April 2024 arXiv papers — page 29

Xinpeng Li, Teng Wang, Jian Zhao, Shuyi Mao

Emotion recognition aims to discern the emotional state of subjects within an image, relying on subject-centric and contextual visual cues. Current approaches typically follow a two-stage pipeline: first localize subjects by off-the-shelf detectors, then perform emotion classification through the late fusion of subject and context features. However, the comp

Jingen Lin, Jinbei Chen, Jianchao Zhang, Haowen Liang

Flat lenses have shown promising applications in miniaturized and ultracompact lightweight optical systems. However, it has been a great challenge in simultaneously achieving broadband achromatism and high numerical aperture. Here, we demonstrate that this long-term dilemma can be broken through by the zone division multiplex of the meta-atoms on a composite

Alexandre Gauthier, Jonathan A. Sobota, Nicolas Gauthier, Costel R. Rotundu

Bi$_2$Se$_3$ has been the focus of intense interest over the past decade due to its topological properties. Bi surfaces are known to form on Bi$_2$Se$_3$ upon exposure to atmosphere, but their electronic structure has not been investigated. We report band structure measurements of such Bi surfaces using angle-resolved photoemission spectroscopy. Measured spe

Sotirios Konstantakos, Jorgen Cani, Ioannis Mademlis, Despina Ioanna Chalkiadaki

Self-Supervised Learning (SSL) is a valuable and robust training methodology for contemporary Deep Neural Networks (DNNs), enabling unsupervised pretraining on a 'pretext task' that does not require ground-truth labels/annotation. This allows efficient representation learning from massive amounts of unlabeled training data, which in turn leads to increased a

Haigang Li, Yan Zhao

We study the insulated conductivity problem which involves two adjacent convex insulators embedded in a bounded domain. It is known that the gradient of solutions may blow up as the distance between the two inclusions tends to zero. However, the sharpness of the blow up rate for general convex insulator case in dimension $n\geq3$ has remained open. The novel

Chien-Yeah Seng

To continue from our previous work Phys.Rev.D 109(2024),073007, we derive the full Standard Model prediction of the most general free neutron differential decay rate with all massive particles (neutron, proton and electron) polarized, including the $\mathcal{O}(1/m_N)$ recoil corrections and $\mathcal{O}(\alpha/\pi)$ radiative corrections. For the latter we

Fangda Chen, Jiacheng Nie, Lichuan Jiang, Zhuoer Zeng

We introduced "Presidifussion," a novel approach to learning and replicating the unique style of calligraphy of President Xu, using a pretrained diffusion model adapted through a two-stage training process. Initially, our model is pretrained on a diverse dataset containing works from various calligraphers. This is followed by fine-tuning on a smaller, specia

C. Gong, C. Lu, Z. Li, Z. Liu

Model-free learning-based control methods have recently shown significant advantages over traditional control methods in avoiding complex vehicle characteristic estimation and parameter tuning. As a primary policy learning method, imitation learning (IL) is capable of learning control policies directly from expert demonstrations. However, the performance of

Pavel Zorin-Kranich

We present a few techniques for proving $L^p$ estimates for martingales. Basic applications to It\^o integration and rough paths are included.

Quan Zhang, Binqi Zeng, Chijin Zhou, Gwihwan Go

Presently, with the assistance of advanced LLM application development frameworks, more and more LLM-powered applications can effortlessly augment the LLMs' knowledge with external content using the retrieval augmented generation (RAG) technique. However, these frameworks' designs do not have sufficient consideration of the risk of external content, thereby

Samet Uzun, Purnanand Elango, Abhinav G. Kamath, Taewan Kim

We propose a nonlinear model predictive control (NMPC) framework based on a direct optimal control method that ensures continuous-time constraint satisfaction and accurate evaluation of the running cost, without compromising computational efficiency. We leverage the recently proposed successive convexification framework for trajectory optimization, where: (1

Lina Azerouk, Binh-Minh Bui-Xuan, Camille Palisoc, Maria Potop-Butucaru

Twin nodes in a static network capture the idea of being substitutes for each other for maintaining paths of the same length anywhere in the network. In dynamic networks, we model twin nodes over a time-bounded interval, noted $(\Delta,d)$-twins, as follows. A periodic undirected time-varying graph $\mathcal G=(G_t)_{t\in\mathbb N}$ of period $p$ is an infin

Hailay Teklehaymanot, Dren Fazlija, Niloy Ganguly, Gourab K. Patro

The absence of explicitly tailored, accessible annotated datasets for educational purposes presents a notable obstacle for NLP tasks in languages with limited resources.This study initially explores the feasibility of using machine translation (MT) to convert an existing dataset into a Tigrinya dataset in SQuAD format. As a result, we present TIGQA, an exper

ATLAS Collaboration

A search for Higgs boson pair production via vector-boson fusion is performed in the Lorentz-boosted regime, where a Higgs boson candidate is reconstructed as a single large-radius jet, using 140 fb$^{-1}$ of proton-proton collision data at $\sqrt{s} = 13$ TeV recorded by the ATLAS detector at the Large Hadron Collider. Only Higgs boson decays into bottom qu

Paola Goatin, Benedetto Piccoli

We propose a new model for multi-lane traffic with moving bottlenecks, e.g., autonomous vehicles (AV). It consists of a system of balance laws for traffic in each lane, coupled in the source terms for lane changing, and fully coupled to ODEs for the AVs' trajectories.More precisely, each AV solves a controlled equation depending on the traffic density, while

Chloé Giraudet, Qian Wu, Jean-Marc Allain

The cornea, as the outer element of the human eye, plays a pivotal role in vision. Any defects in its shape can result in visual impairments. Mechanical defect manifest as shape defects, as the cornea is under to pressure. Our study presents the first comprehensive observation of human corneal deformation throughout its entire thickness during an inflation t

S. Chraibi, F. Iutzeler, J. Malick, A. Rogozin

Many problems in machine learning write as the minimization of a sum of individual loss functions over the training examples. These functions are usually differentiable but, in some cases, their gradients are not Lipschitz continuous, which compromises the use of (proximal) gradient algorithms. Fortunately, changing the geometry and using Bregman divergences

Naveen Kumar, Arpita Chatterjee

We consider a cavity field state, which is created by the atom-cavity field's interaction in the presence of a driven field. The two-level atom passes through the cavity and is driven by a weak classical field. A photon number dependent Stark shift is induced by the atom's dispersive interaction with the cavity field. When the atom is in excited state $|a\ra

Alexander Wong

A plane rooted tree is called a hipster tree if it has no nontrivial automorphisms. Equivalently, a tree is a hipster tree if no two siblings have isomorphic subtrees. We impose the hipster condition on various classes of rooted trees. By approximating the generating function for the number of such trees, we obtain bounds on their exponential growth rates.

Sadjad Anzabi Zadeh, W. Nick Street, Barrett W. Thomas

Deep Reinforcement Learning is an effective tool for drug dosing for chronic condition management. However, the final protocol is generally a black box without any justification for its prescribed doses. This paper addresses this issue by proposing an explainable dosing protocol for warfarin using a Proximal Policy Optimization method combined with Policy Di

Jiajun Liang, Baoquan Zhang, Yunming Ye, Xutao Li

The accurate detection of Mesoscale Convective Systems (MCS) is crucial for meteorological monitoring due to their potential to cause significant destruction through severe weather phenomena such as hail, thunderstorms, and heavy rainfall. However, the existing methods for MCS detection mostly targets on single-frame detection, which just considers the stati

Ye Xu

It is assumed that heavy dark matter $\phi$ with O(TeV) mass captured by the Earth may decay to relativistic light milli-charged particles (MCPs). These MCPs could be measured by the IceCube neutrino telescope. The massless hidden photon model was taken for MCPs to interact with nuclei, so that the numbers and fluxes of expected MCPs may be evaluated at IceC

Motoya Teranishi

We study some density results for integral points on the complement of a closed subvariety in the $n$-dimensional projective space over a number field. For instance, we consider a subvariety whose components consist of $n-1$ hyperplanes plus one smooth quadric hypersurface in general position, or four hyperplanes in general position plus a finite number of c

Jing Hu, Honghu Zhang, Peng Zheng, Jialin Mu

Addressing complex meteorological processes at a fine spatial resolution requires substantial computational resources. To accelerate meteorological simulations, researchers have utilized neural networks to downscale meteorological variables from low-resolution simulations. Despite notable advancements, contemporary cutting-edge downscaling algorithms tailore

Yuhang Zhou, Haolin Li, Siyuan Du, Jiangchao Yao

The popularity of large-scale pre-training has promoted the development of medical foundation models. However, some studies have shown that although foundation models exhibit strong general feature extraction capabilities, their performance on specific tasks is still inferior to task-specific methods. In this paper, we explore a new perspective called ``Know

Muhammad Rizwan, Jure Demšar

Social anxiety represents a prevalent challenge in modern society, affecting individuals across personal and professional spheres. Left unaddressed, this condition can yield substantial negative consequences, impacting social interactions and performance. Further understanding its diverse physical and emotional symptoms becomes pivotal for comprehensive diag

R. Reho, A. R. Botello-Méndez, D. Sangalli, M. J. Verstraete

Van der Waals heterostructures of two-dimensional transition metal dichalcogenides provide a unique platform to engineer optoelectronic devices tuning their optical properties via stacking, twisting, or straining. Using ab initio Many-Body Perturbation Theory, we predict the electronic and optical (absorption and photoluminescence spectra) properties of MoS$

Qingyuan Zhang, Hien Duy Nguyen

Many problems in statistics and machine learning can be formulated as model selection problems, where the goal is to choose an optimal parsimonious model among a set of candidate models. It is typical to conduct model selection by penalizing the objective function via information criteria (IC), as with the pioneering work by Akaike and Schwarz. Via recent wo

Adrien LeCoz, Houssem Ouertatani, Stéphane Herbin, Faouzi Adjed

Image classifiers should be used with caution in the real world. Performance evaluated on a validation set may not reflect performance in the real world. In particular, classifiers may perform well for conditions that are frequently encountered during training, but poorly for other infrequent conditions. In this study, we hypothesize that recent advances in

Robert D. Cousins, Larry Wasserman

This is a writeup, with some elaboration, of the talks by the two authors (a physicist and a statistician) at the first PHYSTAT Informal review on January 24, 2024. We discuss Bayesian and frequentist approaches to dealing with nuisance parameters, in particular, integrated versus profiled likelihood methods. In regular models, with finitely many parameters

Dooyoung Kim, Taewook Ha, Jinseok Hong, Seonji Kim

We introduce the concept of a meta-object, a next-generation virtual object that inherits the form, properties, and functions of its real-world counterpart, enabling seamless synchronization, interaction, and sharing between the physical and virtual worlds. While plenty of today's virtual objects provide some sensory feedback and dynamic behavior, meta-objec

Haojie Zhang, Yimeng Zhuang

Few-shot Named Entity Recognition (NER) aims to extract named entities using only a limited number of labeled examples. Existing contrastive learning methods often suffer from insufficient distinguishability in context vector representation because they either solely rely on label semantics or completely disregard them. To tackle this issue, we propose a uni

Anurag Kumar Pandey, Anil Goyal, Nikhil Sikka

Marketing is one of the high-cost activities for any online platform. With the increase in the number of customers, it is crucial to understand customers based on their dynamic behaviors to design effective marketing strategies. Customer segmentation is a widely used approach to group customers into different categories and design the marketing strategy targ

Enxin Song, Wenhao Chai, Tian Ye, Jenq-Neng Hwang

Recently, integrating video foundation models and large language models to build a video understanding system can overcome the limitations of specific pre-defined vision tasks. Yet, existing methods either employ complex spatial-temporal modules or rely heavily on additional perception models to extract temporal features for video understanding, and they onl

Hu Zhou, Ying-Chang Liang, Chau Yuen

In reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR), an RIS is exploited to assist the primary system and to simultaneously operate as a secondary transmitter by modulating its own information over the incident primary signal from the air. Such an operation is called over-the-air modulation. The existing modulation schemes such as on-of

Constantin-Daniel Nicolae, Sara Sameer, Nathan Sun, Karena Yan

Accurately measuring the cycle lifetime of commercial lithium-ion batteries is crucial for performance and technology development. We introduce a novel hybrid approach combining a physics-based equation with a self-attention model to predict the cycle lifetimes of commercial lithium iron phosphate graphite cells via early-cycle data. After fitting capacity l

Yanbiao Ma, Licheng Jiao, Fang Liu, Lingling Li

In semi-supervised learning, methods that rely on confidence learning to generate pseudo-labels have been widely proposed. However, increasing research finds that when faced with noisy and biased data, the model's representation network is more reliable than the classification network. Additionally, label generation methods based on model predictions often s

Runa Shimada

To study a one parameter deformation of an $S_1$ singularity taking into consideration its differential geometric properties, we give a form representing the deformation using only diffeomorphisms on the source and isometries of the target. Using this form, we study differential geometric properties of $S_1$ singularities and the Whitney umbrellas appearing

Aasif Ahmad Mir, Nina Smirnova, Jeyshankar Ramalingam, Philipp Mayr

The study aims to highlight the growth and development of Indo-German collaborative research over the past three decades. Moreover, this study encompasses an in-depth examination of funding acknowledgements to gain valuable insights into the financial support that underpins these collaborative endeavors. Together with this paper, we provide an openly accessi

Ronghua Liao, Chen Hui, Lang Yuan, Haiqi Zhu

No-Reference Image Quality Assessment (NR-IQA) aims at estimating image quality in accordance with subjective human perception. However, most methods focus on exploring increasingly complex networks to improve the final performance,accompanied by limitations on input images. Especially when applied to high-resolution (HR) images, these methods offen have to

Renqiang Luo, Huafei Huang, Shuo Yu, Xiuzhen Zhang

The design of Graph Transformers (GTs) generally neglects considerations for fairness, resulting in biased outcomes against certain sensitive subgroups. Since GTs encode graph information without relying on message-passing mechanisms, conventional fairness-aware graph learning methods cannot be directly applicable to address these issues. To tackle this chal

Fatemeh P. A. Beik, Chen Greif, Manfred Trummer

We establish necessary and sufficient conditions for invertibility of symmetric three-by-three block matrices having a double saddle-point structure \fb{that guarantee the unique solvability of double saddle-point systems}. We consider various scenarios, including the case where all diagonal blocks are allowed to be rank deficient. Under certain conditions r

Shatanik Bhattacharya, Srijan Bharati Das, Lisa Bugnet, Subrata Panda

Magnetic fields in the stellar interiors are key candidates to explain observed core rotation rates inside solar-like stars along their evolution. Recently, asteroseismic estimates of radial magnetic field amplitudes near the hydrogen-burning shell (H-shell) inside about 24 red-giants (RGs) have been obtained by measuring frequency splittings from their powe

W. C. Haxton, Evan Rule

The Mu2e and COMET experiments are expected to improve existing limits on charged lepton flavor violation (CLFV) by roughly four orders of magnitude. $\mu\rightarrow e$ conversion experiments are typically optimized for electrons produced without nuclear excitation, as this maximizes the electron energy and minimizes backgrounds from the free decay of the mu

Tanmay Sen, Ansuman Das, Mrinmay Sen

Hate speech encompasses verbal, written, or behavioral communication that targets derogatory or discriminatory language against individuals or groups based on sensitive characteristics. Automated hate speech detection plays a crucial role in curbing its propagation, especially across social media platforms. Various methods, including recent advancements in d

S. I. Dimitrov

This paper presents a brief survey of the most important and the most remarkable inequalities involving the basic arithmetic functions.

Xindi Zheng, Yuwei Wu, Yu Pan, Wanyu Lin

Missing data imputation poses a paramount challenge when dealing with graph data. Prior works typically are based on feature propagation or graph autoencoders to address this issue. However, these methods usually encounter the over-smoothing issue when dealing with missing data, as the graph neural network (GNN) modules are not explicitly designed for handli

Erich Novak, Friedrich Pillichshammer

We study lower bounds on the worst-case error of numerical integration in tensor product spaces. As reference we use the $N$-th minimal error of linear rules that use $N$ function values. The information complexity is the minimal number $N$ of function evaluations that is necessary such that the $N$-th minimal error is less than a factor $\varepsilon$ times

S. Balamoorthy, S. V. Bharanedhar

In this paper we have discussed the group vertex magicness of H-join of family of graphs and generalized friendship graph.

Yicheng Gu, Xueyao Zhang, Liumeng Xue, Haizhou Li

Generative Adversarial Network (GAN) based vocoders are superior in both inference speed and synthesis quality when reconstructing an audible waveform from an acoustic representation. This study focuses on improving the discriminator for GAN-based vocoders. Most existing Time-Frequency Representation (TFR)-based discriminators are rooted in Short-Time Fourie

Anuj Nandi, Santabrata Das, Seshadri Majumder, Tilak Katoch

We report the first detection of evolving Low-Frequency Quasi-periodic Oscillation (LFQPO) frequencies in hard X-rays upto $100$ keV with $AstroSat/LAXPC$ during `unusual' outburst phase of Swift J1727.8-1613 in hard-intermediate state (HIMS). The observed LFQPO in $20 - 100$ keV has a centroid $\nu_{_{\rm QPO}}=1.43$ Hz, a coherence factor $Q= 7.14$ and an

Xiongjun Guan, Jianjiang Feng, Jie Zhou

Fingerprint dense registration aims to finely align fingerprint pairs at the pixel level, thereby reducing intra-class differences caused by distortion. Unfortunately, traditional methods exhibited subpar performance when dealing with low-quality fingerprints while suffering from slow inference speed. Although deep learning based approaches shows significant

Ken Yokoyama, Shinji Ito, Tatsuya Matsuoka, Kei Kimura

An online decision-making problem is a learning problem in which a player repeatedly makes decisions in order to minimize the long-term loss. These problems that emerge in applications often have nonlinear combinatorial objective functions, and developing algorithms for such problems has attracted considerable attention. An existing general framework for dea

Nanxu Gong, Wangyang Ying, Dongjie Wang, Yanjie Fu

Feature selection aims to identify the optimal feature subset for enhancing downstream models. Effective feature selection can remove redundant features, save computational resources, accelerate the model learning process, and improve the model overall performance. However, existing works are often time-intensive to identify the effective feature subset with

Xiongjun Guan, Yongjie Duan, Jianjiang Feng, Jie Zhou

Skin distortion is a long standing challenge in fingerprint matching, which causes false non-matches. Previous studies have shown that the recognition rate can be improved by estimating the distortion field from a distorted fingerprint and then rectifying it into a normal fingerprint. However, existing rectification methods are based on principal component r

Nardjess Benoudina, Chaudry Massood Khalique, Ji Lin

This paper discusses the construction of a new $(3+1)$-dimensional Korteweg-de Vries (KdV) equation. By employing the KdV's recursion operator, we extract two equations, and with elemental computation steps, the obtained result is $ 3u_{xyt}+3u_{xzt}-(u_{t}-6uu_{x}+u_{xxx})_{yz}-2\left(u_{x}\partial_{x}^{-1}u_{y}\right)_{xz}-2\left(u_{x}\partial_{x}^{-1}u_{z

Vsevolod K. Malinovskii

The paper is a sketch of systematic presentation of distributional limit theorems and their refinements for compound sums. When analyzing, e.g., ergodic semi-Markov systems with discrete or continuous time, this allows us to separate those aspects that lie within the theory of random processes from those that relate to the classical summation theory. All the

Jing-Yu Ran, Bao Wang, Jun-Jie Wei

A hypothetical photon mass, $m_{\gamma}$, can produce a frequency-dependent vacuum dispersion of light, which leads to an additional time delay between photons with different frequencies when they propagate through a fixed distance. The dispersion measure--redshift measurements of fast radio bursts (FRBs) have been widely used to constrain the rest mass of t

Cheryl Lee, Chunqiu Steven Xia, Longji Yang, Jen-tse Huang

Software debugging is a time-consuming endeavor involving a series of steps, such as fault localization and patch generation, each requiring thorough analysis and a deep understanding of the underlying logic. While large language models (LLMs) demonstrate promising potential in coding tasks, their performance in debugging remains limited. Current LLM-based m

Tunhou Zhang, Shiyu Li, Hsin-Pai Cheng, Feng Yan

Exploring dense connectivity of convolutional operators establishes critical "synapses" to communicate feature vectors from different levels and enriches the set of transformations on Computer Vision applications. Yet, even with heavy-machinery approaches such as Neural Architecture Search (NAS), discovering effective connectivity patterns requires tremendou

Chengpei Xu, Wenjing Jia, Ruomei Wang, Xiaonan Luo

Bottom-up text detection methods play an important role in arbitrary-shape scene text detection but there are two restrictions preventing them from achieving their great potential, i.e., 1) the accumulation of false text segment detections, which affects subsequent processing, and 2) the difficulty of building reliable connections between text segments. Targ

Yali Sun, Mingzu Zhang, Xing Feng, Xing Yang

Reliability assessment of interconnection networks is critical to the design and maintenance of multiprocessor systems. The $(n, k)$-enhanced hypercube $Q_{n,k}$, as a variation of the hypercube $Q_{n}$, was proposed by Tzeng and Wei in 1991. As an extension of traditional edge-connectivity, $h$-extra edge-connectivity of a connected graph $G,$ $\lambda_h(G)

Xiongjun Guan, Jianjiang Feng, Jie Zhou

In order to make 3D fingerprints compatible with traditional 2D flat fingerprints, a common practice is to unfold the 3D fingerprint into a 2D rolled fingerprint, which is then matched with the flat fingerprints by traditional 2D fingerprint recognition algorithms. The problem with this method is that there may be large elastic deformation between the unfold

Xiongjun Guan, Yongjie Duan, Jianjiang Feng, Jie Zhou

Skin distortion is a long standing challenge in fingerprint matching, which causes false non-matches. Previous studies have shown that the recognition rate can be improved by estimating the distortion field from a distorted fingerprint and then rectifying it into a normal fingerprint. However, existing rectification methods are based on principal component r

Zhenrong Zhang, Jianan Liu, Xi Zhou, Tao Huang

Cooperative perception is essential to enhance the efficiency and safety of future transportation systems, requiring extensive data sharing among vehicles on the road, which raises significant privacy concerns. Federated learning offers a promising solution by enabling data privacy-preserving collaborative enhancements in perception, decision-making, and pla

Yichen Liu, Junxi Yu, Cheng-Cheng Liu

We introduce a universal methodology for generating and manipulating altermagnetism in two-dimensional (2D) magnetic van der Waals (MvdW) materials through twisting. We find that a key in-plane 2-fold rotational operation can be achieved in a twisted bilayer of any 2D MvdW material, which takes one of all five 2D Bravais lattices, thereby inducing altermagne

Nardjess Benoudina, Fatima Zohra Boutaf, Nasserdine Kechkar

In this study, the finite volume method is implemented for solving the problem of the semilinear equation: $-d \delta u+ u=u^q (d, q>0) $with a homogeneous Neumann boundary condition. This problem is equivalent to the known stationary Keller-Segel model, which arises in chemotaxis.After discretization, a nonlinear algebraic system is obtained and solved on t

Luke Lee

This paper examines the complex legal landscape surrounding digital assets, analysing how they are defined and regulated as property across various jurisdictions. As digital assets such as cryptocurrencies and non-fungible tokens (NFTs) increasingly integrate with global economies, their intangible nature presents unique challenges to traditional property la

Simon J. Ward, Muhamed Baljevic, Sharon M. Weiss

The response time of a biosensor is a crucial metric in safety-critical applications such as medical diagnostics where an earlier diagnosis can markedly improve patient outcomes. However, the speed at which a biosensor reaches a final equilibrium state can be limited by poor mass transport and long molecular diffusion times that increase the time it takes ta

Shotaro Ishihara, Hiromu Takahashi

Dominant pre-trained language models (PLMs) have demonstrated the potential risk of memorizing and outputting the training data. While this concern has been discussed mainly in English, it is also practically important to focus on domain-specific PLMs. In this study, we pre-trained domain-specific GPT-2 models using a limited corpus of Japanese newspaper art

Elena R. Henderson, Jessie M. Henderson, William V. Oxford, Mitchell A. Thornton

Several cryptographic systems depend upon the computational difficulty of reversing cryptographic hash functions. Robust hash functions transform inputs to outputs in such a way that the inputs cannot be later retrieved in a reasonable amount of time even if the outputs and the function that created them are known. Consequently, hash functions can be cryptog

Mami Deka, Earl P. Bellinger, Shashi M. Kanbur, Sukanta Deb

The effect of metallicity on the theoretical and empirical period-luminosity (PL) relations of Cepheid variables is not well understood and remains a highly debated issue. Here, we examine empirical colour-magnitude diagrams (CMDs) of Classical and Type-II Cepheids in the Magellanic Clouds and compare those with the theoretically predicted instability strip

Yunxiang Zhang, Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim

Self-correction has emerged as a promising solution to boost the reasoning performance of large language models (LLMs), where LLMs refine their solutions using self-generated critiques that pinpoint the errors. This work explores whether small (<= 13B) language models (LMs) have the ability of self-correction on reasoning tasks with minimal inputs from stron

Norihisa Namura, Hiroya Nakao

We present a model of the central pattern generator (CPG) network that can control gait transitions in hexapod robots in a simple manner based on phase reduction. The CPG network consists of six weakly coupled limit-cycle oscillators, whose synchronization dynamics can be described by six phase equations through phase reduction. Focusing on the transitions b

Zhaohui Huang, Zhaocheng Wang, Sheng Chen

In next-generation communications, sub-6GHz and millimeter-wave (mmWave) links typically coexist, with the sub-6GHz link always active and the mmWave link active when high-rate transmission is required. Due to the spatial similarities between sub-6GHz and mmWave channels, sub-6GHz channel information can be utilized to support hybrid beamforming in mmWave co

Pengyu Le

The constant mass function foliation has been shown useful for studying the null Penrose inequality on a null hypersurface, because of the monotonicity formula of Hawking mass along such a foliation. In this paper, we show the global existence of the constant mass aspect function foliation on a nearly spherically symmetric incoming null hypersurface, emanati

Yang Wu, Yao Wan, Hongyu Zhang, Yulei Sui

The Natural Language to Visualization (NL2Vis) task aims to transform natural-language descriptions into visual representations for a grounded table, enabling users to gain insights from vast amounts of data. Recently, many deep learning-based approaches have been developed for NL2Vis. Despite the considerable efforts made by these approaches, challenges per

Ao Wang, Xinyun Zhang

In this paper, we study the metrical theory of the growth rate of digits in L\"{u}roth expansions. More precisely, for $ x\in \left( 0,1 \right] $, let $ \left[ d_1\left( x \right) ,d_2\left( x \right) ,\cdots \right] $ denote the L\"{u}roth expansion of $ x $, we completely determine the Hausdorff dimension of the following sets \begin{align*} E_{\mathrm{su

Chenyang Xu, Ziquan Zhuang

We prove that in any fixed dimension, K-semistable log Fano cone singularities whose volumes are bounded from below by a fixed positive number form a bounded set. As a consequence, we show that the set of local volumes of klt singularities of a fixed dimension has zero as the only accumulation point.

Zehao Zhou, Shane M. Parker

Eigenvalue problems and linear systems of equations involving large symmetric matrices are commonly solved in quantum chemistry using Krylov space methods, such as the Davidson algorithm. The preconditioner is a key component of Krylov space methods that accelerates convergence by improving the quality of new guesses at each iteration. We systematically desi

Hao Song, Xuelian Zhang, Jian Wang, Yanming Sun

High-contrast refractive indexes are pivotal in dielectric metasurfaces for inducing various exotic phenomena, such as the bound state in the continuum (BIC) and electric mirror (EM). However, the limitations of high-index materials are adverse to the practical applications, thus low-contrast metasurfaces with comparable performance are highly desired. Here

Hiroshi Ando

Paszkiewicz's conjecture asserts that given a decreasing sequence $T_1\ge T_2\ge \dots$ of positive contractions on a separable infinite-dimensional Hilbert space $H$, the product $S_n=T_nT_{n-1}\cdots T_1$ converges in the strong operator topology. In these notes, we give an equivalent, more precise formulation of his conjecture. Moreover, we show that the

Taejung Kim, Yonggi Kim, Wootaek Cho, Jong-Hyun Kwak

This study presents a novel, ultralow-power single-sensor-based electronic nose (e-nose) system for real-time gas identification, distinguishing itself from conventional sensor-array-based e-nose systems whose power consumption and cost increase with the number of sensors. Our system employs a single metal oxide semiconductor (MOS) sensor built on a suspende

Juan G. Colonna, Ahmed A. Fares, Márcio Duarte, Ricardo Sousa

Process Mining offers a powerful framework for uncovering, analyzing, and optimizing real-world business processes. Petri nets provide a versatile means of modeling process behavior. However, traditional methods often struggle to effectively compare complex Petri nets, hindering their potential for process enhancement. To address this challenge, we introduce

Xiaoyu Zhang, Pengcheng Yang, Jiawei Feng, Qiang Luo

How intelligence emerges from living beings has been a fundamental question in neuroscience. However, it remains largely unanswered due to the complex neuronal dynamics and intricate connections between neurons in real neural systems. To address this challenge, we leveraged the largest available adult Drosophila connectome data set, and constructed a compreh

Yuehua Zhang, Qingwen Wu, Jiancheng Wu, Xinwu Cao

The theoretical debris supply rate from a tidal disruption of stars can exceed about one hundred times of the Eddington accretion rate for a $10^{6-7}M_{\odot}$ supermassive black hole (SMBH). It is believed that a strong wind will be launched from the disk surface due to the radiation pressure in the case of super-Eddington accretion, which may be one of th

Hai Siong Tan, Kuancheng Wang, Rafe Mcbeth

In this work, we present a novel application of an uncertainty-quantification framework called Deep Evidential Learning in the domain of radiotherapy dose prediction. Using medical images of the Open Knowledge-Based Planning Challenge dataset, we found that this model can be effectively harnessed to yield uncertainty estimates that inherited correlations wit

Tingliang Zhang, Haiwang Zhong, Zhenfei Tan, Xinfei Yan

In this paper, we present Misaka, a visualized swarm testbed for smart grid algorithm evaluation, also an extendable open-source open-hardware platform for developing tabletop tangible swarm interfaces. The platform consists of a collection of custom-designed 3 omni-directional wheels robots each 10 cm in diameter, high accuracy localization through a microd

Maverick S. H. Oh, Anna Nierenberg, Daniel Gilman, Simon Birrer

Flux-ratio anomalies in quadruply imaged quasars are sensitive to the imprint of low-mass dark-matter haloes. The reliability of detection depends on the robustness of the smooth mass model. Optical surveys show that massive early-type galaxies similar to galaxy-scale gravitational lenses depart from perfect ellipticity, exhibiting $m=3$ and $m=4$ multipole

Wei Xu, Jianlong Chen, Zhicheng Ding, Jinyin Wang

This paper explores the importance of text sentiment analysis and classification in the field of natural language processing, and proposes a new approach to sentiment analysis and classification based on the bidirectional gated recurrent units (GRUs) model. The study firstly analyses the word cloud model of the text with six sentiment labels, and then carrie

Dongsheng Wang, Xiaoqin Feng, Zeming Liu, Chuan Wang

Named entity recognition (NER) is a fundamental task in natural language processing that involves identifying and classifying entities in sentences into pre-defined types. It plays a crucial role in various research fields, including entity linking, question answering, and online product recommendation. Recent studies have shown that incorporating multilingu

Christina Sormani wth Wenchuan Tian, Changliang Wang

This report contains a survey of examples of sequences of manifolds with nonnegative scalar curvature including an extreme example with Wenchuan Tian and Changliang Wang. It announces a paper with Wenchaun Tian proving the GH and SWIF convergence of the extreme example. It contains six Open Questions concerning such sequences.

Valeriia Cherepanova, James Zou

Large language models (LLMs) exhibit excellent ability to understand human languages, but do they also understand their own language that appears gibberish to us? In this work we delve into this question, aiming to uncover the mechanisms underlying such behavior in LLMs. We employ the Greedy Coordinate Gradient optimizer to craft prompts that compel LLMs to

Xiu-hui Tan, Jun-qing Xia

Within the mass range of $10^{16}-5\times 10^{18}$ g, primordial black holes (PBHs) persist as plausible candidates for dark matter. Our study involves a reassessment of the constraints on PBHs through a comparative analysis of the cosmic X-ray background (CXB) and the emissions arising from their Hawking evaporation. We identify previously overlooked radiat

Yasuyo Kita, Yudai Fujieda, Ichiro Matsuda, Nobuyuki Kita

In this paper, we propose a method for calculating the three-dimensional (3D) position and orientation of a pallet placed on a shelf on the side of a forklift truck using a 360-degree camera. By using a 360-degree camera mounted on the forklift truck, it is possible to observe both the pallet at the side of the forklift and one several meters ahead. However,

Ikuma Ogasawara, Ken-ichi Amano

In this letter, we use knowledge gained from our recent study to present a technique for separation of nanoparticles such as exosomes, anticancer drugs, and vaccines. The technique involves adding non-adsorptive polymers to a system in which two types of nanoparticles with the same size and zeta potential are dispersed. The different types of nanoparticles c

Leandro Fiorini Aurichi, Paulo Magalhães Júnior, Lucas Real

The notion of ends in an infinite graph $G$ might be modified if we consider them as equivalence classes of infinitely edge-connected rays, rather than equivalence classes of infinitely (vertex-)connected ones. This alternative definition yields the edge-end space $\Omega_E(G)$ of $G$, in which we can endow a natural (edge-)end topology. For every graph $G$,

Samuel Bearden, Tigran M. Abramyan, Dmitry Gil, Jessica Johnson

The movement of small particles and molecules through membranes is widespread and has far-reaching implications. Consequently, the development of mathematical models is essential for understanding these processes on a micro level, leading to deeper insights. In this endeavour, we suggested a model based on a set of empirical equations to predict the transpor

David Jekel, Srivatsav Kunnawalkam Elayavalli

We study upgraded free independence phenomena for unitary elements $u_1$, $u_2$, \dots representing the large-$n$ limit of Haar random unitaries, showing that free independence extends to several larger algebras containing $u_j$ in the ultraproduct of matrices $\prod_{n \to \mathcal{U}} M_n(\mathbb{C})$. Using a uniform asymptotic freeness argument and volum

Zheng Lian, Haiyang Sun, Licai Sun, Zhuofan Wen

Multimodal emotion recognition is an important research topic in artificial intelligence. Over the past few decades, researchers have made remarkable progress by increasing the dataset size and building more effective algorithms. However, due to problems such as complex environments and inaccurate annotations, current systems are hard to meet the demands of