October 2023 arXiv papers — page 129

Kosuke Takahashi, Takahiro Omi, Kosuke Arima, Tatsuya Ishigaki

This paper presents a simple and cost-effective method for synthesizing data to train question-answering systems. For training, fine-tuning GPT models is a common practice in resource-rich languages like English, however, it becomes challenging for non-English languages due to the scarcity of sufficient question-answer (QA) pairs. Existing approaches use que

Junyu Gao, Xinhong Ma, Changsheng Xu

Despite the great progress of unsupervised domain adaptation (UDA) with the deep neural networks, current UDA models are opaque and cannot provide promising explanations, limiting their applications in the scenarios that require safe and controllable model decisions. At present, a surge of work focuses on designing deep interpretable methods with adequate da

Xin Lyu, Avishay Tal, Hongxun Wu, Junzhao Yang

In his breakthrough paper, Raz showed that any parity learning algorithm requires either quadratic memory or an exponential number of samples [FOCS'16, JACM'19]. A line of work that followed extended this result to a large class of learning problems. Until recently, all these results considered learning in the streaming model, where each sample is drawn inde

Haochen Li, Xin Zhou, Luu Anh Tuan, Chunyan Miao

Recently, contrastive learning has become a key component in fine-tuning code search models for software development efficiency and effectiveness. It pulls together positive code snippets while pushing negative samples away given search queries. Among contrastive learning, InfoNCE is the most widely used loss function due to its better performance. However,

Yun Ye, Yanjie Pan, Qually Jiang, Ming Lu

Over-fitting-based image compression requires weights compactness for compression and fast convergence for practical use, posing challenges for deep convolutional neural networks (CNNs) based methods. This paper presents a simple re-parameterization method to train CNNs with reduced weights storage and accelerated convergence. The convolution kernels are re-

Dake Chen, Haoyang Zhang, Hanbin Wang, Yunhao Huo

The large language model (LLM) based agents have demonstrated their capacity to automate and expedite software development processes. In this paper, we focus on game development and propose a multi-agent collaborative framework, dubbed GameGPT, to automate game development. While many studies have pinpointed hallucination as a primary roadblock for deploying

Xiaotie Deng, Dongchen Li, Hanyu Li

AI in Math deals with mathematics in a constructive manner so that reasoning becomes automated, less laborious, and less error-prone. For algorithms, the question becomes how to automate analyses for specific problems. For the first time, this work provides an automatic method for approximation analysis on a well-studied problem in theoretical computer scien

Varun Dolia, Halleh B. Balch, Sahil Dagli, Sajjad Abdollahramezani

Metasurfaces provide a versatile and compact approach to free-space optical manipulation and wavefront shaping. Comprised of arrays of judiciously-arranged dipolar resonators, metasurfaces precisely control the amplitude, polarization, and phase of light, with applications spanning imaging, sensing, modulation, and computing. Three crucial performance metric

Miaomiao Yang, Changwei Yao, Shijin Yan

Age estimation technology is a part of facial recognition and has been applied to identity authentication. This technology achieves the development and application of a juvenile anti-addiction system by authenticating users in the game. Convolutional Neural Network (CNN) and Transformer algorithms are widely used in this application scenario. However, these

Teddy Lazebnik, Orr Spiegel

Zoonotic disease transmission between animals and humans is a growing risk and the agricultural context acts as a likely point of transition, with individual heterogeneity acting as an important contributor. Thus, understanding the dynamics of disease spread in the wildlife-livestock interface is crucial for mitigating these risks of transmission. Specifical

Qingliang Fan, Zijian Guo, Ziwei Mei, Cun-Hui Zhang

Nonlinearity and endogeneity are prevalent challenges in causal analysis using observational data. This paper proposes an inference procedure for a nonlinear and endogenous marginal effect function, defined as the derivative of the nonparametric treatment function, with a primary focus on an additive model that includes high-dimensional covariates. Using the

Sioni Summers, Ioannis Bestintzanos, Giovanni Petrucciani

The Phase-2 Upgrade of the CMS Level-1 Trigger (L1T) will reconstruct particles using the Particle Flow algorithm, connecting information from the tracker, muon, and calorimeter detectors, and enabling fine-grained reconstruction of high level physics objects like jets. We have developed a jet reconstruction algorithm using a cone centred on an energetic see

Yiqiang Yi, Xu Wan, Yatao Bian, Le Ou-Yang

Predicting the docking between proteins and ligands is a crucial and challenging task for drug discovery. However, traditional docking methods mainly rely on scoring functions, and deep learning-based docking approaches usually neglect the 3D spatial information of proteins and ligands, as well as the graph-level features of ligands, which limits their perfo

Soheil Memariansorkhabi

Let $X=\Gamma \backslash \mathbb{B}^{n} $ be an $n$-dimensional complex ball quotient by a torsion-free non-uniform lattice $\Gamma$ whose parabolic subgroups are unipotent. We prove that the volumes of subvarieties of $X$ are controlled by the systole of $X,$ which is the length of the shortest closed geodesic of $X$. There are a number of arithmetic and ge

Handan Borluk, Gulcin M. Muslu, Fábio Natali

In this paper, we determine the spectral instability of periodic odd waves for the defocusing fractional cubic nonlinear Schr\"odinger equation. Our approach is based on periodic perturbations that have the same period as the standing wave solution, and we construct real periodic waves by minimizing a suitable constrained problem. The odd solution generates

Siyao Zhu, Xiaojun Cui, Tianqi Shi

In this paper, we propose a Cauchy type problem to the timelike Lorentzian eikonal equation on a globally hyperbolic space-time. For this equation, as the value of the solution on a Cauchy surface is known, we prove the existence of viscosity solutions on the past set (future set) of the Cauchy surface. Furthermore, when the time orientation of viscosity sol

Amrik Singh, Ravi Srivastava, Bibhas Adhikari, Sandeep Kumar Yadav

In this paper, we extend our earlier proposal of corona product of signed graphs into generalized corona product of signed graphs inspired by the generalized corona product of unsigned graphs. Then we study structural balance and spectral properties of these graphs. Utilizing the notion of coronal of a graph, we determine computable formulae of characteristi

Shreyas Havaldar, Navodita Sharma, Shubhi Sareen, Karthikeyan Shanmugam

Learning from Label Proportions (LLP) is a learning problem where only aggregate level labels are available for groups of instances, called bags, during training, and the aim is to get the best performance at the instance-level on the test data. This setting arises in domains like advertising and medicine due to privacy considerations. We propose a novel alg

Teemu Härkönen, Erik M. Vartiainen, Lasse Lensu, Matthew T. Moores

We propose an approach utilizing gamma-distributed random variables, coupled with log-Gaussian modeling, to generate synthetic datasets suitable for training neural networks. This addresses the challenge of limited real observations in various applications. We apply this methodology to both Raman and coherent anti-Stokes Raman scattering (CARS) spectra, usin

Xin He, Yinchang Ma, Chenhui Zhang, Aiping Fu

The exploration of ferroelectric phase transitions enables an in-depth understanding of ferroelectric switching and promising applications in information storage. However, controllably tuning the dynamics of ferroelectric phase transitions remains challenging owing to inaccessible hidden phases. Here, using protonic gating technology, we create a series of m

Daniel Alvestad, Alexander Rothkopf, Dénes Sexty

We present a simulation strategy for the real-time dynamics of quantum fields, inspired by reinforcement learning. It builds on the complex Langevin approach, which it amends with system specific prior information, a necessary prerequisite to overcome this exceptionally severe sign problem. The optimization process underlying our machine learning approach is

F. Tonolo, L. Bizzocchi, V. M. Rivilla, F. Lique

We report the derivation of rate coefficients for the rotational (de-)excitation of PO$^+$ induced by collisions with H$_2$. The calculations were performed on a four-dimensional potential energy surface, obtained on top of highly accurate $ab$ $initio$ energy points. Preliminary tests pointed out the low influence of the coupling between $j=0$ and the highe

Jianchao Lu, Yuzhe Tian, Yang Zhang, Quan Z. Sheng

Brain--computer interfaces are groundbreaking technology whereby brain signals are used to control external devices. Despite some advances in recent years, electroencephalogram (EEG)-based motor-imagery tasks face challenges, such as amplitude and phase variability and complex spatial correlations, with a need for smaller models and faster inference. In this

Shang Xu

This paper explores the sheaves with the action of a lie algebra and computes their cohomology in a new category. Then in the following sections, We try to generalize a classical result in [GM, Ch. IV] about exterior algebra. We add the action of $\mathfrak{g}_{0}$ and prove that there is still a faithful functor.

Ivan Lee, Nan Jiang, Taylor Berg-Kirkpatrick

What is the relationship between model architecture and the ability to perform in-context learning? In this empirical study, we take the first steps toward answering this question. We evaluate thirteen model architectures capable of causal language modeling across a suite of synthetic in-context learning tasks. These selected architectures represent a broad

Yueh-Lin Chiang

In this paper, we develop a new scaling method to study spectral and Bergman kernels for the k-th tensor power of a line bundle over a complex manifold under local spectral gap condition. In particular, we establish a simple proof of the pointwise asymptotics of spectral and Bergman kernels. As a new result, in the function case, we obtain the leading term o

Hans-Peter Schröcker, Zbyněk Šìr

We solve the so far open problem of constructing all spatial rational curves with rational arc length functions. More precisely, we present three different methods for this construction. The first method adapts a recent approach of (Kalkan et al. 2022) to rational PH curves and requires solving a modestly sized system of linear equations. The second construc

Sardor Murodov, Javlon Rayimbaev, Bobomurat Ahmedov, Abdullo Hakimov

Investigations of electromagnetic interactions between test-charged and magnetized particles are important in the dynamics of the particles in strong gravitational fields around black holes. Here, we study the dynamics of a particle having an electric charge and a magnetic dipole moment in the spacetime of a Schwarzschild black hole in modified gravity (MOG)

Iman Askari, Ali Vaziri, Xuemin Tu, Shen Zeng

Model predictive control (MPC) has proven useful in enabling safe and optimal motion planning for autonomous vehicles. In this paper, we investigate how to achieve MPC-based motion planning when a neural state-space model represents the vehicle dynamics. As the neural state-space model will lead to highly complex, nonlinear and nonconvex optimization landsca

Ziyang Song, Ruijie Zhu, Chuxin Wang, Jiacheng Deng

Self-supervised monocular depth estimation holds significant importance in the fields of autonomous driving and robotics. However, existing methods are typically trained and tested on standard datasets, overlooking the impact of various adverse conditions prevalent in real-world applications, such as rainy days. As a result, it is commonly observed that thes

Ulisse Mini, Peli Grietzer, Mrinank Sharma, Austin Meek

To understand the goals and goal representations of AI systems, we carefully study a pretrained reinforcement learning policy that solves mazes by navigating to a range of target squares. We find this network pursues multiple context-dependent goals, and we further identify circuits within the network that correspond to one of these goals. In particular, we

Yixuan Zhou, Xuanhan Wang, Xing Xu, Lei Zhao

High-resolution representation is necessary for human pose estimation to achieve high performance, and the ensuing problem is high computational complexity. In particular, predominant pose estimation methods estimate human joints by 2D single-peak heatmaps. Each 2D heatmap can be horizontally and vertically projected to and reconstructed by a pair of 1D heat

Jing Liu, Ruihao Gong, Xiuying Wei, Zhiwei Dong

Large Language Models (LLMs) excel in NLP, but their demands hinder their widespread deployment. While Quantization-Aware Training (QAT) offers a solution, its extensive training costs make Post-Training Quantization (PTQ) a more practical approach for LLMs. In existing studies, activation outliers in particular channels are identified as the bottleneck to P

Xiaoyang Song, Wenbo Sun, Maher Nouiehed, Raed Al Kontar

Current techniques for Out-of-Distribution (OoD) detection predominantly rely on quantifying predictive uncertainty and incorporating model regularization during the training phase, using either real or synthetic OoD samples. However, methods that utilize real OoD samples lack exploration and are prone to overfit the OoD samples at hand. Whereas synthetic sa

Jinbo Song, Ruoran Huang, Xinyang Wang, Wei Huang

Industrial systems such as recommender systems and online advertising, have been widely equipped with multi-stage architectures, which are divided into several cascaded modules, including matching, pre-ranking, ranking and re-ranking. As a critical bridge between matching and ranking, existing pre-ranking approaches mainly endure sample selection bias (SSB)

Zihao Xu, Xuan Tang, Yufei Shi, Jianfeng Zhang

In continual learning, the learner learns multiple tasks in sequence, with data being acquired only once for each task. Catastrophic forgetting is a major challenge to continual learning. To reduce forgetting, some existing rehearsal-based methods use episodic memory to replay samples of previous tasks. However, in the process of knowledge integration when l

Carsten Eickhoff

An increasing number of CS researchers are employed in academic non-CS departments where publication output is measured in terms of journal impact factors. To foster recognition of publications in peer-reviewed CS conference proceedings, we analyzed more than 40,000 CS publications and computed journal impact factors for 88 top-ranking conferences across a r

Binghui Wu, Philipp Gysel, Dinil Mon Divakaran, Mohan Gurusamy

Recent research works have proposed machine learning models for classifying IoT devices connected to a network. However, there is still a practical challenge of not having all devices (and hence their traffic) available during the training of a model. This essentially means, during the operational phase, we need to classify new devices not seen in the traini

Jiarong Wei, Yancong Lin, Holger Caesar

Active learning strives to reduce the need for costly data annotation, by repeatedly querying an annotator to label the most informative samples from a pool of unlabeled data, and then training a model from these samples. We identify two problems with existing active learning methods for LiDAR semantic segmentation. First, they overlook the severe class imba

Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye

The fusion of human-centric design and artificial intelligence (AI) capabilities has opened up new possibilities for next-generation autonomous vehicles that go beyond transportation. These vehicles can dynamically interact with passengers and adapt to their preferences. This paper proposes a novel framework that leverages Large Language Models (LLMs) to enh

Nikiforos Mimikos-Stamatopoulos

We present a Mean Field Game approach to obtain the rate function for the empirical measure of interacting particles under McKean-Vlasov dynamics. Although the result is well known, our approach relies on PDE methods and provides another example of the well known connection between Mean Field Games and Large Deviations.

Jiaqi Li, Guilin Qi, Chuanyi Zhang, Yongrui Chen

Multimodal movie genre classification has always been regarded as a demanding multi-label classification task due to the diversity of multimodal data such as posters, plot summaries, trailers and metadata. Although existing works have made great progress in modeling and combining each modality, they still face three issues: 1) unutilized group relations in m

Armin Tabesh

Electromechanics is the field of studying the interaction between microwave resonators and mechanical oscillators. It has been an interesting topic in the recent decade due to its numerous potential applications in science and technology, including ground-state cooling of macroscopic objects, quantum sensing, quantum memory, and quantum transduction. This th

Artur Back de Luca, Kimon Fountoulakis, Shenghao Yang

The growing interest in machine learning problems over graphs with additional node information such as texts, images, or labels has popularized methods that require the costly operation of processing the entire graph. Yet, little effort has been made to the development of fast local methods (i.e. without accessing the entire graph) that extract useful inform

Timothy M. F. Hirsch, Nicolas P. Mauranyapin, Erick Romero, Tina Jin

Integrated acoustic circuits leverage guided acoustic waves for applications ranging from radio-frequency filters to quantum state transfer, biochemical sensing and nanomechanical computing. In many applications it is desirable to have a method for unidirectional acoustic wave emission. In this work we demonstrate directional emission in an integrated single

Ryunosuke Maeda, Tsuyoshi Inoue, Shu-ichiro Inutsuka

Since self-gravity is crucial in the structure formation of the universe, many hydrodynamics simulations with the effect of self-gravity have been conducted. The multigrid method is widely used as a solver for the Poisson equation of the self-gravity; however, the parallelization efficiency of the multigrid method becomes worse when we use a massively parall

Mojun Pan, Junde Liu, Famin Chen, Ji Wang

We present a detailed exposition of the design for time- and angle-resolved photoemission spectroscopy using a UV probe laser source that combines the nonlinear effects of \b{eta}-BaB2O4 and KBe2BO3F2 optical crystals. The photon energy of the probe laser can be switched between 6.0 and 7.2 eV, with the flexibility to operate each photon energy setting under

Yi Dai, Hao Lang, Kaisheng Zeng, Fei Huang

Out-of-distribution (OOD) detection is essential for reliable and trustworthy machine learning. Recent multi-modal OOD detection leverages textual information from in-distribution (ID) class names for visual OOD detection, yet it currently neglects the rich contextual information of ID classes. Large language models (LLMs) encode a wealth of world knowledge

Xingyue Liu, Jiahao Qi, Chen Chen, Kangcheng Bin

Owing to the capacity of performing full-time target search, cross-modality vehicle re-identification (Re-ID) based on unmanned aerial vehicle (UAV) is gaining more attention in both video surveillance and public security. However, this promising and innovative research has not been studied sufficiently due to the data inadequacy issue. Meanwhile, the cross-

Oliwia Kempinski, Marco T. Morazán

Students find their first course in Formal Languages and Automata Theory challenging. In addition to the development of formal arguments, most students struggle to understand nondeterministic computation models. In part, the struggle stems from the course exposing them for the first time to nondeterminism. Often, students find it difficult to understand why

Yiming Li, Xiaoxuan Luo, Yaxin Guo, Jiahuan Ren

Photonic spin-orbit (SO) coupling is an important physical mechanism leading to numerous interesting phenomena in the systems of microcavity photons and exciton-polaritons. We report the effect of SO coupling in a tunable open-access microcavity embedded with anisotropic active media. The SO coupling associated with the TE-TM splitting results in an emergent

Adam R. H. Stevens, Toby Brown, Benedikt Diemer, Annalisa Pillepich

It has been shown in previous publications that the TNG100 simulation quantitatively reproduces the observed reduction in each of the total atomic and total molecular hydrogen gas for galaxies within massive halos, i.e.~dense environments. In this Letter, we study how well TNG50 reproduces the resolved effects of a Virgo-like cluster environment on the gas s

Mustapha Azreg-Aïnou, Kauntey Acharya, Pankaj S. Joshi

The importance and significance of magnetic fields in the astrophysical scenario is well known. Many domains of astrophysical black hole physics such as polarized shadow image, high energy emitting processes and jet formation are dependent on the behavior of the magnetic fields in the vicinity of the compact objects. In light of this, we determine the master

Sai Huang, Yuting Chen, Jiashuo He, Shuo Chang

Automatic modulation classification (AMC) is to identify the modulation format of the received signal corrupted by the channel effects and noise. Most existing works focus on the impact of noise while relatively little attention has been paid to the impact of channel effects. However, the instability posed by multipath fading channels leads to significant pe

Shenyi Pan, Harry Joe

Vine pair-copula constructions exist for a mix of continuous and ordinal variables. In some steps, this can involve estimating a bivariate copula for a pair of mixed continuous-ordinal variables. To assess the adequacy of copula fits for such a pair, diagnostic and visualization methods based on normal score plots and conditional Q-Q plots are proposed. The

Namiko Matsumoto, Arya Mazumdar

In 1-bit compressed sensing, the aim is to estimate a $k$-sparse unit vector $x\in S^{n-1}$ within an $\epsilon$ error (in $\ell_2$) from minimal number of linear measurements that are quantized to just their signs, i.e., from measurements of the form $y = \mathrm{Sign}(\langle a, x\rangle).$ In this paper, we study a noisy version where a fraction of the me

Siyuan Brandon Loh, Aravind Sesagiri Raamkumar

Large Language Models (LLMs) have demonstrated remarkable performance across various information-seeking and reasoning tasks. These computational systems drive state-of-the-art dialogue systems, such as ChatGPT and Bard. They also carry substantial promise in meeting the growing demands of mental health care, albeit relatively unexplored. As such, this study

T. T. Zhao, Rui Li, C. S. Liu

Motivated by the experiment of electrostatic conveyor belt for indirect excitons [A. G. Winbow, \textit{et al.}, Phys. Rev. Lett. \textbf{106}, 196806 (2011)], we study the exciton patterns for understanding the exciton dynamics. By analyzing the exciton diffusion, we find that the patterns mainly come from the photoluminescence of two kinds of excitons. The

Jihye Choi, Shruti Tople, Varun Chandrasekaran, Somesh Jha

Membership Inference Attacks (MIAs) aim to identify specific data samples within the private training dataset of machine learning models, leading to serious privacy violations and other sophisticated threats. Many practical black-box MIAs require query access to the data distribution (the same distribution where the private data is drawn) to train shadow mod

Tatyana Barron, Michael Francis

The $b$-calculus of Melrose is a tool for studying structures on a smooth manifold with a first order degeneracy at a given hypersurface. In this framework, Mendoza defined complex $b$-manifolds. In the spirit of work of Scott, we extend Mendoza's definition to the case of higher-order degeneracies, introducing the notion of a complex $b^k$-manifold for $k$

Wei Ao, Vishnu Naresh Boddeti

Secure inference of deep convolutional neural networks (CNNs) under RNS-CKKS involves polynomial approximation of unsupported non-linear activation functions. However, existing approaches have three main limitations: 1) Inflexibility: The polynomial approximation and associated homomorphic evaluation architecture are customized manually for each CNN architec

Nozima Murodova, Hyungjoon Koo

An executable binary typically contains a large number of machine instructions. Although the statistics of popular instructions is well known, the distribution of non-popular instructions has been relatively under explored. Our finding shows that an arbitrary group of binaries com es with both i) a similar distribution of common machine instructions, and ii)

Ping-Hui Mou, Qing-Quan Jiang, Ke-Jian He, Guo-Ping Li

By considering the negative cosmological constant {\Lambda} as a thermodynamic pressure, we study the thermodynamics and phase transitions of the D-dimensional dyonic AdS black holes (BHs) with quasitopological electromagnetism in Einstein-Gauss-Bonnet (EGB) gravity. The results indicate that the small/large BH phase transition that is similar to the van der

Pandeng Li, Hongtao Xie, Jiannan Ge, Lei Zhang

Unsupervised video hashing usually optimizes binary codes by learning to reconstruct input videos. Such reconstruction constraint spends much effort on frame-level temporal context changes without focusing on video-level global semantics that are more useful for retrieval. Hence, we address this problem by decomposing video information into reconstruction-de

Aparna Elangovan, Jiayuan He, Yuan Li, Karin Verspoor

BERT-based models have had strong performance on leaderboards, yet have been demonstrably worse in real-world settings requiring generalization. Limited quantities of training data is considered a key impediment to achieving generalizability in machine learning. In this paper, we examine the impact of training data quality, not quantity, on a model's general

Jaret J. Vasquez-Lozano, Shuo Li, Andrew D. Greentree

Optical superresolution microscopy is an important field, where nonlinear optical processes or prior information is used to defeat the classical diffraction limit of light. Quantum correlation microscopy uses photon arrival statistics from single photon emitters to aid in the determination of properties including the number of emitters and their relative bri

Vinh Van Duong, Thuc Nguyen Huu, Jonghoon Yim, Byeungwoo Jeon

Plenoptic 2.0 cameras enable high-resolution light field capture by incorporating focused optical designs that differ fundamentally from traditional plenoptic 1.0 systems. These structural differences produce distinct motion characteristics that challenge existing motion estimation (ME) algorithms. In this paper, we first conduct a comprehensive statistical

Sven Hirsch, Jonathan J. Zhu

We prove uniqueness of tangent cones for forced mean curvature flow, at both closed self-shrinkers and round cylindrical self-shrinkers, in any codimension. The corresponding results for mean curvature flow in Euclidean space were proven by Schulze and Colding-Minicozzi respectively. We adapt their methods to handle the presence of the forcing term, which va

Vishnu Iyer, Siddhartha Jain, Robin Kothari, Matt Kovacs-Deak

We study a natural complexity measure of Boolean functions known as the rational degree. Denoted $\textrm{rdeg}(f)$, it is the minimal degree of a rational function that is equal to $f$ on the Boolean hypercube. For total functions $f$, it is conjectured that $\textrm{rdeg}(f)$ is polynomially related to the Fourier degree of $f$, $\textrm{deg}(f)$. Towards

Yu Lu, Guo-Hui Hu

Diffusion of Brownian particles in the tilted periodic potential, usually referred to the washboard potential (WBP), is a well-known model to describe physical systems out of equilibrium. Considering that the biological medium is flexible and thermally fluctuating, a new model, namely the soft matter potential (SMP), is proposed to describe the biological me

Zeyu Cai, Can Zhang, Xunhao Chen, Shanghuan Liu

Coded Aperture Snapshot Spectral Imaging (CASSI) system has great advantages over traditional methods in dynamically acquiring Hyper-Spectral Image (HSI), but there are the following problems. 1) Traditional mask relies on random patterns or analytical design, both of which limit the performance improvement of CASSI. 2) Existing high-quality reconstruction a

Dinesh Kumar Reddy G, Mayank Verma, Ashoke De

Wind energy has emerged as a viable alternative to fossil fuels, with vertical axis wind turbines (VAWTs) gaining popularity due to their efficiency and adaptability. Combining the Actuator Line Method (ALM) with Large Eddy Simulation (LES) enables accurate performance evaluations, facilitating the design and optimization of wind turbines. The present study

Yite Wang, Jiahao Su, Hanlin Lu, Cong Xie

Scaling of deep neural networks, especially Transformers, is pivotal for their surging performance and has further led to the emergence of sophisticated reasoning capabilities in foundation models. Such scaling generally requires training large models from scratch with random initialization, failing to leverage the knowledge acquired by their smaller counter

Tinghui Ouyang, Isao Echizen, Yoshiki Seo

Data outside the problem domain poses significant threats to the security of AI-based intelligent systems. Aiming to investigate the data domain and out-of-distribution (OOD) data in AI quality management (AIQM) study, this paper proposes to use deep learning techniques for feature representation and develop a novel statistical measure for OOD detection. Fir

Chen Zhang, Wanjuan Su, Qingshan Xu, Wenbing Tao

Recently, learning multi-view neural surface reconstruction with the supervision of point clouds or depth maps has been a promising way. However, due to the underutilization of prior information, current methods still struggle with the challenges of limited accuracy and excessive time complexity. In addition, prior data perturbation is also an important but

Lapo Frati, Neil Traft, Jeff Clune, Nick Cheney

This work identifies a simple pre-training mechanism that leads to representations exhibiting better continual and transfer learning. This mechanism -- the repeated resetting of weights in the last layer, which we nickname "zapping" -- was originally designed for a meta-continual-learning procedure, yet we show it is surprisingly applicable in many settings

Zhan Chen, Yidan Zhang, Xiyu Qi, Yongqiang Mao

Height estimation has long been a pivotal topic within measurement and remote sensing disciplines, proving critical for endeavours such as 3D urban modelling, MR and autonomous driving. Traditional methods utilise stereo matching or multisensor fusion, both well-established techniques that typically necessitate multiple images from varying perspectives and a

Chenxi Zhu

Reconfigurable intelligent surface (RIS) is an important candidate technology for 6G. We provide an analysis of RIS-assisted MIMO communication in sparse channel typically found in the mmW or THz range. By exploring the sparse property, we maximize the capacity in the singular space of the channel and developed efficient algorithms for SU-MIMO or DL MU-MIMO.

Yoshimasa Hidaka, Kiyoharu Kawana

We propose an effective field theory for branes with higher-form symmetry as a generalization of ordinary Landau theory, which is an extension of the previous work by Iqbal and McGreevy for one-dimensional objects to an effective theory for $p$-dimensional objects. In the case of a $p$-form symmetry, the fundamental field $\psi[C_p^{}]$ is a functional of $p

Soyun Kim, Jeonghoon Hong, Kenji Watanabe, Takashi Taniguchi

We report the transport properties of ${\alpha}$-RuCl$_3$/bilayer graphene heterostructures, where carrier doping is induced by a work function difference, resulting in distinct electron and hole populations in ${\alpha}$-RuCl3 and bilayer graphene, respectively. Through a comprehensive analysis of multi-channel transport signatures, including Hall measureme

Chen Zhao, Kuan-Jui Su, Chong Wu, Xuewei Cao

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. Method: In this study, we propose a novel

Guoxin Zheng, Yuan Zhu, Kuan-Wen Chen, Byungmin Kang

In metals, electrons in a magnetic field undergo cyclotron motion, leading to oscillations in physical properties called quantum oscillations. This phenomenon has never been seen in a robust insulator because there are no mobile electrons. We report the first exception to this rule. We study a Mott insulator on a kagome lattice which does not order magnetica

Yuhang Lei, Wen Zhao, Liang cui, Xiaoyin Li

For a HOM interferometer with two independent incident pulses, the interference pattern can be affected by adding a dispersion medium on one of the incident directions, but there hasn't been a method to reconstruct the phase constant of the medium from the interference pattern. To solve it, we adapted two phase retrieval algorithms and used them to recover t

Wensheng Lin, Yuna Yan, Lixin Li, Zhu Han

This letter proposes a novel relaying framework, semantic-forward (SF), for cooperative communications towards the sixth-generation (6G) wireless networks. The SF relay extracts and transmits the semantic features, which reduces forwarding payload, and also improves the network robustness against intra-link errors. Based on the theoretical basis for cooperat

Ianto Cannon, Stefano Olivieri, Marco E. Rosti

We perform fully coupled numerical simulations using immersed boundary methods of finite-size spheres and fibres suspended in a turbulent flow for a range of Taylor Reynolds numbers $12.8<Re_\lambda<442$ and solid mass fractions $0\leq M\leq1$. Both spheres and fibres reduce the turbulence intensity with respect to the single-phase flow at all Reynolds numbe

Fu Luo, Xi Lin, Fei Liu, Qingfu Zhang

Neural combinatorial optimization (NCO) is a promising learning-based approach for solving challenging combinatorial optimization problems without specialized algorithm design by experts. However, most constructive NCO methods cannot solve problems with large-scale instance sizes, which significantly diminishes their usefulness for real-world applications. I

Yizhen Zheng, Huan Yee Koh, Jiaxin Ju, Anh T. N. Nguyen

Large language models are a form of artificial intelligence systems whose primary knowledge consists of the statistical patterns, semantic relationships, and syntactical structures of language1. Despite their limited forms of "knowledge", these systems are adept at numerous complex tasks including creative writing, storytelling, translation, question-answeri

Baoming Shi, Yucen Han, Apala Majumdar, Lei Zhang

We study nematic configurations within three-dimensional (3D) cuboids, with planar degenerate boundary conditions on the cuboid faces, in the Landau-de Gennes framework. There are two geometry-dependent variables: the edge length of the square cross-section, $\lambda$, and the parameter $h$, which is a measure of the cuboid height. Theoretically, we prove th

Hwajong Lee, Chan Kim, Seong-Woo Kim

A flow control system is a critical concept for increasing the production capacity of manufacturing systems. To solve the scheduling optimization problem related to the flow control with the aim of improving productivity, existing methods depend on a heuristic design by domain human experts. Therefore, the methods require correction, monitoring, and verifica

Zohair Shafi, Benjamin A. Miller, Ayan Chatterjee, Tina Eliassi-Rad

Recent advances in machine learning (ML) have shown promise in aiding and accelerating classical combinatorial optimization algorithms. ML-based speed ups that aim to learn in an end to end manner (i.e., directly output the solution) tend to trade off run time with solution quality. Therefore, solutions that are able to accelerate existing solvers while main

Zohair Shafi, Benjamin A. Miller, Tina Eliassi-Rad, Rajmonda S. Caceres

Machine learning (ML) approaches are increasingly being used to accelerate combinatorial optimization (CO) problems. We investigate the Set Cover Problem (SCP) and propose Graph-SCP, a graph neural network method that augments existing optimization solvers by learning to identify a smaller sub-problem that contains the solution space. Graph-SCP uses both sup

Anffany Chen, Joseph Maciejko, Igor Boettcher

We study Anderson localization in disordered tight-binding models on hyperbolic lattices. Such lattices are geometries intermediate between ordinary two-dimensional crystalline lattices, which localize at infinitesimal disorder, and Bethe lattices, which localize at strong disorder. Using state-of-the-art computational group theory methods to create large sy

Yang Cai, Ziyun Chen, Jinzhao Wu

We study revenue maximization in multi-item auctions, where bidders have subadditive valuations over independent items. Providing a simple mechanism that is approximately revenue-optimal in this setting is a major open problem in mechanism design. In this paper, we present the first \emph{simple mechanism} whose revenue is at least a \emph{constant fraction}

Quy Thuong Lê, Takehiko Yasuda

We show basic properties of higher Jacobian matrices and higher Jacobian ideals for functions and apply it to obtain two results concerning singularities of functions. Firstly, we prove that a higher Nash blowup algebra is invariant under contact equivalences, which was recently conjectured by Hussain, Ma, Yau and Zuo. Secondly, we obtain an analogue of a re

Sotirios Konstantakos, Despina Ioanna Chalkiadaki, Ioannis Mademlis, Adamantia Anna Rebolledo Chrysochoou

Automated visual firearms classification from RGB images is an important real-world task with applications in public space security, intelligence gathering and law enforcement investigations. When applied to images massively crawled from the World Wide Web (including social media and dark Web sites), it can serve as an important component of systems that att

Hyun Joong Kim, Yong Hyun Song, Jip Kim

While peer-to-peer energy trading has the potential to harness the capabilities of small-scale energy resources, a peer-matching process often overlooks power grid conditions, yielding increased losses, line congestion, and voltage problems. This imposes a great challenge on the distribution system operator (DSO), which can eventually limit peer-to-peer ener

Michael Lingzhi Li, Kosuke Imai

Across a wide array of disciplines, many researchers use machine learning (ML) algorithms to identify a subgroup of individuals who are likely to benefit from a treatment the most (``exceptional responders'') or those who are harmed by it. A common approach to this subgroup identification problem consists of two steps. First, researchers estimate the conditi

Xianghao Kong, Ollie Liu, Han Li, Dani Yogatama

Denoising diffusion models enable conditional generation and density modeling of complex relationships like images and text. However, the nature of the learned relationships is opaque making it difficult to understand precisely what relationships between words and parts of an image are captured, or to predict the effect of an intervention. We illuminate the

Jiajie Luo, Gregory Henselman-Petrusek

The study of persistent homology has contributed new insights and perspectives into a variety of interesting problems in science and engineering. Work in this domain relies on the result that any finitely-indexed persistence module of finite-dimensional vector spaces admits an interval decomposition -- that is, a decomposition as a direct sum of simpler comp

Nazanin Nezami, Hadis Anahideh

Surrogate Optimization (SO) algorithms have shown promise for optimizing expensive black-box functions. However, their performance is heavily influenced by hyperparameters related to sampling and surrogate fitting, which poses a challenge to their widespread adoption. We investigate the impact of hyperparameters on various SO algorithms and propose a Hyperpa