March 2024 arXiv papers — page 114

João Manoel Herrera Pinheiro, Marcelo Becker

Cancer is one of the diseases that kill the most women in the world, with breast cancer being responsible for the highest number of cancer cases and consequently deaths. However, it can be prevented by early detection and, consequently, early treatment. Any development for detection or perdition this kind of cancer is important for a better healthy life. Man

João Helis Bernardo, Daniel Alencar da Costa, Sérgio Queiroz de Medeiros, Uirá Kulesza

Continuous Integration (CI) is a well-established practice in traditional software development, but its nuances in the domain of Machine Learning (ML) projects remain relatively unexplored. Given the distinctive nature of ML development, understanding how CI practices are adopted in this context is crucial for tailoring effective approaches. In this study, w

Ramón J. Aliaga, E. Pernecká, Richard J. Smith

Let $\mathrm{Lip}_0(M)$ be the space of Lipschitz functions on a complete metric space $(M,d)$ that vanish at a point $0\in M$. We investigate its dual $\mathrm{Lip}_0(M)^*$ using the de Leeuw transform, which allows representing each functional on $\mathrm{Lip}_0(M)$ as a (non-unique) measure on $\beta\widetilde{M}$, where $\widetilde{M}$ is the space of pa

Tomer Ezra, Michal Feldman, Maya Schlesinger

We introduce a novel model of contracts with combinatorial actions that accounts for sequential and adaptive agent behavior. As in the standard model, a principal delegates the execution of a costly project to an agent. There are $n$ actions, each one incurring a cost to the agent and inducing a probability distribution over $m$ outcomes; each outcome genera

Navid Ashrafi, Vanessa Neuhaus, Francesco Vona, Nicolina Laura Peperkorn

Identification within media, whether with real or fictional characters, significantly impacts users, shaping their behavior and enriching their social and emotional experiences. Immersive media, like video games, utilize virtual entities such as agents, avatars, or NPCs to connect users with virtual worlds, fostering a heightened sense of immersion and ident

Blaine Hoak, Patrick McDaniel

In this work, we investigate \textit{texture learning}: the identification of textures learned by object classification models, and the extent to which they rely on these textures. We build texture-object associations that uncover new insights about the relationships between texture and object classes in CNNs and find three classes of results: associations t

Jana Bender, Patrick Mischke, Tanita Klas, Florian Binoth

We experimentally study the level mixing, splitting and repulsion of an optically driven atomic multi-level system under two competing interactions. The strength of the optical coupling is increased until it surpasses the atomic hyperfine interaction responsible for mixing the magnetic substates. Due to the multi-level character of the coupled state space, t

Do Hai Son, Tran Thi Thuy Quynh, Le Quang Minh

Ethereum 2.0 is a major upgrade to improve its scalability, throughput, and security. In this version, RANDAO is the scheme to randomly select the users who propose, confirm blocks, and get rewards. However, a vulnerability, referred to as the `Last Revealer Attack' (LRA), compromises the randomness of this scheme by introducing bias to the Random Number Gen

Alexandros Saplaouras

The aim of this work is to present the regularity condition (also known in the literature as structure condition) an integro-differential operator may satisfy in order for the domination principle to hold for (sub-,super-) solutions of polynomial growth. More precisely, the framework presented in Hollender [13], in which power functions are used in order to

Matthew Finlayson, Xiang Ren, Swabha Swayamdipta

Large language model (LLM) providers often hide the architectural details and parameters of their proprietary models by restricting public access to a limited API. In this work we show that, with only a conservative assumption about the model architecture, it is possible to learn a surprisingly large amount of non-public information about an API-protected LL

V. E. Gianolli, S. Bianchi, P-O Petrucci, M. Brusa

The detection of blue-shifted absorption lines likely associated with ionized Iron K-shell transitions in the X-ray spectra of many Active Galactic Nuclei (AGN) suggests the presence of a highly ionized gas outflowing with mildly relativistic velocities (0.03c-0.6c), named Ultra-Fast Outflow (UFO). Within the SUBWAYS project we characterized these winds star

Francesco Minna, Fabio Massacci, Katja Tuma

Background: Helm is a package manager that allows defining, installing, and upgrading applications with Kubernetes (K8s), a popular container orchestration platform. A Helm chart is a collection of files describing all dependencies, resources, and parameters required for deploying an application within a K8s cluster. Objective: The goal of this study is to m

Reza Saeed Kandezy, John Ning Jiang

Artificial intelligence and machine learning is enhancing electric grids by offering data analysis tools that can be used to operate the power grid more reliably. However, the complex nonlinear dynamics, particularly when coupled with multi-scale interactions among Inverter-based renewable energy Resources, calls for effective algorithms for power system app

Akihiro Ozawa, Yasufumi Araki, Kentaro Nomura

Modulation of magnetization in magnetic Weyl semimetals leads to the shift of Weyl points in momentum space, which effectively serves as the chirality-dependent gauge field for the Weyl fermions. Here, we theoretically study such a magnetization-induced chiral gauge field, in a fully spin-polarized Weyl ferromagnet $\rm{Co}_3\rm{Sn}_2\rm{S}_2$. From a tight-

Xavier Erny

The paper has two objectives: proving that the rate of convergence in distribution for mean-field models in CLT regime is $N^{-1/2}$, and obtaining explicit expressions for the infinitesimal generators of two types of measure-valued Markov processes (conditional law of McKean-Vlasov processes, and empirical measures of McKean-Vlasov systems). The proof of th

Li Li, S. K. Roushon

In this note we prove that the affine Artin group of type $\widetilde B_n$ is virtually poly-free. The proof also gives another solution of the $K(\pi, 1)$ problem for $\widetilde B_n$.

Ariel Neufeld, Matthew Ng Cheng En, Ying Zhang

In this paper we develop a Stochastic Gradient Langevin Dynamics (SGLD) algorithm tailored for solving a certain class of non-convex distributionally robust optimisation (DRO) problems. By deriving non-asymptotic convergence bounds, we build an algorithm which for any prescribed accuracy $\varepsilon>0$ outputs an estimator whose expected excess risk is at m

Kaichao You, Runsheng Bai, Meng Cao, Jianmin Wang

PyTorch \texttt{2.x} introduces a compiler designed to accelerate deep learning programs. However, for machine learning researchers, adapting to the PyTorch compiler to full potential can be challenging. The compiler operates at the Python bytecode level, making it appear as an opaque box. To address this, we introduce \texttt{depyf}, a tool designed to demy

Godwin Badu-Marfo, Ranwa Al Mallah, Bilal Farooq

The recent application of Federated Learning algorithms in IOT and Wireless vehicular networks have given rise to newer cyber threats in the mobile environment which hitherto were not present in traditional fixed networks. These threats arise due to the intrinsic nature of wireless transmission medium and other inherent characteristics of mobile networks suc

Chris Kelly, Luhui Hu, Jiayin Hu, Yu Tian

The evolution of text to visual components facilitates people's daily lives, such as generating image, videos from text and identifying the desired elements within the images. Computer vision models involving the multimodal abilities in the previous days are focused on image detection, classification based on well-defined objects. Large language models (LLMs

Zhiqiang Zhao, Wanlin Guo, Zhuhua Zhang

High Nb-containing TiAl alloys exhibit exceptional high-temperature strength and room-temperature ductility, making them widely used in hot-section components of automotive and aerospace engines. However, the lack of accurate interatomic interaction potentials for large-scale modeling severely hampers a comprehensive understanding of the failure mechanism of

Asad Ullah, Helder Vilarinho

We obtain estimates on the decay of correlations, Central Limit Theorem and Large Deviations for dynamical systems admitting an induced weak Gibbs--Markov map, for larger classes of observables with weaker regularity than H\"{o}lder, characterized by suitable moduli of continuity.

Jinhua Liang, Huan Zhang, Haohe Liu, Yin Cao

We introduce WavCraft, a collective system that leverages large language models (LLMs) to connect diverse task-specific models for audio content creation and editing. Specifically, WavCraft describes the content of raw audio materials in natural language and prompts the LLM conditioned on audio descriptions and user requests. WavCraft leverages the in-contex

Luc Enthoven, Masoud Babaie, Fabio Sebastiano

Quantum processors based on color centers in diamond are promising candidates for future large-scale quantum computers thanks to their flexible optical interface, (relatively) high operating temperature, and high-fidelity operation. Similar to other quantum-computing platforms, the electrical interface required to control and read out such qubits may limit b

Kristen C. Dage, Arash Bahramian, Smadar Naoz, Alexey Bobrick

The globular cluster ultraluminous X-ray source, RZ2109, is a complex and unique system which has been detected at X-ray, ultra-violet, and optical wavelengths. Based on almost 20 years of Chandra and XMM-Newton observations, the X-ray luminosity exhibits order-of-magnitude variability, with the peak flux lasting on the order of a few hours. We perform robus

Marco Olivieri, Xenofon Karakonstantis, Mirco Pezzoli, Fabio Antonacci

Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based approaches, particularly in sound field reconstruction. Physics-Informed Neural Networks (PINNs) have emerged as a novel framework, bridging the gap between data-driven and model-base

Henning Reinken, Andreas M. Menzel

The need for structuring on micrometer scales is abundant, for example, in view of phononic applications. We here outline a novel approach based on the phenomenon of active turbulence on the mesoscale. As we demonstrate, a shear-thickening carrier fluid of active microswimmers intrinsically stabilizes regular vortex patterns of otherwise turbulent active sus

Jiahuan Li, Shanbo Cheng, Shujian Huang, Jiajun Chen

Large Language Models (LLM) have demonstrated their strong ability in the field of machine translation (MT), yet they suffer from high computational cost and latency. Therefore, transferring translation knowledge from giant LLMs to medium-sized machine translation models is a promising research direction. However, traditional knowledge distillation methods d

Rito Brata Nath, Tarun Rambha, Maximilian Schiffer

As many public transportation systems around the world transition to electric buses, the planning and operation of fleets can be improved via tailored decision-support tools. In this work, we study the impact of jointly locating charging facilities, assigning electric buses to trips, and determining when and where to charge the buses. We propose a mixed inte

Patrick Lopatto, Matthew Meeker

We consider an $n\times n$ matrix of independent real Gaussian random variables and determine the asymptotic distribution of the smallest gaps between complex eigenvalues.

Kirill Parshukov, Raymond Wiedmann, Andreas P. Schnyder

We study the symmetry requirements for topologically protected spin-polarized Weyl points in 2D altermagnets. The topology is characterized by a quantized $\pi$-Berry phase and the degeneracy is protected by spin-space group symmetries. Gapped phases with finite Chern and/or spin/chirality Chern numbers emerge under different symmetry-breaking mass terms. We

Qiushi Liu, Yuxiang Yang

Optimized quantum control can enhance the performance and noise resilience of quantum metrology. However, the optimization quickly becomes intractable when multiple control operations are applied sequentially. In this work, we propose efficient tensor network algorithms for optimizing strategies of quantum metrology enhanced by a long sequence of control ope

Alain Bretto, Alain Faisant, François Hennecart

We show that the chromatic index of a hypergraph $\mathcal{H}$ satisfies Berge-F\"uredi conjectured bound $\mathrm{q}(\mathcal{H})\le \Delta([\mathcal{H}]_2)+1$ under certain hypotheses on the antirank $\mathrm{ar}(\mathcal{H})$ or on the maximum degree $\Delta(\mathcal{H})$. This provides sharp information in connection with Erd\H{o}s-Faber-Lov\'asz Conject

Luheng Zhao, Prithvi Raj Datla, Weikun Tian, Mohammad Mujahid Aliyu

Quantum thermalization occurs in a broad class of systems from elementary particles to complex materials. Out-of-equilibrium quantum systems have long been understood to either thermalize or retain memory of their initial states, but not both. Here we achieve the first coexistence of thermalization and memory in a quantum system, where we use both Rydberg bl

Shadi Iskander, Kira Radinsky, Yonatan Belinkov

Mitigating social biases typically requires identifying the social groups associated with each data sample. In this paper, we present DAFair, a novel approach to address social bias in language models. Unlike traditional methods that rely on explicit demographic labels, our approach does not require any such information. Instead, we leverage predefined proto

Andrei Jaikin-Zapirain

In this paper, we introduce the notion of $L^2$-subgroup rigid groups and demonstrate that free groups are $L^2$-subgroup rigid. As a consequence, we establish the equivalence between compressibility, inertness, strong inertness, and $L^2$-independence for a finitely generated subgroup of a free group, confirming a conjecture by Dicks and Ventura as well as

Prashant Kumar Nag, Amit Bhagat, R. Vishnu Priya, Deepak kumar Khare

This manuscript presents a methodical examination of the utilization of Artificial Intelligence in the assessment of emotions in texts related to healthcare, with a particular focus on the incorporation of Natural Language Processing and deep learning technologies. We scrutinize numerous research studies that employ AI to augment sentiment analysis, categori

Yu Wang, Xiaogeng Liu, Yu Li, Muhao Chen

With the advent and widespread deployment of Multimodal Large Language Models (MLLMs), the imperative to ensure their safety has become increasingly pronounced. However, with the integration of additional modalities, MLLMs are exposed to new vulnerabilities, rendering them prone to structured-based jailbreak attacks, where semantic content (e.g., "harmful te

Alexander Lipton

An intriguing link between a wide range of problems occurring in physics and financial engineering is presented. These problems include the evolution of small perturbations of linear flows in hydrodynamics, the movements of particles in random fields described by the Kolmogorov and Klein-Kramers equations, the Ornstein-Uhlenbeck and Feller processes, and the

Colm Kelleher, Frédéric Holweck, Péter Lévay

Quantum games embody non-intuitive consequences of quantum phenomena, such as entanglement and contextuality. The Mermin-Peres game is a simple example, demonstrating how two players can utilise shared quantum information to win a no - communication game with certainty, where classical players cannot. In this paper we look at the geometric structure behind s

Xiao-Hui Hu, Cai-Ping Jia, Ye Xing, Fu-Sheng Yu

In this study, we examine the non-leptonic weak decays of doubly charmed baryons, denoted as ${\cal B}_{cc}\to{\cal B}_{c}P$, where ${\cal B}_{cc}$ represents the doubly charmed baryons, specifically $(\Xi_{cc}^{++},\Xi_{cc}^{+},\Omega_{cc}^{+})$. The notation ${\cal B}_{c}$ denotes the singly charmed baryons, specifically $({\cal B}_{\bar{3}},{\cal B}_{6})$

Zainab Alalawi, Paolo Bova, Theodor Cimpeanu, Alessandro Di Stefano

There is general agreement that some form of regulation is necessary both for AI creators to be incentivised to develop trustworthy systems, and for users to actually trust those systems. But there is much debate about what form these regulations should take and how they should be implemented. Most work in this area has been qualitative, and has not been abl

Ali Nouri, Christian Berger, Fredrik Törner

Safety analysis is used to identify hazards and build knowledge during the design phase of safety-relevant functions. This is especially true for complex AI-enabled and software intensive systems such as Autonomous Drive (AD). System-Theoretic Process Analysis (STPA) is a novel method applied in safety-related fields like defense and aerospace, which is also

Jeonghyeok Do, Munchurl Kim

Skeleton-based action recognition, which classifies human actions based on the coordinates of joints and their connectivity within skeleton data, is widely utilized in various scenarios. While Graph Convolutional Networks (GCNs) have been proposed for skeleton data represented as graphs, they suffer from limited receptive fields constrained by joint connecti

Shafat Sharar, Carl D. Hoover

This paper presents a reliability life analysis and preventive maintenance schedule for ducted wind turbines. Ducted wind turbines (DWT) are an emerging segment of the renewable energy industry with innovations that promise reliable, efficient, low-cost energy for consumer and small business markets. Many attempts have been made to build viable ducted turbin

Yulong Pei, Salwa Alamir, Rares Dolga, Sameena Shah

Code revert prediction, a specialized form of software defect detection, aims to forecast or predict the likelihood of code changes being reverted or rolled back in software development. This task is very important in practice because by identifying code changes that are more prone to being reverted, developers and project managers can proactively take measu

Yitian Zhang, Yue Bai, Huan Wang, Yizhou Wang

Current training pipelines in object recognition neglect Hue Jittering when doing data augmentation as it not only brings appearance changes that are detrimental to classification, but also the implementation is inefficient in practice. In this study, we investigate the effect of hue variance in the context of video understanding and find this variance to be

V. Alcayne, D. Cano-Ott, J. Garcia, E. Gonzalez-Romero

The neutron time-of-flight facility n_TOF at CERN is a spallation source dedicated to measurements of neutron-induced reaction cross-sections of interest in nuclear technologies, astrophysics, and other applications. Since 2014, Experimental ARea 2 (EAR2) is operational and delivers a neutron fluence of $4\times 10^7$ neutrons per nominal proton pulse, which

Han Wang, Yhonatan Kvich, Eduardo Pérez, Florian Römer

This study considers the Block-Toeplitz structural properties inherent in traditional multichannel forward model matrices, using Full Matrix Capture (FMC) in ultrasonic testing as a case study. We propose an analytical convolutional forward model that transforms reflectivity maps into FMC data. Our findings demonstrate that the convolutional model excels ove

Ralf Römer, Lukas Brunke, Siqi Zhou, Angela P. Schoellig

Learning models or control policies from data has become a powerful tool to improve the performance of uncertain systems. While a strong focus has been placed on increasing the amount and quality of data to improve performance, data can never fully eliminate uncertainty, making feedback necessary to ensure stability and performance. We show that the control

Julyan Arbel, Stéphane Girard, Hadrien Lorenzo

This work focuses on dimension-reduction techniques for modelling conditional extreme values. Specifically, we investigate the idea that extreme values of a response variable can be explained by nonlinear functions derived from linear projections of an input random vector. In this context, the estimation of projection directions is examined, as approached by

Jongsuk Kim, Hyeongkeun Lee, Kyeongha Rho, Junmo Kim

Recent advancements in self-supervised audio-visual representation learning have demonstrated its potential to capture rich and comprehensive representations. However, despite the advantages of data augmentation verified in many learning methods, audio-visual learning has struggled to fully harness these benefits, as augmentations can easily disrupt the corr

Jinhui Ouyang, Mingzhu Wu, Xinglin Li, Hanhui Deng

Recent advances in EEG-based BCI technologies have revealed the potential of brain-to-robot collaboration through the integration of sensing, computing, communication, and control. In this paper, we present BRIEDGE as an end-to-end system for multi-brain to multi-robot interaction through an EEG-adaptive neural network and an encoding-decoding communication

Keyu Ding, Jing Liu

The light yield and decay constant of BGO were measured at both dry ice and liquid nitrogen temperatures using two SiPMs directly coupled to a $6\times6\times6$ cm$^2$ cubic BGO crystal. With the measured light yield (5.2$\pm$0.3 PE/keV at dry ice temperature and 10.5$\pm$0.4 PE/keV at liquid nitrogen temperature) and decay constants, potential applications

Lixiong Qin, Mei Wang, Xuannan Liu, Yuhang Zhang

With the comprehensive research conducted on various face analysis tasks, there is a growing interest among researchers to develop a unified approach to face perception. Existing methods mainly discuss unified representation and training, which lack task extensibility and application efficiency. To tackle this issue, we focus on the unified model structure,

Nawazish Ali, Abdul Wahid, Rachael Shaw, Karl Mason

Dairy farming consumes a significant amount of energy, making it an energy-intensive sector within agriculture. Integrating renewable energy generation into dairy farming could help address this challenge. Effective battery management is important for integrating renewable energy generation. Managing battery charging and discharging poses significant challen

Shaghayegh Z. Ashtiani, Mohammad Sarabian, Kaveh Laksari, Hessam Babaee

Blood flow reconstruction in the vasculature is important for many clinical applications. However, in clinical settings, the available data are often quite limited. For instance, Transcranial Doppler ultrasound (TCD) is a noninvasive clinical tool that is commonly used in the clinical settings to measure blood velocity waveform at several locations on brain'

Yuhan Liu, Xiuying Chen, Xiaoqing Zhang, Xing Gao

In the digital era, the rapid propagation of fake news and rumors via social networks brings notable societal challenges and impacts public opinion regulation. Traditional fake news modeling typically forecasts the general popularity trends of different groups or numerically represents opinions shift. However, these methods often oversimplify real-world comp

Vittoria Bonanzinga, Shalom Eliahou

Let $R_n=K[x_1,\dots,x_n]$ be the $n$-variable polynomial ring over a field $K$. Let $S_n$ denote the set of monomials in $R_n$. A monomial $u \in S_n$ is a \textit{Gotzmann monomial} if the Borel-stable monomial ideal $\langle u \rangle$ it generates in $R_n$ is a Gotzmann ideal. A longstanding open problem is to determine all Gotzmann monomials in $R_n$. G

Anthony D Stephens, David R Walwyn

Previous work has resulted in the development of an energy model able to calculate wind and solar fleet efficiencies. However, for investment planning purposes, it is necessary to calculate from the lowest economically acceptable efficiencies how much wind and solar generation would be economically justified. The paper explains how this objective has been ac

Kevin Kraschewski, Gregory P. Phlipot, Dennis M. Kochmann

Predicting the mechanics of large structural networks, such as beam-based architected materials, requires a multiscale computational strategy that preserves information about the discrete structure while being applicable to large assemblies of struts. Especially the fracture properties of such beam lattices necessitate a two-scale modeling strategy, since th

Austin Adams

This paper studies the market structure impact of cheaper and faster chains on the Uniswap v3 Protocol. The Uniswap Protocol is the largest decentralized application on Ethereum by both gas and blockspace used, and user behaviors of the protocol are very sensitive to fluctuations in gas prices and market structure due to the economic factors of the Protocol.

Yuxuan Cai, Xinwei He, Dingkang Liang, Ao Tong

Recently, large vision and language models have shown their success when adapting them to many downstream tasks. In this paper, we present a unified framework named CLIP-ADA for Anomaly Detection by Adapting a pre-trained CLIP model. To this end, we make two important improvements: 1) To acquire unified anomaly detection across industrial images of multiple

Hiroaki Kusunose, Satoru Hayami

In this short article, we overview a concept of electronic toroidal multipoles, and their ordering with associated physical properties in non-magnetic and magnetic materials. The toroidal multipoles are introduced as microscopic electronic variables in view of symmetry. They are classified according to crystallographic and magnetic point groups, which allows

Philipp Rodegast, Steffen Maier, Jonas Kneifl, Jörg Fehr

Globally, motorcycles attract vast and varied users. However, since the rate of severe injury and fatality in motorcycle accidents far exceeds passenger car accidents, efforts have been directed toward increasing passive safety systems. Impact simulations show that the risk of severe injury or death in the event of a motorcycle-to-car impact can be greatly r

Young Hyun Yoo, Jii Cha, Changhyeon Kim, Taeuk Kim

While the introduction of contrastive learning frameworks in sentence representation learning has significantly contributed to advancements in the field, it still remains unclear whether state-of-the-art sentence embeddings can capture the fine-grained semantics of sentences, particularly when conditioned on specific perspectives. In this paper, we introduce

Oliver H. Wilson, Wynn C. G. Ho

Matter in compact stars is dense enough that transient events within the star could have sufficiently high energies to produce detectable gravitational waves (GWs). These GWs could be used to constrain the equation of state (EoS) for matter in the star and could reveal that there is more than one type of EoS at play in the population, implying that multiple

Joonwon Jang, Sanghwan Jang, Wonbin Kweon, Minjin Jeon

Large language models (LLMs) are able to solve various tasks with only a few demonstrations utilizing their in-context learning (ICL) abilities. However, LLMs often rely on their pre-trained semantic priors of demonstrations rather than on the input-label relationships to proceed with ICL prediction. In this work, we term this phenomenon as the 'Demonstratio

Lian-Bao Jia

How to describe loop corrections of gravitation is a fundamental challenge in the quantization of Einstein gravity. In this paper, we give it a try in UV-free scheme, including one-loop propagator and two-loop vertex, and the results are effective for graviton loops. This indicates that both loops of the renormalizable Standard Model and the non-renormalizab

Kang Chen, Shiyan Chen, Jiyuan Zhang, Baoyue Zhang

Reconstructing a sequence of sharp images from the blurry input is crucial for enhancing our insights into the captured scene and poses a significant challenge due to the limited temporal features embedded in the image. Spike cameras, sampling at rates up to 40,000 Hz, have proven effective in capturing motion features and beneficial for solving this ill-pos

Shuoguo Zhang, Yu Fan, Dong Wu, Chi Zhang

This paper introduces a novel dynamical pressure boundary condition for weakly-compressible smoothed particle hydrodynamics (WCSPH). Unlike previous methods that rely on indirect approaches or ghost particles, our method integrates the dynamical boundary pressure directly into the SPH approximation of the pressure gradient on near-boundary particles. Additio

Hans Gersbach, Fikri Pitsuwan, Pio Blieske

Bug bounty programs, where external agents are invited to search and report vulnerabilities (bugs) in exchange for rewards (bounty), have become a major tool for companies to improve their systems. We suggest augmenting such programs by inserting artificial bugs to increase the incentives to search for real (organic) bugs. Using a model of crowdsearch, we id

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

A method is presented to reconstruct charged particles with lifetimes between 10 ps and 10 ns, which considers a combination of their decay products and the partial tracks created by the initial charged particle. Using the $\Xi^-$ baryon as a benchmark, the method is demonstrated with simulated events and proton-proton collision data at $\sqrt{s}=13$ TeV, co

Anson Hook, Clayton Ristow

We show that if there are conserved flavor symmetries then some properties of a monopole can depend on $\theta$ even when a fermion is massless. The quantized nature of global symmetries and the fractional nature of the Witten effect can lead to interesting structure. Seen from another point of view, aside from possibly breaking baryon and lepton flavor symm

Paloma Rabaey, Johannes Deleu, Stefan Heytens, Thomas Demeester

Bayesian networks are well-suited for clinical reasoning on tabular data, but are less compatible with natural language data, for which neural networks provide a successful framework. This paper compares and discusses strategies to augment Bayesian networks with neural text representations, both in a generative and discriminative manner. This is illustrated

Hmrishav Bandyopadhyay, Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Aneeshan Sain

In this paper, we explore the unique modality of sketch for explainability, emphasising the profound impact of human strokes compared to conventional pixel-oriented studies. Beyond explanations of network behavior, we discern the genuine implications of explainability across diverse downstream sketch-related tasks. We propose a lightweight and portable expla

Yuncheng Huang, Qianyu He, Yipei Xu, Jiaqing Liang

Current language models have demonstrated their capability to develop basic reasoning, but struggle in more complicated reasoning tasks that require a combination of atomic skills, such as math word problem requiring skills like arithmetic and unit conversion. Previous methods either do not improve the inherent atomic skills of models or not attempt to gener

Nadja Egner, Pierre-Alain Jacqmin, Nelson Martins-Ferreira

The notion of a weakly Mal'tsev category, as it was introduced in 2008 by the third author, is a generalization of the classical notion of a Mal'tsev category. It is well-known that a variety of universal algebras is a Mal'tsev category if and only if its theory admits a Mal'tsev term. In the main theorem of this paper, we prove a syntactic characterization

Nicolaj Schmid, Cornelius von Einem, Cesar Cadena, Roland Siegwart

Autonomous mobile robots are an increasingly integral part of modern factory and warehouse operations. Obstacle detection, avoidance and path planning are critical safety-relevant tasks, which are often solved using expensive LiDAR sensors and depth cameras. We propose to use cost-effective low-resolution ranging sensors, such as ultrasonic and infrared time

Yossi Oren, Amiel Sternberg, Christopher F. McKee, Yakov Faerman

We analyze measurements of the thermal Sunyaev-Zeldovich (tSZ) effect arising in the circumgalactic medium (CGM) of $L^*$ galaxies, reported by Bregman et al. 2022 and Das et al. 2023. In our analysis we use the Faerman et al. 2017 and Faerman et al. 2020 CGM models, a new power-law model (PLM), and the TNG100 simulation. For a given $M_{\rm vir}$, our PLM h

Chan Gao, Linying Tian, Dong Zheng

Wireless systems are of paramount importance for providing ubiquitous data transmission for smart cities. However, due to the broadcasting and openness of wireless channels, such systems face potential security challenges. UAV-assisted covert communication is a supporting technology for improving covert performances and has become a hot issue in the research

William Hornsby, Ander Gray, James Buchanan, Daniel Kenndy

Spherical tokamaks (STs) have many desirable features that make them a suitable choice for fusion power plants. To understand their confinement properties, accurate calculation of turbulent micro-instabilities is necessary for tokamak design. Presented is a novel surrogate model for Micro-tearing modes (MTMs), the micro-instability thought to be dominant in

Ali Nouri, Christian Berger, Fredrik Torner

Using continuous development, deployment, and monitoring (CDDM) to understand and improve applications in a customer's context is widely used for non-safety applications such as smartphone apps or web applications to enable rapid and innovative feature improvements. Having demonstrated its potential in such domains, it may have the potential to also improve

Anthony Couthures, Thomas Mongaillard, Vineeth S. Varma, Samson Lasaulce

We consider a set of consumers in a city or town (who thus generate pollution) whose opinion is governed by a continuous opinion and discrete action (CODA) dynamics model. This dynamics is coupled with an observation signal dynamics, which defines the information the consumers have access to regarding the common pollution. We show that the external observati

Zhiqing Sun, Longhui Yu, Yikang Shen, Weiyang Liu

Current AI alignment methodologies rely on human-provided demonstrations or judgments, and the learned capabilities of AI systems would be upper-bounded by human capabilities as a result. This raises a challenging research question: How can we keep improving the systems when their capabilities have surpassed the levels of humans? This paper answers this ques

Zunnan Xu, Yukang Lin, Haonan Han, Sicheng Yang

Gesture synthesis is a vital realm of human-computer interaction, with wide-ranging applications across various fields like film, robotics, and virtual reality. Recent advancements have utilized the diffusion model and attention mechanisms to improve gesture synthesis. However, due to the high computational complexity of these techniques, generating long and

Marco Letta, Pierluigi Montalbano, Adriana Paolantonio

The complex relationship between climate shocks, migration, and adaptation hampers a rigorous understanding of the heterogeneous mobility outcomes of farm households exposed to climate risk. To unpack this heterogeneity, the analysis combines longitudinal multi-topic household survey data from Nigeria with a causal machine learning approach, tailored to a co

Bianca Gouthier, Dajano Tossici

If the characteristic of a field $k$ is odd any infinitesimal group scheme of $PGL_{2,k}$ lifts to $SL_{2,k}$. In this paper, we prove that this is not true in characteristic $2$ and we give a complete description, up to isomorphism, of infinitesimal unipotent subgroup schemes of $PGL_{2,k}$. Also, the infinitesimal trigonalizable case is considered.

Wonjun Kang, Kevin Galim, Hyung Il Koo

Diffusion models have achieved remarkable success in the domain of text-guided image generation and, more recently, in text-guided image editing. A commonly adopted strategy for editing real images involves inverting the diffusion process to obtain a noisy representation of the original image, which is then denoised to achieve the desired edits. However, cur

Louis H. Rowen

We consider residue structures $R/G$ where $(G,+)$ is an additive subgroup of a ring $(R,+,\cdot)$, not necessarily an ideal. Special instances include Krasner's construction of quotient hyperfields, and Pumpluen's construction of nonassociative algebras. The residue construction, treated formally, satisfies the Noether isomorphism theorems, and also is cast

Stefan Tappe

We provide an existence and uniqueness result for mild solutions to rough partial differential equations in the framework of the semigroup approach. Applications to stochastic partial differential equations driven by infinite dimensional Wiener processes and infinite dimensional fractional Brownian motion are presented as well.

Vipul Arora, Arnab Bhattacharyya, Mathews Boban, Venkatesan Guruswami

We study the problem of robust multivariate polynomial regression: let $p\colon\mathbb{R}^n\to\mathbb{R}$ be an unknown $n$-variate polynomial of degree at most $d$ in each variable. We are given as input a set of random samples $(\mathbf{x}_i,y_i) \in [-1,1]^n \times \mathbb{R}$ that are noisy versions of $(\mathbf{x}_i,p(\mathbf{x}_i))$. More precisely, ea

Ye Yuan, Chen Zhang, Fan Li, Jian Chen

The atmosphere of Triton was probed directly by observing a ground-based stellar occultation on 6 October 2022. This rare event yielded 23 positive light curves collected from 13 separate observation stations contributing to our campaign. The significance of this event lies in its potential to directly validate the modest pressure fluctuation on Triton, a ph

L. Franco, I. A. Bonfil-Rivera, J. F. Huan Lew-Yee, M. Piris

Within the framework of natural orbital functional theory, having a convenient representation of the occupation numbers and orbitals becomes critical for the computational performance of the calculations. Recognizing this, we propose an innovative parametrization of the occupation numbers that takes advantage of the electron-pairing approach used in Piris na

Minyang Zhang, Dong-Xu Chen, Pengxiang Ruan, Jun Liu

The rich structure of transverse spatial modes of structured light has facilitated their extensive applications in quantum information and optical communication. The Laguerre-Gaussian (LG) modes, which carry a well-defined orbital angular momentum (OAM), consist of a complete orthogonal basis describing the transverse spatial modes of light. The application

James P. Best, Anwesha Kanjilal, Alireza Ghafarollahi, Uzair Rehman

The cubic $C$15 CaAl$_2$ Laves phase is a crucial brittle intermetallic precipitate in Mg-Al-Ca alloys. Although knowledge of the mechanical properties of coexisting phases is essential for improved alloy design, the fracture toughness is not yet studied experimentally due to the need for miniaturised testing. Here, micropillar splitting and microcantilever

E. Tholerus, F. J. Casson, S. P. Marsden, T. Wilson

STEP is a spherical tokamak prototype power plant that is being designed to demonstrate net electric power. The design phase involves the exploitation of plasma models to optimise fusion performance subject to satisfying various physics and engineering constraints. A modelling workflow, including integrated core plasma modelling, MHD stability analysis, SOL

Andrés-David Suárez-Gómez, Andres A. Hernandez Ortega

This paper investigates the development and optimization of control algorithms for mobile robotics, with a keen focus on their implementation in Field-Programmable Gate Arrays (FPGAs). It delves into both classical control approaches such as PID and modern techniques including deep learning, addressing their application in sectors ranging from industrial aut

Huan-Qiang Zhou, Qian-Qian Shi, Ian P. McCulloch

Exact matrix product state representations for a type of scale-invariant states are presented, which describe highly degenerate ground states arising from spontaneous symmetry breaking with type-B Goldstone modes in one-dimensional quantum many-body systems. As a possible application, such a representation offers a convenient but powerful means for evaluatin

B. Eslam Panah, S. Zare, H. Hassanabadi

Motivated by the effect of the energy of moving particles in $C-$metric, we first obtain exact accelerating black hole solutions in gravity's rainbow. Then, we study the effects of gravity's rainbow and $C-$metric parameters on the Ricci and Kretschmann scalars, and also the asymptotical behavior of this solution. Next, we indicate how different parameters o