February 2024 arXiv papers — page 15

Jeng Yi Lee

We present a theoretical study of a novel polarization beam splitter (PBS), different to conventional time-reversal symmetry one, where can be totally reflected at two opposite sides with one specific linearly polarized light incident and can be transparent at only one side with its orthogonal linearly polarized light incident. %In addition, intensity of tot

Mingxin Li, Richong Zhang, Zhijie Nie

Sentence Representation Learning (SRL) is a crucial task in Natural Language Processing (NLP), where contrastive Self-Supervised Learning (SSL) is currently a mainstream approach. However, the reasons behind its remarkable effectiveness remain unclear. Specifically, many studies have investigated the similarities between contrastive and non-contrastive SSL f

Eric Bourreau, Gerard Fleury, Phlippe Lacomme

The Job-Shop Scheduling Problem (JSSP) stands as one of the most renowned challenges in scheduling. It is characterized as a disjunctive problem, wherein a solution is fully depicted through an oriented disjunctive graph, with earliest starting times computed using a longest path algorithm. The complexity of solving this problem arises in part from the requi

Deepa Antony, Rupam Barman

In a recent paper, Bilu et al. studied a conjecture of Marques and Lengyel on the $p$-adic valuation of the Tribonacci sequence. In this article, we study the $p$-adic valuation of third order linear recurrence sequences by considering a generalisation of the conjecture of Marques and Lengyel for third order linear recurrence sequences. Suppose that $(x_n)$

Huiyuan Xiong, Jun Shen, Taohong Zhu, Yuelong Pan

High-definition (HD) map is crucial for autonomous driving systems. Most existing works design map elements detection heads based on the DETR decoder. However, the initial queries lack explicit incorporation of physical positional information, and vanilla self-attention entails high computational complexity. Therefore, we propose EAN-MapNet for Efficiently c

Dongyoung Kim, Jinwoo Kim, Junsang Yu, Seon Joo Kim

White balance (WB) algorithms in many commercial cameras assume single and uniform illumination, leading to undesirable results when multiple lighting sources with different chromaticities exist in the scene. Prior research on multi-illuminant WB typically predicts illumination at the pixel level without fully grasping the scene's actual lighting conditions,

Rohit Gurjar, Taihei Oki, Roshan Raj

The matching and linear matroid intersection problems are solvable in quasi-NC, meaning that there exist deterministic algorithms that run in polylogarithmic time and use quasi-polynomially many parallel processors. However, such a parallel algorithm is unknown for linear matroid matching, which generalizes both of these problems. In this work, we propose a

Yang Cao, Shuo Shang, Jun Wang, Wei Zhang

This paper explores providing explainability for session-based recommendation (SR) by path reasoning. Current SR models emphasize accuracy but lack explainability, while traditional path reasoning prioritizes knowledge graph exploration, ignoring sequential patterns present in the session history. Therefore, we propose a generalized hierarchical reinforcemen

Hao Shi, Tatsuya Kawahara

Adapting an automatic speech recognition (ASR) system to unseen noise environments is crucial. Integrating adapters into neural networks has emerged as a potent technique for transfer learning. This study thoroughly investigates adapter-based ASR adaptation in noisy environments. We conducted experiments using the CHiME--4 dataset. The results show that inse

Zhiqiang Tian, Ziming Zhu, Jiang Zeng, Chao-Fei Liu

Materials with ferroelectrically switchable topological properties are of interest for both fundamental physics and practical applications. Using first-principles calculations, we find that stacking ferroelectric $\alpha$-In$_2$Se$_3$ monolayers into a bilayer leads to polarization-dependent band structures, which yields polarization-dependent topological pr

V. N. Nefedov

The present paper is a continuation of the author's previous works, in which necessary and sufficient local extrema at a stationary point of a polynomial or a power series (and thus of an analytic function) are given. It is known that for the case of one variable, the necessary and sufficient conditions of the extremum are closing, i.e., they can be formulat

Qineng Wang, Zihao Wang, Ying Su, Hanghang Tong

Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLMs. In this work, we reevaluate this claim through systematic experiments, where we propose a novel group discussion framework to enrich the set of discussion mechanisms. Interestingly, our results show that a single-agent LLM with strong prompts can

Jingyu Xu, Weixiang Wan, Linying Pan, Wenjian Sun

In response to the demand for real-time performance and control quality in industrial Internet of Things (IoT) environments, this paper proposes an optimization control system based on deep reinforcement learning and edge computing. The system leverages cloud-edge collaboration, deploys lightweight policy networks at the edge, predicts system states, and out

Emilio Calvanese Strinati, George C. Alexandropoulos, Madhusudan Giyyarpuram, Philippe Sehier

This paper introduces the concept of Distributed Intelligent integrated Sensing and Communications (DISAC), which expands the capabilities of Integrated Sensing and Communications (ISAC) towards distributed architectures. Additionally, the DISAC framework integrates novel waveform design with new semantic and goal-oriented communication paradigms, enabling I

Shuhe Zhang, Aiye Wang, Jinghao Xu, Tianci Feng

Fourier ptychographic microscopy (FPM), characterized by high-throughput computational imaging, theoretically provides a cunning solution to the trade-off between spatial resolution and field of view (FOV), which has a promising prospect in the application of digital pathology. However, block reconstruction and then stitching has currently become an unavoida

Victor Ayala, Adriano Da Silva, Anderson F. P. Rojas

In this paper, we study the dynamical behavior of a linear control system on $\R^2$ when the associated matrix has real eigenvalues. Different from the complex case, we show that the position of the control zero relative to the control range can have a strong interference in such dynamics if the matrix is not invertible. In the invertible case, we explicitly

Vanik E. Mkrtchian, Hakob S. Avetisyan, Armen E. Allahverdyan

Detection of scattered light can determine the susceptibility of dielectrics. Such imaging normally holds Rayleigh's limit: details finer than the wavelength of the incident light cannot be determined from the far-field zone. We show that time-modulation of an inhomogeneous dielectric can be used to determine its susceptibility. To this end, we focus on the

Shasha Guo, Lizi Liao, Cuiping Li, Tat-Seng Chua

In this survey, we present a detailed examination of the advancements in Neural Question Generation (NQG), a field leveraging neural network techniques to generate relevant questions from diverse inputs like knowledge bases, texts, and images. The survey begins with an overview of NQG's background, encompassing the task's problem formulation, prevalent bench

Hangyong Shan, Jens-Christian Drawer, Meng Sun, Carlos Anton-Solanas

Bosonic condensation and lasing of exciton-polaritons in microcavities is a fascinating solid-state phenomenon. It provides a versatile platform to study out-of-equilibrium many-body physics and has recently appeared at the forefront of quantum technologies. Here, we study the photon statistics via the second-order temporal correlation function of polariton

Caroline Brosse, Alessio Conte, Vincent Limouzy, Giulia Punzi

A set of vertices in a graph forms a potential maximal clique if there exists a minimal chordal completion in which it is a maximal clique. Potential maximal cliques were first introduced as a key tool to obtain an efficient, though exponential-time algorithm to compute the treewidth of a graph. As a byproduct, this allowed to compute the treewidth of variou

Jiebin Zhang, Eugene J. Yu, Qinyu Chen, Chenhao Xiong

It presents significant challenges to generate comprehensive and accurate Wikipedia articles for newly emerging events under a real-world scenario. Existing attempts fall short either by focusing only on short snippets or by using metrics that are insufficient to evaluate real-world scenarios. In this paper, we construct WIKIGENBENCH, a new benchmark consist

Vincent Cohen-Addad, Tommaso d'Orsi, Anupam Gupta, Euiwoong Lee

We study the approximability of the MaxCut problem in the presence of predictions. Specifically, we consider two models: in the noisy predictions model, for each vertex we are given its correct label in $\{-1,+1\}$ with some unknown probability $1/2 + \epsilon$, and the other (incorrect) label otherwise. In the more-informative partial predictions model, for

Hongshen Xu, Lu Chen, Zihan Zhao, Da Ma

The growing prevalence of visually rich documents, such as webpages and scanned/digital-born documents (images, PDFs, etc.), has led to increased interest in automatic document understanding and information extraction across academia and industry. Although various document modalities, including image, text, layout, and structure, facilitate human information

Denis Fufaev, Evgenij Troitsky

We introduce and study some new uniform structures for Hilbert $C^*$-modules over an algebra $A$. In particular, we prove that in some cases they have the same totally bounded sets. To define one of them, we introduce a new class of $A$-functionals: locally adjointable functionals, which have interesting properties in this context and seem to be of independe

Jörn Tebbe, Christoph Zimmer, Ansgar Steland, Markus Lange-Hegermann

Active learning of physical systems must commonly respect practical safety constraints, which restricts the exploration of the design space. Gaussian Processes (GPs) and their calibrated uncertainty estimations are widely used for this purpose. In many technical applications the design space is explored via continuous trajectories, along which the safety nee

Alexander Rolf Korsch, Niccolò Fiaschi, Simon Gröblacher

In recent years, nanomechanical oscillators in thin films of superfluid helium have attracted attention in the field of optomechanics due to their exceptionally low mechanical dissipation and optical scattering. Mechanical excitations in superfluid thin films - so-called third sound waves - can interact with the optical mode of an optical microresonator by m

Hongshen Xu, Ruisheng Cao, Su Zhu, Sheng Jiang

Previous work on spoken language understanding (SLU) mainly focuses on single-intent settings, where each input utterance merely contains one user intent. This configuration significantly limits the surface form of user utterances and the capacity of output semantics. In this work, we first propose a Multi-Intent dataset which is collected from a realistic i

Sung-Soo Byun, Kohei Noda

Non-Hermitian Wishart matrices were introduced in the context of quantum chromodynamics with a baryon chemical potential. These provide chiral extensions of the elliptic Ginibre ensembles as well as non-Hermitian extensions of the classical Wishart/Laguerre ensembles. In this work, we investigate eigenvalues of non-Hermitian Wishart matrices in the symmetry

Julian Ma, Davide Crapis

Automated market makers (AMMs) allocate fee revenue \textit{proportional} to the amount of liquidity investors deposit. In this paper, we study the economic consequences of the competition between passive liquidity providers (LPs) caused by this allocation rule. We employ a game-theoretic model in which $N$ strategic agents optimally provide liquidity and tw

Najko Jahn

The ongoing controversy surrounding transformative agreements, which aim to transition subscription-based journal publishing to full open access, highlights the need for large-scale studies assessing the impact of these agreements on hybrid open access. By combining multiple open data sources, including cOAlition S Journal Checker, Crossref, and OpenAlex, th

Marc Comadran, Cristina Manuel

We develop a quantum kinetic theory for photons in the presence of an axion background and in the collisioness limit. In deriving the classical regime of our quantum kinetic equations, we observe that they capture well known features of axion electrodynamics. By projecting the Wigner function onto a polarization basis, relating the Wigner matrix function wit

Romaine Kerjouan, Michael Rosticher, Aurélie Pierret, Kenji Watanabe

The sharp Dirac cone of the electronic dispersion confers to graphene a remarkable sensitivity to strain. It is usually encoded in scalar and pseudo-vector potentials, induced by the modification of hopping parameters, which have given rise to new phenomena at the nanoscale such as giant pseudomagnetic fields and valley polarization. Here, we unveil the effe

Haoxiang Guan, Jiyan He, Shuxin Zheng, En-Hong Chen

Large language models (LLMs) have demonstrated impressive performance on many tasks. However, to achieve optimal performance, specially designed prompting methods are still needed. These methods either rely on task-specific few-shot examples that require a certain level of domain knowledge, or are designed to be simple but only perform well on a few types of

Bashir Olaniyi Sadiq

Edge detection as a pre-processing stage is a fundamental and important aspect of the number plate extraction system. This is due to the fact that the identification of a particular vehicle is achievable using the number plate because each number plate is unique to a vehicle. As such, the characters of a number plate system that differ in lines and shapes ca

Calliope Ryan-Smith

In computer science, combinatorics, and model theory, the VC dimension is a central notion underlying far-reaching topics such as error rate for decision rules, combinatorial measurements of classes of finite structures, and neo-stability theory. In all cases, it measures the capacity for a collection of sets $\mathcal{F}\subseteq\mathscr{P}(X)$ to shatter s

Anton Montag, Flore K. Kunst

In this paper, we show that three different generalized similarities enclose all unitary and anti-unitary symmetries that induce exceptional points in lower-dimensional non-Hermitian systems. We prove that the generalized similarity conditions result in a larger class of systems than any class defined by a unitary or anti-unitary symmetry. Further we highlig

Deng Wang, Olga Mena

The integrated Sachs-Wolfe effect (ISW) describes how CMB photons pick up a net blue or redshift when traversing the time-varying gravitational potentials between the last scattering surface and us. Deviations from its standard amplitude could hint new physics. We show that reconstructing the amplitude of the ISW effect as a function of the redshift may prov

Genni Fragnelli, Dimitri Mugnai, Amine Sbai

We study the null controllability for a degenerate/singular wave equation with drift in non divergence form. In particular, considering a control localized on the non degenerate boundary point, we provide some conditions for the boundary controllability via energy methods and boundary observability.

Joachim Grimstad, Andrey Morozov

This paper presents a new approach to the solution of Probabilistic Risk Assessment (PRA) models using the combination of Reinforcement Learning (RL) and Graph Neural Networks (GNNs). The paper introduces and demonstrates the concept using one of the most popular PRA models - Fault Trees. This paper's original idea is to apply RL algorithms to solve a PRA mo

Svein Høgemo

Leaf powers and $k$-leaf powers have been studied for over 20 years, but there are still several aspects of this graph class that are poorly understood. One such aspect is the leaf rank of leaf powers, i.e. the smallest number $k$ such that a graph $G$ is a $k$-leaf power. Computing the leaf rank of leaf powers has proved a hard task, and furthermore, result

Angelo Pignatelli

A re-calculation of a known family of formulas of PI is carried out, revisiting the old Archimedes' algorithm. This allows to identify a general family equation and three new simple formulas of Pi in terms of the golden ratio PHI in the form of infinite nested square roots, with some geometrical properties that enhance the link between the circle and the gol

Seongyeol Kim, Juan Pablo Gonzalez-Aguilera, Philippe Piot, Gongxiaohui Chen

Beams with cross-plane coupling or extreme asymmetries between the two transverse phase spaces are often encountered in particle accelerators. Flat beams with large transverse-emittance ratios are critical for future linear colliders. Similarly, magnetized beams with significant cross-plane coupling are expected to enhance the performance of electron cooling

Mengjie Ren, Boxi Cao, Hongyu Lin, Cao Liu

Instruction Fine-tuning~(IFT) is a critical phase in building large language models~(LLMs). Previous works mainly focus on the IFT's role in the transfer of behavioral norms and the learning of additional world knowledge. However, the understanding of the underlying mechanisms of IFT remains significantly limited. In this paper, we design a knowledge interve

Luigi Palmieri, Chiara Boldrini, Lorenzo Valerio, Andrea Passarella

Fully decentralized learning is gaining momentum for training AI models at the Internet's edge, addressing infrastructure challenges and privacy concerns. In a decentralized machine learning system, data is distributed across multiple nodes, with each node training a local model based on its respective dataset. The local models are then shared and combined t

Claudia Angelini, Daniela De Canditiis, Italia De Feis, Antonella Iuliano

We propose AFTNet, a novel network-constraint survival analysis method based on the Weibull accelerated failure time (AFT) model solved by a penalized likelihood approach for variable selection and estimation. When using the log-linear representation, the inference problem becomes a structured sparse regression problem for which we explicitly incorporate the

Ritam Ghosh

Affective states regulate our day to day to function and has a tremendous effect on mental and physical health. Detection of affective states is of utmost importance for mental health monitoring, smart entertainment selection and dynamic workload management. In this paper, we discussed relevant literature on affective state detection using physiology data, t

Jizhi Zhang, Keqin Bao, Wenjie Wang, Yang Zhang

The new kind of Agent-oriented information system, exemplified by GPTs, urges us to inspect the information system infrastructure to support Agent-level information processing and to adapt to the characteristics of Large Language Model (LLM)-based Agents, such as interactivity. In this work, we envisage the prospect of the recommender system on LLM-based Age

Karl D. Briegel, Nick R. von Grafenstein, Julia C. Draeger, Peter Blümler

Microscopy enables detailed visualization and understanding of minute structures or processes. While cameras have significantly advanced optical, infrared, and electron microscopy, imaging nuclear magnetic resonance (NMR) signals on a camera has remained elusive. Here, we employ nitrogen-vacancy (NV) centers in diamond as a quantum sensor, which converts NMR

Orfeu Bertolami, A. E. Bernardini

We argue that time crystal properties naturally arise from phase-space noncommutative quantum mechanics. In order to exemplify our point we consider the 2-dimensional noncommutative quantum harmonic oscillator and show that it exibihits periodic oscillations that can be identified as time crystals.

Houri Ziaeepour

Our Universe is ruled by quantum mechanics and should be treated as a quantum system. $SU(\infty)$-QGR is a recently proposed quantum model for the Universe, in which gravity is associated to $SU(\infty)$ symmetry of its Hilbert space. Fragmentation of its infinite dimensional state due to random quantum fluctuations divides the Universe to approximately iso

Tina Yao, Endrit Pajaziti, Michael Quail, Silvia Schievano

Computational fluid dynamics (CFD) can be used for evaluation of hemodynamics. However, its routine use is limited by labor-intensive manual segmentation, CFD mesh creation, and time-consuming simulation. This study aims to train a deep learning model to both generate patient-specific volume-meshes of the pulmonary artery from 3D cardiac MRI data and directl

Jack Tacchi, Chiara Boldrini, Andrea Passarella, Marco Conti

Humans are known to structure social relationships according to certain patterns, such as the Ego Network Model (ENM). These patterns result from our innate cognitive limits and can therefore be observed in the vast majority of large human social groups. Until recently, the main focus of research was the structural characteristics of this model. The main aim

Iason Giannopoulos, Iacopo Mochi, Michaela Vockenhuber, Yasin Ekinci

Extreme ultraviolet (EUV) lithography is the leading lithography technique in CMOS mass production, moving towards the sub-10 nm half-pitch (HP) regime with the ongoing development of the next generation high-numerical aperture (high-NA) EUV scanners. Hitherto, EUV interference lithography (EUV-IL) utilizing transmission gratings has been a powerful patterni

Zhengqing Zang, Chenyu Lin, Chenwei Tang, Tao Wang

Existing object detection models are mainly trained on large-scale labeled datasets. However, annotating data for novel aerial object classes is expensive since it is time-consuming and may require expert knowledge. Thus, it is desirable to study label-efficient object detection methods on aerial images. In this work, we propose a zero-shot method for aerial

Hadi Tabealhojeh, Soumava Kumar Roy, Peyman Adibi, Hossein Karshenas

Meta-learning problem is usually formulated as a bi-level optimization in which the task-specific and the meta-parameters are updated in the inner and outer loops of optimization, respectively. However, performing the optimization in the Riemannian space, where the parameters and meta-parameters are located on Riemannian manifolds is computationally intensiv

A. Bermúdez, D. Gómez, D. González-Peñas

This work proposes a methodology combining distributed and lumped models to simulate the current distribution in an indirect heat resistance furnace and, in particular, to compute the current to be supplied in order to obtain a desired power. The distributed model is a time-harmonic eddy current problem which has been numerically solved by using a finite ele

Xi Wang, Xiaotong Zhao, Juncheng Wang, You Li

We consider joint beamforming and stream allocation to maximize the weighted sum rate (WSR) for non-coherent joint transmission (NCJT) in user-centric cell-free MIMO networks, where distributed access points (APs) are organized in clusters to transmit different signals to serve each user equipment (UE). We for the first time consider the common limits of max

Jesse Huhtala, Iiro Vilja

LSZ-type reduction formulae are derived for gauge fields and fermions in curved spacetime. The formulae are derived using a conserved current method applicable also to flat spacetimes. The method generalizes to more general quantum field theories. The formulae are then applied to a few problems to illustrate their use.

Siqi Ren, Zhifei Zhang

In this paper, we investigate the long-time dynamics of the linearized 2-D Euler equations around a hyperbolic tangent flow $(\tanh y,0)$. A key difference compared to previous results is that the linearized operator has an embedding eigenvalue, which has a significant impact on the dynamics of the linearized system. For the first mode, the dynamics consists

Ulrich Bunke, Benjamin Duenzinger

We show that the equivariant $E$-theory category $\mathrm{E}_{\mathrm{sep}}^{G}$ for separable $C^{*}$-algebras is a compactly assembled stable $\infty$-category. We derive this result as a consequence of the shape theory for $C^{*}$-algebras developed by Blackadar and Dardarlat and a new construction of $\mathrm{E}_{\mathrm{sep}}^{G}$. As an application we

Dennis Junger, Max Westing, Christopher P. Freitag, Achim Guldner

Progressing digitalization and increasing demand and use of software cause rises in energy- and resource consumption from information and communication technologies (ICT). This raises the issue of sustainability in ICT, which increasingly includes the sustainability of the software products themselves and the art of creating sustainable software. To this end

Robin Cockett, Jean-Simon Pacaud Lemay, Priyaa Varshinee Srinivasan

Drazin inverses are a fundamental algebraic structure which have been extensively deployed in semigroup theory, ring theory, and matrix theory. Drazin inverses can also be defined for endomorphisms in any category. However, beyond a paper by Puystjens and Robinson from 1987, there has been almost no further development of Drazin inverses in category theory.

Julian Coda-Forno, Marcel Binz, Jane X. Wang, Eric Schulz

Large language models (LLMs) have significantly advanced the field of artificial intelligence. Yet, evaluating them comprehensively remains challenging. We argue that this is partly due to the predominant focus on performance metrics in most benchmarks. This paper introduces CogBench, a benchmark that includes ten behavioral metrics derived from seven cognit

Łukasz Kułacz

In terms of complex radio environments especially in dense urban areas, a very interesting topic is considered - the utilization of reconfigurable intelligent surfaces. Basically, based on simple controls of the angle of reflection of the signal from the surface, it is possible to achieve different effects in a radio communication system. Maximizing or minim

Wenhong Zhu, Hongkun Hao, Zhiwei He, Yiming Ai

Current language models decode text token by token according to probabilistic distribution, and determining the appropriate candidates for the next token is crucial to ensure generation quality. This study introduces adaptive decoding, a mechanism that dynamically empowers language models to ascertain a sensible candidate set during generation. Specifically,

Youngseung Jeon, Jaehoon Kim, Sohyun Park, Yunyong Ko

Considerable efforts are currently underway to mitigate the negative impacts of echo chambers, such as increased susceptibility to fake news and resistance towards accepting scientific evidence. Prior research has presented the development of computer systems that support the consumption of news information from diverse political perspectives to mitigate the

Benedikt Eggemeier, Ananthu Krishnan Anilkumar, Klaus Dolag

We identify axion miniclusters collapsing in the radiation-dominated era and follow them to redshift $z=99$ with N-body simulations. We find that the majority of the densest miniclusters end up in the center of larger minicluster halos at late times. Soon after their formation, the miniclusters exhibit NFW profiles but they subsequently develop a steeper inn

Rybin Nikita, Dmitrii Maksimov, Yuriy Zaikov, Alexander Shapeev

Fluoride salts demonstrate significant potential for applications in next-generation nuclear reactors, necessitating a comprehensive understanding of their thermophysical properties for technological advancements. Experimental measurement of these properties poses challenges, due to factors such as high temperatures, impurity control, and corrosion. Conseque

Krzysztof Smutek

We investigate the fluctuations of the nodal number (count of the phase singularities) in a natural extension of the well-known complex planar Berry Random Wave Model - Berry (2002) - obtained by considering two independent real Berry Random Waves, with distinct energies $E_1, E_2 \to \infty$ (at possibly $\neq$ speeds). Our framework relaxes the conditions

Zvi Shem-Tov, Lior Silberman

We work toward the arithmetic quantum unique ergodicity (AQUE) conjecture for sequences of Hecke--Maass forms on hyperbolic $4$-manifolds. We show that limits of such forms can only scar on totally geodesic $3$-submanifolds, and in fact that all ergodic components of the microlocal lift other than the uniform measure arise from the uniform measures on these

Jin Liu, Huiyuan Fu, Chuanming Wang, Huadong Ma

Exposure correction aims to enhance images suffering from improper exposure to achieve satisfactory visual effects. Despite recent progress, existing methods generally mitigate either overexposure or underexposure in input images, and they still struggle to handle images with mixed exposure, i.e., one image incorporates both overexposed and underexposed regi

Akash Gupta, Ivaxi Sheth, Vyas Raina, Mark Gales

With the recent emergence of powerful instruction-tuned large language models (LLMs), various helpful conversational Artificial Intelligence (AI) systems have been deployed across many applications. When prompted by users, these AI systems successfully perform a wide range of tasks as part of a conversation. To provide some sort of memory and context, such a

Bowen Wang, Ningning Zhang, Yuyan Chen, Alejandro Martinez

In recent years, self-burrowing probes have been studied since they can be suitable for soil monitoring in locations with limited access such as outer space bodies and underneath existing structures. We study the performance of a self-burrowing probe under different gravity conditions, from low gravity (i.e., 1/6g, 1/3g and 1g) to high gravity (i.e., 5g, 10g

Polona Repolusk

The exploration of weakly toll convexity is the focus of this investigation. A weakly toll walk is any walk $W: u, w_1, \ldots , w_{k-1}, v$ between $u$ and $v$ such that $u$ is adjacent only to the vertex $w_1$, which can appear more than once in the walk, and $v$ is adjacent only to the vertex $w_{k-1}$, which can appear more than once in the walk. Through

Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler, Samuel Dooley

Pareto front profiling in multi-objective optimization (MOO), i.e., finding a diverse set of Pareto optimal solutions, is challenging, especially with expensive objectives that require training a neural network. Typically, in MOO for neural architecture search (NAS), we aim to balance performance and hardware metrics across devices. Prior NAS approaches simp

Amornthep Tita, Burin Gumjudpai, Pornrad Srisawad

Background cosmological dynamics for a universe with matter, a scalar field non-minimally derivative coupling to Einstein tensor under power-law potential and holographic vacuum energy is considered here. The holographic IR cutoff scale is apparent horizon which, for accelerating universe, forms a trapped null surface in the same spirit as blackhole's event

Martin Tancer

A collection of unit cubes with integer coordinates in $\mathbb R^3$ is an animal if its union is homeomorphic to the 3-ball. Pach's animal problem asks whether any animal can be transformed to a single cube by adding or removing cubes one by one in such a way that any intermediate step is an animal as well. Here we provide an example of an animal that canno

Mengnan Zhao, Lihe Zhang, Yuqiu Kong, Baocai Yin

Fast Adversarial Training (FAT) has gained increasing attention within the research community owing to its efficacy in improving adversarial robustness. Particularly noteworthy is the challenge posed by catastrophic overfitting (CO) in this field. Although existing FAT approaches have made strides in mitigating CO, the ascent of adversarial robustness occurs

Kenneth Enevoldsen, Emil Trenckner Jessen, Rebekah Baglini

Named entity recognition is one of the cornerstones of Danish NLP, essential for language technology applications within both industry and research. However, Danish NER is inhibited by a lack of available datasets. As a consequence, no current models are capable of fine-grained named entity recognition, nor have they been evaluated for potential generalizabi

Prajit T. Rajendran, Kevin Creusy, Vivien Garnes

Short form content has permeated into the video creator space over the past few years, led by industry leading products such as TikTok, YouTube Shorts and Instagram Reels. YouTube in particular was previously synonymous with being the main hub for long form video content consumption. The monetization of long form videos was easier as it allowed multiple adve

Lukas Kühne, Xavier Roulleau

This paper further studies the matroid realization space of a specific deformation of the regular $n$-gon with its lines of symmetry. Recently, we obtained that these particular realization spaces are birational to the elliptic modular surfaces $\Xi_{1}(n)$ over the modular curve $X_1(n)$. Here, we focus on the peculiar cases when $n=7,8$ in more detail. We

Rishubh Parihar, Abhijnya Bhat, Abhipsa Basu, Saswat Mallick

Diffusion Models (DMs) have emerged as powerful generative models with unprecedented image generation capability. These models are widely used for data augmentation and creative applications. However, DMs reflect the biases present in the training datasets. This is especially concerning in the context of faces, where the DM prefers one demographic subgroup v

Wei Zhang, Xiangyuan Guan, Lu Yunhong, Jie Zhang

Logs produced by extensive software systems are integral to monitoring system behaviors. Advanced log analysis facilitates the detection, alerting, and diagnosis of system faults. Log parsing, which entails transforming raw log messages into structured templates, constitutes a critical phase in the automation of log analytics. Existing log parsers fail to id

Álvaro López-Chilet, Zhaoyi Liu, Jon Ander Gómez, Carlos Alvarez

This paper proposes a method for Acoustic Constrained Segmentation (ACS) in audio recordings of vehicles driven through a production test track, delimiting the boundaries of surface types in the track. ACS is a variant of classical acoustic segmentation where the sequence of labels is known, contiguous and invariable, which is especially useful in this work

Michelle Roost, Karel Devriendt, Giulio Zucal, Jürgen Jost

Discrete curvatures are quantities associated to the nodes and edges of a graph that reflect the local geometry around them. These curvatures have a rich mathematical theory and they have recently found success as a tool to analyze networks across a wide range of domains. In this work, we consider the problem of constructing graphs with a prescribed set of d

Toshiyuki Kodama, Toshihiro Nakanishi, Kei Sawada, Satoshi Tomita

We report numerical studies of microwave bianisotropies by magnetochiral (MCh) metasurfaces consisting of double Z-type gammadions with perpendicularly magnetized substrates. The metasurfaces' effective polarizability tensor, extracted from calculated reflection and transmission coefficients, has components of the non-reciprocal moving-type bianisotropy as w

T. De Kerf, S. Sels, S. Samsonova, S. Vanlanduit

The high incidence of oil spills in port areas poses a serious threat to the environment, prompting the need for efficient detection mechanisms. Utilizing automated drones for this purpose can significantly improve the speed and accuracy of oil spill detection. Such advancements not only expedite cleanup operations, reducing environmental harm but also enhan

Sen Xu, Shikui Wei, Tao Ruan, Lixin Liao

Deep superpixel algorithms have made remarkable strides by substituting hand-crafted features with learnable ones. Nevertheless, we observe that existing deep superpixel methods, serving as mid-level representation operations, remain sensitive to the statistical properties (e.g., color distribution, high-level semantics) embedded within the training dataset.

Thomas O. Rot

On 13-01-2024 the annual wintersymposium of the Koninlijk Wiskundig Genootschap (KWG) was held in the academiegebouw in Utrecht. The symposium had the theme ``inzichtelijk abstract''. Thomas Rot gave a lecture on his favourite theorem from topology. This article is a written account of this lecture. Audience comprised mostly of high school teachers and that

Tom Kretzschmar, Sebastian Ritter, Anand Kumar, Tobias Vogl

Single photon emitters (SPEs) are a key component for their use as pure photon source in quantum technologies. In this study, we investigate the generation of SPEs from drop-casted hexagonal boron nitride (hBN) nanoflakes, examining the influence of the immersion solution and the source of hBN. We show that, depending on the utilized supplier and solution th

Milad Solbi, Kayoomars Karami

Here, we investigate the formation of primordial black holes (PBHs) in non-minimal coupling Gauss-Bonnet inflationary model in the presence of power-law potentials. We employ a two part coupling function to enhance primordial curvatures at small scales as well as satisfy Planck measurements at the CMB scale. Moreover, our model satisfies the swampland criter

Marcel Wever

Automated machine learning (AutoML) aims to select and configure machine learning algorithms and combine them into machine learning pipelines tailored to a dataset at hand. For supervised learning tasks, most notably binary and multinomial classification, aka single-label classification (SLC), such AutoML approaches have shown promising results. However, the

Sukalpa Kundu, Jayanta Dutta

Recent numerical simulations have shown that the unstable disk within the central regime of the primordial gas cloud fragments to form multiple protostars on several scales. Their evolution depends on the mass accretion phenomenon, interaction with the surrounding medium and radiative feedback respectively. In this work, we use a fast semi-analytical framewo

Jingrui Yu, Dipankar Nandi, Roman Seidel, Gangolf Hirtz

Human pose estimation (HPE) in the top-view using fisheye cameras presents a promising and innovative application domain. However, the availability of datasets capturing this viewpoint is extremely limited, especially those with high-quality 2D and 3D keypoint annotations. Addressing this gap, we leverage the capabilities of Neural Radiance Fields (NeRF) tec

Dominik Czernia, Piotr Konieczny, Marcin Perzanowski, Beata Nowicka

A new magnetic phase with $T_C = 72 \ \mathrm K$ was obtained by exposing the three-dimensional $\mathrm{\{ [Mn^{II}(H_2O)_2]_2[Nb^{IV}(CN)_8] \cdot 4H_2O \} _n}$ coordination ferrimagnet ($T_C = 49 \ \mathrm K$) to air, oxygen, nitrogen, and argon-based plasma. The X-ray powder diffraction pattern revealed that the unit cell shrank after plasma treatment, l

Tim Maurice Julitz, Nadine Schlüter, Manuel Löwer

This research article presents a methodical data-based approach to systematically identify key factors in safety-related failure scenarios, with a focus on complex product-environmental systems in the era of Industry 4.0. The study addresses the uncertainty arising from the growing complexity of modern products. The method uses scenario analysis and focuses

Jorge Martín-Morales

We provide an algorithm for computing the number of integral points lying in certain triangles that do not have integral vertices. We use techniques from Algebraic Geometry such as the Riemann-Roch formula for weighted projective planes and resolution of singularities. We analyze the complexity of the method and show that the worst case is given by the Fibon

Zhangkai Ni, Juncheng Wu, Zian Wang, Wenhan Yang

This paper aims to address a common challenge in deep learning-based image transformation methods, such as image enhancement and super-resolution, which heavily rely on precisely aligned paired datasets with pixel-level alignments. However, creating precisely aligned paired images presents significant challenges and hinders the advancement of methods trained

Yuan Ge, Yilun Liu, Chi Hu, Weibin Meng

With contributions from the open-source community, a vast amount of instruction tuning (IT) data has emerged. Given the significant resource allocation required for training and evaluating models, it is advantageous to have an efficient method for selecting high-quality IT data. However, existing methods for instruction data selection have limitations such a

Tomohiro Sugiyama, Shin-ichi Tanigawa

The celebrated result of Gortler-Healy-Thurston (independently, Jackson-Jord\'an for $d=2$) shows that the global rigidity of graphs realised in the $d$-dimensional Euclidean space is a generic property. Extending this result to the global rigidity problem in $\ell_p$-spaces remains an open problem. In this paper we affirmatively solve this problem when $d=2