February 2024 arXiv papers — page 14

Lorenzo Bini, Fatemeh Nassajian Mojarrad, Margarita Liarou, Thomas Matthes

This paper presents FlowCyt, the first comprehensive benchmark for multi-class single-cell classification in flow cytometry data. The dataset comprises bone marrow samples from 30 patients, with each cell characterized by twelve markers. Ground truth labels identify five hematological cell types: T lymphocytes, B lymphocytes, Monocytes, Mast cells, and Hemat

Ines Sorrentino, Giulio Romualdi, Daniele Pucci

This paper proposes a novel sensor fusion based on Unscented Kalman Filtering for the online estimation of joint-torques of humanoid robots without joint-torque sensors. At the feature level, the proposed approach considers multimodal measurements (e.g. currents, accelerations, etc.) and non-directly measurable effects, such as external contacts, thus leadin

A. R. Mazza, J. Yan, S. Middey, J. S. Gardner

Many of the most exciting materials discoveries in fundamental condensed matter physics are made in systems hosting some degree of intrinsic disorder. While disorder has historically been regarded as something to be avoided in materials design, it is often of central importance to correlated and quantum materials. This is largely driven by the conceptual and

Bertrand Even, Christophe Giraud, Nicolas Verzelen

We investigate the existence of a fundamental computation-information gap for the problem of clustering a mixture of isotropic Gaussian in the high-dimensional regime, where the ambient dimension $p$ is larger than the number $n$ of points. The existence of a computation-information gap in a specific Bayesian high-dimensional asymptotic regime has been conje

Niclas Göring, Florian Hess, Manuel Brenner, Zahra Monfared

In science we are interested in finding the governing equations, the dynamical rules, underlying empirical phenomena. While traditionally scientific models are derived through cycles of human insight and experimentation, recently deep learning (DL) techniques have been advanced to reconstruct dynamical systems (DS) directly from time series data. State-of-th

Craig W. Schmidt, Varshini Reddy, Haoran Zhang, Alec Alameddine

Tokenization is a foundational step in natural language processing (NLP) tasks, bridging raw text and language models. Existing tokenization approaches like Byte-Pair Encoding (BPE) originate from the field of data compression, and it has been suggested that the effectiveness of BPE stems from its ability to condense text into a relatively small number of to

Lo-Yao Yeh, Sheng-Po Tseng, Chia-Hsun Lu, Chih-Ya Shen

In recent years, the notion of federated learning (FL) has led to the new paradigm of distributed artificial intelligence (AI) with privacy preservation. However, most current FL systems suffer from data privacy issues due to the requirement of a trusted third party. Although some previous works introduce differential privacy to protect the data, however, it

Jordi Cabot

There is a growing need for better development methods and tools to keep up with the increasing complexity of new software systems. New types of user interfaces, the need for intelligent components, sustainability concerns, ... bring new challenges that we need to handle. In the last years, model-driven engineering has been key to improving the quality and p

Seoyeon Kim, Kwangwook Seo, Hyungjoo Chae, Jinyoung Yeo

Recent approaches in domain-specific named entity recognition (NER), such as biomedical NER, have shown remarkable advances. However, they still lack of faithfulness, producing erroneous predictions. We assume that knowledge of entities can be useful in verifying the correctness of the predictions. Despite the usefulness of knowledge, resolving such errors w

Robert M. Guralnick, Cai Heng Li, Lei Wang, Binzhou Xia

The classification of factorizations $G=HK$ of finite almost simple groups, proposed by Wielandt in 1979 and pursued through several partial classifications, has remained open in one major case. We settle that case: for every finite almost simple classical group $G$, we determine all factorizations $G=HK$ in which both $H$ and $K$ have a unique nonsolvable c

Shigeki Akiyama, Paul Grosskopf, Benoît Loridant, Wolfgang Steiner

The Knuth Twin Dragon is a compact subset of the plane with fractal boundary of Hausdorff dimension $s = (\log \lambda)/(\log \sqrt{2})$, $\lambda^3 = \lambda^2 + 2$. Although the intersection with a generic line has Hausdorff dimension $s-1$, we prove that this does not occur for lines with rational parameters. We further describe the intersection of the Tw

A. N. Zarezad, A. Qaiumzadeh, J. Barnaś, A. Dyrdał

The topological charge-Hall effect (TCHE) and the topological spin-Hall effect (TSHE), arising from ferromagnetic (FM) and antiferromagnetic (AFM) skyrmions, respectively; can be elucidated through the emergence of spin-dependent Berry gauge fields that affect the adiabatic flow of electrons within the skyrmion texture. TCHE is absent in systems with parity-

Antonio L. Maroto, Alfredo D. Miravet

We study the dynamics of Abelian gauge fields invariant under transverse diffeomorphisms (TDiff) in cosmological contexts. We show that in the geometric optics approximation, very much as for Diff invariant theories, the corresponding massless gauge bosons propagate along null geodesics and particle number is conserved. In addition, the polarization vectors

Shun-Cai Zhao, Xin Li, Ping Yang

Simulating the cold $^{87}Rb$ atom with a three-level quantum system interacting with two orthogonal standing-wave fields, the localization within half-wavelength domain in the x-y plane is achieved by monitoring the probe absorption. Within the half-wavelength domain, the single absorption peak increases from 0.2 to 1.0 via the spontaneously generated coher

Dimitri Bytchenkoff, Michael Speckbacher, Peter Balazs

Kernel theorems, in general, provide a convenient representation of bounded linear operators. For the operator acting on a concrete function space, this means that its action on any element of the space can be expressed as a generalised integral operator, in a way reminiscent of the matrix representation of linear operators acting on finite dimensional vecto

Qingjing Wang, Wenhao Ding, Qing He, Ping Wang

In railway transportation, the evaluation of track geometry is an indispensable requirement to ensure the safety and comfort of railway vehicles. A promising approach is to directly use vehicle dynamic responses to assess the impact of track geometry defects. However, the computational cost of obtaining the dynamic response of the vehicle body using dynamics

Abdulrahman Bin Rabiah, Anas Alsoliman, Yugarshi Shashwat, Silas Richelson

Connected and Autonomous vehicles stand to drastically improve the safety and efficiency of the transportation system in the near future while also reducing pollution. These systems leverage communication to coordinate among vehicles and infrastructure in service of a number of safety and efficiency driver assist and even fully autonomous applications. Attac

Bi-Yang Tan, Yueshui Zhang, Hua-Chen Zhang, Wei Tang

The low-energy physics of Tomonaga-Luttinger liquids (TLLs) is controlled by the Luttinger parameter. We demonstrate that this parameter can be extracted from a single wave function for one-component TLLs with periodic boundary condition. This method relies on the fact that TLLs are described by conformal field theory in which crosscap states can be construc

Beibei Shen, Yunxin Zhang

Cellular migration is crucial for biological processes including embryonic development, immune response, and wound healing. The myosin-clutch model is a framework that describes how cells control migration through the interactions between myosin, the clutch mechanism, and the substrate. This model is related to how cells regulate adhesion, generate traction

Sangjoon Park, Yong Bae Kim, Jee Suk Chang, Seo Hee Choi

As advancements in the field of breast cancer treatment continue to progress, the assessment of post-surgical cosmetic outcomes has gained increasing significance due to its substantial impact on patients' quality of life. However, evaluating breast cosmesis presents challenges due to the inherently subjective nature of expert labeling. In this study, we pre

Jin Hwa Lee, Stefano Sarao Mannelli, Andrew Saxe

Diverse studies in systems neuroscience begin with extended periods of curriculum training known as `shaping' procedures. These involve progressively studying component parts of more complex tasks, and can make the difference between learning a task quickly, slowly or not at all. Despite the importance of shaping to the acquisition of complex tasks, there is

Christian Antić

The author has recently introduced abstract algebraic frameworks of analogical proportions and similarity within the general setting of universal algebra. The purpose of this paper is to build a bridge from similarity to analogical proportions by formulating the latter in terms of the former. The benefit of this similarity-based approach is that the connecti

Rosalba Fittipaldi, Roman Hartmann, Maria Teresa Mercaldo, Sachio Komori

Materials with strongly correlated electrons exhibit physical properties that are often difficult to predict as they result from the interactions of large numbers of electrons combined with several quantum degrees of freedom. The layered oxide perovskite Sr$_2$RuO$_4$ is a strongly correlated electron material that has been intensively investigated since its

A. Bazzani, F. Capoani, M. Giovannozzi

In this paper, we analyze the adiabatic crossing of a resonance for Hamiltonian systems when a double-resonance condition is satisfied by the linear frequency at an elliptic fixed point. We discuss in detail the phase-space structure on a class of Hamiltonians and area-preserving maps with an elliptic fixed point in the presence of a time-dependent exciter.

Virgil V. Baran, Jens Paaske

Superconductor-semiconductor hybrid devices, involving quantum dots interfaced with floating and/or grounded superconductors, have reached a level of complexity which calls for the development of versatile and numerically efficient modelling tools. Here, we propose an extension of the surrogate model solver for sub-gap states [Phys. Rev. B 108, L220506 (2023

Garazi Muguruza, Florian Speelman

We introduce Port-Based State Preparation (PBSP), a teleportation task where Alice holds a complete classical description of the target state and Bob's correction operations are restricted to only tracing out registers. We show a protocol that implements PBSP with error decreasing exponentially in the number of ports, in contrast to the polynomial trade-off

Julian Reichinger, Thomas Krismayer, Jan Rellermeyer

Modern, large scale monitoring systems have to process and store vast amounts of log data in near real-time. At query time the systems have to find relevant logs based on the content of the log message using support structures that can scale to these amounts of data while still being efficient to use. We present our novel Compressed Probabilistic Retrieval a

Shivani Sharma, David Greenberg

Cloud microphysics has important consequences for climate and weather phenomena, and inaccurate representations can limit forecast accuracy. While atmospheric models increasingly resolve storms and clouds, the accuracy of the underlying microphysics remains limited by computationally expedient bulk moment schemes based on simplifying assumptions. Droplet-bas

Xujun Liu, Gexin Yu

An induced matching in a graph $G$ is a matching such that its end vertices also induce a matching. A $(1^{\ell}, 2^k)$-packing edge-coloring of a graph $G$ is a partition of its edge set into disjoint unions of $\ell$ matchings and $k$ induced matchings. Gastineau and Togni (2019), as well as Hocquard, Lajou, and Lu\v{z}ar (2022), have conjectured that ever

Esther Galby, Andrea Munaro, Shizhou Yang

We investigate a relaxation of the notion of fractional treewidth-fragility, namely fractional tree-independence-number-fragility. In particular, we obtain polynomial-time approximation schemes for meta-problems such as finding a maximum-weight sparse induced subgraph satisfying a given $\mathsf{CMSO}_2$ formula on fractionally tree-independence-number-fragi

Changho Choi, Minho Kim, Junhyeok Lee, Hyoung-Kyu Song

We propose LatentSwap, a simple face swapping framework generating a face swap latent code of a given generator. Utilizing randomly sampled latent codes, our framework is light and does not require datasets besides employing the pre-trained models, with the training procedure also being fast and straightforward. The loss objective consists of only three term

Maher Boudabra, Kais Hamza

One of aims of this note is to capture the interest of the mathematical community to a novel transformation, which we shall call Brownian symmetrization. This transformation arises from the solution of the planar Skorokhod embedding problem. Brownian symmetrization shares some properties with the famous Steiner symmetrization. However, we show that these two

Edgar Knobloch, Arik Yochelis

Rogue waves are an intriguing nonlinear phenomenon arising across different scales, ranging from ocean waves through optics to Bose-Einstein condensates. We describe the emergence of rogue-like wave dynamics in a reaction-diffusion system that arise as a result of a subcritical Turing instability. This state is present in a regime where all time-independent

Xing Cheng, Yunrui Zheng

In this article, we consider energy-critical complex Ginzburg-Landau equation in three and four dimensions. We give the dynamics when the energy of the initial data is equal to the energy of the stationary solution.

Alexandre Huchet, Jean-Baptiste Melin

We used an unbiased CMB lensing mass estimator on 468 SPT-SZ clusters from the SPT-SZ and the Planck public data, the first such estimation using combined ground- and space-based data. We measured the average ratio between CMB lensing and SZ mass to be $M_{\rm CMBlens}/M_{\rm SZ} = 0.98 \pm 0.19$ (stat.) $\pm 0.03$ (syst.). The average CMB lensing mass from

Tianxu An, Joonho Lee, Marko Bjelonic, Flavio De Vincenti

Challenges in real-world robotic applications often stem from managing multiple, dynamically varying entities such as neighboring robots, manipulable objects, and navigation goals. Existing multi-agent control strategies face scalability limitations, struggling to handle arbitrary numbers of entities. Additionally, they often rely on engineered heuristics fo

Jiachun Li, Pengfei Cao, Chenhao Wang, Zhuoran Jin

Large language models exhibit high-level commonsense reasoning abilities, especially with enhancement methods like Chain-of-Thought (CoT). However, we find these CoT-like methods lead to a considerable number of originally correct answers turning wrong, which we define as the Toxic CoT problem. To interpret and mitigate this problem, we first utilize attribu

Ai-Wei Guan, Chuan-Fu Yang, Natalia P. Bondarenko

In this paper, we consider the recovery of third-order differential operators from two spectra, as well as fourth-order or fifth-order differential operators from three spectra, where these differential operators are endowed with complex-valued distributional coefficients. For the case of multiple spectra, we first establish the relationship between spectra

Mengdi Wang

Let $\tau_k$ be the $k$-fold divisor function. By constructing an approximant of $\tau_k$, denoted as $\tau_k^*$, which is a normalized truncation of the $k$-fold divisor function, we prove that when $\exp\left(C\log^{1/2}X(\log\log X)^{1/2}\right)\leq H\leq X$ and $C>0$ is sufficiently large, the following estimate holds for almost all $x\in[X,2X]$: \[ \Big

Stevan Pilipović, Nenad Teofanov, Filip Tomić

Inductive and projective type sequence spaces of sub- and super-exponential growth, and the corresponding inductive and projective limits of modulation spaces are considered as a framework for almost diagonalization of pseudo-differential operators. Moreover, recent results of the first author and B. Prangoski related to the almost diagonalization of pseudo-

Trideb Shit, Rishav Hui, Marco Di Liberto, Diptiman Sen

We study Hanbury Brown-Twiss spatial intensity correlations in femtosecond laser-fabricated photonic Su-Schrieffer-Heeger lattices using coherent input states with tunable phases. By mapping intensity correlations to the two-body quantum walk, we experimentally simulate edge bound states in the continuum (BICs) of two indistinguishable bosons. These two-body

Joakim Blikstad, Ola Svensson, Radu Vintan, David Wajc

The classic theorem of Vizing (Diskret. Analiz.'64) asserts that any graph of maximum degree $\Delta$ can be edge colored (offline) using no more than $\Delta+1$ colors (with $\Delta$ being a trivial lower bound). In the online setting, Bar-Noy, Motwani and Naor (IPL'92) conjectured that a $(1+o(1))\Delta$-edge-coloring can be computed online in $n$-vertex g

Andreas Møller Slavensky, Mads-Peter V. Christensen, Bjørk Hammer

Global optimization of atomistic structure rely on the generation of new candidate structures in order to drive the exploration of the potential energy surface (PES) in search for the global minimum energy (GM) structure. In this work, we discuss a type of structure generation, which locally optimizes structures in complementary energy (CE) landscapes. These

Rafael Orozco, Felix J. Herrmann, Peng Chen

Bayesian optimal experimental design (OED) seeks to conduct the most informative experiment under budget constraints to update the prior knowledge of a system to its posterior from the experimental data in a Bayesian framework. Such problems are computationally challenging because of (1) expensive and repeated evaluation of some optimality criterion that typ

Eugene A. Eliseev, Yuri O. Zagorodniy, Victor N. Pavlikov, Oksana V. Leshchenko

To describe the polar properties of the nanosized HfxZr1-xO2-y, we evolve the "effective" Landau-Ginzburg-Devonshire (LGD) model based on the parametrization of the Landau expansion coefficients for the polar and antipolar orderings. We have shown that the effective LGD model can predict the influence of screening conditions and size effects on phase diagram

James R. Ashford

Social media platforms such as Twitter have a fundamental role in facilitating the spread and discussion of ideas online through the concept of retweeting and replying. However, these features also contribute to the spread of mis/disinformation during the vaccine rollout of the COVID-19 pandemic. Using COVID-19 vaccines as a case study, we analyse multiple s

Nihal V. Nayak, Yiyang Nan, Avi Trost, Stephen H. Bach

We introduce Bonito, an open-source model for conditional task generation that converts unannotated text into task-specific training datasets for instruction tuning. We aim to enable zero-shot task adaptation of large language models on users' specialized, private data. We train Bonito by fine-tuning a pretrained large language model on a new large-scale dat

Andreas Bluhm, Leevi Leppäjärvi, Ion Nechita

In the quest for robust and universal quantum devices, the notion of simulation plays a crucial role, both from a theoretical and from an applied perspective. In this work, we go beyond the simulation of quantum channels and quantum measurements, studying what it means to simulate a collection of measurements, which we call a multimeter. To this end, we firs

Jia Guo, Chenyang Yang

Graph neural networks (GNNs) have been shown promising in optimizing power allocation and link scheduling with good size generalizability and low training complexity. These merits are important for learning wireless policies under dynamic environments, which partially come from the matched permutation equivariance (PE) properties of the GNNs to the policies

Ziying Pan, Kun Wang, Gang Li, Feihong He

The class-conditional image generation based on diffusion models is renowned for generating high-quality and diverse images. However, most prior efforts focus on generating images for general categories, e.g., 1000 classes in ImageNet-1k. A more challenging task, large-scale fine-grained image generation, remains the boundary to explore. In this work, we pre

Taeho Kang, Youngki Lee

We present EgoTAP, a heatmap-to-3D pose lifting method for highly accurate stereo egocentric 3D pose estimation. Severe self-occlusion and out-of-view limbs in egocentric camera views make accurate pose estimation a challenging problem. To address the challenge, prior methods employ joint heatmaps-probabilistic 2D representations of the body pose, but heatma

Dmitrijs Trizna, Luca Demetrio, Battista Biggio, Fabio Roli

Living-off-the-land (LOTL) techniques pose a significant challenge to security operations, exploiting legitimate tools to execute malicious commands that evade traditional detection methods. To address this, we present a robust augmentation framework for cyber defense systems as Security Information and Event Management (SIEM) solutions, enabling the detecti

Lin Wang, Guido Burkard

We investigate the valley relaxation due to intervalley coupling in a single-electron bilayer graphene quantum dot. The valley relaxation is assisted by both the emission of acoustic phonons via the deformation potential and bond-length change mechanisms and $1/f$ charge noise. In the perpendicular magnetic-field dependence of the valley relaxation time $T_1

Emanuele Caputo, Nicola Cavallucci

The goal of this paper is to continue the study of the relation between the Poincar\'e inequality and the lower bounds of Minkowski content of separating sets, initiated in our previous work [Caputo, Cavallucci: Poincar\'e inequality and energy of separating sets, arXiv 2401.02762]. A new shorter proof is provided. An intermediate tool is the study of the lo

Umang Bhatt, Holli Sargeant

Algorithmic resignation is a strategic approach for managing the use of artificial intelligence (AI) by embedding governance directly into AI systems. It involves deliberate and informed disengagement from AI, such as restricting access AI outputs or displaying performance disclaimers, in specific scenarios to aid the appropriate and effective use of AI. By

Nida Itrat Abbasi, Guy Laban, Tamsin Ford, Peter B Jones

The interdisciplinary nature of Child-Robot Interaction (CRI) fosters incorporating measures and methodologies from many established domains. However, when employing CRI approaches to sensitive avenues of health and wellbeing, caution is critical in adapting metrics to retain their safety standards and ensure accurate utilisation. In this work, we conducted

Gianni Bosi, Roberto Daris, Gabriele Sbaiz

Let $X$ be an arbitrary set. Then a topology $t$ on $X$ is said to be completely useful if every upper semicontinuous linear (total) preorder $\precsim$ on $X$ can be represented by an upper semicontinuous real-valued order preserving function. In this paper, appealing, simple and new characterizations of completely useful topologies will be proved, therefor

Vadim K. Weinstein, Steven M. LaValle

This paper formally defines a robot system, including its sensing and actuation components, as a general, topological dynamical system. The focus is on determining general conditions under which various environments in which the robot can be placed are indistinguishable. A key result is that, under very general conditions, covering maps witness such indistin

Won-Kwang Park

This study investigates the applicability of Kirchhoff migration (KM) for a fast identification of unknown objects in a real-world limited-aperture inverse scattering problem. To demonstrate the theoretical basis for the applicability including unique determination of objects, the imaging function of the KM was formulated using a uniformly convergent infinit

Nikhil Jha, Martino Trevisan, Marco Mellia, Daniel Fernandez

In response to growing concerns about user privacy, legislators have introduced new regulations and laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) that force websites to obtain user consent before activating personal data collection, fundamental to providing targeted advertising. The cornerstone of t

Bing Li, Dong Zhang, Cheng Huang, Yun Xian

Camera with a fisheye or ultra-wide lens covers a wide field of view that cannot be modeled by the perspective projection. Serious fisheye lens distortion in the peripheral region of the image leads to degraded performance of the existing head pose estimation models trained on undistorted images. This paper presents a new approach for head pose estimation th

Santosh Thoduka, Nico Hochgeschwender, Juergen Gall, Paul G. Plöger

An object handover between a robot and a human is a coordinated action which is prone to failure for reasons such as miscommunication, incorrect actions and unexpected object properties. Existing works on handover failure detection and prevention focus on preventing failures due to object slip or external disturbances. However, there is a lack of datasets an

Feiya Li, Chunyun Fu, Dongye Sun, Jian Li

Point cloud maps generated via LiDAR sensors using extensive remotely sensed data are commonly used by autonomous vehicles and robots for localization and navigation. However, dynamic objects contained in point cloud maps not only downgrade localization accuracy and navigation performance but also jeopardize the map quality. In response to this challenge, we

Juuso Manninen, Robert H. Blick, Francesco Massel

We propose an integrated nonlinear superconducting device based on a nanoelectromechanical shuttle. The system can be described as a qubit coupled to a bosonic mode. The topology of the circuit gives rise to an adjustable qubit/mechanical coupling, allowing the experimenter to tune between linear and quadratic coupling in the mechanical degrees of freedom. O

Yang Li, Shenglan Yuan, Shengyuan Xu

Stochastic vegetation-water dynamical systems play a pivotal role in ecological stability, biodiversity, water resource management, and adaptation to climate change. This research proposes a machine learning-based method for analyzing rare events in stochastic vegetation-water dynamical systems with multiplicative Gaussian noise. Utilizing the Freidlin-Wentz

Tian Lin, Hengshan Hu

This article presents the Huygens method as a novel alternative to the Cagniard-de Hoop method, offering insights into wave generation mechanisms and facilitating wave decomposition. It holds promise for various linear boundary problems and provides results without singularities. The study defines surface waves, including the Rayleigh wave, and identifies a

Eilidh Jack, Craig Alexander, Elinor M Jones

In recent years, the integration of gamification into educational settings has garnered significant attention as a means to enhance student engagement and learning outcomes. By leveraging gamified elements such as points and leaderboards, educators aim to promote active participation, motivation, and deeper understanding among students. This study investigat

Subhabrata Dutta, Joykirat Singh, Soumen Chakrabarti, Tanmoy Chakraborty

Despite superior reasoning prowess demonstrated by Large Language Models (LLMs) with Chain-of-Thought (CoT) prompting, a lack of understanding prevails around the internal mechanisms of the models that facilitate CoT generation. This work investigates the neural sub-structures within LLMs that manifest CoT reasoning from a mechanistic point of view. From an

Ke Xue, Xi Lin, Yunqi Shi, Shixiong Kai

Placement is crucial in the physical design, as it greatly affects power, performance, and area metrics. Recent advancements in analytical methods, such as DREAMPlace, have demonstrated impressive performance in global placement. However, DREAMPlace has some limitations, e.g., may not guarantee legalizable placements under the same settings, leading to fragi

Jan E. Grabowski, Andrew N. W. Hone, Wookyung Kim

We extend recent work of the third author and Kouloukas by constructing deformations of integrable cluster maps corresponding to the Dynkin types $A_{2N}$, lifting these to higher-dimensional maps possessing the Laurent property and demonstrating integrality of the deformations for $N\leq 3$. This provides the first infinite class of examples (in arbitrarily

Miriam Louise Carnot, Eric Peukert, Bogdan Franczyk

In the efforts for safer roads, ensuring adequate vertical clearance above roadways is of great importance. Frequently, trees or other vegetation is growing above the roads, blocking the sight of traffic signs and lights and posing danger to traffic participants. Accurately estimating this space from simple images proves challenging due to a lack of depth in

Chunming Tang, Shajie Xing, Wen Huang, Jinbao Jian

In this paper, a restricted memory quasi-Newton bundle method for minimizing a locally Lipschitz continuous function over a Riemannian manifold is proposed. The curvature information of the objective function is approximated by applying a Riemannian version of the quasi-Newton updating formulas. A Riemannian subgradient aggregation technique is proposed and

Joanne Lin, Nantheera Anantrasirichai, David Bull

Instance segmentation for low-light imagery remains largely unexplored due to the challenges imposed by such conditions, for example shot noise due to low photon count, color distortions and reduced contrast. In this paper, we propose an end-to-end solution to address this challenging task. Our proposed method implements weighted non-local blocks (wNLB) in t

Saikat Gayen, Rashmi Sagar, Sarvesh Mangla, Prasun Dutta

Power spectrum of \HI 21-cm radiation is one of the promising probes to study large scale structure of the universe and understand galaxy formation and evolution. The presence of foregrounds, that are orders of magnitude larger in the same frequency range of the redshifted 21-cm signal has been one of the largest observational challenges. The foreground cont

Ahmed Ghorbel, Wassim Hamidouche, Luce Morin

Neural fields, also known as implicit neural representations (INRs), have shown a remarkable capability of representing, generating, and manipulating various data types, allowing for continuous data reconstruction at a low memory footprint. Though promising, INRs applied to video compression still need to improve their rate-distortion performance by a large

Zezhong Ding, Yongan Xiang, Shangyou Wang, Xike Xie

In the realm of distributed systems tasked with managing and processing large-scale graph-structured data, optimizing graph partitioning stands as a pivotal challenge. The primary goal is to minimize communication overhead and runtime cost. However, alongside the computational complexity associated with optimal graph partitioning, a critical factor to consid

Haakon Andresen, Evan P. O'Connor, Oliver Eggenberger Andersen, Sean M. Couch

In this work we extended an energy-integrated neutrino transport method to facilitate efficient, yet precise, modeling of compact astrophysical objects. We particularly focus on core-collapse supernovae. We implemented a gray neutrino-transport framework from the literature into FLASH and performed a detailed evaluation of its accuracy in core-collapse super

Jiacheng Lin, Jiajun Chen, Kunyu Peng, Xuan He

This paper introduces the task of Auditory Referring Multi-Object Tracking (AR-MOT), which dynamically tracks specific objects in a video sequence based on audio expressions and appears as a challenging problem in autonomous driving. Due to the lack of semantic modeling capacity in audio and video, existing works have mainly focused on text-based multi-objec

Ronan Mouchoux, Laurent Moulin, Nicolas Striebig

Broken links in websites external resources pose a serious threat to cybersecurity and the credibility of websites. They can be hijacked to eavesdrop user traffic or to inject malicious software. In this paper, we present the first result of an ongoing research. We focus on the prevalence of broken links in external resources on home pages of the most visite

Shin-ichiro Seki

We present a new proof of the extended double shuffle relation for multiple zeta values which notably does not rely on the use of integrals. This proof is based on a formula recently obtained by Maesaka, Watanabe, and the author.

Nikolai Chugai, Victor Utrobin

We propose a model for the origin of the broad He II 4686A emission in the early spectrum of type II SN~2020jfo. The 4686A line is emitted presumably by dense fragments embedded into a hot gas of the forward shock wave. The fragments are produced as a result of a heavy braking of the dense low-mass shell at the ejecta boundary and a simultaneous Rayleigh-Tay

Annabel L Davies, A E Ades, Julian PT Higgins

Quantitative evidence synthesis methods aim to combine data from multiple medical trials to infer relative effects of different interventions. A challenge arises when trials report continuous outcomes on different measurement scales. To include all evidence in one coherent analysis, we require methods to `map' the outcomes onto a single scale. This is partic

Jonathan Cutler, Luke Pebody, Amites Sarkar

Given a set of integers $A$ and an integer $k$, write $A+k\cdot A$ for the set $\{a+kb:a\in A,b\in A\}$. Hanson and Petridis showed that if $|A+A|\le K|A|$ then $|A+2\cdot A|\le K^{2.95}|A|$. At a presentation of this result, Petridis stated that the highest known value for $\frac{\log(|A+2\cdot A|/|A|)}{\log(|A+A|/|A|)}$ (bounded above by 2.95) was $\frac{\

Celal Alagoz

Human Activity Recognition (HAR) has been extensively studied, with recent emphasis on the implementation of advanced Machine Learning (ML) and Deep Learning (DL) algorithms for accurate classification. This study investigates the efficacy of two ML algorithms, eXtreme Gradient Boosting (XGBoost) and MiniRocket, in the realm of HAR using data collected from

Jingmin Xia, Yucen Han

The smectic C (smC) phase represents a unique class of liquid crystal phases characterised by the layered arrangement of molecules with tilted orientations with respect to layer normals. Building upon the real-valued tensorial smectic A (smA) model in [Xia et al., PRL, 126, 177801 (2021)], we propose a new continuum mathematical model for smC (and smA) by in

Qiang Zhang, Peter Cui, David Yan, Jingkai Sun

Recently, humanoid robots have made significant advances in their ability to perform challenging tasks due to the deployment of Reinforcement Learning (RL), however, the inherent complexity of humanoid robots, including the difficulty of designing complicated reward functions and training entire sophisticated systems, still poses a notable challenge. To conq

Joo Chan Lee, Taejune Kim, Eunbyung Park, Simon S. Woo

There have been significant advancements in anomaly detection in an unsupervised manner, where only normal images are available for training. Several recent methods aim to detect anomalies based on a memory, comparing or reconstructing the input with directly stored normal features (or trained features with normal images). However, such memory-based approach

Yunwei Bai, Ying Kiat Tan, Shiming Chen, Yao Shu

Few-shot learning (FSL) commonly requires a model to identify images (queries) that belong to classes unseen during training, based on a few labelled samples of the new classes (support set) as reference. So far, plenty of algorithms involve training data augmentation to improve the generalization capability of FSL models, but outlier queries or support imag

B. Hagedorn, C. Cicone, M. Sarzi, A. Saintonge

We derived molecular gas fractions ($f_\mathrm{mol}=M_\mathrm{mol}/M_*$) and depletion times ($\tau_\mathrm{mol}= M_\mathrm{mol}/\mathrm{SFR} $) for 353 galaxies representative of the local star-forming population with $10^{8.5}\,M_\odot < M_* < 10^{10.5}\,M_\odot$ drawn from the ALLSMOG and xCOLDGASS surveys of CO(2-1) and CO(1-0) line emission. By adding c

Wenjian Sun, Linying Pan, Jingyu Xu, Weixiang Wan

Autonomous driving technology can improve traffic safety and reduce traffic accidents. In addition, it improves traffic flow, reduces congestion, saves energy and increases travel efficiency. In the relatively mature automatic driving technology, the automatic driving function is divided into several modules: perception, decision-making, planning and control

Mark Pencovitch

Expanding on work by Conway, Orson, and Powell, we study the isotopy classes rel. boundary of nonorientable, compact, locally flatly embedded surfaces in $D^4$ with knot group $\mathbb{Z}_2$. In particular we show that if two such surfaces have fixed knot boundary $K$ in $S^4$ such that $\vert \det(K) \vert =1$, the same normal Euler number, and the same non

Esa Järvenpää, Maarit Järvenpää, Markus Myllyoja, Örjan Stenflo

We consider the Hausdorff dimension of random covering sets generated by balls and general measures in Euclidean spaces. We prove, for a certain parameter range, a conjecture by Ekstr\"om and Persson concerning the exact value of the dimension in the special case of radii $(n^{-\alpha})_{n=1}^\infty$. For generating balls with an arbitrary sequence of radii,

Ergun Akleman, Bekir Tevfik Akgun, Adil Alpkocak

In this article, we present a framework for developing context-sensitive luminance correction formulas that can produce constant luminance perception for foreground objects. Our formulas make the foreground object slightly translucent to mix with the blurred version of the background. This mix can quickly produce any desired illusion of luminance in foregrou

Bruno Henriques, Benjamin Allaert, Jean-Philippe Vandeborre

With the growing demand for immersive digital applications, the need to understand and reconstruct 3D scenes has significantly increased. In this context, inpainting indoor environments from a single image plays a crucial role in modeling the internal structure of interior spaces as it enables the creation of textured and clutter-free reconstructions. While

Diogo L. M. Souza, Fernando S. Borges, Enrique C. Gabrick, Lucas E. Bentivoglio

Spiral waves are spatial-temporal patterns that can emerge in different systems as heart tissues, chemical oscillators, ecological networks and the brain. These waves have been identified in the neocortex of turtles, rats, and humans, particularly during sleep-like states. Although their functions in cognitive activities remain until now poorly understood, t

Bashir Kazimi, Karina Ruzaeva, Stefan Sandfeld

In this work, we explore the potential of self-supervised learning with Generative Adversarial Networks (GANs) for electron microscopy datasets. We show how self-supervised pretraining facilitates efficient fine-tuning for a spectrum of downstream tasks, including semantic segmentation, denoising, noise \& background removal, and super-resolution. Experiment

Mihaela Cătălina Stoian, Alex Tatomir, Thomas Lukasiewicz, Eleonora Giunchiglia

Deep learning models have shown their strengths in various application domains, however, they often struggle to meet safety requirements for their outputs. In this paper, we introduce PiShield, the first package ever allowing for the integration of the requirements into the neural networks' topology. PiShield guarantees compliance with these requirements, re

Shuo Yang, Gjergji Kasneci

Wide usage of ChatGPT has highlighted the potential of reinforcement learning from human feedback. However, its training pipeline relies on manual ranking, a resource-intensive process. To reduce labor costs, we propose a self-supervised text ranking approach for applying Proximal-Policy-Optimization to fine-tune language models while eliminating the need fo

Mriganka Sandilya, Javed Akhtar, Manash Jyoti Sarmah, Himangshu Prabal Goswami

In the nonequilibrium steadystate of electronic transport across a spin-resolved quantronic junction, we investigate the role of cotunneling on the emergent statistics under phase-different adiabatic modulation of the reservoirs' chemical potentials. By explicitly identifying the sequential and inelastic cotunneling rates, we numerically evaluate the geometr

Xinjian Luo, Yangfan Jiang, Fei Wei, Yuncheng Wu

Diffusion models have recently gained significant attention in both academia and industry due to their impressive generative performance in terms of both sampling quality and distribution coverage. Accordingly, proposals are made for sharing pre-trained diffusion models across different organizations, as a way of improving data utilization while enhancing pr