May 2023 arXiv papers — page 68

Emilio Minichiello, Manuel Rivera, Mahmoud Zeinalian

We explain how to derive an explicit formula for a natural transformation relating the (left adjoints of) the homotopy coherent nerve and the Dwyer-Kan simplicial classifying space functor. The formula is derived using a method introduced by Szczarba when comparing two different chain models of a fibration. This note may be taken as a companion to section 3

Narutatsu Ri, Bill Sun, Sam Davidson, Zhou Yu

Although significant progress has been made in developing methods for Grammatical Error Correction (GEC), addressing word choice improvements has been notably lacking and enhancing sentence expressivity by replacing phrases with advanced expressions is an understudied aspect. In this paper, we focus on this area and present our investigation into the task of

Jinghu Liu, Zhihao Xu

Recent studies on disorder-induced many-body localization (MBL) in non-Hermitian quantum systems have attracted great interest. However, the non-Hermitian disorder-free MBL still needs to be clarified. We consider a one-dimensional interacting Stark model with nonreciprocal hoppings having time-reversal symmetry, the properties of which are boundary dependen

Ran Liu, Billy Pik Lik Lau, Khairuldanial Ismail, Achala Chathuranga

Simultaneous localization and mapping (SLAM) is paramount for unmanned systems to achieve self-localization and navigation. It is challenging to perform SLAM in large environments, due to sensor limitations, complexity of the environment, and computational resources. We propose a novel approach for localization and mapping of autonomous vehicles using radio

Zekun Qiu, Zhipu Xie, Zehua Ji, Yuhao Mao

The increasing maturity of big data applications has led to a proliferation of models targeting the same objectives within the same scenarios and datasets. However, selecting the most suitable model that considers model's features while taking specific requirements and constraints into account still poses a significant challenge. Existing methods have focuse

Yuting Wu, Chengyang Yi

Inspired by [1, 13], we prove Michael-Simon type inequalities for smooth symmetric uniformly positive define (0, 2)-tensor fields on compact submanifolds in Euclidean space by the Alexandrov-Bakelman-Pucci (ABP) method.

Yifu Qiu, Yftah Ziser, Anna Korhonen, Edoardo M. Ponti

Hallucinations pose a significant challenge to the reliability of neural models for abstractive summarisation. While automatically generated summaries may be fluent, they often lack faithfulness to the original document. This issue becomes even more pronounced in low-resource settings, such as cross-lingual transfer. With the existing faithful metrics focusi

Siqi Liu, Weixi Feng, Tsu-jui Fu, Wenhu Chen

Making image retrieval methods practical for real-world search applications requires significant progress in dataset scales, entity comprehension, and multimodal information fusion. In this work, we introduce \textbf{E}ntity-\textbf{D}riven \textbf{I}mage \textbf{S}earch (EDIS), a challenging dataset for cross-modal image search in the news domain. EDIS cons

Songnian Xu

Let $G$ be a graph with vertex set $V(G)$, $f$ a permutation of $V(G)$. Define $\delta_f(G)=|d(x,y)-d(f(x),f(y))|$ and $\delta_f(G)=\Sigma\delta_f(x,y)$, where the sum is taken over all unordered pair $x$, $y$ of distinct vertices of $G$. $\delta_f(x,U)=\Sigma\delta_f(x,y)$, where $U\subseteq V(G)$ and $y\in U$. Let $\pi(G)$ denote the smallest positive valu

Hongfei Xue, Qijie Shao, Peikun Chen, Pengcheng Guo

UniSpeech has achieved superior performance in cross-lingual automatic speech recognition (ASR) by explicitly aligning latent representations to phoneme units using multi-task self-supervised learning. While the learned representations transfer well from high-resource to low-resource languages, predicting words directly from these phonetic representations in

Ran Zhou, Xin Li, Lidong Bing, Erik Cambria

In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the linguistic gap by training on pseudo-labeled target-language data. However, due to sub-optimal performance on target languages, the pseudo labels are often noisy and limit the overall performance. In this work, we aim to improve self-training for cross-lingual NER b

Samuel Cahyawijaya, Holy Lovenia, Tiezheng Yu, Willy Chung

Large language models (LLMs) that are tuned with instructions have demonstrated remarkable capabilities in various tasks and languages. However, their ability to generalize to underrepresented languages is limited due to the scarcity of available data. Additionally, directly adapting new languages to instruction-tuned LLMs can result in catastrophic forgetti

Yang Deng, Lizi Liao, Liang Chen, Hongru Wang

Conversational systems based on Large Language Models (LLMs), such as ChatGPT, show exceptional proficiency in context understanding and response generation. However, despite their impressive capabilities, they still possess limitations, such as providing randomly-guessed answers to ambiguous queries or failing to refuse users' requests, both of which are co

Robert Reynolds

Various product and sum relationships are established using special functions, specifically involving Special functions. These relationships are derived from formulas inspired by the finite sum that incorporates the Hurwitz-Lerch zeta function.

Jiang Liu, Chun Pong Lau, Zhongliang Guo, Yuxiang Guo

The increasingly pervasive facial recognition (FR) systems raise serious concerns about personal privacy, especially for billions of users who have publicly shared their photos on social media. Several attempts have been made to protect individuals from being identified by unauthorized FR systems utilizing adversarial attacks to generate encrypted face image

William Fiore, Lina Levin, Maura A. McLaughlin, Akash Anumarlapudi

We present timing solutions for 21 pulsars discovered in 350 MHz surveys using the Green Bank Telescope (GBT). All were discovered in the Green Bank North Celestial Cap pulsar survey, with the exception of PSR J0957-0619, which was found in the GBT 350 MHz Drift-scan pulsar survey. The majority of our timing observations were made with the GBT at 820 MHz. Wi

Wenxuan Wang, Jingyuan Huang, Chang Chen, Jiazhen Gu

The exponential growth of social media platforms, such as Facebook and TikTok, has revolutionized communication and content publication in human society. Users on these platforms can publish multimedia content that delivers information via the combination of text, audio, images, and video. Meanwhile, the multimedia content release facility has been increasin

Tao Zhuo, Zhiyong Cheng, Zan Gao, Hehe Fan

Continual Learning (CL) aims at incrementally learning new tasks without forgetting the knowledge acquired from old ones. Experience Replay (ER) is a simple and effective rehearsal-based strategy, which optimizes the model with current training data and a subset of old samples stored in a memory buffer. To further reduce forgetting, recent approaches extend

Sihan Wang, Jingran Xu, Yong Zeng

Symbiotic radio (SR) is a promising technology of spectrum- and energy-efficient wireless systems, for which the key idea is to use cognitive backscattering communication to achieve mutualistic spectrum and energy sharing with passive backscatter devices (BDs). In this paper, a reconfigurable intelligent surface (RIS) based SR system is considered, where the

Zeyu Xiao, Jiawang Bai, Zhihe Lu, Zhiwei Xiong

The goal of image restoration (IR), a fundamental issue in computer vision, is to restore a high-quality (HQ) image from its degraded low-quality (LQ) observation. Multiple HQ solutions may correspond to an LQ input in this poorly posed problem, creating an ambiguous solution space. This motivates the investigation and incorporation of prior knowledge in ord

Yuma Fujimoto, Kaito Ariu, Kenshi Abe

Learning in games considers how multiple agents maximize their own rewards through repeated games. Memory, an ability that an agent changes his/her action depending on the history of actions in previous games, is often introduced into learning to explore more clever strategies and discuss the decision-making of real agents like humans. However, such games wi

Simon K. Schnyder, John J. Molina, Ryoichi Yamamoto, Matthew S. Turner

During epidemics people may reduce their social and economic activity to lower their risk of infection. Such social distancing strategies will depend on information about the course of the epidemic but also on when they expect the epidemic to end, for instance due to vaccination. Typically it is difficult to make optimal decisions, because the available info

Shumin Deng, Shengyu Mao, Ningyu Zhang, Bryan Hooi

Event-centric structured prediction involves predicting structured outputs of events. In most NLP cases, event structures are complex with manifold dependency, and it is challenging to effectively represent these complicated structured events. To address these issues, we propose Structured Prediction with Energy-based Event-Centric Hyperspheres (SPEECH). SPE

Nan Ma, Ying Yang, Dongkai Zhou

Partial nephrectomy (PN) is common surgery in urology. Digitization of renal anatomies brings much help to many computer-aided diagnosis (CAD) techniques during PN. However, the manual delineation of kidney vascular system and tumor on each slice is time consuming, error-prone, and inconsistent. Therefore, we proposed an entire renal anatomies extraction met

Ping Sun, Ze-Chun Hu, Wei Sun

Let $X_{d_1,d_2}$ be an $F$-random variable with numerator and denominator degrees of freedom $d_1$ and $d_2$, respectively. We investigate the inequality: $P\{|X_{d_1,d_2}-E[X_{d_1,d_2}]|\le \sqrt{{\rm Var}(X_{d_1,d_2})}\}\ge P\{|W-E[W]|\le \sqrt{{\rm Var}(W)}\}$, where $W$ is a standard normal random variable or a $\chi^2(d_1)$ random variable. We prove th

Siyuan Chen, Mengyue Wu, Kenny Q. Zhu, Kunyao Lan

Empowering chatbots in the field of mental health is receiving increasing amount of attention, while there still lacks exploration in developing and evaluating chatbots in psychiatric outpatient scenarios. In this work, we focus on exploring the potential of ChatGPT in powering chatbots for psychiatrist and patient simulation. We collaborate with psychiatris

Valentin Nkana Ngan, Giovanni Stabile, Andrea Mola, Gianluigi Rozza

This article presents a Galerkin projection-based reduced-order modelling (ROM) approach for segregated fluid-structure interaction (FSI) problems, formulated within an Arbitrary Lagrangian Eulerian (ALE) framework at low Reynolds numbers using the Finite Volume Method (FVM). The ROM is constructed using Proper Orthogonal Decomposition (POD) and incorporates

Ziyue Jiang, Qian Yang, Jialong Zuo, Zhenhui Ye

Stutter removal is an essential scenario in the field of speech editing. However, when the speech recording contains stutters, the existing text-based speech editing approaches still suffer from: 1) the over-smoothing problem in the edited speech; 2) lack of robustness due to the noise introduced by stutter; 3) to remove the stutters, users are required to d

Congqi Cao, Yue Lu, Peng Wang, Yanning Zhang

Semi-supervised video anomaly detection (VAD) is a critical task in the intelligent surveillance system. However, an essential type of anomaly in VAD named scene-dependent anomaly has not received the attention of researchers. Moreover, there is no research investigating anomaly anticipation, a more significant task for preventing the occurrence of anomalous

Meghana Sistla, Swarat Chaudhuri, Thomas Reps

This paper presents a new data structure, called \emph{Weighted Context-Free-Language Ordered BDDs} (WCFLOBDDs), which are a hierarchically structured decision diagram, akin to Weighted BDDs (WBDDs) enhanced with a procedure-call mechanism. For some functions, WCFLOBDDs are exponentially more succinct than WBDDs. They are potentially beneficial for represent

Paul R. Estrada, Richard. H. Durisen

The Cassini spacecraft provided key measurements during its more than twelve year mission that constrain the absolute age of Saturn's rings. These include the extrinsic micrometeoroid flux at Saturn, the volume fraction of non-icy pollutants in the rings, and a measurement of the ring mass. These observations taken together limit the ring exposure age to be

Wenxiao Cai, Ke Jin, Jinyan Hou, Cong Guo

Semantic segmentation of drone images is critical for various aerial vision tasks as it provides essential semantic details to understand scenes on the ground. Ensuring high accuracy of semantic segmentation models for drones requires access to diverse, large-scale, and high-resolution datasets, which are often scarce in the field of aerial image processing.

Mengqi Huang, Zhendong Mao, Quan Wang, Yongdong Zhang

Existing autoregressive models follow the two-stage generation paradigm that first learns a codebook in the latent space for image reconstruction and then completes the image generation autoregressively based on the learned codebook. However, existing codebook learning simply models all local region information of images without distinguishing their differen

Rodrigo Fresneda, Lucas de Souza, Dmitri Vassilevich

We propose a relation between the $\eta$ invariant on a manifold with boundary, the $\eta$ invariants of edge states, and the $\eta$ invariant in an infinite volume limit. With the example of planar fermions with bag and chiral bag boundary conditions we show that this relation holds whenever edge states are sufficiently well-localized near the boundary. As

Mei Wang, Weihong Deng

In many real-world applications, face recognition models often degenerate when training data (referred to as source domain) are different from testing data (referred to as target domain). To alleviate this mismatch caused by some factors like pose and skin tone, the utilization of pseudo-labels generated by clustering algorithms is an effective way in unsupe

Ya-Nan Zhu, Jingwei Liang, Xiaoqun Zhang

Federated learning (FL) is a distributed learning paradigm that allows several clients to learn a global model without sharing their private data. In this paper, we generalize a primal dual fixed point (PDFP) \cite{PDFP} method to federated learning setting and propose an algorithm called Federated PDFP (FPDFP) for solving composite optimization problems. In

Domingos Djinja, Sergei Silvestrov, Alex Behakanira Tumwesigye

Conditions for linear integral operators on $L_p$ over measure spaces to satisfy the polynomial covariance type commutation relations are described in terms of defining kernels of the corresponding integral operators. Representation by integral operators are studied both for general polynomial covariance commutation relations and for important classes of pol

Haoqin Tu, Yitong Li, Fei Mi, Zhongliang Yang

Incorporating visual knowledge into text-only dialogue systems has become a potential direction to imitate the way humans think, imagine, and communicate. However, existing multimodal dialogue systems are either confined by the scale and quality of available datasets or the coarse concept of visual knowledge. To address these issues, we provide a new paradig

Ashleigh Simonis, Alexander Hrabski, Yulin Pan

As presented in Annenkov & Shrira (2009), when a surface gravity wave field is subjected to an abrupt perturbation of external forcing, its spectrum evolves on a ``fast'' dynamic time scale of $O(\varepsilon^{-2})$, with $\varepsilon$ a measure of wave steepness. This observation poses a challenge to wave turbulence theory that predicts an evolution with a k

Mingkun Li, Peng Xu, Chun-Guang Li, Jun Guo

In this paper, we address a highly challenging yet critical task: unsupervised long-term person re-identification with clothes change. Existing unsupervised person re-id methods are mainly designed for short-term scenarios and usually rely on RGB cues so that fail to perceive feature patterns that are independent of the clothes. To crack this bottleneck, we

Wei Liu, Jun Wang, Haozhao Wang, Ruixuan Li

A self-explaining rationalization model is generally constructed by a cooperative game where a generator selects the most human-intelligible pieces from the input text as rationales, followed by a predictor that makes predictions based on the selected rationales. However, such a cooperative game may incur the degeneration problem where the predictor overfits

Xu-Jia Ouyang, Yong Zhang, Albert Zijlstra, Chuan-Peng Zhang

The young, compact, very high surface brightness but low excitation planetary nebula (PN) BD+303639 is one of the very few PNe that have been reported to exhibit the 21cm HI emission line. As part of a long-term programme to search for circumstellar atomic hydrogen, we observed the 21cm feature toward BD+303639 with the Five-hundred-meter Aperture Spherical

Naoto Ohsaka, Riku Togashi

Beyond accuracy, there are a variety of aspects to the quality of recommender systems, such as diversity, fairness, and robustness. We argue that many of the prevalent problems in recommender systems are partly due to low-dimensionality of user and item embeddings, particularly when dot-product models, such as matrix factorization, are used. In this study, w

Lequn Chen, Xiling Yao, Wenhe Feng, Youxiang Chew

Real-time defect detection is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM). Traditional in-situ monitoring approach utilizes a single sensor (i.e., acoustic, visual, or thermal sensor) to capture the complex process dynamic behaviors, which is insufficient for defect detection with high accuracy and robustness. This paper p

Ziwei Zhu, Zhaocheng Liu, Changxi Zheng

We present a fully differentiable framework for seamlessly integrating wave optical components with geometrical lenses, offering an approach to enhance the performance of large-scale end-to-end optical systems. In this study, we focus on the integration of a metalens, a geometrical lens, and image data. Through the use of gradient-based optimization techniqu

Michał Stęchły, Lanruo Gao, Boniface Yogendran, Enrico Fontana

In this paper, we aim to expand the understanding of the relationship between the composition of the Hamiltonian in the Quantum Approximate Optimization Algorithm (QAOA) and the corresponding cost landscape characteristics. QAOA is a prominent example of a Variational Quantum Algorithm (VQA), which is most commonly used for combinatorial optimization. The su

Xinyu Zhang, Hefei Huang, Xu Jia, Dong Wang

The shutter strategy applied to the photo-shooting process has a significant influence on the quality of the captured photograph. An improper shutter may lead to a blurry image, video discontinuity, or rolling shutter artifact. Existing works try to provide an independent solution for each issue. In this work, we aim to re-expose the captured photo in post-p

Jianyu Zhao, Yuyang Rong, Yiwen Guo, Yifeng He

Semantic understanding of programs has attracted great attention in the community. Inspired by recent successes of large language models (LLMs) in natural language understanding, tremendous progress has been made by treating programming language as another sort of natural language and training LLMs on corpora of program code. However, programs are essentiall

Mingshuai Dong, Yuxuan Bai, Shimin Wei, Xiuli Yu

Grasping specific objects in complex and irregularly stacked scenes is still challenging for robotics. Because the robot is not only required to identify the object's grasping posture but also needs to reason the manipulation relationship between the objects. In this paper, we propose a manipulation relationship reasoning network with a multi-scale feature a

Xichao Zhang, Jing Xia, Oleg A. Tretiakov, Motohiko Ezawa

The world is full of fluids that flow. The fluid nature of flowing skyrmionic quasiparticles is of fundamental physical interest and plays an essential role in the transport of many skyrmions. Here, we report the laminar and transiently disordered dynamic behaviors of many magnetic skyrmions flowing in a pipe channel. The skyrmion flow driven by a uniform cu

Yiming Zhang, Sravani Nanduri, Liwei Jiang, Tongshuang Wu

Toxicity annotators and content moderators often default to mental shortcuts when making decisions. This can lead to subtle toxicity being missed, and seemingly toxic but harmless content being over-detected. We introduce BiasX, a framework that enhances content moderation setups with free-text explanations of statements' implied social biases, and explore i

Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri

Reproducing kernel Hilbert $C^*$-module (RKHM) is a generalization of reproducing kernel Hilbert space (RKHS) by means of $C^*$-algebra, and the Perron-Frobenius operator is a linear operator related to the composition of functions. Combining these two concepts, we present deep RKHM, a deep learning framework for kernel methods. We derive a new Rademacher ge

Shui-Jing Tang, Mingjie Zhang, Jialve Sun, Jia-Wei Meng

Vibrational spectroscopy is a ubiquitous technology that derives the species, constituents, and morphology of an object from its natural vibrations. However, the vibrational spectra of mesoscopic particles - including most biological cells - have remained hidden from existing technologies. These particles are expected to vibrate faintly at megahertz to gigah

Abhinendra Singh

Dense suspensions of fine particles are significant in numerous biological, industrial, and natural phenomena. They also provide an ideal tool to develop statistical mechanics description for out-of-equilibrium systems. Predicting the bulk response of such materials has been challenging since these systems often undergo liquid-solid transitions upon a small

Siyuan Wang, Zhongyu Wei, Meng Han, Zhihao Fan

Logical reasoning over incomplete knowledge graphs to answer complex logical queries is a challenging task. With the emergence of new entities and relations in constantly evolving KGs, inductive logical reasoning over KGs has become a crucial problem. However, previous PLMs-based methods struggle to model the logical structures of complex queries, which limi

Li Pan, Lv Peizhuo, Chen Kai, Zhang Shengzhi

Compared to traditional neural networks with a single output channel, a multi-exit network has multiple exits that allow for early outputs from the model's intermediate layers, thus significantly improving computational efficiency while maintaining similar main task accuracy. Existing model stealing attacks can only steal the model's utility while failing to

Yaoting Wang, Yuanchao Li, Paul Pu Liang, Louis-Philippe Morency

Fusing multiple modalities has proven effective for multimodal information processing. However, the incongruity between modalities poses a challenge for multimodal fusion, especially in affect recognition. In this study, we first analyze how the salient affective information in one modality can be affected by the other, and demonstrate that inter-modal incon

Yang Chen, Vedaant Shah, Alan Ritter

Large language models (LLMs) combined with instruction tuning have shown significant progress in information extraction (IE) tasks, exhibiting strong generalization capabilities to unseen datasets by following annotation guidelines. However, their applicability to low-resource languages remains limited due to lack of both labeled data for fine-tuning, and un

Aaron Chan, Anant Kharkar, Roshanak Zilouchian Moghaddam, Yevhen Mohylevskyy

Software vulnerabilities bear enterprises significant costs. Despite extensive efforts in research and development of software vulnerability detection methods, uncaught vulnerabilities continue to put software owners and users at risk. Many current vulnerability detection methods require that code snippets can compile and build before attempting detection. T

Riddhi S. Gupta, Neereja Sundaresan, Thomas Alexander, Christopher J. Wood

To run large-scale algorithms on a quantum computer, error-correcting codes must be able to perform a fundamental set of operations, called logic gates, while isolating the encoded information from noise~\cite{Harper2019,Ryan-Anderson2021,Egan2021fault, Chen2022calibrated, Sundaresan2022matching, ryananderson2022implementing, Postler2022demonstration, Google

Marc Delcroix, Naohiro Tawara, Mireia Diez, Federico Landini

Combining end-to-end neural speaker diarization (EEND) with vector clustering (VC), known as EEND-VC, has gained interest for leveraging the strengths of both methods. EEND-VC estimates activities and speaker embeddings for all speakers within an audio chunk and uses VC to associate these activities with speaker identities across different chunks. EEND-VC ge

Yufan Zhou, Ruiyi Zhang, Tong Sun, Jinhui Xu

Recent text-to-image generation models have demonstrated impressive capability of generating text-aligned images with high fidelity. However, generating images of novel concept provided by the user input image is still a challenging task. To address this problem, researchers have been exploring various methods for customizing pre-trained text-to-image genera

R. H. Durisen, Paul. R. Estrada

The Cassini mission provided key measurements needed to determine the absolute age of Saturn's rings, including the extrinsic micrometeoroid flux at Saturn, the volume fraction of non-icy pollutants in the rings, and the total ring mass. These three factors constrain the ring age to be no more than a few 100 Myr (Kempf et al., 2023). Observations during the

Amin Jafarimoghaddam, Manuel Soler

In this research, we consider the commercial aircraft trajectory optimization problem for a general cruise model with arbitrary spatial wind fields to be solved using the Pontryagin maximum principle. The model features two fundamental controls, namely, throttle setting (which appears as a singular control) and heading angle (appearing as a regular control).

Shahid Nawaz, Muhammad Saleem, F. V. Kusmartsev, Dalaver H. Anjum

Data Science is a multidisciplinary field that plays a crucial role in extracting valuable insights and knowledge from large and intricate datasets. Within the realm of Data Science, two fundamental components are Information Theory (IT) and Statistical Mechanics (SM), which provide a theoretical framework for understanding dataset properties. IT enables eff

Shuman Sun, Beichen Wang, Kaikai Liu, Mark Harrington

The generation of ultra-low noise microwave and mmWave in miniaturized, chip-based platforms can transform communication, radar, and sensing systems. Optical frequency division that leverages optical references and optical frequency combs has emerged as a powerful technique to generate microwaves with superior spectral purity than any other approaches. We de

Wenbo Shi, Robert Malaney

With sub-threshold quantum error correction on quantum hardware still out of reach, quantum error mitigation methods are currently deemed an attractive option for implementing certain applications on near-term noisy quantum devices. One such application is quantum routing - the ability to map an incoming quantum signal into a superposition of paths. In this

Sheng Tian, Jihai Dong, Jintang Li, Wenlong Zhao

Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones. As instances that appear in the real world are naturally connected and can be represented with graphs, graph neural networks become increasingly popular in tackling the anomaly detection problem. Despite the promising results, research on ano

Ta-Chung Chi, Ting-Han Fan, Li-Wei Chen, Alexander I. Rudnicky

The use of positional embeddings in transformer language models is widely accepted. However, recent research has called into question the necessity of such embeddings. We further extend this inquiry by demonstrating that a randomly initialized and frozen transformer language model, devoid of positional embeddings, inherently encodes strong positional informa

Xiaoshui Huang, Guofeng Mei, Jian Zhang

The emerging topic of cross-source point cloud (CSPC) registration has attracted increasing attention with the fast development background of 3D sensor technologies. Different from the conventional same-source point clouds that focus on data from same kind of 3D sensor (e.g., Kinect), CSPCs come from different kinds of 3D sensors (e.g., Kinect and { LiDAR}).

Sylvain E. Cappell, Edward Y. Miller

The mesh matrix $Mesh(G,T_0)$ of a connected finite graph $G=(V(G),E(G))=(vertices, edges) \ of \ G$ of with respect to a choice of a spanning tree $T_0 \subset G$ is defined and studied. It was introduced by Trent \cite{Trent1,Trent2}. Its characteristic polynomial $det(X \cdot Id -Mesh(G,T_0))$ is shown to equal $\Sigma_{j=0}^{N} \ (-1)^j \ ST_{j}(G,T_0)\

Pei Zhang, Nur Anisah Mohamed, Adriana Irawati Nur Ibrahim

This paper discusses a new type of anticipated backward stochastic differential equation with a time-delayed generator (DABSDEs, for short) driven by fractional Brownian motion, also known as fractional BSDEs, with Hurst parameter $H\in(1/2,1)$, which extends the results of the anticipated backward stochastic differential equation to the case of the drive is

Bohan Wu, Roberto Martin-Martin, Li Fei-Fei

In this paper, we propose a method to create visuomotor mobile manipulation solutions for long-horizon activities. We propose to leverage the recent advances in simulation to train visual solutions for mobile manipulation. While previous works have shown success applying this procedure to autonomous visual navigation and stationary manipulation, applying it

Shaojie Ma, Hongwei Jia, Yangang Bi, Shangqiang Ning

Owing to the chirality of Weyl nodes characterized by the first Chern number, a Weyl system supports one-way chiral zero modes under a magnetic field, which underlies the celebrated chiral anomaly. As a generalization of Weyl nodes from three-dimensional to five-dimensional physical systems, Yang monopoles are topological singularities carrying nonzero secon

Sangli Teng, Ashkan Jasour, Ram Vasudevan, Maani Ghaffari

This paper reports a novel result: with proper robot models on matrix Lie groups, one can formulate the kinodynamic motion planning problem for rigid body systems as \emph{exact} polynomial optimization problems that can be relaxed as semidefinite programming (SDP). Due to the nonlinear rigid body dynamics, the motion planning problem for rigid body systems

Se Kwon Kim, Suk Bum Chung

Recent years have seen a number of instances where magnetism and superconductivity intrinsically coexist. Our focus is on the case where spin-triplet superconductivity arises out of ferromagnetism, and we make a hydrodynamic analysis of the effect of a charge supercurrent on magnetic topological defects like domain walls and merons. We find that the emergent

Daliang Ouyang, Su He, Guozhong Zhang, Mingzhu Luo

Remarkable effectiveness of the channel or spatial attention mechanisms for producing more discernible feature representation are illustrated in various computer vision tasks. However, modeling the cross-channel relationships with channel dimensionality reduction may bring side effect in extracting deep visual representations. In this paper, a novel efficien

Nick Alonso, Jeff Krichmar, Emre Neftci

Backpropagation (BP), the standard learning algorithm for artificial neural networks, is often considered biologically implausible. In contrast, the standard learning algorithm for predictive coding (PC) models in neuroscience, known as the inference learning algorithm (IL), is a promising, bio-plausible alternative. However, several challenges and questions

John Cardy

We propose a solution to the puzzle of dimensional reduction in the random field Ising model, inverting the question and asking: to what random problem in $D=d+2$ dimensions does a pure system in $d$ dimensions correspond? We consider two models: a continuum binary fluid, and a lattice gas which maps exactly onto an Ising model. In both cases we show that th

Binfeng Xu, Zhiyuan Peng, Bowen Lei, Subhabrata Mukherjee

Augmented Language Models (ALMs) blend the reasoning capabilities of Large Language Models (LLMs) with tools that allow for knowledge retrieval and action execution. Existing ALM systems trigger LLM thought processes while pulling observations from these tools in an interleaved fashion. Specifically, an LLM reasons to call an external tool, gets halted to fe

Sayak Chakrabarty, Konstantin Makarychev

We show that a simple single-pass semi-streaming variant of the Pivot algorithm for Correlation Clustering gives a (3 + {\epsilon})-approximation using O(n/{\epsilon}) words of memory. This is a slight improvement over the recent results of Cambus, Kuhn, Lindy, Pai, and Uitto, who gave a (3 + {\epsilon})-approximation using O(n log n) words of memory, and Be

Christopher Thomas, Sébastien Burdin, Claudine Lacroix

In this work, we study the effects of a transverse magnetic field in a Kondo lattice model with two $f$ orbitals interacting with the conduction electrons. The $f$ electrons that are present on the same site interact through Hund's coupling, while on neighboring sites they interact through intersite exchange. We consider here that part of $f$ electrons are l

Sean Monahan

We provide an overview of the combinatorial theory of horospherical varieties using coloured fans, a generalization of the combinatorial theory of toric varieties using polyhedral fans.

Kazuya Shinjo, Shigetoshi Sota, Seiji Yunoki, Takami Tohyama

The recent experimental observations of loop current in Sr$_{2}$IrO$_{4}$, YBa$_{2}$Cu$_{3}$O$_{7}$, and Sr$_{14}$Cu$_{24}$O$_{41}$ have inspired a theoretical study that broadly redefines loop current as a manifestation of quantum liquid crystals. Using the density-matrix renormalization group method, we investigate the emergence of spin loop-current (sLC)

Dahlia Malkhi, Maofan Yin

This article will take you on a journey to the core of blockchains, their Byzantine consensus engine, where HotStuff emerged as a new algorithmic foundation for the classical Byzantine generals consensus problem. The first part of the article underscores the theoretical advances HotStuff enabled, including several models in which HotStuff-based solutions clo

J. P. Morgan, Ilham Variansyah, Todd S. Palmer, Kyle E. Niemeyer

To find deterministic solutions to the transient $S_N$ neutron transport equation, iterative schemes are typically used to treat the scattering (and fission) source terms. We explore the one-cell inversion iteration scheme to do this on the GPU and make comparisons to a source iteration scheme. We examine convergence behavior, through the analysis of spectra

Lei Guo, Wei Chen, Yuxuan Sun, Bo Ai

Semantic communication, an intelligent communication paradigm that aims to transmit useful information in the semantic domain, is facilitated by deep learning techniques. Robust semantic features can be learned and transmitted in an analog fashion, but it poses new challenges to hardware, protocol, and encryption. In this paper, we propose a digital semantic

Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic

Flexible models for probability distributions are an essential ingredient in many machine learning tasks. We develop and investigate a new class of probability distributions, which we call a Squared Neural Family (SNEFY), formed by squaring the 2-norm of a neural network and normalising it with respect to a base measure. Following the reasoning similar to th

Emily Ren

A bond in a graph is a minimal nonempty edge-cut. A connected graph $G$ is dual Hamiltonian if the vertex set can be partitioned into two subsets $X$ and $Y$ such that the subgraphs induced by $X$ and $Y$ are both trees. There is much interest in studying the longest cycles and largest bonds in graphs. H. Wu conjectured that any longest cycle must meet any l

Yiwei Wang, Bryan Hooi, Fei Wang, Yujun Cai

Relation extraction (RE) aims to extract the relations between entity names from the textual context. In principle, textual context determines the ground-truth relation and the RE models should be able to correctly identify the relations reflected by the textual context. However, existing work has found that the RE models memorize the entity name patterns to

Daichi Kashino, Simon J. Lilly, Robert A. Simcoe, Rongmon Bordoloi

The physical conditions of the circumgalactic medium are probed by intervening absorption-line systems in the spectrum of background quasi-stellar objects out to the epoch of cosmic reionization. A correlation between the ionization state of the absorbing gas and the nature of the nearby galaxies has been suggested by the sources detected either in Lyalpha o

Rui-Qi Wang, Tianmin Lei, Yue-Wen Fang

Rapidly increasing interest in low-dimensional materials is driven by the emerging requirement to develop nanoscale solid-state devices with novel functional properties that are not available in three-dimensional bulk phases. Among the well-known low-dimensional systems, complex transition metal oxide interface holds promise for broad applications in electro

Chun Pong Lau, Jiang Liu, Rama Chellappa

The increasingly pervasive facial recognition (FR) systems raise serious concerns about personal privacy, especially for billions of users who have publicly shared their photos on social media. Several attempts have been made to protect individuals from unauthorized FR systems utilizing adversarial attacks to generate encrypted face images to protect users f

Haoqi Zheng, Qihuang Zhong, Liang Ding, Zhiliang Tian

Text classification tasks often encounter few shot scenarios with limited labeled data, and addressing data scarcity is crucial. Data augmentation with mixup has shown to be effective on various text classification tasks. However, most of the mixup methods do not consider the varying degree of learning difficulty in different stages of training and generate

Allan Zhou, Kaien Yang, Yiding Jiang, Kaylee Burns

The recent success of neural networks as implicit representation of data has driven growing interest in neural functionals: models that can process other neural networks as input by operating directly over their weight spaces. Nevertheless, constructing expressive and efficient neural functional architectures that can handle high-dimensional weight-space obj

Yuming Shi, Yi Shi, Adam Wasserman

Accurate first-principles calculations for the energies, charge distributions, and spin symmetries of many-electron systems are essential to understand and predict the electronic and structural properties of molecules and materials. Kohn-Sham density functional theory (KS-DFT) stands out among electronic-structure methods due to its balance of accuracy and c

Alvaro Cintas Canto, Jasmin Kaur, Mehran Mozaffari Kermani, Reza Azarderakhsh

This survey is on forward-looking, emerging security concerns in post-quantum era, i.e., the implementation attacks for 2022 winners of NIST post-quantum cryptography (PQC) competition and thus the visions, insights, and discussions can be used as a step forward towards scrutinizing the new standards for applications ranging from Metaverse, Web 3.0 to deeply

Yosuke Ashida, Ken'ichiro Nakazato, Takuji Tsujimoto

Fluxes of the diffuse supernova neutrino background (DSNB) are calculated based on a new modeling of galactic chemical evolution, where a variable stellar initial mass function (IMF) depending on the galaxy type is introduced and black hole (BH) formation from the failed supernova is considered for progenitors heavier than 18$M_{\odot}$. The flux calculation

Inbal Livni Navon, Charlotte Peale, Omer Reingold, Judy Hanwen Shen

Many companies rely on advertising platforms such as Google, Facebook, or Instagram to recruit a large and diverse applicant pool for job openings. Prior works have shown that equitable bidding may not result in equitable outcomes due to heterogeneous levels of competition for different types of individuals. Suggestions have been made to address this problem

Shuai Shao, Yu Guan, Bing Zhai, Paolo Missier

Human activity recognition (HAR) is one of the core research themes in ubiquitous and wearable computing. With the shift to deep learning (DL) based analysis approaches, it has become possible to extract high-level features and perform classification in an end-to-end manner. Despite their promising overall capabilities, DL-based HAR may suffer from overfitti