July 2023 arXiv papers — page 91

Saptarshi Majumdar, Laura Foini, Thierry Giamarchi, Alberto Rosso

We study an incommensurate XXZ spin chain coupled to a collection of local harmonic baths. At zero temperature, by varying the strength of the coupling to the bath the chain undergoes a quantum phase transition between a Luttinger liquid phase and a spin density wave (SDW). As opposed to the standard mechanism, the SDW emerges in the absence of the opening o

Yi-Hsin Chen, Si-Cun Chen, Yi-Hsin Chen, Yen-Yu Lin

This work addresses continuous space-time video super-resolution (C-STVSR) that aims to up-scale an input video both spatially and temporally by any scaling factors. One key challenge of C-STVSR is to propagate information temporally among the input video frames. To this end, we introduce a space-time local implicit neural function. It has the striking featu

Fiona Sloothaak, Lorenzo Federico

We study a cascading edge failure mechanism on a connected random graph with a prescribed degree sequence, sampled using the configuration model. This mechanism prescribes that every edge failure puts an additional strain on the remaining network, possibly triggering more failures. We show that under a critical loading mechanism that depends on the global st

Kadri Kurt, Tamaz Kaladze, Melik Buğra Yeşil

In the frame of shallow water model the influence of free surface action on nonlinear propagation of planetary ULF magnetized Rossby waves in the weakly ionized ionospheric D-, E-, and F-layers is revealed. Relevant nonlinear dynamic equations satisfying several conservation laws are obtained and investigated. The role of Hall and Pedersen conductivities is

Randy Stefan Tanuwijaya, Hong Liang, Jiawei Xi, Tsz Kit Yung

Metasurfaces have recently opened up applications in the quantum regime, including quantum tomography and the generation of quantum entangled states. With their capability to store a vast amount of information by utilizing the various geometric degrees of freedom of nanostructures, metasurfaces are expected to be useful for processing quantum information. In

Adrien Laurent

The aromatic bicomplex is an algebraic tool based on aromatic Butcher trees and used in particular for the explicit description of volume-preserving affine-equivariant numerical integrators. The present work defines new tools inspired from variational calculus such as the Lie derivative, different concepts of symmetries, and Noether's theory in the context o

Yuwei Jiang, David Johnson

In 2015, the United Nations put forward 17 Sustainable Development Goals (SDGs) to be achieved by 2030, where data has been promoted as a focus to innovating sustainable development and as a means to measuring progress towards achieving the SDGs. In this study, we propose a systematic approach towards discovering data types and sources that can be used for S

Krishna Teja Chitty-Venkata, Sparsh Mittal, Murali Emani, Venkatram Vishwanath

Recent years have seen a phenomenal rise in performance and applications of transformer neural networks. The family of transformer networks, including Bidirectional Encoder Representations from Transformer (BERT), Generative Pretrained Transformer (GPT) and Vision Transformer (ViT), have shown their effectiveness across Natural Language Processing (NLP) and

Harry van der Graaf, Niels van Bakel, Bram Bouwens, Martin Breidenbach

The Rasnik 3-point alignment system, now widely applied in particle physics experiments and in the instrumentation of gravitational wave experiments, can be used as N-point alignment system by daisy chain N individual 3-point systems. The conceptual implementation of Rasnik chains in C3 is presented. The proper operation of a laser diode and a CMOS image sen

Xingrong Dong, Zhaoxian Wu, Qing Ling, Zhi Tian

This paper studies distributed online learning under Byzantine attacks. The performance of an online learning algorithm is often characterized by (adversarial) regret, which evaluates the quality of one-step-ahead decision-making when an environment provides adversarial losses, and a sublinear bound is preferred. But we prove that, even with a class of state

Yuheng Huang, Jiayang Song, Zhijie Wang, Shengming Zhao

The recent performance leap of Large Language Models (LLMs) opens up new opportunities across numerous industrial applications and domains. However, erroneous generations, such as false predictions, misinformation, and hallucination made by LLMs, have also raised severe concerns for the trustworthiness of LLMs', especially in safety-, security- and reliabili

Natalia P. Bondarenko

In this paper, we consider a class of matrix functions, which contains regularization matrices of Mirzoev and Shkalikov for differential operators with distribution coefficients of order $n \ge 2$. We show that every matrix function of this class is associated with some differential expression. Moreover, we construct the family of associated matrices for a f

Andrei Rodin

Kolmogorov's Calculus of Problems is an interpretation of Heyting's intuitionistic propositional calculus published by A.N. Kolmogorov in 1932. Unlike Heyting's intended interpretation of this calculus, Kolmogorov's interpretation does not comply with the philosophical principles of Mathematical Intuitionism. This philosophical difference between Kolmogorov

A. Neronov, D. Semikoz, J. Aublin, M. Lamoureux

We show that the IceCube observation of the Galactic neutrino flux component confirms the hint of detection of neutrinos from the Galactic Ridge (the inner part of the Milky Way disk within the Galactic longitude |l|<30 degrees), previously reported by the ANTARES collaboration. This confirmation indicates that the bulk of the high-energy flux from the Galac

Runwei Ding, Yuhang Wen, Jinfu Liu, Nan Dai

Human skeletons and RGB sequences are both widely-adopted input modalities for human action recognition. However, skeletons lack appearance features and color data suffer large amount of irrelevant depiction. To address this, we introduce human parsing feature map as a novel modality, since it can selectively retain spatiotemporal features of the body parts,

Maosu Li, Yijie Wu, Anthony G. O. Yeh, Fan Xue

Many existing 3D semantic segmentation methods, deep learning in computer vision notably, claimed to achieve desired results on urban point clouds. Thus, it is significant to assess these methods quantitatively in diversified real-world urban scenes, encompassing high-rise, low-rise, high-density, and low-density urban areas. However, existing public benchma

Shouwei Ruan, Yinpeng Dong, Hang Su, Jianteng Peng

Visual recognition models are not invariant to viewpoint changes in the 3D world, as different viewing directions can dramatically affect the predictions given the same object. Although many efforts have been devoted to making neural networks invariant to 2D image translations and rotations, viewpoint invariance is rarely investigated. As most models process

Shamil Mamedov, A. René Geist, Jan Swevers, Sebastian Trimpe

Accurately predicting deformable linear object (DLO) dynamics is challenging, especially when the task requires a model that is both human-interpretable and computationally efficient. In this work, we draw inspiration from the pseudo-rigid body method (PRB) and model a DLO as a serial chain of rigid bodies whose internal state is unrolled through time by a d

Ahmad Al-Badawi

We investigate the behavior of the regular modified gravity (MOG) static spherically symmetric black hole (BH) under massless scalar perturbation, gravitational perturbation, and massless Dirac perturbation. The dimensionless parameter $\left( \alpha \right) $ distinguishes this BH from a Schwarzschild BH. We derive the effective potential equations for thre

Bao Duong, Thin Nguyen

Heretofore, learning the directed acyclic graphs (DAGs) that encode the cause-effect relationships embedded in observational data is a computationally challenging problem. A recent trend of studies has shown that it is possible to recover the DAGs with polynomial time complexity under the equal variances assumption. However, this prohibits the heteroscedasti

Dongyu Yao, Boheng Li

Self-training approach recently secures its position in domain adaptive semantic segmentation, where a model is trained with target domain pseudo-labels. Current advances have mitigated noisy pseudo-labels resulting from the domain gap. However, they still struggle with erroneous pseudo-labels near the boundaries of the semantic classifier. In this paper, we

Chao Li, Gangyi Chen

In this paper, we introduce the concept of general $L_p$ projection body and general $L_p$ centroid body of general measures with positive homogeneity density function, and prove the corresponding extreme inequalities. Meanwhile, we also study their measure comparison problem and monotone inequalities.

Virginia Vassilevska Williams, Yinzhan Xu, Zixuan Xu, Renfei Zhou

The main contribution of this paper is a new improved variant of the laser method for designing matrix multiplication algorithms. Building upon the recent techniques of [Duan, Wu, Zhou, FOCS 2023], the new method introduces several new ingredients that not only yield an improved bound on the matrix multiplication exponent $\omega$, but also improve the known

Miguel Corrales, Hussein Hoteit, Matteo Ravasi

The inversion of petrophysical parameters from seismic data represents a fundamental step in the process of characterizing the subsurface. We propose a novel, data-driven approach named Seis2Rock that utilizes optimal basis functions learned from well log information to directly link band-limited petrophysical reflectivities to pre-stack seismic data. Seis2R

Jianan Xu, Xinrong Ma

In this paper, we establish three new and general transformations with sixteen parameters and bases via Abel's lemma on summation by parts. As applications, we set up a lot of new transformations of basic hypergeometric series. Among include some new results of Gasper and Rahman's quadratic, cubic, and quartic transformations. Furthermore, we put forward the

Krishnendu Gongopadhyay, Tejbir Lohan, Chandan Maity

An element of a group is called $\textit{strongly reversible}$ or $\textit{strongly real}$ if it can be expressed as a product of two involutions. We provide necessary and sufficient conditions for an element of $\mathrm{SL}(n,\mathbb{C})$ to be a product of two involutions. In particular, we classify the strongly reversible conjugacy classes in $\mathrm{SL}

Arghajit Jana, Arka Chatterjee, Hsiang-Kuang Chang, Prantik Nandi

We studied the broadband X-ray spectra of {\it Swift}/BAT selected low-accreting AGNs using the observations from {\it XMM-Newton}, {\it Swift}, and {\it NuSTAR} in the energy range of $0.5-150$~keV. Our sample consists of 30 AGNs with Eddington ratio, $\lambda_{\rm Edd}<10^{-3}$. We extracted several coronal parameters from the spectral modelling, such as t

Jiarong Wu, Lili Wei, Yanyan Jiang, Shing-Chi Cheung

Programming by example (PBE) is an emerging programming paradigm that automatically synthesizes programs specified by user-provided input-output examples. Despite the convenience for end-users, implementing PBE tools often requires strong expertise in programming language and synthesis algorithms. Such a level of knowledge is uncommon among software develope

Johannes Ruf

In July 2023, Nasdaq announced a `Special Rebalance' of the Nasdaq-100 index to reduce the index weights of its large constituents. A rebalance as suggested currently by Nasdaq index methodology may have several undesirable effects. These effects can be avoided by a different, but simple rebalancing strategy. Such rebalancing is easily computable and guarant

Yi Qin, Linhu Li

We unveil an exotic phenomenon arising from the intricate interplay between non-Hermiticity and many-body physics, namely an occupation-dependent particle separation for hardcore bosons in a one-dimensional lattice driven by uni-directional non-Hermitian pumping. Taking hardcore bosons as an example, we find that a pair of particles occupying the same unit c

P Chaddah

This is a Comment on: Thermodynamic determination of the equilibrium first-order phase-transition line hidden by hysteresis, published in Sci Rep 13, 6876 (2023); arXiv:2303.00327, by K. Matsuura et al. We stress that the lower hysteresis line should not be used to infer any thermodynamic quantity below the temperature at which the lower hysteresis line show

Reza Ahmadvand, Sarah Safura Sharif, Yaser Mike Banad

Energy efficiency and reliability have long been crucial factors for ensuring cost-effective and safe missions in autonomous systems computers. With the rapid evolution of industries such as space robotics and advanced air mobility, the demand for these low size, weight, and power (SWaP) computers has grown significantly. This study focuses on introducing an

Sijie Ren, Jian Wang, Shipeng Wang, Weihua Yang

A graph $G$ is called $C_{2k+1}$-free if it does not contain any cycle of length $2k+1$. In 1981, Haggkvist, Faudree and Schelp showed that every $n$-vertex triangle-free graph with more than $\frac{(n-1)^2}{4}+1$ edges is bipartite. In this paper, we extend their result and show that for $1\leq t\leq 2k-2$ and $n\geq 318t^2k$, every $n$-vertex $C_{2k+1}$-fr

Jingyuan Yang, Qirui Huang, Tingting Ding, Dani Lischinski

Visual Emotion Analysis (VEA) aims at predicting people's emotional responses to visual stimuli. This is a promising, yet challenging, task in affective computing, which has drawn increasing attention in recent years. Most of the existing work in this area focuses on feature design, while little attention has been paid to dataset construction. In this work,

Yuwei Chuai, Haoye Tian, Nicolas Pröllochs, Gabriele Lenzini

Developing interventions that successfully reduce engagement with misinformation on social media is challenging. One intervention that has recently gained great attention is X/Twitter's Community Notes (previously known as "Birdwatch"). Community Notes is a crowdsourced fact-checking approach that allows users to write textual notes to inform others about po

Atsushi Matsuda, Mohammad R. K. Mofrad

Recent studies have suggested that the Nuclear Pore Complex (NPC) plays a significant role in mechanotransduction. When a force is exerted, the NPC's diameter widens, leading to an increased molecular flux into the nucleus. In this study, we sought to further explore this phenomenon and quantitativelly assess the impact of pore dilation on molecular transpor

Ardeline M. Buhphang, Rishabh Goswami, Amit Kuber

We show that a monomial algebra $\Lambda$ over an algebraically closed field $K$ is self-injective if and only if each map $\mathrm{soc}(_{\Lambda}\Lambda)\to \ _{\Lambda}\Lambda$ can be extended to an endomorphism of $_{\Lambda}\Lambda$, and provide a complete classification of such algebras. As a consequence, we show that the class of self-injective monomi

Yi Zhou, Meixuan Wu, Chengzhou Ouyang, Min Zhu

Biomedical research has revealed the crucial role of miRNAs in the progression of many diseases, and computational prediction methods are increasingly proposed for assisting biological experiments to verify miRNA-disease associations (MDAs). However, the generalizability and explainability are currently underemphasized. It's significant to generalize effecti

Wendi Yu, Zhichao Hou, Xiaorui Liu

Polynomial graph filters have been widely used as guiding principles in the design of Graph Neural Networks (GNNs). Recently, the adaptive learning of the polynomial graph filters has demonstrated promising performance for modeling graph signals on both homophilic and heterophilic graphs, owning to their flexibility and expressiveness. In this work, we condu

Jian-Guo He, Yong Shao, Shi-Jie Gao, Xiang-Dong Li

It is widely accepted that quite a number of double compact objects (DCOs) in the Milky Way can be identified by future space-based gravitational wave (GW) detectors, while systematic investigations on the detection of the GW sources in nearby galaxies are still lacking. In this paper, we present calculations of potential populations of GW sources for all ty

Zhihong Shi, Qingwen Wu, Zhen Yan, Bing Lyu

We explore the timing and spectral properties of GRS 1915+105 based on X-ray observations of NICER and Insight-HXMT during the long outburst from 2017 to 2021. We find a new class of variability in the rising stage of the outburst that differs from the formerly reported patterns of light curves. This new variability pattern, which we name class $\psi$, is ch

Anastasios N. Zachos

We prove that Fermat constants do not completely determine Clairaut constants for three branching geodesics that meet at the weighted Fermat-Torricelli point on a surface of revolution, except the case of a standard sphere in $\mathbb{R}^{3}.$

Lei Ma, Peng Xue, Yuning Gu, Yue Zhao

In recent years, the demand for dental implants has surged, driven by their high success rates and esthetic advantages. However, accurate prediction of missing teeth for precise digital implant planning remains a challenge due to the intricate nature of dental structures and the variability in tooth loss patterns. This study presents a novel framework for ac

Shruti Aggarwal, Satyabrata Adhikari

Realignment operation has a significant role in detecting bound as well as free entanglement. Just like partial transposition, it is also based on permutations of the matrix elements. However, the physical implementation of realignment operation is not known yet. In this letter, we address the problem of experimental realization of realignment operation and

Zhenwen Liang, Dian Yu, Xiaoman Pan, Wenlin Yao

Reasoning in mathematical domains remains a significant challenge for relatively small language models (LMs). Many current methods focus on specializing LMs in mathematical reasoning and rely heavily on knowledge distillation from powerful but inefficient large LMs (LLMs). In this work, we explore a new direction that avoids over-reliance on LLM teachers, in

Kiana Kheiri, Hamid Karimi

This study presents a thorough examination of various Generative Pretrained Transformer (GPT) methodologies in sentiment analysis, specifically in the context of Task 4 on the SemEval 2017 dataset. Three primary strategies are employed: 1) prompt engineering using the advanced GPT-3.5 Turbo, 2) fine-tuning GPT models, and 3) an inventive approach to embeddin

Sahil Tyagi, Martin Swany

In distributed training, deep neural networks (DNNs) are launched over multiple workers concurrently and aggregate their local updates on each step in bulk-synchronous parallel (BSP) training. However, BSP does not linearly scale-out due to high communication cost of aggregation. To mitigate this overhead, alternatives like Federated Averaging (FedAvg) and S

Po-Chen Kuo, Jhen-Dong Lin, Yin-Chun Huang, Yueh-Nan Chen

Nanoscale Fano resonances, with applications from telecommunications to ultra-sensitive biosensing, have prompted extensive research. We demonstrate that a superconducting qubit, jointly coupled to microwave waveguides and an inter-digital transducer composite device, can exhibit acoustic Fano resonances. Our analytical framework, leveraging the Taylor serie

Abhayjeet Singh, Arjun Singh Mehta, Ashish Khuraishi K S, Deekshitha G

Automatic speech recognition (ASR) performance has improved drastically in recent years, mainly enabled by self-supervised learning (SSL) based acoustic models such as wav2vec2 and large-scale multi-lingual training like Whisper. A huge challenge still exists for low-resource languages where the availability of both audio and text is limited. This is further

Shuhan Tan, Boris Ivanovic, Xinshuo Weng, Marco Pavone

Simulation forms the backbone of modern self-driving development. Simulators help develop, test, and improve driving systems without putting humans, vehicles, or their environment at risk. However, simulators face a major challenge: They rely on realistic, scalable, yet interesting content. While recent advances in rendering and scene reconstruction make gre

Zifeng Cheng, Qingyu Zhou, Zhiwei Jiang, Xuemin Zhao

Few-shot sequence labeling aims to identify novel classes based on only a few labeled samples. Existing methods solve the data scarcity problem mainly by designing token-level or span-level labeling models based on metric learning. However, these methods are only trained at a single granularity (i.e., either token level or span level) and have some weaknesse

Wuyuan Xie, Miaohui Wang, Di Lin, Boxin Shi

With the rapid development of high-resolution 3D vision applications, the traditional way of manipulating surface detail requires considerable memory and computing time. To address these problems, we introduce an efficient surface detail processing framework in 2D normal domain, which extracts new normal feature representations as the carrier of micro geomet

Zhuoxiao Chen, Yadan Luo, Zheng Wang, Mahsa Baktashmotlagh

Unsupervised domain adaptation (DA) with the aid of pseudo labeling techniques has emerged as a crucial approach for domain-adaptive 3D object detection. While effective, existing DA methods suffer from a substantial drop in performance when applied to a multi-class training setting, due to the co-existence of low-quality pseudo labels and class imbalance is

Shrey Chowdhary, Jan Lebert, Shai Dickman, Charles Gordon

Voltage-sensitive fluorescence imaging is widely used to image action potential waves in the heart. However, while the electrical waves trigger mechanical contraction, imaging needs to be performed with pharmacologically contraction-inhibited hearts, limiting studies of the coupling between cardiac electrophysiology and tissue mechanics. Here, we introduce a

Yadan Luo, Zhuoxiao Chen, Zhen Fang, Zheng Zhang

Achieving a reliable LiDAR-based object detector in autonomous driving is paramount, but its success hinges on obtaining large amounts of precise 3D annotations. Active learning (AL) seeks to mitigate the annotation burden through algorithms that use fewer labels and can attain performance comparable to fully supervised learning. Although AL has shown promis

Heng Zhu, Avishek Ghosh, Arya Mazumdar

Motivated by the need for communication-efficient distributed learning, we investigate the method for compressing a unit norm vector into the minimum number of bits, while still allowing for some acceptable level of distortion in recovery. This problem has been explored in the rate-distortion/covering code literature, but our focus is exclusively on the "hig

Atsushi Shirafuji, Yutaka Watanobe

Referring to solution programs written by other users is helpful for learners in programming education. However, current online judge systems just list all solution programs submitted by users for references, and the programs are sorted based on the submission date and time, execution time, or user rating, ignoring to what extent the programs can be helpful

Xiaoxiao Peng, Shijie Zhou

Controlling complex dynamical systems has been a topic of considerable interest in academic circles in recent decades. While existing works have primarily focused on closed-loop control schemes with infinite-time durations, this paper introduces a novel finite-time, closed-loop stochastic controller that pays special attention to control time and energy and

Haotian Dong, Enhui Ma, Lubo Wang, Miaohui Wang

Semantic scene completion (SSC) requires an accurate understanding of the geometric and semantic relationships between the objects in the 3D scene for reasoning the occluded objects. The popular SSC methods voxelize the 3D objects, allowing the deep 3D convolutional network (3D CNN) to learn the object relationships from the complex scenes. However, the curr

Zhiqi Chen, Yuri Nikolayevsky, Joseph A. Wolf, Shaoxiang Zhang

The geodesic orbit property is useful and interesting in itself, and it plays a key role in Riemannian geometry. It implies homogeneity and has important classes of Riemannian manifolds as special cases. Those classes include weakly symmetric Riemannian manifolds and naturally reductive Riemannian manifolds. The corresponding results for indefinite metric ma

Hang Que, Jie Yang, Chao-Kai Wen, Shuqiang Xia

The millimeter-wave (mmWave) communication technology, which employs large-scale antenna arrays, enables inherent sensing capabilities. Simultaneous localization and mapping (SLAM) can utilize channel multipath angle estimates to realize integrated sensing and communication design in 6G communication systems. However, existing works have ignored the signific

Jinlong Li, Runsheng Xu, Xinyu Liu, Baolu Li

Due to the lack of enough real multi-agent data and time-consuming of labeling, existing multi-agent cooperative perception algorithms usually select the simulated sensor data for training and validating. However, the perception performance is degraded when these simulation-trained models are deployed to the real world, due to the significant domain gap betw

Siwei Yang, Hanrong Ye, Dan Xu

This paper targets the problem of multi-task dense prediction which aims to achieve simultaneous learning and inference on a bunch of multiple dense prediction tasks in a single framework. A core objective in design is how to effectively model cross-task interactions to achieve a comprehensive improvement on different tasks based on their inherent complement

Yin Tang, Tao Chen, Xiruo Jiang, Yazhou Yao

Few-shot video object segmentation (FSVOS) aims to segment dynamic objects of unseen classes by resorting to a small set of support images that contain pixel-level object annotations. Existing methods have demonstrated that the domain agent-based attention mechanism is effective in FSVOS by learning the correlation between support images and query frames. Ho

Yiwen Shan, Dong Hu, Zhi Wang

Due to the high flexibility and remarkable performance, low-rank approximation methods has been widely studied for color image denoising. However, those methods mostly ignore either the cross-channel difference or the spatial variation of noise, which limits their capacity in real world color image denoising. To overcome those drawbacks, this paper is propos

Het Mankad, Sanil Rao, Brian Van Straalen, Phillip Colella

We present a first look at ProtoX, a code generation framework for stencil and pointwise operations that occur frequently in the numerical solution of partial differential equations. ProtoX has Proto as its library frontend and SPIRAL as the backend. Proto is a C++ based domain specific library which optimizes the algorithms used to compute the numerical sol

Yifan Zhang, Cheng Wei, Shangyou Wu, Zhengting He

Decision-makers in GIS need to combine a series of spatial algorithms and operations to solve geospatial tasks. For example, in the task of facility siting, the Buffer tool is usually first used to locate areas close or away from some specific entities; then, the Intersect or Erase tool is used to select candidate areas satisfied multiple requirements. Thoug

Haofu Liao, Aruni RoyChowdhury, Weijian Li, Ankan Bansal

We present a new formulation for structured information extraction (SIE) from visually rich documents. It aims to address the limitations of existing IOB tagging or graph-based formulations, which are either overly reliant on the correct ordering of input text or struggle with decoding a complex graph. Instead, motivated by anchor-based object detectors in v

Xiaohang Ren, Xingyu Chen, Pengfei Yao, Heung-Yeung Shum

The SOTA face swap models still suffer the problem of either target identity (i.e., shape) being leaked or the target non-identity attributes (i.e., background, hair) failing to be fully preserved in the final results. We show that this insufficient disentanglement is caused by two flawed designs that were commonly adopted in prior models: (1) counting on on

Zhiding Liang, Hanrui Wang

In this era of incessant advancements in quantum computing, bridging the gap between quantum algorithms' hardware requisites and available devices has become crucial. A prime focus in this context is the Software and System Level support for quantum computers, which has shown promising potential in significantly decreasing this gap. However, a noteworthy def

Xin Bao, Ying Lv, Zeng-Qi Ou

In this paper, we study the following fractional Schr\"{o}dinger equation with prescribed mass \begin{equation*} \left\{ \begin{aligned} &(-\Delta)^{s}u=\lambda u+a(x)|u|^{p-2}u,\quad\text{in $\mathbb{R}^{N}$},\\ &\int_{\mathbb{R}^{N}}|u|^{2}dx=c^{2},\quad u\in H^{s}(\mathbb{R}^{N}), \end{aligned} \right. \end{equation*} where $0<s<1$, $N>2s$, $2+\frac{4s}{N

Feng Ji, Wee Peng Tay, Antonio Ortega

In this expository article, we provide a self-contained overview of the notion of convolution embedded in different theories: from the classical Fourier theory to the theory of algebraic signal processing. We discuss their relations and differences. Toward the end, we provide an opinion on whether there is a consistent approach to convolution that unifies se

Huizhi Wang, Yong Zeng

Multiple-input multiple-output (MIMO) has become a key technology for contemporary wireless communication systems. For typical MIMO systems, antenna arrays are separated by half of the signal wavelength, which are termed collocated arrays. In this paper, we ask the following question: For future wireless communication systems, is it possible to achieve bette

Chen Qian, Wei Liu, Hongzhang Liu, Nuo Chen

Software development is a complex task that necessitates cooperation among multiple members with diverse skills. Numerous studies used deep learning to improve specific phases in a waterfall model, such as design, coding, and testing. However, the deep learning model in each phase requires unique designs, leading to technical inconsistencies across various p

Yifei Shi, Junhua Xi, Dewen Hu, Zhiping Cai

Learning-based multi-view stereo (MVS) has by far centered around 3D convolution on cost volumes. Due to the high computation and memory consumption of 3D CNN, the resolution of output depth is often considerably limited. Different from most existing works dedicated to adaptive refinement of cost volumes, we opt to directly optimize the depth value along eac

Mehrad Jaloli, Marzia Cescon

In this paper, models of the blood glucose (BG) dynamics in people with Type 1 diabetes (T1D) in response to moderate intensity aerobic activity are derived from physiology-based first principles and system identification experiments. We show that by enhancing insulin-dependent glucose utilization by the tissues in two phases, a rapid short-term increase in

Yanna Lin, Haotian Li, Leni Yang, Aoyu Wu

Computational notebooks have become increasingly popular for exploratory data analysis due to their ability to support data exploration and explanation within a single document. Effective documentation for explaining chart findings during the exploration process is essential as it helps recall and share data analysis. However, documenting chart findings rema

Ning Chen, Ying-nan Mao, Zhaolong Teng

We study the origin of the global $B-L$ symmetry in a class of flavor-unified theories with gauge groups of ${\rm SU}(N\geq 6)$. In particular, we focus on the ${\rm SU}(8)$ theory which can minimally embed three-generational SM fermions non-trivially. A reformulation of the third law for the flavor sector proposed by Georgi is useful to manifest the underly

Yidan Sun, Mayank Kejriwal

Complex systems research and network science have recently been used to provide novel insights into economic phenomena such as patenting behavior and innovation in firms. Several studies have found that increased mobility of inventors, manifested through firm switching or transitioning, is associated with increased overall productivity. This paper proposes a

Xiaohuan Pei, Yanxi Li, Minjing Dong, Chang Xu

With the increasing number of new neural architecture designs and substantial existing neural architectures, it becomes difficult for the researchers to situate their contributions compared with existing neural architectures or establish the connections between their designs and other relevant ones. To discover similar neural architectures in an efficient an

Yijiao Zhang, Zhongyi Zhu

With the increasing availability of datasets, developing data fusion methods to leverage the strengths of different datasets to draw causal effects is of great practical importance to many scientific fields. In this paper, we consider estimating the quantile treatment effects using small validation data with fully-observed confounders and large auxiliary dat

K. C. W. Li, R. Neveling, P. Adsley, H. Fujita

Recent measurements indicate that the previously established upper limit for the $\gamma$-decay branch of the $3_{1}^{-}$ resonance in $^{12}\textrm{C}$ at $E_{x} = 9.641(5)$ MeV may be incorrect. As a result, the $3_{1}^{-}$ resonance has been suggested as a significant resonance for mediating the triple-$\alpha$ reaction at high temperatures above 2 GK. Ac

Mingyuan Fan, Cen Chen, Chengyu Wang, Wenmeng Zhou

Split learning enables collaborative deep learning model training while preserving data privacy and model security by avoiding direct sharing of raw data and model details (i.e., sever and clients only hold partial sub-networks and exchange intermediate computations). However, existing research has mainly focused on examining its reliability for privacy prot

Phil Attard

The paradox of Bose-Einstein condensation is that phenomena such as the $\lambda$-transition heat capacity and superfluid flow are macroscopic, whereas the occupancy of the ground state is microscopic. This contradiction is resolved with a simple derivation for ideal bosons that shows Bose-Einstein condensation is into multiple low-lying states, not just the

Xia Liu

In this paper, we provide a vertical height estimate for compact special Weingarten surface of elliptic type in warped product

Fei Yang

In recent years, commonsense reasoning has received more and more attention from academic community. We propose a new lexical inference task, Mental and Physical Classification (MPC), to handle commonsense reasoning in a reasoning graph. Mental words relate to mental activities, which fall into six categories: Emotion, Need, Perceiving, Reasoning, Planning a

Kayode Inadagbo, Baran Arig, Nisanur Alici, Murat Isik

This study presents advanced neural network architectures including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTMs), and Deep Belief Networks (DBNs) for enhanced ECG signal analysis using Field Programmable Gate Arrays (FPGAs). We utilize the MIT-BIH Arrhythmia Database for training and validation

Patrick Mukala

For a long time, the Von Neumann has been a successful model of computation for sequential computing .Many models including the dataflow model have been unsuccessfully developed to emulate the same results in parallel computing. It is widely accepted that high performance computation is better-achieved using parallel architectures and is seen as the basis fo

Yuhao Zhao, Anina Leuch, Oded Zilberberg, Antonio Štrkalj

Electron optics deals with condensed matter platforms for manipulating and guiding electron beams with high efficiency and robustness. Common devices rely on the spatial confinement of the electrons into one-dimensional channels. Recently, there is growing interest in electron optics applications in two dimensions, which heretofore are almost exclusively bas

Kaveh Safavigerdini, Koundinya Nouduri, Ramakrishna Surya, Andrew Reinhard

We present a pipeline for predicting mechanical properties of vertically-oriented carbon nanotube (CNT) forest images using a deep learning model for artificial intelligence (AI)-based materials discovery. Our approach incorporates an innovative data augmentation technique that involves the use of multi-layer synthetic (MLS) or quasi-2.5D images which are ge

Patrick Mukala

A myriad of applications ranging from engineering and scientific simulations, image and signal processing as well as high-sensitive data retrieval demand high processing power reaching up to teraflops for their efficient execution. While a standard serial computer would require clock-cycles of less than one per second in this instance, parallel computing is

Gelei Deng, Yi Liu, Yuekang Li, Kailong Wang

Large Language Models (LLMs) have revolutionized Artificial Intelligence (AI) services due to their exceptional proficiency in understanding and generating human-like text. LLM chatbots, in particular, have seen widespread adoption, transforming human-machine interactions. However, these LLM chatbots are susceptible to "jailbreak" attacks, where malicious us

Sunisth Kumar, Davide Liu, Alexandre Boulenger

We propose an efficient modeling framework for cross-lingual named entity recognition in semi-structured text data. Our approach relies on both knowledge distillation and consistency training. The modeling framework leverages knowledge from a large language model (XLMRoBERTa) pre-trained on the source language, with a student-teacher relationship (knowledge

Xin Guo, Lihong Li, Sareh Nabi, Rabih Salhab

Motivated by bid recommendation in online ad auctions, this paper considers a general class of multi-level and multi-agent games, with two major characteristics: one is a large number of anonymous agents, and the other is the intricate interplay between competition and cooperation. To model such complex systems, we propose a novel and tractable bi-objective

Félix Baril Boudreau, Erik Holmes, Khoa D. Nguyen

Let $\sum a_nx^n\in\bar{\mathbb{Q}}[[x]]$ be the power series representation of a rational function and let $f:\ \{0,1,\ldots\}\rightarrow \bar{\mathbb{Q}}$ be a so-called almost quasi-polynomial. Under a necessary stability condition, we prove that $\sum f(n)a_nx^n$ satisfies the P\'olya-Carlson dichotomy: it is either a rational function or it cannot be ex

Yao Wei, Yanchao Sun, Ruijie Zheng, Sai Vemprala

We introduce DualMind, a generalist agent designed to tackle various decision-making tasks that addresses challenges posed by current methods, such as overfitting behaviors and dependence on task-specific fine-tuning. DualMind uses a novel "Dual-phase" training strategy that emulates how humans learn to act in the world. The model first learns fundamental co

Patrick Mukala

Audit trails are evidential indications of activities performers in any logs. Modern reactive systems such as transaction processing systems, management information systems, decision support systems and even executive management systems log activities of users as they perform their daily tasks for a number of reasons and perhaps one of the most important is

Patrick Mukala

Formal verification is at the heart of model validation and correctness. With model checking, invaluable realizations have been accomplished in software engineering and particularly in software development. By means of this approach, complex applications can be simulated and their performance forecasted in light with requirements at hands and expected perfor

Daniele Cuomo

This thesis treats networks providing quantum computation based on distributed paradigms. Compared to architectures relying on one processor, a network promises to be more scalable and less fault-prone. Developing a distributed system able to provide practical quantum computation comes with many challenges, each of which need to be faced with careful analysi

Mennatullah Siam, Konstantinos G. Derpanis, Richard P. Wildes

Few-shot video object segmentation (FS-VOS) aims at segmenting video frames using a few labelled examples of classes not seen during initial training. In this paper, we present a simple but effective temporal transductive inference (TTI) approach that leverages temporal consistency in the unlabelled video frames during few-shot inference. Key to our approach