April 2024 arXiv papers — page 71

David Montero, César Aybar, Chaonan Ji, Guido Kraemer

Advancements in Earth system science have seen a surge in diverse datasets. Earth System Data Cubes (ESDCs) have been introduced to efficiently handle this influx of high-dimensional data. ESDCs offer a structured, intuitive framework for data analysis, organising information within spatio-temporal grids. The structured nature of ESDCs unlocks significant op

Sergio Moroni

We present a stability result for ground states of a Schr\"odinger-Poisson system in $(2+1)$ dimension, modelling the propagation of a light beam through a liquid crystal with nonlocal nonlinear response. The core of the proof is a coercivity bound on the second derivative of the action, where non scaling nonlinearities and the coupled system present the maj

Felix Kuchelmeister

A dataset with two labels is linearly separable if it can be split into its two classes with a hyperplane. This inflicts a curse on some statistical tools (such as logistic regression) but forms a blessing for others (e.g. support vector machines). Recently, the following question has regained interest: What is the probability that the data are linearly sepa

Yixiang Zhuang, Baoping Cheng, Yao Cheng, Yuntao Jin

Speech-driven facial animation methods usually contain two main classes, 3D and 2D talking face, both of which attract considerable research attention in recent years. However, to the best of our knowledge, the research on 3D talking face does not go deeper as 2D talking face, in the aspect of lip-synchronization (lip-sync) and speech perception. To mind the

Zhan Peng, Xinyi Ye, Weiyue Zhao, Tianqi Liu

In this paper, we present RStab, a novel framework for video stabilization that integrates 3D multi-frame fusion through volume rendering. Departing from conventional methods, we introduce a 3D multi-frame perspective to generate stabilized images, addressing the challenge of full-frame generation while preserving structure. The core of our approach lies in

Zeyu Ling, Bo Han, Yongkang Wongkan, Han Lin

Conditional human motion synthesis (HMS) aims to generate human motion sequences that conform to specific conditions. Text and audio represent the two predominant modalities employed as HMS control conditions. While existing research has primarily focused on single conditions, the multi-condition human motion synthesis remains underexplored. In this study, w

T. Moretti, M. Milanesio, R. Cardella, T. Kugathasan

Samples of the monolithic silicon pixel ASIC prototype produced in 2022 within the framework of the Horizon 2020 MONOLITH ERC Advanced project were irradiated with 70 MeV protons up to a fluence of 1 x 1016 neq/cm2, and then tested using a beam of 120 GeV/c pions. The ASIC contains a matrix of 100 \mu m pitch hexagonal pixels, readout out by low noise and ve

Ana Luiza Tenório, Peter Arndt, Hugo Luiz Mariano

We expand \v{C}ech cohomology of a topological space $X$ with values in a presheaf on $X$ to \v{C}ech cohomology of a commutative ring with unity $R$ with values in a presheaf on $R$. The strategy is to observe that both the set of open subsets of $X$ and the set of ideals of $R$ provide examples of a (semicartesian) quantale. We study a particular pair of (

Walter S. Mathis, Maria Ferrara, John Cahill, Sneha Karmani

This paper presents TimelinePTC, a web-based tool developed to improve the collection and analysis of Pathways to Care (PTC) data in first episode psychosis (FEP) research. Accurately measuring the duration of untreated psychosis (DUP) is essential for effective FEP treatment, requiring detailed understanding of the patient's journey to care. However, tradit

Mustafa R. Kılınç, Michael Massmann

In this paper, we analyse the influence of estimating a constant term on the bias of the conditional sum-of-squares (CSS) estimator in a stationary or non-stationary type-II ARFIMA ($p_1$,$d$,$p_2$) model. We derive expressions for the estimator's bias and show that the leading term can be easily removed by a simple modification of the CSS objective function

Carlo Pagani, Hidenori Sonoda

We discuss the field independent additive constant in Wilson actions carefully within the exact renormalization group formalism. The additive constant does not affect the correlation functions of fields normalized by the partition function, and for that reason it is often ignored. But it is an essential part of the partition function, and in the limit where

Meir Lederman, Uzi Pereg

Semantic security is considered with unreliable entanglement assistance, due to one of two reasons: Interception or loss. We consider two corresponding models. In the first model, Eve may intercept the entanglement resource. In the second model, Eve is passive, and the resource may dissipate to the environment beyond her reach. We derive achievable rates for

Peibo Li, Maarten de Rijke, Hao Xue, Shuang Ao

The next Point of Interest (POI) recommendation task is to predict users' immediate next POI visit given their historical data. Location-Based Social Network (LBSN) data, which is often used for the next POI recommendation task, comes with challenges. One frequently disregarded challenge is how to effectively use the abundant contextual information present i

Guanhua Chen, Wenhan Yu, Xiao Lu, Xiao Zhang

While Retrieval-Augmented Generation (RAG) plays a crucial role in the application of Large Language Models (LLMs), existing retrieval methods in knowledge-dense domains like law and medicine still suffer from a lack of multi-perspective views, which are essential for improving interpretability and reliability. Previous research on multi-view retrieval often

Louie Bernhardt, Volker Schlue, Dongxiao Yu

In light of recent work of the third author, we revisit a classic example given by Fritz John of a semi-linear wave equation which exhibits finite in time blow up for all compactly supported data. We present the construction of future global solutions from asymptotic data given in arXiv:2204.12870(2022) for this specific example, and clarify the relation of

Indranil Biswas, Swarnava Mukhopadhyay

Let $G$ be a semisimple complex algebraic group with a simple Lie algebra $\mathfrak{g}$, and let $\mathcal{M}^0_{G}$ denote the moduli stack of topologically trivial stable $G$-bundles on a smooth projective curve $C$. Fix a theta characteristic $\kappa$ on $C$ which is even in case $\dim{\mathfrak{g}}$ is odd. We show that there is a nonempty Zariski open

Shentong Mo, Xufang Luo, Yansen Wang, Dongsheng Li

Visual task adaptation has been demonstrated to be effective in adapting pre-trained Vision Transformers (ViTs) to general downstream visual tasks using specialized learnable layers or tokens. However, there is yet a large-scale benchmark to fully explore the effect of visual task adaptation on the realistic and important medical domain, particularly across

Mikel F. Barbat, Jürgen Schaffner-Bielich, Laura Tolos

We present a comprehensive study of compact stars admixed with non-self annihilating self-interacting fermionic dark matter, delineating the dependence on the nuclear equation of state by considering the two limiting parametrized equations of state for neutron star matter obtained by smoothly matching the low-density chiral effective theory and the high-dens

Rafael F. Oliveira, Gladston J. P. Moreira, Vander L. S. Freitas, Eduardo J. S. Luz

Arrhythmias, detectable through electrocardiograms (ECGs), pose significant health risks, underscoring the need for accurate and efficient automated detection techniques. While recent advancements in graph-based methods have demonstrated potential to enhance arrhythmia classification, the challenge lies in effectively representing ECG signals as graphs. This

Atsu Kokuvi Angélo Passah, Rodrigo C. de Lamare, Arsenia Chorti

User authentication in future wireless communication networks is expected to become more complicated due to their large scale and heterogeneity. Furthermore, the computational complexity of classical cryptographic approaches based on public key distribution can be a limiting factor for using in simple, low-end Internet of things (IoT) devices. This paper pro

Sander Noels, Sébastien Viaene, Tijl De Bie

This paper addresses a long-standing problem in the field of accounting: mapping company-specific ledger accounts to a standardized chart of accounts. We propose a novel solution, TopoLedgerBERT, a unique sentence embedding method devised specifically for ledger account mapping. This model integrates hierarchical information from the charts of accounts into

Sairam Pamulaparthi Venkata, Yuxin Fu, Yibin Fu, Hooman Danesh

Bilayers, soft substrates coated with stiff films, are commonly found in nature with examples including skin tissue, vesicles, and organ membranes. They exhibit different types of instabilities when subjected to compression, depending on the contrast in material properties between the two components. In this work, we unravel the mechanisms behind wrinkling i

Zhaodonghui Li, Haitao Yuan, Huiming Wang, Gao Cong

Query rewrite, which aims to generate more efficient queries by altering a SQL query's structure without changing the query result, has been an important research problem. In order to maintain equivalence between the rewritten query and the original one during rewriting, traditional query rewrite methods always rewrite the queries following certain rewrite r

Michael-Sam Vidza, Marcin Budka, Wei Koong Chai, Mark Thrush

In aquaculture, disease spread models often neglect the dynamic interactions between farms, hindering accuracy. This study enhances the Katz index (KI) to incorporate spatial and temporal patterns of fish movement, improving the prediction of farms susceptible to disease via live fish transfers. We modified the Katz index to create models like the Weighted K

Rahul K. Gupta, Sherif Fahmy, Max Chevron, Enea Figini

In Part-I, we presented an optimal day-ahead scheduling scheme for dispatching active distribution networks accounting for the flexibility provided by electric vehicle charging stations (EVCSs) and other controllable resources such as battery energy storage systems (BESSs). Part-II presents the intra-day control layer for tracking the dispatch plan computed

Jemal Yimer Damte, Jiri Houskaa

Two-dimensional transition metal dichalcogenides (TMDs) are highly promising candidates for various applications due to their unique electrical, optical, mechanical, and chemical properties. Furthermore, heterostructures consisting of TMDs with metals, oxides, and conductive materials have attracted significant research interest due to their exceptional elec

Frederik Walter, Omer Sabary, Antonia Wachter-Zeh, Eitan Yaakobi

Composite DNA is a recent method to increase the base alphabet size in DNA-based data storage.This paper models synthesizing and sequencing of composite DNA and introduces coding techniques to correct substitutions, losses of entire strands, and symbol deletion errors. Non-asymptotic upper bounds on the size of codes with $t$ occurrences of these error types

Xingtai Gui, Tengteng Huang, Haonan Shao, Haotian Yao

The future instance prediction from a Bird's Eye View(BEV) perspective is a vital component in autonomous driving, which involves future instance segmentation and instance motion prediction. Existing methods usually rely on a redundant and complex pipeline which requires multiple auxiliary outputs and post-processing procedures. Moreover, estimated errors on

Yang Luo, Zangwei Zheng, Zirui Zhu, Yang You

The increase in parameter size of multimodal large language models (MLLMs) introduces significant capabilities, particularly in-context learning, where MLLMs enhance task performance without updating pre-trained parameters. This effectiveness, however, hinges on the appropriate selection of in-context examples, a process that is currently biased towards visu

Siyu Chen, David Sankoff

We propose a model of the immunity to a cyclical epidemic disease taking account not only of seasonal boosts during the infectious season, but also of residual immunity remaining from one season to the next. The focus is on the exponential waning process over successive cycles, imposed on the temporal distribution of infections or exposures over a season. Th

Marcel Stachak, Julian Geus, Gaston Pugliese, Felix Freiling

Modern eBike on-board computers are basically small PCs that not only offer motor control, navigation, and performance monitoring, but also store lots of sensitive user data. The Bosch Nyon series of board computers are cutting-edge devices from one of the market leaders in the eBike business, which is why they are especially interesting for forensics. There

Rahul K. Gupta, Sherif Fahmy, Max Chevron, Riccardo Vasapollo

This paper proposes a grid-aware scheduling and control framework for Electric Vehicle Charging Stations (EVCSs) for dispatching the operation of an active power distribution network. The framework consists of two stages. In the first stage, we determine an optimal day-ahead power schedule at the grid connection point (GCP), referred to as the dispatch plan.

Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright, Bernd Bischl

While machine learning (ML) models are increasingly used due to their high predictive power, their use in understanding the data-generating process (DGP) is limited. Understanding the DGP requires insights into feature-target associations, which many ML models cannot directly provide due to their opaque internal mechanisms. Feature importance (FI) methods pr

Yilong Chen, Zongyi Xu, xiaoshui Huang, Ruicheng Zhang

Weakly supervised LiDAR semantic segmentation has made significant strides with limited labeled data. However, most existing methods focus on the network training under weak supervision, while efficient annotation strategies remain largely unexplored. To tackle this gap, we implement LiDAR semantic segmentation using scatter image annotation, effectively int

Miao Cai, Jiang-Shan Tang, Ming-Yuan Chen, Keyu Xia

Phase transitions, non-Hermiticity and nonreciprocity play central roles in fundamental physics. However, the triple interplay of these three fields is of lack in the quantum domain. Here, we show nonreciprocal parity-time-symmetric phase transition in a non-Hermitian chiral quantum electrodynamical system, caused by the directional system dissipation. In re

Kexin Wang, Butian Zhang, Chengyu Yan, Luojun Du

Centrosymmetric materials with site inversion asymmetries possess hidden spin polarization, which remains challenging to be converted into spin currents because the global inversion symmetry is still conserved. This study demonstrates the spin-polarized DC circular photocurrents (CPC) in centrosymmetric transition metal dichalcogenides (TMDCs) at normal inci

Timothée Crin-Barat, Stefan Škondrić, Alessandro Violini

We present a weak-strong uniqueness result for the inhomogeneous Navier-Stokes (INS) equations in $\mathbb{R}^d$ ($d=2,3$) for bounded initial densities that are far from vacuum. Given a strong solution within the class employed in Paicu, Zhang and Zhang (2013) and Chen, Zhang and Zhao (2016), and a Leray-Hopf weak solution, we establish that they coincide i

Y. Kamiya, R. Nishimura, S. Mitsui, Z. Wang

We have developed a two-dimensional neutron imager based on a semiconductor pixelated sensor, especially designed for experiments measuring of a spatial and a temporal behavior of quantum bound states of ultra-cold neutrons. Through these measurements, we expect to measure the ratio between the inertial and gravitational masses of neutrons and to test the eq

Ross Greer, Bjørk Antoniussen, Andreas Møgelmose, Mohan Trivedi

Object detection is crucial for ensuring safe autonomous driving. However, data-driven approaches face challenges when encountering minority or novel objects in the 3D driving scene. In this paper, we propose VisLED, a language-driven active learning framework for diverse open-set 3D Object Detection. Our method leverages active learning techniques to query

Emanuele Triuzzi, Riccardo Mengoni, Francesco Micucci, Domenico Bonanni

Molecular docking is an essential step in the drug discovery process involving the detection of three-dimensional poses of a ligand inside the active site of the protein. In this paper, we address the Molecular Docking search phase by formulating the problem in QUBO terms, suitable for an annealing approach. We propose a problem formulation as a weighted sub

Peace Azugo, Hein Venter, Mike Wa Nkongolo

Cybersecurity faces challenges in identifying and mitigating ransomware, which is important for protecting critical infrastructures. The absence of datasets for distinguishing normal versus abnormal network behaviour hinders the development of proactive detection strategies against ransomware. An obstacle in proactive prevention methods is the absence of com

Muhammad Osama Nusrat, Waseem Shahzad, Saad Ahmed Jamal

Depression is a significant issue nowadays. As per the World Health Organization (WHO), in 2023, over 280 million individuals are grappling with depression. This is a huge number; if not taken seriously, these numbers will increase rapidly. About 4.89 billion individuals are social media users. People express their feelings and emotions on platforms like Twi

Christian Näther, Daniel Herzinger, Stefan-Lukas Gazdag, Jan-Philipp Steghöfer

Networks such as the Internet are essential for our connected world. Quantum computing poses a threat to this heterogeneous infrastructure since it threatens fundamental security mechanisms. Therefore, a migration to post-quantum-cryptography (PQC) is necessary for networks and their components. At the moment, there is little knowledge on how such migrations

Hanna Kindlund, Theodora Ciobanu, Suneel Kodambaka, C. V. Ciobanu

Cubic rocksalt structured transition-metal carbides, nitrides, and related alloys (TMC/Ns) are attractive for a wide variety of applications, notably as hard, wear-resistant material. To-date, valence electron concentration (VEC) is used as a good indicator of stability and mechanical properties of these refractory compounds. In this perspective, we argue fo

Beichen Li, Yuanfang Guo, Heqi Peng, Yangxi Li

Deep neural networks are vulnerable to backdoor attacks. Among the existing backdoor defense methods, trigger reverse engineering based approaches, which reconstruct the backdoor triggers via optimizations, are the most versatile and effective ones compared to other types of methods. In this paper, we summarize and construct a generic paradigm for the typica

Erik Nikolov

For a smooth projective variety $X$ of dimension $d \geq 5$ over an algebraically closed field $k$ of characteristic zero, it is shown in this paper that the bounded derived category of the Hilbert scheme of three points $X^{[3]}$ admits a semi-orthogonal sequence of length $\binom{d-3}{2}$. Each subcategory in this sequence is equivalent to the derived cate

Zeke Xia, Ming Hu, Dengke Yan, Xiaofei Xie

Federated Learning (FL) as a promising distributed machine learning paradigm has been widely adopted in Artificial Intelligence of Things (AIoT) applications. However, the efficiency and inference capability of FL is seriously limited due to the presence of stragglers and data imbalance across massive AIoT devices, respectively. To address the above challeng

Tapio Kurkinen

We characterize regular boundary points in terms of a barrier family for a general form of a parabolic equation that generalizes both the standard parabolic $p$-Laplace equation and the normalized version arising from stochastic game theory. Using this result we prove geometric conditions that ensure regularity by constructing suitable barrier families. We a

Tomasz Goliński, Grzegorz Jakimowicz, Aneta Sliżewska

The set of partial isometries in a W*-algebra possesses a structure of Banach Lie groupoid. In this paper the differential structure on the set of partial isometries over the restricted Grassmannian is constructed, which makes it into a Banach Lie groupoid.

Zeke Xia, Ming Hu, Dengke Yan, Ruixuan Liu

Although Split Federated Learning (SFL) is good at enabling knowledge sharing among resource-constrained clients, it suffers from the problem of low training accuracy due to the neglect of data heterogeneity and catastrophic forgetting. To address this issue, we propose a novel SFL approach named KoReA-SFL, which adopts a multi-model aggregation mechanism to

Aleksei Dorkin, Kairit Sirts

We present our submission to the unconstrained subtask of the SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages for morphological annotation, POS-tagging, lemmatization, character- and word-level gap-filling. We developed a simple, uniform, and computationally lightweight approach based on the adapters framework using

Nicolas Daans, Vítězslav Kala, Jakub Krásenský, Pavlo Yatsyna

We discuss the phenomenon where an element in a number field is not integrally represented by a given positive definite quadratic form, but becomes integrally represented by this form over a totally real extension of odd degree. We prove that this phenomenon happens infinitely often, and, conversely, establish finiteness results about the situation when the

Diego Calanzone, Stefano Teso, Antonio Vergari

Large language models (LLMs) are a promising venue for natural language understanding and generation tasks. However, current LLMs are far from reliable: they are prone to generate non-factual information and, more crucially, to contradict themselves when prompted to reason about beliefs of the world. These problems are currently addressed with large scale fi

Satoshi Imamura, Akihiko Kasagi, Eiji Yoshida

Geometry optimization is an important task in quantum chemical calculations to analyze the characteristics of molecules. A top concern on it is a long execution time because time-consuming energy and gradient calculations are repeated across several to tens of steps. In this work, we present a scheme to estimate the execution times of geometry optimization o

Gazi Hasin Ishrak, Zalish Mahmud, MD. Zami Al Zunaed Farabe, Tahera Khanom Tinni

Deepfake technology, derived from deep learning, seamlessly inserts individuals into digital media, irrespective of their actual participation. Its foundation lies in machine learning and Artificial Intelligence (AI). Initially, deepfakes served research, industry, and entertainment. While the concept has existed for decades, recent advancements render deepf

Shengxuan Zhou

In this paper, we show that the Calabi volume and Mabuchi volume of Bergman spaces on the product of a projective manifold and a projective space is infinite. Our result is inspired by a conjecture of Shiffman-Zelditch in [arXiv:2303.11559].

Ali Rasekh, Sepehr Kazemi Ranjbar, Milad Heidari, Wolfgang Nejdl

Large Vision Language Models (VLMs), such as CLIP, have significantly contributed to various computer vision tasks, including object recognition and object detection. Their open vocabulary feature enhances their value. However, their black-box nature and lack of explainability in predictions make them less trustworthy in critical domains. Recently, some work

Daniel Brosch, Diane Puges

The inducibility of a graph represents its maximum density as an induced subgraph over all possible sequences of graphs of size growing to infinity. This invariant of graphs has been extensively studied since its introduction in $1975$ by Pippenger and Golumbic. In $2017$, Czabarka, Sz\'ekely and Wagner extended this notion to leaf-labeled rooted binary tree

Rodrigo Falcão, Arghavan Hosseinzadeh

A typical user interacts with many digital services nowadays, providing these services with their data. As of now, the management of privacy preferences is service-centric: Users must manage their privacy preferences according to the rules of each service provider, meaning that every provider offers its unique mechanisms for users to control their privacy se

Ralf Schindler, Taichi Yasuda

We study a strengthening of $\mathrm{MM}^{++}$ which is called $\mathrm{MM}^{\ast, ++}$ and which was introduced by Asper\'o and Schindler. We force its bounded version $\mathrm{MM}^{\ast, ++}_{\mathfrak{c}}$, which is stronger than both $\mathrm{MM}^{++}(\mathfrak{c})$ as well as $\mathrm{BMM}^{++}$, by $\mathbb{P}_{\max}$ forcing over a determinacy model $

Callum McCall, Helen Jermak, Iain A. Steele, Iván Agudo

We present the results of seven years of multicolour photometric monitoring of a sample of 31 $\gamma$-ray bright blazars using the RINGO3 polarimeter on the Liverpool Telescope from 2013--2020. We explore the relationships between simultaneous observations of flux in three optical wavebands along with Fermi $\gamma$-ray data in order to explore the radiatio

Francesco Esposito, Mario Marietti, Grant T. Barkley, Christian Gaetz

We introduce the concepts of an amazing hypercube decomposition and a double shortcut for it, and use these new ideas to formulate a conjecture implying the Combinatorial Invariance Conjecture of the Kazhdan--Lusztig polynomials for the symmetric group. This conjecture has the advantage of being combinatorial in nature. The appendix by Grant T. Barkley and C

Jiahong Xiang, Xiaoyang Xu, Fanchu Kong, Mingyuan Wu

Recently, multiple Automated Program Repair (APR) techniques based on Large Language Models (LLMs) have been proposed to enhance the repair performance. While these techniques mainly focus on the single-line or hunk-level repair, they face significant challenges in real-world application due to the limited repair task scope and costly statement-level fault l

Dmytro Shvetsov, Joonas Ariva, Marharyta Domnich, Raul Vicente

Deep learning is dramatically transforming the field of medical imaging and radiology, enabling the identification of pathologies in medical images, including computed tomography (CT) and X-ray scans. However, the performance of deep learning models, particularly in segmentation tasks, is often limited by the need for extensive annotated datasets. To address

Kristian Bredies, José A. Iglesias, Daniel Walter

We consider the set of extremal points of the generalized unit ball induced by gradient total variation seminorms for vector-valued functions on bounded Euclidean domains. These are central to the understanding of sparse solutions and sparse optimization algorithms for variational problems posed among such functions. For cases in which either the domain or t

François Rottenberg, Thomas Feys, Nuutti Tervo

The current evolution towards a massive number of antennas and a large variety of transceiver architectures forces to revisit the conventional techniques used to improve the fundamental power amplifier (PA) linearity-efficiency trade-off. Most of the digital linearization techniques rely on PA measurements using a dedicated feedback receiver. However, in mod

Alessandra Bassani, Beatrice Del Bo, Alfio Ferrara, Marta Mangini

The Latin language has received attention from the computational linguistics research community, which has built, over the years, several valuable resources, ranging from detailed annotated corpora to sophisticated tools for linguistic analysis. With the recent advent of large language models, researchers have also started developing models capable of genera

Andrew D. McRae

We show that solutions to the popular convex matrix LASSO problem (nuclear-norm--penalized linear least-squares) have low rank under similar assumptions as required by classical low-rank matrix sensing error bounds. Although the purpose of the nuclear norm penalty is to promote low solution rank, a proof has not yet (to our knowledge) been provided outside v

Anthony Yazdani, Alban Bornet, Philipp Khlebnikov, Boya Zhang

Adverse drug events (ADEs) are a major safety issue in clinical trials. Thus, predicting ADEs is key to developing safer medications and enhancing patient outcomes. To support this effort, we introduce CT-ADE, a dataset for multilabel ADE prediction in monopharmacy treatments. CT-ADE encompasses 2,497 drugs and 168,984 drug-ADE pairs from clinical trial resu

Baltasar Pérez, Víctor A. Araña, Javier Perez-Mato, Francisco Cabrera

This letter presents a 360{\deg} phase detector cell for performing phase-shift measurements on multiple output systems. An analog phase detector, capable of detecting a maximum range of {\pm}90{\deg}, has been used to perform a double multiplication of two signals, both in-phase and phase-shifted. The proposed solution broadens the frequency range beyond ot

Shaohao Zhu, Jiacheng Zhou, Anjun Chen, Mingming Bai

The sim-to-real gap poses a significant challenge in RL-based multi-agent exploration due to scene quantization and action discretization. Existing platforms suffer from the inefficiency in sampling and the lack of diversity in Multi-Agent Reinforcement Learning (MARL) algorithms across different scenarios, restraining their widespread applications. To fill

Wietse Boon, Erik Nilsson

We propose nodal auxiliary space preconditioners for facet and edge virtual elements of lowest order by deriving discrete regular decompositions on polytopal grids and generalizing the Hiptmair-Xu preconditioner to the virtual element framework. The preconditioner consists of solving a sequence of elliptic problems on the nodal virtual element space, combine

Jayashree Pati, Rajendra S. Dhaka

We report the electrochemical sodium-ion kiinetics and distribution of relaxation time (DRT) analysis of a newly designed mixed polyanionic NaFe$_{1.6}$V$_{0.4}$(PO$_{4}$)(SO$_{4}$)$_{2}$@CNT composite as a cathode. The specific capacity of 104 mAhg$^{-1}$ is observed at 0.1~C with the average working voltage of $\sim$3~V. Intriguingly, a remarkable rate cap

William Macpherson, Geoffrey Goodell

As global governments intensify efforts to operationalize retail central bank digital currencies (CBDCs), the imperative for architectures that preserve user privacy has never been more pronounced. This paper advances an existing retail CBDC framework developed at University College London. Utilizing the capabilities of the Comet research framework, our prop

Manuel Schlierf

While there are various results on the long-time behavior of the Willmore flow, the Helfrich flow with non-zero spontaneous curvature as its natural generalization is not yet well-understood. Past results for the gradient flow of a locally area- and volume-constrained Willmore flow indicate the existence of finite-time singularities which corresponds to the

Georgios Kouros, Minye Wu, Sushruth Nagesh, Xianling Zhang

Inverse rendering aims to reconstruct the scene properties of objects solely from multiview images. However, it is an ill-posed problem prone to producing ambiguous estimations deviating from physically accurate representations. In this paper, we utilize Neural Microfacet Fields (NMF), a state-of-the-art neural inverse rendering method to illustrate the inhe

Gustav A. Lunde, Emil V. Damm, Peter A. V. Gade, Jalal Kazempour

The Danish system operator, Energinet, has recently introduced an innovative grid code called the P90 requirement, which allows stochastic flexible resources to bid their flexibility in Nordic ancillary service markets, contingent upon a minimum 90\% probability of successfully realizing the reserve capacity bid. For limited-energy resources, Energinet impos

Belle, Belle II Collaborations, :, I. Adachi

We report a determination of the CKM angle $\phi_{3}$, also known as $\gamma$, from a combination of measurements using samples of up to 711~fb$^{-1}$ from the Belle experiment and up to 362~fb$^{-1}$ from the Belle II experiment. We combine results from analyses of $B^+\to DK^+, B^+\to D\pi^+$, and $B^+ \to D^{*}K^+$ decays, where $D$ is an admixture of $D^

Junya Shiraishi, Sara Cavallero, Shashi Raj Pandey, Fabio Saggese

This paper considers energy-efficient connectivity for Internet of Things (IoT) devices in a coexistence scenario between two distinctive communication models: pull- and push-based. In pull-based, the base station (BS) decides when to retrieve a specific type of data from the IoT devices, while in push-based, the IoT device decides when and which data to tra

Samuel Barnier, Chris Done

Large scale, strong magnetic fields are often evoked in black hole accretion flows, for jet launching in the low/hard state and to circumvent the thermal instability in the high/soft state. Here we show how these ideas are strongly challenged by X-ray polarization measurements from IXPE. Quite general arguments show that equipartition large scale fields in t

Ziqiang Shi, Rujie Liu

Diffusion generative modelling (DGM) based on stochastic differential equations (SDEs) with score matching has achieved unprecedented results in data generation. In this paper, we propose a novel fast high-quality generative modelling method based on high-order Langevin dynamics (HOLD) with score matching. This motive is proved by third-order Langevin dynami

Husnain Shahid, Miguel Angel Vazquez, Laurent Reynaud, Fanny Parzysz

By effectively implementing the strategies for resource allocation, the capabilities, and reliability of non-terrestrial networks (NTN) can be enhanced. This leads to enhance spectrum utilization performance while minimizing the unmet system capacity, meeting quality of service (QoS) requirements and overall system optimization. In turn, a wide range of appl

Marika M. Cusick, Glenn M. Chertow, Douglas K. Owens, Michelle Y. Williams

Changing clinical algorithms to remove race adjustment has been proposed and implemented for multiple health conditions. Removing race adjustment from estimated glomerular filtration rate (eGFR) equations may reduce disparities in chronic kidney disease (CKD), but has not been studied in clinical practice after implementation. Here, we assessed whether imple

F. Agurto-Sepúlveda, J. Oliva, M. Oyarzo, D. R. G Schleicher

In this paper we study the qualitative features induces by corrections to GR coming from String Theory, on the shadows of rotating black holes. We deal with the slowly rotating black hole solutions up to order $\mathcal{O}(a^3)$, to first order in $\alpha'$, including also the dilaton. We provide a detailed characterization of the geometry, as well as the in

Marharyta Domnich, Raul Vicente

A pressing issue in the adoption of AI models is the increasing demand for more human-centric explanations of their predictions. To advance towards more human-centric explanations, understanding how humans produce and select explanations has been beneficial. In this work, inspired by insights of human cognition we propose and test the incorporation of two no

Eugenia Celada, Tommaso Giani, Jaco ter Hoeve, Luca Mantani

We present SMEFiT3.0, an updated global SMEFT analysis of Higgs, top quark, and diboson production data from the LHC complemented by electroweak precision observables (EWPOs) from LEP and SLD. We consider recent inclusive and differential measurements from the LHC Run II, alongside with a novel implementation of the EWPOs based on independent calculations of

Julian Geus, Jenny Ottmann, Felix Freiling

Due to the increasing security standards of modern smartphones, forensic data acquisition from such devices is a growing challenge. One rather generic way to access data on smartphones in practice is to use the local backup mechanism offered by the mobile operating systems. We study the suitability of such mechanisms for forensic data acquisition by performi

Peter A. V. Gade, Henrik W. Bindner, Jalal Kazempour

The P90 requirement of the Danish transmission system operator, Energinet, incentivizes flexible resources with stochastic power consumption/production baseline to bid in Nordic ancillary service markets with the minimum reliability of 90%, i.e., letting them cause reserve shortfall with the probability of up to 10%. Leveraging this requirement, we develop a

Tommy Meier, Yeohoon Yoon, Laura Teuerle, Ali Solgi

Photolithography is believed to be a complementary technique to large-area printing, allowing for nanometer-scale integration and offering cost-efficiency. For organic electronics though, adapting photolithography is very challenging due to chemical incompatibilities. However, with the help of Alexander Zakhidov, orthogonal resins opened up the prospect of a

Viktor Rindert, Steffen Richter, Philipp Kühne, Alexander Ruder

A generalized approach derived from Blochs equation of motion of nuclear magnetic moments is presented to model the frequency, magnetic field, spin density, and temperature dependencies in the electromagnetic permeability tensor for materials with magnetic resonances. The resulting tensor model predicts characteristic polarization signatures which can be obs

Junming Hou, Zihan Cao, Naishan Zheng, Xuan Li

Vision transformer family has dominated the satellite pan-sharpening field driven by the global-wise spatial information modeling mechanism from the core self-attention ingredient. The standard modeling rules within these promising pan-sharpening methods are to roughly stack the transformer variants in a cascaded manner. Despite the remarkable advancement, t

Jingqun Tang, Chunhui Lin, Zhen Zhao, Shu Wei

Text-centric visual question answering (VQA) has made great strides with the development of Multimodal Large Language Models (MLLMs), yet open-source models still fall short of leading models like GPT4V and Gemini, partly due to a lack of extensive, high-quality instruction tuning data. To this end, we introduce a new approach for creating a massive, high-qu

Ata Koklu, Yusuf Guven, Tufan Kumbasar

In this paper, we tackle the task of generating Prediction Intervals (PIs) in high-risk scenarios by proposing enhancements for learning Interval Type-2 (IT2) Fuzzy Logic Systems (FLSs) to address their learning challenges. In this context, we first provide extra design flexibility to the Karnik-Mendel (KM) and Nie-Tan (NT) center of sets calculation methods

Avraham Gal

We study to what extent the unique observation of $\Lambda\Lambda$ hypernuclei by their weak decay into known $\Lambda$ hypernuclei, with lifetimes of order 10$^{-10}$ s, rules out the existence of a deeply bound doubly-strange (${\cal S}$=$-$2) $H$ dibaryon. Treating ${_{\Lambda\Lambda}^{~~6}}{\rm He}$ (the Nagara emulsion event) in a realistic $\Lambda-\La

Yusuf Guven, Ata Koklu, Tufan Kumbasar

General Type-2 (GT2) Fuzzy Logic Systems (FLSs) are perfect candidates to quantify uncertainty, which is crucial for informed decisions in high-risk tasks, as they are powerful tools in representing uncertainty. In this paper, we travel back in time to provide a new look at GT2-FLSs by adopting Zadeh's (Z) GT2 Fuzzy Set (FS) definition, intending to learn GT

Fotis Farakos, Alex Kehagias, Nikolaos Liatsos

We revisit the genuine Fayet-Iliopoulos terms of 4D N=1 supergravity. Such terms are commonly believed to preserve a global symmetry, and therefore they are in conflict with the principles of quantum gravity. However, we find that generically there do exist supersymmetric terms that break explicitly the specific global symmetry, while preserving gauge invari

Marius Schmidt-Mengin, Alexis Benichoux, Shibeshih Belachew, Nikos Komodakis

Annotating lots of 3D medical images for training segmentation models is time-consuming. The goal of weakly supervised semantic segmentation is to train segmentation models without using any ground truth segmentation masks. Our work addresses the case where only image-level categorical labels, indicating the presence or absence of a particular region of inte

Christopher Lang, Alexander Braun, Lars Schillingmann, Abhinav Valada

Multi-task networks can potentially improve performance and computational efficiency compared to single-task networks, facilitating online deployment. However, current multi-task architectures in point cloud perception combine multiple task-specific point cloud representations, each requiring a separate feature encoder and making the network structures bulky

Armin Wolf

Constraint satisfaction or optimisation models -- even if they are formulated in high-level modelling languages -- need to be reduced into an equivalent format before they can be solved by the use of Quantum Computing. In this paper we show how Boolean and integer FlatZinc builtins over finite-domain integer variables can be equivalently reformulated as line

Majid Khazaee, Milad Hasani, Sam Riahi, Lasse Rosendahl

Accurately estimating the complex motion of the heart can unlock enormous potential for kinetic energy harvesting. This paper presents a foundational dataset for heart kinetic motion through in-vivo tests and investigates the most influential factors in heart kinetic motion. In-vivo tests on a living pig's heart, with signal processing, were carried out to s