February 2024 arXiv papers — page 45

Hanqi Yan, Lingjing Kong, Lin Gui, Yuejie Chi

Counterfactual generation lies at the core of various machine learning tasks, including image translation and controllable text generation. This generation process usually requires the identification of the disentangled latent representations, such as content and style, that underlie the observed data. However, it becomes more challenging when faced with a s

Philipp Isserstedt, Daniel Jaroszewski, Wolfgang Mergenthaler, Felix Paul

We explore the applicability of quantum annealing to the approximation task of curve fitting. To this end, we consider a function that shall approximate a given set of data points and is written as a finite linear combination of standardized functions, e.g., orthogonal polynomials. Consequently, the decision variables subject to optimization are the coeffici

Anastasiia Fadeeva, Philippe Schlattner, Andrii Maksai, Mark Collier

The adoption of tablets with touchscreens and styluses is increasing, and a key feature is converting handwriting to text, enabling search, indexing, and AI assistance. Meanwhile, vision-language models (VLMs) are now the go-to solution for image understanding, thanks to both their state-of-the-art performance across a variety of tasks and the simplicity of

Weibin Sun, Lulu Fan, Yunkun Han, Kirsten K. Knudsen

We present a UV to millimeter spectral energy distribution (SED) analysis of 16 hyperluminous, dust-obscured quasars at z $\sim$ 3, selected by the \textit{Wide-field Infrared Survey Explorer}. We aim to investigate the physical properties of these quasars, with a focus on their molecular gas content. We decompose the SEDs into three components: stellar, col

Ying Han, Hangman Chen, Yongwen Sun, Jian Liu

Recent research in multi-principal element alloys (MPEAs) has increasingly focused on the exploration and exploitation of short-range order (SRO) to enhance material performance. However, the understanding of SRO formation and the precise tuning of it within MPEAs remains poorly understood, limiting the comprehension of its impact on material properties and

Chang Geng, Peng-Hui Chen, Fei Niu, Zu-Xing Yang

Within the framework of the dinuclear system model, this study delves into the impact of various nuclear mass models on evaluating the fusion probability of superheavy nuclei. Nuclear mass models, as crucial inputs to the DNS model, exhibit slight variations in binding energy, quadrupole deformation, and extrapolation ability; these subtle differences can si

Pierre Berger

We construct analytic surface symplectomorphisms with unstable elliptic fixed points; this solves a problem of Birkhoff (1927). More precisely, we construct analytic symplectomorphisms of the sphere and of the disk which are transitive, with respectively only 2 and 1 periodic points. This also solves problems of proposed by Herman (1998), Fayad-Katok (2004)

Somnath Banerjee, Sayan Layek, Rima Hazra, Animesh Mukherjee

In this study, we tackle a growing concern around the safety and ethical use of large language models (LLMs). Despite their potential, these models can be tricked into producing harmful or unethical content through various sophisticated methods, including 'jailbreaking' techniques and targeted manipulation. Our work zeroes in on a specific issue: to what ext

Yuzhe Zhang, Yipeng Zhang, Yidong Gan, Lina Yao

Causal graph recovery is traditionally done using statistical estimation-based methods or based on individual's knowledge about variables of interests. They often suffer from data collection biases and limitations of individuals' knowledge. The advance of large language models (LLMs) provides opportunities to address these problems. We propose a novel method

Huijie Tang, Federico Berto, Zihan Ma, Chuanbo Hua

Large-scale multi-agent pathfinding (MAPF) presents significant challenges in several areas. As systems grow in complexity with a multitude of autonomous agents operating simultaneously, efficient and collision-free coordination becomes paramount. Traditional algorithms often fall short in scalability, especially in intricate scenarios. Reinforcement Learnin

Jai Pal

This study delves into the analysis of financial markets through the lens of Wyckoff Phases, a framework devised by Richard D. Wyckoff in the early 20th century. Focusing on the accumulation pattern within the Wyckoff framework, the research explores the phases of trading range and secondary test, elucidating their significance in understanding market dynami

Ailin Deng, Zhirui Chen, Bryan Hooi

Large Vision-Language Models (LVLMs) are susceptible to object hallucinations, an issue in which their generated text contains non-existent objects, greatly limiting their reliability and practicality. Current approaches often rely on the model's token likelihoods or other internal information, instruction tuning on additional datasets, or incorporating comp

James P. Darby, Angela F. Harper, Joseph R. Nelson, Andrew J. Morris

In this work we used $\textit{ab-initio}$ random structure searching (AIRSS) to carry out a systematic search for crystalline Na-Ge materials at both 0 and 10 GPa. The high-throughput structural relaxations were accelerated using a machine-learned interatomic potential (MLIP) fit to density-functional theory (DFT) reference data, allowing $\sim$1.5 million s

Ievgen I. Arkhipov, Fabrizio Minganti, Adam Miranowicz, Şahin K. Özdemir

Non-Hermitian systems have attracted much interest in recent decades, driven partly by the existence of exotic spectral singularities, known as exceptional points (EPs), where the dimensionality of the system evolution operator is reduced. Among various intriguing applications, the discovery of EPs has suggested the potential for implementing a symmetric mod

Hui Lin, Zhiheng Ma, Rongrong Ji, Yaowei Wang

This paper focuses on semi-supervised crowd counting, where only a small portion of the training data are labeled. We formulate the pixel-wise density value to regress as a probability distribution, instead of a single deterministic value. On this basis, we propose a semi-supervised crowd-counting model. Firstly, we design a pixel-wise distribution matching

Francesco Verdelli, Yu-Chen Wei, Kripa Joseph, Mohamed S. Abdelkhalik

Vibrational strong coupling can modify chemical reaction pathways in unconventional ways. Thus far, Fabry-Perot cavities formed by pairs of facing mirrors have been mostly utilized to achieve vibrational strong coupling. In this study, we demonstrate the application of plasmonic microparticle arrays defining non-local metasurfaces that can sustain surface la

Lina Yang, Mahmoud I. Hussein

Atomic motion in nanopillars standing on the surface of a silicon membrane generates vibrons, which are wavenumber-independent phonons that act as local resonances. These vibrons couple with heat-carrying phonons traveling along the base membrane causing a reduction in the in-plane lattice thermal conductivity. In this work, we examine isolated silicon and g

Ismael Agchar, Ilja Baumann, Franziska Braun, Paula Andrea Perez-Toro

In recent years, machine learning, and in particular generative adversarial neural networks (GANs) and attention-based neural networks (transformers), have been successfully used to compose and generate music, both melodies and polyphonic pieces. Current research focuses foremost on style replication (eg. generating a Bach-style chorale) or style transfer (e

Stefanos Chaliasos, Jens Ernstberger, David Theodore, David Wong

Zero-knowledge proofs (ZKPs) have evolved from being a theoretical concept providing privacy and verifiability to having practical, real-world implementations, with SNARKs (Succinct Non-Interactive Argument of Knowledge) emerging as one of the most significant innovations. Prior work has mainly focused on designing more efficient SNARK systems and providing

Robin Denz, Nina Timmesfeld

Kaplan-Meier curves stratified by treatment allocation are the most popular way to depict causal effects in studies with right-censored time-to-event endpoints. If the treatment is randomly assigned and the sample size of the study is adequate, this method produces unbiased estimates of the population-averaged counterfactual survival curves. However, in obse

S. M. Iftiquar

Whispering gallery mode (WGM) of resonance occurs when a traveling wave faces grazing reflection and confinement at the inner surface of a spherical cavity. Such a resonance was observed with micrometer sized liquid droplet and with light of sub-micron wavelength. The resonance occurs when specific boundary conditions are fulfilled, following a relation betw

Tongyi Liang, Han-Xiong Li

Existing models encounter bottlenecks in balancing performance and computational efficiency when modeling long sequences. Although the state space model (SSM) has achieved remarkable success in handling long sequence tasks, it still faces the problem of large number of parameters. In order to further improve the efficiency of SSM, we propose a new state spac

Shunyu Liu, Jie Zhou, Qunxi Zhu, Qin Chen

Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text. However, a notable challenge in ABSA lies in precisely determining the aspects' boundaries (start and end indices), especially for long ones, due to users' colloquial expressions. We propose DiffusionABSA, a nov

Jonas Ney, Patrick Matalla, Vincent Lauinger, Laurent Schmalen

In this work, we present a high-throughput field programmable gate array (FPGA) demonstrator of an artificial neural network (ANN)-based equalizer. The equalization is performed and illustrated in real-time for a 30 GBd, two-level pulse amplitude modulation (PAM2) optical communication system.

Martin Žonda, Wolfgang Belzig, Edward Goldobin, Tomáš Novotný

We present a theoretical investigation of the stochastic dynamics of a damped particle in a tilted periodic potential with a double well per period. By applying the matrix continued fraction technique to the Fokker-Planck equation in conjunction with the full counting statistics and master equation approaches, we determine the rates of specific processes con

Junjie Wang, Yong Wang, Haoting Zhang, Ziyang Yang

Machine learning potential (MLP) has been a popular topic in recent years for its potential to replace expensive first-principles calculations in some large systems. Meanwhile, message passing networks have gained significant attention due to their remarkable accuracy, and a wave of message passing networks based on Cartesian coordinates has emerged. However

David Sommer, Robert Gruhlke, Max Kirstein, Martin Eigel

Sampling from probability densities is a common challenge in fields such as Uncertainty Quantification (UQ) and Generative Modelling (GM). In GM in particular, the use of reverse-time diffusion processes depending on the log-densities of Ornstein-Uhlenbeck forward processes are a popular sampling tool. In Berner et al. [2022] the authors point out that these

Tongyi Liang, Han-Xiong Li

Although deep learning-based methods have shown great success in spatiotemporal predictive learning, the framework of those models is designed mainly by intuition. How to make spatiotemporal forecasting with theoretical guarantees is still a challenging issue. In this work, we tackle this problem by applying domain knowledge from the dynamical system to the

Martin Benfeghoul, Umais Zahid, Qinghai Guo, Zafeirios Fountas

In an unfamiliar setting, a model-based reinforcement learning agent can be limited by the accuracy of its world model. In this work, we present a novel, training-free approach to improving the performance of such agents separately from planning and learning. We do so by applying iterative inference at decision-time, to fine-tune the inferred agent states ba

Fernando G. Jeronimo, Pei Wu

Quantum entanglement is a key enabling ingredient in diverse applications. However, the presence of unwanted adversarial entanglement also poses challenges in many applications. In this paper, we explore methods to "break" quantum entanglement. Specifically, we construct a dimension-independent k-partite disentangler (like) channel from bipartite unentangled

Dominik Joho, Jonas Schwinn, Kirill Safronov

Collision detection is one of the most time-consuming operations during motion planning. Thus, there is an increasing interest in exploring machine learning techniques to speed up collision detection and sampling-based motion planning. A recent line of research focuses on utilizing neural signed distance functions of either the robot geometry or the swept vo

Anthony Sudbery

The projection postulate is a description of the effect on a quantum system, assumed to be in a pure state, of a measurement of an observable with a discrete spectrum, in nonrelativistic quantum mechanics. It is often called "von Neumann's projection postulate" or "the L\"uders rule". This paper is an examination of the versions of this postulate due to Dira

Florian Starke

This is my dissertation about digraphs ordered by pp-constructability. We study in particular smooth digraphs, i.e., digraphs without sources or sinks, tournaments and semicomplete digraphs, orientations of paths and cycles, digraphs with at most four vertices, and orientations of trees.

Yinxuan Zhao, Mei Zhang

Let $(Z_n)$ be a supercritical branching process with immigration in a random environment. The small positive values and some lower deviation inequalities for $Z$ are investigated. Based on these results, the central limit theorem of $\log Z_n$ and the Edgeworth expansion are obtained. The study is taken under the assumption that each individual produces $0$

Holger Arndt

In the evolving landscape of digital education, chatbots have emerged as potential game-changers, promising personalized and adaptive learning experiences. This research undertook an in-depth exploration of ChatGPT's potential as an educational tool, focusing on user perceptions, experiences and learning outcomes. Through a mixed-methods approach, a diverse

Billel Guelmame, Stéphane Junca, Didier Clamond, Robert L. Pego

A nondispersive, conservative regularisation of the inviscid Burgers equation is proposed and studied. Inspired by a related regularisation of the shallow water system recently introduced by Clamond and Dutykh, the new regularisation provides a family of Galilean-invariant interpolants between the inviscid Burgers equation and the Hunter-Saxton equation. It

Anna Galanou, Khushboo Bindlish, Luca Preibsch, Yvonne-Anne Pignolet

Confidential computing alleviates the concerns of distrustful customers by removing the cloud provider from their trusted computing base and resolves their disincentive to migrate their workloads to the cloud. This is facilitated by new hardware extensions, like AMD's SEV Secure Nested Paging (SEV-SNP), which can run a whole virtual machine with confidential

Zijun Long, Xuri Ge, Richard Mccreadie, Joemon Jose

Text-to-image retrieval aims to find the relevant images based on a text query, which is important in various use-cases, such as digital libraries, e-commerce, and multimedia databases. Although Multimodal Large Language Models (MLLMs) demonstrate state-of-the-art performance, they exhibit limitations in handling large-scale, diverse, and ambiguous real-worl

Yiming Huang, Juan Zhang, Lian Tao, Zhengwei Li

The Chasing All Transients Constellation Hunters (CATCH) space mission is an intelligent constellation consisting of 126 micro-satellites in three types (A, B, and C), designed for X-ray observation with the objective of studying the dynamic universe. Currently, we are actively developing the first Pathfinder (CATCH-1) for the CATCH mission, specifically for

Billel Guelmame, Didier Clamond, Stéphane Junca

We prove in this note the local (in time) well-posedness of a broad class of $2 \times 2$ symmetrisable hyperbolic system involving additional non-local terms. The latest result implies the local well-posedness of the non dispersive regularisation of the Saint-Venant system with uneven bottom introduced by Clamond, Dutykh and Mitsotakis. We also prove that,

Guy Horowitz, Yonatan Sommer, Moran Koren, Nir Rosenfeld

When users stand to gain from certain predictions, they are prone to act strategically to obtain favorable predictive outcomes. Whereas most works on strategic classification consider user actions that manifest as feature modifications, we study a novel setting in which users decide -- in response to the learned classifier -- whether to at all participate (o

Matteo Risso, Francesco Daghero, Beatrice Alessandra Motetti, Daniele Jahier Pagliari

Miniaturized autonomous unmanned aerial vehicles (UAVs) are gaining popularity due to their small size, enabling new tasks such as indoor navigation or people monitoring. Nonetheless, their size and simple electronics pose severe challenges in implementing advanced onboard intelligence. This work proposes a new automatic optimization pipeline for visual pose

Zhe Wang, Siqi Fan, Xiaoliang Huo, Tongda Xu

In autonomous driving, cooperative perception makes use of multi-view cameras from both vehicles and infrastructure, providing a global vantage point with rich semantic context of road conditions beyond a single vehicle viewpoint. Currently, two major challenges persist in vehicle-infrastructure cooperative 3D (VIC3D) object detection: $1)$ inherent pose err

Sudeshna Roy, Hongyi Xiao, Vasileios Angelidakis, Thorsten Pöschel

The thermal and mechanical behaviors of powders are important for various additive manufacturing technologies. For powder bed fusion, capturing the temperature profile and the packing structure of the powders prior to melting is challenging due to both the various pathways of heat transfer and the complicated properties of powder system. Furthermore, these t

Maysam Behmanesh, Maks Ovsjanikov

Graph contrastive learning (GCL) aligns node representations by classifying node pairs into positives and negatives using a selection process that typically relies on establishing correspondences within two augmented graphs. The conventional GCL approaches incorporate negative samples uniformly in the contrastive loss, resulting in the equal treatment of neg

Anna-Lee Jessop, Allan J. Millsteed, Jacob J. K. Kirkensgaard, Jeremy Shaw

The sponge-like biomineralised calcite materials found in echinoderm skeletons are of interest in terms of both structure formation and biological function. Despite their crystalline atomic structure, they exhibit curved interfaces that have been related to known triply-periodic minimal surfaces. Here, we investigate the endoskeleton of the sea urchin Cidari

Nathanaël Carraz Rakotonirina, Marco Baroni

Transformer-based language models (LMs) track contextual information through large, hard-coded input windows. We introduce MemoryPrompt, a leaner approach in which the LM is complemented by a small auxiliary recurrent network that passes information to the LM by prefixing its regular input with a sequence of vectors, akin to soft prompts, without requiring L

Daniel Gibert, Giulio Zizzo, Quan Le, Jordi Planes

Deep learning-based malware detectors have been shown to be susceptible to adversarial malware examples, i.e. malware examples that have been deliberately manipulated in order to avoid detection. In light of the vulnerability of deep learning detectors to subtle input file modifications, we propose a practical defense against adversarial malware examples ins

Zhihao Cao, Zizhou Luo

Functional near-infrared spectroscopy (fNIRS) is a valuable non-invasive tool for monitoring brain activity. The classification of fNIRS data in relation to conscious activity holds significance for advancing our understanding of the brain and facilitating the development of brain-computer interfaces (BCI). Many researchers have turned to deep learning to ta

Juhye Ha, Hyeon Jeon, DaEun Han, Jinwook Seo

Large language models (LLMs) have facilitated significant strides in generating conversational agents, enabling seamless, contextually relevant dialogues across diverse topics. However, the existing LLM-driven conversational agents have fixed personalities and functionalities, limiting their adaptability to individual user needs. Creating personalized agent

Denys Dutykh

This manuscript investigates the deformation of ice layers under a moving block, focusing on the theoretical framework and mathematical models that describe the mechanical and thermal properties of ice and its interaction with underlying water. It delves into the formulation of the problem, discussing the assumptions and conditions applied, such as neglectin

Xiaolong Wang, Yile Wang, Sijie Cheng, Peng Li

Recent work has made a preliminary attempt to use large language models (LLMs) to solve the stance detection task, showing promising results. However, considering that stance detection usually requires detailed background knowledge, the vanilla reasoning method may neglect the domain knowledge to make a professional and accurate analysis. Thus, there is stil

Alonso Rodriguez-Navarro

Knowing which countries contribute the most to pushing the boundaries of knowledge in science and technology has social and political importance. However, common citation metrics do not adequately measure this contribution. This measure requires more stringent metrics appropriate for the highly influential breakthrough papers that push the boundaries of know

Balázs Szegedy, Domonkos Czifra, Péter Kőrösi-Szabó

Define an optimizer as having memory $k$ if it stores $k$ dynamically changing vectors in the parameter space. Classical SGD has memory $0$, momentum SGD optimizer has $1$ and Adam optimizer has $2$. We address the following questions: How can optimizers make use of more memory units? What information should be stored in them? How to use them for the learnin

Billel Guelmame, Didier Clamond, Stéphane Junca

Recently, a Hamiltonian regularised shallow water (Saint-Venant) system has been introduced by Clamond and Dutykh. This system is Galilean invariant, linearly non-dispersive and conserves formally an $H^1$-like energy. In this paper, we generalise this regularisation for the barotropic Euler system preserving the same properties. We prove the local (in time)

Nishil Mehta, Shabnam Iyyani

Gamma-ray bursts (GRBs) are commonly attributed to the demise of massive stars or the merger of binary compact objects. However, their varied emission characteristics strongly imply the existence of multiple GRB classes based on progenitor types, radiation mechanisms, central engines etc. This study utilizes unsupervised clustering with the Nested Gaussian M

Jianhong Wang, Yang Li, Yuan Zhang, Wei Pan

Ad hoc teamwork poses a challenging problem, requiring the design of an agent to collaborate with teammates without prior coordination or joint training. Open ad hoc teamwork (OAHT) further complicates this challenge by considering environments with a changing number of teammates, referred to as open teams. One promising solution in practice to this problem

Dawood Kothawala, Sahil Saini

The workshop was organized at IUCAA on Oct 30 - Nov 3, 2023 as a compact discussion and discourse meeting with a threadbare exposition and discussion of the various aspects and the questions arising. It was occasioned by the visit of Professor Hakan Andreasson of the Gothenburg Technical University, Sweden. He has been exploring with his collaborators the Ei

Siddhant Midha, Madhur Parashar, Anuj Bathla, David A. Broadway

Quantum Diamond Microscopy using Nitrogen-Vacancy (NV) defects in diamond crystals has enabled the magnetic field imaging of a wide variety of nanoscale current profiles. Intimately linked with the imaging process is the problem of reconstructing the current density, which provides critical insight into the structure under study. This manifests as a non-triv

Xavier Riley, Drew Edwards, Simon Dixon

Automatic music transcription (AMT) has achieved high accuracy for piano due to the availability of large, high-quality datasets such as MAESTRO and MAPS, but comparable datasets are not yet available for other instruments. In recent work, however, it has been demonstrated that aligning scores to transcription model activations can produce high quality AMT t

Maria Teresa Rossi, Alessandro Tundo, Leonardo Mariani

Configuring and evolving dashboards in complex and large-scale Systems-of-Systems (SoS) can be an expensive and cumbersome task due to the many Key Performance Indicators (KPIs) that are usually collected and have to be arranged in a number of visualizations. Unfortunately, setting up dashboards is still a largely manual and error-prone task requiring extens

Arnaud Gloter, Nakahiro Yoshida

The adaptive quasi-likelihood analysis is developed for a degenerate diffusion process. Asymptotic normality and moment convergence are proved for the quasi-maximum likelihood estimators and quasi-Bayesian estimators, in the adaptive scheme.

Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla

Causal discovery in the presence of missing data introduces a chicken-and-egg dilemma. While the goal is to recover the true causal structure, robust imputation requires considering the dependencies or, preferably, causal relations among variables. Merely filling in missing values with existing imputation methods and subsequently applying structure learning

Aditya Desu, Xuanli He, Qiongkai Xu, Wei Lu

As machine- and AI-generated content proliferates, protecting the intellectual property of generative models has become imperative, yet verifying data ownership poses formidable challenges, particularly in cases of unauthorized reuse of generated data. The challenge of verifying data ownership is further amplified by using Machine Learning as a Service (MLaa

Gianluca Janka, Maria Mendes Martins, Xiaojie Ni, Zaher Salman

The Low-Energy Muon beamline (LEM) at the Paul Scherrer Institute currently stands as the world's only facility providing a continuous beam of low-energy muons with keV energies for conducting muon spin rotation experiments on a nanometer depth scale in heterostructures and near a sample's surface. As such, optimizing the beam quality to reach its full poten

Chen Zhao, Ting Yu, Zhigao Zheng, Song Jin

Core decomposition is a well-established graph mining problem with various applications that involves partitioning the graph into hierarchical subgraphs. Solutions to this problem have been developed using both bottom-up and top-down approaches from the perspective of vertex convergence dependency. However, existing algorithms have not effectively harnessed

Sabrina Herbst, Vincenzo De Maio, Ivona Brandic

With the advent of the Post-Moore era, the scientific community is faced with the challenge of addressing the demands of current data-intensive machine learning applications, which are the cornerstone of urgent analytics in distributed computing. Quantum machine learning could be a solution for the increasing demand of urgent analytics, providing potential t

Andrés G. Jirón, Luis B. Castro, Angel E. Obispo, Antonio S. de Castro

This work re-examines the issue of spin-$1$ particles in a ($2+1$)-dimensional Duffin-Kemmer-Petiau oscillator (DKPO) in the presence of an external magnetic field. By following the appropriate procedure for the spin-$1$ sector of the Duffin-Kemmer-Petiau (DKP) theory, the previously used $6\times 6$ representation in the literature is shown to be reducible

Khalil Zakeri, Albrecht von Faber, Arthur Ernst

The experimental investigations of the magnetic interactions in an atomically thin magnetic layer are essential to understand the physics of low-dimensional magnets. The full spectrum of collective magnetic excitations (magnons) would provide an access to these fundamental interactions on the atomic scale. Here in order to be able to excite the magnons by me

Shyam Sundar Ghoshal, Billel Guelmame, Animesh Jana, Stéphane Junca

This paper deals with the optimal regularity for entropy solutions of conservation laws. For this purpose, we use two key ingredients: (a) fine structure of entropy solutions and (b) fractional $BV$ spaces. We show that optimality of the regularizing effect for the initial value problem from $L^\infty$ to fractional Sobolev space and fractional $BV$ spaces i

Meguya Ryu, Simonas Varapnickas, Darius Gailevicius, Domas Paipulas

Determination of refractive index of micro-disks of a calcinated ($1100^\circ$C in air) photo-resist SZ2080$^\mathrm{TM}$ was carried out using transmission and reflection spectroscopy. Interference fringes at specific wavenumbers/wavelengths were selected for determination of the optical thickness, hence, the refractive index when the thickness of micro-dis

Armand Stricker, Patrick Paroubek

During task-oriented dialogues (TODs), human users naturally introduce chitchat that is beyond the immediate scope of the task, interfering with the flow of the conversation. To address this issue without the need for expensive manual data creation, we use few-shot prompting with Llama-2-70B to enhance the MultiWOZ dataset with user backstories, a typical ex

Yue Cui, Liuyi Yao, Zitao Li, Yaliang Li

Vertical Federated Learning (VFL) has emerged as a popular machine learning paradigm, enabling model training across the data and the task parties with different features about the same user set while preserving data privacy. In production environment, VFL usually involves one task party and one data party. Fair and economically efficient feature trading is

Adri Gomez Martin, Carlos Fernandez del Cerro, Monica Abella Garcia, Manuel Desco Menendez

Most efforts in Computer Vision focus on natural images or artwork, which differ significantly both in size and contents from the kind of data biomedical image processing deals with. Thus, Transfer Learning models often prove themselves suboptimal for these tasks, even after manual finetuning. The development of architectures from scratch is oftentimes unfea

Neil D. Barrie, Chengcheng Han

We present a minimal framework that realises successful Dirac Leptogenesis through the Affleck-Dine mechanism. A single right-handed neutrino and a neutrinophillic Higgs doublet are introduced to the Standard Model, which couple via a Yukawa interaction. The inflationary setting is induced by a combination of the two Higgs doublets, with their global symmetr

Aleksey A. Shlyapnikov

The purpose of this work is to supplement the "Stars with solar-type activity" catalog with information about confirmed exoplanets and exoplanet candidates. To do this, cross-identification of the catalog stars with data from the NASA exoplanet archive was carried out. This article presents the distribution of the number of suspected stars with exoplanets by

Zengrong Hao, Ricardo García-Mayoral

Turbulent flows over porous substrates are studied via a systematic exploration of the dependence of the flow properties on the substrate parameters, including permeability $K$, grain pitch $L$, and depth $h$. The study uses direct numerical simulations mainly for staggered-cube substrates with $L^+\approx10$ - $50$, $\sqrt{K}/L\approx0.01$ - $0.25$, and dep

Tong Hui, Manuel J. Fernandez Gonzalez, Matteo Fumagalli

Pushing tasks performed by aerial manipulators can be used for contact-based industrial inspections. Underactuated aerial vehicles are widely employed in aerial manipulation due to their widespread availability and relatively low cost. Industrial infrastructures often consist of diverse oriented work surfaces. When interacting with such surfaces, the coupled

Javier Navarro, Ricard Ravell Rodríguez, Mikel Sanz

The Cram\'er-Rao bound serves as a crucial lower limit for the mean squared error of an estimator in frequentist parameter estimation. Paradoxically, it requires highly accurate prior knowledge of the estimated parameter for constructing the optimal unbiased estimator. In contrast, Bhattacharyya bounds offer a more robust estimation framework with respect to

Aleksey A. Shlyapnikov

A new version of CSSTA catalog of the lower main-sequence stars with solar-type activity was presented. It comprises 314618 objects, and the database that is realized on its basis is a developing project that contains hyperlinks to the original photometric and spectral observations.

Jiwoong Im, Nayoung Kwon, Taewoo Park, Jiheon Woo

We propose a communication-efficient collaborative inference framework in the domain of edge inference, focusing on the efficient use of vision transformer (ViT) models. The partitioning strategy of conventional collaborative inference fails to reduce communication cost because of the inherent architecture of ViTs maintaining consistent layer dimensions acro

Aneesh Sivasankaran, Laura Blecha, Paul Torrey, Luke Zoltan Kelley

Feedback from active galactic nuclei (AGN) can strongly impact the host galaxies by driving high-velocity winds that impart substantial energy and momentum to the interstellar medium (ISM). In this work, we study the impact of these winds in isolated galaxies using high-resolution hydrodynamics simulations. Our simulations use the explicit ISM and stellar ev

Surojit Dalui, Arpan Krishna Mitra, Deeshani Mitra, Subir Ghosh

In this paper, we propose a new Analogue Gravity example - a spinning (or Kerr) Black Hole in an extended fluid model. The fluid model receives Berry curvature contributions and applies to electron dynamics in Condensed Matter lattice systems in the hydrodynamic limit. We construct the acoustic metric for sonic fluctuations that obey a structurally relativis

Fengming Lin, Yan Xia, Michael MacRaild, Yash Deo

The automated segmentation of cerebral aneurysms is pivotal for accurate diagnosis and treatment planning. Confronted with significant domain shifts and class imbalance in 3D Rotational Angiography (3DRA) data from various medical institutions, the task becomes challenging. These shifts include differences in image appearance, intensity distribution, resolut

Yiping Jin, Leo Wanner, Alexander Shvets

Online hate detection suffers from biases incurred in data sampling, annotation, and model pre-training. Therefore, measuring the averaged performance over all examples in held-out test data is inadequate. Instead, we must identify specific model weaknesses and be informed when it is more likely to fail. A recent proposal in this direction is HateCheck, a su

Fengming Lin, Yan Xia, Michael MacRaild, Yash Deo

Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models. Since cross-modality medical data exhibit significant intra and inter-domain shifts and most are unlabelled, UDA is more important while challenging in medical image analysis. This paper pro

Kazuki Kitajima, Daichi Haraguchi, Seiichi Uchida

This paper addresses the challenging task of estimating font impressions from real font images. We use a font dataset with annotation about font impressions and a convolutional neural network (CNN) framework for this task. However, impressions attached to individual fonts are often missing and noisy because of the subjective characteristic of font impression

Zhefan Wang, Yuanqing Yu, Wendi Zheng, Weizhi Ma

LLM-based agents have gained considerable attention for their decision-making skills and ability to handle complex tasks. Recognizing the current gap in leveraging agent capabilities for multi-agent collaboration in recommendation systems, we introduce MACRec, a novel framework designed to enhance recommendation systems through multi-agent collaboration. Unl

Rubén Gómez González, Vicente Garzó

The Boltzmann kinetic equation for dilute granular suspensions under simple (or uniform) shear flow (USF) is considered to determine the non-Newtonian transport properties of the system. In contrast to previous attempts based on a coarse-grained description, our suspension model accounts for the real collisions between grains and particles of the surrounding

Jiawei Wu, Yanglin Hu, Akshay Bansal, Marco Tomamichel

Weak coin flipping is a cryptographic primitive in which two mutually distrustful parties generate a shared random bit to agree on a winner via remote communication. While a stand-alone secure weak coin flipping protocol can be constructed from noiseless quantum communication channels, its composability remains unexplored. In this work, we demonstrate that n

S. Riggi, G. Umana, C. Trigilio, C. Bordiu

Generation of science-ready data from processed data products is one of the major challenges in next-generation radio continuum surveys with the Square Kilometre Array (SKA) and its precursors, due to the expected data volume and the need to achieve a high degree of automated processing. Source extraction, characterization, and classification are the major s

Yuhe Ding, Bo Jiang, Aijing Yu, Aihua Zheng

Transfer learning methods endeavor to leverage relevant knowledge from existing source pre-trained models or datasets to solve downstream target tasks. With the increase in the scale and quantity of available pre-trained models nowadays, it becomes critical to assess in advance whether they are suitable for a specific target task. Model transferability estim

David Wölfle, Kevin Förderer, Tobias Riedel, Lukas Landwich

This article aims at facilitating the widespread application of Energy Management Systems (EMSs), especially on buildings and cities, in order to support the realization of future carbon-neutral energy systems. We claim that economic viability is a severe issue for the utilization of EMSs at scale and that the provisioning of forecasting and optimization alg

M. Aoyagi, R. G. Bose, S. Chun, E. Gau

XL-Calibur is a balloon-borne Compton polarimeter for X-rays in the $\sim$15-80 keV range. Using an X-ray mirror with a 12 m focal length for collecting photons onto a beryllium scattering rod surrounded by CZT detectors, a minimum-detectable polarization as low as $\sim$3% is expected during a 24-hour on-target observation of a 1 Crab source at 45$^{\circ}$

Nomvelo Karabo Sibisi

This paper uses convolutions of the gamma density and the one-sided stable density to construct higher level densities. The approach is applied to constructing a 4-parameter Mittag-Leffler density, whose Laplace transform is a corresponding Mittag-Leffler function, which is completely monotone (CM) by construction. Laplace transforms of mixtures of the stabl

Shengyang Huang, Jianwen Mo

With the explosive growth of data, continual learning capability is increasingly important for neural networks. Due to catastrophic forgetting, neural networks inevitably forget the knowledge of old tasks after learning new ones. In visual classification scenario, a common practice of alleviating the forgetting is to constrain the backbone. However, the impa

Lina Jaurigue

Forecasting timeseries based upon measured data is needed in a wide range of applications and has been the subject of extensive research. A particularly challenging task is the forecasting of timeseries generated by chaotic dynamics. In recent years reservoir computing has been shown to be an effective method of forecasting chaotic dynamics and reconstructin

Yonatan Sivan, Ieng-Wai Un, Subhajit Sarkar

Using the Boltzmann transport model, we show that, somewhat unintuitively, ballistic transport of electrons in metals is weaker than diffusive transport. This happens because the femtosecond-scale collision rates of the non-thermal electrons makes their mean-free path negligible. Our predictions are correlated with various photoluminescence and nonlinear opt

Alba de las Heras, David Schmidt, Julio San Román, Javier Serrano

The landscape of ultrafast structured light pulses has recently evolved driven by the capability of high-order harmonic generation (HHG) to up-convert orbital angular momentum (OAM) from the infrared to the extreme-ultraviolet (EUV) spectral regime. Accordingly, HHG has been proven to produce EUV vortex pulses at the femtosecond timescale. Here we demonstrat

Hong Yao, Cheng Li, Houbing Zhou, Ning Wang

We propose two functions to distinguish fission-like events from quasi-elastic (QE) scattering and deep inelastic collisions (DIC), for a better analysis of the measured mass-total kinetic energy distributions of binary fragments formed in fusion-fission reactions. We note that the ratio of capture to DIC events evidently decreases with the decreasing of the