March 2024 arXiv papers — page 102

Tengzhou Hu

A Lie algebroid is a generalization of Lie algebra that provides a general framework to describe the symmetries of a manifold. In this paper, we generalize the Kodaira vanishing theorem, which is a basic result in complex geometry, to Kahler Lie algebroids. The generalization of the Kodaira vanishing theorem states that the kernel of the Lie algebroid Laplac

Evgenios Gryparis, Georgios C. Georgiou

The effect of wall slip on the apparent flow curves of viscoplastic materials obtained using torsional parallel plate rheometers is analysed by considering Herschel-Bulkley fluids and assuming that slip occurs above a critical wall shear stress, the slip yield stress {\tau}_c, taken to be lower than the yield stress, {\tau}_0. Thus, different flow regimes ar

Meng Song

Representation learning is a fundamental task in machine learning, aiming at uncovering structures from data to facilitate subsequent tasks. However, what is a good representation for planning and reasoning in a stochastic world remains an open problem. In this work, we posit that learning a distance function is essential to allow planning and reasoning in t

Lakshadeep Naik, Thorbjørn Mosekjær Iversen, Aljaz Kramberger, Norbert Krüger

Accurate 6D object pose estimation is essential for various robotic tasks. Uncertain pose estimates can lead to task failures; however, a certain degree of error in the pose estimates is often acceptable. Hence, by quantifying errors in the object pose estimate and acceptable errors for task success, robots can make informed decisions. This is a challenging

Weicao Deng, Min Li, Ming-Min Zhao, Min-Jian Zhao

Hybrid beamforming is vital in modern wireless systems, especially for massive MIMO and millimeter-wave (mmWave) deployments, offering efficient directional transmission with reduced hardware complexity. However, effective beamforming in multi-user scenarios relies heavily on accurate channel state information, the acquisition of which often requires signifi

Sharief Saleh, Qamar Bader, Malek Karaim, Mohamed Elhabiby

Autonomous vehicles (AVs) are poised to revolutionize the transportation industry by enhancing traffic efficiency and road safety. However, achieving optimal vehicular autonomy demands an uninterrupted and precise positioning solution, especially in deep urban environments. 5G mmWave holds immense potential to provide such a service due to its accurate range

Xiaoqin Bai, Juan Bai, Boris A. Malomed, Rongcao Yang

We investigate the dynamics of optical Airy beams in the one-dimensional fractional Schr\"odinger equation with a harmonic-oscillator (HO) potential subjected to modulation along the propagation distance. Deriving general solutions for propagating beams and particular solutions for Airy waves with/without chirp, we study analytically the spectrum conversion

Stephen R. Clark, Craig McGregor

Many countries have commenced a transition from fossil fuel-based electricity generation systems to sustainable systems based on wind and solar generation. It is often noted that the least cost approach would involve a massive scale-up in the building of variable renewables, supported by battery storage and gas peaking plants. The required backup should be f

Steven Chaplick, Martin Frohn, Steven Kelk, Johann Lottermoser

In this article we prove that the minimum-degree greedy algorithm, with adversarial tie-breaking, is a $(2/3)$-approximation for the Maximum Independent Set problem on interval graphs. We show that this is tight, even on unit interval graphs of maximum degree 3. We show that on chordal graphs, the greedy algorithm is a $(1/2)$-approximation and that this is

Matthew D. Watson, Swagata Acharya, James E. Nunn, Laxman Nagireddy

We present the evolution of the electronic structure of CrSBr from its antiferromagnetic ground state to the paramagnetic phase above T_N=132 K, in both experiment and theory. Low temperature angle-resolved photoemission spectroscopy (ARPES) results are obtained using a novel method to overcome sample charging issues, revealing quasi-2D valence bands in the

Yuta Suzuki

In this paper, we prove an asymptotic formula for the number of relatively prime pairs in the Piatetski-Shapiro sequence of arbitrarily large order. This improves the result of Pimsert, Srichan and Tangsupphathawat (2023), the order of which was restricted to be $<\frac{3}{2}$. The key ingredients of the proof are a simple averaging trick and an extension of

Maurice H. P. M. van Putten

The Hubble expansion of the Universe is considered in the classical limit of a Big Bang quantum cosmology. In an IR-consistent coupling to the the bare cosmological constant, we infer a dark energy as a relic of the Big Bang by loss of time-translation invariance on a Hubble time-scale. This dark energy is identified with the trace $J$ of the Schouten tensor

Korbinian Staudacher, Ludwig Schmid, Johannes Zeiher, Robert Wille

Quantum circuit synthesis describes the process of converting arbitrary unitary operations into a gate sequence of a fixed universal gate set, usually defined by the operations native to a given hardware platform. Most current synthesis algorithms are designed to synthesize towards a set of single qubit rotations and an additional entangling two qubit gate,

Xiaoyu Li, Wenwen Min, Shunfang Wang, Changmiao Wang

Spatially resolved transcriptomics represents a significant advancement in single-cell analysis by offering both gene expression data and their corresponding physical locations. However, this high degree of spatial resolution entails a drawback, as the resulting spatial transcriptomic data at the cellular level is notably plagued by a high incidence of missi

Rhombik Roy, Andrea Trombettoni, Barnali Chakrabarti

We numerically study the expansion dynamics of initially localized dipolar bosons in a homogeneous 1D optical lattice for different initial states. Comparison is made to interacting bosons with contact interaction. For shallow lattices the expansion is unimodal and ballistic, while strong lattices suppress tunneling. However for intermediate lattice depths a

Nouhaila Innan, Muhammad Al-Zafar Khan, Alberto Marchisio, Muhammad Shafique

In this study, we explore the innovative domain of Quantum Federated Learning (QFL) as a framework for training Quantum Machine Learning (QML) models via distributed networks. Conventional machine learning models frequently grapple with issues about data privacy and the exposure of sensitive information. Our proposed Federated Quantum Neural Network (FedQNN)

Junyang Wu, Yun Gu, Guang-Zhong Yang

Robot assisted endoluminal intervention is an emerging technique for both benign and malignant luminal lesions. With vision-based navigation, when combined with pre-operative imaging data as priors, it is possible to recover position and pose of the endoscope without the need of additional sensors. In practice, however, aligning pre-operative and intra-opera

Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic

Kernel conditional mean embeddings (CMEs) offer a powerful framework for representing conditional distribution, but they often face scalability and expressiveness challenges. In this work, we propose a new method that effectively combines the strengths of deep learning with CMEs in order to address these challenges. Specifically, our approach leverages the e

Hongbo Chu, Qiehe Sun, Jiawen Li, Yuxuan Chen

Histopathological whole slide image (WSI) analysis with deep learning has become a research focus in computational pathology. The current paradigm is mainly based on multiple instance learning (MIL), in which approaches with Transformer as the backbone are well discussed. These methods convert WSI tasks into sequence tasks by representing patches as tokens i

Jinshi Sai, Hsi-Wei Yen, Masahiro N. Machida, Nagayoshi Ohashi

We present the results of our mosaic observations of a single Class 0 protostar IRAS 15398$-$3359 with Atacama Compact Array (ACA) in the CO $J=2\mbox{-}1$ line. The new observations covering a $\sim\!2'$ square region revealed elongated redshifted and blueshifted components, which are located at distances of $\sim\!30''\mbox{-}75''$ on the northern and sout

Federico Talamucci

In the context of holonomic constrained systems the identification of virtual displacements is clear and consolidated: this gives the possibility, once the class of displacements have been combined with Newton's equations, to write the correct equations of motion for the constrained system. The method combines d'Alembert principle with the Lagrange formalism

Ke Lin, Yiyang Luo, Zijian Zhang, Ping Luo

Generative linguistic steganography attempts to hide secret messages into covertext. Previous studies have generally focused on the statistical differences between the covertext and stegotext, however, ill-formed stegotext can readily be identified by humans. In this paper, we propose a novel zero-shot approach based on in-context learning for linguistic ste

Ayoub Ghriss, Masashi Sugiyama, Alessandro Lazaric

The current thesis aims to explore the reinforcement learning field and build on existing methods to produce improved ones to tackle the problem of learning in high-dimensional and complex environments. It addresses such goals by decomposing learning tasks in a hierarchical fashion known as Hierarchical Reinforcement Learning. We start in the first chapter b

Tianhe Wu, Kede Ma, Jie Liang, Yujiu Yang

While Multimodal Large Language Models (MLLMs) have experienced significant advancement in visual understanding and reasoning, their potential to serve as powerful, flexible, interpretable, and text-driven models for Image Quality Assessment (IQA) remains largely unexplored. In this paper, we conduct a comprehensive and systematic study of prompting MLLMs fo

Minhyuk Seo, Seongwon Cho, Minjae Lee, Diganta Misra

Online learning methods often rely on supervised data. However, under data distribution shifts, such as in continual learning (CL), where continuously arriving online data streams incorporate new concepts (e.g., classes), real-time manual annotation is impractical due to its costs and latency, which hinder real-time adaptation. To alleviate this, 'name-only'

Athanasios Koliogiorgos, Richard Korytár

We present an efficient method of calculating the vibrational spectrum of a magnetic molecule adsorbed on a superconductor, directly related to the first derivative of the tunneling $IV$ curve. The work is motivated by a recent scanning-tunneling spectroscopy of lead phthalocyanine on superconducting Pb(100), showing a wealth of vibrational excitations, the

Yan Wang, Humphrey O. Obie, Zhuying Li, Flora D. Salim

The pleasure that often comes with eating can be further enhanced with intelligent technology, as the field of human-food interaction suggests. However, knowledge on how to design such pleasure-supporting eating systems is limited. To begin filling this knowledge gap, we designed "GustosonicSense", a novel gustosonic eating system that utilizes wireless earb

Zhuowei Li, Miao Zhang, Xiaotian Lin, Meng Yin

This paper introduces GAgent: an Gripping Agent designed for open-world environments that provides advanced cognitive abilities via VLM agents and flexible grasping abilities with variable stiffness soft grippers. GAgent comprises three primary components - Prompt Engineer module, Visual-Language Model (VLM) core and Workflow module. These three modules enha

Prayushi Faldu, Indrajit Bhattacharya, Mausam

An essential requirement for a real-world Knowledge Base Question Answering (KBQA) system is the ability to detect the answerability of questions when generating logical forms. However, state-of-the-art KBQA models assume all questions to be answerable. Recent research has found that such models, when superficially adapted to detect answerability, struggle t

Yangguang Zhong, Shuai Yue, Huawei Liu, Yuexing Xia

Carrier transport in nanodevices plays a crucial role in determining their functionality. In the post-Moore era, the behavior of carriers near surface or interface domains the function of the whole devices. However, the femtosecond dynamics and nanometer-scale movement of carriers pose challenges for imaging their behavior. Techniques with high spatial-tempo

Ranran Wang, Qi Liu, Jinyu Xia, Yongmo Hu

In this paper, we investigate a novel form of approximate orthogonality that is based on integral orthogonality. Additionally, we establish the fundamental properties of this new approximate orthogonality and examine its capability to preserve mappings of orthogonality. Moreover, we explore the relationship between this new approximate orthogonality and othe

Xiaoshan Luo, Zhenyu Wang, Pengyue Gao, Jian Lv

Recent advances in deep learning generative models (GMs) have created high capabilities in accessing and assessing complex high-dimensional data, allowing superior efficiency in navigating vast material configuration space in search of viable structures. Coupling such capabilities with physically significant data to construct trained models for materials dis

Vishnu Kumar, Sudhanshu Tiwari, Gayathri Pillai, Rudra Pratap

Piezoelectric micro-electro-mechanical systems have significant market potential owing to their superior capabilities of transduction to those of standard capacitive and piezoresistive devices. However, piezoelectric films are often lossy, which reduces the quality factor of devices and affects their performance. It is thus important to examine all sources o

Irinel Caprini

Modifications of the QCD perturbative expansions by the subtraction of the dominant infrared renormalon have been proposed recently as attempts to solve the long-standing discrepancy between fixed-order and contour-improved perturbation theory for the hadronic $\tau$ decays. In this approach, the modified perturbative series is supplemented by a modified glu

Nian Wang, Huashi Xu, Tianyou Wang, Zhizhao Che

Cavitation is a common phenomenon in nature and has numerous applications. In contrast to a cavitation bubble in a free domain, a cavitation bubble in a thin tube is restricted by the tube wall, which is expected to significantly affect bubble evolution but its mechanism is still unclear. In this study, the dynamics of a cavitation bubble in a thin circular

Mohammad Ali Labbaf-Khaniki, Mohammad Manthouri, Hanieh Ajami

Fault detection and diagnosis (FDD) is a crucial task for ensuring the safety and efficiency of industrial processes. We propose a novel FDD methodology for the Tennessee Eastman Process (TEP), a widely used benchmark for chemical process control. The model employs two separate Transformer branches, enabling independent processing of input data and potential

Tian Zhao, Timothy L. Molloy

We formulate the discrete-time inverse optimal control problem of inferring unknown parameters in the objective function of an optimal control problem from measurements of optimal states and controls as a nonlinear filtering problem. This formulation enables us to propose a novel extended Kalman filter (EKF) for solving inverse optimal control problems in a

Nikhil Churamani, Saksham Checker, Fethiye Irmak Dogan, Hao-Tien Lewis Chiang

It is critical for robots to explore Federated Learning (FL) settings where several robots, deployed in parallel, can learn independently while also sharing their learning with each other. This collaborative learning in real-world environments requires social robots to adapt dynamically to changing and unpredictable situations and varying task settings. Our

Dongyu Yan, Guanyu Huang, Fengyu Quan, Haoyao Chen

Panoramic observation using fisheye cameras is significant in virtual reality (VR) and robot perception. However, panoramic images synthesized by traditional methods lack depth information and can only provide three degrees-of-freedom (3DoF) rotation rendering in VR applications. To fully preserve and exploit the parallax information within the original fish

Yizhe Huang, Yuwen Chen, Ke Zhu, Pengfei Li

With the rapid industrial development, many dye pollutants have entered the water, along with heavy metals like As, leading to complex pollution that threatens the ecological environment and human health. Therefore, designing an effective strategy for treating complex dye wastewater is urgent. Herein, we have constructed high-purity pyrite FeS$_2$ nanoplates

Yongyeon Kim, Byung-Won On, Ingyu Lee

In social network service platforms, crime suspects are likely to use cybercrime coded words for communication by adding criminal meanings to existing words or replacing them with similar words. For instance, the word 'ice' is often used to mean methamphetamine in drug crimes. To analyze the nature of cybercrime and the behavior of criminals, quickly detecti

Yuwen Chen, Ke Zhu, Yizhe Huang, Xin Li

The coexistence of tetracycline (TC) and arsenite (As(III)) in livestock wastewater threatens public health, and the heterogeneous Fenton-like system is a practical approach for the simultaneous removal of TC and As(III). In this work, fine CoFe$_2$O$_4$ nanoparticles are facilely anchored on heretically porous carbon (CoFe$_2$O$_4$@PC) via a microwave-assis

Ali Shokri, Ibrahim Jameel Mujhid, Mehdi Mirakhorli

To implement important quality attributes of software such as architectural security tactics, developers incorporate API of software frameworks, as building blocks, to avoid re-inventing the wheel and improve their productivity. However, this is a challenging and error-prone task, especially for novice programmers. Despite the advances in the field of API-ba

Yuwen Chen, Ke Zhu, Wenlei Qin, Zhiwei Jiang

Spinel oxides are recognized as promising Fenton-like catalysts for the degradation of antibiotics. However, the catalytic performance is restrained by the poor electron transfer rate (ETR). Herein, hollow NiCo2O4@C nanocages are rationally designed and prepared to accelerate ETR in peroxymonosulfate (PMS) activation for tetracycline (TC) degradation.

Uiwon Hwang, Jonghyun Lee, Juhyeon Shin, Sungroh Yoon

In the face of the deep learning model's vulnerability to domain shift, source-free domain adaptation (SFDA) methods have been proposed to adapt models to new, unseen target domains without requiring access to source domain data. Although the potential benefits of applying data augmentation to SFDA are attractive, several challenges arise such as the depende

Yuhong Cao, Rui Zhao, Yizhuo Wang, Bairan Xiang

In this work, we propose a deep reinforcement learning (DRL) based reactive planner to solve large-scale Lidar-based autonomous robot exploration problems in 2D action space. Our DRL-based planner allows the agent to reactively plan its exploration path by making implicit predictions about unknown areas, based on a learned estimation of the underlying transi

Haifeng Luo, Navneet Garg, Mark Holm, Tharmalingam Ratnarajah

This paper investigates a robust joint power allocation and beamforming scheme for in-band full-duplex multi-cell multi-user (IBFD-MCMU) networks. A mean-squared error (MSE) minimization problem is formulated with constraints on the power budgets and residual self-interference (RSI) power. The problem is not convex, so we decompose it into two sub-problems:

Qilong Zhao, Yifei Zhang, Mengdan Zhu, Siyi Gu

Explanation supervision aims to enhance deep learning models by integrating additional signals to guide the generation of model explanations, showcasing notable improvements in both the predictability and explainability of the model. However, the application of explanation supervision to higher-dimensional data, such as 3D medical images, remains an under-ex

Deyi Ji, Lanyun Zhu, Siqi Gao, Qi Zhu

In this paper, we address the challenge of Multi-Object Tracking (MOT) in moving Unmanned Aerial Vehicle (UAV) scenarios, where irregular flight trajectories, such as hovering, turning left/right, and moving up/down, lead to significantly greater complexity compared to fixed-camera MOT. Specifically, changes in the scene background not only render traditiona

Eftekhar Hossain, Omar Sharif, Mohammed Moshiul Hoque, Sarah M. Preum

Internet memes have become a powerful means for individuals to express emotions, thoughts, and perspectives on social media. While often considered as a source of humor and entertainment, memes can also disseminate hateful content targeting individuals or communities. Most existing research focuses on the negative aspects of memes in high-resource languages,

Chengpeng Huang, Rui Song, Shang Gao, Yu Guo

The scalability limitations of public blockchains have hindered their widespread adoption in real-world applications. While the Ethereum community is pushing forward in zk-rollup (zero-knowledge rollup) solutions, such as introducing the ``blob transaction'' in EIP-4844, Layer 2 networks encounter a data availability problem: storing transactions completely

Tianhao Dai, Chengyu Huang, Lizi Liao

Existing neural response generation models have achieved impressive improvements for two-party conversations, which assume that utterances are sequentially organized. However, many real-world dialogues involve multiple interlocutors and the structure of conversational context is much more complex, e.g. utterances from different interlocutors can occur "in pa

Hsiang-Wei Huang, Cheng-Yen Yang, Wenhao Chai, Zhongyu Jiang

In the field of multi-object tracking (MOT), traditional methods often rely on the Kalman filter for motion prediction, leveraging its strengths in linear motion scenarios. However, the inherent limitations of these methods become evident when confronted with complex, nonlinear motions and occlusions prevalent in dynamic environments like sports and dance. T

Wei Zhang, Feng Qiu, Chen Liu, Lincheng Li

Affective Behavior Analysis aims to facilitate technology emotionally smart, creating a world where devices can understand and react to our emotions as humans do. To comprehensively evaluate the authenticity and applicability of emotional behavior analysis techniques in natural environments, the 6th competition on Affective Behavior Analysis in-the-wild (ABA

Rabimba Karanjai, Weidong Shi

Artificial General Intelligence falls short when communicating role specific nuances to other systems. This is more pronounced when building autonomous LLM agents capable and designed to communicate with each other for real world problem solving. Humans can communicate context and domain specific nuances along with knowledge, and that has led to refinement o

Hao Wei, Bowen Liu, Minqing Zhang, Peilun Shi

Generalist foundation model has ushered in newfound capabilities in medical domain. However, the contradiction between the growing demand for high-quality annotated data with patient privacy continues to intensify. The utilization of medical artificial intelligence generated content (Med-AIGC) as an inexhaustible resource repository arises as a potential sol

Simon A. Lee, Timothy Lindsey

Large Language Models (LLMs) have become a pivotal research area, potentially making beneficial contributions in fields like healthcare where they can streamline automated billing and decision support. However, the frequent use of specialized coded languages like ICD-10, which are regularly updated and deviate from natural language formats, presents potentia

Chenxing Jiang, Yiming Luo, Boyu Zhou, Shaojie Shen

In recent years, implicit online dense mapping methods have achieved high-quality reconstruction results, showcasing great potential in robotics, AR/VR, and digital twins applications. However, existing methods struggle with slow texture modeling which limits their real-time performance. To address these limitations, we propose a NeRF-based dense mapping met

Hoyoung Kim, Sehyun Hwang, Suha Kwak, Jungseul Ok

Training and validating models for semantic segmentation require datasets with pixel-wise annotations, which are notoriously labor-intensive. Although useful priors such as foundation models or crowdsourced datasets are available, they are error-prone. We hence propose an effective framework of active label correction (ALC) based on a design of correction qu

Sourav Chakraborty, Lijun Chen

We study incentivized exploration for the multi-armed bandit (MAB) problem with non-stationary reward distributions, where players receive compensation for exploring arms other than the greedy choice and may provide biased feedback on the reward. We consider two different non-stationary environments: abruptly-changing and continuously-changing, and propose r

Norio Inui

The magnetic properties of a circular graphene nanoribbon (carbon belt) in a magnetic field parallel to its central axis is studied using a tight-binding model. Orbital magnetic susceptibility is calculated using an analytical expression of the energy eigenvalues as a function of the magnetic flux density for any size, and its temperature dependence is consi

Masaki Hidaka, Minoru Itoh

We show that the Schur polynomials in all primitive $n$th roots of unity are $1$, $0$, or $-1$, if $n$ has at most two distinct odd prime factors. This result can be regarded as a generalization of properties of the coefficients of the cyclotomic polynomial and its multiplicative inverse. The key to the proof is the concept of a unimodular system of vectors.

Chao Yang, Zhen Zhao

In this paper, we study \lambda-biharmonic hypersurfaces in the product space L^{m}\times\mathbb{R}, where L^{m} is an Einstein space and \mathbb{R} is a real line. We prove that \lambda-biharmonic hypersurfaces with constant mean curvature in L^{m}\times\mathbb{R} are either minimal or vertical cylinders, and obtain some classification results for \lambda$-

Mude Hui, Zihao Wei, Hongru Zhu, Fei Xia

Volumetric optical microscopy using non-diffracting beams enables rapid imaging of 3D volumes by projecting them axially to 2D images but lacks crucial depth information. Addressing this, we introduce MicroDiffusion, a pioneering tool facilitating high-quality, depth-resolved 3D volume reconstruction from limited 2D projections. While existing Implicit Neura

Tianyi Zhang, Kaining Huang, Weiming Zhi, Matthew Johnson-Roberson

Humans have the remarkable ability to construct consistent mental models of an environment, even under limited or varying levels of illumination. We wish to endow robots with this same capability. In this paper, we tackle the challenge of constructing a photorealistic scene representation under poorly illuminated conditions and with a moving light source. We

Aissa Bouhali, Issam Louhichi

A major open question in the theory of Toeplitz operator on the Bergman space of the unit disk of the complex plane is to fully characterize the set of all Toeplitz operators that commute with a given one. In [2], the second author described the sum $S = T_{e^{im\theta}f} +T_{e^{il\theta}g}$, where $f$ and $g$ are radial functions, that commutes with the sum

Cong Ding, Zhijun Luo

We classify smooth Euler-symmetric varieties corresponding to the symbol system generated by a single reduced polynomial.

Yusuf Abu Muhanna, Issam Louhichi

We use properties of the hyperbolic metric and properties of the modular function to show that the Bohr's radius for covering maps onto hyperbolic domains is greater or equal to exponential minus pi. This includes almost all known classes of analytic functions.

David Bowman, Sehyun Ji

Following the recent ideas of Guillen and Silvestre in $[9]$, we prove that the Fisher information is non-increasing along the flow of the isotropic Landau equation. We then use this fact to deduce global existence for the equation $\partial_t f = (-\Delta)^{-1}f \cdot \Delta f + f^2$ under a relatively lax set of conditions on the initial data. In particula

Sean Ye, Matthew Gombolay

Trajectory prediction and generation are crucial for autonomous robots in dynamic environments. While prior research has typically focused on either prediction or generation, our approach unifies these tasks to provide a versatile framework and achieve state-of-the-art performance. While diffusion models excel in trajectory generation, their iterative sampli

Md Arafat Habib, Pedro Enrique Iturria-Rivera, Yigit Ozcan, Medhat Elsayed

This paper introduces an innovative method for predicting wireless network traffic in concise temporal intervals for Open Radio Access Networks (O-RAN) using a transformer architecture, which is the machine learning model behind generative AI tools. Depending on the anticipated traffic, the system either launches a reinforcement learning-based traffic steeri

Eugene Ku

Knowledge Distillation (KD) aims to transfer a more capable teacher model's knowledge to a lighter student model in order to improve the efficiency of the model, making it faster and more deployable. However, the student model's optimization process over the noisy pseudo labels (generated by the teacher model) is tricky and the amount of pseudo labels one ca

Vitaly Alifanov, Kamil Almetov, Ivan Kornienko, Arsen Mutalapov

High-quality software products rely on both well-written source code and timely detection and thorough reporting of bugs. However, some programmers view bug reports as negative assessments of their work, leading them to withhold reporting bugs, thereby detrimentally impacting projects. Through a survey of 102 programmers, we discovered that only a third of t

Fan Zhang, Zhaohan Wang, Xin Lyu, Siyuan Zhao

Speech-driven gesture generation is an emerging field within virtual human creation. However, a significant challenge lies in accurately determining and processing the multitude of input features (such as acoustic, semantic, emotional, personality, and even subtle unknown features). Traditional approaches, reliant on various explicit feature inputs and compl

Don N. Page

If two initially unbound black holes of masses M_1 and M_2, total mass M = M_1 + M_2, reduced mass mu = M_1 M_2/(M_1+M_2), and initial relative velocity v << c(4 mu/M) in otherwise empty space are captured into a bound orbit by emitting gravitational radiation, the inspiral time to coalescence increases monotonically to infinity as the impact parameter b app

Jiawei Li, Sitong Li, Shanshan Wang, Yicheng Zeng

Deploying machine learning in open environments presents the challenge of encountering diverse test inputs that differ significantly from the training data. These out-of-distribution samples may exhibit shifts in local or global features compared to the training distribution. The machine learning (ML) community has responded with a number of methods aimed at

Yang Cao, Haolong Xiang, Hang Zhang, Ye Zhu

Anomaly detection is a longstanding and active research area that has many applications in domains such as finance, security, and manufacturing. However, the efficiency and performance of anomaly detection algorithms are challenged by the large-scale, high-dimensional, and heterogeneous data that are prevalent in the era of big data. Isolation-based unsuperv

Ziqi Zhou, Minghui Li, Wei Liu, Shengshan Hu

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Ne

Andrew Geng, Pin-Yu Chen

When evaluating the performance of a pre-trained model transferred to a downstream task, it is imperative to assess not only the in-distribution (ID) accuracy of the downstream model but also its capacity to generalize and identify out-of-distribution (OOD) samples. In this paper, we unveil the hidden costs associated with intrusive fine-tuning techniques. S

Jun Liu, Zhenglun Kong, Pu Zhao, Changdi Yang

Structured pruning for large language models (LLMs) has garnered significant academic interest due to its ability to efficiently compress and accelerate LLMs by eliminating redundant weight groups at a coarse-grained granularity. Current structured pruning methods for LLMs typically depend on a singular granularity for assessing weight importance, resulting

Shichao Kan, Yuhai Deng, Jiale Fu, Lihui Cen

Retrieval-augmented generation (RAG) with large language models (LLMs) plays a crucial role in question answering, as LLMs possess limited knowledge and are not updated with continuously growing information. Most recent work on RAG has focused primarily on text-based or large-image retrieval, which constrains the broader application of RAG models. We recogni

Zhekai Li, Kun Han, Xu Cai, Renxin Yang

The diode rectifier unit-based high voltage direct current (DRU-HVDC) transmission with grid-forming (GFM) wind turbine is becoming a promising scheme for offshore wind farm(OWF) integration due to its high reliability and low cost. In this scheme, the AC network of the OWF and the DRU has completely different synchronization mechanisms and power flow charac

Yin Li, Bo Liu, Rajalakshmi Nanadakumar

Acoustic sensing manifests great potential in various applications that encompass health monitoring, gesture interface and imaging by leveraging the speakers and microphones on smart devices. However, in ongoing research and development in acoustic sensing, one problem is often overlooked: the same speaker, when used concurrently for sensing and other tradit

Zhehui Huang, Guangyao Shi, Gaurav S. Sukhatme

Routing problems are common in mobile robotics, encompassing tasks such as inspection, surveillance, and coverage. Depending on the objective and constraints, these problems often reduce to variants of the Traveling Salesman Problem (TSP), with solutions traditionally derived by translating high-level objectives into an optimization formulation and using mod

Zixuan Wu, Sean Ye, Manisha Natarajan, Matthew C. Gombolay

Reinforcement Learning (RL)-based motion planning has recently shown the potential to outperform traditional approaches from autonomous navigation to robot manipulation. In this work, we focus on a motion planning task for an evasive target in a partially observable multi-agent adversarial pursuit-evasion game (PEG). Pursuit-evasion problems are relevant to

G. B. de Gracia, B. M. Pimentel

Taking into account the recent developments associated with duality in physics, this article is focused on investigating the properties of a tensor generalization of the electrodynamics dual to the standard vector model even considering the full radiative corrections. A discussion on duality is extended for non-linearly interacting models. The alternative te

Lucy Jiang, Crescentia Jung, Mahika Phutane, Abigale Stangl

While audio description (AD) is the standard approach for making videos accessible to blind and low vision (BLV) people, existing AD guidelines do not consider BLV users' varied preferences across viewing scenarios. These scenarios range from how-to videos on YouTube, where users seek to learn new skills, to historical dramas on Netflix, where a user's goal

Katie Buchhorn, Kerrie Mengersen, Edgar Santos-Fernandez, James McGree

Data collected from arrays of sensors are essential for informed decision-making in various systems. However, the presence of anomalies can compromise the accuracy and reliability of insights drawn from the collected data or information obtained via statistical analysis. This study aims to develop a robust Bayesian optimal experimental design (BOED) framewor

Zhenxiao Fu, Min Yang, Cheng Chu, Yilun Xu

Variational quantum circuits (VQCs) have become a powerful tool for implementing Quantum Neural Networks (QNNs), addressing a wide range of complex problems. Well-trained VQCs serve as valuable intellectual assets hosted on cloud-based Noisy Intermediate Scale Quantum (NISQ) computers, making them susceptible to malicious VQC stealing attacks. However, tradi

Jon Goohs, Georgel Savin, Lucas Starks, Josiah Dykstra

Variations of the Flip-It game have been applied to model network cyber operations. While Flip-It can accurately express uncertainty and loss of control, it imposes no essential resource constraints for operations. Capture the flag (CTF) style competitive games, such as Flip-It , entail uncertainties and loss of control, but also impose realistic constraints

B. Dotson, P. Metzger, J. Hafner, A. Shackelford

This study examines the characteristics, composition, and origin of fine particle debris samples collected following the launch of the first Starship orbital test flight, which suggests a new launch pad failure mode previously unknown. Particle shapes, sizes, bulk densities, and VIS/NIR/MIR spectra, of collected fine particle material from Port Isabel, TX, w

Yuansan Liu, Sudanthi Wijewickrema, Christofer Bester, Stephen O'Leary

Finding effective representations for time series data is a useful but challenging task. Several works utilize self-supervised or unsupervised learning methods to address this. However, there still remains the open question of how to leverage available label information for better representations. To answer this question, we exploit pre-existing techniques i

Yuwen Chen, Nicholas Konz, Hanxue Gu, Haoyu Dong

Accurately translating medical images between different modalities, such as Computed Tomography (CT) to Magnetic Resonance Imaging (MRI), has numerous downstream clinical and machine learning applications. While several methods have been proposed to achieve this, they often prioritize perceptual quality with respect to output domain features over preserving

Ning Xi, Xiao Wang, Yunjing Gao, Yunfeng Jiang

Quantum integrability emerging near a quantum critical point (QCP) is manifested by exotic excitation spectrum that is organized by the associated algebraic structure. A well known example is the emergent $E_8$ integrability near the QCP of a transverse field Ising chain (TFIC), which was long predicted theoretically and initially proposed to be realized in

Amirreza Esmaeili, Iman Saberi, Fatemeh H. Fard

Parameter Efficient Fine-Tuning (PEFT) methods are proposed as an alternative fine-tuning approach for Large Language Models (LLM) to minimize high training costs. While prior research demonstrates the effectiveness of PEFT methods in knowledge transfer using smaller language models, their application to larger LLMs, particularly in low-resource and unseen p

Jack Saunders, Sajad Saeedi, Adam Hartshorne, Binbin Xu

Station-keeping tasks for high-altitude balloons show promise in areas such as ecological surveys, atmospheric analysis, and communication relays. However, identifying the optimal time and position to launch a latex high-altitude balloon is still a challenging and multifaceted problem. For example, tasks such as forest fire tracking place geometric constrain

Rui Wang, Hailong Guo, Jiaming Liu, Huaxia Li

In this paper, we introduce StableGarment, a unified framework to tackle garment-centric(GC) generation tasks, including GC text-to-image, controllable GC text-to-image, stylized GC text-to-image, and robust virtual try-on. The main challenge lies in retaining the intricate textures of the garment while maintaining the flexibility of pre-trained Stable Diffu

Mahdi Alehdaghi, Pourya Shamsolmoali, Rafael M. O. Cruz, Eric Granger

A key challenge in visible-infrared person re-identification (V-I ReID) is training a backbone model capable of effectively addressing the significant discrepancies across modalities. State-of-the-art methods that generate a single intermediate bridging domain are often less effective, as this generated domain may not adequately capture sufficient common dis

Rongwu Xu

Humor, a culturally nuanced aspect of human language, poses challenges for computational understanding and generation, especially in Chinese humor, which remains relatively unexplored in the NLP community. This paper investigates the capability of state-of-the-art language models to comprehend and generate Chinese humor, specifically focusing on training the

Mariia Khan, Yue Qiu, Yuren Cong, Jumana Abu-Khalaf

Multi-class multi-instance segmentation is the task of identifying masks for multiple object classes and multiple instances of the same class within an image. The foundational Segment Anything Model (SAM) is designed for promptable multi-class multi-instance segmentation but tends to output part or sub-part masks in the "everything" mode for various real-wor