December 2023 arXiv papers — page 127

Yifei Chen, Arturo Gamboa-Gonzalez, Michael Wehner, Xiaobin Xiong

We present a novel pneumatic augmentation to traditional electric motor-actuated legged robot to increase intermittent power density to perform infrequent explosive hopping behaviors. The pneumatic system is composed of a pneumatic pump, a tank, and a pneumatic actuator. The tank is charged up by the pump during regular hopping motion that is created by the

Dianshu Liao, Shidong Pan, Xiaoyu Sun, Xiaoxue Ren

LLM-based code generation tools are essential to help developers in the software development process. Existing tools often disconnect with the working context, i.e., the code repository, causing the generated code to be not similar to human developers. In this paper, we propose a novel code generation framework, dubbed A^3-CodGen, to harness information with

Jingyao Wang, Yi Ren, Zeen Song, Jianqi Zhang

Meta-learning enables rapid generalization to new tasks by learning knowledge from various tasks. It is intuitively assumed that as the training progresses, a model will acquire richer knowledge, leading to better generalization performance. However, our experiments reveal an unexpected result: there is negative knowledge transfer between tasks, affecting ge

Ji Liu, Juncheng Jia, Tianshi Che, Chao Huo

As a promising approach to deal with distributed data, Federated Learning (FL) achieves major advancements in recent years. FL enables collaborative model training by exploiting the raw data dispersed in multiple edge devices. However, the data is generally non-independent and identically distributed, i.e., statistical heterogeneity, and the edge devices sig

Zhihua Chen, Kai Wu, Shao-Ming Fei

The quantum network correlations play significant roles in long distance quantum communication,quantum cryptography and distributed quantum computing. Generally it is very difficult to characterize the multipartite quantum network correlations such as nonlocality, entanglement and steering. In this paper, we propose the network and the genuine network quantu

Hui Zhao, Bing-Zhao Li

The polar wavelet transform (PWT) has been proven to be a powerful mathematical tool for signal and image processing in recent years. Due to the increasing demand for directional representations of signals in engineering, it is impossible to fully exploit the intrinsic directional features of signals to describe high-dimensional signals like images. Focusing

Varun Sharma

Affective computing is a field of study that focuses on developing systems and technologies that can understand, interpret, and respond to human emotions. Speech Emotion Recognition (SER), in particular, has got a lot of attention from researchers in the recent past. However, in many cases, the publicly available datasets, used for training and evaluation, a

Praveen Kumar Singya, Behrooz Makki, Antonio D'Errico, Mohamed-Slim Alouini

Moving towards $6^{\text{th}}$ generation (6G), backhaul networks require significant improvements to support new use-cases with restricted joint capacity and availability requirements. In this paper, we investigate the potentials and challenges of joint sub-teraHertz (sub-THz) and free space optical (FSO), in short sub-THz-FSO, multi-hop networks as a candi

Teng Hu, Jiangning Zhang, Ran Yi, Yuzhen Du

Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. Although anomaly generation methods have been proposed to augment the anomaly data, they either suffer from poor generation authenticity or inaccurate alignment between the generated anom

Ali Farghadan, Junoh Jung, Rutvij Bhagwat, Aaron Towne

We present an extension of the RSVD-$\Delta t$ algorithm initially developed for resolvent analysis of statistically stationary flows to handle harmonic resolvent analysis of time-periodic flows. The harmonic resolvent operator, as proposed by \citet{Padovanetal20}, characterizes the linearized dynamics of time-periodic flows in the frequency domain, and its

Venkat Venkatasubramanian, Arun Sankar E M, Abhishek Sivaram

Patterns in ecological systems such as mussel beds have been of considerable interest for a long time. Several physicochemical mechanisms have been proposed for their formation. Here, we propose a novel framework based on economics and game theory. Since mussels are biological agents instinctively driven by the survival purpose, we mathematically model this

Siqi Wang, Shaoyuan Li, Li Yin, Xiang Yin

This paper investigates the problem of designing control policies that satisfy high-level specifications described by signal temporal logic (STL) in unknown, stochastic environments. While many existing works concentrate on optimizing the spatial robustness of a system, our work takes a step further by also considering temporal robustness as a critical metri

Yong Tao

By using the Landau-Ginzburg-Wilson paradigm, we show that, near a quantum critical point (QCP), Cooper pairs at zero temperature would obey a nonlinear relativistic equation, where the imaginary time emerges as a novel dimension. This relativistic equation is applicable to certain superconductors at zero temperature for which the Faber-Pippard coherence len

Guojie Hu, Qingqing Wu, Kui Xu, Jian Ouyang

We investigate multiuser uplink communication from multiple single-antenna users to a base station (BS), which is equipped with a movable-antenna (MA) array and adopts zero-forcing receivers to decode multiple signals. We aim to optimize the MAs' positions at the BS, to minimize the total transmit power of all users subject to the minimum rate requirement. A

Yougang Lyu, Jitai Hao, Zihan Wang, Kai Zhao

Multiple defendants in a criminal fact description generally exhibit complex interactions, and cannot be well handled by existing Legal Judgment Prediction (LJP) methods which focus on predicting judgment results (e.g., law articles, charges, and terms of penalty) for single-defendant cases. To address this problem, we propose the task of multi-defendant LJP

Zixi Wang, M. Cenk Gursoy

Federated learning (FL) is a framework which allows multiple users to jointly train a global machine learning (ML) model by transmitting only model updates under the coordination of a parameter server, while being able to keep their datasets local. One key motivation of such distributed frameworks is to provide privacy guarantees to the users. However, prese

Ao Wang, Hui Chen, Zijia Lin, Jungong Han

Segment Anything Model (SAM) has shown impressive zero-shot transfer performance for various computer vision tasks recently. However, its heavy computation costs remain daunting for practical applications. MobileSAM proposes to replace the heavyweight image encoder in SAM with TinyViT by employing distillation, which results in a significant reduction in com

Hemanth Manjunatha, Panagiotis Tsiotras

Deep learning has revolutionized autonomous driving by enabling vehicles to perceive and interpret their surroundings with remarkable accuracy. This progress is attributed to various deep learning models, including Mediated Perception, Behavior Reflex, and Direct Perception, each offering unique advantages and challenges in enhancing autonomous driving capab

Jiaxin Gao, Yuxiao Hu, Qinglong Cao, Siqi Dai

Time series forecasting (TSF) holds significant importance in modern society, spanning numerous domains. Previous representation learning-based TSF algorithms typically embrace a contrastive learning paradigm featuring segregated trend-periodicity representations. Yet, these methodologies disregard the inherent high-impact noise embedded within time series d

Tianqianjin Lin, Kaisong Song, Zhuoren Jiang, Yangyang Kang

Heterogeneous graph neural networks have become popular in various domains. However, their generalizability and interpretability are limited due to the discrepancy between their inherent inference flows and human reasoning logic or underlying causal relationships for the learning problem. This study introduces a novel solution, HG-SCM (Heterogeneous Graph as

Huajian Li, Longjian Li, Jiajian Liang, Weinan Dai

In recent years, machine learning (ML) has brought effective approaches and novel techniques to economic decision, investment forecasting, and risk management, etc., coping the variable and intricate nature of economic and financial environments. For the investment in stock market, this research introduces a pioneering quantitative fusion model combining sto

T. U. Ito, W. Higemoto, K. Shimomura

In positive muon spin rotation and relaxation ($\mu^+$SR) spectroscopy, positive muons ($\mu^+$) implanted into solid oxides are conventionally treated as immobile spin-probes at interstitial sites below room temperature. This is because each $\mu^+$ is thought to be tightly bound to an oxygen atom in the host lattice to form a muonic analogue of the hydroxy

Shu Li, Lu Lu, Jianfeng Wang

In this paper, we study the graph-theoretic analogues of vector Laplacian (or Helmholtz operator) and vector Laplace equation. We determine the graph matrix representation of vector Laplacian and obtain the dimension of solution space of vector Laplace equation on graphs.

Sieun Lee, Jeong-Eun Lee, Carlos Contreras Peña, Doug Johnstone

Variability in the brightness of Young Stellar Objects (YSOs) is a common phenomenon that can be caused by changes in various factors, including accretion, extinction, disk morphology, interactions between the disk and the stellar photosphere, and the rotation of hot or cold magnetic spots on the stellar photosphere. Analyzing the variability on different ti

Jianbiao Mei, Yu Yang, Mengmeng Wang, Junyu Zhu

Semantic scene completion (SSC) aims to predict the semantic occupancy of each voxel in the entire 3D scene from limited observations, which is an emerging and critical task for autonomous driving. Recently, many studies have turned to camera-based SSC solutions due to the richer visual cues and cost-effectiveness of cameras. However, existing methods usuall

Shiryu Ueno, Yusei Yamada, Shunsuke Nakatsuka, Kunihito Kato

In this study, we benchmark query strategies for deep actice learning~(DAL). DAL reduces annotation costs by annotating only high-quality samples selected by query strategies. Existing research has two main problems, that the experimental settings are not standardized, making the evaluation of existing methods is difficult, and that most of experiments were

Dusan Guller

This paper is a continuation of our work concerning the logical and computational foundations of multi-step fuzzy inference. We bring further results on the implementation of the Mamdani-Assilian type of fuzzy rules and inference in Goedel logic with truth constants. In our previous work, we have provided translation of Mamdani-Assilian fuzzy rules to formul

Amirhosein Chahe, Chenan Wang, Abhishek Jeyapratap, Kaidi Xu

This paper introduces an attacking mechanism to challenge the resilience of autonomous driving systems. Specifically, we manipulate the decision-making processes of an autonomous vehicle by dynamically displaying adversarial patches on a screen mounted on another moving vehicle. These patches are optimized to deceive the object detection models into misclass

Adam Moss, P. Bergeron, Mukremin Kilic, Gracyn Jewett

We report the discovery of spectroscopic variations in the magnetic DBA white dwarf SDSS J091016.43+210554.2. Follow-up time-resolved spectroscopy at the Apache Point Observatory (APO) and the MMT show significant variations in the H absorption lines over a rotation period of 7.7 or 11.3 h. Unlike recent targets that show similar discrepancies in their H and

Letian Zhang, Ming Li, Chen Chen, Jie Xu

Neural radiance fields (NeRF) is a promising approach for generating photorealistic images and representing complex scenes. However, when processing data sequentially, it can suffer from catastrophic forgetting, where previous data is easily forgotten after training with new data. Existing incremental learning methods using knowledge distillation assume that

Kennedy Efosa Ehimwenma, Safiya Al Sharji, Maruf Raheem

The probability of an event is in the range of [0, 1]. In a sample space S, the value of probability determines whether an outcome is true or false. The probability of an event Pr(A) that will never occur = 0. The probability of the event Pr(B) that will certainly occur = 1. This makes both events A and B thus a certainty. Furthermore, the sum of probabiliti

Yiming Zhang, Dongning Guo

This paper introduces a novel approach to radio resource allocation in multi-cell wireless networks using a fully scalable multi-agent reinforcement learning (MARL) framework. A distributed method is developed where agents control individual cells and determine spectrum and power allocation based on limited local information, yet achieve quality of service (

Orr Zohar, Alejandro Lozano, Shelly Goel, Serena Yeung

Object detection is integral to a bevy of real-world applications, from robotics to medical image analysis. To be used reliably in such applications, models must be capable of handling unexpected - or novel - objects. The open world object detection (OWD) paradigm addresses this challenge by enabling models to detect unknown objects and learn discovered ones

James Paolo Rili

Semiconductors are currently an active topic of study due to the endless range of applications in electronic hardware and computer engineering. In this experiment, the material properties (i.e. resistivity $ρ$, Hall coefficient $R_{H}$, and mobility $μ$) of a doped GaAs sheet is described by utilizing Hall Effect and the Van der Pauw method with varying temp

Kennedy E. Ehimwenma, Junfeng Wang, Ze Zheng, Hongyu Zhou

A red-black (RB) tree is a data structure with red and black nodes coloration. The red and black color of nodes make up the principal component for balancing a RB tree. A balanced tree has an equal number of black nodes on any simple path. But when a black leaf node is deleted, a double-black (DB) node is formed, thus, causing a reduction in black heights an

Boyu Shi, Shiyu Xia, Xu Yang, Haokun Chen

Recently, Stitchable Neural Networks (SN-Net) is proposed to stitch some pre-trained networks for quickly building numerous networks with different complexity and performance trade-offs. In this way, the burdens of designing or training the variable-sized networks, which can be used in application scenarios with diverse resource constraints, are alleviated.

Ishita Mediratta, Qingfei You, Minqi Jiang, Roberta Raileanu

Despite recent progress in offline learning, these methods are still trained and tested on the same environment. In this paper, we compare the generalization abilities of widely used online and offline learning methods such as online reinforcement learning (RL), offline RL, sequence modeling, and behavioral cloning. Our experiments show that offline learning

Thinh Pham, Chi Tran, Dat Quoc Nguyen

The research study of detecting multiple intents and filling slots is becoming more popular because of its relevance to complicated real-world situations. Recent advanced approaches, which are joint models based on graphs, might still face two potential issues: (i) the uncertainty introduced by constructing graphs based on preliminary intents and slots, whic

Jirui Guo, Ban Lin, Hao Zou

This paper studies the derived equivalence between Calabi--Yau mixed branches using the B-brane hemisphere partition function in anomalous gauged linear sigma models (GLSMs). For a family of anomalous $U(2)$ GLSMs, we study the infrared behavior of B-branes under RG flow and the variation of FI parameters in the quantum K\"ahler moduli space. As characterize

Shu Yin, Chao Gao, Zhen Wang

With the rise of social media, the spread of fake news has become a significant concern, potentially misleading public perceptions and impacting social stability. Although deep learning methods like CNNs, RNNs, and Transformer-based models like BERT have enhanced fake news detection, they primarily focus on content, overlooking social context during news pro

Junlong Mao, Huiyi Tang, Yi Zhang, Fengxia Liu

The proliferation of Deep Neural Networks (DNN) in commercial applications is expanding rapidly. Simultaneously, the increasing complexity and cost of training DNN models have intensified the urgency surrounding the protection of intellectual property associated with these trained models. In this regard, DNN watermarking has emerged as a crucial safeguarding

Jiahong Wu, Nan Liu, Wei Kang

In this paper, we consider the case that sharing many secrets among a set of participants using the threshold schemes. All secrets are assumed to be statistically independent and the weak secure condition is focused on. Under such circumstances we investigate the infimum of the (average) information ratio and the (average) randomness ratio for any structure

Nguyen Van Thu, Pham Duy Thanh

We investigate the effect of non-zero temperature to the condensate fraction of a homogeneous dilute weakly interacting Bose gas in very low-temperature region. Within inproved Hartree-Fock approximation, the Cornwall-Jackiw-Tomboulis effective action approach shows that the thermal fluctuations make the condensate fraction decrease as a second and fourth-or

Ruyue Liu, Rong Yin, Yong Liu, Weiping Wang

Graph Comparative Learning (GCL) is a self-supervised method that combines the advantages of Graph Convolutional Networks (GCNs) and comparative learning, making it promising for learning node representations. However, the GCN encoders used in these methods rely on the Fourier transform to learn fixed graph representations, which is inherently limited by the

Yuntao Shou, Tao Meng, Wei Ai, Fangze Fu

Multi-modal conversation emotion recognition (MCER) aims to recognize and track the speaker's emotional state using text, speech, and visual information in the conversation scene. Analyzing and studying MCER issues is significant to affective computing, intelligent recommendations, and human-computer interaction fields. Unlike the traditional single-utteranc

Raymond Cheng, Rui Wang, Yuesheng Xu

Learning methods in Banach spaces are often formulated as regularization problems which minimize the sum of a data fidelity term in a Banach norm and a regularization term in another Banach norm. Due to the infinite dimensional nature of the space, solving such regularization problems is challenging. We construct a direct sum space based on the Banach spaces

Sumit Sanwal

In modern-day organizations, many software applications require critical input to decide the next steps in the application workflow and approval. One of the most important inputs to decide the subsequent course of action is the key performance indicator-based scoring for the entities used in the application. Computing the right score for the entities in the

Vinicius Soares Silva Marques

DevBots are automated tools that perform various tasks in order to support software development. They are a growing trend and have been used in repositories to automate repetitive tasks, as code generators, and as collaborators in eliciting requirements and defining architectures. In this study, we analyzed 24 articles to investigate the state of the art of

Wenjun Shao, Chunfeng Wu, Xun-Li Feng

In the preceding Comment [1] it was claimed that the third-order Hamiltonian obtained in our original paper [2] is not Hermitian for general situations when considering time-dependence and the way of deriving the effective third-order expansion is not very rigorous. To reply the comment we should emphasize the following three points: first of all, the third-

Brett McInnes

A ``large'' AdS black hole can attain equilibrium with its own Hawking radiation, and in that condition it is thought to be dual to a strongly coupled field theory, also at equilibrium. But the interior of the black hole is by no means static: the geometry of spatial sections lying inside the event horizon evolves at some rate. This prompts the obvious quest

Yongkang Yin, Xu Li, Ying Shan, Yuexian Zou

Speaker diarization in real-world videos presents significant challenges due to varying acoustic conditions, diverse scenes, the presence of off-screen speakers, etc. This paper builds upon a previous study (AVR-Net) and introduces a novel multi-modal speaker diarization system, AFL-Net. The proposed AFL-Net incorporates dynamic lip movement as an additional

Qing-Hua Zhang, Lemin Lai, Shao-Ming Fei

We study the steerability for arbitrary dimensional bipartite systems based on the correlation matrices given by local special unitary groups. We present families of steering criteria for bipartite quantum states in terms of parameterized correlation matrices. We show that these steering criteria may detect more steerable states than the existing steering cr

H. V. Almeida Silva, D. Dalmazi, R. R. Lino dos Santos, E. L. Mendonça

In this work, we systematically derive explicit expressions for the Poincar\'e Group generators on arbitrary-rank tensors and spinor-tensors in $D=3+1$ and $D=2+1$ spacetimes, thus generalizing previous works in the literature for the groups $ISO(3,1)$ and $ISO(2,1)$. From the Casimir eigenvalue equations, we demonstrate in a model-independent way the Fierz-

Alaa Selim, Junbo Zhao

This paper introduces a cyber-physical testbed that integrates the Real-Time Digital Simulator (RTDS) with the Real-Time Automation Controller (RTAC) to enhance cybersecurity in electrical distribution networks. Focused on addressing vulnerabilities to cyber attacks, our testbed employs an advanced control algorithm developed in Python and executed through r

Kaiming Shen, Ziping Zhao, Yannan Chen, Zepeng Zhang

Fractional programming (FP) arises in various communications and signal processing problems because several key quantities in the field are fractionally structured, e.g., the Cram\'{e}r-Rao bound, the Fisher information, and the signal-to-interference-plus-noise ratio (SINR). A recently proposed method called the quadratic transform has been applied to the F

Jianwei Li, Tianchi Zhang, Ian En-Hsu Yen, Dongkuan Xu

Transformer-based models, such as BERT, have been widely applied in a wide range of natural language processing tasks. However, one inevitable side effect is that they require massive memory storage and inference cost when deployed in production. Quantization is one of the popularized ways to alleviate the cost. However, the previous 8-bit quantization strat

Zaber Ibn Abdul Hakim, Najibul Haque Sarker, Rahul Pratap Singh, Bishmoy Paul

A thorough comprehension of textual data is a fundamental element in multi-modal video analysis tasks. However, recent works have shown that the current models do not achieve a comprehensive understanding of the textual data during the training for the target downstream tasks. Orthogonal to the previous approaches to this limitation, we postulate that unders

Nan Li, Ehsan Taheri, Ilya Kolmanovsky, Dimitar Filev

In this paper, we develop a computationally-efficient approach to minimum-time trajectory optimization using input-output data-based models, to produce an end-to-end data-to-control solution to time-optimal planning/control of dynamic systems and hence facilitate their autonomous operation. The approach integrates a non-parametric data-based model for trajec

Carlos Molina-Jimenez, Sandra Milena Felizia

The use of computer technology to automate the enforcement of law is a promising alternative to simplify bureaucratic procedures. However, careless automation might result in an inflexible and dehumanise law enforcement system driven by algorithms that do not account for the particularities of individuals or minorities. In this paper, we argue that hybrid sm

Zhen Li, Li-Wei Wang, Xulong Wang, Zhi-Kang Lin

Non-Hermitian effects have emerged as a new paradigm for the manipulation of phases of matter that profoundly changes our understanding of non-equilibrium systems, introducing novel concepts such as exceptional points and spectral topology, as well as exotic phenomena such as non-Hermitian skin effects (NHSEs). Most existing studies, however, focus on non-He

Benjamin Idini, Francis Nimmo

Titan's ice shell floats on top of a global ocean revealed by the large tidal Love number $k_2 = 0.616\pm0.067$ registered by Cassini. The Cassini observation exceeds the predicted $k_2$ by one order of magnitude in the absence of an ocean, and is 3-$\sigma$ away from the predicted $k_2$ if the ocean is pure water resting on top of a rigid ocean floor. Previ

Shoshana Elgart, Mark B. Flegg, Somya Mehra, Jennifer A. Flegg

The epidemiological behavior of Plasmodium vivax malaria occurs across spatial scales including within-host, population, and metapopulation levels. On the within-host scale, P. vivax sporozoites inoculated in a host may form latent hypnozoites, the activation of which drives secondary infections and accounts for a large proportion of P. vivax illness; on the

Tyler Spears, P. Thomas Fletcher

Our understanding of the human connectome is fundamentally limited by the resolution of diffusion MR images. Reconstructing a connectome's constituent neural pathways with tractography requires following a continuous field of fiber directions. Typically, this field is found with simple trilinear interpolation in low-resolution, noisy diffusion MRIs. However,

Jianwei Li, Sheng Liu, Qi Lei

Language models trained via federated learning (FL) demonstrate impressive capabilities in handling complex tasks while protecting user privacy. Recent studies indicate that leveraging gradient information and prior knowledge can potentially reveal training samples within FL setting. However, these investigations have overlooked the potential privacy risks t

Nyle Siddiqui, Praveen Tirupattur, Mubarak Shah

In this work, we present a novel approach to multi-view action recognition where we guide learned action representations to be separated from view-relevant information in a video. When trying to classify action instances captured from multiple viewpoints, there is a higher degree of difficulty due to the difference in background, occlusion, and visibility of

Aparajithan Venkateswaran, Jishnu Das, Tyler H. McCormick

Contact tracing is one of the most important tools for preventing the spread of infectious diseases, but as the experience of COVID-19 showed, it is also next-to-impossible to implement when the disease is spreading rapidly. We show how to substantially improve the efficiency of contact tracing by combining standard microeconomic tools that measure heterogen

Houcheng Su, Daixian Liu, Mengzhu Wang, Wei Wang

Fully test-time adaptation (FTTA) adapts a model that is trained on a source domain to a target domain during the testing phase, where the two domains follow different distributions and source data is unavailable during the training phase. Existing methods usually adopt entropy minimization to reduce the uncertainty of target prediction results, and improve

Hudson Hilal, Pramit Saha

As the use of Lithium-ion batteries continues to grow, it becomes increasingly important to be able to predict their remaining useful life. This work aims to compare the relative performance of different machine learning algorithms, both traditional machine learning and deep learning, in order to determine the best-performing algorithms for battery cycle lif

Andong Hua, Jindong Gu, Zhiyu Xue, Nicholas Carlini

With the prevalence of the Pretraining-Finetuning paradigm in transfer learning, the robustness of downstream tasks has become a critical concern. In this work, we delve into adversarial robustness in transfer learning and reveal the critical role of initialization, including both the pretrained model and the linear head. First, we discover the necessity of

Ellis R. Crabtree, Juan M. Bello-Rivas, Ioannis G. Kevrekidis

A valuable step in the modeling of multiscale dynamical systems in fields such as computational chemistry, biology, materials science and more, is the representative sampling of the phase space over long timescales of interest; this task is not, however, without challenges. For example, the long term behavior of a system with many degrees of freedom often ca

Alysson Bessani, Miguel Correia, Tobias Distler, Rüdiger Kapitza

A recent paper by Gupta et al. (EuroSys'23) challenged the usefulness of trusted component (TC) based Byzantine fault-tolerant (BFT) protocols to lower the replica group size from $3f+1$ to $2f+1$, identifying three limitations of such protocols and proposing that TCs should be used instead to improve the performance of BFT protocols. Here, we point out flaw

A. Shulevski, M. Brienza, F. Massaro, R. Morganti

We present the discovery of a very extended (550 kpc) and low-surface-brightness ($ 3.3 \mu \mathrm{Jy} \, arcsec^{-2} $ at 144 MHz) radio emission region in Abell 1318. These properties are consistent with its characterisation as an active galactic nucleus (AGN) remnant radio plasma, based on its morphology and radio spectral properties. We performed a broa

Shanmuka Shivashankara, Patti Rizzo, Nicole Cafe

Divergences that occur in density matrices of decay and scattering processes are shown to be regularized by tracing and unitarity or the optical theorem. These divergences are regularized by the lifetime of the decaying particle or the total scattering cross section. Also, this regularization is shown to give the expected helicities of final particles. The d

Tyson Klingner

We give a complete, self-contained computation of the spectral data parametrising Higgs bundles in the generic fibres of the $\mathrm{SO}_{2n+1}$-Hitchin fibration where the Higgs fields are $L$-twisted endomorphisms. Although the spectral data is known in the literature, we develop a new approach to spectral data, which takes advantage of Hecke modification

Parisa Ramezani, Yasaman Khorsandmanesh, Emil Björnson

Reconfigurable intelligent surface (RIS) is a newly-emerged technology that might fundamentally change how wireless networks are operated. Though extensively studied in recent years, the practical limitations of RIS are often neglected when assessing the performance of RIS-assisted communication networks. One of these limitations is that each RIS element is

Leonardo P. C. da Cruz, Jaume LLibre

A center of a differential system in the plane $\mathbb{R}^2$ is an equilibrium point $p$ having a neighborhood $U$ such that $U\setminus \{p\}$ is filled of periodic orbits. A center $p$ is global when $\mathbb{R}^2\setminus \{p\}$ is filled of periodic orbits. In general is a difficult problem to distinguish the centers from the foci for a given class of d

David R. Bellamy, Bhawesh Kumar, Cindy Wang, Andrew Beam

In this work we introduce Labrador, a pre-trained Transformer model for laboratory data. Labrador and BERT were pre-trained on a corpus of 100 million lab test results from electronic health records (EHRs) and evaluated on various downstream outcome prediction tasks. Both models demonstrate mastery of the pre-training task but neither consistently outperform

Raviteja Anantha, Tharun Bethi, Danil Vodianik, Srinivas Chappidi

Large language models (LLMs) have the remarkable ability to solve new tasks with just a few examples, but they need access to the right tools. Retrieval Augmented Generation (RAG) addresses this problem by retrieving a list of relevant tools for a given task. However, RAG's tool retrieval step requires all the required information to be explicitly present in

Hongyi Gu, Chi Zhang, Zidan Yu, Christoph Rettenmeier

Functional MRI (fMRI) is an important tool for non-invasive studies of brain function. Over the past decade, multi-echo fMRI methods that sample multiple echo times has become popular with potential to improve quantification. While these acquisitions are typically performed with Cartesian trajectories, non-Cartesian trajectories, in particular spiral acquisi

Poorva Garg, Steven Holtzen, Guy Van den Broeck, Todd Millstein

Probabilistic programming languages (PPLs) are expressive means for creating and reasoning about probabilistic models. Unfortunately hybrid probabilistic programs, involving both continuous and discrete structures, are not well supported by today's PPLs. In this paper we develop a new approximate inference algorithm for hybrid probabilistic programs that fir

Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov

Second-order methods such as KFAC can be useful for neural net training. However, they are often memory-inefficient since their preconditioning Kronecker factors are dense, and numerically unstable in low precision as they require matrix inversion or decomposition. These limitations render such methods unpopular for modern mixed-precision training. We addres

Hazem Sallouha, Sharief Saleh, Sibren De Bast, Zhuangzhuang Cui

The inherent limitations in scaling up ground infrastructure for future wireless networks, combined with decreasing operational costs of aerial and space networks, are driving considerable research interest in multisegment ground-air-space (GAS) networks. In GAS networks, where ground and aerial users share network resources, ubiquitous and accurate user loc

Thorsten Prüstel, Martin Meier-Schellersheim

Biological cells can exchange messages through soluble molecules or membrane-bound receptors. In particular in the latter case, the interaction is usually located in specific regions of the interacting cells and may depend on or induce local morphological features or reorganizations of the membrane-associated or membrane-proximal biochemistry. Examples are i

Xinyuan Song, Liang Yang, Chuang Deng

Grain boundary (GB) migration stands as a linchpin process governing microstructural evolution in polycrystalline materials. Over the past decade, the concept of shear coupling, quantified through the shear coupling factor, has transformed our understanding and driven the development of theoretical frameworks for unifying GB behaviors. In this study, we intr

Nazar Pyvovar, Lingze Duan

Fabry-Perot interferometers have been widely studied and used for well over a century. However, they have always been treated as stationary devices in the past. In this paper, we investigate the optical transmission of a longitudinally moving Fabry-Perot interferometer within the framework of relativity and establish a general relation between the transmissi

Annalivia Polselli

The presence of units with extreme values in the dependent and/or independent variables (i.e., vertical outliers, leveraged data) has the potential to severely bias regression coefficients and/or standard errors. This is common with short panel data because the researcher cannot advocate asymptotic theory. Example include cross-country studies, cell-group an

Matthew Stover

For any $g_1, g_2 \ge 0$, this paper shows that there is a cocompact lattice $\Gamma < \mathrm{PU}(2,1)$ such that the ball quotient $\Gamma \backslash \mathbb{B}^2$ is birational to a product $C_1 \times C_2$ of smooth projective curves $C_j$ of genus $g_j$. The only prior examples were $\mathbb{P}^1 \times \mathbb{P}^1$, due to Deligne--Mostow and rediscov

Chen Liang, Donghua Yang, Zhiyu Liang, Hongzhi Wang

In recent times, the field of unsupervised representation learning (URL) for time series data has garnered significant interest due to its remarkable adaptability across diverse downstream applications. Unsupervised learning goals differ from downstream tasks, making it tricky to ensure downstream task utility by focusing only on temporal feature characteriz

G. Di Bello, A. Ponticelli, F. Pavan, V. Cataudella

The physics of quantum states beyond thermodynamic equilibrium represents a fascinating and cutting-edge research. Using numerical state-of-the-art approaches, we observe dynamical quantum phase transitions in the dissipative two-qubit Rabi model. By quenching the qubits-oscillator coupling, the system (Rabi + Environment) exhibits dynamical quantum phase tr

Mithila Sivakumar, Alvine Boaye Belle, Jinjun Shan, Kimya Khakzad Shahandashti

In the ever-evolving landscape of software engineering, the emergence of large language models (LLMs) and conversational interfaces, exemplified by ChatGPT, is nothing short of revolutionary. While their potential is undeniable across various domains, this paper sets out on a captivating expedition to investigate their uncharted territory, the exploration of

Tianjin Huang, Tianlong Chen, Zhangyang Wang, Shiwei Liu

Vision Transformers have been rapidly uprising in computer vision thanks to their outstanding scaling trends, and gradually replacing convolutional neural networks (CNNs). Recent works on self-supervised learning (SSL) introduce siamese pre-training tasks, on which Transformer backbones continue to demonstrate ever stronger results than CNNs. People come to

Janos Barbero, Ana Franceschi, Luca Giliberto, Patrick Phuoc Do

[18F]-florbetaben positron emission tomography (PET) imaging is an established marker of {\ss}-Amyloid (A{\ss}) that is being increasingly used to assess A{\ss} deposition in AD. This study presents a novel, explainable machine learning-based biomarker for assessing A{\ss}+ positivity based on [18F]-florbetaben PET scans. We analyzed 163 scans acquired at ou

Matthew W. Davies, Laura Iacconi, David J. Mulryne

Single-field models of inflation might lead to amplified scalar fluctuations on small scales due, for example, to a transient ultra-slow-roll phase. It was argued by Kristiano $\&$ Yokoyama in arXiv:2211.03395 that the enhanced amplitude of the scalar power spectrum on small scales has the potential to induce a sizeable 1-loop correction to the spectrum at l

Xuan Shen, Peiyan Dong, Lei Lu, Zhenglun Kong

Large Language Models (LLMs) stand out for their impressive performance in intricate language modeling tasks. However, their demanding computational and memory needs pose obstacles for broad use on edge devices. Quantization is then introduced to boost LLMs' on-device efficiency. Recent works show that 8-bit or lower weight quantization is feasible with mini

Masashi Wakaiki

Let $-A$ be the generator of a bounded $C_0$-semigroup $(e^{-tA})_{t \geq 0}$ on a Hilbert space. First we study the long-time asymptotic behavior of the Cayley transform $V_{\omega}(A) := (A-\omega I) (A+\omega I)^{-1}$ with $\omega >0$. We give a decay estimate for $\|V_{\omega}(A)^nA^{-1}\|$ when $(e^{-tA})_{t \geq 0}$ is polynomially stable. Considering

Jasmin Vural, Claire Merker, Moritz Löffler, Daniel Leuenberger

In a joint effort, MeteoSwiss and Deutscher Wetterdienst (DWD) address the need for improving the initial state of the atmospheric boundary layer (ABL) by exploiting ground-based profiling observations that aim to fill the existing observational gap in the ABL. We implemented the brightness temperature observations from ground-based microwave radiometers (MW

Tessa Han, Yasha Ektefaie, Maha Farhat, Marinka Zitnik

Post hoc explanations have emerged as a way to improve user trust in machine learning models by providing insight into model decision-making. However, explanations tend to be evaluated based on their alignment with prior knowledge while the faithfulness of an explanation with respect to the model, a fundamental criterion, is often overlooked. Furthermore, th

Sydney A. Shavalier, J. Daniel Gezelter

Reverse non-equilibrium molecular dynamics (RNEMD) simulations were used to study heat transport in solvated gold interfaces which have been functionalized with a low molecular weight thiolated polyethylene glycol (PEG). The gold interfaces studied included (111), (110), and (100) facets, as well as spherical nanoparticles with radii of 10 and 20 {\AA}. The

Ran Zhang, Aida Kostikova, Christoph Leiter, Jonas Belouadi

Artificial Intelligence (AI) has witnessed rapid growth, especially in the subfields Natural Language Processing (NLP), Machine Learning (ML) and Computer Vision (CV). Keeping pace with this rapid progress poses a considerable challenge for researchers and professionals in the field. In this arXiv report, the second of its kind, which covers the period from

Keming Zhang, Tharindu Jayasinghe, Joshua S. Bloom

Modern surveys often deliver hundreds of thousands of stellar spectra at once, which are fit to spectral models to derive stellar parameters/labels. Therefore, the technique of Amortized Neural Posterior Estimation (ANPE) stands out as a suitable approach, which enables the inference of large number of targets as sub-linear/constant computational costs. Leve