April 2024 arXiv papers — page 57

F. H. B. Somhorst, B. K. Sauër, S. N. van den Hoven, J. J. Renema

We present a scalable scheme to achieve photon distillation, i.e. the preparation of a single photon with reduced indistinguishability error out of multiple imperfect photons, based on multiphoton interference in Fourier matrices. Our scheme achieves arbitrary error reduction in a single step, removing the need to concatenate multiple rounds of the protocol.

Rene Allerstorfer, Llorenç Escolà-Farràs, Arpan Akash Ray, Boris Skoric

Motivated by the fact that coherent states may offer practical advantages it was recently shown that a continuous-variable (CV) quantum position verification (QPV) protocol using coherent states could be securely implemented if and only if attackers do not pre-share any entanglement. In the discrete-variable (DV) analogue of that protocol it was shown that m

Subhajit Kar, Ramkrishna Das, Tapas Baug

We report a multi-wavelength study of the Wolf Rayet (WR) star: [KSF2015] 1381-19L, which is located in the solar metallicity region (Z=0.014) of the Milky Way Galaxy, strongly obscured by the interstellar dust. We perform a detailed characterization of the stellar atmosphere by fitting the spectral emission lines observed in the Optical and Near-InfraRed (N

Ali Rostami Shirazi, Hosein Haghi, Akram Hasani Zonoozi, Ahmad Farahani Asl

The Spitzer instability leads to the formation of a black hole sub-system (BHSub) at the center of a star cluster providing energy to luminous stars (LSs) and increasing their rate of evaporation. When the self-depletion time of the BHSub exceeds the evaporation time of the LSs, a dark star cluster (DSC) will appear. Using the NBODY7 code, we performed a com

Igor O. Sokolov, Gert-Jan Both, Art D. Bochevarov, Pavel A. Dub

Kohn-Sham Density Functional Theory (KS-DFT) provides the exact ground state energy and electron density of a molecule, contingent on the as-yet-unknown universal exchange-correlation (XC) functional. Recent research has demonstrated that neural networks can efficiently learn to represent approximations to that functional, offering accurate generalizations t

Ruairi Moran, Sheila Bagley, Seth Kasmann, Rob Martin

This paper introduces a novel NMPC formulation for real-time obstacle avoidance on heavy equipment by modeling both vehicle and obstacles as convex superellipsoids. The combination of this approach with the separating hyperplane theorem and Optimization Engine (OpEn) allows to achieve efficient obstacle avoidance in autonomous heavy equipment and robotics. W

Paul L. Schechter, Dominique Sluse, Erik A. Zaborowski, Alex Drlica-Wagner

A quadruply lensed source, J125856.3-031944, has been discovered using the DELVE survey and WISE W1 - W2 colors. Followup direct imaging carried out with the Magellan Baade 6.5 m telescope is analyzed, as is spectroscopy from the 2.5 m Nordic Optical Telescope. The lensed image configuration is kite-like, with the major axis of the lensing galaxy along the s

Samuel M. Corson, Sam Hughes, Philip Möller, Olga Varghese

We prove that affine Coxeter groups are profinitely rigid.

Mahmoud Abu-samha, L. B. Madsen, N. I. Shvetsov-Shilovski

We investigate the effects of the multielectron polarization of the ion described by the induced dipole potential in photoelectron momentum distributions produced in ionization of the CO molecule by a strong laser field. We present results of the numerical solution of the time-dependent Schr\"{o}dinger equation in three spatial dimensions and semiclassical s

Emil Dare, Markus Kiderlen, Christoph Thaele

Consider a uniformly distributed random linear subspace $L$ and a stochastically independent random affine subspace $E$ in $\mathbb{R}^n$, both of fixed dimension. For a natural class of distributions for $E$ we show that the intersection $L\cap E$ admits a density with respect to the invariant measure. This density depends only on the distance $d(o,E \cap L

Thomas Ortner, Horst Petschenig, Athanasios Vasilopoulos, Roland Renner

There is a growing demand for low-power, autonomously learning artificial intelligence (AI) systems that can be applied at the edge and rapidly adapt to the specific situation at deployment site. However, current AI models struggle in such scenarios, often requiring extensive fine-tuning, computational resources, and data. In contrast, humans can effortlessl

Tommaso Gastaldi

This paper establishes that conditioning the probability of execution of new orders on the self-generated historical trading information (HTI) of a trading strategy is a necessary condition for a statistical trading edge. It is shown, in particular, that, given any trading strategy S that does not use its own HTI, it is always possible to construct a new str

Marcus Hilbrich, Ninon De Mecquenem

Managing software artifacts is one of the most essential aspects of computer science. It enables to develop, operate, and maintain software in an engineer-like manner. Therefore, numerous concrete strategies, methods, best practices, and concepts are available. A combination of such methods must be adequate, efficient, applicable, and effective for a concret

Aaron Buchwald, Stephen Buttolph, Andrew Lewis-Pye, Patrick O'Grady

Snowman is the consensus protocol implemented by the Avalanche blockchain and is part of the Snow family of protocols, first introduced through the original Avalanche leaderless consensus protocol. A major advantage of Snowman is that each consensus decision only requires an expected constant communication overhead per processor in the `common' case that the

Alexander Rogiers, Maarten Buyl, Bo Kang, Tijl De Bie

KamerRaad is an AI tool that leverages large language models to help citizens interactively engage with Belgian political information. The tool extracts and concisely summarizes key excerpts from parliamentary proceedings, followed by the potential for interaction based on generative AI that allows users to steadily build up their understanding. KamerRaad's

Guibiao Liao, Jiankun Li, Zhenyu Bao, Xiaoqing Ye

Exploiting 3D Gaussian Splatting (3DGS) with Contrastive Language-Image Pre-Training (CLIP) models for open-vocabulary 3D semantic understanding of indoor scenes has emerged as an attractive research focus. Existing methods typically attach high-dimensional CLIP semantic embeddings to 3D Gaussians and leverage view-inconsistent 2D CLIP semantics as Gaussian

Xiaoning Liu, Zongwei Wu, Ao Li, Florin-Alexandru Vasluianu

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and be

Anjith George, Sebastien Marcel

Heterogeneous Face Recognition (HFR) focuses on matching faces from different domains, for instance, thermal to visible images, making Face Recognition (FR) systems more versatile for challenging scenarios. However, the domain gap between these domains and the limited large-scale datasets in the target HFR modalities make it challenging to develop robust HFR

Alexander W. Byard, Brian Cai, Nathan P. Jones, Lucy H. Vuong

We undertake the study of profinite quandles. We provide several constructions of profinite quandles from profinite groups, and from other profinite quandle. We characterize which subquandles of profinite quandles are again profinite. Finally, we provide a characterization of algebraically connected profinite quandles in terms of the profinite completion of

Adam Janovsky, Łukasz Chmielewski, Petr Svenda, Jan Jancar

With 5394 security certificates of IT products and systems, the Common Criteria for Information Technology Security Evaluation have bred an ecosystem entangled with various kind of relations between the certified products. Yet, the prevalence and nature of dependencies among Common Criteria certified products remains largely unexplored. This study devises a

Julian Dörfler, Christian Ikenmeyer

We determine all functional closure properties of finite $\mathbb{N}$-weighted automata, even all multivariate ones, and in particular all multivariate polynomials. We also determine all univariate closure properties in the promise setting, and all multivariate closure properties under certain assumptions on the promise, in particular we determine all multiv

Jonas Ricker, Dennis Assenmacher, Thorsten Holz, Asja Fischer

Recent advances in the field of generative artificial intelligence (AI) have blurred the lines between authentic and machine-generated content, making it almost impossible for humans to distinguish between such media. One notable consequence is the use of AI-generated images for fake profiles on social media. While several types of disinformation campaigns a

Jin-Duk Park, Yong-Min Shin, Won-Yong Shin

A series of graph filtering (GF)-based collaborative filtering (CF) showcases state-of-the-art performance on the recommendation accuracy by using a low-pass filter (LPF) without a training process. However, conventional GF-based CF approaches mostly perform matrix decomposition on the item-item similarity graph to realize the ideal LPF, which results in a n

Zheru Qiu, Neetesh Singh, Yang Liu, Xinru Ji

Microwaves generated by optical techniques have demonstrated unprecedentedly low noise and hold significance in various applications such as communication, radar, instrumentation, and metrology. To date, the purest microwave signals are generated using optical frequency division with femtosecond mode-locked lasers. However, many femtosecond laser combs have

Siru Zhong, Xixuan Hao, Yibo Yan, Ying Zhang

Urbanization challenges underscore the necessity for effective satellite image-text retrieval methods to swiftly access specific information enriched with geographic semantics for urban applications. However, existing methods often overlook significant domain gaps across diverse urban landscapes, primarily focusing on enhancing retrieval performance within s

Yu Hou, Jin-Duk Park, Won-Yong Shin

A recent study has shown that diffusion models are well-suited for modeling the generative process of user-item interactions in recommender systems due to their denoising nature. However, existing diffusion model-based recommender systems do not explicitly leverage high-order connectivities that contain crucial collaborative signals for accurate recommendati

Chenyang Zhu, Kai Li, Yue Ma, Chunming He

This paper introduces MultiBooth, a novel and efficient technique for multi-concept customization in image generation from text. Despite the significant advancements in customized generation methods, particularly with the success of diffusion models, existing methods often struggle with multi-concept scenarios due to low concept fidelity and high inference c

Ning Yang, Shuo Chen, Haijun Zhang, Randall Berry

Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the central network, incorporating edge nodes close to end devices. This expansion facilitates the implementation of large-scale "connected things" within edge networks. The advent of applications necessitating real-time, high-quality service presents several challenges, such as

GuangZhen Wang, Xiaolei Li, Nan Liang

In this work, we investigate the phenomenologically emergent dark energy (PEDE) model and its generalized form, namely the generalized emergent dark energy (GEDE) model, which introduces a free parameter \unboldmath {\( \Delta \)} that can discriminate between the \unboldmath{$\mathrm{\Lambda}$}CDM model and the PEDE model. Fitting the emergent dark energy (

Mathias Thorsager, Victor Croisfelt, Junya Shiraishi, Petar Popovski

This paper introduces EcoPull, a sustainable Internet of Things (IoT) framework empowered by tiny machine learning (TinyML) models for fetching images from wireless visual sensor networks. Two types of learnable TinyML models are installed in the IoT devices: i) a behavior model and ii) an image compressor model. The first filters out irrelevant images for t

Jarno Alanko, Davide Cenzato, Nicola Cotumaccio, Sung-Hwan Kim

The LCP array is an important tool in stringology, allowing to speed up pattern matching algorithms and enabling compact representations of the suffix tree. Recently, Conte et al. [DCC 2023] and Cotumaccio et al. [SPIRE 2023] extended the definition of this array to Wheeler DFAs and, ultimately, to arbitrary labeled graphs, proving that it can be used to eff

Christian Lange

We construct examples of (effective) closed orbifolds which are covered by manifolds, but not finitely so.

Wenyi Xiao, Ziwei Huang, Leilei Gan, Wanggui He

The rapidly developing Large Vision Language Models (LVLMs) have shown notable capabilities on a range of multi-modal tasks, but still face the hallucination phenomena where the generated texts do not align with the given contexts, significantly restricting the usages of LVLMs. Most previous work detects and mitigates hallucination at the coarse-grained leve

Anwesha Das, Zekun Wu, Iza Škrjanec, Anna Maria Feit

Visual highlighting can guide user attention in complex interfaces. However, its effectiveness under limited attentional capacities is underexplored. This paper examines the joint impact of visual highlighting (permanent and dynamic) and dual-task-induced cognitive load on gaze behaviour. Our analysis, using eye-movement data from 27 participants viewing 150

Wilson Román Acevedo, Myriam H. Aguirre, Beatriz Noheda, Diego Rubi

Neuromorphic computing aims to mimic the architecture and the information processing mechanisms of the mammalian brain, appearing as the only avenue that offers significant energy savings compared to the standard digital computers. Memcapacitive devices (which can change their capacitance between different non-volatile states upon the application of electric

Piotr Wilczyński, Wiktoria Mieleszczenko-Kowszewicz, Przemysław Biecek

If AI is the new electricity, what should we do to keep ourselves from getting electrocuted? In this work, we explore factors related to the potential of large language models (LLMs) to manipulate human decisions. We describe the results of two experiments designed to determine what characteristics of humans are associated with their susceptibility to LLM ma

Marco Bianucci, Mauro Bologna, Riccardo Mannella

Research on stochastic differential equations (SDE) involving both additive and multiplicative noise has been extensive. In situations where the primary process is driven by a multiplicative stochastic process, additive white noise typically represents an intrinsic and unavoidable fast factor, including phenomena like thermal fluctuations, inherent uncertain

Christian Pinto, Dong Li, Thaleia Dimitra Doudali, Christina Giannoula

The future of computing systems is inevitably embracing a disaggregated and composable pattern: from clusters of computers to pools of resources that can be dynamically combined together and tailored around applications requirements. Transitioning to this new paradigm requires ground-breaking research, ranging from new hardware architectures up to new models

Mingyu Huang, Shasha Zhou, Ke Li

We are living in an era of "big literature", where scientific literature is expanding exponentially. While this growth presents new opportunities, it complicates mapping global scientific research landscapes, as manual review methods become infeasible. Recent advancements in machine learning, complex networks, and natural language processing have enabled num

Vladimir Spokoiny

This note extends the results of classical parametric statistics like Fisher and Wilks theorem to modern setups with a high or infinite parameter dimension, limited sample size, and possible model misspecification. We consider a special class of stochastically linear smooth (SLS) models satisfying three major conditions: the stochastic component of the log-l

Rebecca Durst, Michael Neilan

In this paper, we construct and analyze divergence-free finite element methods for the Stokes problem on smooth domains. The discrete spaces are based on the Scott-Vogelius finite element pair of arbitrary polynomial degree greater than two. By combining the Piola transform with the classical isoparametric framework, and with a judicious choice of degrees of

Qi Zhengyang, Liu Zijing, Zhang Jiying, Cao He

Due to the vast design space of molecules, generating molecules conditioned on a specific sub-structure relevant to a particular function or therapeutic target is a crucial task in computer-aided drug design. Existing works mainly focus on specific tasks, such as linker design or scaffold hopping, each task requires training a model from scratch, and many we

Jiaxiang Liang, Minghui Du, Peng Xu

Based on the gravitoelectromagnetic formalism and inspired by the rich analogies between electrodynamics and general relativity, we try one step further along this line and suggest a new counterpart in the gravitoelectromagnetic world analogue to the electromagnetic physics. A counterpart model of the MagnetoHydroDynamics that could help us to understand the

Laura Hjort Blicher, Peter Emil Carstensen, Jacob Bendsen, Henrik Linden

Physiological whole-body models are valuable tools for the development of novel drugs where understanding the system aspects is important. This paper presents a generalized model that encapsulates the structure and flow of whole-body human physiology. The model contains vascular, interstitial, and cellular subcompartments for each organ. Scaling of volumes a

Alejandro Aguirre, Philipp G. Haselwarter, Markus de Medeiros, Kwing Hei Li

Probabilistic programs often trade accuracy for efficiency, and thus may, with a small probability, return an incorrect result. It is important to obtain precise bounds for the probability of these errors, but existing verification approaches have limitations that lead to error probability bounds that are excessively coarse, or only apply to first-order prog

Sumedh Rasal

Recent advancements in artificial intelligence have propelled the capabilities of Large Language Models, yet their ability to mimic nuanced human reasoning remains limited. This paper introduces a novel conceptual enhancement to LLMs, termed the Artificial Neuron, designed to significantly bolster cognitive processing by integrating external memory systems.

Jun Diao, Lin Zhou

We revisit sequential outlier hypothesis testing and derive bounds on achievable exponents when both the nominal and anomalous distributions are unknown. The task of outlier hypothesis testing is to identify the set of outliers that are generated from an anomalous distribution among all observed sequences where the rest majority are generated from a nominal

Parth Garud, Kiumars Aryana, Cosmin Constantin Popescu, Steven Vitale

Electrically tunable optical devices present diverse functionalities for manipulating electromagnetic waves by leveraging elements capable of reversibly switching between different optical states. This adaptability in adjusting their responses to electromagnetic waves after fabrication is crucial for developing more efficient and compact optical systems for

Marah Abdin, Jyoti Aneja, Hany Awadalla, Ahmed Awadallah

We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone. Our

Grazia Ragone, Paolo Buono, Rosa Lanzilotti

This workshop proposal focuses on best practices in UI/UX design for AI applications aimed at children, emphasising safety, engagement, and ethics. It aims to address the challenge of measuring the safety, trustworthiness, and reliability of interactions between children and AI systems. Through collaborative discussions, participants will explore effective d

Jason Saied, Jeffrey Marshall, Namit Anand, Eleanor G. Rieffel

We introduce state-of-the-art protocols to distill indistinguishable photons, reducing distinguishability error rates by a factor of $n$, while using a modest amount of resources scaling only linearly in $n$. Our resource requirements are both significantly lower and have fewer hardware requirements than previous works, making large-scale distillation experi

Amita Gnanapandithan, Li Qian, Hoi-Kwong Lo

Quantum protocols including quantum key distribution and blind quantum computing often require the preparation of quantum states of known dimensions. Here, we show that, rather surprisingly, hidden multi-dimensional modulation is often performed by practical devices. This violates the dimensional assumption in quantum protocols, thus creating side channels a

Zheye Deng, Chunkit Chan, Weiqi Wang, Yuxi Sun

The task of condensing large chunks of textual information into concise and structured tables has gained attention recently due to the emergence of Large Language Models (LLMs) and their potential benefit for downstream tasks, such as text summarization and text mining. Previous approaches often generate tables that directly replicate information from the te

Tim Gehrunger, Richard Pink

Consider a hyperelliptic curve of genus $g$ over a field $K$ of characteristic zero. After extending $K$ we can view it as a marked curve with its $2g+2$ Weierstrass points. We present an explicit algorithm to compute the stable reduction of this marked curve for a valuation of residue characteristic $2$ over a finite extension of $K$. In the cases $g\le2$ w

Louis Dijkstra, Tania Schink, Ronja Foraita

Despite extensive safety assessments of drugs prior to their introduction to the market, certain adverse drug reactions (ADRs) remain undetected. The primary objective of pharmacovigilance is to identify these ADRs (i.e., signals). In addition to traditional spontaneous reporting systems (SRSs), electronic health (EHC) data is being used for signal detection

Mauricio Lima, Katherine Deck, Oliver R. A. Dunbar, Tapio Schneider

Machine learning is playing an increasing role in hydrology, supplementing or replacing physics-based models. One notable example is the use of recurrent neural networks (RNNs) for forecasting streamflow given observed precipitation and geographic characteristics. Training of such a model over the continental United States (CONUS) has demonstrated that a sin

Tracey K. M. Lee, H. W. Chan, K. H. Leo, Effie Chew

Time series (TS) data have consistently been in short supply, yet their demand remains high for training systems in prediction, modeling, classification, and various other applications. Synthesis can serve to expand the sample population, yet it is crucial to maintain the statistical characteristics between the synthesized and the original TS : this ensures

Rajika L. Kuruwita, Christoph Federrath, Marina Kounkel

(Edited) Many fast rotator stars (rotation periods of < 2 days) are found in unresolved binaries with separations of tens of au. This correlation leads to the question of whether the formation of binary stars inherently produces fast rotators. We aim to understand whether the formation of companions plays a role in spinning up stars. We use magneto-hydrodyna

Po-Ting Lai, Elisabeth Coudert, Lucila Aimo, Kristian Axelsen

Expert curation is essential to capture knowledge of enzyme functions from the scientific literature in FAIR open knowledgebases but cannot keep pace with the rate of new discoveries and new publications. In this work we present EnzChemRED, for Enzyme Chemistry Relation Extraction Dataset, a new training and benchmarking dataset to support the development of

Klaus Heeger, Hendrik Molter

In this work, we study the computational (parameterized) complexity of $P \mid r_j, p_j=p \mid \sum_j w_j U_j$. Here, we are given $m$ identical parallel machines and $n$ jobs with equal processing time, each characterized by a release date, a due date, and a weight. The task is to find a feasible schedule, that is, an assignment of the jobs to starting time

Xing Huang, Quan Sun, Datao Gong, Piljun Gwak

We present the design and characterization of the first full chain precision timing prototype ASIC, named ETL Readout Chip version 1 (ETROC1) for the CMS MTD endcap timing layer (ETL) upgrade. The ETL utilizes Low Gain Avalanche Diode (LGAD) sensors to detect charged particles, with the goal to achieve a time resolution of 40 - 50 ps per hit, and 30 - 40 ps

Marina Lotti, Nicolò Decarli, Gianni Pasolini, Davide Dardari

In next-generation vehicular environments, precise localization is crucial for facilitating advanced applications such as autonomous driving. As automation levels escalate, the demand rises for enhanced accuracy, reliability, energy efficiency, update rate, and reduced latency in position information delivery. In this paper, we propose the exploitation of ba

Tiffany Y. Y. Lo, Watson Levens, David J. T. Sumpter

The way in which a social network is generated, in terms of how individuals attach to each other, determines the properties of the resulting network. Here we study an intuitively appealing `friend of a friend' model, where a network is formed by each newly added individual attaching first to a randomly chosen target and then to $n_q\geq 1$ randomly chosen fr

Ali Khalesi, Petros Elia

The work considers the $N$-server distributed computing scenario with $K$ users requesting functions that are linearly-decomposable over an arbitrary basis of $L$ real (potentially non-linear) subfunctions. In our problem, the aim is for each user to receive their function outputs, allowing for reduced reconstruction error (distortion) $\epsilon$, reduced co

Jung-hun Kim, Milan Vojnovic, Se-Young Yun

In this study, we consider the infinitely many-armed bandit problems in a rested rotting setting, where the mean reward of an arm may decrease with each pull, while otherwise, it remains unchanged. We explore two scenarios regarding the rotting of rewards: one in which the cumulative amount of rotting is bounded by $V_T$, referred to as the slow-rotting case

V. Uma

In this article we describe the $T_{comp}$-equivariant topological $K$-ring of a $T$-{\it cellular} complete toric variety. We further show that $K_{T_{comp}}^0(X)$ is isomorphic as an $R(T_{comp})$-algebra to the ring of piecewise Laurent polynomial functions on the associated fan denoted $PLP(\Delta)$. Furthermore, we compute a basis for $K_{T_{comp}}^0(X)

Dawoon E. Kim, Laura Di Gesu, Frédéric Marin, Alan P. Marscher

X-ray polarimetry has been suggested as a prominent tool for investigating the geometrical and physical properties of the emissions from active galactic nuclei (AGN). The successful launch of the Imaging X-ray Polarimetry Explorer (IXPE) on 9 December 2021 has expanded the previously restricted scope of polarimetry into the X-ray domain, enabling X-ray polar

Mana Masuda, Jinhyung Park, Shun Iwase, Rawal Khirodkar

While recent advancements in animatable human rendering have achieved remarkable results, they require test-time optimization for each subject which can be a significant limitation for real-world applications. To address this, we tackle the challenging task of learning a Generalizable Neural Human Renderer (GNH), a novel method for rendering animatable human

Shuo Zhang, Xin Chen, Zixuan Wang

The automobile plays an important role in the economic activities of mankind, especially in the metropolis. Under the circumstances, the demand of quick search for available parking spaces has become a major concern for the automobile drivers. Meanwhile, the public sense of privacy is also awaking, the image-based parking space recognition methods lack the a

Lu Han, Xu-Yang Chen, Han-Jia Ye, De-Chuan Zhan

Multivariate time series forecasting plays a crucial role in various fields such as finance, traffic management, energy, and healthcare. Recent studies have highlighted the advantages of channel independence to resist distribution drift but neglect channel correlations, limiting further enhancements. Several methods utilize mechanisms like attention or mixer

Serge N. Gavrilov, Ilya O. Poroshin, Ekaterina V. Shishkina, Yulia A. Mochalova

We consider a quite general problem concerning a linear free oscillation of a discrete mass-spring-damper system. This discrete sub-system is embedded into a one-dimensional continuum medium described by the linear telegraph equation. In a particular case, the discrete sub-system can move along the continuum one at a sub-critical speed. Provided that the dis

Dawid Strzelczyk, Miha Rot, Gregor Kosec, Maciej Matyka

In this paper, two mesh-free CFD solvers for pore-scale fluid flow through porous media are considered, namely the Lattice Boltzmann Method with the two relaxation time collision term and the direct Navier-Stokes solver under the artificial compressibility limit. The porous media is built with a regular arrangement of spherical grains with variable radii, wh

Denis V. Vasilyev, Athreya Shankar, Raphael Kaubruegger, Peter Zoller

We study optimal quantum sensing of multiple physical parameters using repeated measurements. In this scenario, the Fisher information framework sets the fundamental limits on sensing performance, yet the optimal states and corresponding measurements that attain these limits remain to be discovered. To address this, we extend the Fisher information approach

David E. Evans, Corey Jones

We consider the problem of building non-invertible quantum symmetries (as characterized by actions of unitary fusion categories) on noncommutative tori. We introduce a general method to construct actions of fusion categories on inductive limit C*-algberas using finite dimenionsal data, and then apply it to obtain AT-actions of arbitrary Haagerup-Izumi catego

Siyuan Shen, Langwen Huang, Marcin Chrapek, Timo Schneider

The shift towards high-bandwidth networks driven by AI workloads in data centers and HPC clusters has unintentionally aggravated network latency, adversely affecting the performance of communication-intensive HPC applications. As large-scale MPI applications often exhibit significant differences in their network latency tolerance, it is crucial to accurately

Víctor Franco-Sánchez, Arnau Martí-Llobet, Ramon Ferrer-i-Cancho

Consider a linguistic structure formed by $n$ elements, for instance, subject, direct object and verb ($n=3$) or subject, direct object, indirect object and verb ($n=4$). We investigate whether the frequency of the $n!$ possible orders is constrained by two principles. First, entropy minimization, a principle that has been suggested to shape natural communic

Omid Hurson

In the first part of the Thesis, we reformulate the Murakami-Ohtsuki-Yamada state-sum description of the level n Jones polynomial of an oriented link in terms of a suitable braided monoidal category whose morphisms are Q[q, q-1] s-linear combinations of oriented trivalent planar graphs, and give a corresponding description for the HOMFLY-PT polynomial. In th

Florian Nettersheim, Stephan Arlt, Michael Rademacher

Online advertising represents a main instrument for publishers to fund content on the World Wide Web. Unfortunately, a significant number of online advertisements often accommodates potentially malicious content, such as cryptojacking hidden in web banners - even on reputable websites. In order to protect Internet users from such online threats, the thorough

Meghana Bhat, Saipriya Dubey, Shreedevi K. Masuti, Tomohiro Okuma

Let $(A, \mathfrak{m})$ be a Gorenstein local ring, and $\mathcal{F} =\{F_n \}_{n\in \mathbb{Z}}$ a Hilbert filtration. In this paper, we give a criterion for Gorensteinness of the associated graded ring of $\mathcal{F}$ in terms of the Hilbert coefficients of $\mathcal{F}$ in some cases. As a consequence we recover and extend a result proved by Okuma, Watan

Ryan A. L. Schoop, Gijs Hendriks, Tristan van Leeuwen, Chris L. de Korte

Ultrafast ultrasound imaging insonifies a medium with one or a combination of a few plane waves at different beam-steered angles instead of many focused waves. It can achieve much higher frame rates, but often at the cost of reduced image quality. Deep learning approaches have been proposed to mitigate this disadvantage, in particular for single plane wave i

Jakob Richter, Jonas Nitzler, Luca Pegolotti, Karthik Menon

Boundary condition (BC) calibration to assimilate clinical measurements is an essential step in any subject-specific simulation of cardiovascular fluid dynamics. Bayesian calibration approaches have successfully quantified the uncertainties inherent in identified parameters. Yet, routinely estimating the posterior distribution for all BC parameters in 3D sim

YuanDong Wang, Zhen-Gang Zhu, Gang Su

To control the magnon transport in magnetic systems is of great interest in magnonics. Due to the feasibility of electric field, how to generate and manipulate magnon with pure electrical method is one of the most desired goals. Here we propose that the magnon spin current is generated by applying time-dependent electric field, where the coupling between the

Spencer W. Jolly

Radially-polarized light beams present very interesting and useful behavior for creating small intensity spots when tightly-focused, and manipulating nanostructures or charged particles. The modeling of the propagation of such vector beams, however, is almost always done using the lowest-order fundamental radially-polarized beam due to the complexity of vect

Guangquan Zeng, Lan Wang, Liang Gao, Hang Yang

The origin of diverse kinematic morphologies observed in low-mass galaxies is unclear. In this study, we investigate the kinematic morphologies of central galaxies with stellar mass $10^{8.5-9.0} M_\odot$ at $z=0$ in the TNG50-1 cosmological simulation. The majority of the low-mass galaxies in TNG50-1 are dispersion-dominated, consistent with observations. B

Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su

We present the results and the main findings of SemEval-2024 Task 8: Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection. The task featured three subtasks. Subtask A is a binary classification task determining whether a text is written by a human or generated by a machine. This subtask has two tracks: a monolingual track focused so

Ying-Jie Zhang, Yijie Zhu, Qing Li, Zhe-Ning Xiang

It is well-known that magnetic moments are very harmful to superconductivity. A typical example is the element Mn whose compounds usually exhibit strong magnetism. Thus, it is very hard to achieve superconductivity in materials containing Mn. Here, we report enhanced superconductivity with the superconducting transition temperature ($T_\text{c}$) up to a rec

Ryotaro Koshoji, Taisuke Ozaki

Efficient heuristics have predicted many functional materials such as high-temperature superconducting hydrides, while inorganic structural chemistry explains why and how the crystal structures are stabilized. Here we develop the paired mathematical programming formalism for searching and systematizing the structural prototypes of crystals. The first is the

Georgios Amanatidis, Elliot Anshelevich, Christopher Jerrett, Alexandros A. Voudouris

We consider a voting problem in which a set of agents have metric preferences over a set of alternatives, and are also partitioned into disjoint groups. Given information about the preferences of the agents and their groups, our goal is to decide an alternative to approximately minimize an objective function that takes the groups of agents into account. We c

Peter Beelen, Maria Montanucci, Jonathan Tilling Niemann, Luciane Quoos

The problem of understanding whether two given function fields are isomorphic is well-known to be difficult, particularly when the aim is to prove that an isomorphism does not exist. In this paper we investigate a family of maximal function fields that arise as Galois subfields of the Hermitian function field. We compute the automorphism group, the Weierstra

Peter Frankl, Andrey Kupavskii

A family of subsets of $[n]$ is $r$-wise agreeing if for any $r$ sets from the family there is an element $x$ that is either contained in all or contained in none of the $r$ sets. The study of such families is motivated by questions in discrete optimization. In this paper, we determine the size of the largest non-trivial $r$-wise agreeing family. This can be

Guanhua Zhao, Yu Gu, Xuhan Sheng, Yujie Hu

With the popularity of social media platforms and retouching tools, more people are beautifying their facial photos, posing challenges for fields requiring photo authenticity. To address this issue, some work has proposed makeup removal methods, but they cannot revert images involving geometric deformations caused by retouching. To tackle the problem of faci

Rakesh Chatterjee, Hui-Shun Kuan, Frank Julicher, Vasily Zaburdaev

Microphase separation is common in active biological systems as exemplified by the separation of RNA and DNA-rich phases in the cell nucleus driven by the transcriptional activity of polymerase enzymes acting similarly to amphiphiles in a microemulsion. Here we propose an analytically tractable model of an active microemulsion to investigate how the activity

John P. Farmer, Giovanni Zevi Della Porta

Plasma wakefields offer high acceleration gradients, orders of magnitude larger than conventional RF accelerators. However, the achievable luminosity remains relatively low, typically limited by repetition rate and the charge accelerated per shot. In this work, we show that a train of drive bunches can be harnessed to accelerate multiple witness bunches in a

Richard Einsele, Roland Mitrić

We introduce a novel methodology for simulating the excited-state dynamics of extensive molecular aggregates in the framework of the long-range corrected time-dependent density-functional tight-binding fragment molecular orbital method (FMO-LC-TDDFTB) combined with the mean-field Ehrenfest method. The electronic structure of the system is described in a quas

Manuel Dubinsky, César Massri, Gabriel Taubin

Spanning trees are fundamental objects in graph theory. The spanning tree set size of an arbitrary graph can be very large. This limitation discourages its analysis. However interesting patterns can emerge in small cases. In this article we introduce \emph{tinygarden}, a java package for validating hypothesis, testing properties and discovering patterns from

Wei Ye, Peng Xiao, Xiaofan Xu, Xiang Zhu

In this work, we address the central problem about how to effectively find the available precision limit of unknown parameters. In the framework of the quantum Ziv-Zakai bound (QZZB), we employ noiseless linear amplification (NLA)techniques to an initial coherent state (CS) as the probe state, and focus on whether the phase estimation performance is improved

Giacomo Baldan, Francesco Manara, Gregorio Frassoldati, Alberto Guardone

A numerical investigation of the flow evolution over a pitching NACA 0012 airfoil incurring in deep dynamic stall phenomena is presented. The experimental data at Reynolds number Re = 135 000 and reduced frequency k = 0.1, provided by Lee and Gerontakos, are compared to numerical simulations using different turbulence models. After a preliminary space and ti

Luca Iorio, Raffaele Ardito, Stefano Gonella

The signature topological feature of Maxwell lattices is their polarization, which manifests as an unbalance in stiffness between opposite edges of a finite domain. The manifestation of this asymmetry is especially dramatic in the case of soft lattices undergoing large nonlinear deformation under concentrated loads, where the excess of softness at the soft e

Jelle Vekeman, Dries Vandenabeele, Nikolaus Doppelhammer, Elisabeth Vandeurzen

Despite the widespread use of zeolites in chemical industry, their formation process is not fully understood due to the complex and heterogeneous structure of traditional synthesis media. Hydrated silicate ionic liquids (HSILs) have been proposed as an alternative. They are truly homogeneous and transparent mixtures with low viscosity, facilitating experimen

Mattia Corti

Alzheimer's disease is the most common dementia worldwide. Its pathological development is well known to be connected with the accumulation of two toxic proteins: tau protein and amyloid-$\beta$. Mathematical models and numerical simulations can predict the spreading patterns of misfolded proteins in this context. However, the calibration of the model parame