November 2024 arXiv papers — page 186

Dakota K. Keblbeck, Eric Mayotte, Uwe Greife, Kyle G. Leach

We present the characterization of cosmogenic muon backgrounds for the Colorado Underground Research Institute (CURIE), located in the Edgar Experimental Mine (EEM) in Idaho Springs, Colorado. The CURIE facility at the EEM offers a versatile shallow underground environment, with accessible horizontal tunnel access and stable rock formations ideal for low-bac

Zijun Gao, Qingyuan Zhao

Existing tools for explaining complex models and systems are associational rather than causal and do not provide mechanistic understanding. We propose a new notion called counterfactual explainability for causal attribution that is motivated by the concept of genetic heritability in twin studies. Counterfactual explainability extends methods for global sensi

Xinyu Xu, Huazhen Liu, Tao Zhang, Huilin Xiong

Semantic segmentation is an important branch of image processing and computer vision. With the popularity of deep learning, various convolutional neural networks have been proposed for pixel-level classification and segmentation tasks. In practical scenarios, however, imaging angles are often arbitrary, encompassing instances such as water body images from r

Kun Yi, Jingru Fei, Qi Zhang, Hui He

While numerous forecasters have been proposed using different network architectures, the Transformer-based models have state-of-the-art performance in time series forecasting. However, forecasters based on Transformers are still suffering from vulnerability to high-frequency signals, efficiency in computation, and bottleneck in full-spectrum utilization, whi

Yuntong Zhang, Jiawei Wang, Dominic Berzin, Martin Mirchev

Critical open source software systems undergo significant validation in the form of lengthy fuzz campaigns. The fuzz campaigns typically conduct a biased random search over the domain of program inputs, to find inputs which crash the software system. Such fuzzing is useful to enhance the security of software systems in general since even closed source softwa

Arianna Tinari

The tension of $B\to K^{(*)}\bar\ell\ell$ decays with the Standard Model (SM) can be attributed to a short-distance (SD) $b s\bar\ell\ell$ interaction. We show two methods to disentangle this effect from long-distance (LD) dynamics. Firstly, we perform a comparison of the inclusive $b\to s\bar\ell\ell$ rate at high $q^2=m^2_{\ell\ell}\geq 15~\rm GeV^2$ with

Hambur Wang

This study explores the impact of appearance discrimination in the labor market and whether education can mitigate this issue. A statistical analysis of approximately 1.058 million job advertisements in China from 2008 to 2010 found that about 7.7% and 2.6% of companies had explicit requirements regarding candidates' appearance and height, particularly in po

Oliver Stein

We define two $L$-functions associated to a common vector valued eigenform $f$ transforming with the ``finite'' Weil representation. The first one can be seen as a standard zeta function defined by the eigenvalues of $f$. The second one can be interpreted as standard $L$-function defined as an Euler product where each $p$-factor is a rational function in ter

Maryam Mahdavifar, Farhad Khoeini, Francois M. Peeters

The impact of vacancies on spin-resolved electronic properties of quantum dots (QDs) in phosphorene-based junctions, are investigated numerically. Regardless of the crystal orientation, a phosphorene nanoribbon (PNR) containing a monovacancy is found to exhibit a topological quasi-flat band that emerges within the band gap. The electronic properties of QDs,

Yiwei Zhang, Jin Gao, Fudong Ge, Guan Luo

Bird's-eye-view (BEV) map layout estimation requires an accurate and full understanding of the semantics for the environmental elements around the ego car to make the results coherent and realistic. Due to the challenges posed by occlusion, unfavourable imaging conditions and low resolution, \emph{generating} the BEV semantic maps corresponding to corrupted

Onil Boussim

This paper generalizes the changes-in-changes (CIC) model to handle discrete treatments with more than two categories, extending the binary case of Athey and Imbens (2006). While the original CIC model is well-suited for binary treatments, it cannot accommodate multi-category discrete treatments often found in economic and policy settings. Although recent wo

Megha Emerse, Lucas Goehring

Thin films or sheets subjected to external forces often undergo mechanical instability, leading to regular patterns of wrinkles, folds, and creases. As can be anticipated from the difficulty of flattening a curved globe, any natural curvature of the sheet will have a strong influence on these instabilities. Here, we develop a non-invasive synthetic schlieren

Marco Gallo, Sunra Mosconi, Marco Squassina

We study concavity properties of positive solutions to the Logarithmic Schr\"odinger equation $-\Delta u=u\, \log u^2$ in a general convex domain with Dirichlet conditions. To this aim, we analyse the auxiliary Lane-Emden problems $-\Delta u = \sigma\, (u^q-u)$ and build, for any $\sigma>0$ and $q>1$, solutions $u_q$ such that $u_q^{(1-q)/2}$ is convex. By c

Filipe R. Cordeiro, Gustavo Carneiro

An important stage of most state-of-the-art (SOTA) noisy-label learning methods consists of a sample selection procedure that classifies samples from the noisy-label training set into noisy-label or clean-label subsets. The process of sample selection typically consists of one of the two approaches: loss-based sampling, where high-loss samples are considered

Sanaz Saki Norouzi, Adrita Barua, Antrea Christou, Nikita Gautam

Knowledge graphs (KGs) are increasingly utilized for data integration, representation, and visualization. While KG population is critical, it is often costly, especially when data must be extracted from unstructured text in natural language, which presents challenges, such as ambiguity and complex interpretations. Large Language Models (LLMs) offer promising

Lutfi Eren Erdogan, Vijay Anand Raghava Kanakagiri, Kurt Keutzer, Zhen Dong

One of the major bottlenecks for efficient deployment of neural network based recommendation systems is the memory footprint of their embedding tables. Although many neural network based recommendation systems could benefit from the faster on-chip memory access and increased computational power of hardware accelerators, the large embedding tables in these mo

Haw-Shiuan Chang, Nanyun Peng, Mohit Bansal, Anil Ramakrishna

Contrastive decoding (CD) (Li et al., 2023) improves the next-token distribution of a large expert language model (LM) using a small amateur LM. Although CD is applied to various LMs and domains to enhance open-ended text generation, it is still unclear why CD often works well, when it could fail, and how we can make it better. To deepen our understanding of

Xiaobin Zhao, Pengcheng Liao, Francesco Anna Mele, Ulysse Chabaud

Quantum state tomography, a fundamental tool for quantum physics, usually requires a number of state copies that scale exponentially with the system size, owing to the intricate quantum correlations between subsystems. We show that, in bosonic systems, the nature of correlations indeed fully determines this scaling. Motivated by the Hong-Ou-Mandel effect and

Xingyu Hu, Lijun Zhang, Dejian Meng, Ye Han

In this study, we propose GITSR, an effective framework for Graph Interaction Transformer-based Scene Representation for multi-vehicle collaborative decision-making in intelligent transportation system. In the context of mixed traffic where Connected Automated Vehicles (CAVs) and Human Driving Vehicles (HDVs) coexist, in order to enhance the understanding of

Franco Strocchi

This note focuses the problem of motivating the use of gauge symmetries (being the identity on the observables) from general principles, beyond their practical success, starting from global gauge symmetries and then by emphasizing the substantially different role of local gauge symmetries. In the latter case, a deterministic time evolution of the local field

Mingyue Yuan, Jieshan Chen, Zhenchang Xing, Aaron Quigley

The rise of Large Language Models (LLMs) has streamlined frontend interface creation through tools like Vercel's V0, yet surfaced challenges in design quality (e.g., accessibility, and usability). Current solutions, often limited by their focus, generalisability, or data dependency, fall short in addressing these complexities. Moreover, none of them examine

Swapan Jana, Sourav Pal

Famous results due to von Neumann, Sz.-Nagy and Arveson assert that the following four statements are equivalent; a Hilbert space operator $T$ is a contraction; the closed unit disk $\overline{\mathbb D}$ is a spectral set for $T$; $T$ can be dilated to a Hilbert space isometry; $\overline{\mathbb D}$ is a complete spectral set for $T$. In this article, we s

Qiang Hu, Xiaofei Xie, Sen Chen, Lei Ma

Large Language Model (LLM) is changing the software development paradigm and has gained huge attention from both academia and industry. Researchers and developers collaboratively explore how to leverage the powerful problem-solving ability of LLMs for specific domain tasks. Due to the wide usage of LLM-based applications, e.g., ChatGPT, multiple works have b

Jianjun Sun, Zhenwei Niu, Yihao Dong, Fenglin Zhang

This paper presents an autonomous aerial system specifically engineered for operation in challenging marine GNSS-denied environments, aimed at transporting small cargo from a target vessel. In these environments, characterized by weakly textured sea surfaces with few feature points, chaotic deck oscillations due to waves, and significant wind gusts, conventi

Yean Cheng, Ziqi Cai, Ming Ding, Wendi Zheng

We introduce DreamPolish, a text-to-3D generation model that excels in producing refined geometry and high-quality textures. In the geometry construction phase, our approach leverages multiple neural representations to enhance the stability of the synthesis process. Instead of relying solely on a view-conditioned diffusion prior in the novel sampled views, w

Bianca Trinkenreich, Zixuan Feng, Rudrajit Choudhuri, Marco Gerosa

The sustainability of open source software (OSS) projects hinges on contributor retention. Interpersonal challenges can inhibit a feeling of welcomeness among contributors, particularly from underrepresented groups, which impacts their decision to continue with the project. How much this impact is, varies among individuals, underlining the importance of a th

Fang Sun, Zijie Huang, Haixin Wang, Huacong Tang

Accurately predicting long-horizon molecular dynamics (MD) trajectories remains a significant challenge, as existing deep learning methods often struggle to retain fidelity over extended simulations. We hypothesize that one key factor limiting accuracy is the difficulty of capturing interactions that span distinct spatial and temporal scales, ranging from hi

Kejia Li, Mengen Wang

CsPbI3 has been recognized as a promising candidate for optoelectronic device applications. To further improve the efficiency of the devices, it is imperative to better understand the surface properties of CsPbI3, which affect charge carrier transport and defect formation properties. In this study, we perform density functional theory calculations to explore

Jaume de Haro, Supriya Pan

In this work, we focus on two important aspects of modern cosmology: reheating and Hubble constant tension within the framework of a unified model, namely, quintessential inflation connecting the early inflationary era and late-time cosmic acceleration. In the context of reheating, we use instant preheating and gravitational reheating, two viable reheating m

Xiayang Xiao, Zhuoxuan Li, Haipeng Wang

Current mainstream SAR image object detection methods still lack robustness when dealing with unknown objects in open environments. Open-set detection aims to enable detectors trained on a closed set to detect all known objects and identify unknown objects in open-set environments. The key challenges are how to improve the generalization to potential unknown

Daniel Csillag, Claudio José Struchiner, Guilherme Tegoni Goedert

When a machine learning model is deployed, its predictions can alter its environment, as better informed agents strategize to suit their own interests. With such alterations in mind, existing approaches to uncertainty quantification break. In this work we propose a new framework, Strategic Conformal Prediction, which is capable of robust uncertainty quantifi

Hui Lin, Danfeng Hong, Shuhang Ge, Chuyao Luo

Remote Sensing Image Captioning (RSIC) presents unique challenges and plays a critical role in applications. Traditional RSIC methods often struggle to produce rich and diverse descriptions. Recently, with advancements in VLMs, efforts have emerged to integrate these models into the remote sensing domain and to introduce descriptive datasets specifically des

Chia-Chun Lo

We show that the spectrum of a Schr\"odinger eigenvalue problem posed on a closed Riemannian manifold $M$ with non-negative potential can be approached by that of Robin eigenvalue problems with constant positive boundary parameter posed on a sequence of domains in $M$. We construct these Robin problems by means of a homogenisation procedure. We show a simila

Han Yang, Yanlong Zang, Ziwei Liu

Virtual try-on (VTON) transfers a target clothing image to a reference person, where clothing fidelity is a key requirement for downstream e-commerce applications. However, existing VTON methods still fall short in high-fidelity try-on due to the conflict between the high diversity of dressing styles (\eg clothes occluded by pants or distorted by posture) an

Javier Esparza, Mogens Nielsen

We survey 25 years of research on decidability issues for Petri nets. We collect results on the decidability of important properties, equivalence notions, and temporal logics.

Steven Finch

N. G. de Bruijn (1958) studied the asymptotic expansion of iterates of sin$(x)$ with $0 < x \leq \pi/2$. Bencherif & Robin (1994) generalized this result to increasing analytic functions $f(x)$ with an attractive fixed point at 0 and $x > 0$ suitably small. Mavecha & Laohakosol (2013) formulated an algorithm for explicitly deriving required parameters. We re

O. O. Prykhodko, L. V. Zadorozhna

We investigate the ground-state structures and vortex configurations in a two-component Bose-Einstein condensate (BEC) under the influence of spin-orbital-angular-momentum coupling (SOAMC) with a high spatial inhomogeneity and high characteristic orbital angular momentum. By modulating the coupling strength, we uncover two distinct quantum phases: a stripe p

Taha Sochi

In this paper of "The Epistemology of Contemporary Physics" series we investigate the epistemological significance and sensibility (and hence interpretability and interpretation) of classical mechanics in its Newtonian and non-Newtonian formulations. As we will see, none of these formulations provide a clear and consistent framework for understanding the phy

Xinliang Zhou, Yuzhe Han, Zhisheng Chen, Chenyu Liu

In this paper, we address the challenges in automatic sleep stage classification, particularly the high computational cost, inadequate modeling of bidirectional temporal dependencies, and class imbalance issues faced by Transformer-based models. To address these limitations, we propose BiT-MamSleep, a novel architecture that integrates the Triple-Resolution

Xuran Meng, Jingfei Zhang, Yi Li

Gaussian graphical regressions have emerged as a powerful approach for regressing the precision matrix of a Gaussian graphical model on covariates, which, unlike traditional Gaussian graphical models, can help determine how graphs are modulated by high dimensional subject-level covariates, and recover both the population-level and subject-level graphs. To fi

R. Allen LaCour, Joseph P. Heindel, Ruoqi Zhao, Teresa Head-Gordon

A wide variety of reactions are reported to be dramatically accelerated in aqueous microdroplets, making them a promising platform for environmentally clean chemical synthesis. However to fully utilize the microdroplets for accelerating chemical reactions requires a fundamental understanding of how microdroplet chemistry differs from that of a homogeneous ph

Margherita Solci

We study the asymptotic behaviour of double-well energies perturbed by a higher-order fractional term, which, in the one-dimensional case, take the form $$ \frac{1}{\varepsilon}\int_I W(u(x))dx+\varepsilon^{2(k+s)-1}\frac{s(1-s)}{2^{1-s}}\int_{I\times I} \frac{|u^{(k)}(x)-u^{(k)}(y)|^2}{|x-y|^{1+2s}} dx\,dy $$ defined on the higher-order fractional Sobolev s

Quanxin Hu, Shengshan Qin, Yi Peng, Yuke Song

The topological properties observed in iron-based superconductors extend our understanding of vortex Majorana quasiparticle excitations in unexpected ways. Vortex Majorana physics has been extensively studied within the context of the topologically protected surface Dirac state. By employing an in-situ strain device, we demonstrate that uniaxial strain can g

Zhenyu Wang, Yali Li, Hengshuang Zhao, Shengjin Wang

The current trend in computer vision is to utilize one universal model to address all various tasks. Achieving such a universal model inevitably requires incorporating multi-domain data for joint training to learn across multiple problem scenarios. In point cloud based 3D object detection, however, such multi-domain joint training is highly challenging, beca

Chunlu Chen, Ji Liu, Haowen Tan, Xingjian Li

While recent years have witnessed the advancement in big data and Artificial Intelligence (AI), it is of much importance to safeguard data privacy and security. As an innovative approach, Federated Learning (FL) addresses these concerns by facilitating collaborative model training across distributed data sources without transferring raw data. However, the ch

Shapeng Jiang, Lijia Wei, Chen Zhang

In recent years, large language models (LLMs) have attracted attention due to their ability to generate human-like text. As surveys and opinion polls remain key tools for gauging public attitudes, there is increasing interest in assessing whether LLMs can accurately replicate human responses. This study examines the potential of LLMs, specifically ChatGPT-4o

Sorina Cretu, Nicolas Folastre, David Troadec, Ingrid Marie Andersen

All-solid-state batteries hold great promise for electric vehicle applications due to their enhanced safety and higher energy density. However, further performance optimization requires a deeper understanding of their degradation mechanisms, particularly at the nanoscale. This study investigates the real-time degradation processes of an oxide-based all-solid

Minghao Li, Dmitrii Avdiukhin, Rana Shahout, Nikita Ivkin

Federated Learning (FL) trains deep models across edge devices without centralizing raw data, preserving user privacy. However, client heterogeneity slows down convergence and limits global model accuracy. Clustered FL (CFL) mitigates this by grouping clients with similar representations and training a separate model for each cluster. In practice, client dat

Shuo Tan, Rui Liu, Xuesong Han, XianLei Long

Deploying Convolutional Neural Networks (CNNs) on resource-constrained devices necessitates efficient management of computational resources, often via distributed environments susceptible to latency from straggler nodes. This paper introduces the Flexible Coded Distributed Convolution Computing (FCDCC) framework to enhance straggler resilience and numerical

Siqi Chen, Zhiqiang Wang, Xianqi Deng, Yili Shen

Rational design of next-generation functional materials relied on quantitative predictions of their electronic structures beyond single building blocks. First-principles quantum mechanical (QM) modeling became infeasible as the size of a material grew beyond hundreds of atoms. In this study, we developed a new computational tool integrating fragment-based gr

Maximilian Fleissner, Maedeh Zarvandi, Debarghya Ghoshdastidar

The explainable clustering problem was first posed by Moshkovitz et al. (ICML 2020) and studies how well an axis-aligned decision tree with $K$ leaves can approximate a given clustering. The performance of the tree is measured via the \textit{price of explainability}, defined as the ratio between the clustering cost of the tree (where every leaf is a cluster

Shi Yin, Hongqi Tan, Li Ming Chong, Haofeng Liu

Background: Cone-beam computed tomography (CBCT) plays a crucial role in image-guided radiotherapy, but artifacts and noise make them unsuitable for accurate dose calculation. Artificial intelligence methods have shown promise in enhancing CBCT quality to produce synthetic CT (sCT) images. However, existing methods either produce images of suboptimal quality

Olga Mashkova, Fernando Zhapa-Camacho, Robert Hoehndorf

Ontology embeddings map classes, roles, and individuals in ontologies into $\mathbb{R}^n$, and within $\mathbb{R}^n$ similarity between entities can be computed or new axioms inferred. For ontologies in the Description Logic $\mathcal{EL}^{++}$, several optimization-based embedding methods have been developed that explicitly generate models of an ontology. H

M. C. Carrisi, S. Pennisi, T. Ruggeri, T. Arima

A new expression is here proposed for the production term Q in the Boltzmann Equation. This new expression satisfies the H-Theorem (non approximated) and, in the transition to Ordinary Thermodynamics gives a result which is independent on the number N of the moments which are used both if the Maxwellian Iteration or the Eckart method are used.

Bing Cao, Xingxin Xu, Pengfei Zhu, Qilong Wang

Image fusion aims to integrate complementary information from multiple input images acquired through various sources to synthesize a new fused image. Existing methods usually employ distinct constraint designs tailored to specific scenes, forming fixed fusion paradigms. However, this data-driven fusion approach is challenging to deploy in varying scenarios,

Meng Gao, Zhuan Ning, Yu Tian, Hongbao Zhang

In this paper, we extend the study of holographic superfluids from planar topology to spherical topology, inspired by recent studies on Bose-Einstein condensation (BEC) on shell-shaped geometry. We investigate the superfluid phase transition from normal fluid and its Quasi-Normal Modes (QNMs) on the sphere. It turns out that the critical temperature for the

Lance J. Dixon, Shuo Xin

Recently, the maximally-helicity-violating four-point form factor for the chiral stress-energy tensor in planar $\mathcal{N}=4$ super Yang-Mills was computed to three loops at the level of the symbol associated with multiple polylogarithms. It exhibits {\it antipodal self-duality}, or invariance under the combined action of a kinematic map and reversing the

Dipanjan Majumdar, Giulio Vita, Rubina Ramponi, Nina Glover

The effects of trees on urban flows are often determined using computational fluid dynamics approaches which typically use a quadratic drag formulation based on the leaf-area density $a$ and a volumetric drag coefficient $C_{d}^V$ to model vegetation. In this paper, we develop an analytical model for the flow within a vegetation canopy and identify that the

R. A. Evarestov, Yu. E. Kitaev, S. S. Novikov

Group theoretical aspects of the three temperature-dependent and temperature-reversible experimentally observed phase transitions in the KNbO3 crystal (cubic-tetragonal, tetragonal-orthorhombic, orthorhombic-rhombohedral) in the framework of the group-subgroup relationship tree have been discussed. The ab initio DFT-HSE06 LCAO calculations of the electron an

Faseeh Ahmad, Jonathan Styrud, Volker Krueger

In this paper, we propose an approach that combines Vision Language Models (VLMs) and Behavior Trees (BTs) to address failures in robotics. Current robotic systems can handle known failures with pre-existing recovery strategies, but they are often ill-equipped to manage unknown failures or anomalies. We introduce VLMs as a monitoring tool to detect and ident

Alexander Jenkins, Thiernithi Variddhisai, Ahmed El-Medany, Fu Siong Ng

Graph Signal Processing (GSP) provides a powerful framework for analysing complex, interconnected systems by modelling data as signals on graphs. While recent advances have enabled graph topology learning from observed signals, existing methods often struggle with time-varying systems and real-time applications. To address this gap, we introduce AdaCGP, a sp

Jasmina Karabegovic

This paper introduces an explicit algorithm for computing perfect public equilibrium (PPE) payoffs in repeated games with imperfect public monitoring, public randomization, and discounting. The method adapts the established framework by Abreu, Pearce, and Stacchetti (1990) into a practical tool that balances theoretical accuracy with computational efficiency

Jiawei Zhao, Kejiang Chen, Weiming Zhang, Nenghai Yu

Large Language Models (LLMs) are susceptible to jailbreak attacks that can induce them to generate harmful content. Previous jailbreak methods primarily exploited the internal properties or capabilities of LLMs, such as optimization-based jailbreak methods and methods that leveraged the model's context-learning abilities. In this paper, we introduce a novel

Xinyu Geng, Jiaming Wang, Jun Xu

Deep learning has excelled in medical image classification, but its clinical application is limited by poor interpretability. Capsule networks, known for encoding hierarchical relationships and spatial features, show potential in addressing this issue. Nevertheless, traditional capsule networks often underperform due to their shallow structures, and deeper v

Asbjørn Holk, Claudia Strauch, Lukas Trottner

In recent years, denoising diffusion models have become a crucial area of research due to their abundance in the rapidly expanding field of generative AI. While recent statistical advances have delivered explanations for the generation ability of idealised denoising diffusion models for high-dimensional target data, implementations introduce thresholding pro

Mingyue Jian, N. Siddharth

Large language models (LLMs) are trained on data assumed to include natural language pragmatics, but do they actually behave like pragmatic speakers? We attempt to answer this question using the Rational Speech Act (RSA) framework, which models pragmatic reasoning in human communication. Using the paradigm of a reference game constructed from the TUNA corpus

Feng Mo, Lin Xiao, Qiya Song, Xieping Gao

Graph neural networks (GNNs) have become crucial in multimodal recommendation tasks because of their powerful ability to capture complex relationships between neighboring nodes. However, increasing the number of propagation layers in GNNs can lead to feature redundancy, which may negatively impact the overall recommendation performance. In addition, the exis

Llŷr Humphries, Huw Morgan, David Kuridze

Small-scale brightenigs are ubiquitous, dynamic and energetic phenomena found in the chromopshere. An advanced filter-detection algorithm applied to high-resolution observations from the Interface Region Imaging Spectrograph enables the detection of these brightenings close to the noise level. This algorithm also tracks the movement of these brightenings and

Lilian Menn, Elif Sacikara

In this paper, we contribute to previously known results on lattices constructed by algebraic function fields, or function field lattices in short. First, motivated by the non-well-roundedness property of certain hyperelliptic function field lattices (Ates and Stichtenoth, 2016), we explore the successive minima of these lattices in detail. We also study the

Xiaoyi Su, Zhixin Zhou, Rui Luo

Conformal inference is a statistical method used to construct prediction sets for point predictors, providing reliable uncertainty quantification with probability guarantees. This method utilizes historical labeled data to estimate the conformity or nonconformity between predictions and true labels. However, conducting conformal inference for hidden states u

Abhishek Ghosh, Rajesh K. Singh

In this article, we study weighted estimates for a general class of lacunary maximal functions on homogeneous groups. As an application we derive improved weighted estimates for the lacunary maximal function associated to the Kor\'anyi spherical means as well as for the lacunary maximal function associated to codimension two spheres in the Heisenberg group.

Ranran Yang

In the context of high fossil fuel consumption and inefficiency within China's energy systems, effective demand-side management is essential. This study examines the thermal characteristics of various building types across different functional areas, utilizing the concept of body coefficient to integrate their unique structural and energy use traits into a d

Melih Emin Can, Alexandre Trilles

We show that the vague specification property is strictly weaker than most of the specification-like properties, by establishing its equivalence with the asymptotic average shadowing property. In particular, we see that the weak specification property implies the vague specification property, but the converse does not hold, answering the question posed by Do

David M T Kuo

In this comprehensive study, we conduct a theoretical investigation into the Stark shift of topological states (TSs) in finite armchair graphene nanoribbons (AGNRs) and heterostructures under transverse electric fields. Our focus centers on the multiple end zigzag edge states of AGNRs and the interface states of $9-7-9$ AGNR heterostructures. For the formal

Zibin Zhao, Guilong Li, Huanbo Luo, Bin Liu

The $1/r$ long-range interaction, induced by laser illumination, offers a mechanism for the implementation of stable self-trapping in Bose-Einstein condensates (BECs) in the three-dimensional free space. Using the variational approximation and numerical solutions, we find that self-trapped states in this setting , with attractive nonlocal and repulsive local

Han Wang, Binbin Chen, Tieying Zhang, Baoxiang Wang

Effective communication is an essential component in collaborative multi-agent systems. Situations where explicit messaging is not feasible have been common in human society throughout history, which motivate the study of implicit communication. Previous works on learning implicit communication mostly rely on theory of mind (ToM), where agents infer the ment

Soumyajit Mandal, Piotr Maj, Grzegorz W. Deptuch

A low-power integer-N frequency synthesizer for flexible on-chip clock generation has been designed in 65 nm CMOS technology. The circuit can be programmed to generate two independent low-jitter clocks between 30 MHz and 3 GHz that are locked a 10-50 MHz reference input. The design uses a phase-locked loop (PLL) with a dual-tuned LC voltage-controlled oscill

Yizhe Ji, Quanyu Tang, Wei Wang, Hao Zhang

An invariant for cospectral graphs is a property shared by all cospectral graphs. In this paper, we establish three novel arithmetic invariants for cospectral graphs, revealing deep connections between spectral properties and combinatorial structures. More precisely, one of our main results shows that for any two cospectral graphs $G$ and $H$ with adjacency

Susanne C. Brenner, Li-yeng Sung

We present a new error analysis for finite element methods for a linear-quadratic elliptic optimal control problem with Neumann boundary control and pointwise control constraints. It can be applied to standard finite element methods when the coefficient s in the elliptic operator are smooth and also to multiscale finite element methods when the coefficients

Alon Hayman, Eli Kraisler, Tamar Stein

Spin is a fundamental property of any many-electron system. The ability of density functional theory to accurately predict the physical properties of a system, while varying its spin, is crucial for describing magnetic materials and high-spin molecules, spin flip, magnetization and demagnetization processes. Within density functional theory, when using vario

Langming Liu, Dingxuan Zhou

Federated learning is an efficient machine learning tool for dealing with heterogeneous big data and privacy protection. Federated learning methods with regularization can control the level of communications between the central and local machines. Stochastic gradient descent is often used for implementing such methods on heterogeneous big data, to reduce the

Chengting Yu, Fengzhao Zhang, Ruizhe Chen, Aili Wang

Knowledge Distillation (KD), a learning manner with a larger teacher network guiding a smaller student network, transfers dark knowledge from the teacher to the student via logits or intermediate features, with the aim of producing a well-performed lightweight model. Notably, many subsequent feature-based KD methods outperformed the earliest logit-based KD m

Chun-Yueh Chiang

In this paper we consider a class of conjugate discrete-time Riccati equations (CDARE), arising originally from the linear quadratic regulation problem for discrete-time antilinear systems. Recently, we have proved the existence of the maximal solution to the CDARE with a nonsingular control weighting matrix under the framework of the constructive method. Ou

Qihe Pan, Zhen Zhao, Zicheng Wang, Sifan Long

A plethora of text-guided image editing methods has recently been developed by leveraging the impressive capabilities of large-scale diffusion-based generative models especially Stable Diffusion. Despite the success of diffusion models in producing high-quality images, their application to small object generation has been limited due to difficulties in align

Peizheng Li, Xinyi Lin, Adnan Aijaz

Error detection and correction are essential for ensuring robust and reliable operation in modern communication systems, particularly in complex transmission environments. However, discussions on these topics have largely been overlooked in semantic communication (SemCom), which focuses on transmitting meaning rather than symbols, leading to significant impr

Yifan Deng, Hao Zhou, Hewei Gao

Purpose: With the development of spectral CT, several novel spectral filters have been introduced to modulate the spectra, such as split filters and spectral modulators. However, due to the finite size of the focal spot of X-ray source, these filters cause spectral mixing in the penumbra region. Traditional spectrum estimation methods fail to account for it,

Jitesh Joshi, Sos S. Agaian, Youngjun Cho

Remote photoplethysmography (rPPG) enables non-invasive extraction of blood volume pulse signals through imaging, transforming spatial-temporal data into time series signals. Advances in end-to-end rPPG approaches have focused on this transformation where attention mechanisms are crucial for feature extraction. However, existing methods compute attention dis

Haoxiang Zhao, Lixuan An, Daning Zhang, Xiong Yang

The rapid development of modern energy applications drives an urgent need to enhance the dielectric strength of energy storage dielectrics for higher power density. Interface design is a promising strategy to regulate the crucial charge transport process determining dielectric strength. However, the targeted exploitation of interface effects on charge transp

Langming Liu, Wanyu Wang, Xiangyu Zhao, Zijian Zhang

Recommender systems play a pivotal role across practical scenarios, showcasing remarkable capabilities in user preference modeling. However, the centralized learning paradigm predominantly used raises serious privacy concerns. The federated recommender system (FedRS) addresses this by updating models on clients, while a central server orchestrates training w

William F. Bradley

We investigate the patterns of incorrect answers produced by large language models (LLMs) during evaluation. These errors exhibit highly non-intuitive behaviors unique to each model. By analyzing these patterns, we measure the similarities between LLMs and construct a taxonomy that categorizes them based on their error correlations. Our findings reveal that

Sige Liu, Nan Li, Yansha Deng, Tony Q. S. Quek

The rapid progress of artificial intelligence (AI) and computer vision (CV) has facilitated the development of computation-intensive applications like Visual Question Answering (VQA), which integrates visual perception and natural language processing to generate answers. To overcome the limitations of traditional VQA constrained by local computation resource

Yue Fu, Wenquan Liu, Yunhan Wang, Chang-Kui Duan

Quantum information processing (QIP) based on high-dimensional quantum systems provides unique advantages and new potentials where high-dimensional quantum correlations (QCs) play vital roles. Exploring the resistance of QCs against noises is crucial as QCs are fragile due to complex and unavoidable system-environment interactions. In this study, we investig

Langming Liu, Xiangyu Zhao, Chi Zhang, Jingtong Gao

Transformer models have achieved remarkable success in sequential recommender systems (SRSs). However, computing the attention matrix in traditional dot-product attention mechanisms results in a quadratic complexity with sequence lengths, leading to high computational costs for long-term sequential recommendation. Motivated by the above observation, we propo

Rohit Singh, Anil Kumar Yerrapragada, Radha Krishna Ganti

Random Access is a critical procedure using which a User Equipment (UE) identifies itself to a Base Station (BS). Random Access starts with the UE transmitting a random preamble on the Physical Random Access Channel (PRACH). In a conventional BS receiver, the UE's specific preamble is identified by correlation with all the possible preambles. The PRACH signa

Ram M. Adar

Living cells actively regulate their volume in response to changes in the extra-cellular environment, such as osmolarity and chemo-attractant concentration. While the basic physical mechanisms of volume regulation are understood from the classic "pump-leak" model, it does not provide an explicit expression for the volume during dynamic regulation and can ben

Haotong Du, Quanming Yao, Juzheng Zhang, Yang Liu

Subgraph-based methods have proven to be effective and interpretable in predicting drug-drug interactions (DDIs), which are essential for medical practice and drug development. Subgraph selection and encoding are critical stages in these methods, yet customizing these components remains underexplored due to the high cost of manual adjustments. In this study,

S. Hümmerich, K. Bernhard, E. Paunzen

The magnetic chemically peculiar Ap stars exhibit an extreme spread of rotational velocities, the reason of which is not well understood. Ap stars with rotational periods of 50 days or longer are know as super-slowly rotating Ap (ssrAp) stars. Photometrically variable Ap stars are commonly termed alpha2 Canum Venaticorum (ACV) variables. Our study aims at en

William F. Bradley

As large language models (LLMs) become increasingly powerful, traditional evaluation metrics tend to saturate, making it challenging to distinguish between models. We propose a general method to transform existing LLM evaluations into a series of progressively more difficult tasks. These enhanced evaluations emphasize reasoning capabilities and can reveal re

Yahel Jacobs, Reut Dayan, Uri Shaham

Graphs play a central role in modeling complex relationships in data, yet most graph learning methods falter when faced with cold-start nodes--new nodes lacking initial connections--due to their reliance on adjacency information. To tackle this, we propose SPARC, a groundbreaking framework that introduces a novel approach to graph learning by utilizing gener

Mika Hämäläinen

We showcase that ChatGPT can be used to disambiguate lemmas in two endangered languages ChatGPT is not proficient in, namely Erzya and Skolt Sami. We augment our prompt by providing dictionary translations of the candidate lemmas to a majority language - Finnish in our case. This dictionary augmented generation approach results in 50\% accuracy for Skolt Sam