December 2023 arXiv papers — page 122

Salvatore Spina

Thanks to its heterogeneity, the Biscari Archive, one of the most representative family's archives in Sicily, in a new digital historical study, became a valuable set of computable data that can lead historians to reconstruct the history of the city of Catania and Sicily. Ignazio Paterno' Castello and his wife Anna, princes of Biscari, were the promoters of

Youjia Zhang, Zikai Song, Junqing Yu, Yawei Luo

Generating multi-view images from a single input view using image-conditioned diffusion models is a recent advancement and has shown considerable potential. However, issues such as the lack of consistency in synthesized views and over-smoothing in extracted geometry persist. Previous methods integrate multi-view consistency modules or impose additional super

Nikhel Gupta, Zeeshan Hayder, Ray P. Norris, Minh Hyunh

We present a novel multimodal dataset developed by expert astronomers to automate the detection and localisation of multi-component extended radio galaxies and their corresponding infrared hosts. The dataset comprises 4,155 instances of galaxies in 2,800 images with both radio and infrared modalities. Each instance contains information on the extended radio

Dong Yao, Jieming Zhu, Jiahao Xun, Shengyu Zhang

Recent research in self-supervised contrastive learning of music representations has demonstrated remarkable results across diverse downstream tasks. However, a prevailing trend in existing methods involves representing equally-sized music clips in either waveform or spectrogram formats, often overlooking the intrinsic part-whole hierarchies within music. In

Nicholas Fraser Boardman, Vivienne Wild, Kate Rowlands, Natalia Vale Asari

Gas-phase abundances in galaxies are the products of those galaxies' evolutionary histories. The star-formation history (SFH) of a region might therefore be expected to influence that region's present day gaseous abundances. Here, we employ data from the MaNGA survey to explore how local gas metallicities relate to star-formation histories of galaxy regions.

Simon Klix, Nils Albartus, Julian Speith, Paul Staat

Intellectual Property (IP) theft is a cause of major financial and reputational damage, reportedly in the range of hundreds of billions of dollars annually in the U.S. alone. Field Programmable Gate Arrays (FPGAs) are particularly exposed to IP theft, because their configuration file contains the IP in a proprietary format that can be mapped to a gate-level

Simrandeep Kaur, Tanima Chanda, Kazi Rafsanjani Amin, Divya Sahani

Fractional quantum Hall (FQH) phases emerge due to strong electronic interactions and are characterized by anyonic quasiparticles, each distinguished by unique topological parameters, fractional charge, and statistics. In contrast, the integer quantum Hall (IQH) effects can be understood from the band topology of non-interacting electrons. We report a surpri

Haozhe Jia, Yan Li, Hengfei Cui, Di Xu

In this work, we focus on exploring explicit fine-grained control of generative facial image editing, all while generating faithful facial appearances and consistent semantic details, which however, is quite challenging and has not been extensively explored, especially under an one-shot scenario. We identify the key challenge as the exploration of disentangl

Saeejith Nair, Chi-en Amy Tai, Yuhao Chen, Alexander Wong

Manually tracking nutritional intake via food diaries is error-prone and burdensome. Automated computer vision techniques show promise for dietary monitoring but require large and diverse food image datasets. To address this need, we introduce NutritionVerse-Synth (NV-Synth), a large-scale synthetic food image dataset. NV-Synth contains 84,984 photorealistic

Xiaoyu Dong, Zhenning Cai

We study the iterative methods for large moment systems derived from the linearized Boltzmann equation. By Fourier analysis, it is shown that the direct application of the block symmetric Gauss-Seidel (BSGS) method has slower convergence for smaller Knudsen numbers. Better convergence rates for dense flows are then achieved by coupling the BSGS method with t

Gao Huang, Song Li, Hang Xu

We investigate the phase retrieval problem perturbed by dense bounded noise and sparse outliers that can change an adversarially chosen $s$-fraction of the measurement vector. The adversarial sparse outliers may exhibit dependence on both the observation and the measurement. We demonstrate that the nonlinear least absolute deviation based on amplitude measur

Shreema Subhash Bhatt, Bipul Saurabh

In this article, we prove $K$-stability for a family of $C^*$-algebras, which are generated by a finite set of unitaries and isometries satisfying twisted commutation relations. This family includes the $C^*$-algebra of doubly non-commuting isometries and free twist of isometries. Next, we consider the $C^*$-algebra $A_{\mathcal{V}}$ generated by an $n$-tupl

Hongliang Dai, Ziqian Zeng

For the task of fine-grained entity typing (FET), due to the use of a large number of entity types, it is usually considered too costly to manually annotating a training dataset that contains an ample number of examples for each type. A common way to address this problem is to use distantly annotated training data that contains incorrect labels. However, the

Alexandru Dimca

In this paper we collect the main properties of free curves in the complex projective plane and a lot of conjectures and open problems, both old and new. In the quest to understand the mystery of free curves, many tools were developed and many results were obtained, which apply to any reduced plane curve, and some of them are recorded here.

Carsten Wiuf, Chuang Xu

In this note, we use a result by Harris (1957) to show that there always exists a stationary measure (not necessarily a distribution) on a closed irreducible component of a stochastic reaction network. This measure might not be unique. In particular, any weakly reversible stochastic reaction network has a stationary measure on all closed irreducibe component

Linjie Fu, Xia Li, Xiuding Cai, Yingkai Wang

Radiation therapy serves as an effective and standard method for cancer treatment. Excellent radiation therapy plans always rely on high-quality dose distribution maps obtained through repeated trial and error by experienced experts. However, due to individual differences and complex clinical situations, even seasoned expert teams may need help to achieve th

Mads Chr Hansen, Carsten Wiuf, Chuang Xu

We study continuous-time Markov chains on the non-negative integers under mild regularity conditions (in particular, the set of jump vectors is finite and both forward and backward jumps are possible). Based on the so-called flux balance equation, we derive an iterative formula for calculating stationary measures. Specifically, a stationary measure $\pi(x)$

Qinggang Zhang, Junnan Dong, Hao Chen, Daochen Zha

Large Language Models (LLMs) have demonstrated remarkable capabilities in many real-world applications. Nonetheless, LLMs are often criticized for their tendency to produce hallucinations, wherein the models fabricate incorrect statements on tasks beyond their knowledge and perception. To alleviate this issue, researchers have explored leveraging the factual

Tenghao Deng, Yan Sun

In recent years, with the continuous advancement of deep learning and the emergence of large-scale human motion datasets, human motion prediction technology has gradually gained prominence in various fields such as human-computer interaction, autonomous driving, sports analysis, and personnel tracking. This article introduces common model architectures in th

Song-Jin O

We propose a novel scheme for combining efficiently the truncated-unity functional renormalization group (TUFRG) and the mean-field theory. It follows a method of Wang, Eberlein and Metzner that uses only the two-particle irreducible part of the vertex as an input for the mean-field treatment. In the TUFRG, the neglect of fluctuation effects from other chann

Alexey Yurtin

This article presents a method for recovering missing values in multidimensional time series. The method combines neural network technologies and an algorithm for searching snippets (behavioral patterns of a time series). It includes the stages of data preprocessing, recognition and reconstruction, using convolutional and recurrent neural networks. Experimen

Yueyao Yu, Yin Zhang

Since its introduction in 2017, Transformer has emerged as the leading neural network architecture, catalyzing revolutionary advancements in many AI disciplines. The key innovation in Transformer is a Self-Attention (SA) mechanism designed to capture contextual information. However, extending the original Transformer design to models of greater depth has pro

Ping Zou

The quantum approximate optimization algorithm (QAOA) is one of the canonical algorithms designed to find approximate solutions to combinatorial optimization problems in current noisy intermediate-scale quantum (NISQ) devices. It is an active area of research to exhibit its speedup over classical algorithms. The performance of the QAOA at low depths is limit

Wenbin Guo, Zhao Li, Xin Wang, Zirui Chen

Knowledge graphs often suffer from incompleteness issues, which can be alleviated through information completion. However, current state-of-the-art deep knowledge convolutional embedding models rely on external convolution kernels and conventional convolution processes, which limits the feature interaction capability of the model. This paper introduces a nov

Hao Yin, Bayu Jayawardhana, Stephan Trenn

This paper studies the contraction property of time-varying differential-algebraic equation (DAE) systems by embedding them to higher-dimension ordinary differential equation (ODE) systems. The first result pertains to the equivalence of the contraction of a DAE system and the uniform global exponential stability (UGES) of its variational DAE system. Such eq

Fuxiang Huang, Lei Zhang, Xiaowei Fu, Suqi Song

Mixed-Modal Image Retrieval (MMIR) as a flexible search paradigm has attracted wide attention. However, previous approaches always achieve limited performance, due to two critical factors are seriously overlooked. 1) The contribution of image and text modalities is different, but incorrectly treated equally. 2) There exist inherent labeling noises in describ

Yan Tan, Liucang Wu, Wenqi Liu

In this article, we develop a new adaptive event-triggered asymptotic control scheme for strict-feedback systems with fast time-varying parameters. To deal with time-varying parameters with unknown variation boundaries in the feedback path and the input path, we construct three adaptive laws for parameter estimation, two for the uncertain parameters in the f

Yifei Zong, David Barajas-Solano, Alexandre M. Tartakovsky

We propose a physics-informed machine learning method for uncertainty quantification in high-dimensional inverse problems. In this method, the states and parameters of partial differential equations (PDEs) are approximated with truncated conditional Karhunen-Lo\`eve expansions (CKLEs), which, by construction, match the measurements of the respective variable

Jaehoon Hahm, Hayeon Kim, Young June Park

Variational Quantum Algorithms (VQAs) are expected to be promising algorithms with quantum advantages that can be run at quantum computers in the close future. In this work, we review simple rules in basic quantum circuits, and propose a simplification method, Measurement Simplification, that simplifies the expression for the measurement of quantum circuit.

Shivam Kumar Mishra, Sarveshwar Sharma, Sudip Sengupta

An analytical study of the charged particle dynamics in the presence of an elliptically polarized electromagnetic wave and a uniform axial magnetic field, is presented. It is found that for $g\omega_{0}/ \omega' = \pm 1$, maximum energy gain occurs respectively for linear and circular polarization; $\omega_{0}$ and $\omega'$ respectively being the cyclotron

Andrey Goglachev

This article presents the PaSTiLa algorithm for automated labeling of large time series on a cluster with GPUs. The method automatically selects snippet length values based on the new proposed criterion and allows to search for patterns with high performance. Experiments showed high accuracy of pattern search and the advantage of the method compared to analo

Liang Dai, Kejie Lyu, Chengcheng Zhang, Guangming Zhao

Guaranteed display (GD) advertising is a critical component of advertising since it provides publishers with stable revenue and enables advertisers to target specific audiences with guaranteed impressions. However, smooth pacing control for online ad delivery presents a challenge due to significant budget disparities, user arrival distribution drift, and dyn

Jiawen Yi, Guo Chen

The fundamental goal of the Text-to-SQL task is to translate natural language question into SQL query. Current research primarily emphasizes the information coupling between natural language questions and schemas, and significant progress has been made in this area. The natural language questions as the primary task requirements source determines the hardnes

Qian Shao, Ye Dai, Haochao Ying, Kan Xu

Uveitis demands the precise diagnosis of anterior chamber inflammation (ACI) for optimal treatment. However, current diagnostic methods only rely on a limited single-modal disease perspective, which leads to poor performance. In this paper, we investigate a promising yet challenging way to fuse multimodal data for ACI diagnosis. Notably, existing fusion para

Giovanni Barbarino, Sven-Erik Ekström, Carlo Garoni, David Meadon

We study the spectral properties of flipped Toeplitz matrices of the form $H_n(f)=Y_nT_n(f)$, where $T_n(f)$ is the $n\times n$ Toeplitz matrix generated by the function $f$ and $Y_n$ is the $n\times n$ exchange (or flip) matrix having $1$ on the main anti-diagonal and $0$ elsewhere. In particular, under suitable assumptions on $f$, we establish an alternati

Yifan Liu, Tiecheng Song, Chengye Xian, Ruiyuan Chen

Crater detection can provide valuable information for humans to explore the topography and understand the history of extraterrestrial planets. Due to the significantly varying scenario distributions, existing detection models trained on known labelled crater datasets are hardly effective when applied to new unlabelled planets. To address this issue, we propo

Wenhang Liu, Meng Ren, Kun Song, Michael Yu Wang

During complex object manipulation, manipulator systems often face the configuration disconnectivity problem due to closed-chain constraints. Although regrasping can be adopted to get a piecewise connected manipulation, it is a challenging problem to determine whether there is a planning result without regrasping. To address this problem, a novel planning fr

Yiwen Hu, Xin Liu, Shi Feng, Xu Liu

In computational optics, numerical modeling of diffraction between arbitrary planes offers unparalleled flexibility. However, existing methods suffer from the trade-off between computational accuracy and efficiency. To resolve this dilemma, we present a novel approach that rigorously and efficiently models wave propagation between two arbitrary planes. This

Yin Jiang

In this work the non-abelian gauge theory is reformulated in a local inertial frame with the presence of a background rotation. With this new formalism the influence of the background rotation on the color deconfinement transition for a SU(2) pure gluon system. The KvBLL caloron, which is a color neutral and asymptotically nontrivial solution of Yang-Mills e

Aleksandr V. Petrov, Craig Macdonald

Sequential Recommendation is a popular recommendation task that uses the order of user-item interaction to model evolving users' interests and sequential patterns in their behaviour. Current state-of-the-art Transformer-based models for sequential recommendation, such as BERT4Rec and SASRec, generate sequence embeddings and compute scores for catalogue items

Minghui Zhang, Hao Zheng, Yawen Huang, Ling Shao

Shape modeling of volumetric medical images is crucial for quantitative analysis and surgical planning in computer-aided diagnosis. To alleviate the burden of expert clinicians, reconstructed shapes are typically obtained from deep learning models, such as Convolutional Neural Networks (CNNs) or transformer-based architectures, followed by the marching cube

Kalibinuer Tiliwalidi

Nowadays, the susceptibility of deep neural networks (DNNs) has garnered significant attention. Researchers are exploring patch-based physical attacks, yet traditional approaches, while effective, often result in conspicuous patches covering target objects. This leads to easy detection by human observers. Recently, novel camera-based physical attacks have em

Qiuhai Yan, Aiwen Jiang, Kang Chen, Long Peng

Image restoration has always been a cutting-edge topic in the academic and industrial fields of computer vision. Since degradation signals are often random and diverse, "all-in-one" models that can do blind image restoration have been concerned in recent years. Early works require training specialized headers and tails to handle each degradation of concern,

Ayman Kachmar, Vladimir Lotoreichik

We obtain an upper bound on the lowest magnetic Neumann eigenvalue of a bounded, convex, smooth, planar domain with moderate intensity of the homogeneous magnetic field. This bound is given as a product of a purely geometric factor expressed in terms of the torsion function and of the lowest magnetic Neumann eigenvalue of the disk having the same maximal val

Song Yu, Zhengyu Zong

Let $X$ be a toric Calabi-Yau 3-fold and let $L\subset X$ be an Aganagic-Vafa outer brane. We prove two versions of open WDVV equations for the open Gromov-Witten theory of $(X,L)$. The first version of the open WDVV equation leads to the construction of a semi-simple (formal) Frobenius manifold and the second version leads to the construction of a flat (for

Miriam Shiffman, Ryan Giordano, Tamara Broderick

Differential expression (DE) plays a fundamental role toward illuminating the molecular mechanisms driving a difference between groups (e.g., due to treatment or disease). While any analysis is run on particular cells/samples, the intent is to generalize to future occurrences of the treatment or disease. Implicitly, this step is justified by assuming that pr

Xudong Li, Timin Gao, Runze Hu, Yan Zhang

The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods typically rely on feature extraction from upstream semantic backbone networks, assuming that all extracted features are relevant. However, we make a key observation that not all features are beneficial, and some may even be harmful, necessitating careful selection. Empiricall

Jinpeng Xu, Xiaoguang Luo, Xi Lin, Xi Zhang

Two-dimensional (2D) material photodetectors have gained great attention as potential elements for optoelectronic applications. However, the linearity of the photoresponse is often compromised by the carrier interaction, even in 2D photodiodes. In this study, we present a new device concept of dual-floating van der Waals heterostructures (vdWHs) photodiode b

Mikami Hirasawa, Ryota Hiura, Makoto Sakuma

A marked strongly invertible knot is a triple $(K,h,\delta)$ of a knot $K$ in $S^3$, a strong inversion $h$ of $K$, and a subarc $\delta \subset \operatorname{Fix}(h)\cong S^1$ bounded by $\operatorname{Fix}(h)\cap K\cong S^0$. An invariant Seifert surface for $(K,h,\delta)$ is an $h$-invariant Seifert surface for $K$ that intersects $\operatorname{Fix}(h)$

Guoyi Yang, Stephen Burgess, C Mary Schooling

Background: Immortal time is a period of follow-up during which death or the study outcome cannot occur by design. Bias from immortal time has been increasingly recognized in epidemiologic studies. However, the fundamental causes and structures of bias from immortal time have not been explained systematically using a structural approach. Methods: We use an e

Arun Kumar Karngala, Chanan Singh, Le Xie

Distribution system end users are transforming from passive to active participants, marked by the push towards widespread adoption of edge-level Distributed Energy Resources (DERs). This paper addresses the challenges in distribution system planning arising from these dynamic changes. We introduce a bottom-up probabilistic approach that integrates these edge

Anthony Cintron Roman, Jennifer Wortman Vaughan, Valerie See, Steph Ballard

This paper introduces a no-code, machine-readable documentation framework for open datasets, with a focus on responsible AI (RAI) considerations. The framework aims to improve comprehensibility, and usability of open datasets, facilitating easier discovery and use, better understanding of content and context, and evaluation of dataset quality and accuracy. T

Hugues Beauchesne, Zong-En Chen, Cheng-Wei Chiang

Weak supervision searches have in principle the advantages of both being able to train on experimental data and being able to learn distinctive signal properties. However, the practical applicability of such searches is limited by the fact that successfully training a neural network via weak supervision can require a large amount of signal. In this work, we

Baichu Yu, Masahito Hayashi

In quantum theory, a quantum state on a composite system of two parties realizes a non-negative probability with any measurement element with a tensor product form. However, there also exist non-quantum states which satisfy the above condition. Such states are called beyond-quantum states, and cannot be detected by standard Bell tests. To distinguish a beyon

Bolin Han

Conformal field theory (CFT) has become an active area of research beyond its origins in statistical physics and attracted much attention due to its intrinsic mathematical interest, which reveals deep connections with other diverse branches of mathematics. We study a specific subclass of CFTs that involve either uncoupled or coupled free fermions. Coupled fr

Lifu Tu, Semih Yavuz, Jin Qu, Jiacheng Xu

Large Language Models (LLMs) have demonstrated a powerful ability for text generation. However, achieving optimal results with a given prompt or instruction can be challenging, especially for billion-sized models. Additionally, undesired behaviors such as toxicity or hallucinations can manifest. While much larger models (e.g., ChatGPT) may demonstrate streng

Cody Gilbert, McCleary Philbin, Kayla Wright

In this paper, we give matrix formulae for non-orientable surfaces that provide the Laurent expansion for quasi-cluster variables, generalizing the orientable surface matrix formulae by Musiker-Williams. We additionally use our matrix formulas to prove the skein relations for the elements in the quasi-cluster algebra associated to curves on the non-orientabl

Faria Huq, Jeffrey P. Bigham, Nikolas Martelaro

Large language models (LLMs) that have been trained on a corpus that includes large amount of code exhibit a remarkable ability to understand HTML code. As web interfaces are primarily constructed using HTML, we design an in-depth study to see how LLMs can be used to retrieve and locate important elements for a user given query (i.e. task description) in a w

Yating Wu, Xiaoyan Liu, Tao Chu

Due to that the polarization states in optical fibers change randomly during transmission, polarization-independent (PID) devices are demanded to receive lights with arbitrary polarization states. Compared with their orthogonal polarization states, the optical profiles of various modes of the same polarization are similar, and their directions of the main el

Jinseok Seol, Minseok Gang, Sang-goo Lee, Jaehui Park

Neural network approaches in recommender systems have shown remarkable success by representing a large set of items as a learnable vector embedding table. However, infrequent items may suffer from inadequate training opportunities, making it difficult to learn meaningful representations. We examine that in attribute and context-aware settings, the poorly lea

Xuan He, Danny H. K. Tsang, Yize Chen

Global climate challenge is demanding urgent actions for decarbonization, while electric power systems take the major roles in clean energy transition. Due to the existence of spatially and temporally dispersed renewable energy resources and the uneven distribution of carbon emission intensity throughout the grid, it is worth investigating future load planni

Cédric Arhancet, Lukas Hagedorn, Christoph Kriegler, Pierre Portal

This paper investigates the functional calculus of the harmonic oscillator on each Moyal-Groenewold plane, the noncommutative phase space which is a fundamental object in quantum mechanics. Specifically, we show that the harmonic oscillator admits a bounded $\mathrm{H}^\infty(\Sigma_\omega)$ functional calculus for any angle $0 < \omega < \frac{\pi}{2}$ and

Fan Liu, Xi Lin, Yuting Yan, Xuetao Gan

Tunable photovoltaic photodetectors are of significant relevance in the fields of programmable and neuromorphic optoelectronics. However, their widespread adoption is hindered by intricate architectural design and energy consumption challenges. This study employs a nonvolatile MoTe2/hBN/graphene semi-floating photodetector to address these issues. Programed

Savya Khosla, Zhen Zhu, Yifei He

This paper explores Memory-Augmented Neural Networks (MANNs), delving into how they blend human-like memory processes into AI. It covers different memory types, like sensory, short-term, and long-term memory, linking psychological theories with AI applications. The study investigates advanced architectures such as Hopfield Networks, Neural Turing Machines, C

Gargi Mitra, Chanyuan Liu, Pritam Dash, Yingao Elaine Yao

Modern industrial control systems (ICS) increasingly host their Supervisory Control and Data Acquisition (SCADA) services in the cloud to reduce the costs of large-scale automation. To protect site-SCADA communications, ICS operators commonly use VPN tunneling and standard security practices. We show that, despite these security measures, an on-path Internet

Prakash Gawas, Antoine Legrain, Louis-Martin Rousseau

Modern business models have enabled service systems to leverage a large pool of casual employees with flexible hours, paid based on piece rates, to fulfill on-demand work. These systems have been successfully implemented in sectors such as ride-sharing, delivery services, and microtasks. However, because casual employees engage infrequently and may lack expe

Malihe Heydari-Fard, Mohammad Rahim Bordbar, Golnaz Mohammadi

We present the exact time-dependent solutions on inhomogeneous spherically symmetric space-time in the conformal invariant Weyl gravity. For this purpose, the subclass of the Lemaitre-Tolman metric which is supported by an anisotropic fluid is used. For the first time, the exact solutions of the dynamical equations are obtained for two special cases. One of

Alexandr Garbali, Ajeeth Gunna

We consider partition functions on the $N\times N$ square lattice with the local Boltzmann weights given by the $R$-matrix of the $U_{t}(\widehat{sl}(n+1|m))$ quantum algebra. We identify boundary states such that the square lattice can be viewed on a conic surface. The partition function $Z_N$ on this lattice computes the weighted sum over all possible clos

Lei Zhang, Fangxun Shu, Tianyang Liu, Sucheng Ren

The increasing availability of image-text pairs has largely fueled the rapid advancement in vision-language foundation models. However, the vast scale of these datasets inevitably introduces significant variability in data quality, which can adversely affect the model performance. This highlights the critical role of data filtering, not only to enhance train

Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi

Emerging non-volatile memory (NVM)-based Computing-in-Memory (CiM) architectures show substantial promise in accelerating deep neural networks (DNNs) due to their exceptional energy efficiency. However, NVM devices are prone to device variations. Consequently, the actual DNN weights mapped to NVM devices can differ considerably from their targeted values, in

Xincong Liu, Tingfa Xu, Ying Wang, Zhinong Yu

Siamese network-based trackers have shown remarkable success in aerial tracking. Most previous works, however, usually perform template matching only between the initial template and the search region and thus fail to deal with rapidly changing targets that often appear in aerial tracking. As a remedy, this work presents Building Appearance Collection Tracki

Zhanjie Zhang, Quanwei Zhang, Guangyuan Li, Wei Xing

Artistic style transfer aims to repaint the content image with the learned artistic style. Existing artistic style transfer methods can be divided into two categories: small model-based approaches and pre-trained large-scale model-based approaches. Small model-based approaches can preserve the content strucuture, but fail to produce highly realistic stylized

Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi

Architectures that incorporate Computing-in-Memory (CiM) using emerging non-volatile memory (NVM) devices have become strong contenders for deep neural network (DNN) acceleration due to their impressive energy efficiency. Yet, a significant challenge arises when using these emerging devices: they can show substantial variations during the weight-mapping proc

Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer

In this paper, we empirically study the optimization dynamics of multi-task learning, particularly focusing on those that govern a collection of tasks with significant data imbalance. We present a simple yet effective method of pre-training on high-resource tasks, followed by fine-tuning on a mixture of high/low-resource tasks. We provide a thorough empirica

Zhiyuan Wang, Zhiyu Ma, Wenzhe Wei, Jialu Chang

We report on the development and performance evaluation of an ultra-stable clock laser for an $\rm ^{27}Al^+$ optical clock. The thermal noise limited ultra-stable laser is developed based on a 30 cm long ultra-stable cavity. Three independent evaluation methods, including the frequency noise summation method, the three-cornered hat (TCH) method, and the opt

Henrik Bachmann

We give explicit expressions for MacMahon's generalized sums-of-divisors $q$-series $A_r$ and $C_r$ by relating them to (odd) multiple Eisenstein series. Recently, these sums-of-divisors have been studied in the context of quasimodular forms, vertex algebras, $N=4$ $SU(N)$ Super-Yang-Mills theory, and the study of congruences of partitions. We relate them to

Yiheng Xu, Pranav Sivaraman, Hariharan Devarajan, Kathryn Mohror

Parallel applications can spend a significant amount of time performing I/O on large-scale supercomputers. Fast near-compute storage accelerators called burst buffers can reduce the time a processor spends performing I/O and mitigate I/O bottlenecks. However, determining if a given application could be accelerated using burst buffers is not straightforward e

Kalpalata Dash, P. C. Dash, R. N. Panda, Susmita Kar

We study the purely leptonic decays of heavy-flavored charged pseudoscalar (P) and vector (V) mesons ($D_{(s)}^{(*)+}$, $B_{(c)}^{(*)+}$) in the relativistic independent quark (RIQ) model based on an average flavor-independent confining potential in equally mixed scalar-vector harmonic form. We first compute the mass spectra of the ground-state-mesons and fi

Shrutheesh R. Iyer, Anwesan Pal, Jiaming Hu, Akanimoh Adeleye

We consider the problem of building an assistive robotic system that can help humans in daily household cleanup tasks. Creating such an autonomous system in real-world environments is inherently quite challenging, as a general solution may not suit the preferences of a particular customer. Moreover, such a system consists of multi-objective tasks comprising

Pak Hang Chris Lau, Chen-Te Ma, Jeff Murugan, Masaki Tezuka

We model backreaction in AdS$_2$ JT gravity via a proposed boundary dual Sachdev-Ye-Kitaev quantum dot coupled to Dirac fermion matter and study it from the perspective of quantum entanglement and chaos. The boundary effective action accounts for the backreaction through a linear coupling of the Dirac fermions to the Gaussian-random two-body Majorana interac

Qiyuan Nie, Zhi Zeng, Hao Ma, Litao Yang

Rare event search experiments using germanium detectors are operated in underground laboratories to minimize the background induced by cosmic rays. However, the cosmogenic activation in germanium crystals on the ground during fabrication and transportation generates long half-life radionuclides and contributes a considerable background. We simulated the prod

Jing Hou, Guang Chen, Ruiqi Zhang, Zhijun Li

The promotion of large-scale applications of reinforcement learning (RL) requires efficient training computation. While existing parallel RL frameworks encompass a variety of RL algorithms and parallelization techniques, the excessively burdensome communication frameworks hinder the attainment of the hardware's limit for final throughput and training effects

Zehuan Huang, Hao Wen, Junting Dong, Yaohui Wang

Generating multiview images from a single view facilitates the rapid generation of a 3D mesh conditioned on a single image. Recent methods that introduce 3D global representation into diffusion models have shown the potential to generate consistent multiviews, but they have reduced generation speed and face challenges in maintaining generalizability and qual

Haokai Hong, Liang Feng, Min Jiang, Kay Chen Tan

Multi-objective optimization problems (MOPs) require the simultaneous optimization of conflicting objectives. Real-world MOPs often exhibit complex characteristics, including high-dimensional decision spaces, many objectives, or computationally expensive evaluations. While population-based evolutionary computation has shown promise in addressing diverse MOPs

Victor D. Mendoza-Rubio, Victor H. Jorge-Pérez

In this paper, we aim to obtain some results under the condition that the dual of a module over a commutative Noetherian ring has finite Gorenstein dimension. In this direction, we derive results involving vanishing of Ext as well as the freeness or totally reflexivity of modules. For instance, we provide a generalization of a celebrated theorem by Auslander

Renchi Yang, Jing Tang

Given an undirected graph G, the effective resistance r(s,t) measures the dissimilarity of node pair s,t in G, which finds numerous applications in real-world problems, such as recommender systems, combinatorial optimization, molecular chemistry, and electric power networks. Existing techniques towards pairwise effective resistance estimation either trade ap

Heegyu Kim, Hyunsouk Cho

Caution: This paper includes offensive words that could potentially cause unpleasantness. The fast-paced evolution of generative language models such as GPT-4 has demonstrated outstanding results in various NLP generation tasks. However, due to the potential generation of offensive words related to race or gender, various Controllable Text Generation (CTG) m

Jai Kannan

In software engineering, the meticulous configuration of software tools is crucial in ensuring optimal performance within intricate systems. However, the complexity inherent in selecting optimal configurations is exacerbated by the high-dimensional search spaces presented in modern applications. Conventional trial-and-error or intuition-driven methods are bo

Wei Sun

In this paper, we shall study the weak solution to the supercritical deformed Hermitian-Yang-Mills equation in the boundary case.

Marco Aurelio Galvani Cunha, John C. Crocker, Andrea J. Liu

Biopolymer networks from the intracellular to tissue scale display high rigidity and tensile stress while having coordinations well below the normal threshold for mechanical rigidity. The elastic filaments in these networks are often severed by enzymes in a tension-inhibited manner. The effects of such pruning on the mechanics of prestressed networks have no

Yuhui Liu

In this paper, it is proved that every sufficiently large even integer can be represented as the sum of two squares of primes, two cubes of primes, two biquadrates of primes and 16 powers of 2. Furthermore, there are at least 5.313% odd integers that can be represented as one square of prime, one cube of prime and one biquadrate of prime. This result constit

Xincheng Yu, Dongyue Guo, Jianwei Zhang, Yi Lin

Radio speech echo is a specific phenomenon in the air traffic control (ATC) domain, which degrades speech quality and further impacts automatic speech recognition (ASR) accuracy. In this work, a time-domain recognition-oriented speech enhancement (ROSE) framework is proposed to improve speech intelligibility and also advance ASR accuracy based on convolution

Jiaming Liu, Yue Wu, Maoguo Gong, Qiguang Miao

3D Single Object Tracking (SOT) stands a forefront task of computer vision, proving essential for applications like autonomous driving. Sparse and occluded data in scene point clouds introduce variations in the appearance of tracked objects, adding complexity to the task. In this research, we unveil M3SOT, a novel 3D SOT framework, which synergizes multiple

Panos Achlioptas, Alexandros Benetatos, Iordanis Fostiropoulos, Dimitris Skourtis

In this work, we systematically study the problem of personalized text-to-image generation, where the output image is expected to portray information about specific human subjects. E.g., generating images of oneself appearing at imaginative places, interacting with various items, or engaging in fictional activities. To this end, we focus on text-to-image sys

R. Nakabe, C. J. Auton, S. Endo, H. Fujioka

Neutron transmission experiments can offer a new type of highly sensitive search for time-reversal invariance violating (TRIV) effects in nucleon-nucleon interactions via the same enhancement mechanism observed for large parity violating (PV) effects in neutron-induced compound nuclear processes. In these compound processes, the TRIV cross-section is given a

Catherine Felce, Steinunn Liorsdóttir, Lior Pachter

We observe that the time averaged continuous Price equation is identical to the positive momentum virial theorem, and we discuss the applications and implications of this connection.

Renchi Yang

Similarity search over a bipartite graph aims to retrieve from the graph the nodes that are similar to each other, which finds applications in various fields such as online advertising, recommender systems etc. Existing similarity measures either (i) overlook the unique properties of bipartite graphs, or (ii) fail to capture high-order information between no

Mehala Balamurali, Ehsan Mihankhah

Accurate and efficient object detection is crucial for safe and efficient operation of earth-moving equipment in mining. Traditional 2D image-based methods face limitations in dynamic and complex mine environments. To overcome these challenges, 3D object detection using point cloud data has emerged as a comprehensive approach. However, training models for mi

Jaeseok Byun, Dohoon Kim, Taesup Moon

We consider a critical issue of false negatives in Vision-Language Pre-training (VLP), a challenge that arises from the inherent many-to-many correspondence of image-text pairs in large-scale web-crawled datasets. The presence of false negatives can impede achieving optimal performance and even lead to a significant performance drop. To address this challeng

S M Iftiquar

Hydrogenated amorphous silicon is well known for its various alloys and wide ranging opto-electronic properties. Hydrogenated silicon sub-oxide (aSiO:H) is one of them. The effect of boron doping on optoelectronic properties of the aSiO:H have been investigated with intrinsic and delta-doped materials. The Urbach energy of the tail and midgap defect states o