December 2024 arXiv papers — page 80

S. Spezzano, E. Redaelli, P. Caselli, O. Sipilä

Pre-stellar cores are the first steps in the process of star and planet formation. However, the dynamical and chemical evolution of pre-stellar cores is still not well understood. We aim at estimating the central density of the pre-stellar core IRAS16293E and at carrying out an inventory of molecular species towards the density peak of the core. We observed

Jie Liu, Sze-Man Ngai, Lei Ouyang

For fractals on Riemannian manifolds, the theory of iterated function systems often does not apply well directly, as fractal sets are often defined by relations that are multivalued or non-contractive. To overcome this difficulty, we introduce the notion of iterated relation systems. We study the attractor of an iterated relation system and formulate a condi

Xinliang Lyu, Naoki Kawashima

We make Kadanoff's block idea into a reliable three-dimensional (3D) real space renormalization group (RG) method. Kadanoff's idea, expressed in spin representation, offers a qualitative intuition for clarifying scaling behavior in criticality, but has difficulty as a quantitative tool due to uncontrolled approximations. A tensor-network reformulation equips

Junki Mori, Kosuke Kihara, Taiki Miyagawa, Akinori F. Ebihara

Federated learning (FL) commonly assumes that the server or some clients have labeled data, which is often impractical due to annotation costs and privacy concerns. Addressing this problem, we focus on a source-free domain adaptation task, where (1) the server holds a pre-trained model on labeled source domain data, (2) clients possess only unlabeled data fr

R. Neuhäuser, D. L. Neuhäuser, M. Mugrauer, D. Luge

The pulsating variable star Mira (omikron Ceti) was observed by David Fabricius (Frisia) in 1596 and 1609. We review suggested previous detections (e.g. China, Hipparchos). We analyze all Mira records from Fabricius in their historical context. Fabricius measured the separation of Mira to other stars to \pm 1.6-1.7'. From his texts, we derive a brightness (s

Federico Ronchetti, Valentina Akishina, Edvard Andreassen, Nora Bluhme

Due to the increase of data volumes expected for the LHC Run 3 and Run 4, the ALICE Collaboration designed and deployed a new, energy efficient, computing model to run Online and Offline O$^2$ data processing within a single software framework. The ALICE O$^2$ Event Processing Nodes (EPN) project performs online data reconstruction using GPUs (Graphic Proces

Hai-Xiao Wang, Zhichao Wang

We delve into the challenge of semi-supervised node classification on the Contextual Stochastic Block Model (CSBM) dataset. Here, nodes from the two-cluster Stochastic Block Model (SBM) are coupled with feature vectors, which are derived from a Gaussian Mixture Model (GMM) that corresponds to their respective node labels. With only a subset of the CSBM node

Xuekang Zhu, Xiaochen Ma, Lei Su, Zhuohang Jiang

The mesoscopic level serves as a bridge between the macroscopic and microscopic worlds, addressing gaps overlooked by both. Image manipulation localization (IML), a crucial technique to pursue truth from fake images, has long relied on low-level (microscopic-level) traces. However, in practice, most tampering aims to deceive the audience by altering image se

Sait Akturk, Justin Valentine, Junaid Ahmad, Martin Jagersand

This paper introduces the TactiMesh Teleoperator Interface (TTI), a novel predictive visual and haptic system designed explicitly for human-in-the-loop robot control using a head-mounted display (HMD). By employing simultaneous localization and mapping (SLAM)in tandem with a space carving method (CARV), TTI creates a real time 3D surface mesh of remote envir

Tim Austin

Ergodic theory includes several notions of entropy for probability-preserving actions of countable groups. These include Kolmogorov--Sinai entropy based on F\o lner sequences for amenable groups, entropy defined using a random ordering of the group, and Bowen's sofic entropy for sofic groups. In this work we pursue these notions across an analogy between erg

Jesper Byggmästar, Damian Sobieraj, Jan S. Wróbel, Daniel K. Schreiber

Tungsten-based low-activation high-entropy alloys are possible candidates for next-generation fusion reactors due to their exceptional tolerance to irradiation, thermal loads, and stress. We develop an accurate and efficient machine-learned interatomic potential for the W-Ta-Cr-V system and use it in hybrid Monte Carlo molecular dynamics simulations of order

Xin Su, Zhuoran Zheng

With the rising imaging resolution of handheld devices, existing multi-exposure image fusion algorithms struggle to generate a high dynamic range image with ultra-high resolution in real-time. Apart from that, there is a trend to design a manageable and editable algorithm as the different needs of real application scenarios. To tackle these issues, we introd

Boqun Zhao, Chongjun Ouyang, Xingqi Zhang, Yuanwei Liu

A continuous-aperture array (CAPA)-based secure transmission framework is proposed to enhance physical layer security. Continuous current distributions, or beamformers, are designed to maximize the secrecy transmission rate under a power constraint and to minimize the required transmission power for achieving a specific target secrecy rate. On this basis, th

Yash Pathak, Laxman Prasad Goswami, Bansi Dhar Malhotra, Rishu Chaujar

In this work, we have implemented an accurate machine-learning approach for predicting various key analog and RF parameters of Negative Capacitance Field-Effect Transistors (NCFETs). Visual TCAD simulator and the Python high-level language were employed for the entire simulation process. However, the computational cost was found to be excessively high. The m

Keyao Peng

In this work, we initially compute the integral MW-motivic cohomology groups associated with Stiefel varieties. Then we proceed to establish the integral MW-motive decomposition of Stiefel varieties, which proves the conjecture in our previous work.

Zhuoran Jin, Hongbang Yuan, Tianyi Men, Pengfei Cao

Despite the significant progress made by existing retrieval augmented language models (RALMs) in providing trustworthy responses and grounding in reliable sources, they often overlook effective alignment with human preferences. In the alignment process, reward models (RMs) act as a crucial proxy for human values to guide optimization. However, it remains unc

Carys Harvey, Stephen Clark, Douglas Brown, Konstantinos Meichanetzidis

We present a variety of methods for training complex-valued word embeddings, based on the classical Skip-gram model, with a straightforward adaptation simply replacing the real-valued vectors with arbitrary vectors of complex numbers. In a more "physically-inspired" approach, the vectors are produced by parameterised quantum circuits (PQCs), which are unitar

Romain Dalidet, Laurent Labonté, Gregory Sauder, Sébastien Tanzilli

Optical phase measurements play a key role in the detection of macroscopic parameters such as position, velocity, and displacement. They also permit to qualify the microscopic properties of photonic waveguides such as polarization mode dispersion, refractive index difference, and chromatic dispersion. In the quest for ever-better measurement performance and

Yuyue Liu, Haihan Duan, Wei Cai

User-Generated Content (UGC) refers to any form of content, such as posts and images, created by users rather than by professionals. In recent years, UGC has become an essential part of the evolving video game industry, influencing both game culture and community dynamics. The ability for users to actively contribute to the games they engage with has shifted

Kaiwen Huang, Tao Zhou, Huazhu Fu, Yizhe Zhang

The limited availability of labeled data has driven advancements in semi-supervised learning for medical image segmentation. Modern large-scale models tailored for general segmentation, such as the Segment Anything Model (SAM), have revealed robust generalization capabilities. However, applying these models directly to medical image segmentation still expose

Domiziano Doria, Simone Martino, Matteo Becchi, Giovanni M. Pavan

In general, comprehension of any type of complex system depends on the resolution used to examine the phenomena occurring within it. However, identifying a priori, for example, the best time frequencies/scales to study a certain system over-time, or the spatial distances at which correlations, symmetries, and fluctuations are, most often non-trivial. Here we

David Senovilla-Sanz

Let $C$ be a cusp in $(\mathbb C^2,\mathbf 0)$ with Puiseux pair $(n,m)$. This paper is devoted to show how the semimodule of differential values of $C$ determines a subset of the roots of the Bernstein-Sato polynomial of $C$. We add more precise results when the multiplicity of the cusp is $n\leq 4$.

Fumiyoshi Kobayashi, Hidetaka Manabe, Gregory A. L. White, Terry Farrelly

Quantum error correction (QEC) is essential for fault-tolerant quantum computation. Often in QEC errors are assumed to be independent and identically distributed and can be discretised to a random Pauli error during the execution of a quantum circuit. In real devices, however, the noise profile is much more complex and contains non-trivial spatiotemporal cor

Navid Ansari, Hans-Peter Seidel, Vahid Babaei

This paper introduces a novel and scalable framework for uncertainty estimation and separation with applications in data driven modeling in science and engineering tasks where reliable uncertainty quantification is critical. Leveraging an ensemble of quantile regression (E-QR) models, our approach enhances aleatoric uncertainty estimation while preserving th

Giordano d'Aloisio, Luca Traini, Federica Sarro, Antinisca Di Marco

Language models have proven successful across a wide range of software engineering tasks, but their significant computational costs often hinder their practical adoption. To address this challenge, researchers have begun applying various compression strategies to improve the efficiency of language models for code. These strategies aim to optimize inference l

Jiaxiang Liu, Yuan Wang, Jiawei Du, Joey Tianyi Zhou

Artificial intelligence has advanced in Medical Visual Question Answering (Med-VQA), but prevalent research tends to focus on the accuracy of the answers, often overlooking the reasoning paths and interpretability, which are crucial in clinical settings. Besides, current Med-VQA algorithms, typically reliant on singular models, lack the robustness needed for

Jiaqi Yang, Chu'ai Zhang, Zhengbao Wang, Xinyue Cao

3D point cloud registration is a fundamental problem in computer vision, computer graphics, robotics, remote sensing, and etc. Over the last thirty years, we have witnessed the amazing advancement in this area with numerous kinds of solutions. Although a handful of relevant surveys have been conducted, their coverage is still limited. In this work, we presen

Junuk Cha, Mengwei Ren, Krishna Kumar Singh, He Zhang

We present a lighting-aware image editing pipeline that, given a portrait image and a text prompt, performs single image relighting. Our model modifies the lighting and color of both the foreground and background to align with the provided text description. The unbounded nature in creativeness of a text allows us to describe the lighting of a scene with any

Kun Yan, Zied Bouraoui, Fangyun Wei, Chang Xu

The aim of multi-label few-shot image classification (ML-FSIC) is to assign semantic labels to images, in settings where only a small number of training examples are available for each label. A key feature of the multi-label setting is that an image often has several labels, which typically refer to objects appearing in different regions of the image. When e

Dong Xie, Chunling Xu

Squeezed light can exponentially increase the signal-to-noise ratio (SNR) of dispersive qubit readout, especially using a combination of injected external squeezing (IES) and intracavity squeezing (ICS). We further investigate whether IES and ICS can also exponentially improve the measurement precision of temperature. In the case of fully thermalized qubits

Tiago Rodrigues de Almeida, Tim Schreiter, Andrey Rudenko, Luigi Palmieiri

Accurate human activity and trajectory prediction are crucial for ensuring safe and reliable human-robot interactions in dynamic environments, such as industrial settings, with mobile robots. Datasets with fine-grained action labels for moving people in industrial environments with mobile robots are scarce, as most existing datasets focus on social navigatio

Pavel O. Kiparisov, Viktor V. Lagutov

This study presents a framework for assessing urban critical infrastructure resilience during extreme events, such as hurricanes. The approach combines GIS and network analysis with open remote sensing data of the aftermath, vector data on infrastructure, and socio-demographic attributes of populations in affected areas. Using Panama City as an example case

Juan Casado, Yasser Hendy, Nikola Faltova, Dana Kovaleva

The number of known star clusters, associations, and moving groups in the vicinity of the Sun has increased significantly due to the use of Gaia data. We investigated the existence and properties of open superclusters (OSC) within 500 pc of the Sun, with a particular focus on a newly identified OSC, designated HC8. Using advanced Gaia-derived astrometric dat

Yuga Yano, Akinobu Mizutani, Yukiya Fukuda, Daiju Kanaoka

To facilitate human--robot interaction (HRI) tasks in real-world scenarios, service robots must adapt to dynamic environments and understand the required tasks while effectively communicating with humans. To accomplish HRI in practice, we propose a novel indoor dynamic map, task understanding system, and response generation system. The indoor dynamic map opt

Gianni Vercelli, Saverio Iacono, Luca Martini, Michele Zardetto

This study explores the application of Virtual Reality (VR) to industrial safety training by adapting immersive design principles from cultural heritage education. The SAMPAI simulator, developed for the IPLOM refinery in Busalla (Italy), offers a controlled environment that enhances spatial awareness and procedural retention in high-risk scenarios. The proj

Muhammad Sohail Ibrahim, Muhammad Usman, Jeong-A Lee

Convolutional Neural Networks (CNNs) are crucial in various applications, but their deployment on resource-constrained edge devices poses challenges. This study presents the Sum-of-Products (SOP) units for convolution, which utilize low-latency left-to-right bit-serial arithmetic to minimize response time and enhance overall performance. The study proposes a

Samaa Salah, Mohamed Mokhtar Hefny, Nabil M. El-Siragy, Abdelhamid Elshaer

Cyanobacteria are prokaryotic microorganisms that possess the capability to convert light energy into chemical energy through the process of photosynthesis. These organisms exhibit numerous potential applications, including the production of bioethanol, the synthesis of food colorants, the development of dietary supplements, and the provision of raw material

Andrew G. T. Pyo, Yuta Nagano, Martina Milighetti, James Henderson

The biophysical interactions between the T cell receptor (TCR) and its ligands determine the specificity of the cellular immune response. However, the immense diversity of receptors and ligands has made it challenging to discover generalizable rules across the distinct binding affinity landscapes created by different ligands. Here, we present an optimization

Petr Laštovička, Jan Legerský

One of the questions in Rigidity Theory is whether a realization of the vertices of a graph in the plane is flexible, namely, if it allows a continuous deformation preserving the edge lengths. A flexible realization of a connected graph in the plane exists if and only if the graph has a NAC-coloring, which is a surjective edge coloring by two colors such tha

Jincheol Jung, Hongju Jeong, Eui-Nam Huh

This study analyzes the performance of domain-specific Large Language Models (LLMs) for the medical field by integrating Retrieval-Augmented Generation (RAG) systems within a federated learning framework. Leveraging the inherent advantages of federated learning, such as preserving data privacy and enabling distributed computation, this research explores the

Jáchym Herynek, Stefan Edelkamp

In robotics, coordinating a group of robots is an essential task. This work presents the communication-constrained multi-agent multi-goal path planning problem and proposes a graph-search based algorithm to address this task. Given a fleet of robots, an environment represented by a weighted graph, and a sequence of goals, the aim is to visit all the goals wi

Jonas Allerbeck, Laric Bobzien, Nils Krane, S. Eve Ammerman

Controlling electron dynamics at optical clock rates is a fundamental challenge in lightwave-driven nanoelectronics. Here, we demonstrate ultrafast charge-state manipulation of individual selenium vacancies in monolayer and bilayer tungsten diselenide (WSe$_2$) using picosecond terahertz (THz) source pulses, focused onto the picocavity of a scanning tunnelin

Jiaqi Xiong, Nan Yin, Shiyang Liang, Haoyang Li

Inferencing Gene Regulatory Networks (GRNs) from gene expression data is a pivotal challenge in systems biology, and several innovative computational methods have been introduced. However, most of these studies have not considered the skewed degree distribution of genes. Specifically, some genes may regulate multiple target genes while some genes may be regu

Simran Khanuja, Vivek Iyer, Claire He, Graham Neubig

Beyond conventional paradigms of translating speech and text, recently, there has been interest in automated transcreation of images to facilitate localization of visual content across different cultures. Attempts to define this as a formal Machine Learning (ML) problem have been impeded by the lack of automatic evaluation mechanisms, with previous work rely

Lifeng Qiao, Peng Ye, Yuchen Ren, Weiqiang Bai

Foundation models have made significant strides in understanding the genomic language of DNA sequences. However, previous models typically adopt the tokenization methods designed for natural language, which are unsuitable for DNA sequences due to their unique characteristics. In addition, the optimal approach to tokenize DNA remains largely under-explored, a

Xingyu Lyu, Qianqian Xu, Zhiyong Yang, Shaojie Lyu

Real-world datasets often exhibit a long-tailed distribution, where vast majority of classes known as tail classes have only few samples. Traditional methods tend to overfit on these tail classes. Recently, a new approach called Imbalanced SAM (ImbSAM) is proposed to leverage the generalization benefits of Sharpness-Aware Minimization (SAM) for long-tailed d

Chenqi Li, Boyan Gao, Gabriel Jones, Timothy Denison

Deep learning models have demonstrated exceptional performance in a variety of real-world applications. These successes are often attributed to strong base models that can generalize to novel tasks with limited supporting data while keeping prior knowledge intact. However, these impressive results are based on the availability of a large amount of high-quali

Guillermo B. Morales, Miguel A. Muñoz, Yuhai Tu

The brain encodes external stimuli through patterns of neural activity, forming internal representations of the world. Recent experiments show that neural representations for a given stimulus change over time. However, the mechanistic origin for the observed "representational drift" (RD) remains unclear. Here, we propose a biologically-realistic computationa

Jesse Heyninck

Conditional independence is a crucial concept supporting adequate modelling and efficient reasoning in probabilistics. In knowledge representation, the idea of conditional independence has also been introduced for specific formalisms, such as propositional logic and belief revision. In this paper, the notion of conditional independence is studied in the alge

Corentin Bertrand, Pauline Besserve, Michel Ferrero, Thomas Ayral

Noise is often regarded as a limitation of quantum computers. In this work, we show that in the dynamical mean field theory (DMFT) approach to strongly-correlated systems, it can actually be harnessed to our advantage. Indeed, DMFT maps a lattice model onto an impurity model, namely a finite system coupled to a dissipative bath. While standard approaches req

Alexander Pütz, Julia Sauter

Reineke and independent other authors proved that every projective variety arises as a quiver Grassmannian. We prove the claim in the title by restricting Reineke's isomorphism to Grassmannians for a fully exact subcategory.

Muyao Niu, Zhuoxiao Li, Yifan Zhan, Huy H. Nguyen

Many surveillance cameras switch between daytime and nighttime modes based on illuminance levels. During the day, the camera records ordinary RGB images through an enabled IR-cut filter. At night, the filter is disabled to capture near-infrared (NIR) light emitted from NIR LEDs typically mounted around the lens. While RGB-based AI algorithm vulnerabilities h

Taein Son, Soo Won Seo, Jisong Kim, Seok Hwan Lee

Video Action Detection (VAD) entails localizing and categorizing action instances within videos, which inherently consist of diverse information sources such as audio, visual cues, and surrounding scene contexts. Leveraging this multi-modal information effectively for VAD poses a significant challenge, as the model must identify action-relevant cues with pre

CONUS Collaboration, E. Sanchez Garcia, N. Ackermann, S. Armbruster

CONUS+ is an experiment aiming at detecting coherent elastic neutrino-nucleus scattering (CE$\nu$NS) of reactor antineutrinos on germanium nuclei in the fully coherent regime, continuing the CONUS physics program conducted at the Brokdorf nuclear power plant (KBR), Germany. The CONUS+ experiment is installed in the Leibstadt nuclear power plant (KKL), Switze

Rodrigo Nicolau Almeida, Guram Bezhanishvili

We investigate the set-theoretic strength of several maximality principles that play an important role in the study of modal and intuitionistic logics. We focus on the well-known Fine and Esakia maximality principles, present two formulations of each, and show that the stronger formulations are equivalent to the Axiom of Choice (AC), while the weaker ones to

Minkyoung Kim, Yunha Kim, Hyeram Seo, Heejung Choi

Large language models (LLMs) have exhibited outstanding performance in natural language processing tasks. However, these models remain susceptible to adversarial attacks in which slight input perturbations can lead to harmful or misleading outputs. A gradient-based defensive suffix generation algorithm is designed to bolster the robustness of LLMs. By append

Gert Aarts, Lingxiao Wang, Kai Zhou

Diffusion models are currently the leading generative AI approach used for image generation in e.g. DALL-E and Stable Diffusion. In this talk we relate diffusion models to stochastic quantisation in field theory and employ it to generate configurations for scalar fields on a two-dimensional lattice. We end with some speculations on possible applications.

Fatemeh Froughirad, Reza Bakhoda Eshtivani, Hamed Khajavi, Amir Rastgoo

Deep learning models, specifically convolutional neural networks, have transformed the landscape of image classification by autonomously extracting features directly from raw pixel data. This article introduces an innovative image classification model that employs three consecutive inception blocks within a convolutional neural networks framework, providing

Kunat Pipatanakul, Potsawee Manakul, Natapong Nitarach, Warit Sirichotedumrong

This paper introduces Typhoon 2, a series of text and multimodal large language models optimized for the Thai language. The series includes models for text, vision, and audio. Typhoon2-Text builds on state-of-the-art open models, such as Llama 3 and Qwen2, and we perform continual pre-training on a mixture of English and Thai data. We employ post-training te

Takuya Inoue, Teppei Okumura, Shohei Saga, Atsushi Taruya

We investigate cosmological constraints on local position invariance (LPI), a key aspect of the Einstein equivalence principle (EEP), through asymmetric galaxy clustering. The LPI asserts that the outcomes of the non-gravitational experiments are identical regardless of location in spacetime and has been tested through measurements of the gravitational redsh

Chockalingam Senthilnathan

A series of recent papers have modified the classical variational phase-field fracture models to successfully predict both the nucleation and propagation of cracks in brittle fracture under general loading conditions. This is done through the introduction of a consistent crack nucleation driving force in the phase field governing equations, which results in

Joseph W. P. Wilkinson, Katrin Bolsmann, Thiago L. M. Guedes, Markus Müller

Ultracold trapped atomic ions excited into highly energetic Rydberg states constitute a promising platform for scalable quantum information processing. Elementary building blocks for such tasks are high-fidelity and sufficiently fast entangling two-qubit gates, which can be achieved via strong dipole-dipole interactions between microwave-dressed Rydberg ions

Paloma Piot, Javier Parapar

Automatic detection of hate and abusive language is essential to combat its online spread. Moreover, recognising and explaining hate speech serves to educate people about its negative effects. However, most current detection models operate as black boxes, lacking interpretability and explainability. In this context, Large Language Models (LLMs) have proven e

Rafael Ayllón-Gavilán, Francisco José Martínez-Estudillo, David Guijo-Rubio, César Hervás-Martínez

Ordinal Classification (OC) addresses those classification tasks where the labels exhibit a natural order. Unlike nominal classification, which treats all classes as mutually exclusive and unordered, OC takes the ordinal relationship into account, producing more accurate and relevant results. This is particularly critical in applications where the magnitude

Jonathan Kriewald, Ana M. Teixeira

The Inverse Seesaw mechanism remains one of the most attractive explanations for the lightness of neutrino masses, allowing for natural low-scale realisations. We consider the prospects of a simple extension via 3 generations of sterile fermions - the so called ISS(3,3) - in what concerns numerous lepton flavour observables. In order to facilitate a connecti

Jens Kohl, Luisa Gloger, Rui Costa, Otto Kruse

As LLM-based applications reach millions of customers, ensuring their scalability and continuous quality improvement is critical for success. However, the current workflows for developing, maintaining, and operating (DevOps) these applications are predominantly manual, slow, and based on trial-and-error. With this paper we introduce the Generative AI Toolkit

Dominik Werner Wolf, Alexander Braun, Markus Ulrich

'A trustworthy representation of uncertainty is desirable and should be considered as a key feature of any machine learning method' (Huellermeier and Waegeman, 2021). This conclusion of Huellermeier et al. underpins the importance of calibrated uncertainties. Since AI-based algorithms are heavily impacted by dataset shifts, the automotive industry needs to s

Chen Wang, Roderick C. I. MacKenzie, Uli Würfel, Dieter Neher

Organic photovoltaics are a promising solar cell technology well-suited to mass production using roll-to-roll processes. The efficiency of lab-scale solar cells has exceeded 20% and considerable attention is currently being given to understanding and minimising the remaining loss mechanisms preventing higher efficiencies. While recent efficiency improvements

Lina Gong, Chen Wang, Yujun Huang, Di Cui

Seamless user interface (i.e., UI) translation has emerged as a pivotal technique for modern mobile developers, addressing the challenge of developing separate UI applications for Android and HarmonyOS platforms due to fundamental differences in layout structures and development paradigms. In this paper, we present UITrans, the first automated UI translation

Alexis Commereuc, Emmanuelle Rio, François Boulogne

Acquiring a comprehensive understanding of the interplay between foam friction and surface roughness is essential for achieving precise control over their flow dynamics. In particular, a major challenge is to reduce friction, which can be achieved with rough surfaces in the situation where a liquid infuses the asperities. In this study, we propose to explore

Alejandrina Aranda, Oscar Dieste, José Ignacio Panach, Natalia Juristo

Background: Various factors determine analyst effectiveness during elicitation. While the literature suggests that elicitation technique and time are influential factors, other attributes could also play a role. Aim: Determine aspects that may have an influence on analysts' ability to identify certain elements of the problem domain. Methodology: We conducted

Xiwen Geng, Suyun Zhao, Yixin Yu, Borui Peng

Clustering traditionally aims to reveal a natural grouping structure within unlabeled data. However, this structure may not always align with users' preferences. In this paper, we propose a personalized clustering method that explicitly performs targeted representation learning by interacting with users via modicum task information (e.g., $\textit{must-link}

Katharina Henn, Michaela Lestakova, Kevin Logan, Jakob Hartig

Slums, informal settlements, and deprived areas are urban regions characterized by poverty. According to the United Nations, over one billion people reside in these areas, and this number is projected to increase. Additionally, these settlements are integral components of urban systems. We conducted a bibliometrical analysis and scoping study using the Web o

C. Maria Keet, Zubeida Casmod Khan

Competency Questions (CQs) are widely used in ontology development by guiding, among others, the scoping and validation stages. However, very limited guidance exists for formulating CQs and assessing whether they are good CQs, leading to issues such as ambiguity and unusable formulations. To solve this, one requires insight into the nature of CQs for ontolog

Anand Manaparambil, Cătălin Paşcu Moca, Gergely Zaránd, Ireneusz Weymann

A magnetic impurity with a larger $S=1$ spin remains partially screened by the Kondo effect when embedded in a metal. However, when placed within an $s$-wave superconductor, the interplay between the superconducting energy gap $\Delta$ and the Kondo temperature $T_K$ induces a quantum phase transition from an underscreened doublet Kondo to an unscreened trip

Oliver Sefrin, Sabine Wölk

The "hybrid agent for quantum-accessible reinforcement learning", as defined in (Hamann and W\"olk, 2022), provides a proven quasi-quadratic speedup and is experimentally tested. However, the standard version can only be applied to episodic learning tasks with fixed episode length. In many real-world applications, the information about the necessary number o

Nicolò Bellini, Claudio Bonanno, Francesco D'Angelo, Massimo D'Elia

The sphaleron rate is a key phenomenological quantity both for the axion thermal production in the Early Universe and the Chiral Magnetic Effect occurring in the Quark-Gluon Plasma in presence of a background magnetic field. In this talk we present an extension of our recent determination of the sphaleron rate, in the SU(3) gauge theory, based on the determi

Chuang Yang, Bingxuan Zhao, Qing Zhou, Qi Wang

The rapid advancement of deep generative models (DGMs) has significantly advanced research in computer vision, providing a cost-effective alternative to acquiring vast quantities of expensive imagery. However, existing methods predominantly focus on synthesizing remote sensing (RS) images aligned with real images in a global layout view, which limits their a

Nikolaos Kolomvakis, Emil Björnson

Holographic multiple-input multiple-output (MIMO) systems represent a spatially constrained MIMO architecture with a massive number of antennas with small antenna spacing as a close approximation of a spatially continuous electromagnetic aperture. Accurate channel modeling is essential for realizing the full potential of this technology. In this paper, we in

Jie-Jing Shao, Bo-Wen Zhang, Xiao-Wen Yang, Baizhi Chen

Travel planning stands out among real-world applications of \emph{Language Agents} because it couples significant practical demand with a rigorous constraint-satisfaction challenge. However, existing benchmarks primarily operate on a slot-filling paradigm, restricting agents to synthetic queries with pre-defined constraint menus, which fails to capture the o

Andreas Mueller

Parallel manipulators, also called parallel kinematics machines (PKM), enable robotic solutions for highly dynamic handling and machining applications. The safe and accurate design and control necessitates high-fidelity dynamics models. Such modeling approaches have already been presented for PKM with simple limbs (i.e. each limb is a serial kinematic chain)

Hongliang Wo, Bingying Pan, Die Hu, Yu Feng

Elucidating spin correlations in the parent compounds of high-temperature superconductors is crucial for understanding superconductivity. We used neutron scattering to study spin correlations in Li$_{1-x}$Fe$_x$ODFeSe, an insulating material with reduced electron carriers compared to its superconducting counterpart ($T_c$ = 41 K), serving as the undoped pare

Neetha Jambigi, Joshua Hammesfahr, Moritz Mueller, Thomas Bach

Capturing the workload of a database and replaying this workload for a new version of the database can be an effective approach for regression testing. However, false positive errors caused by many factors such as data privacy limitations, time dependency or non-determinism in multi-threaded environment can negatively impact the effectiveness. Therefore, we

Alex Tamkin, Miles McCain, Kunal Handa, Esin Durmus

How are AI assistants being used in the real world? While model providers in theory have a window into this impact via their users' data, both privacy concerns and practical challenges have made analyzing this data difficult. To address these issues, we present Clio (Claude insights and observations), a privacy-preserving platform that uses AI assistants the

Thomas Vonk, Ulf-G. Meißner

We analyze the large-$N_c$ structure of the baryon-baryon potential derived in the framework of SU(3) chiral perturbation theory up to next-to-leading order including contact interactions as well as one-meson and two-meson exchange diagrams. Moreover, we assess the impact of SU(3) symmetry breaking from a large-$N_c$ perspective and show that the leading ord

Bin Li, Xiao Zhu, Junyi Wang

Data compression technology is able to reduce data size, which can be applied to lower the cost of task offloading in mobile edge computing (MEC). This paper addresses the practical challenges for robust trajectory and scheduling optimization based on data compression in the unmanned aerial vehicle (UAV)-assisted MEC, aiming to minimize the sum energy cost o

Pei Chen, Fudong Wang, Yixuan Tong, Jingdong Chen

Recently, the surge of efficient and automated 3D AI-generated content (AIGC) methods has increasingly illuminated the path of transforming human imagination into complex 3D structures. However, the automated generation of 3D content is still significantly lags in industrial application. This gap exists because 3D modeling demands high-quality assets with sh

Muhammad Mansur Zubairu, Abdullahi Umar, Fatma Salim Al-Kharousi

Let $[n]$ be a finite chain $\{1, 2, \ldots, n\}$, and let $\mathcal{LS}_{n}$ be the semigroup consisting of all isotone and order-decreasing partial transformations on $[n]$. Moreover, let $\mathcal{SS}_{n} = \{\alpha \in \mathcal{LS}_{n} : \, 1 \in \text{Dom } \alpha\}$ be the subsemigroup of $\mathcal{LS}_{n}$, consisting of all transformations in $\mathc

Vladislav Popkov, Carlo Presilla, Mario Salerno

We analytically investigate the Liouvillian exceptional point manifolds (LEPMs) of a two-qubit open system, where one qubit is coupled to a dissipative polarization bath. Exploiting a Z_2 symmetry, we block-diagonalize the Liouvillian and show that one symmetry block yields two planar LEPMs while the other one exhibits a more intricate, multi-sheet topology.

Manuel Del Piano, Stefan Hohenegger, Francesco Sannino

In previous work we have developed a model-independent, effective description of quantum deformed, spherically symmetric and static black holes in four dimensions. The deformations of the metric are captured by two functions of the physical distance to the horizon, which are provided in the form of self-consistent Taylor series expansions. While this approac

Shankar Bhamidi, Amarjit Budhiraja, Akshay Sakanaveeti

We study inhomogeneous random graphs with a finite type space. For a natural generalization of the model as a dynamic network-valued process, the paper establishes the following results: (a) Functional central limit theorems for the infinite vector of microscopic type-densities and characterizations of the limits as infinite-dimensional conditionally Gaussia

Krishnendu Gongopadhyay, Shrinit Singh

We provide a general structural criterion implying that a group has infinite $m$-almost palindromic width. In particular, we prove that both HNN extensions and free products exhibit infinite $m$-almost palindromic width, with the unique exception of the infinite dihedral group among free products. This framework extends and strengthens the results of \cite{M

Xiangtong Wang, Hang Zhao

Let $G$ be a countable infinite amenable group, $K$ a finite-dimensional compact metrizable space, and $(K^G,\sigma)$ the full $G$-shift on $K^G$. For any $r\in [0,{\rm mdim}(K^G,\sigma))$, we construct a minimal subshift $(X,\sigma)$ of $(K^G,\sigma)$ with mdim$(X,\sigma)=r$. Furthermore, we construct a subshift of $([0,1]^G,\sigma)$ such that its mean dime

Xiaobao Wu, Liangming Pan, Yuxi Xie, Ruiwen Zhou

Data contamination hinders fair LLM evaluation by introducing test data into newer models' training sets. Existing studies solve this challenge by updating benchmarks with newly collected data. However, they fail to guarantee contamination-free evaluation as the newly collected data may contain pre-existing knowledge, and their benchmark updates rely on inte

Jintao Li, Shuaijie Qian

We consider Merton's problem with proportional transaction costs. It is well known that the optimal investment strategy is characterized by two trading boundaries, the buy boundary and the sell boundary, between which lies the no-trading region. We investigate how these two trading boundaries vary with the transaction cost rates. We show that the cost-adjust

Debashis Naskar, Subhashis Das, Sara Rodriguez Gonzalez

The extensive dissemination of false information in social networks affects netizens social lives, morals, and behaviours. When a neighbour expresses strong emotions (e.g., fear, anger, excitement) based on a false statement, these emotions can be transmitted to others, especially through interactions on social media. Therefore, exploring the mechanism that

Charmin Asirimath, Chathuranga Weeraddana, Sumudu Samarakoon, Jayampathy Ratnayake

This work explores the advantages of using persistence diagrams (PDs), topological signatures of raw point cloud data, in a point-to-point communication setting. PD is a structural semantics in the sense that it carries information about the shape and structure of the data. Instead of transmitting raw data, the transmitter communicates its PD semantics, and

ChengAo Shen, Zhengzhang Chen, Dongsheng Luo, Dongkuan Xu

Causal discovery is an imperative foundation for decision-making across domains, such as smart health, AI for drug discovery and AIOps. Traditional statistical causal discovery methods, while well-established, predominantly rely on observational data and often overlook the semantic cues inherent in cause-and-effect relationships. The advent of Large Language

Aneta Zugecova, Dominik Macko, Ivan Srba, Robert Moro

The capabilities of recent large language models (LLMs) to generate high-quality content indistinguishable by humans from human-written texts raises many concerns regarding their misuse. Previous research has shown that LLMs can be effectively misused for generating disinformation news articles following predefined narratives. Their capabilities to generate

Ludwig Winkler

The analysis of dynamical systems is a fundamental tool in the natural sciences and engineering. It is used to understand the evolution of systems as large as entire galaxies and as small as individual molecules. With predefined conditions on the evolution of dy-namical systems, the underlying differential equations have to fulfill specific constraints in ti