November 2024 arXiv papers — page 103

Shouvick Mondal, Tse-Hsun Chen

Test case prioritization (TCP) has been an effective strategy to optimize regression testing. Traditionally, test cases are ordered based on some heuristic and rerun against the version under test with the goal of yielding a high failure throughput. Almost four decades of TCP research has seen extensive contributions in the light of individual prioritization

J. Pérez-Aracil, C. Peláez-Rodríguez, Ronan McAdam, Antonello Squintu

Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these extreme events remains challenging, as their complex interactions with large-scale atmospheric and climatic variables are difficult to capture with traditional statistical and dynamical models. This work presents a general method for d

Eleni Batziou, John Fearnley, Spencer Gordon, Ruta Mehta

We study functions $f : [0, 1]^d \rightarrow [0, 1]^d$ that are both monotone and contracting, and we consider the problem of finding an $\varepsilon$-approximate fixed point of $f$. We show that the problem lies in the complexity class UEOPL. We give an algorithm that finds an $\varepsilon$-approximate fixed point of a three-dimensional monotone contraction

Giulio Rossolini, Tommaso Baldi, Alessandro Biondi, Giorgio Buttazzo

Distributed learning frameworks, which partition neural network models across multiple computing nodes, enhance efficiency in collaborative edge-cloud systems, but may also introduce new vulnerabilities to evasion attacks, often in the form of adversarial perturbations. In this work, we present a new threat model that explores the feasibility of generating u

J. A. Gracey

We evaluate the Green's function for the insertion of the second moment of the twist-$2$ flavour nonsinglet Wilson operator in a quark $2$-point function in all three different single scale external momentum configurations at four loops in the MSbar scheme and the chiral limit. One configuration is where the operator is inserted at zero momentum while the ot

Boyuan Jiang, Xiaobin Hu, Donghao Luo, Qingdong He

Although image-based virtual try-on has made considerable progress, emerging approaches still encounter challenges in producing high-fidelity and robust fitting images across diverse scenarios. These methods often struggle with issues such as texture-aware maintenance and size-aware fitting, which hinder their overall effectiveness. To address these limitati

Somnath Roy, Mattia Coccolo, Miguel A. F. Sanjuán

We investigate how a constant time delay influences a parametric autoresonant system. This is a nonlinear system driven by a parametrically chirped force with a negative delay-feedback that maintains adiabatic phase locking with the driving frequency. This phase locking results in a continuous amplitude growth, regardless of parameter changes. Our study reve

ALICE Collaboration

The distribution of angular correlations between prompt charm hadrons and primary charged particles in pp collisions is sensitive to the charm-quark hadronisation process. In this letter, charm-baryon correlations are measured for the first time by studying the azimuthal-angle difference between charged particles and prompt $\Lambda_{\rm c}^{+}$ baryons prod

V. Guzey

Using the leading twist approach (LTA) to nuclear shadowing (NS), we calculate the ratio of the diffractive-to-total DIS cross sections $R_{\rm diff/tot}$ for a heavy nucleus and proton and confirm that $R_{\rm diff/tot} \approx 0.5-1$ in contrast with $R_{\rm diff/tot} \approx 1.5-2$ in the dipole model. We show that the magnitude of $R_{\rm diff/tot}$ is c

Di Wu, Wentao Liu, Shuang-Qing Wu, Robert B. Mann

By viewing black hole solutions as topological defects in thermodynamic parameter space, we unveil a novel topological class and two new topological subclasses, respectively denoted $W^{0-\leftrightarrow 1+}$, $\overline{W}^{1+}$, and $\hat{W}^{1+}$, that extend beyond the four established categories proposed by Wei et al. [Phys. Rev. D 110, L081501 (2024)].

Laurent Schmalen, Vincent Lauinger, Jonas Ney, Norbert Wehn

In this paper, we highlight recent advances in the use of machine learning for implementing equalizers for optical communications. We highlight both algorithmic advances as well as implementation aspects using conventional and neuromorphic hardware.

Muhammad Usman, Azka Rehman, Abdullah Shahid, Abd Ur Rehman

Despite advances in deep learning for estimating brain age from structural MRI data, incorporating functional MRI data is challenging due to its complex structure and the noisy nature of functional connectivity measurements. To address this, we present the Multitask Adversarial Variational Autoencoder, a custom deep learning framework designed to improve bra

Jarah Evslin, Hengyuan Guo, Hui Liu, Baiyang Zhang

We have recently claimed that the domain wall in the 3+1 dimensional $\phi^4$ double-well model can be constructed as a squeezed, coherent state and that at one loop it has a finite tension given general, but unspecified, renormalization conditions. In the present note, we justify this claim by showing that the tadpole is finite and the infrared divergences

Matheus F. S. Alves, R. R. Cuzinatto, C. A. M. de Melo, L. G. Medeiros

In this paper, the emission of gravitational waves in quadratic gravity theory is examined. The wave equations for massless and massive perturbations are derived, followed by the calculation of the energy and angular momentum radiated. In the quadrupole approximation, and taking into account only the transverse-traceless modes, it is shown that the theory av

R. Kusdiantara, H. Susanto, T. F. Adriano, N. Karjanto

This study investigates the existence and stability of localized states in the discrete nonlinear Schr\"odinger (DNLS) equation with quadratic and cubic nonlinearities, describing the so-called quantum droplets and bubbles. Those states exist within an interval known as the pinning region, as we vary a control parameter. Within the interval, multistable stat

Muhammad Zakwan, Giancarlo Ferrari-Trecate

The control of large-scale cyber-physical systems requires optimal distributed policies relying solely on limited communication with neighboring agents. However, computing stabilizing controllers for nonlinear systems while optimizing complex costs remains a significant challenge. Neural Networks (NNs), known for their expressivity, can be leveraged to param

Siripak Sangsinsorn, Sonia Dsoke, Oana Cojocaru-Mirédin

A new era for energy storage devices, such as rechargeable batteries, has been opened in the last decades. However, commercially available energy storage devices are based mainly on critical elements such as Li, Co, Mn, P, Ni, and graphite opening sustainability concerns for the industry and society. Yet, these elements are crucial for both, battery cells as

Yuxin Wang, Qi Liu, Jinyu Xia, Shuaizhe Huang

In this paper, we introduce a new constant for Banach spaces, denoted as $\widetilde{C}_{\mathrm{NJ}}^p(\xi, v, X)$. We provide calculations for both the lower and upper bounds of this constant, as well as its exact values in certain Banach spaces. Furthermore, we give the inequality relationship between the $\widetilde{C}_{\mathrm{NJ}}^p(\xi, v, X)$ constan

Nacim Oijid

The study of SAT and its variants has provided numerous NP-complete problems, from which most NP-hardness results were derived. Due to the NP-hardness of SAT, adding constraints to either specify a more precise NP-complete problem or to obtain a tractable one helps better understand the complexity class of several problems. In 1984, Tovey proved that bounded

KM3NeT Collaboration, S. Aiello, A. Albert, A. R. Alhebsi

Indirect dark matter detection methods are used to observe the products of dark matter annihilations or decays originating from astrophysical objects where large amounts of dark matter are thought to accumulate. With neutrino telescopes, an excess of neutrinos is searched for in nearby dark matter reservoirs, such as the Sun and the Galactic Centre, which co

Jacki O'Neill, Vukosi Marivate, Barbara Glover, Winnie Karanu

This white paper is the output of a multidisciplinary workshop in Nairobi (Nov 2023). Led by a cross-organisational team including Microsoft Research, NEPAD, Lelapa AI, and University of Oxford. The workshop brought together diverse thought-leaders from various sectors and backgrounds to discuss the implications of Generative AI for the future of work in Afr

Chris Nill, Sylvain de Léséleuc, Christian Groß, Igor Lesanovsky

Rydberg dressing traditionally refers to a technique where interactions between cold atoms are imprinted through the far off-resonant continuous-wave excitation of high-lying Rydberg states. Dipolar interactions between these electronic states are then translated into effective interactions among ground state atoms. Motivated by recent experiments, we invest

Joe de Keizer, Rémi Lenain, Raphaël Porcher, Sarah Zoha

In a clinical trial, the random allocation aims to balance prognostic factors between arms, preventing true confounders. However, residual differences due to chance may introduce near-confounders. Adjusting on prognostic factors is therefore recommended, especially because the related increase of the power. In this paper, we hypothesized that G-computation a

Antonio Iannizzotto, Giovanni Porru

We discuss two optimization problems related to the fractional $p$-Laplacian. First, we prove the existence of at least one minimizer for the principal eigenvalue of the fractional $p$-Laplacian with Dirichlet conditions, with a bounded weight function varying in a rearrangement class. Then, we investigate the maximization of the energy functional for genera

Einari Vaaras, Manu Airaksinen, Okko Räsänen

Self-supervised learning (SSL) is a data-driven learning approach that utilizes the innate structure of the data to guide the learning process. In contrast to supervised learning, which depends on external labels, SSL utilizes the inherent characteristics of the data to produce its own supervisory signal. However, one frequent issue with SSL methods is repre

Dengke Zhang, Fagui Liu, Quan Tang

Open-vocabulary semantic segmentation aims to assign semantic labels to each pixel without being constrained by a predefined set of categories. While Contrastive Language-Image Pre-training (CLIP) excels in zero-shot classification, it struggles to align image patches with category embeddings because of its incoherent patch correlations. This study reveals t

Ryui Kaneko, Daichi Kagamihara, Ippei Danshita

We develop a random sampling method for calculating the time evolution of the R\'{e}nyi entanglement entropy after a quantum quench from an insulating state in free boson systems. Because of the non-Gaussian nature of the initial state, calculating the R\'{e}nyi entanglement entropy calls for the exponential cost of computing a matrix permanent. We numerical

Emirhan Böge, Yasemin Gunindi, Erchan Aptoula, Nihan Alp

Neuron importance assessment is crucial for understanding the inner workings of artificial neural networks (ANNs) and improving their interpretability and efficiency. This paper introduces a novel approach to neuron significance assessment inspired by frequency tagging, a technique from neuroscience. By applying sinusoidal contrast modulation to image inputs

Wang Qun, Liu Yang, Lin Qingquan, Jiang Ling

We introduce Xmodel-1.5, a 1-billion-parameter multilingual large language model pretrained on 2 trillion tokens, designed for balanced performance and scalability. Unlike most large models that use the BPE tokenizer, Xmodel-1.5 employs a custom unigram tokenizer with 65,280 tokens, optimizing both efficiency and accuracy. The model delivers competitive resu

Nguyen Xuan Tung, Trinh Van Chien, Dinh Thai Hoang, Won Joo Hwang

Jointly optimizing power allocation and device association is crucial in Internet-of-Things (IoT) networks to ensure devices achieve their data throughput requirements. Device association, which assigns IoT devices to specific access points (APs), critically impacts resource allocation. Many existing works often assume all data throughput requirements are sa

Maurice Rohr, Sebastian Dill

Depth cameras are an interesting modality for capturing vital signs such as respiratory rate. Plenty approaches exist to extract vital signs in a controlled setting, but in order to apply them more flexibly for example in multi-camera settings, a simulated environment is needed to generate enough data for training and testing of new algorithms. We show first

Maja Pavlovic, Massimo Poesio

With the increasing capabilities of LLMs, recent studies focus on understanding whose opinions are represented by them and how to effectively extract aligned opinion distributions. We conducted an empirical analysis of three straightforward methods for obtaining distributions and evaluated the results across a variety of metrics. Our findings suggest that sa

Kenjiro Oya

Abstract This paper proposes a novel approach to Bermudan swaption hedging by applying the deep hedging framework to address limitations of traditional arbitrage-free methods. Conventional methods assume ideal conditions, such as zero transaction costs, perfect liquidity, and continuous-time hedging, which often differ from real market environments. This dis

Rinsy Thomas, Jobil Thomas, Minu Joy

A sharp step on a chaotic potential can enhance primordial curvature fluctuations on smaller scales to the $\mathcal{O}(10^{-2})$ to form primordial black holes (PBHs). The present study discusses an inflationary potential with a sharp step that results in the formation of PBHs in four distinct mass ranges. Also this inflationary model allows the separate co

Selma Moqvist, Weilong Chen, Mathias Schreiner, Feliks Nüske

Using normalizing flows and reweighting, Boltzmann Generators enable equilibrium sampling from a Boltzmann distribution, defined by an energy function and thermodynamic state. In this work, we introduce Thermodynamic Interpolation (TI), which allows for generating sampling statistics in a temperature-controllable way. We introduce TI flavors that work direct

Quentin Bateux, Jonathan Koss, Patrick W. Sweeney, Erika Edwards

The digitization of natural history collections over the past three decades has unlocked a treasure trove of specimen imagery and metadata. There is great interest in making this data more useful by further labeling it with additional trait data, and modern deep learning machine learning techniques utilizing convolutional neural nets (CNNs) and similar netwo

Dmitry S Ivanov, Tatiana E Itina

Modern semiconductor applications demand precise laser processing at the nanometer scale, requiring a detailed understanding of phase transitions and structural modifications. Accurate control over laser-induced processes in semiconductors is essential for generating surface structures and modifying surface properties. In this study, we present a numerical i

Ishrath Ahamed, Chamith Dilshan Ranathunga, Dinuka Sandun Udayantha, Benny Kai Kiat Ng

Accurate people counting in smart buildings and intelligent transportation systems is crucial for energy management, safety protocols, and resource allocation. This is especially critical during emergencies, where precise occupant counts are vital for safe evacuation. Existing methods struggle with large crowds, often losing accuracy with even a few addition

Rutger Hendrix, Federica Proietto Salanitri, Concetto Spampinato, Simone Palazzo

We introduce FedEvPrompt, a federated learning approach that integrates principles of evidential deep learning, prompt tuning, and knowledge distillation for distributed skin lesion classification. FedEvPrompt leverages two sets of prompts: b-prompts (for low-level basic visual knowledge) and t-prompts (for task-specific knowledge) prepended to frozen pre-tr

Huali Xu, Li Liu, Tianpeng Liu, Shuaifeng Zhi

Existing cross-domain few-shot learning (CDFSL) methods, which develop source-domain training strategies to enhance model transferability, face challenges with large-scale pre-trained models (LMs) due to inaccessible source data and training strategies. Moreover, fine-tuning LMs for CDFSL demands substantial computational resources, limiting practicality. Th

Zichen Song, Sitan Huang, Yuxin Wu, Zhongfeng Kang

Evaluating the importance of different layers in large language models (LLMs) is crucial for optimizing model performance and interpretability. This paper first explores layer importance using the Activation Variance-Sparsity Score (AVSS), which combines normalized activation variance and sparsity to quantify each layer's contribution to overall model perfor

Thibault Clérice, Juliette Janes, Hugo Scheithauer, Sarah Bénière

We present a novel, open-access dataset designed for semantic layout analysis, built to support document recreation workflows through mapping with the Text Encoding Initiative (TEI) standard. This dataset includes 7,254 annotated pages spanning a large temporal range (1600-2024) of digitised and born-digital materials across diverse document types (magazines

Jonas Hawellek, Athin Mohan, Hadi Aghaee, Christian Deppe

This paper explores communication over a two-sender, two-receiver classical interference channel, enhanced by the availability of entanglement resources between transmitters. The central contributions are an inner and outer bound on the capacity region for a general interference channel with entangled transmitters. It addresses the persistent challenge of th

Guillaume Voisin, Ismaël Cognard, Melaine Saillenfest, Thomas Tauris

Context: Timing of pulsar PSR J0337+1715 provides a unique opportunity to test the strong equivalence principle (SEP) with a strongly self-gravitating object. This is due to its unique situation in a triple stellar system with two white dwarfs. Aims: Our previous study suggested the presence of a strong low-frequency signal in the timing residuals. We set ou

Chaoqun Li, Huanqian Yan, Lifeng Zhou, Tairan Chen

Adversarial attacks in the physical world pose a significant threat to the security of vision-based systems, such as facial recognition and autonomous driving. Existing adversarial patch methods primarily focus on improving attack performance, but they often produce patches that are easily detectable by humans and struggle to achieve environmental consistenc

Xavier Bou, Gabriele Facciolo, Rafael Grompone von Gioi, Jean-Michel Morel

Oriented object detection predicts orientation in addition to object location and bounding box. Precisely predicting orientation remains challenging due to angular periodicity, which introduces boundary discontinuity issues and symmetry ambiguities. Inspired by classical works on edge and corner detection, this paper proposes to represent orientation in orie

Shinnosuke Koyama, Joji Nasu

We investigate the thermal Hall effect in the Shastry-Sutherland model, incorporating interactions between quasiparticle excitations. In this model, with strong nearest-neighbor interactions, the ground state is well described by the direct product of spin-singlet states, and the elementary excitations to spin-triplet states are known as triplons. In candida

David Shulman, Itai Dattner

This paper introduces an adaptive physics-guided neural network (APGNN) framework for predicting quality attributes from image data by integrating physical laws into deep learning models. The APGNN adaptively balances data-driven and physics-informed predictions, enhancing model accuracy and robustness across different environments. Our approach is evaluated

Shuai Gong, Chaoran Cui, Chunyun Zhang, Wenna Wang

Federated domain generalization (FedDG) aims to improve the global model generalization in unseen domains by addressing data heterogeneity under privacy-preserving constraints. A common strategy in existing FedDG studies involves sharing domain-specific knowledge among clients, such as spectrum information, class prototypes, and data styles. However, this kn

Camille Grange, Marion Lavignac, Valentina Pozzoli, Eric Bourreau

Quantum Approximate Optimization Algorithm (QAOA) is one of the most short-term promising quantum-classical algorithm to solve unconstrained combinatorial optimization problems. It alternates between the execution of a parametrized quantum circuit and a classical optimization. There are numerous levers for enhancing QAOA performances, such as the choice of q

Rang Meng, Xingyu Zhang, Yuming Li, Chenguang Ma

Recent work on human animation usually involves audio, pose, or movement maps conditions, thereby achieves vivid animation quality. However, these methods often face practical challenges due to extra control conditions, cumbersome condition injection modules, or limitation to head region driving. Hence, we ask if it is possible to achieve striking half-body

Xiaofei Zhu, Jiawei Cheng, Zhou Yang, Zhuo Chen

Multimodal emotion recognition in conversation (MER) aims to accurately identify emotions in conversational utterances by integrating multimodal information. Previous methods usually treat multimodal information as equal quality and employ symmetric architectures to conduct multimodal fusion. However, in reality, the quality of different modalities usually v

Andreas Athanasiou, Konstantinos Chatzikokolakis, Catuscia Palamidessi

Quantitative Information Flow (QIF) provides a robust information-theoretical framework for designing secure systems with minimal information leakage. While previous research has addressed the design of such systems under hard constraints (e.g. application limitations) and soft constraints (e.g. utility), scenarios often arise where the core system's behavio

Kedi Zheng, Qixin Chen, Yi Wang, Chongqing Kang

Having a better understanding of how locational marginal prices (LMPs) change helps in price forecasting and market strategy making. This paper investigates the fundamental distribution of the congestion part of LMPs in high-dimensional Euclidean space using an unsupervised approach. LMP models based on the lossless and lossy DC optimal power flow (DC-OPF) a

Chi Liu, Jiangxia Cao, Rui Huang, Kai Zheng

In large-scale content recommendation systems, retrieval serves as the initial stage in the pipeline, responsible for selecting thousands of candidate items from billions of options to pass on to ranking modules. Traditionally, the dominant retrieval method has been Embedding-Based Retrieval (EBR) using a Deep Neural Network (DNN) dual-tower structure. Howev

Yilin Song, Ruixiao Zhang

In this article, we investigate the global well-posedness for the defocusing, cubic nonlinear Schr\"{o}dinger equation posed on $\T^3$ with intial data lying in its critical space $H^\frac{1}{2}(\T^3)$. By establishing the linear profile decomposition, and applied this to the concentration-compactness/rigidity argument, we prove that if the solution remains

César Quilodrán-Casas, Christopher Waite, Nicole Alhadeff, Diyona Dsouza

Climate change poses an urgent global threat, needing the rapid identification and deployment of innovative solutions. We hypothesise that many of these solutions already exist within scientific literature but remain underutilised. To address this gap, this study employs a curated dataset sourced from OpenAlex, a comprehensive repository of scientific papers

Li Zeng, Chao Feng, Xiaofan Wang, Huaiqian Yi

Over the last decade, external seeded free electron lasers (FELs) have achieved significant advancements across various disciplines, progressively establishing themselves as indispensable tools in fields ranging from fundamental science to industrial applications. The performance of seeded FELs is critically dependent on the quality of the frequency up-conve

Anna Goldie, Azalia Mirhoseini, Jeff Dean

In 2020, we introduced a deep reinforcement learning method capable of generating superhuman chip layouts, which we then published in Nature and open-sourced on GitHub. AlphaChip has inspired an explosion of work on AI for chip design, and has been deployed in state-of-the-art chips across Alphabet and extended by external chipmakers. Even so, a non-peer-rev

Yuming Qin, Junchen Liu

This paper is concerned with existence, uniqueness and stability of the solution for the 3D Prandtl equation in a polynomial weighted Sobolev space. The main novelty of this paper is to directly prove the long time well-posedness to 3D Prandtl equation under monotonicity condition $\partial_{z} u >0$ and a special structural assumption $v=Ku$ $\big(\partial_

Jae Choon Cha, Min Hoon Kim

In 2009, Calegari constructed smooth homotopy 4-spheres from monodromies of fibered knots. We prove that all these are diffeomorphic to the standard 4-sphere. Our method uses 5-dimensional handlebody techniques and results on mapping class groups of 3-dimensional handlebodies. As an application, we present potential counterexamples to the smooth 4-dimensiona

Arnav Mejari, Maitreya Vaghulade, Paarshva Chitaliya, Arya Telang

In recent years, the global and Indian government efforts in monitoring and collecting data related to the fisheries industry have witnessed significant advancements. Despite this wealth of data, there exists an untapped potential for leveraging artificial intelligence based technological systems to benefit Indian fishermen in coastal areas. To fill this voi

Kanghyun Kim, Min Jun Kim

To enable versatile robot manipulation, robots must detect task-relevant poses for different purposes from raw scenes. Currently, many perception algorithms are designed for specific purposes, which limits the flexibility of the perception module. We present a general problem formulation called 3D scene-to-pose-set matching, which directly matches the corres

Tymofii Nikolaienko, Harshil Patel, Aniruddha Panda, Subodh Madhav Joshi

Physics-Informed Neural Networks (PINNs) have emerged as an influential technology, merging the swift and automated capabilities of machine learning with the precision and dependability of simulations grounded in theoretical physics. PINNs are often employed to solve algebraic or differential equations to replace some or even all steps of multi-stage computa

Claus Metzner, Achim Schilling, Andreas Maier, Patrick Krauss

Reservoir computing - information processing based on untrained recurrent neural networks with random connections - is expected to depend on the nonlinear properties of the neurons and the resulting oscillatory, chaotic, or fixpoint dynamics of the network. However, the required degree of nonlinearity and the range of suitable dynamical regimes for a given t

János Barát, Stijn Cambie, Geňa Hahn, Davide Mattiolo

Since its beginnings, every Cycles and Colourings workshop holds one or two open problem sessions; this document contains the problems (together with notes regarding the current state of the art and related bibliography) presented by participants of the 32nd edition of the workshop which took place in Poprad, Slovakia during September 8-13, 2024 (see the wor

Sanjaya Paudel, Cristiano G. Sabiu, Suk-Jin Yoon, Pierre-Alain Duc

We report the discovery of a rare isolated group of five dwarf galaxies located at z = 0.0086 ($D$ = 36 Mpc). All member galaxies are star-forming, blue, and gas-rich with $g-r$ indices ranging from 0.2 to 0.6 mag, and two of them show signs of ongoing mutual interaction. The most massive member of the group has a stellar mass that is half of the Small Magel

Jonas M. Maas, Falk K. Wittel

Determining the creep compliances of orthotropic composite materials requires experiments in at least three different uniaxial and biaxial loading directions. Up to date, data respecting multiple climates and all anatomical directions are sparse for hygro-responsive materials like Norway spruce. Consequently, simulation models of wood frequently over-simplif

Shian Jia, Xinbo Wang, Mingli Song, Gang Chen

The operating system (OS) is the backbone of modern computing, providing essential services and managing resources for computer hardware and software. This review paper offers an in-depth analysis of operating systems' evolution, current state, and prospects. We begin with an overview of the concept and significance of operating systems in the digital era. I

Himanshu Chhabra, R. Inkulu

Constant workspace algorithms use a constant number of words in addition to the read-only input to the algorithm. In this paper, we devise algorithms to efficiently compute relative hulls in the plane using a constant workspace. Specifically, we devise algorithms for the following three problems: (i) Given two simple polygons P and Q with P \subset Q, comput

Yoshiyuki Kawamura

In this report, a simple experimental system is shown, by which the temperature rise of global warming due to greenhouse gases can be demonstrated quantitatively. The system configuration is similar to that of the earth-atmosphere-space system based on a simple gray atmosphere model. The space (heat sink), the ground surface and the radiation power from the

François Dubois

We propose to define a notion of state of the opinion in order to link politician popularity estimations and voting intentions. We present two ways of modelling: a classical approach and quantum modelling. We test these ideas on data obtained during the French presidential election of April 2012.

Calla Beauregard, Christopher M. Danforth, Peter Sheridan Dodds

The common phrase 'representation matters' asserts that media has a measurable and important impact on civic society's perception of self and others. The representation of health in media, in particular, may reflect and perpetuate a society's disease burden. Here, for the top 10 major causes of death in the United States, we examine how cinematic representat

Danying Yu, Kun Ding, Xianfeng Chen, Luqi Yuan

The dislocation created in the topological material lays the foundation of many significant findings to control light but requires delicate fabrication of the material. To extend its flexibility and reconfigurability, we propose the magnetic dislocation concept and unveil its properties in a representative model, which effectively combines the topological de

Yoshiki Obinata, Haoyu Jia, Kento Kawaharazuka, Naoaki Kanazawa

Robot systems capable of executing tasks based on language instructions have been actively researched. It is challenging to convey uncertain information that can only be determined on-site with a single language instruction to the robot. In this study, we propose a system that includes ambiguous parts as template variables in language instructions to communi

Dan He, Guofen Wang, Weisheng Li, Yucheng Shu

Multimodal image fusion (MMIF) integrates information from different modalities to obtain a comprehensive image, aiding downstream tasks. However, existing research focuses on complementary information fusion and training strategies, overlooking the critical role of underlying architectural components like normalization and convolution kernels. We reevaluate

Antonio Avilés, Mikołaj Krupski

We prove that every Lindel\"of scattered subspace of a $\Sigma$-product of first-countable spaces is $\sigma$-compact. In particular, we obtain the result stated in the title. This answers some questions of Tkachuk from [Houston J. Math. 48 (2022), no. 1, 171--181].

Jung-Woo Chang, Ke Sun, David Xia, Xinyu Zhang

Vibrometry-based side channels pose a significant privacy risk, exploiting sensors like mmWave radars, light sensors, and accelerometers to detect vibrations from sound sources or proximate objects, enabling speech eavesdropping. Despite various proposed defenses, these involve costly hardware solutions with inherent physical limitations. This paper presents

Yanhao Sun, RunZe Tian, Xiao Han, XinYao Liu

With the emergence of large-scale Text-to-Image(T2I) models and implicit 3D representations like Neural Radiance Fields (NeRF), many text-driven generative editing methods based on NeRF have appeared. However, the implicit encoding of geometric and textural information poses challenges in accurately locating and controlling objects during editing. Recently,

Weihao Zhong, Yinhao Xiao, Minghui Xu, Xiuzhen Cheng

Short video platforms have become important channels for news dissemination, offering a highly engaging and immediate way for users to access current events and share information. However, these platforms have also emerged as significant conduits for the rapid spread of misinformation, as fake news and rumors can leverage the visual appeal and wide reach of

Jingyuan Zhou, Longhao Yan, Jinhao Liang, Kaidi Yang

It is recognized that the control of mixed-autonomy platoons comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) can enhance traffic flow. Among existing methods, Multi-Agent Reinforcement Learning (MARL) appears to be a promising control strategy because it can manage complex scenarios in real time. However, current research

Karthik R, Ashutosh Srivastava, Soumen Midya, Akbar Shanu

Miniaturization of electronic components has led to overheating, increasing power consumption and causing early circuit failures. Conventional heat dissipation methods are becoming inadequate due to limited surface area and higher short-circuit risks. This study presents a fast, low-cost, and scalable technique using 2D hexagonal boron nitride (hBN) coatings

Jiawei Zhou, Linye Lyu, Daojing He, Yu Li

Adversarial camouflage is a widely used physical attack against vehicle detectors for its superiority in multi-view attack performance. One promising approach involves using differentiable neural renderers to facilitate adversarial camouflage optimization through gradient back-propagation. However, existing methods often struggle to capture environmental cha

Yanzhao Fang

The goal of multi-object tracking (MOT) is to detect and track all objects in a scene across frames, while maintaining a unique identity for each object. Most existing methods rely on the spatial-temporal motion features and appearance embedding features of the detected objects in consecutive frames. Effectively and robustly representing the spatial and appe

Zhendong Liu, Yi Nian, Yuehan Qin, Henry Peng Zou

How can models effectively detect out-of-distribution (OOD) samples in complex, multi-label settings without extensive retraining? Existing OOD detection methods struggle to capture the intricate semantic relationships and label co-occurrences inherent in multi-label settings, often requiring large amounts of training data and failing to generalize to unseen

Yang Xiao, Rohan Kumar Das

Transformers and their variants have achieved great success in speech processing. However, their multi-head self-attention mechanism is computationally expensive. Therefore, one novel selective state space model, Mamba, has been proposed as an alternative. Building on its success in automatic speech recognition, we apply Mamba for spoofing attack detection.

Mayank Raikwar, Nikita Polyanskii, Sebastian Müller

This paper is a Systematization of Knowledge (SoK) on Directed Acyclic Graph (DAG)-based consensus protocols, analyzing their performance and trade-offs within the framework of consistency, availability, and partition tolerance inspired by the CAP theorem. We classify DAG-based consensus protocols into availability-focused and consistency-focused categories,

Manabu Machida, Tsutomu Mitsuie, Shinji Nakamura, Takashi Kusaka

Neonatal hypoxic-ischemic encephalopathy (HIE) is a significant cause of neonatal mortality and developmental disabilities. It has been revealed that the temporal behavior of the cerebral blood volume (CBV) carries information on the degree of hypoxia-ischemia. CBV can be estimated by means of near-infrared spectroscopy. The change of CBV after the insult is

Zhanke Zhou, Jianing Zhu, Fengfei Yu, Xuan Li

Deep neural networks have enabled numerous studies and applications on both Euclidean data, such as images and text, and non-Euclidean data, such as graphs. Because these networks may process private data, their deployment raises concerns about privacy leakage. Model inversion attacks (MIAs) exploit access to a trained model to reconstruct training examples

Taha Sochi

In this paper of "The Epistemology of Contemporary Physics" series we investigate Newton's third law and discuss and analyze its epistemological significance from some aspects with special attention to its relation to the principle of conservation of linear and angular momentum. The main issue in this investigation is the potential violations of this law acc

Tom Denecker, Yukii Torii Chew, Oscar Guillemant, Genki Watanabe

Lasers are the workhorse of quantum engineering in the atomic-molecular-optic community. However, phase noise of the laser, which can be especially large in popular semiconductor-based lasers, can limit fidelity of operation. Here, we present a fully-fiberized instrument detecting and correcting the fast, sub-microsecond, phase fluctuations of lasers. We dem

Yan Hu, Xu Zuo, Yujia Zhou, Xueqing Peng

Backgrounds: Information extraction (IE) is critical in clinical natural language processing (NLP). While large language models (LLMs) excel on generative tasks, their performance on extractive tasks remains debated. Methods: We investigated Named Entity Recognition (NER) and Relation Extraction (RE) using 1,588 clinical notes from four sources (UT Physician

Laura O'Mahony, Nikola S. Nikolov, David JP O'Sullivan

Recent efforts to understand intermediate representations in deep neural networks have commonly attempted to label individual neurons and combinations of neurons that make up linear directions in the latent space by examining extremal neuron activations and the highest direction projections. In this paper, we show that this approach, although yielding a good

Naitian Zhou, David Bamman

Narrative film is a composition of writing, cinematography, editing, and performance. While much computational work has focused on the writing or visual style in film, we conduct in this paper a computational exploration of acting performance. Applying speech emotion recognition models and a variationist sociolinguistic analytical framework to a corpus of po

Kavindie Katuwandeniya, Leimin Tian, Dana Kulić

This paper investigates the application of Video Foundation Models (ViFMs) for generating robot data summaries to enhance intermittent human supervision of robot teams. We propose a novel framework that produces both generic and query-driven summaries of long-duration robot vision data in three modalities: storyboards, short videos, and text. Through a user

Fatahlla Moreh, Yusuf Hasan, Bilal Zahid Hussain, Mohammad Ammar

Micro Crack detection using deep neural networks (DNNs) through an automated pipeline using wave fields interacting with the damaged areas is highly sought after. These high-dimensional spatio-temporal crack data are limited, and these datasets have large dimensions in the temporal domain. The dataset presents a substantial class imbalance, with crack pixels

Giovanni Iacovelli, Chandan Kumar Sheemar, Wali Ullah Khan, Asad Mahmood

In this article, we propose the integration of the Holographic Multiple Input Multiple Output (HMIMO) as a transformative solution for next generation Non-Terrestrial Networks (NTNs), addressing key challenges, such as high hardware costs, launch expenses, and energy inefficiency. Traditional NTNs are constrained by the financial and operational limitations

Yongfan Liu, Hyoukjun Kwon

Stereo depth estimation is a fundamental component in augmented reality (AR), which requires low latency for real-time processing. However, preprocessing such as rectification and non-ML computations such as cost volume require significant amount of latency exceeding that of an ML model itself, which hinders the real-time processing required by AR. Therefore

Yuhong Gao, Bo Li, Mijie Shi, Shaoxia Chen

We aim to provide more insights into the applicability to solar coronal seismology of the much-studied discrete leaky modes (DLMs) in classic analyses. Under linear ideal pressureless MHD, we examine two-dimensional (2D) axial fundamental kink motions that arise when localized velocity exciters impact some symmetric slab equilibria. Continuous structuring is

Atsushi Kudo

Numerical weather prediction (NWP) centers around the world operate a variety of NWP models. In addition, recent advances in AI-driven NWP models have further increased the availability of NWP outputs. While this expansion holds the potential to improve forecast accuracy, it raises a critical question: which prediction is the most plausible? If the NWP model