March 2024 arXiv papers — page 67

Samuel S. Welborn, Bjoern Enders, Chris Harris, Peter Ercius

Recent advancements in detector technology have significantly increased the size and complexity of experimental data, and high-performance computing (HPC) provides a path towards more efficient and timely data processing. However, movement of large data sets from acquisition systems to HPC centers introduces bottlenecks owing to storage I/O at both ends. Thi

Mikhail Drobyshevskiy, Denis Aivazov, Denis Turdakov, Alexander Yatskov

Online network crawling tasks require a lot of efforts for the researchers to collect the data. One of them is identification of important nodes, which has many applications starting from viral marketing to the prevention of disease spread. Various crawling algorithms has been suggested but their efficiency is not studied well. In this paper we compared six

Duojun Huang, Xinyu Xiong, De-Jun Fan, Feng Gao

Deep learning-based techniques have proven effective in polyp segmentation tasks when provided with sufficient pixel-wise labeled data. However, the high cost of manual annotation has created a bottleneck for model generalization. To minimize annotation costs, we propose a deep active learning framework for annotation-efficient polyp segmentation. In practic

Qihan Huang, Jie Song, Jingwen Hu, Haofei Zhang

Concept Bottleneck Models (CBMs), which break down the reasoning process into the input-to-concept mapping and the concept-to-label prediction, have garnered significant attention due to their remarkable interpretability achieved by the interpretable concept bottleneck. However, despite the transparency of the concept-to-label prediction, the mapping from th

Pavla Krotka, Martin Posch, Mohamed Gewily, Günter Höglinger

Utilizing non-concurrent control data (NCC) in the analysis of late-entering arms in platform trials has recently received considerable attention. While incorporating NCC can lead to increased power and lower sample sizes, it might introduce bias to the effect estimators if temporal drifts are present. Aiming to mitigate this potential bias, we propose vario

Stamatina C. Barakou, Costas S. Tzafestas, Kimon P. Valavanis

This survey paper focuses on quadrotor- and multirotor- based cooperative aerial manipulation. Emphasis is first given on comparing and evaluating prototype systems that have been implemented and tested in real-time in diverse application environments. Underlying modeling and control approaches are also discussed and compared. The outcome of the survey allow

Yujian Liu, Ruoxuan Wu, Xinjie Shen, Zihuang Lu

In the realm of digital pathology, multi-magnification Multiple Instance Learning (multi-mag MIL) has proven effective in leveraging the hierarchical structure of Whole Slide Images (WSIs) to reduce information loss and redundant data. However, current methods fall short in bridging the domain gap between pretrained models and medical imaging, and often fail

Hengyu Zhang, Xuehan Wang, Jingbo Tan, Jintao Wang

The next generation wireless communication networks are required to support high-mobility scenarios, such as reliable data transmission for high-speed railways. Nevertheless, widely utilized multi-carrier modulation, the orthogonal frequency division multiplex (OFDM), cannot deal with the severe Doppler spread brought by high mobility. To address this proble

Kevin Leahy, Ho Chit Siu

Conveying human goals to autonomous systems (AS) occurs both when the system is being designed and when it is being operated. The design-step conveyance is typically mediated by robotics and AI engineers, who must appropriately capture end-user requirements and concepts of operations, while the operation-step conveyance is mediated by the design, interfaces,

C. Nguyen, G. Loi, T. Russell, Y. Yang

One of the key risks for a Carbon Capture Storage (CCS) is injectivity decline. Evaporation of the connate brine in near-wellbore region during CO2 injection may result in drying-up the rock yielding the mobilisation and migration of clay particles leading to decline rock permeability and consequent loss of well injectivity. Influx of the reservoir brine int

Erwan Mahe, Rouwaida Abdallah, Sara Tucci-Piergiovanni, Pierre-Yves Piriou

This paper presents a novel adversary model specifically tailored to distributed systems, aiming to assess the security of blockchain networks. Building upon concepts such as adversarial assumptions, goals, and capabilities, our proposed adversary model classifies and constrains the use of adversarial actions based on classical distributed system models, def

Jiaxin Liu, Yi Yang, Kar Yan Tam

In this paper, we introduce the Financial-STS task, a financial domain-specific NLP task designed to measure the nuanced semantic similarity between pairs of financial narratives. These narratives originate from the financial statements of the same company but correspond to different periods, such as year-over-year comparisons. Measuring the subtle semantic

Yulan Hu, Sheng Ouyang, Zhirui Yang, Ge Chen

Graphs are ubiquitous in real-world scenarios and encompass a diverse range of tasks, from node-, edge-, and graph-level tasks to transfer learning. However, designing specific tasks for each type of graph data is often costly and lacks generalizability. Recent endeavors under the "Pre-training + Fine-tuning" or "Pre-training + Prompt" paradigms aim to desig

Daniel Trippa, Cesare Campagnano, Maria Sofia Bucarelli, Gabriele Tolomei

Machine Unlearning, the process of selectively eliminating the influence of certain data examples used during a model's training, has gained significant attention as a means for practitioners to comply with recent data protection regulations. However, existing unlearning methods face critical drawbacks, including their prohibitively high cost, often associat

Yu-Chen Shen, Li Gao, Hao-Chung Cheng

In this paper, we consider the standard quantum information decoupling, in which Alice aims to decouple her system from the environment by local operations and discarding some of her systems. To achieve an $\varepsilon$-decoupling with trace distance as the error criterion, we establish a near-optimal one-shot characterization for the largest dimension of th

T. Rodzinka, E. Dionis, L. Calmels, S. Beldjoudi

The effective control of atomic coherence with cold atoms has made atom interferometry an essential tool for quantum sensors and precision measurements. The performance of these interferometers is closely related to the operation of large wave packet separations. We present here a novel approach for atomic beam splitters based on the stroboscopic stabilizati

Mirko Signorelli, Sophie Retif

In recent years, the growing availability of biomedical datasets featuring numerous longitudinal covariates has motivated the development of several multi-step methods for the dynamic prediction of survival outcomes. These methods employ either mixed-effects models or multivariate functional principal component analysis to model and summarize the longitudina

Elena Camuffo, Umberto Michieli, Jijoong Moon, Daehyun Kim

Improving model robustness in case of corrupted images is among the key challenges to enable robust vision systems on smart devices, such as robotic agents. Particularly, robust test-time performance is imperative for most of the applications. This paper presents a novel approach to improve robustness of any classification model, especially on severely corru

Christian Döbler

We extend the Malliavin theory for $L^2$-functionals on product probability spaces that has recently been developed by Decreusefond and Halconruy (2019) and by Duerinckx (2021), by characterizing the domains and investigating the actions of the three Malliavin operators in terms of the infinite Hoeffding decomposition in $L^2$, which we identify as the natur

Ajian Liu, Shuai Xue, Jianwen Gan, Jun Wan

Domain generalization (DG) based Face Anti-Spoofing (FAS) aims to improve the model's performance on unseen domains. Existing methods either rely on domain labels to align domain-invariant feature spaces, or disentangle generalizable features from the whole sample, which inevitably lead to the distortion of semantic feature structures and achieve limited gen

Weiqiang He, Hendrik Fichtenberger, Pan Peng

We study differentially private (DP) algorithms for recovering clusters in well-clustered graphs, which are graphs whose vertex set can be partitioned into a small number of sets, each inducing a subgraph of high inner conductance and small outer conductance. Such graphs have widespread application as a benchmark in the theoretical analysis of spectral clust

Lincoln Murr

Ethereum's current Gasper consensus mechanism, which combines the Latest Message Driven Greediest Heaviest Observed SubTree (LMD-GHOST) fork choice rule with the probabilistic Casper the Friendly Finality Gadget (FFG) finality overlay, finalizes transactions in 64 to 95 blocks, an approximate 15-minute delay. This finalization latency impacts user experience

Saurabh K. Shukla

Non-renormalisable versions of $SO(10)$\, based on irreducible representations with lesser degrees of freedom, are free of running into the catastrophe of non-perturbativity of standard model gauge couplings in contrast to the renormalisable versions having tensors with many degrees of freedom. $16_H$ is the smallest representation, participates in Yukawa La

Gordon R. M. Robb, Josh G. M. Walker, Gian-Luca Oppo, Thorsten Ackemann

We show that a Bose--Einstein Condensate illuminated by a far off-resonant optical pump field and its retroreflection from a feedback mirror can produce stable, localised structures known as optomechanical droplets. We show that these droplets could be used to measure the acceleration of a BEC via continuous monitoring of the position of the droplet via the

Ralph Willox, Takafumi Mase, Alfred Ramani, Basil Grammaticos

We study the link between the degree growth of integrable birational mappings of order higher than two and their singularity structures. The higher order mappings we use in this study are all obtained by coupling mappings that are integrable through spectral methods, typically belonging to the QRT family, to a variety of linearisable ones. We show that by ju

Fernando Acero, Zhibin Li

Recent advancements in reinforcement learning (RL) have led to remarkable achievements in robot locomotion capabilities. However, the complexity and ``black-box'' nature of neural network-based RL policies hinder their interpretability and broader acceptance, particularly in applications demanding high levels of safety and reliability. This paper introduces

Sheresh Zahoor, Anthony C. Constantinou, Tim M Curtis, Mohammed Hasanuzzaman

Diabetes, a pervasive and enduring health challenge, imposes significant global implications on health, financial healthcare systems, and societal well-being. This study undertakes a comprehensive exploration of various structural learning algorithms to discern causal pathways amongst potential risk factors influencing diabetes progression. The methodology i

Haedam Oh, Nived Chebrolu, Matias Mattamala, Leonard Freißmuth

Many LiDAR place recognition systems have been developed and tested specifically for urban driving scenarios. Their performance in natural environments such as forests and woodlands have been studied less closely. In this paper, we analyzed the capabilities of four different LiDAR place recognition systems, both handcrafted and learning-based methods, using

Oliver A. Thompson, Alexander J. Richings, Brad K. Gibson, Claude-André Faucher-Giguère

Our ability to trace the star-forming molecular gas is important to our understanding of the Universe. We can trace this gas using CO emission, converting the observed CO intensity into the H$_2$ gas mass of the region using the CO-to-H$_2$ conversion factor (Xco). In this paper, we use simulations to study the conversion factor and the molecular gas within

Michael John Fanous, Paloma Casteleiro Costa, Cagatay Isil, Luzhe Huang

The integration of deep learning techniques with biophotonic setups has opened new horizons in bioimaging. A compelling trend in this field involves deliberately compromising certain measurement metrics to engineer better bioimaging tools in terms of cost, speed, and form-factor, followed by compensating for the resulting defects through the utilization of d

Andreas Chatziafratis, Spyridon Kamvissis

In this note, we announce a systematic analysis of continuous dependence on the data in classical spaces for the initial-boundary-value problem of the diffusion equation on the half-line, with data that are not necessarily compatible at the quadrant corner. This is based on a recent approach to rigorously analyzing integral representations derived via the un

Francesco Macheda, Francesco Mauri, Thibault Sohier

Plasmons and polar phonons are elementary electrodynamic excitations of matter. In 2d and at long wavelengths, they couple to light and act as the system polaritons. They also dictate the scattering of charged carriers. Van der Waals heterostructures offer the opportunity to couple excitations from different layers via long-range Coulomb interactions, modify

Ryoji Takano

In this paper, we prove the large deviation principle (LDP) for stochastic differential equations driven by stochastic integrals in one dimension. The result can be proved with a minimal use of rough path theory, and this implies the LDP for many class of rough volatility models, and it characterizes the asymptotic behavior of implied volatility. First, we i

Jianeng Wang, Matias Mattamala, Christina Kassab, Guillaume Burger

Self-balancing exoskeletons are a key enabling technology for individuals with mobility impairments. While the current challenges focus on human-compliant hardware and control, unlocking their use for daily activities requires a scene perception system. In this work, we present Exosense, a vision-centric scene understanding system for self-balancing exoskele

Sergey I. Agafonov, Vladimir S. Matveev

We show that if $n$ functionally independent commutative quadratic in momenta integrals for the geodesic flow of a Riemannian or pseudo-Riemannian metric on an $n$-dimensional manifold are simultaneously diagonalisable at the tangent space to every point, then they come from the St\"ackel construction, so the metric admits orthogonal separation of variables.

Ali Ezati, Mohammadreza Dezyani, Rajib Rana, Roozbeh Rajabi

Convolutional neural networks (CNNs) and their variations have shown effectiveness in facial expression recognition (FER). However, they face challenges when dealing with high computational complexity and multi-view head poses in real-world scenarios. We introduce a lightweight attentional network incorporating multi-scale feature fusion (LANMSFF) to tackle

Michal Johanis, Luděk Zajíček

Our note is a complement to recent articles \cite{JS1} (2011) and \cite{JS2} (2013) by M. Jim\'enez-Sevilla and L. S\'anchez-Gonz\'alez which generalise (the basic statement of) the classical Whitney extension theorem for $C^1$-smooth real functions on $\mathbb R^n$ to the case of real functions on $X$ (\cite{JS1}) and to the case of mappings from $X$ to $Y$

Chandrasheel Bhagwat, Shubham Jaiswal

In this article, we realize some groups as Galois groups over rational numbers and finite extension of rational numbers by studying right splitting of some exact sequences, Galois correspondence and algebraic operations on Galois representations.

Wolfgang Glatzel, Michaela Kraus

Yellow Hypergiants (YHGs) are massive stars that are commonly interpreted to be in a post-red supergiant evolutionary state. These objects can undergo outbursts on timescales of decades, which are suspected to be due to instabilities in the envelope. To test this conjecture, the stability of envelope models for YHGs with respect to infinitesimal, radial pert

Sreemayee Aditya, Deepak Dhar, Diptiman Sen

We introduce a one-dimensional correlated-hopping model of spinless fermions in which a particle can hop between two neighboring sites only if the sites to the left and right of those two sites have different particle numbers. Using a bond-to-site mapping, this model involving four-site terms can be mapped to an assisted pair-flipping model involving only th

Jaroslav Merc, Peter Velez, Stéphane Charbonnel, Olivier Garde

The poorly studied variable star V390 Sco, previously classified as a Mira pulsator, was detected in a brightening event by the ESA Gaia satellite in September 2023. This work presents an analysis of available archival multifrequency photometric data of this target, along with our spectroscopic observations. Our findings lead to the conclusion that V390 Sco

Xiaoxue Cheng, Junyi Li, Wayne Xin Zhao, Ji-Rong Wen

Chain-of-Thought (CoT) prompting can enhance the reasoning capabilities of large language models (LLMs), establishing itself as a primary approach to solving complex reasoning tasks. Existing CoT synthesis approaches usually focus on simpler reasoning tasks and thus result in low-quality and inconsistent CoT prompts. In response to this challenge, we present

Kimyeong Lee, Kaiwen Sun, Haowu Wang

The Freudenthal--Tits magic square $\mathfrak{m}(\mathbb{A}_1,\mathbb{A}_2)$ for $\mathbb{A}=\mathbb{R},\mathbb{C},\mathbb{H},\mathbb{O}$ of semi-simple Lie algebras can be extended by including the sextonions $\mathbb{S}$. A series of non-reductive Lie algebras naturally appear in the new row associated with the sextonions, which we will call the \textit{in

Lennart Heeren, Adwait Datar, Antonio Mendez Gonzalez, Herbert Werner

In this paper, we propose a model reduction technique for linear parameter varying (LPV) systems based on available tools for fixed-structure controller synthesis. We start by transforming a model reduction problem into an equivalent controller synthesis problem by defining an appropriate generalized plant. The controller synthesis problem is then solved by

Shyam Balaji, Guillem Domènech, Gabriele Franciolini, Alexander Ganz

It has been recently proposed that Hawking evaporation might slow down after a black hole has lost about half of its mass. Such an effect, called "memory burden", is parameterized as a suppression in the mass loss rate by negative powers $n$ of the black hole entropy and could considerably extend the lifetime of a black hole. We study the impact of memory bu

Li Conghui

Electrohydrodynamics is a discipline that studies the interaction between fluid motion and electric field. Finite element method, finite difference method and other numerical simulations are effective numerical calculation methods for electrofluid dynamics models. In this paper, the finite element format of the electrofluid dynamics model is established, and

Diego Alberici, Pierluigi Contucci, Emanuele Mingione, Filippo Zimmaro

A family of multispecies Ising models on generalized regular random graphs is investigated in the thermodynamic limit. The architecture is specified by class-dependent couplings and magnetic fields. We prove that the magnetizations, neighbours correlations and free energy converge to suitable functions evaluated at the solution of a belief propagation fixed

Eray Guven, Gunes Karabulut Kurt

A precise incident wave angle estimation in aerial communication is a key enabler in sixth-generation wireless communication network. With this goal, a generic 3-dimensional (3D) channel model is analyzed for air-to-air (A2A) networks under antenna misalignment, radio frequency impairments and polarization loss. The unique aspects of each aerial node are hig

Adrian Röfer, Iman Nematollahi, Tim Welschehold, Wolfram Burgard

Sample efficient learning of manipulation skills poses a major challenge in robotics. While recent approaches demonstrate impressive advances in the type of task that can be addressed and the sensing modalities that can be incorporated, they still require large amounts of training data. Especially with regard to learning actions on robots in the real world,

Neil T. Lewis, Mark R. England, James A. Screen, Ruth Geen

Coupled climate model simulations designed to isolate the effects of Arctic sea-ice loss often apply artificial heating, either directly to the ice or through modification of the surface albedo, to constrain sea-ice in the absence of other forcings. Recent work has shown that this approach may lead to an overestimation of the climate response to sea-ice loss

M V Vismaya, M V Sangaranarayanan

A unified algebraic structure is shown to exist among various equations for the critical temperatures pertaining to diverse two- and three-dimensional lattices. This isomorphism is a pointer to the straight-forward extension of two-dimensional results to corresponding three dimensional analogues.

Xinyu Shi, Zecheng Hao, Zhaofei Yu

The remarkable success of Vision Transformers in Artificial Neural Networks (ANNs) has led to a growing interest in incorporating the self-attention mechanism and transformer-based architecture into Spiking Neural Networks (SNNs). While existing methods propose spiking self-attention mechanisms that are compatible with SNNs, they lack reasonable scaling meth

Zhi-Gang Hu, Yi-Meng Gao, Jian-Fei Liu, Hao Yang

Cavity optomechanical systems have enabled precision sensing of magnetic fields, by leveraging the optical resonance-enhanced readout and mechanical resonance-enhanced response. Previous studies have successfully achieved scalable and reproducible microcavity optomechanical magnetometry (MCOM) by incorporating Terfenol-D thin films into high-quality ($Q$) fa

Yutong Hu, Kehan Wen, Fisher Yu

Learning dexterous locomotion policy for legged robots is becoming increasingly popular due to its ability to handle diverse terrains and resemble intelligent behaviors. However, joint manipulation of moving objects and locomotion with legs, such as playing soccer, receive scant attention in the learning community, although it is natural for humans and smart

Marilena Crupi, Antonino Ficarra

In this paper, we give a new criterion for the Cohen-Macaulayness of vertex splittable ideals, a family of monomial ideals recently introduced by Moradi and Khosh-Ahang. Our result relies on a Betti splitting of the ideal and provides an inductive way of checking the Cohen-Macaulay property. As a result, we obtain characterizations for Gorenstein, level and

Virginia Morini, Valentina Pansanella, Katherine Abramski, Erica Cau

Social media platforms are online fora where users engage in discussions, share content, and build connections. This review explores the dynamics of social interactions, user-generated contents, and biases within the context of social media analysis (analyzing works that use the tools offered by complex network analysis and natural language processing) throu

Francisco Mena, Diego Arenas, Marcela Charfuelan, Marlon Nuske

Earth observation (EO) applications involving complex and heterogeneous data sources are commonly approached with machine learning models. However, there is a common assumption that data sources will be persistently available. Different situations could affect the availability of EO sources, like noise, clouds, or satellite mission failures. In this work, we

Katharina Ledebur, Alexandra Kautzky-Willer, Stefan Thurner, Peter Klimek

As populations age, the rise of multimorbidity poses a significant healthcare challenge. However, our ability to quantitatively forecast the progression of multimorbidity remains limited. Leveraging a nationwide dataset comprising approximately 45 million hospital stays spanning 17 years in Austria, we develop a new compartmental model for chronic disease tr

Partha Pratim Ghosh, Benedikt Jahnel, Sanjoy Kumar Jhawar

We derive large- and moderate-deviation results in random networks given as planar directed navigations on homogeneous Poisson point processes. In this non-Markovian routing scheme, starting from the origin, at each consecutive step a Poisson point is joined by an edge to its nearest Poisson point to the right within a cone. We establish precise exponential

Bing-Hai Qin, Wen Qin, Ling-Yun Dai

In this paper, a coherent study of the $e^+e^-$ annihilation into $K^+K^-\pi^0$, $K^0_SK^0_L\pi^0$ and $K^0_SK^\pm\pi^\mp$ is carried out within the framework of resonance chiral theory. The amplitudes are fixed by fitting to the experimental cross-section and invariant mass spectrum. With these amplitudes, one can calculate the hadronic vacuum polarization

Ponkoj Chandra Shill, Md. Azizul Hakim

As robots become increasingly integrated into various industries, understanding how humans respond to robotic failures is critical. This study systematically examines trust dynamics and system design by analyzing human reactions to robot failures. We conducted a four-stage survey to explore how trust evolves throughout human-robot interactions. The first sta

Saad Noufel, Nadir Maaroufi, Mehdi Najib, Mohamed Bakhouya

Inpainting, for filling missing image regions, is a crucial task in various applications, such as medical imaging and remote sensing. Trending data-driven approaches efficiency, for image inpainting, often requires extensive data preprocessing. In this sense, there is still a need for model-driven approaches in case of application constrained with data avail

Pablo Marcos-Manchón, Roberto Alcover-Couso, Juan C. SanMiguel, Jose M. Martínez

Diffusion models represent a new paradigm in text-to-image generation. Beyond generating high-quality images from text prompts, models such as Stable Diffusion have been successfully extended to the joint generation of semantic segmentation pseudo-masks. However, current extensions primarily rely on extracting attentions linked to prompt words used for image

Subhajit Saha, Md Sahidullah, Swagatam Das

The state-of-the-art audio deepfake detectors leveraging deep neural networks exhibit impressive recognition performance. Nonetheless, this advantage is accompanied by a significant carbon footprint. This is mainly due to the use of high-performance computing with accelerators and high training time. Studies show that average deep NLP model produces around 6

Anton Dmytriiev, Markus Böttcher

Context. Blazar flares provide a window into the extreme physical processes occurring in relativistic outflows. Most numerical codes used for modeling blazar emission during flares utilize a simplified continuous-loss description of particle cooling due to the inverse Compton (IC) process, neglecting non-continuous (discrete) effects that arise in the Klein-

Jiahui Zhang

In this paper, a theoretical method for the efficient creation and detection of deeply bound molecules in three-state $\Lambda$-type and five-state M-type molecular systems is proposed. The method is based on the three-state coincident pulses technique and the generalized five-state coincident pulses technique. For the three-state system, the technique can e

Tingyu Lin, Robert Sablatnig

In analyzing vast amounts of digitally stored historical image data, existing content-based retrieval methods often overlook significant non-semantic information, limiting their effectiveness for flexible exploration across varied themes. To broaden the applicability of image retrieval methods for diverse purposes and uncover more general patterns, we innova

Nikhil Raghav, Md Sahidullah

Clustering speaker embeddings is crucial in speaker diarization but hasn't received as much focus as other components. Moreover, the robustness of speaker diarization across various datasets hasn't been explored when the development and evaluation data are from different domains. To bridge this gap, this study thoroughly examines spectral clustering for both

Jordan Haden

We present a family of selfinjective algebras of type D, which arise from the 3-preprojective algebras of type A by taking a $\mathbb{Z}_3$-quotient. We show that a subset of these are themselves 3-preprojective algebras, and that the associated 2-representation-finite algebras are fractional Calabi-Yau. In addition, we show our work is connected to modular

Aprem P. Joy, Achim Rosch

The Kitaev spin liquid realizes an emergent static $\mathbb{Z}_2$ gauge field with vison excitations coupled to Majorana fermions. We consider Kitaev models stacked on top of each other, weakly coupled by Heisenberg interaction $\propto J_\perp$. This inter-layer coupling breaks the integrability of the model and makes the gauge fields dynamic. \new{Conserva

Arash Hajisharifi, Rahul Halder, Michele Girfoglio, Andrea Beccari

The numerical treatment of fluid-particle systems is a very challenging problem because of the complex coupling phenomena occurring between the two phases. Although accurate mathematical modelling is available to address this kind of application, the computational cost of the numerical simulations is very expensive. The use of the most modern high-performanc

Marco Favier, Toon Calders, Sam Pinxteren, Jonathan Meyer

It is widely accepted that biased data leads to biased and thus potentially unfair models. Therefore, several measures for bias in data and model predictions have been proposed, as well as bias mitigation techniques whose aim is to learn models that are fair by design. Despite the myriad of mitigation techniques developed in the past decade, however, it is s

Martin Messmer, Benjamin Kiefer, Leon Amadeus Varga, Andreas Zell

In this paper, we explore the application of Unmanned Aerial Vehicles (UAVs) in maritime search and rescue (mSAR) missions, focusing on medium-sized fixed-wing drones and quadcopters. We address the challenges and limitations inherent in operating some of the different classes of UAVs, particularly in search operations. Our research includes the development

Zheyuan He, Zihao Li, Sen Yang, He Ye

Large Language Models (LLMs) have emerged as powerful tools across various domains within cyber security. Notably, recent studies are increasingly exploring LLMs applied to the context of blockchain security (BS). However, there remains a gap in a comprehensive understanding regarding the full scope of applications, impacts, and potential constraints of LLMs

Francesco Di Felice, Alberto Remus, Stefano Gasperini, Benjamin Busam

Estimating the pose of objects through vision is essential to make robotic platforms interact with the environment. Yet, it presents many challenges, often related to the lack of flexibility and generalizability of state-of-the-art solutions. Diffusion models are a cutting-edge neural architecture transforming 2D and 3D computer vision, outlining remarkable

Samet Ok, Axel Schwope, David A. H. Buckley, Jaco Brink

In this study, we report a discovery from XMM-Newton, which involves the identification and subsequent examination of a newly discovered polar-type cataclysmic variable named XMM J152737.4-205305.9. The discovery was made by matching the XMM-Newton data archive with the cataclysmic variable candidate catalog provided by Gaia Data Release 3. The utilization o

Gabriel F. Rodríguez Ruiz, Adrian Reich, Alexander Shnirman, Jörg Schmalian

We study the joint dynamics of the phase bias $\phi$ and the propagating Majorana fermions of the edge modes in Josephson junctions containing 2D time-reversal invariant topological superconductors (TRITOPS). We consider TRITOPS-TRITOPS junctions, as well as junctions between topological and non-topological superconductors (TRITOPS-S). Both types of junction

Frederik Thiele, Niklas Lamberty, Thomas Hummel, Tim Bartley

Cryogenic opto-electronic interconnects are gaining increasing interest as a means to control and read out cryogenic electronic components. The challenge is to achieve sufficient signal integrity with low heat load processing. In this context, we demonstrate the opto-electronic bias and readout of a commercial four-pixel superconducting nanowire single-photo

Shuqian Sheng, Yi Xu, Luoyi Fu, Jiaxin Ding

The majority of automatic metrics for evaluating NLG systems are reference-based. However, the challenge of collecting human annotation results in a lack of reliable references in numerous application scenarios. Despite recent advancements in reference-free metrics, it has not been well understood when and where they can be used as an alternative to referenc

Zhenyu Mao, Jialong Li, Dongming Jin, Munan Li

Recent advancements in large language models (LLMs) have highlighted the potential for vulnerability detection, a crucial component of software quality assurance. Despite this progress, most studies have been limited to the perspective of a single role, usually testers, lacking diverse viewpoints from different roles in a typical software development life-cy

Deborah Schwarcz, Nadav Schneider, Gal Oren, Uri Steinitz

Neutronic calculations for reactors are a daunting task when using Monte Carlo (MC) methods. As high-performance computing has advanced, the simulation of a reactor is nowadays more readily done, but design and optimization with multiple parameters is still a computational challenge. MC transport simulations, coupled with machine learning techniques, offer p

Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior

The study performs large-eddy simulations of supersonic free jet flows using the Discontinuous Galerkin Spectral Element Method (DGSEM). The main objective of the present work is to assess the resolution requirements for adequate simulation of such flows with the DGSEM approach. The study looked at the influence of the mesh and the spatial discretization acc

Oskar A. Sultanov

The influence of multiplicative white noise on the resonance capture of strongly nonlinear oscillatory systems under chirped-frequency excitations is investigated. It is assumed that the intensity of the perturbation decays polynomially with time, and its frequency grows according to a power low. Resonant solutions with a growing amplitude and phase, synchro

Tim Salzmann, Markus Ryll, Alex Bewley, Matthias Minderer

Visual relationship detection aims to identify objects and their relationships in images. Prior methods approach this task by adding separate relationship modules or decoders to existing object detection architectures. This separation increases complexity and hinders end-to-end training, which limits performance. We propose a simple and highly efficient deco

Seonghyuk Im, Hyunwoo Lee

Dirac's theorem states that any $n$-vertex graph $G$ with even integer $n$ satisfying $\delta(G) \geq n/2$ contains a perfect matching. We generalize this to $k$-uniform linear hypergraphs by proving the following. Any $n$-vertex $k$-uniform linear hypergraph $H$ with minimum degree at least $\frac{n}{k} + \Omega(1)$ contains a matching that covers at least

PeiYing Lee, HauYun Guo, Berlin Chen

End-to-End Neural Diarization with Encoder-Decoder based Attractor (EEND-EDA) is an end-to-end neural model for automatic speaker segmentation and labeling. It achieves the capability to handle flexible number of speakers by estimating the number of attractors. EEND-EDA, however, struggles to accurately capture local speaker dynamics. This work proposes an a

Jonathan Ditlevsen, Jan Frahm

The pair of real reductive groups $(G,H)=(\operatorname{GL}(n+1,\mathbb{R}),\operatorname{GL}(n,\mathbb{R}))$ is a strong Gelfand pair, i.e. the multiplicities $\dim\operatorname{Hom}_H(\pi|_H,\tau)$ are either $0$ or $1$ for all irreducible Casselman-Wallach representations $\pi$ of $G$ and $\tau$ of $H$. This paper is concerned with the construction of exp

Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang

Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications. Recent advances in Foundation Models (FMs) have fundamentally reshaped the paradigm of model design for time series analysis, boosting various downstream tasks in practice. Th

Márcio Ferreira, Michał Bejger

We present a new inference framework for neutron star astrophysics based on conditional variational autoencoders. Once trained, the generator block of the model reconstructs the neutron star equation of state from a given set of mass-radius observations. While the pressure of dense matter is the focus of the present study, the proposed model is flexible enou

Ananya Singha, Bhavya Chopra, Anirudh Khatry, Sumit Gulwani

Automated insight generation is a common tactic for helping knowledge workers, such as data scientists, to quickly understand the potential value of new and unfamiliar data. Unfortunately, automated insights produced by large-language models can generate code that does not correctly correspond (or align) to the insight. In this paper, we leverage the semanti

Patrick Serwene

We prove that an exotic fusion system described by Grazian on a subgroup of the Monster group is block-exotic, thus proving that exotic and block-exotic fusion systems are the same for all $p$-groups with sectional rank 3, where $p \geq 5$.

Seungkwon Kim, Sangyeon Kim, Seung-Hun Nam

Portrait stylization is a challenging task involving the transformation of an input portrait image into a specific style while preserving its inherent characteristics. The recent introduction of Stable Diffusion (SD) has significantly improved the quality of outcomes in this field. However, a practical stylization framework that can effectively filter harmfu

Neuton Li, Jihua Zhang, Dragomir N. Neshev, Andrey A. Sukhorukov

Sum frequency generation (SFG) has multiple applications, from optical sources to imaging, where efficient conversion requires either long interaction distances or large field concentrations in a quadratic nonlinear material. Metasurfaces provide an essential avenue to enhanced SFG due to resonance with extreme field enhancements with an integrated ultrathin

Finn Behrendt, Debayan Bhattacharya, Lennart Maack, Julia Krüger

Supervised deep learning techniques show promise in medical image analysis. However, they require comprehensive annotated data sets, which poses challenges, particularly for rare diseases. Consequently, unsupervised anomaly detection (UAD) emerges as a viable alternative for pathology segmentation, as only healthy data is required for training. However, rece

Dan Ciubotaru, Ju-Lee Kim

For an irreducible smooth representation of a connected reductive $p$-adic group, two important associated invariants are the wavefront set and the (partly conjectural) Langlands parameter. While a wavefront set consists of $p$-adic nilpotent orbits, one constituent of the Langlands parameter is a complex nilpotent orbit in the dual Lie algebra. For unipoten

Marcos Sánchez-Élez, Inmaculada Pardines, Pablo García, Guadalupe Miñana

The use of new technologies in higher education has surprisingly emphasized students' tendency to adopt a passive behavior in class. Participation and interaction of students are essential to improve academic results. This paper describes an educational experiment aimed at the promotion of students' autonomous learning by requiring them to generate test type

Gianluca Grilletti, Ivano Ciardelli

The aim of this paper is to study the complexity of the model checking problem MC for inquisitive propositional logic InqB and for inquisitive modal logic InqM, that is, the problem of deciding whether a given finite structure for the logic satisfies a given formula. In recent years, this problem has been thoroughly investigated for several variations of dep

Elie Rouphael, Manas Mejari, Mihaly Petreczky, Lotfi Belkoura

In this paper, we consider stochastic realization theory of Linear Switched Systems (LSS) with i.i.d. switching. We characterize minimality of stochastic LSSs and show existence and uniqueness (up to isomorphism) of minimal LSSs in innovation form. We present a realization algorithm to compute a minimal LSS in innovation form from output and input covariance

Stefan Trapp, Joachim Warschat

Already since the 1950s TRIZ shows that patents and the technical contradictions they solve are an important source of inspiration for the development of innovative products. However, TRIZ is a heuristic based on a historic patent analysis and does not make use of the ever-increasing number of latest technological solutions in current patents. Because of the

Benjamin Delarue, Daniel Monclair, Andrew Sanders

By constructing a non-empty domain of discontinuity in a suitable homogeneous space, we prove that every torsion-free projective Anosov subgroup is the monodromy group of a locally homogeneous contact Axiom A dynamical system with a unique basic hyperbolic set on which the flow is conjugate to the refraction flow of Sambarino. Under the assumption of irreduc