Dadas, Małgorzata Grębowiec

Retrieval-augmented generation (RAG) is becoming an increasingly popular technique for integrating internal knowledge bases with large language models. In a typical RAG pipeline, three models are used, responsible for the retrieval, reranking, and generation stages. In this article, we focus on the reranking problem for the Polish language, examining the per

Sulav Ghimire, Kanakesh V. Kkuni, Gabriel M. G. Guerreiro, Emerson D. Guest

This paper studies control interactions between grid-forming (GFM) converters exhibited by power and frequency oscillations in a weakly connected offshore wind power plant (WPP). Two GFM controls are considered, namely virtual synchronous machine (VSM) and virtual admittance (VAdm) based GFM. The GFM control methods are implemented in wind turbine generators

Ziling Liu, Jinyu Yang, Mingqi Gao, Feng Zheng

Controllable video editing has demonstrated remarkable potential across diverse applications, particularly in scenarios where capturing or re-capturing real-world videos is either impractical or costly. This paper introduces a novel and efficient system named Place-Anything, which facilitates the insertion of any object into any video solely based on a pictu

Yuwei Yang, Siqi Ouyang, Xueyu Hu, Mingyue Zheng

Structure-based drug design aims at generating high affinity ligands with prior knowledge of 3D target structures. Existing methods either use conditional generative model to learn the distribution of 3D ligands given target binding sites, or iteratively modify molecules to optimize a structure-based activity estimator. The former is highly constrained by da

KhayTze Peong, Seiichi Uchida, Daichi Haraguchi

Recent diffusion-based generative models show promise in their ability to generate text images, but limitations in specifying the styles of the generated texts render them insufficient in the realm of typographic design. This paper proposes a typographic text generation system to add and modify text on typographic designs while specifying font styles, colors

Ashok K. Singal

The electromagnetic energy-momentum of a moving charged spherical capacitor may be calculated by a 4-vector Lorentz transformation from the energy in the rest frame. However, energy-momentum of the moving system computed directly from electromagnetic fields yields extra terms; in particular a factor of 4/3 in momentum appears, similar to that encountered in

Kei Nakatsuru, Seiichi Uchida

Kerning is the task of setting appropriate horizontal spaces for all possible letter pairs of a certain font. One of the difficulties of kerning is that the appropriate space differs for each letter pair. Therefore, for a total of 52 capital and small letters, we need to adjust $52 \times 52 = 2704$ different spaces. Another difficulty is that there is neith

JUST Team, Chengze Liu, Ying Zu, Fabo Feng

The Jiao Tong University Spectroscopic Telescope (JUST) is a 4.4-meter f/6.0 segmentedmirror telescope dedicated to spectroscopic observations. The JUST primary mirror is composed of 18 hexagonal segments, each with a diameter of 1.1 m. JUST provides two Nasmyth platforms for placing science instruments. One Nasmyth focus fits a field of view of 10 arcmin an

Tetta Kondo, Shumpei Takezaki, Daichi Haraguchi, Seiichi Uchida

Fonts have huge variations in their styles and give readers different impressions. Therefore, generating new fonts is worthy of giving new impressions to readers. In this paper, we employ diffusion models to generate new font styles by interpolating a pair of reference fonts with different styles. More specifically, we propose three different interpolation a

Jinlan Fu, Shenzhen Huangfu, Hang Yan, See-Kiong Ng

Large Language Models (LLMs) have recently showcased remarkable generalizability in various domains. Despite their extensive knowledge, LLMs still face challenges in efficiently utilizing encoded knowledge to develop accurate and logical reasoning processes. To mitigate this problem, we introduced Hint-before-Solving Prompting (HSP), which guides the model t

Chun-Lin Ji, Tao Yu, Peng Gao, Fei Wang

Object detection, a crucial aspect of computer vision, has seen significant advancements in accuracy and robustness. Despite these advancements, practical applications still face notable challenges, primarily the inaccurate detection or missed detection of small objects. In this paper, we propose YOLO-TLA, an advanced object detection model building on YOLOv

Jie Yin, Ang Li, Wei Xi, Wenxian Yu

We introduce Ground-Fusion, a low-cost sensor fusion simultaneous localization and mapping (SLAM) system for ground vehicles. Our system features efficient initialization, effective sensor anomaly detection and handling, real-time dense color mapping, and robust localization in diverse environments. We tightly integrate RGB-D images, inertial measurements, w

Miaoxin Wang, Xiao Wu, Jun Lin, Zhongfeng Wang

Convolutional neural networks (CNNs) with large kernels, drawing inspiration from the key operations of vision transformers (ViTs), have demonstrated impressive performance in various vision-based applications. To address the issue of computational efficiency degradation in existing designs for supporting large-kernel convolutions, an FPGA-based inference ac

Zachary J. Lythgoe, Thomas F. Long, Michael J. Buchholz, Anthony R. Livernois

Phasor measurement units (PMUs) provide a high-resolution view of the power system at the locations where they are placed. As such, it is desirable to place them in bulk in low voltage distribution circuits. However, the power consumption of a PMU/micro-PMU is in the order of Watts (W) that results in them requiring an external power supply, which in turn in

Phuong Dinh Mai, Duc-Trong Le, Tuan-Anh Hoang, Dung D. Le

In this paper, we tackle the problem of computing a sequence of rankings with the guarantee of the Pareto-optimal balance between (1) maximizing the utility of the consumers and (2) minimizing unfairness between producers of the items. Such a multi-objective optimization problem is typically solved using a combination of a scalarization method and linear pro

Yichi Zhang, Zhuo Chen, Lei Liang, Huajun Chen

Multi-modal knowledge graph completion (MMKGC) aims to predict the missing triples in the multi-modal knowledge graphs by incorporating structural, visual, and textual information of entities into the discriminant models. The information from different modalities will work together to measure the triple plausibility. Existing MMKGC methods overlook the imbal

Peng Gao, Peng Wang, Feng Gao, Fei Wang

As a long-term vision in the field of artificial intelligence, the core goal of embodied intelligence is to improve the perception, understanding, and interaction capabilities of agents and the environment. Vision-language navigation (VLN), as a critical research path to achieve embodied intelligence, focuses on exploring how agents use natural language to c

Andy Trung Huynh, Benjamin Zwick, Michael Halle, Adam Wittek

Computational simulations using methods such as the finite element (FE) method rely on high-quality meshes for achieving accurate results. This study introduces a method for creating a high-quality hexahedral mesh using the Open Anatomy Project's brain atlas. Our atlas-based FE hexahedral mesh of the brain mitigates potential inaccuracies and uncertainties d

Pylyp Kuznietsov, Igor Girka, Igor Kyryllin, Andrii Sotnikov

This article provides the description of current training processes and structure of education in quantum physics at the Education and Research Institute "School of Physics and Technology" (SPT) of V.N. Karazin Kharkiv National University, Ukraine. Crucial feature of quantum education at the SPT is the involvement of scientists and experts from national and

Zhenning Zhang, Yunan Zhang, Suyu Ge, Guangwei Weng

The advent of large language models (LLMs) brings an opportunity to minimize the effort in search engine result page (SERP) organization. In this paper, we propose GenSERP, a framework that leverages LLMs with vision in a few-shot setting to dynamically organize intermediate search results, including generated chat answers, website snippets, multimedia data,

Yuyue Zhou, Banafshe Felfeliyan, Shrimanti Ghosh, Jessica Knight

Conventional deep learning models deal with images one-by-one, requiring costly and time-consuming expert labeling in the field of medical imaging, and domain-specific restriction limits model generalizability. Visual in-context learning (ICL) is a new and exciting area of research in computer vision. Unlike conventional deep learning, ICL emphasizes the mod

Miao Xin, Zhongrui You, Zihan Zhang, Taoran Jiang

We present SpaceAgents-1, a system for learning human and multi-robot collaboration (HMRC) strategies under microgravity conditions. Future space exploration requires humans to work together with robots. However, acquiring proficient robot skills and adept collaboration under microgravity conditions poses significant challenges within ground laboratories. To

Bin Liang, Ang Li, Jingqian Zhao, Lin Gui

Stance detection is a challenging task that aims to identify public opinion from social media platforms with respect to specific targets. Previous work on stance detection largely focused on pure texts. In this paper, we study multi-modal stance detection for tweets consisting of texts and images, which are prevalent in today's fast-growing social media plat

Siqi Meng, Shaohua Wu, Jiaming Zhang, Junlan Cheng

In the coming sixth generation (6G) communication era, to provide seamless and ubiquitous connections, the space-air-ground-sea integrated network (SAGSIN) is envisioned to address the challenges of communication coverage in areas with difficult conditions, such as the forest, desert, and sea. Considering the fundamental limitations of the SAGSIN including l

Ang Li, Jingqian Zhao, Bin Liang, Lin Gui

Stance detection is critical for understanding the underlying position or attitude expressed toward a topic. Large language models (LLMs) have demonstrated significant advancements across various natural language processing tasks including stance detection, however, their performance in stance detection is limited by biases and spurious correlations inherent

Taegyun Kim, Ji Oon Lee

We consider the 2-spin spherical Sherrington--Kirkpatrick model without external magnetic field where the interactions between the spins are given as random variables with heavy-tailed distribution. We show that the free energy exhibits a sharp phase transition depending on the location of the largest eigenvalue of the interaction matrix. We also prove the o

Leonardo N. Coregliano, Maryanthe Malliaris

We develop a theory of high-arity PAC learning, which is statistical learning in the presence of "structured correlation". In this theory, hypotheses are either graphs, hypergraphs or, more generally, structures in finite relational languages, and i.i.d. sampling is replaced by sampling an induced substructure, producing an exchangeable distribution. Our mai

Rui Yang, Boming Yang, Sixun Ouyang, Tianwei She

In the domain of Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated promise in text-generation tasks. However, their educational applications, particularly for domain-specific queries, remain underexplored. This study investigates LLMs' capabilities in educational scenarios, focusing on concept graph recovery and question-answe

Rama Adithya Varanasi, Aditya Vashistha, Nicola Dell

This paper presents Saharaline, an intervention designed to provide collective social support for teachers in low-income schools. Implemented as a WhatsApp-based helpline, Saharaline enables teachers to reach out for personalized, long-term assistance with a wide range of problems and stressors, including pedagogical, emotional, and technological challenges.

Nicholas Hu

We formulate generalized Brascamp-Lieb inequalities for representations of bipartite quivers and establish necessary and sufficient conditions for such inequalities. Notably, we show contra Lieb that Gaussians do not saturate certain types of quiver Brascamp-Lieb inequalities.

Samraj Moorjani, Adit Krishnan, Hari Sundaram

As large-scale language models become the standard for text generation, there is a greater need to tailor the generations to be more or less concise, targeted, and informative, depending on the audience/application. Existing control approaches primarily adjust the semantic (e.g., emotion, topics), structural (e.g., syntax tree, parts-of-speech), and lexical

Baichuan Zhou, Ying Hu, Xi Weng, Junlong Jia

We present the TinyLLaVA framework that provides a unified perspective in designing and analyzing the small-scale Large Multimodal Models (LMMs). We empirically study the effects of different vision encoders, connection modules, language models, training data and training recipes. Our extensive experiments showed that better quality of data combined with bet

Xuhao Wu, Liming Wang, Hong-Tao An, Min Ju

We explore the potential manifestation of a hexaquark, the H particle, as a constituent within neutron stars. The H particle, characterized by a quark composition of $uuddss$, is constructed using the framework of Chromomagnetic Interaction (CMI). Specifically, we contemplate the flavor-singlet state H with $J^P=0^+$. Our computations indicate that the three

Lars Kristiansen, Juvenal Murwanashyaka

We introduce a first-order theory $\mathsf{Seq}$ which is mutually interpretable with Robinson's $\mathsf{Q}$. The universe of a standard model for $\mathsf{Seq}$ consists of sequences. We prove that $\mathsf{Seq}$ directly interprets the adjuctive set theory $\mathsf{AST}$, and we prove that $\mathsf{Seq}$ interprets the tree theory $\mathsf{T}$ and the set

Yujia Huang, Adishree Ghatare, Yuanzhe Liu, Ziniu Hu

We study the problem of symbolic music generation (e.g., generating piano rolls), with a technical focus on non-differentiable rule guidance. Musical rules are often expressed in symbolic form on note characteristics, such as note density or chord progression, many of which are non-differentiable which pose a challenge when using them for guided diffusion. W

Shouhei Honda

This short note provides a survey on rigidity and almost rigidity results of Green functions in a non-smooth setting. We also make some observation on the Cheeger-Yau inequality on RCD spaces of non-negative Ricci curvature with applications.

Shen Li, Liuyi Yao, Jinyang Gao, Lan Zhang

To support various applications, a prevalent and efficient approach for business owners is leveraging their valuable datasets to fine-tune a pre-trained LLM through the API provided by LLM owners or cloud servers. However, this process carries a substantial risk of model misuse, potentially resulting in severe economic consequences for business owners. Thus,

Kiwamu Watanabe

Let $X$ be a complex smooth Fano variety of dimension at least four. In this paper, we classify such $X$ when the pseudoindex is at least $n-2$ and the Picard number greater than one. We also discuss the relations between pseudoindex and other invariants of Fano varieties.

Guanwenqing He, Ke Wan, Kazushi Maruo, Toshio Shimokawa

Recent years, large scale clinical data like patient surveys and medical record data are playing an increasing role in medical data science. These large-scale clinical data, collectively referred to as "real-world data (RWD)". It is expected to be widely used in large-scale observational studies of specific diseases, personal medicine or precise medicine, fi

Faith Johnson, Bryan Bo Cao, Kristin Dana, Shubham Jain

Map representations learned by expert demonstrations have shown promising research value. However, the field of visual navigation still faces challenges due to the lack of real-world human-navigation datasets that can support efficient, supervised, representation learning of environments. We present a Landmark-Aware Visual Navigation (LAVN) dataset to allow

Ganesh Sapkota, Sanjay Madria

In modern battlefield scenarios, the reliance on GPS for navigation can be a critical vulnerability. Adversaries often employ tactics to deny or deceive GPS signals, necessitating alternative methods for the localization and navigation of mobile troops. Range-free localization methods such as DV-HOP rely on radio-based anchors and their average hop distance

Haeji Jung, Changdae Oh, Jooeon Kang, Jimin Sohn

Approaches to improving multilingual language understanding often struggle with significant performance gaps between high-resource and low-resource languages. While there are efforts to align the languages in a single latent space to mitigate such gaps, how different input-level representations influence such gaps has not been investigated, particularly with

Daniel M. Kane, Anthony Ostuni, Kewen Wu

Spurred by the influential work of Viola (Journal of Computing 2012), the past decade has witnessed an active line of research into the complexity of (approximately) sampling distributions, in contrast to the traditional focus on the complexity of computing functions. We build upon and make explicit earlier implicit results of Viola to provide superconstant

Spencer Rarrick, Ranjita Naik, Sundar Poudel, Vishal Chowdhary

Neural Machine Translation (NMT) continues to improve in quality and adoption, yet the inadvertent perpetuation of gender bias remains a significant concern. Despite numerous studies on gender bias in translations into English from weakly gendered-languages, there are no benchmarks for evaluating this phenomenon or for assessing mitigation strategies. To add

Liping Yin, Anna Little, Matthew Hirn

Motivated by modern data applications such as cryo-electron microscopy, the goal of classic multi-reference alignment (MRA) is to recover an unknown signal $f: \mathbb{R} \to \mathbb{R}$ from many observations that have been randomly translated and corrupted by additive noise. We consider a generalization of classic MRA where signals are also corrupted by a

Kai-Xuan Zhang, Hanshu Xu, Jihoon Keum, Xiangqi Wang

Perpendicular magnetic anisotropy (PMA) of magnets is paramount for electrically controlled spintronics due to their intrinsic potentials for higher memory density, scalability, thermal stability and endurance, surpassing an in-plane magnetic anisotropy (IMA). Nickel film is a long-lived fundamental element ferromagnet, yet its electrical transport behavior

J. Francis Baer, Maxwell Johnson, Peter Marek

We study the Adams-Novikov spectral sequence in $\mathbb{F}_p$-synthetic spectra, computing the synthetic analogs of $\mathrm{BP}$ and its cooperations to identify the synthetic Adams-Novikov $\mathrm{E}_2$-page, computed in a range with a synthetic algebraic Novikov spectral sequence. We then identify deformations associated to the Cartan-Eilenberg and alge

Qiyuan He, Yizhong Wang, Wenya Wang

Large language models (LLMs) have demonstrated remarkable proficiency in understanding and generating responses to complex queries through large-scale pre-training. However, the efficacy of these models in memorizing and reasoning among large-scale structured knowledge, especially world knowledge that explicitly covers abundant factual information remains qu

Yan Lei, Liang Pang, Yuanzhuo Wang, Huawei Shen

The questionnaire is a professional research methodology used for both qualitative and quantitative analysis of human opinions, preferences, attitudes, and behaviors. However, designing and evaluating questionnaires demands significant effort due to their intricate and complex structure. Questionnaires entail a series of questions that must conform to intric