October 2023 arXiv papers — page 146

Sumanth Varambally, Yi-An Ma, Rose Yu

Discovering causal relationships from time series data is significant in fields such as finance, climate science, and neuroscience. However, contemporary techniques rely on the simplifying assumption that data originates from the same causal model, while in practice, data is heterogeneous and can stem from different causal models. In this work, we relax this

Song Wen, Guian Fang, Renrui Zhang, Peng Gao

Recent advancements in text-to-image models, particularly diffusion models, have shown significant promise. However, compositional text-to-image models frequently encounter difficulties in generating high-quality images that accurately align with input texts describing multiple objects, variable attributes, and intricate spatial relationships. To address thi

Laura Plein, Tegawendé F. Bissyandé

Bugs are notoriously challenging: they slow down software users and result in time-consuming investigations for developers. These challenges are exacerbated when bugs must be reported in natural language by users. Indeed, we lack reliable tools to automatically address reported bugs (i.e., enabling their analysis, reproduction, and bug fixing). With the rece

Yuchen Yang

Situated in the intersection of audiovisual archives, computational methods, and immersive interactions, this work probes the increasingly important accessibility issues from a two-fold approach. Firstly, the work proposes an ontological data model to handle complex descriptors (metadata, feature vectors, etc.) with regard to user interactions. Secondly, thi

Chakkrit Tantithamthavorn, Norman Chen

Teaching software testing presents difficulties due to its abstract and conceptual nature. The lack of tangible outcomes and limited emphasis on hands-on experience further compound the challenge, often leading to difficulties in comprehension for students. This can result in waning engagement and diminishing motivation over time. In this paper, we introduce

A K M Amanat Ullah, William Delamare, Khalad Hasan

Large curved displays have emerged as a powerful platform for collaboration, data visualization, and entertainment. These displays provide highly immersive experiences, a wider field of view, and higher satisfaction levels. Yet, large curved displays are not commonly available due to their high costs. With the recent advancement of Head Mounted Displays (HMD

Yingyan Zeng, Xiaoyu Chen, Ran Jin

It is challenging but important to save annotation efforts in streaming data acquisition to maintain data quality for supervised learning base learners. We propose an ensemble active learning method to actively acquire samples for annotation by contextual bandits, which is will enforce the exploration-exploitation balance and leading to improved AI modeling

Cesar Laguna

In order for telescopes to obtain good and precise images they need to see through atmospheric turbulence. To accomplish this and compensate for atmospheric turbulence we use Adaptive Optics technologies. In this thesis we analyze the variations in phase delays across phase plates which simulate atmospheric turbulence in order to characterize them and determ

Juhi Jang, Pranava Chaitanya Jayanti, Igor Kukavica

We examine a micro-scale model of superfluidity derived by Pitaevskii in 1959 which describes the interacting dynamics between superfluid He-4 and its normal fluid phase. This system consists of the nonlinear Schr\"odinger equation and the incompressible, inhomogeneous Navier-Stokes equations, coupled to each other via a bidirectional nonlinear relaxation me

Hanul Kim, Siyoung Q. Choi

When subjected to an external stress that exceeds the yield stress ($\sigma_\mathrm{Y}$), yield stress fluids (YSFs) undergo a solid-to-liquid transition. Despite the extensive studies, there has been limited attention to the process of liquid-to-solid transition. This work examines the solidification of YSFs through capillary imbibition, easily observed in

M. Sharif, Saba Naz

This paper examines the structure of a gravitationally vacuum star (also known as gravastar) in the background of ${f}(\Re,\mathcal{T}^{2})$ theory. This hypothetical object can be treated as a substitute of a black hole, with three regions: (i) the internal region, (ii) the intrinsic shell and (iii) the outer region. We examine these geometries using Finch-

Carson Stark, Bohkyung Chun, Casey Charleston, Varsha Ravi

This work introduces a robotics platform which embeds a conversational AI agent in an embodied system for natural language understanding and intelligent decision-making for service tasks; integrating task planning and human-like conversation. The agent is derived from a large language model, which has learned from a vast corpus of general knowledge. In addit

Y. Onika Laxmi, Ningthoujam Media, T. Ibungochouba Singh

In this paper, the modified Hawking radiation and the P-v criticality of Reissner-Nordstr{\"o}m-de Sitter (RNdS) black hole are investigated by using Dirac equation with Lorentz violation theory in curved space time. Taking cosmological constant as the thermodynamic pressure and its conjugate quantity as thermodynamic volume, the analogy between the RNdS bla

Shuaichen Chang, Eric Fosler-Lussier

Large language models (LLMs) with in-context learning have demonstrated impressive generalization capabilities in the cross-domain text-to-SQL task, without the use of in-domain annotations. However, incorporating in-domain demonstration examples has been found to greatly enhance LLMs' performance. In this paper, we delve into the key factors within in-domai

Zhongtian Chen, Edmund Lau, Jake Mendel, Susan Wei

We investigate phase transitions in a Toy Model of Superposition (TMS) using Singular Learning Theory (SLT). We derive a closed formula for the theoretical loss and, in the case of two hidden dimensions, discover that regular $k$-gons are critical points. We present supporting theory indicating that the local learning coefficient (a geometric invariant) of t

Nguyen Khoi Tran, Samodha Pallewatta, M. Ali Babar

With the rapid rise in Software Supply Chain (SSC) attacks, organisations need thorough and trustworthy visibility over the entire SSC of their software inventory to detect risks early and identify compromised assets rapidly in the event of an SSC attack. One way to achieve such visibility is through SSC metadata, machine-readable and authenticated documents

Wen-Long Zhang, Xiu-Juan Li, Yu-Peng Yang, Shuang-Xi Yi

As one class of the most important objects in the universe, magnetars can produce a lot of different frequency bursts including X-ray bursts. In \cite{2022ApJS..260...24C}, 75 X-ray bursts produced by magnetar SGR J1935+2154 during an active period in 2020 are published, including the duration and net photon counts of each burst, and waiting time based on th

Gabin An, Juyeon Yoon, Thomas Bach, Jingun Hong

We report our experience of using failure symptoms, such as error messages or stack traces, to identify flaky test failures in a Continuous Integration (CI) pipeline for a large industrial software system, SAP HANA. Although failure symptoms are commonly used to identify similar failures, they have not previously been employed to detect flaky test failures.

Nour Khoudari, Sulaiman Almatrudi, Rabie Ramadan, Joy Carpio

The energy demand of vehicles, particularly in unsteady drive cycles, is affected by complex dynamics internal to the engine and other powertrain components. Yet, in many applications, particularly macroscopic traffic flow modeling and optimization, structurally simple approximations to the complex vehicle dynamics are needed that nevertheless reproduce the

A K M Amanat Ullah, William Delamare, Khalad Hasan

Large curved displays inside Virtual Reality environments are becoming popular for visualizing high-resolution content during analytical tasks, gaming or entertainment. Prior research showed that such displays provide a wide field of view and offer users a high level of immersion. However, little is known about users' performance (e.g., pointing speed and ac

Gaia Collaboration, A. Krone-Martins, C. Ducourant, L. Galluccio

Context. Strongly lensed quasars are fundamental sources for cosmology. The Gaia space mission covers the entire sky with the unprecedented resolution of $0.18$" in the optical, making it an ideal instrument to search for gravitational lenses down to the limiting magnitude of 21. Nevertheless, the previous Gaia Data Releases are known to be incomplete for sm

Xu Chao

The Hubble Constant observed at high redshift and low redshift are inconsistent, representing one of the urgent issues to be resolved in the field of cosmology. The discovery of gravitational waves opens a new window for addressing this problem. For instance, the GW170817 event, through the coordinated observation of electromagnetic and gravitational wave si

Amir Sabzi, Rut Vora, Swati Goswami, Margo Seltzer

The widespread adoption of encryption in network protocols has significantly improved the overall security of many Internet applications. However, these protocols cannot prevent network side-channel leaks -- leaks of sensitive information through the sizes and timing of network packets. We present NetShaper, a system that mitigates such leaks based on the pr

Kiwan Park, Yudong Luo, Toshitaka Kajino

We investigate the impact of magnetic fields on the potential barrier between two interacting nuclei. We addressed this by solving the Boltzmann equation and Maxwell's theory in the presence of a magnetic field, resulting in the determination of magnetized permittivity. Additionally, we derived the magnetized Debye potential, which combines the conventional

Lin Liu, Xinxin Fan, Chulong Zhang, Jingjing Dai

Multimodal medical image fusion plays an instrumental role in several areas of medical image processing, particularly in disease recognition and tumor detection. Traditional fusion methods tend to process each modality independently before combining the features and reconstructing the fusion image. However, this approach often neglects the fundamental common

Tian Yan, Xueyang Li, Sifat Ut Taki, Saeid Mehrdad

Gem5, an open-source, flexible, and cost-effective simulator, is widely recognized and utilized in both academic and industry fields for hardware simulation. However, the typically time-consuming nature of simulating programs on Gem5 underscores the need for a predictive model that can estimate simulation time. As of now, no such dataset or model exists. In

Shyam Narayanan

We provide optimal lower bounds for two well-known parameter estimation (also known as statistical estimation) tasks in high dimensions with approximate differential privacy. First, we prove that for any $\alpha \le O(1)$, estimating the covariance of a Gaussian up to spectral error $\alpha$ requires $\tilde{\Omega}\left(\frac{d^{3/2}}{\alpha \varepsilon} +

Richard Ehrenborg, Gábor Hetyei, Margaret Readdy

We study two classes of permutations intimately related to the visual proof of Spitzer's lemma and Huq's generalization of the Chung-Feller theorem. Both classes of permutations are counted by the Fuss-Catalan numbers. The study of one class leads to a generalization of results of Flajolet from continued fractions to continuants. The study of the other class

Die Gan, Siyu Xie, Zhixin Liu, Jinhu Lv

In this paper, we consider the distributed filtering problem over sensor networks such that all sensors cooperatively track unknown time-varying parameters by using local information. A distributed forgetting factor least squares (FFLS) algorithm is proposed by minimizing a local cost function formulated as a linear combination of accumulative estimation err

HyeAnn Lee, Donghwan Lee

The goal of this paper is to propose a new Q-learning algorithm with a dummy adversarial player, which is called dummy adversarial Q-learning (DAQ), that can effectively regulate the overestimation bias in standard Q-learning. With the dummy player, the learning can be formulated as a two-player zero-sum game. The proposed DAQ unifies several Q-learning vari

Zhiqing Wei, Yueyue Liang, Zeyang Meng, Zhiyong Feng

Neighbor discovery (ND) is a key step in wireless ad hoc network, which directly affects the efficiency of wireless networking. Improving the speed of ND has always been the goal of ND algorithms. The classical ND algorithms lose packets due to the collision of multiple packets, which greatly affects the speed of the ND algorithms. Traditional methods detect

Dorian Goldfeld, Eric Stade, Michael Woodbury

This paper presents a very simple explicit description of Langlands Eisenstein series for ${\rm SL}(n,\mathbb Z)$. The functional equations of these Eisenstein series are heuristically derived from the functional equations of certain divisor sums and certain Whittaker functions that appear in the Fourier coefficients of the Eisenstein series. We conjecture t

Min Ren, Muchan Tao, Xuecai Hu, Xiaotong Liu

Depression, a highly prevalent mental illness, affects over 280 million individuals worldwide. Early detection and timely intervention are crucial for promoting remission, preventing relapse, and alleviating the emotional and financial burdens associated with depression. However, patients with depression often go undiagnosed in the primary care setting. Unli

Zhikang Dong, Bin Chen, Xiulong Liu, Pawel Polak

Music recommendation for videos attracts growing interest in multi-modal research. However, existing systems focus primarily on content compatibility, often ignoring the users' preferences. Their inability to interact with users for further refinements or to provide explanations leads to a less satisfying experience. We address these issues with MuseChat, a

Zekun Yang, Jiaxi Liu, Hongyue Duyang, Wanlei Guo

The directionality information of incoming neutrinos is essential to atmospheric neutrino oscillation analysis since it is directly related to the oscillation baseline length. Large homogeneous liquid scintillator detectors, while offering excellent energy resolution, are traditionally very limited in their capabilities of measuring event directionality. In

Zhizheng Pan, Xianzhong Zheng, Xu Kong

To understand in what mass regime star-forming galaxies (SFGs) build up central mass concentration most actively, we present a study on the luminosity-weighted stellar age radial gradient ($\nabla_{\rm age}$) distribution of $\sim3600$ low-redshift SFGs using the MaNGA Pipe3D data available in the SDSS DR17. The mean age gradient is negative, with $\nabla_{\

Shalini Choudhury, Sushovan Das, Sanjoy Paul, Prasanthi Maddala

Private 5G networks will soon be ubiquitous across the future-generation smart wireless access infrastructures hosting a wide range of performance-critical applications. A high-performing User Plane Function (UPF) in the data plane is critical to achieving such stringent performance goals, as it governs fast packet processing and supports several key control

Haoxiang Luo, Jian Luo, Athanasios V. Vasilakos

In recent years, artificial intelligence (AI) and machine learning (ML) are reshaping society's production methods and productivity, and also changing the paradigm of scientific research. Among them, the AI language model represented by ChatGPT has made great progress. Such large language models (LLMs) serve people in the form of AI-generated content (AIGC)

Kyle Gilman, David Hong, Jeffrey A. Fessler, Laura Balzano

Streaming principal component analysis (PCA) is an integral tool in large-scale machine learning for rapidly estimating low-dimensional subspaces from very high-dimensional data arriving at a high rate. However, modern datasets increasingly combine data from a variety of sources, and thus may exhibit heterogeneous quality across samples. Standard streaming P

Clarissa R. Do Ó, Saavidra Perera, Jérôme Maire, Jayke S. Nguyen

The Gemini Planet Imager (GPI) is a high contrast imaging instrument that aims to detect and characterize extrasolar planets. GPI is being upgraded to GPI 2.0, with several subsystems receiving a re-design to improve the instrument's contrast. To enable observations on fainter targets and increase stability on brighter ones, one of the upgrades is to the ada

Minghan Qin, Yifan Liu, Yuelang Xu, Xiaochen Zhao

One crucial aspect of 3D head avatar reconstruction lies in the details of facial expressions. Although recent NeRF-based photo-realistic 3D head avatar methods achieve high-quality avatar rendering, they still encounter challenges retaining intricate facial expression details because they overlook the potential of specific expression variations at different

Aleksandar Arandjelović, Geoffrey Kingston, Pavel V. Shevchenko

We investigate insurance purchases when bequest motives are age-varying and life insurance and life annuities both carry loads. The existing life cycle literature assumes bequests are normal goods without being either necessities or luxuries. Much of the literature also assumes implicitly that life annuity loads are negative. A key finding of the literature

Juan Yao

In this work, we report on a novel quantum state reconstruction process based on the disentanglement algorithm. Using variational quantum circuits, we disentangle the quantum state to a product of computational zero states. Inverse evolution of the zero states reconstructs the quantum state up to an overall phase. By sequentially disentangling the qubit one

Chau Pham, Boyi Liu, Yingxiang Yang, Zhengyu Chen

Discussion and debate among Large Language Models (LLMs) have gained considerable attention due to their potential to enhance the reasoning ability of LLMs. Although natural language is an obvious choice for communication due to LLM's language understanding capability, the token sampling step needed when generating natural language poses a potential risk of

Ziwei Ji, Tiezheng Yu, Yan Xu, Nayeon Lee

Large language models (LLMs) have shown promise for generative and knowledge-intensive tasks including question-answering (QA) tasks. However, the practical deployment still faces challenges, notably the issue of "hallucination", where models generate plausible-sounding but unfaithful or nonsensical information. This issue becomes particularly critical in th

Yanqi Song, Yusen Wu, Sujuan Qin, Qiaoyan Wen

The Quantum Approximate Optimization Algorithm (QAOA) is an extensively studied variational quantum algorithm utilized for solving optimization problems on near-term quantum devices. A significant focus is placed on determining the effectiveness of training the $n$-qubit QAOA circuit, i.e., whether the optimization error can converge to a constant level as t

Michael Feffer, Nikolas Martelaro, Hoda Heidari

Prior work has established the importance of integrating AI ethics topics into computer and data sciences curricula. We provide evidence suggesting that one of the critical objectives of AI Ethics education must be to raise awareness of AI harms. While there are various sources to learn about such harms, The AI Incident Database (AIID) is one of the few atte

Wenzhuo Zhou

We study offline reinforcement learning (RL) which seeks to learn a good policy based on a fixed, pre-collected dataset. A fundamental challenge behind this task is the distributional shift due to the dataset lacking sufficient exploration, especially under function approximation. To tackle this issue, we propose a bi-level structured policy optimization alg

Piotr Przytycki, Yeeka Yau

We prove that the smallest elements of Shi parts and cone type parts exist and form Garside shadows. The latter resolves a conjecture of Parkinson and the second author as well as a conjecture of Hohlweg, Nadeau and Williams.

Peng Wei, Han-Xiong Li

Numerous industrial thermal processes and fluid processes can be described by distributed parameter systems (DPSs), wherein many process parameters and variables vary in space and time. Early internal abnormalities in the DPS may develop into uncontrollable thermal failures, causing serious safety incidents. In this study, the multiscale information fusion i

Peng Di, Jianguo Li, Hang Yu, Wei Jiang

Code Large Language Models (Code LLMs) have gained significant attention in the industry due to their wide applications in the full lifecycle of software engineering. However, the effectiveness of existing models in understanding non-English inputs for multi-lingual code-related tasks is still far from well studied. This paper introduces CodeFuse-13B, an ope

Atsuhiro Umemoto, Tatsuhiro Naka, Takuya Shiraishi, Osamu Sato

Fine-grained nuclear emulsion films have been developed as a tracking detector with nanometric spatial resolution to be used in direction-sensitive dark matter searches, thanks to novel readout technologies capable of exploiting this unprecedented resolution. Emulsion detectors are time insensitive. Therefore, a directional dark matter search with such detec

Norio Ota

This study theoretically predicts the specific Polycyclic Aromatic Hydrocarbon (PAH) molecules to reproduce both astronomically observed Infrared Bands (IR) and Diffuse Interstellar Bands (DIB). In our recent paper, we could reproduce IR by the hydrocarbon pentagon-hexagon combined PAH molecules using Density Functional Theory (DFT). Found molecules were (C5

Ling Zhou

We extend classical tools from rational homotopy theory to topological data analysis by introducing persistent Sullivan minimal models of persistent topological spaces. Our main result establishes that the interleaving distance between such models in the homotopy category of CDGAs is stable with respect to the homotopy interleaving distance of the underlying

Kamalesh Jana, Yonghao Mi, Søren H. Møller, Dong Hyuk Ko

The ability to manipulate the space-time structure of light waves diversifies light-matter interaction and light-driven applications. Conventionally, metasurfaces are employed to locally control the amplitude and phase of light fields by the material response and structure of small meta-atoms. However, the fixed spatial structures of metasurfaces offer limit

Jinyu Cai, Yunhe Zhang, Jicong Fan

This paper studies the problem of detecting anomalous graphs using a machine learning model trained on only normal graphs, which has many applications in molecule, biology, and social network data analysis. We present a self-discriminative modeling framework for anomalous graph detection. The key idea, mathematically and numerically illustrated, is to learn

Arthur dos Santos, Jayr Pereira, Rodrigo Nogueira, Bruno Masiero

The increasing number of scientific publications in acoustics, in general, presents difficulties in conducting traditional literature surveys. This work explores the use of a generative pre-trained transformer (GPT) model to automate a literature survey of 116 articles on data-driven speech enhancement methods. The main objective is to evaluate the capabilit

Zhaofeng Shi, Qingbo Wu, Fanman Meng, Linfeng Xu

Audio-Visual Segmentation (AVS) aims to extract the sounding object from a video frame, which is represented by a pixel-wise segmentation mask for application scenarios such as multi-modal video editing, augmented reality, and intelligent robot systems. The pioneering work conducts this task through dense feature-level audio-visual interaction, which ignores

Nitin Kohli, Michael Carl Tschantz

We use decision theory to compare variants of differential privacy from the perspective of prospective study participants. We posit the existence of a preference ordering on the set of potential consequences that study participants can incur, which enables the analysis of individual utility functions. Drawing upon the theory of measurement, we argue that cha

Amisha Srivastava, Sanjay Das, Navnil Choudhury, Rafail Psiakis

Power side-channel attacks exploit the dynamic power consumption of cryptographic operations to leak sensitive information of encryption hardware. Therefore, it is necessary to conduct power side-channel analysis for assessing the susceptibility of cryptographic systems and mitigating potential risks. Existing power side-channel analysis primarily focuses on

Yukun Cheng, Wei Chen, Lun Li, Bo Ai

Deep learning based decoding networks have shown significant improvement in decoding LDPC codes, but the neural decoders are limited by rate-matching operations such as puncturing or extending, thus needing to train multiple decoders with different code rates for a variety of channel conditions. In this correspondence, we propose a Multi-Task Learning based

Surjit S. Wadhwa, Natalia R. Landin, Petar Kostic, Oliver Vince

The orbital stability of contact binary systems has been receiving considerable attention recently. Theoretical studies indicate that merger is likely to occur at very low mass ratios, but the actual mass ratio at which merger may take place is likely to be variable and dependent on the mass of the primary. We consider the effects of metal content on the orb

Benjamin Kane, Lenhart Schubert

In many NLP applications that involve interpreting sentences within a rich context -- for instance, information retrieval systems or dialogue systems -- it is desirable to be able to preserve the sentence in a form that can be readily understood without context, for later reuse -- a process known as ``decontextualization''. While previous work demonstrated t

Ran Wei, Nathan Lambert, Anthony McDonald, Alfredo Garcia

Model-based Reinforcement Learning (MBRL) aims to make agents more sample-efficient, adaptive, and explainable by learning an explicit model of the environment. While the capabilities of MBRL agents have significantly improved in recent years, how to best learn the model is still an unresolved question. The majority of MBRL algorithms aim at training the mod

Salil Koner, Sheng Luo

Projection-based testing for mean trajectory differences in two groups of irregularly and sparsely observed functional data has garnered significant attention in the literature because it accommodates a wide spectrum of group differences and (non-stationary) covariance structures. This article presents the derivation of the theoretical power function and the

Nick Polson, Vadim Sokolov

Our goal is to provide a review of deep learning methods which provide insight into structured high-dimensional data. Rather than using shallow additive architectures common to most statistical models, deep learning uses layers of semi-affine input transformations to provide a predictive rule. Applying these layers of transformations leads to a set of attrib

Arnaud Ducrot, Hao Kang

In this paper, we study the existence of traveling wave solutions and the spreading speed for the solutions of an age-structured epidemic model with nonlocal diffusion. Our proofs make use of the comparison principles both to construct suitable sub/super-solutions and to prove the regularity of traveling waves solutions.

Kartikeya Singh, Charuvaran Adhivarahan, Karthik Dantu

Historically, feature-based approaches have been used extensively for camera-based robot perception tasks such as localization, mapping, tracking, and others. Several of these approaches also combine other sensors (inertial sensing, for example) to perform combined state estimation. Our work rethinks this approach; we present a representation learning mechan

Prashanth Gurunath Shivakumar, Jari Kolehmainen, Yile Gu, Ankur Gandhe

Second pass rescoring is a critical component of competitive automatic speech recognition (ASR) systems. Large language models have demonstrated their ability in using pre-trained information for better rescoring of ASR hypothesis. Discriminative training, directly optimizing the minimum word-error-rate (MWER) criterion typically improves rescoring. In this

C. J. Hatchwell, M. Bergin, B. Carr, M. G. Barr

A scanning helium microscope typically utilises a thermal energy helium atom beam, with an energy and wavelength (<100 meV, ~0.05 nm) particularly sensitive to surface structure. An angular detector stage for a scanning helium microscope is presented that facilitates the in-situ measurement of scattering distributions from a sample. We begin by demonstrating

Bowen Zhang, Zhijin Qin, Geoffrey Ye Li

Camera sensors have been widely used in intelligent robotic systems. Developing camera sensors with high sensing efficiency has always been important to reduce the power, memory, and other related resources. Inspired by recent success on programmable sensors and deep optic methods, we design a novel video compressed sensing system with spatially-variant comp

Benjamin Kane, Lenhart Schubert

Many practical applications of dialogue technology require the generation of responses according to a particular developer-specified persona. While a variety of personas can be elicited from recent large language models, the opaqueness and unpredictability of these models make it desirable to be able to specify personas in an explicit form. In previous work,

Pratik Brahma, Sayeef Salahuddin

We show that a classical spin liquid phase can emerge from an ordered magnetic state in the two-dimensional frustrated Shastry-Sutherland Ising lattice due to lateral confinement. Two distinct classical spin liquid states are stabilized (i) long-range spin-correlated dimers, and (ii) exponentially decaying spin-correlated disordered states, depending on widt

Nuoya Xiong, Zhihan Liu, Zhaoran Wang, Zhuoran Yang

We study multi-agent reinforcement learning (MARL) for the general-sum Markov Games (MGs) under the general function approximation. In order to find the minimum assumption for sample-efficient learning, we introduce a novel complexity measure called the Multi-Agent Decoupling Coefficient (MADC) for general-sum MGs. Using this measure, we propose the first un

Toru Kitagawa, Sokbae Lee, Chen Qiu

We consider a decision maker who faces a binary treatment choice when their welfare is only partially identified from data. We contribute to the literature by anchoring our finite-sample analysis on mean square regret, a decision criterion advocated by Kitagawa, Lee, and Qiu (2022). We find that optimal rules are always fractional, irrespective of the width

Tapas Tripura, Souvik Chakraborty

Learning and predicting the dynamics of physical systems requires a profound understanding of the underlying physical laws. Recent works on learning physical laws involve generalizing the equation discovery frameworks to the discovery of Hamiltonian and Lagrangian of physical systems. While the existing methods parameterize the Lagrangian using neural networ

Luwei Yang, Tao Liu, David J. Hill

This paper studies a crucial problem in power system balancing control, i.e., the multi-time slot economic dispatch (MTSED) problem, for power grids with substantial renewables, synchronous generators (SGs), and energy storage devices (ESDs). The target of MTSED is to optimally coordinate active/reactive power outputs of all controllable units to meet a fore

Cheng Peng, Xi Yang, Kaleb E Smith, Zehao Yu

Objective To develop soft prompt-based learning algorithms for large language models (LLMs), examine the shape of prompts, prompt-tuning using frozen/unfrozen LLMs, transfer learning, and few-shot learning abilities. Methods We developed a soft prompt-based LLM model and compared 4 training strategies including (1) fine-tuning without prompts; (2) hard-promp

Xiulong Liu, Zhikang Dong, Peng Zhang

In recent years, there has been a growing emphasis on the intersection of audio, vision, and text modalities, driving forward the advancements in multimodal research. However, strong bias that exists in any modality can lead to the model neglecting the others. Consequently, the model's ability to effectively reason across these diverse modalities is compromi

Tatsuki Koga, Kamalika Chaudhuri, David Page

Patient privacy is a major barrier to healthcare AI. For confidentiality reasons, most patient data remains in silo in separate hospitals, preventing the design of data-driven healthcare AI systems that need large volumes of patient data to make effective decisions. A solution to this is collective learning across multiple sites through federated learning wi

Kazuhiro Kuruma, Benjamin Pingault, Cleaven Chia, Michael Haas

The ability to control phonons in solids is key for diverse quantum applications, ranging from quantum information processing to sensing. Often, phonons are sources of noise and decoherence, since they can interact with a variety of solid-state quantum systems. To mitigate this, quantum systems typically operate at milli-Kelvin temperatures to reduce the num

Nebiyou Yismaw, Ulugbek S. Kamilov, M. Salman Asif

Deep learning-based methods deliver state-of-the-art performance for solving inverse problems that arise in computational imaging. These methods can be broadly divided into two groups: (1) learn a network to map measurements to the signal estimate, which is known to be fragile; (2) learn a prior for the signal to use in an optimization-based recovery. Despit

Wei Dong, Dawei Yan, Zhijun Lin, Peng Wang

The advent of high-capacity pre-trained models has revolutionized problem-solving in computer vision, shifting the focus from training task-specific models to adapting pre-trained models. Consequently, effectively adapting large pre-trained models to downstream tasks in an efficient manner has become a prominent research area. Existing solutions primarily co

Zhi-Yong Wang, Hing Cheung So, Abdelhak M. Zoubir

To alleviate the bias generated by the l1-norm in the low-rank tensor completion problem, nonconvex surrogates/regularizers have been suggested to replace the tensor nuclear norm, although both can achieve sparsity. However, the thresholding functions of these nonconvex regularizers may not have closed-form expressions and thus iterations are needed, which i

Dayong Ren, Zhe Ma, Yuanpei Chen, Weihang Peng

Recently, Spiking Neural Networks (SNNs), enjoying extreme energy efficiency, have drawn much research attention on 2D visual recognition and shown gradually increasing application potential. However, it still remains underexplored whether SNNs can be generalized to 3D recognition. To this end, we present Spiking PointNet in the paper, the first spiking neur

Sambuddha Chakrabarti, Hosna Khajeh, Thomas R Nudell, Mohammad Reza Hesamzadeh

This paper considers the investment coordination problem for the long term transmission capacity expansion in a situation where there are multiple regional Transmission Planners (TPs), each acting in order to maximize the utility in only its own region. In such a setting, any particular TP does not normally have any incentive to cooperate with the neighborin

Priyam Halder, Satyaki Goswami, Protyusha Halder, Uday Ghosh

Millisecond pulsars (MSP) are an important subclass of rotation powered pulsars (RPP), traditionally defined as those with $P_s < 20-30$~ms and $B_s \lesssim 10^{10}$~G. We re-examine this definition by applying Gaussian mixture model (GMM) analysis to identify distinct clusters within the RPP population and find that the MSPs appear to be better demarcated

Dihan Dai, Yekaterina Epshteyn, Akil Narayan

The shallow water flow model is widely used to describe water flows in rivers, lakes, and coastal areas. Accounting for uncertainty in the corresponding transport-dominated nonlinear PDE models presents theoretical and numerical challenges that motivate the central advances of this paper. Starting with a spatially one-dimensional hyperbolicity-preserving, po

Masahiro Yamamoto

Since the invention of computers, communication through natural language (actual human language) has been a dream technology. However, natural language is extremely difficult to mathematically formulate, making it difficult to realize as an algorithm without considering programming. While there have been numerous technological developments, one cannot say th

Erfan Darzi, Florian Dubost, N. M. Sijtsema, P. M. A van Ooijen

Federated learning offers a privacy-preserving framework for medical image analysis but exposes the system to adversarial attacks. This paper aims to evaluate the vulnerabilities of federated learning networks in medical image analysis against such attacks. Employing domain-specific MRI tumor and pathology imaging datasets, we assess the effectiveness of kno

K. Niranjan Kumar, Irfan Essa, Sehoon Ha

Humanoid robots are well suited for human habitats due to their morphological similarity, but developing controllers for them is a challenging task that involves multiple sub-problems, such as control, planning and perception. In this paper, we introduce a method to simplify controller design by enabling users to train and fine-tune robot control policies us

Bruno Silva, Leonardo Nunes, Roberto Estevão, Vijay Aski

Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding across various domains, including healthcare and finance. For some tasks, LLMs achieve similar or better performance than trained human beings, therefore it is reasonable to employ human exams (e.g., certification tests) to assess the performance of LLMs.

Tasmeen Zaman Ornee, Md Kamran Chowdhury Shisher, Clement Kam, Yin Sun

In this study, we investigate a context-aware status updating system consisting of multiple sensor-estimator pairs. A centralized monitor pulls status updates from multiple sensors that are monitoring several safety-critical situations (e.g., carbon monoxide density in forest fire detection, machine safety in industrial automation, and road safety). Based on

Orhan Eren Akgün, Arif Kerem Dayı, Stephanie Gil, Angelia Nedić

We introduce the Projected Push-Pull algorithm that enables multiple agents to solve a distributed constrained optimization problem with private cost functions and global constraints, in a collaborative manner. Our algorithm employs projected gradient descent to deal with constraints and a lazy update rule to control the trade-off between the consensus and o

Georgia R. Hartzenberg, Michael J. Cowley, Andrew M. Hopkins, Rebecca J. Allen

To explore the role environment plays in influencing galaxy evolution at high redshifts, we study $2.0\leq z<4.2$ environments using the FourStar Galaxy Evolution (ZFOURGE) survey. Using galaxies from the COSMOS legacy field with ${\rm log(M_{*}/M_{\odot})}\geq9.5$, we use a seventh nearest neighbour density estimator to quantify galaxy environment, dividing

Yiyou Sun

This thesis makes considerable contributions to the realm of machine learning, specifically in the context of open-world scenarios where systems face previously unseen data and contexts. Traditional machine learning models are usually trained and tested within a fixed and known set of classes, a condition known as the closed-world setting. While this assumpt

František Herman, Richard Hlubina

The size of the Cooper pair $\xi_{\rm pair}$ is one of the basic characteristics of a superconductor, but it is not possible to measure it directly. It might be argued that $\xi_{\rm pair}$ can be determined from the value $\xi_{\rm slope}$ extracted from the measurement of the slope of $H_{c2}$ close to $T_c$. Taking into account both pair-conserving and pa

Hira Naveed

As machine learning (ML) components become increasingly integrated into software systems, the emphasis on the ethical or responsible aspects of their use has grown significantly. This includes building ML-based systems that adhere to human-centric requirements, such as fairness, privacy, explainability, well-being, transparency and human values. Meeting thes

Jingyang Xiang, Siqi Li, Jun Chen, Shipeng Bai

The study of sparsity in Convolutional Neural Networks (CNNs) has become widespread to compress and accelerate models in environments with limited resources. By constraining N consecutive weights along the output channel to be group-wise non-zero, the recent network with 1$\times$N sparsity has received tremendous popularity for its three outstanding advanta

Shuoguang Yang, Xuezhou Zhang, Mengdi Wang

Multi-level optimization has gained increasing attention in recent years, as it provides a powerful framework for solving complex optimization problems that arise in many fields, such as meta-learning, multi-player games, reinforcement learning, and nested composition optimization. In this paper, we study the problem of distributed multi-level optimization o