March 2024 arXiv papers — page 119

Abuzar Royesh, Olamide Oladeji

Despite the need for financial data on company activities in developing countries for development research and economic analysis, such data does not exist. In this project, we develop and evaluate two Natural Language Processing (NLP) based techniques to address this issue. First, we curate a custom dataset specific to the domain of financial text data on de

Tao Chen, Andreu Cecilia, Daniele Astolfi, Lei Wang

In remote estimation of cyber-physical systems (CPSs), sensor measurements transmitted through network may be attacked by adversaries, leading to leakage risk of privacy (e.g., the system state), and/or failure of the remote estimator. To deal with this problem, a chaotic masking protocol is proposed in this paper to secure the sensor measurements transmissi

N. Galikyan, Sh. Khlghatyan, A. A. Kocharyan, V. G. Gurzadyan

Physics-informed neural network (PINN) analysis of the dynamics of S-stars in the vicinity of the supermassive black hole in the Galactic center is performed within General Relativity treatment. The aim is to reveal the role of possible extended mass (dark matter) configuration in the dynamics of the S-stars, in addition to the dominating central black hole'

Shi-Dong Liang

We extend the three-dimensional noncommutative relations of the positions and momenta operators to those in the four dimension. Using the Bopp shift technique, we give the Heisenberg representation of these noncommutative algebras and endow the noncommutative parameters associated with the Planck constant, Planck length and cosmological constant. As an analo

Kaiyin Huang, Wenlei Li, Shaoyun Shi, Zhiguo Xu

Poincar\'{e}'s classical results [H. Poincar\'{e}, Sur l'int\'{e}gration des \'{e}quations diff\'{e}rentielles du premier order et du premier degr\'{e} I and II, Rend. Circ. Mat. Palermo 5 (1891) 161-191; 11 (1897) 193-239] first provide a link between the existence of analytic first integrals and the resonant relations for analytic dynamical systems. In thi

Yongyu Mu, Peinan Feng, Zhiquan Cao, Yuzhang Wu

In this study, we reveal an in-context learning (ICL) capability of multilingual large language models (LLMs): by translating the input to several languages, we provide Parallel Input in Multiple Languages (PiM) to LLMs, which significantly enhances their comprehension abilities. To test this capability, we design extensive experiments encompassing 8 typical

Annan Fan, Shi-Dong Liang

We introduce the velocity field of the Bloch electrons and propose the velocity field approach to characterize the topological invariants of quantum states. We find that the zero modes of the velocity field flow play the roles of effective topological charges or defects. A key global property of the zero modes is topological invariant against the parameter d

Sipeng Zheng, Bohan Zhou, Yicheng Feng, Ye Wang

In this paper, we propose \textbf{UniCode}, a novel approach within the domain of multimodal large language models (MLLMs) that learns a unified codebook to efficiently tokenize visual, text, and potentially other types of signals. This innovation addresses a critical limitation in existing MLLMs: their reliance on a text-only codebook, which restricts MLLM'

Ken Yamamoto, Takashi Bando, Hirokazu Yanagawa, Yoshihiro Yamazaki

The double Pareto distribution is a heavy-tailed distribution with a power-law tail, that is generated via geometric Brownian motion with an exponentially distributed observation time. In this study, we examine a modified model wherein the exponential distribution of the observation time is replaced with a continuous uniform distribution. The probability den

Dengjun Guo, Lifeng Zhao

We consider the three-dimensional incompressible Euler equation \begin{equation*}\left\{\begin{aligned} &\partial_t \Omega+U \cdot \nabla \Omega-\Omega\cdot \nabla U=0 \\ &\Omega(x,0)=\Omega_0(x) \end{aligned}\right. \end{equation*} under the assumption that $\Omega^z$ is helical and in the absence of vorticity stretching. Assuming that the initial vorticity

Yuxuan Zhao, Peiyu Liao, Siting Liu, Jiaxi Jiang

This paper presents an innovative approach to 3D mixed-size placement in heterogeneous face-to-face (F2F) bonded 3D ICs. We propose an analytical framework that utilizes a dedicated density model and a bistratal wirelength model, effectively handling macros and standard cells in a 3D solution space. A novel 3D preconditioner is developed to resolve the topol

Xihan Li, Xing Li, Lei Chen, Xing Zhang

Implementing Boolean functions with circuits consisting of logic gates is fundamental in digital computer design. However, the implemented circuit must be exactly equivalent, which hinders generative neural approaches on this task due to their occasionally wrong predictions. In this study, we introduce a generative neural model, the "Circuit Transformer", wh

Minh Tran, Di Chang, Maksim Siniukov, Mohammad Soleymani

Human-human communication is like a delicate dance where listeners and speakers concurrently interact to maintain conversational dynamics. Hence, an effective model for generating listener nonverbal behaviors requires understanding the dyadic context and interaction. In this paper, we present an effective framework for creating 3D facial motions in dyadic in

Haoran Xu, Yilin Wu

Emergence of regular spatial patterns is a hallmark in living matter ranging from subcellular organelles to developing embryos and to ecosystems. Mechanisms for the formation of ordered spatial patterns in biology often require chemical signaling that coordinates cellular behavior and differentiation. Here we discovered a novel route to large-scale regular p

Shiqing Wei, Prashanth Krishnamurthy, Farshad Khorrami

Designing control inputs that satisfy safety requirements is crucial in safety-critical nonlinear control, and this task becomes particularly challenging when full-state measurements are unavailable. In this work, we address the problem of synthesizing safe and stable control for control-affine systems via output feedback (using an observer) while reducing t

Sungmin Cha, Kyunghyun Cho

Continual learning (CL) aims to train a model on a sequence of tasks (i.e., a CL scenario) while balancing the trade-off between plasticity (learning new tasks) and stability (retaining prior knowledge). The dominantly adopted conventional evaluation protocol for CL algorithms selects the best hyperparameters (e.g., learning rate, mini-batch size, regulariza

Linwei Chen, Lin Gu, Ying Fu

Despite recent advancements in semantic segmentation, where and what pixels are hard to segment remains largely unexplored. Existing research only separates an image into easy and hard regions and empirically observes the latter are associated with object boundaries. In this paper, we conduct a comprehensive analysis of hard pixel errors, categorizing them i

Hui Hu, Jia Wang, Xia-Ji Liu

By using both diagrammatic theory and Chevy ansatz approach, we derive an exact set of equations, which determines the spectral function of Fermi polarons with multiple particle-hole excitations at nonzero temperature. In the diagrammatic theory, we find out the complete series of Feynman diagrams for the multi-particle vertex functions, when the unregulariz

Jerrin Bright, Bavesh Balaji, Harish Prakash, Yuhao Chen

Precise Human Mesh Recovery (HMR) with in-the-wild data is a formidable challenge and is often hindered by depth ambiguities and reduced precision. Existing works resort to either pose priors or multi-modal data such as multi-view or point cloud information, though their methods often overlook the valuable scene-depth information inherently present in a sing

Jie Li, Jiaying Wen, Tongxin Yang, Fenglin Cai

In this paper, we introduce a new dataset in the medical field of Traumatic Brain Injury (TBI), called TBI-IT, which includes both electronic medical records (EMRs) and head CT images. This dataset is designed to enhance the accuracy of artificial intelligence in the diagnosis and treatment of TBI. This dataset, built upon the foundation of standard text and

Mark Chaimovich, Aviel Chaimovich

A novel type of a multiscale approach, called Relative Resolution (RelRes), can correctly retrieve the behavior of various nonpolar liquids, whilst speeding up molecular simulations by almost an order of magnitude. In this approach in a single system, molecules switch their resolution in terms of their relative separation, with near neighbors interacting via

Jie Liu, Barzan Mozafari

Query rewriting is an effective technique for refining poorly written queries before they reach the query optimizer. However, manual rewriting is not scalable, as it is prone to errors and requires deep expertise. Traditional query rewriting algorithms fall short too: rule-based approaches fail to generalize to new query patterns, while synthesis-based metho

Pasquale Balsebre, Weiming Huang, Gao Cong

Large Language Models (LLMs) are poised to play an increasingly important role in our lives, providing assistance across a wide array of tasks. In the geospatial domain, LLMs have demonstrated the ability to answer generic questions, such as identifying a country's capital; nonetheless, their utility is hindered when it comes to answering fine-grained questi

Xianzhe Chen, Hong Ren, Cunhua Pan, Zhangjie Peng

This paper investigates a reconfigurable intelligent surface (RIS)-aided wideband massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system with low-resolution analog-to-digital converters (ADCs). Frequency-selective Rician fading channels are considered, and the OFDM data transmission process is presented in time

Dong Yuan, Eti Rastogi, Gautam Naik, Sree Prasanna Rajagopal

LLMs are revolutionizing NLP tasks. However, the use of the most advanced LLMs, such as GPT-4, is often prohibitively expensive for most specialized fields. We introduce HEAL, the first continuously trained 13B LLaMA2-based LLM that is purpose-built for medical conversations and measured on automated scribing. Our results demonstrate that HEAL outperforms GP

Liang Wu, X. -G. Ma

On modern industrial assembly lines, many intelligent algorithms have been developed to replace or supervise workers. However, we found that there were bottlenecks in both training datasets and real-time performance when deploying algorithms on actual assembly line. Therefore, we developed a promising strategy for expanding industrial datasets, which utilize

Jaerin Lee, Daniel Sungho Jung, Kanggeon Lee, Kyoung Mu Lee

We introduce SemanticDraw, a new paradigm of interactive content creation where high-quality images are generated in near real-time from given multiple hand-drawn regions, each encoding prescribed semantic meaning. In order to maximize the productivity of content creators and to fully realize their artistic imagination, it requires both quick interactive int

Muhammad Adnan, Akhil Arunkumar, Gaurav Jain, Prashant J. Nair

Transformers have emerged as the underpinning architecture for Large Language Models (LLMs). In generative language models, the inference process involves two primary phases: prompt processing and token generation. Token generation, which constitutes the majority of the computational workload, primarily entails vector-matrix multiplications and interactions

Enric Boix-Adsera

Distillation is the task of replacing a complicated machine learning model with a simpler model that approximates the original [BCNM06,HVD15]. Despite many practical applications, basic questions about the extent to which models can be distilled, and the runtime and amount of data needed to distill, remain largely open. To study these questions, we initiate

Seokjin Moon, David T. Limmer

The reactive uptake of $\mathrm{N_2O_5}$ on sea-spray aerosol plays a key role in regulating NO$_\mathrm{x}$ concentration in the troposphere. Despite numerous field and laboratory studies, a microscopic understanding of its heterogeneous reactivity remains unclear. Here, we use molecular simulation and theory to elucidate the chlorination of $\mathrm{N_2O_5

Xingyuan Xu, David J. Moss

We report a dual-polarization radio frequency (RF) channelizer based on microcombs. With the tailored mismatch between the FSRs of the active and passive MRRs, wideband RF spectra can be channelized into multiple segments featuring digital compatible bandwidths via the Vernier effect. Due to the use of dual polarization states, the number of channelized spec

Andrey Davydov, Martin Engilberge, Mathieu Salzmann, Pascal Fua

Even the best current algorithms for estimating body 3D shape and pose yield results that include body self-intersections. In this paper, we present CLOAF, which exploits the diffeomorphic nature of Ordinary Differential Equations to eliminate such self-intersections while still imposing body shape constraints. We show that, unlike earlier approaches to addr

C. Chesnokov, R. Farajzadeh, K. O. K. Prempeh, S. Kahrobaei

This paper discusses axi-symmetric flow during CO2 injection into a non-adiabatic reservoir accounting for Joule-Thomson cooling and steady-state heat exchange between the reservoir and the adjacent layers by Newtons law. An exact solution for this 1D problem is derived and a new method for model validation by comparison with quasi 2D analytical heat-conduct

Lei Wang, Jieming Bian, Letian Zhang, Chen Chen

Federated learning (FL) allows collaborative machine learning training without sharing private data. While most FL methods assume identical data domains across clients, real-world scenarios often involve heterogeneous data domains. Federated Prototype Learning (FedPL) addresses this issue, using mean feature vectors as prototypes to enhance model generalizat

Michael Larsen, Pham Huu Tiep

If $G$ is a finite classical group, linear or unitary in any characteristic, and orthogonal in odd characteristic, we give an approximate formula for $\chi(g)$ in which the error term is much smaller than the estimate, when $g\in G$ is an element with large centralizer and $\chi\in \mathrm{Irr}(G)$ is an irreducible character of low degree. As an application

Michael Larsen, Pham Huu Tiep

For every finite quasisimple group of Lie type $G$, every irreducible character $\chi$ of $G$, and every element $g$ of $G$, we give an exponential upper bound for the character ratio $|\chi(g)|/\chi(1)$ with exponent linear in $\log_{|G|} |g^G|$, or, equivalently, in the ratio of the support of $g$ to the rank of $G$. We give several applications, including

Nail Kashaev, Martin Plávala, Victor H. Aguiar

A judge observes the joint probabilistic choice rule of two decision makers: the frequency of action pairs across pairs of local covariates. The rule is separable if behavior can be generated as if the decision makers were in separate rooms, unable to communicate at the time of choice. Separability allows arbitrary correlation in tastes, beliefs, information

Bin Wang, Jingtao Shi

This paper is concerned with the relationship between general maximum principle and dynamic programming principle for the stochastic recursive optimal control problem with jumps, where the control domain is not necessarily convex. Relations among the adjoint processes, the generalized Hamiltonian function and the value function are proved, under the assumpti

Chen Chen, Ella T. Lifset, Yichen Han, Arkajyoti Roy

While standalone Voice Assistants (VAs) are promising to support older adults' daily routine and wellbeing management, onboarding and setting up these devices can be challenging. Although some older adults choose to seek assistance from technicians and adult children, easy set up processes that facilitate independent use are still critical, especially for th

Yingfa Xie, Haoda Fu, Yuan Huang, Vladimir Pozdnyakov

Patients with type 2 diabetes need to closely monitor blood sugar levels as their routine diabetes self-management. Although many treatment agents aim to tightly control blood sugar, hypoglycemia often stands as an adverse event. In practice, patients can observe hypoglycemic events more easily than hyperglycemic events due to the perception of neurogenic sy

David J. Moss

Feedback control plays a crucial role in improving system accuracy and stability for a variety of scientific and engineering applications. Here, we theoretically and experimentally investigate the implementation of feedback control in microwave photonic (MWP) transversal filter systems based on optical microcomb sources, which offer advantages in achieving h

Jennifer Hsia, Afreen Shaikh, Zhiruo Wang, Graham Neubig

Retrieval-augmented generation (RAG) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation. In this work, we introduce RAGGED, a framework for systematically evaluating RAG systems

Jie Liu, Xuequn Shang, Xiaolin Han, Kai Zheng

Anomaly detection in dynamic graphs presents a significant challenge due to the temporal evolution of graph structures and attributes. The conventional approaches that tackle this problem typically employ an unsupervised learning framework, capturing normality patterns with exclusive normal data during training and identifying deviations as anomalies during

Russell Boey, Yourong Wang, Emily Kendall, Richard Easther

We numerically simulate the motion of a black hole as it plunges radially through an ultralight dark matter soliton. We investigate the timescale in which dynamical friction reduces the kinetic energy of the black hole to a minimum, and consider the sensitivity of this timescale to changes in the ULDM particle mass, the total soliton mass, and the mass of th

Qinyu Zhao, Ming Xu, Kartik Gupta, Akshay Asthana

Large vision-language models (LVLMs), designed to interpret and respond to human instructions, occasionally generate hallucinated or harmful content due to inappropriate instructions. This study uses linear probing to shed light on the hidden knowledge at the output layers of LVLMs. We demonstrate that the logit distributions of the first tokens contain suff

Fan Zhang, Wei Qin, Weijieying Ren, Lei Wang

In the real-world setting, data often follows a long-tailed distribution, where head classes contain significantly more training samples than tail classes. Consequently, models trained on such data tend to be biased toward head classes. The medium of this bias is imbalanced gradients, which include not only the ratio of scale between positive and negative gr

Yinggui Wang, Yuanqing Huang, Jianshu Li, Le Yang

The utilization of personal sensitive data in training face recognition (FR) models poses significant privacy concerns, as adversaries can employ model inversion attacks (MIA) to infer the original training data. Existing defense methods, such as data augmentation and differential privacy, have been employed to mitigate this issue. However, these methods oft

Xiao Ma, Shengfeng He, Hezhe Qiao, Dong Ma

Enabling efficient and accurate deep neural network (DNN) inference on microcontrollers is non-trivial due to the constrained on-chip resources. Current methodologies primarily focus on compressing larger models yet at the expense of model accuracy. In this paper, we rethink the problem from the inverse perspective by constructing small/weak models directly

Dali Zhu, Wenli Zhang, Hualin Zeng, Xiaohao Liu

Remote photoplethysmography (rPPG) technique extracts blood volume pulse (BVP) signals from subtle pixel changes in video frames. This study introduces rFaceNet, an advanced rPPG method that enhances the extraction of facial BVP signals with a focus on facial contours. rFaceNet integrates identity-specific facial contour information and eliminates redundant

Xuan Du Trinh, Nengkun Yu

We prove that adaptive strategies offer no advantage over non-adaptive ones for learning and testing Pauli channels using entangled inputs. This key observation allows us to characterize the query complexity for several fundamental tasks by translating optimal classical estimation algorithms into the quantum setting. First, we determine the tight query compl

Martin Weyssow, Aton Kamanda, Xin Zhou, Houari Sahraoui

Evaluating the alignment of large language models (LLMs) with user-defined coding preferences is a challenging endeavour that requires a deep assessment of LLMs' outputs. Existing methods and benchmarks rely primarily on automated metrics and static analysis tools, which often fail to capture the nuances of user instructions and LLM outputs. To address this

Jinsheng Li, Wei Cui, Xu Zhang

Current spectral compressed sensing methods via Hankel matrix completion employ symmetric factorization to demonstrate the low-rank property of the Hankel matrix. However, previous non-convex gradient methods only utilize asymmetric factorization to achieve spectral compressed sensing. In this paper, we propose a novel nonconvex projected gradient descent me

Zhao Wang, Xiaomeng Li, Na Li, Longlong Shu

This study aimed to develop a deep learning model for the classification of bearing faults in wind turbine generators from acoustic signals. A convolutional LSTM model was successfully constructed and trained by using audio data from five predefined fault types for both training and validation. To create the dataset, raw audio signal data was collected and p

Hugo Laurençon, Léo Tronchon, Victor Sanh

Using vision-language models (VLMs) in web development presents a promising strategy to increase efficiency and unblock no-code solutions: by providing a screenshot or a sketch of a UI, a VLM could generate the code to reproduce it, for instance in a language like HTML. Despite the advancements in VLMs for various tasks, the specific challenge of converting

Ahmed Masry, Mehrad Shahmohammadi, Md Rizwan Parvez, Enamul Hoque

Charts provide visual representations of data and are widely used for analyzing information, addressing queries, and conveying insights to others. Various chart-related downstream tasks have emerged recently, such as question-answering and summarization. A common strategy to solve these tasks is to fine-tune various models originally trained on vision tasks

Chris Kelly, Luhui Hu, Bang Yang, Yu Tian

With the emergence of large language models (LLMs) and vision foundation models, how to combine the intelligence and capacity of these open-sourced or API-available models to achieve open-world visual perception remains an open question. In this paper, we introduce VisionGPT to consolidate and automate the integration of state-of-the-art foundation models, t

Arnab Raha, Deepak A. Mathaikutty, Soumendu K. Ghosh, Shamik Kundu

This paper introduces FlexNN, a Flexible Neural Network accelerator, which adopts agile design principles to enable versatile dataflows, enhancing energy efficiency. Unlike conventional convolutional neural network accelerator architectures that adhere to fixed dataflows (such as input, weight, output, or row stationary) for transferring activations and weig

Benjamin Ramtoula, Daniele De Martini, Matthew Gadd, Paul Newman

This paper adapts a general dataset representation technique to produce robust Visual Place Recognition (VPR) descriptors, crucial to enable real-world mobile robot localisation. Two parallel lines of work on VPR have shown, on one side, that general-purpose off-the-shelf feature representations can provide robustness to domain shifts, and, on the other, tha

Hyunji Lee, Doyoung Kim, Jihoon Jun, Sejune Joo

In this work, we introduce a semiparametric token-sequence co-supervision training method. It trains a language model by simultaneously leveraging supervision from the traditional next token prediction loss which is calculated over the parametric token embedding space and the next sequence prediction loss which is calculated over the nonparametric sequence e

Amit Singh, Chun-Yu Lin, Chung-I Huang, Fang-Pang Lin

Traffic congestion is one of the major issues in urban areas, particularly when traffic loads exceed the roads capacity, resulting in higher petrol consumption and carbon emissions as well as delays and stress for road users. In Asia, the traffic situation can be further deteriorated by road sharing of scooters. How to control the traffic flow to mitigate th

Maxime Burchi, Krishna C. Puvvada, Jagadeesh Balam, Boris Ginsburg

Humans are adept at leveraging visual cues from lip movements for recognizing speech in adverse listening conditions. Audio-Visual Speech Recognition (AVSR) models follow similar approach to achieve robust speech recognition in noisy conditions. In this work, we present a multilingual AVSR model incorporating several enhancements to improve performance and a

Hyesang Cho, Junil Choi

In this paper, we propose algorithms to minimize the energy consumption in millimeter wave/terahertz multi-user downlink communication systems. To ensure coverage in blockage-vulnerable high frequency systems, we consider cooperative rate-splitting (CRS) and transmission over multiple time blocks, where via CRS, multiple users cooperate to assist a blocked u

Andreas Lietz

We answer a question of Woodin by showing that assuming an inaccessible cardinal $\kappa$ which is a limit of ${<}\kappa$-supercompact cardinals exists, there is a stationary set preserving forcing $\mathbb{P}$ so that $V^{\mathbb P}\models``\mathrm{NS}_{\omega_1}\text{ is }\omega_1\text{-dense}"$. We also introduce a new forcing axiom $\mathrm{QM}$, show it

Satoru Hayami, Hiroaki Kusunose

We overview recent developments of electronic orderings and associated cross correlations in condensed matter physics based on a complete set of multipole representations (electric, magnetic, electric toroidal, and magnetic toroidal multipoles) with distinct space-time inversion symmetries. By means of the symmetry-adapted complete basis set in any physical

Andreas Lietz

We prove an iteration theorem which guarantees for a wide class of nice iterations of $\omega_1$-preserving forcings that $\omega_1$ is not collapse, at the price of needing large cardinals to burn as fuel. More precisely, we show that a nice iteration of $\omega_1$-preserving forcings which force SRP at successor steps and preserves old stationary sets does

Emad A. Alghamdi, Reem I. Masoud, Deema Alnuhait, Afnan Y. Alomairi

The swift progress and widespread acceptance of artificial intelligence (AI) systems highlight a pressing requirement to comprehend both the capabilities and potential risks associated with AI. Given the linguistic complexity, cultural richness, and underrepresented status of Arabic in AI research, there is a pressing need to focus on Large Language Models (

Kevin Ye, Ida Sadeghi, Michael Xu, Jack Van Sambeek

We demonstrate synthesis of BaZr(S,Se)3 chalcogenide perovskite alloys by selenization of BaZrS3 thin films. The anion-exchange process produces films with tunable composition and band gap without changing the orthorhombic perovskite crystal structure or the film microstructure. The direct band gap is tunable between 1.5 and 1.9 eV. The alloy films made in t

Andreas Lietz

Usuba has asked whether the $\kappa$-mantle, the intersection of all grounds that extend to $V$ via a forcing of size ${<}\kappa$, is always a model of ZFC. We give a negative answers by constructing counterexamples where $\kappa$ is a Mahlo cardinal, $\kappa=\omega_1$ and where $\kappa$ is the successor of a regular uncountable cardinal.

Guanghua Li, Wensheng Lu, Wei Zhang, Defu Lian

The proliferation of fake news has had far-reaching implications on politics, the economy, and society at large. While Fake news detection methods have been employed to mitigate this issue, they primarily depend on two essential elements: the quality and relevance of the evidence, and the effectiveness of the verdict prediction mechanism. Traditional methods

Rachel J. Carrington, Ian L. Dryden, Madeleine Ellis, James O. Goulding

Mapping deprivation in urban areas is important, for example for identifying areas of greatest need and planning interventions. Traditional ways of obtaining deprivation estimates are based on either census or household survey data, which in many areas is unavailable or difficult to collect. However, there has been a huge rise in the amount of new, non-tradi

Benjamin Rombaut, Filipe R. Cogo, Ahmed E. Hassan

Dependabot, a popular dependency management tool, includes a compatibility score feature that helps client packages assess the risk of accepting a dependency update by leveraging knowledge from "the crowd". For each dependency update, Dependabot calculates this compatibility score as the proportion of successful updates performed by other client packages tha

Y. L. Ma, Q. H. Lao, X. Cheng, B. T. Wang

Sun-as-a-star spectroscopic characteristics of solar flares can be used as a benchmark for the detection and analyses of stellar flares. Here, we study the Sun-as-a-star properties of an X1.0 solar flare using high-resolution spectroscopic data obtained by the Chinese $\mathrm{H} \alpha$ Solar Explorer (CHASE). A noise reduction algorithm based on discrete F

Touchchai Chotisorayuth, Andreas Tiffeau-Mayer

An individual's adaptive immune receptor (AIR) repertoire records immune history due to the exquisite antigen specificity of AIRs. Reading this record requires computational approaches for inferring receptor function from sequence, as the diversity of possible receptor-antigen pairs vastly outstrips experimental knowledge. Identification of AIRs with similar

Christopher A. Lee, Waseem Abbas

This paper presents a novel approach for resilient distributed consensus in multiagent networks when dealing with adversarial agents imprecision in states observed by normal agents. Traditional resilient distributed consensus algorithms often presume that agents have exact knowledge of their neighbors' states, which is unrealistic in practical scenarios. We

Khalid Kabir Dandago, Long Zhang, Wei Pan

Stability and satisfactory performance are critical control requirements for Unmanned Aerial Vehicle (UAV) applications. While conventional control systems for UAVs aim to ensure flight stability and safe operation while accomplishing tasks, UAVs may experience various flight faults that can degrade performance or, in severe cases, lead to instability. Unsat

Benjamin Spreng, Calum Shelden, Tao Gong, Jeremy N. Munday

Quantum and thermal fluctuations are fundamental to a plethora of phenomena within quantum optics, including the Casimir effect that acts between closely separated surfaces typically found in MEMS and NEMS devices. Particularly promising for engineering and harnessing these forces are systems out of thermal equilibrium. Recently, semiconductors with external

Katerina Koleva, Maurizio Vergari, Tanja Kojić, Sebastian Möller

A virtual embodiment can benefit conversational agents, but it is unclear how their personalities and non-verbal behavior influence the User Experience and Social Presence in Augmented Reality (AR). We asked 30 users to converse with a virtual assistant who gives recommendations about city activities. The participants interacted with two different personalit

Tanja Kojić, Maurizio Vergari, Marco Podratz, Sebastian Möller

Millions of people have seen their daily habits transform, reducing physical activity and leading to mental health issues. This study explores how virtual characters impact motivation for well-being. Three prototypes with cartoon, robotic, and human-like avatars were tested by 22 participants. Results show that animated virtual avatars, especially with exten

A. V. Ivanov

The paper discusses an applicability criterion for a cutoff regularization in the coordinate representation in the Euclidean space with a dimension larger than two. It is shown that the set of functions satisfying the criterion is not empty. As an example, an explicit function is presented. It is proved by explicit construction that there are functions satis

Rodrique G. M. Badr, Lukas Hauer, Doris Vollmer, Friederike Schmid

Understanding the dynamics of drops on polymer-coated surfaces is crucial for optimizing applications such as self-cleaning materials or microfluidic devices. While the static and dynamic properties of deposited drops have been well characterised, a microscopic understanding of the underlying dynamics is missing. In particular, it is unclear how drop dynamic

K. Howil, Ł. Wyrzykowski, K. Kruszyńska, P. Zieliński

Identifying black holes is essential for comprehending the development of stars and uncovering novel principles of physics. Gravitational microlensing provides an exceptional opportunity to examine an undetectable population of black holes in the Milky Way. In particular, long-lasting events are likely to be associated with massive lenses, including black ho

Panagiotis Dorlis, Nick E. Mavromatos, Sotirios-Neilos Vlachos

In this work, we elaborate further on a cosmological model of inflation that characterises Chern-Simons (CS) gravity models inspired from string theory. Such models are known to belong to the class of the so-called String-Inspired Running Vacuum Cosmologies. In particular, by applying methods of dynamical systems, commonly used in scalar-field cosmology, we

Ruihua Qiao, Tao Jiang, Wei Yu

This paper investigates the fronthaul compression problem in a user-centric cloud radio access network, in which single-antenna users are served by a central processor (CP) cooperatively via a cluster of remote radio heads (RRHs). To satisfy the fronthaul capacity constraint, this paper proposes a transform-compress-forward scheme, which consists of well-des

Miroslav Engliš, El-Hassan Youssfi

We~describe the Dirichlet space of $M$-harmonic functions, i.e.~functions annihilated by the invariant Laplacian on~the unit ball of the complex $n$-space, as~the limit of the analytic continuation (in~the spirit of Rossi and Vergne) of the corresponding weighted Bergman spaces. Characterizations in terms of tangential derivatives are given, and the associat

Nicolas Chahine, Sira Ferradans, Jean Ponce

Blind image quality assessment (BIQA) approaches, while promising for automating image quality evaluation, often fall short in real-world scenarios due to their reliance on a generic quality standard applied uniformly across diverse images. This one-size-fits-all approach overlooks the crucial perceptual relationship between image content and quality, leadin

Elena Denisova

We prove K-stability of smooth Fano 3-folds of Picard rank 3 and degree 20 that satisfy very explicit generality condition.

Carlos Uriarte

Partial differential equations have a wide range of applications in modeling multiple physical, biological, or social phenomena. Therefore, we need to approximate the solutions of these equations in computationally feasible terms. Nowadays, among the most popular numerical methods for solving partial differential equations in engineering, we encounter the fi

Stefan Evans, Johann Rafelski

We offer a 2nd look at the recently claimed improvement of Euler-Heisenberg-Schwinger (EHS) action [PRL \textbf{122} (2019) no.21, 211602 and post-publication correction]: We demonstrate a difference to the claimed concordance with the Schwinger-Dyson series starting at the two-loop level.

Gonzalo Arranz, Yuenong Ling, Sam Costa, Konrad Goc

We introduce a closure model for wall-modeled large-eddy simulation (WMLES), referred to as the Building-block Flow Model (BFM). The foundation of the model rests on the premise that a finite collection of simple flows encapsulates the essential physics necessary to predict more complex scenarios. The BFM is implemented using artificial neural networks and i

Youssef Kora, Hadi Zadeh-Haghighi, Terrence C Stewart, Khabat Heshami

We study the performance of an Ising spin network for quantum reservoir computing (QRC) in linear and non-linear memory tasks. We investigate the extent to which quantumness enhances performance by monitoring the behaviour of quantum entanglement, which we quantify by the partial transpose of the density matrix. In the most general case where the effects of

Connor Lee, Matthew Anderson, Nikhil Raganathan, Xingxing Zuo

We present the first publicly-available RGB-thermal dataset designed for aerial robotics operating in natural environments. Our dataset captures a variety of terrain across the United States, including rivers, lakes, coastlines, deserts, and forests, and consists of synchronized RGB, thermal, global positioning, and inertial data. We provide semantic segment

Yuexin Bian, Xiaohan Fu, Rajesh K. Gupta, Yuanyuan Shi

In this paper, we introduce a novel framework for building learning and control, focusing on ventilation and thermal management to enhance energy efficiency. We validate the performance of the proposed framework in system model learning via two case studies: a synthetic study focusing on the joint learning of temperature and CO2 fields, and an application to

Yuki Kondo, Riku Miyata, Fuma Yasue, Taito Naruki

In this paper, we analyze and discuss ShadowFormer in preparation for the NTIRE2023 Shadow Removal Challenge [1], implementing five key improvements: image alignment, the introduction of a perceptual quality loss function, the semi-automatic annotation for shadow detection, joint learning of shadow detection and removal, and the introduction of new data augm

Lei Gao, Yue Niu, Tingting Tang, Salman Avestimehr

Language models (LMs) have greatly propelled the research on natural language processing. However, LMs also raise concerns regarding the generation of biased or toxic content and the potential disclosure of private information from the training dataset. In this work, we present a new efficient approach, Ethos, that rectifies LMs to mitigate toxicity and bias

Ahmad Abu Sleem, Mohammed Alromema, Mohammad A. M. Abdel-Aal

"This study provides a modified Bass model to deal with trend curves for basic issues of relevance to individuals from all over the world, for which we collected 16 data sets from 2004 to 2022 and that are available on Google servers as "google trends". It was discovered that the Bass model did not forecast well for curves that have a mono peak with a sharp

Angela Jin, Niloufar Salehi

Accountable use of AI systems in high-stakes settings relies on making systems contestable. In this paper we study efforts to contest AI systems in practice by studying how public defenders scrutinize AI in court. We present findings from interviews with 17 people in the U.S. public defense community to understand their perceptions of and experiences scrutin

Ariel Shlosberg, Alex Kwiatkowski, Akira Kyle, Graeme Smith

Quantum key distribution (QKD) seeks to provide a method of generating cryptographically-secure keys between remote parties while guaranteeing unconditional security. Implementations of high-dimensional QKD using dispersive-optics (DO-QKD) have been proposed to allow for multiple secure bits to be transmitted per photon while remaining cost-effective and sca

Sven Jacobs, Steffen Jaschke

This study investigates the application of large language models, specifically GPT-4, to enhance programming education. The research outlines the design of a web application that uses GPT-4 to provide feedback on programming tasks, without giving away the solution. A web application for working on programming tasks was developed for the study and evaluated w

Yahya Ladghami, Brahim Asfour, Amine Bouali, Ahmed Errahmani

In this paper, we examine the restricted phase space (RPS) thermodynamics for charged AdS black holes by considering the impact of quantum gravity on the event horizon area. The primary aim of this work is to elucidate the influence of quantum gravitational effects on thermodynamic behaviors, critical phenomena, phase transitions, and the stability of black

Duván Cardona, Vishvesh Kumar, Michael Ruzhansky

In this work, we introduce a global theory of subelliptic pseudo-differential operators on arbitrary homogeneous vector bundles over orientable compact homogeneous manifolds. We will show that a global pseudo-differential calculus can be associated to the operators acting on any pair of homogeneous vector-bundles with base space $M,$ if the compact Lie group