October 2023 arXiv papers — page 85

Helmut Prodinger

Motzkin excursions and meanders are revisited. This is considered in the context of forbidden patterns. Previous work by Asinowski, Banderier, Gittenberger, and Roitner is continued. Motzkin paths of bounded height are considered, leading to matrix equations and also to continued fractions. The enumeration is done by properly setting up bivariate generating

Fengbo Gu, Jiangfeng Zhou, Junhui Liao, Yuanning Gao

According to many dark matter models, a potential signal registered in a detector would feature a single-scattering nuclear recoil (NR). So, it is crucial to calibrate the detector's response to NR events. The conventional calibrations implement $\sim$ keV to MeV neutrons, which can be produced by an accelerator, a neutron generator, or a radioactive source.

Oleksandra Ivanova, Javier Licandro, Fernando Moreno, Igor Luk'yanyk

We present the results of observations of asteroid (248370) QN$_{173}$ obtained during July 2021 - January 2022 with three telescopes. Our analysis revealed the presence of the dust tail for about half of a year. The direct images of the asteroid were obtained with broad-band filters. No emissions were revealed in the spectra, and the spectrum of the asteroi

Emmanuel Klu, Sameer Sethi, DJ Passey, Donald Martin

Understanding the long-term impact of algorithmic interventions on society is vital to achieving responsible AI. Traditional evaluation strategies often fall short due to the complex, adaptive and dynamic nature of society. While reinforcement learning (RL) can be a powerful approach for optimizing decisions in dynamic settings, the difficulty of realistic e

J. Tang, K. Jiang, C. Xu, M. J. Cabral

Nominally phase-pure $\gamma$-$Ga_2O_3$ was deposited on (100) $MgAl_2O_4$ within a narrow temperature window centered at $\sim$470 $^{\circ}$C using metal-organic chemical vapor deposition (MOCVD). The film deposited at 440 $^{\circ}$C exhibited either poor crystallization or an amorphous structure; the film grown at 500 $^{\circ}$C contained both $\beta$-$

Hao Hei, Yin Huang

Currently, there is much controversy surrounding the interpretation of the $\Xi(2030)$ and $\Xi(2012)$ as traditional hadrons containing double strange quarks. In particular, the ratios of the partial decay widths into $\Lambda\bar{K}$ and $\Sigma\bar{K}$ for $\Xi(2030)$ cannot obtain a suitable explanation under the $qss$ three quark structure~\cite{Xiao:20

Shanshan Han, Vishal Chakraborty, Michael Goodrich, Sharad Mehrotra

This paper addresses volume leakage (i.e., leakage of the number of records in the answer set) when processing keyword queries in encrypted key-value (KV) datasets. Volume leakage, coupled with prior knowledge about data distribution and/or previously executed queries, can reveal both ciphertexts and current user queries. We develop a solution to prevent vol

Xiangjue Dong, Ziwei Zhu, Zhuoer Wang, Maria Teleki

Pre-trained Language Models are widely used in many important real-world applications. However, recent studies show that these models can encode social biases from large pre-training corpora and even amplify biases in downstream applications. To address this challenge, we propose Co$^2$PT, an efficient and effective debias-while-prompt tuning method for miti

Olumide E. Ojo, Olaronke O. Adebanji, Alexander Gelbukh, Hiram Calvo

Zero-shot classification enables text to be classified into classes not seen during training. In this study, we examine the efficacy of zero-shot learning models in classifying healthcare consultation responses from Doctors and AI systems. The models evaluated include BART, BERT, XLM, XLM-R and DistilBERT. The models were tested on three different datasets b

Dai Shi

A mathematical model to study the flow evolution in RB convection laden with finite-sized particles after a flow perturbation is developed with an Euler-Lagrange viewpoint. A linear analysis is conducted by combining the averaged flow-scale momentum equation with the averaged particle-scale heat exchange equation. A flow mode of regular oscillation, which is

Zipeng Xiao, Zhongkai Hao, Bokai Lin, Zhijie Deng

Neural operators, as an efficient surrogate model for learning the solutions of PDEs, have received extensive attention in the field of scientific machine learning. Among them, attention-based neural operators have become one of the mainstreams in related research. However, existing approaches overfit the limited training data due to the considerable number

Fruzsina J. Agocs, Alex H. Barnett

We present a high-order boundary integral equation (BIE) method for the frequency-domain acoustic scattering of a point source by a singly-periodic, infinite, corrugated boundary. We apply it to the accurate numerical study of acoustic radiation in the neighborhood of a sound-hard two-dimensional staircase modeled after the El Castillo pyramid. Such staircas

Libai Xu, Nancy Reid, Dehan Kong

Composite likelihood usually ignores dependencies among response components, while variational approximation to likelihood ignores dependencies among parameter components. We derive a Gaussian variational approximation to the composite log-likelihood function for Poisson and Gamma regression models with crossed random effects. We show consistency and asympto

Yuyang Han, Christian Pederson, Bethany E. Matthews, Nicholas S. Yama

The need of near-surface color centers in diamond for quantum technologies motivates the controlled doping of specific extrinsic impurities into the crystal lattice. Recent experiments have shown that this can be achieved by momentum transfer from a surface precursor via ion implantation, an approach known as ``recoil implantation.'' Here, we extend this tec

Mackenzie Lach, Steph Sallum, Ravinder Banyal, Natalie Batalha

The Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy (SCALES) instrument is a lenslet-based integral field spectrograph that will operate at 2 to 5 microns, imaging and characterizing colder (and thus older) planets than current high-contrast instruments. Its spatial resolution for distant science targets and/or close-in disks and companions

Kishorkumar Sarva

We investigate the effects of fibre morphologies, such as single and granular chain with torus bead on liquid film evolution using experimental and axi-symmetric numerical simulations with a one-fluid formulation. We introduce a non-dimensional parameter 'Bead Ratio'($BR$), that is, the ratio of bead diameter to the film height. When both the $BR$ and its di

Wenxuan Wang, Wenxiang Jiao, Jingyuan Huang, Ruyi Dai

This paper identifies a cultural dominance issue within large language models (LLMs) due to the predominant use of English data in model training (e.g., ChatGPT). LLMs often provide inappropriate English-culture-related answers that are not relevant to the expected culture when users ask in non-English languages. To systematically evaluate the cultural domin

Grace Diehl, Julie A. Adams

Robotic collectives for military and disaster response applications require coalition formation algorithms to partition robots into appropriate task teams. Collectives' missions will often incorporate tasks that require multiple high-level robot behaviors or services, which coalition formation must accommodate. The highly dynamic and unstructured application

Stephan I. Tzenov, Zhichu Chen, Hailong Wu

Based on the technique of the discrete one-turn transfer maps, the problem of linear coupling between horizontal and vertical betatron oscillations in an accelerator has been treated exactly and entirely in explicit form. The stability region in the fractional part of the horizontal and the vertical betatron tune space as a function of the linear coupling st

Paul Manns, Vanja Nikolić

We consider optimal control problems that have binary-valued control input functions and a perimeter regularization. We develop and analyze a trust-region algorithm that solves a sequence of subproblems in which the regularization term and the binarity constraint are relaxed by a non-convex energy functional. We show how the parameter that controls the disti

Ming-Hao Hsu, Kai-Wei Chang, Shang-Wen Li, Hung-yi Lee

Ever since the development of GPT-3 in the natural language processing (NLP) field, in-context learning (ICL) has played an essential role in utilizing large language models (LLMs). By presenting the LM utterance-label demonstrations at the input, the LM can accomplish few-shot learning without relying on gradient descent or requiring explicit modification o

Ardian Nata Atmaja

Using the BPS Lagrangian method, we show that gravity theory coupled to matter in various dimensions may possess Bogomol'nyi-like equations, which are first-order differential equations, satisfying the Einstein equations and the Euler-Lagrange equations of classical fields ($U(1)$ gauge and scalar fields). In particular we consider static and spherically sym

Christopher Eling

We show that the incompressible Euler equations in three spatial dimensions can be expressed in terms of an abelian gauge theory with a topological BF term. A crucial part of the theory is a 3-form field strength, which is dual to a material invariant local helicity in the fluid. In one version of the theory, there is an additional 2-form field strength, wit

Zijie Pan, Jiachen Lu, Xiatian Zhu, Li Zhang

High-resolution 3D object generation remains a challenging task primarily due to the limited availability of comprehensive annotated training data. Recent advancements have aimed to overcome this constraint by harnessing image generative models, pretrained on extensive curated web datasets, using knowledge transfer techniques like Score Distillation Sampling

I. A. Ivanov, Kyung Taec Kim

We present results of relativistic calculations of even order harmonic generation from various atomic targets. The even order harmonics appear due to the relativistic non-dipole effects. We take these relativistic effects into account by using an approach based on the solution of the time-dependent Dirac equation. The spectra of the non-dipole even harmonics

Gregor Sauer, Mirco Kolarczik, Rodrigo Gomez, Johanna Conrad

Photon-number-resolving (PNR) detectors are a key enabling technology in photonic quantum information processing. Here, we demonstrate the PNR capacity of conventional superconducting nanowire single-photon detectors by performing ultra-high-resolution time-tagging of the detector-generated electrical pulses. This method provides a viable approach for PNR wi

Timon Schapeler, Niklas Lamberty, Thomas Hummel, Fabian Schlue

We apply principal component analysis (PCA) to a set of electrical output signals from a commercially available superconducting nanowire single-photon detector (SNSPD) to investigate their photon-number-resolving capability. We find that the rising edge as well as the amplitude of the electrical signal have the most dependence on photon number. Accurately me

Esteban Segarra Martinez, Ryan P. McMahan

Point clouds are a 3D space representation of an environment that was recorded with a high precision laser scanner. These scanners can suffer from environmental interference such as surface shading, texturing, and reflections. Because of this, point clouds may be contaminated with fake or incorrect colors. Current open source or proprietary tools offer limit

P. A. Nosov, Yi-Ming Wu, S. Raghu

We study de Haas-van Alphen oscillations in a marginal Fermi liquid resulting from a three-dimensional metal tuned to a quantum-critical point (QCP). We show that the conventional approach based on extensions of the Lifshitz-Kosevich formula for the oscillation amplitudes becomes inapplicable when the correlation length exceeds the cyclotron radius. This bre

Jane Panangaden

We begin with the higher-weight modular symbols introduced by Shokurov, which generalize Manin's weight-2 modular symbols. We then define higher-weight limiting modular symbols associated to vertical geodesics with one endpoint at an irrational real number, by means of a limiting procedure on Shokurov's modular symbols. These are analogous to the Manin-Marco

Etsuko Ishii, Yan Xu, Bryan Wilie, Ziwei Ji

Inference, especially those derived from inductive processes, is a crucial component in our conversation to complement the information implicitly or explicitly conveyed by a speaker. While recent large language models show remarkable advances in inference tasks, their performance in inductive reasoning, where not all information is present in the context, is

S. Rajagopal, P. Vanchinathan

Generalising the concept of a complete permutation polynomial over a finite field, we define completness to level $k$ for $k\ge1$ in fields of odd characteristic. We construct two families of polynomials that satisfy the condition of high level completeness for all finite fields, and two more families complete to the maximum level a possible for large collec

Alzayat Saleh, Alex Olsen, Jake Wood, Bronson Philippa

Image classification is a crucial task in modern weed management and crop intervention technologies. However, the limited size, diversity, and balance of existing weed datasets hinder the development of deep learning models for generalizable weed identification. In addition, the expensive labelling requirements of mainstream fully-supervised weed classifiers

Abhinav Agarwalla, Xuhua Huang, Jason Ziglar, Francesco Ferroni

State-of-the-art lidar panoptic segmentation (LPS) methods follow bottom-up segmentation-centric fashion wherein they build upon semantic segmentation networks by utilizing clustering to obtain object instances. In this paper, we re-think this approach and propose a surprisingly simple yet effective detection-centric network for both LPS and tracking. Our ne

Benjamin Grace, Karl Wette, Susan M. Scott, Ling Sun

In this work we characterise the performance of a new search technique designed to be sensitive to the remnants of binary neutron star systems. Sensitivity estimates of the new method on simulated data are competitive against those of other work. Previous searches for a gravitational-wave signal from a possible neutron star remnant of the binary neutron star

Yichuan Deng, Zhao Song, Shenghao Xie, Chiwun Yang

In the realm of deep learning, transformers have emerged as a dominant architecture, particularly in natural language processing tasks. However, with their widespread adoption, concerns regarding the security and privacy of the data processed by these models have arisen. In this paper, we address a pivotal question: Can the data fed into transformers be reco

Youngkyu Lee, Jongho Park, Chang-Ock Lee

The performance of neural networks has been significantly improved by increasing the number of channels in convolutional layers. However, this increase in performance comes with a higher computational cost, resulting in numerous studies focused on reducing it. One promising approach to address this issue is group convolution, which effectively reduces the co

Jordan Bryan, Peter Hoff

Motivated by applications to water quality monitoring using fluorescence spectroscopy, we develop the source apportionment model for high dimensional profiles of dissolved organic matter (DOM). We describe simple methods to estimate the parameters of a linear source apportionment model, and show how the estimates are related to those of ordinary and generali

Javier Hernandez, Jina Suh, Judith Amores, Kael Rowan

The rise of AI conversational agents has broadened opportunities to enhance human capabilities across various domains. As these agents become more prevalent, it is crucial to investigate the impact of different affective abilities on their performance and user experience. In this study, we surveyed 745 respondents to understand the expectations and preferenc

Jia-Qi Wang, Xiao-Jun Jiang, Jie Zheng, Hanna Kellermann

We report the confirmation of a sub-Saturn-size exoplanet, TOI-1194 b with a mass about $0.456_{-0.051}^{+0.055}$ $M_{J}$, and a very low mass companion star with a mass of about $96.5\pm1.5$ $M_J$, TOI-1251 B. Exoplanet candidates provided by the Transiting Exoplanet Survey Satellite (TESS) are suitable for further follow-up observations by ground-based tel

Haitian Jiang, Renjie Liu, Zengfeng Huang, Yichuan Wang

Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that the forward pass iteratively reduces a graph-regularized energy function of interest. In this way, node embeddings produced at the output layer dually serve as both predictive fea

Adeel A. Khan

Notes on algebraic stacks, prepared for an 11-lecture course at the NCTS, Taipei, during the fall of 2022.

Tianchi Yang, Minghui Song, Zihan Zhang, Haizhen Huang

Generative retrieval, which is a new advanced paradigm for document retrieval, has recently attracted research interests, since it encodes all documents into the model and directly generates the retrieved documents. However, its power is still underutilized since it heavily relies on the "preprocessed" document identifiers (docids), thus limiting its retriev

You Li, Jinhui Yin, Yuming Lin

Pretrained language models are expected to effectively map input text to a set of vectors while preserving the inherent relationships within the text. Consequently, designing a white-box model to compute metrics that reflect the presence of specific internal relations in these vectors has become a common approach for post-hoc interpretability analysis of pre

Travis J. Thieme, Shih-Ping Lai, Nagayoshi Ohashi, John J. Tobin

Protostellar disks are a ubiquitous part of the star formation process and the future sites of planet formation. As part of the Early Planet Formation in Embedded Disks (eDisk) large program, we present high-angular resolution dust continuum ($\sim40\,$mas) and molecular line ($\sim150\,$mas) observations of the Class 0 protostar, IRAS 15398-3359. The dust c

Hanbo Bi, Yingchao Feng, Zhiyuan Yan, Yongqiang Mao

Few-shot segmentation (FSS) is proposed to segment unknown class targets with just a few annotated samples. Most current FSS methods follow the paradigm of mining the semantics from the support images to guide the query image segmentation. However, such a pattern of `learning from others' struggles to handle the extreme intra-class variation, preventing FSS

Huayu Li, Ana S. Carreon-Rascon, Xiwen Chen, Geng Yuan

Medical time series data are indispensable in healthcare, providing critical insights for disease diagnosis, treatment planning, and patient management. The exponential growth in data complexity, driven by advanced sensor technologies, has presented challenges related to data labeling. Self-supervised learning (SSL) has emerged as a transformative approach t

Zhenran Xu, Yulin Chen, Baotian Hu, Min Zhang

Zero-shot entity linking (EL) aims at aligning entity mentions to unseen entities to challenge the generalization ability. Previous methods largely focus on the candidate retrieval stage and ignore the essential candidate ranking stage, which disambiguates among entities and makes the final linking prediction. In this paper, we propose a read-and-select (ReS

Yunrui Li, Zahra Zarei, Phu N. Tran, Yifei Wang

Active nematics are dense systems of rodlike particles that consume energy to drive motion at the level of the individual particles. They exist in natural systems like biological tissues and artificial materials such as suspensions of self-propelled colloidal particles or synthetic microswimmers. Active nematics have attracted significant attention in recent

Spiro Gicev, Lloyd C. L. Hollenberg, Muhammad Usman

With quantum devices rapidly approaching qualities and scales needed for fault tolerance, the validity of simplified error models underpinning the study of quantum error correction needs to be experimentally evaluated. In this work, we have assessed the performance of IBM superconducting quantum computer devices implementing heavy-hexagon code syndrome measu

Abhisek Chakraborty, Anirban Bhattacharya, Debdeep Pati

We commonly encounter the problem of identifying an optimally weight adjusted version of the empirical distribution of observed data, adhering to predefined constraints on the weights. Such constraints often manifest as restrictions on the moments, tail behaviour, shapes, number of modes, etc., of the resulting weight adjusted empirical distribution. In this

Jieao Zhu, Zhongzhichao Wan, Linglong Dai, Tie Jun Cui

Electromagnetic information theory (EIT) is an emerging interdisciplinary subject that integrates classical Maxwell electromagnetics and Shannon information theory. The goal of EIT is to uncover the information transmission mechanisms from an electromagnetic (EM) perspective in wireless systems. Existing works on EIT are mainly focused on the analysis of EM

Ji-Bing Yuan, Zhi-Min Tang, Ya-Ju Song, Shi-Qing Tang

We investigate the utilization of a single generalized dephasing qubit for sensing a quantum reservoir, where the antisymmetric coupling between the qubit and its reservoir is broken. It is found that in addition to the decay factor encoding channel, the antisymmetric coupling breaking gives rise to another phase factor encoding channel. We introduce an opti

Yulin Chen, Zhenran Xu, Baotian Hu, Min Zhang

Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base. One of the key challenges comes from insufficient labeled data for specific domains. Although dense retrievers have achieved excellent performance on several benchmarks, their performance decreases significantly when only a limited amount of in-domain labeled

Xiang Shi, Jiawei Liu, Yinpeng Liu, Qikai Cheng

The advent of Large Language Models (LLMs) has shown the potential to improve relevance and provide direct answers in web searches. However, challenges arise in validating the reliability of generated results and the credibility of contributing sources, due to the limitations of traditional information retrieval algorithms and the LLM hallucination problem.

Qingru Zhang, Dhananjay Ram, Cole Hawkins, Sheng Zha

Pretrained transformer models have demonstrated remarkable performance across various natural language processing tasks. These models leverage the attention mechanism to capture long- and short-range dependencies in the sequence. However, the (full) attention mechanism incurs high computational cost - quadratic in the sequence length, which is not affordable

Dengfa Liu, Hongbo Li

Functional bootstrapping is a core technique in Fully Homomorphic Encryption (FHE). For large plaintext, to evaluate a general function homomorphically over a ciphertext, in the FHEW/TFHE approach, since the function in look-up table form is encoded in the coefficients of a test polynomial, the degree of the polynomial must be high enough to hold the entire

Ria Rashid, Gopavaram Raghunath, Vasant Badugu, Nandakumar Nambath

An automated sizing approach for analog circuits using evolutionary algorithms is presented in this paper. A targeted search of the search space has been implemented using a particle generation function and a repair-bounds function that has resulted in faster convergence to the optimal solution. The algorithms are tuned and modified to converge to a better o

Hongwei Yao, Jian Lou, Zhan Qin

Prompts have significantly improved the performance of pretrained Large Language Models (LLMs) on various downstream tasks recently, making them increasingly indispensable for a diverse range of LLM application scenarios. However, the backdoor vulnerability, a serious security threat that can maliciously alter the victim model's normal predictions, has not b

Yeisson Acevedo, Danilo Hernández García, Gabriel Loaiza, Oscar Londoño

We obtain the optimal system's generating operators associated with the kind generalization of the Levinson Smith equation. Using those operators we characterize all invariant solutions associated with this equation. Moreover, we present the variational symmetries and the corresponding conservation laws, using Noether's theorem. Finally, we classify the Lie

Ayoub El Hanchi, Murat A. Erdogdu

We study the performance of empirical risk minimization on the $p$-norm linear regression problem for $p \in (1, \infty)$. We show that, in the realizable case, under no moment assumptions, and up to a distribution-dependent constant, $O(d)$ samples are enough to exactly recover the target. Otherwise, for $p \in [2, \infty)$, and under weak moment assumption

Junxiong Jia, Deyu Meng, Zongben Xu, Fang Yao

This paper addresses Bayesian inference related to partial differential equations (PDEs), particularly nonparametric regression constrained by PDEs. To effectively encode prior information, we propose a novel framework that learns a prediction function of the prior distribution from historical training datasets. We introduce hyper-prior and hyper-posterior d

David Kogan, Dimitrios Diamantidis, John Wakeley, Wai-Tong Louis Fan

The correlation among the gene genealogies at different loci is crucial in biology, yet challenging to understand because such correlation depends on many factors including genetic linkage, recombination, natural selection and population structure. Based on a diploid Wright-Fisher model with a single mating type and partial selfing for a constant large popul

Vineet Barwal, Hirofumi Suto, Tomohiro Taniguchi, Yuya Sakuraba

We develop a high-throughput method for measuring the composition dependence of magnetoresistance (MR) and spin-transfer-torque (STT) effects in current-perpendicular-to-plane giant magnetoresistance (CPP-GMR) devices and report its application to the CoFe system. The method is based on the use of composition-gradient films deposited by combinatorial sputter

Kun Li, Shengling Wang, Hongwei Shi, Xiuzhen Cheng

Spatial crowdsourcing (SC) engages large worker pools for location-based tasks, attracting growing research interest. However, prior SC task allocation approaches exhibit limitations in computational efficiency, balanced matching, and participation incentives. To address these challenges, we propose a graph-based allocation framework optimized for massive he

Linrui Zhang, Zhenghao Peng, Quanyi Li, Bolei Zhou

Driving safety is a top priority for autonomous vehicles. Orthogonal to prior work handling accident-prone traffic events by algorithm designs at the policy level, we investigate a Closed-loop Adversarial Training (CAT) framework for safe end-to-end driving in this paper through the lens of environment augmentation. CAT aims to continuously improve the safet

Dongshen Han, Chaoning Zhang, Sheng Zheng, Chang Lu

As Segment Anything Model (SAM) becomes a popular foundation model in computer vision, its adversarial robustness has become a concern that cannot be ignored. This works investigates whether it is possible to attack SAM with image-agnostic Universal Adversarial Perturbation (UAP). In other words, we seek a single perturbation that can fool the SAM to predict

Cong Yao

In this report, we introduce DocXChain, a powerful open-source toolchain for document parsing, which is designed and developed to automatically convert the rich information embodied in unstructured documents, such as text, tables and charts, into structured representations that are readable and manipulable by machines. Specifically, basic capabilities, inclu

Changzhu Liu, Ruisi He, Yong Niu, Zhu Han

Reconfigurable intelligent surface (RIS) emerges as an efficient and promising technology for the next wireless generation networks and has attracted a lot of attention owing to the capability of extending wireless coverage by reflecting signals toward targeted receivers. In this paper, we consider a RIS-assisted high-speed train (HST) communication system t

Joshua Rosaler, Dhruv Desai, Bhaskarjit Sarmah, Dimitrios Vamvourellis

We initiate a novel approach to explain the predictions and out of sample performance of random forest (RF) regression and classification models by exploiting the fact that any RF can be mathematically formulated as an adaptive weighted K nearest-neighbors model. Specifically, we employ a recent result that, for both regression and classification tasks, any

Luke Hagar, Nathaniel T. Stevens

Bayesian hypothesis tests leverage posterior probabilities, Bayes factors, or credible intervals to inform data-driven decision making. We propose a framework for power curve approximation with such hypothesis tests. We present a fast approach to explore the approximate sampling distribution of posterior probabilities when the conditions for the Bernstein-vo

Deepak Nathani, David Wang, Liangming Pan, William Yang Wang

Language Models (LMs) have shown impressive performance in various natural language tasks. However, when it comes to natural language reasoning, LMs still face challenges such as hallucination, generating incorrect intermediate reasoning steps, and making mathematical errors. Recent research has focused on enhancing LMs through self-improvement using feedbac

Md Rashedul Hasan, Jiawei Li, Iftekhar Ahmed, Hamid Bagheri

The growing adoption of declarative software specification languages, coupled with their inherent difficulty in debugging, has underscored the need for effective and automated repair techniques applicable to such languages. Researchers have recently explored various methods to automatically repair declarative software specifications, such as template-based r

Subhodh Kotekal, Soumyabrata Kundu

Heteroskedasticity testing in nonparametric regression is a classic statistical problem with important practical applications, yet fundamental limits are unknown. Adopting a minimax perspective, this article considers the testing problem in the context of an $\alpha$-H\"{o}lder mean and a $\beta$-H\"{o}lder variance function. For $\alpha > 0$ and $\beta \in

Takahito Takeda, Kengo Takase, Vladimir N. Strocov, Masaaki Tanaka

Two-dimensional (2D) systems, such as high-temperature superconductors, surface states of topological insulators, and layered materials, have been intensively studied using vacuum-ultraviolet (VUV) angle-resolved photoemission spectroscopy (ARPES). In semiconductor films (heterostructures), quantum well (QW) states arise due to electron/hole accumulations at

Yujun Zhu, Ju Ming, Jie Zhu, Zhongming Wang

In this paper, we propose a low rank approximation method for efficiently solving stochastic partial differential equations. Specifically, our method utilizes a novel low rank approximation of the stiffness matrices, which can significantly reduce the computational load and storage requirements associated with matrix inversion without losing accuracy. To dem

Wendy Hui, Wai Kwong Lau

This paper proposes the use of causal modeling to detect and mitigate algorithmic bias. We provide a brief description of causal modeling and a general overview of our approach. We then use the Adult dataset, which is available for download from the UC Irvine Machine Learning Repository, to develop (1) a prediction model, which is treated as a black box, and

Ruihong Ma, Engui Fan

The Ostrovsky-Vakhnenko (OV) equation \begin{align*} &u_{txx}-3\kappa u_x+3u_xu_{xx}+uu_{xxx}=0 \end{align*} is a short wave model of the well-known Degasperis-Procesi equation and admits a $3\times 3$ matrix Lax pair. In this paper, we study the soliton resolution and asymptotic stability of $N$-loop soliton solutions for the OV equation with Schwartz initi

Jianing Wang, Qiushi Sun, Nuo Chen, Chengyu Wang

The recent success of large pre-trained language models (PLMs) heavily hinges on massive labeled data, which typically produces inferior performance in low-resource scenarios. To remedy this dilemma, we study self-training as one of the predominant semi-supervised learning (SSL) approaches, which utilizes large-scale unlabeled data to generate synthetic exam

Imtiaz Khan, Waqas Ahmed, Tianjun Li, Shabbar Raza

Because there are a few typos in the supersymmetry breaking sfermion masses and trilinear soft term, regarding the current Large Hadron Collider (LHC) and dark matter searches, we revisit a three-family Pati-Salam model based on intersecting D6-branes in Type IIA string theory on a $\mathbf{T^6/(\Z_2\times \Z_2)}$ orientifold with a realistic phenomenology.

Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to

Huanyao Rong, Wei You, Xiaofeng Wang, Tianhao Mao

In this paper, we propose a novel directed fuzzing solution named AFLRun, which features target path-diversity metric and unbiased energy assignment. Firstly, we develop a new coverage metric by maintaining extra virgin map for each covered target to track the coverage status of seeds that hit the target. This approach enables the storage of waypoints into t

Siru Ouyang, Shuohang Wang, Yang Liu, Ming Zhong

Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks. However, it remains unclear whether the existing focus of NLP research accurately captures the genuine requirements of human users. This paper provides a comprehensive analysis of the divergence between current NLP research a

Xintong Zhao, Kyle Langlois, Jacob Furst, Scott McClellan

Research methods and procedures are core aspects of the research process. Metadata focused on these components is critical to supporting the FAIR principles, particularly reproducibility. The research reported on in this paper presents a methodological framework for metadata documentation supporting the reproducibility of research producing Metal Organic Fra

Nathan A. Wassermann, Yongchang Li, Alexander J. Myers, Christopher A. Kantzos

Previous work on additively-manufactured oxide dispersion strengthened alloys focused on experimental approaches, resulting in larger dispersoid sizes and lower number densities than can be achieved with conventional powder metallurgy. To improve the as-fabricated microstructure, this work integrates experiments with a thermodynamic and kinetic modeling fram

Yi Song, Xihao Zhang, Xiaoyuan Xie, Songqiang Chen

Failure indexing is a longstanding crux in software testing and debugging, the goal of which is to automatically divide failures (e.g., failed test cases) into distinct groups according to the culprit root causes, as such multiple faults in a faulty program can be handled independently and simultaneously. This community has long been plagued by two challenge

Cedegao E. Zhang, Katherine M. Collins, Adrian Weller, Joshua B. Tenenbaum

Mathematics is one of the most powerful conceptual systems developed and used by the human species. Dreams of automated mathematicians have a storied history in artificial intelligence (AI). Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems. In this work

Xingyu Ma, Minghuan Zeng, Huaiming Guo, Shiping Feng

The recently observed an intimate link between the nature of the strange metallic normal-state and superconductivity in the overdoped electron-doped cuprate superconductors is calling for an explanation. Here the intrinsic correlation between the strength of the low-temperature linear-in-temperature normal-state resistivity and superconducting transition tem

Zengle Zhang, Jiazu Zhou

The authors gave an affine isoperimetric inequality \cite{LYZ2010} that gives a lower bound for the volume of a polar body and the equality holds if and only if the body is a simplex. In this paper, we give a functional isoperimetric inequality for log-concave functions that contains the affine isoperimetric inequality of Lutwak, Yang and Zhang in \cite{LYZ2

Junhwi Bak, Albina Tropina, James Creel, Richard B. Miles

In this work, the thermionic cooling effect during thermionic discharges with parallel plate electrodes at 1 Torr is investigated. Time-resolved observation of electron emission and surface temperature is realized in addition to the typical steady state characterization. Surface cooling by the electron emission, initiated by plasma ignition, is directly capt

Jacob Hartzer, Srikanth Saripalli

This work presents a centralized multi-IMU filter framework with online intrinsic and extrinsic calibration for unsynchronized inertial measurement units that is robust against changes in calibration parameters. The novel EKF-based method estimates the positional and rotational offsets of the system of sensors as well as their intrinsic biases without the us

Sukla Pal, Stephen Powell

We investigate quench dynamics of spin ice after removal of a strong magnetic field along the [100] crystal direction, using Monte Carlo simulations and theoretical arguments. We show how the early-time relaxation of the magnetization can be understood in terms of nucleation and growth of strings of flipped spins, in agreement with an effective stochastic mo

Jinseong Park, Yong-Sik Shin, Sanghyun Kim

Physical human-robot interactions (pHRIs) can improve robot autonomy and reduce physical demands on humans. In this paper, we consider a collaborative task with a considerably long object and no prior knowledge of the object's parameters. An integrated control framework with an online object parameter estimator and a Cartesian object-aware impedance controll

Zhenmei Shi, Junyi Wei, Yingyu Liang

Neural networks have achieved remarkable empirical performance, while the current theoretical analysis is not adequate for understanding their success, e.g., the Neural Tangent Kernel approach fails to capture their key feature learning ability, while recent analyses on feature learning are typically problem-specific. This work proposes a unified analysis fr

Xianglong Bai, Zengfu Wang, Quan Pan, Tao Yun

We address the challenge of tracking an unknown number of targets in strong clutter environments using measurements from a radar sensor. Leveraging the range-Doppler spectra information, we identify the measurement classes, which serve as additional information to enhance clutter rejection and data association, thus bolstering the robustness of target tracki

Yixuan Tang, Yi Yang, Allen H Huang, Andy Tam

In the financial domain, conducting entity-level sentiment analysis is crucial for accurately assessing the sentiment directed toward a specific financial entity. To our knowledge, no publicly available dataset currently exists for this purpose. In this work, we introduce an entity-level sentiment classification dataset, called \textbf{FinEntity}, that annot

Dayang Wang, Yongshun Xu, Shuo Han, Zhan Wu

Low-dose computed tomography (LDCT) offers reduced X-ray radiation exposure but at the cost of compromised image quality, characterized by increased noise and artifacts. Recently, transformer models emerged as a promising avenue to enhance LDCT image quality. However, the success of such models relies on a large amount of paired noisy and clean images, which

Yixiao Zhang, Akira Maezawa, Gus Xia, Kazuhiko Yamamoto

Creating music is iterative, requiring varied methods at each stage. However, existing AI music systems fall short in orchestrating multiple subsystems for diverse needs. To address this gap, we introduce Loop Copilot, a novel system that enables users to generate and iteratively refine music through an interactive, multi-round dialogue interface. The system

Muhammed Fatih Balin, Dominique LaSalle, Ümit V. Çatalyürek

Training large scale Graph Neural Networks (GNNs) requires significant computational resources, and the process is highly data-intensive. One of the most effective ways to reduce resource requirements is minibatch training coupled with graph sampling. GNNs have the unique property that items in a minibatch have overlapping data. However, the commonly impleme

Abdul-Nasah Soale, Yuexiao Dong

Data visualization and dimension reduction for regression between a general metric space-valued response and Euclidean predictors is proposed. Current Fr\'ech\'et dimension reduction methods require that the response metric space be continuously embeddable into a Hilbert space, which imposes restriction on the type of metric and kernel choice. We relax this