March 2024 arXiv papers — page 123

Elizabeth Qian, Dayoung Kang, Vignesh Sella, Anirban Chaudhuri

Machine learning (ML) methods, which fit to data the parameters of a given parameterized model class, have garnered significant interest as potential methods for learning surrogate models for complex engineering systems for which traditional simulation is expensive. However, in many scientific and engineering settings, generating high-fidelity data on which

Naiara Korta Martiartu, Parisa Salemi Yolgunlu, Martin Frenz, Michael Jaeger

We present the first fully two-dimensional attenuation imaging technique developed for pulse-echo ultrasound systems. Unlike state-of-the-art techniques, which use line-by-line acquisitions, our method uses steered emissions to constrain attenuation values at each location with multiple crossing wave paths, essential to resolve the spatial variations of this

Isaac Hobday, Paul Stevenson, James Benstead

Quantum computing can potentially provide advantages for specific computational tasks. The simulation of fermionic systems is one such task that lends itself well to quantum computation, with applications in nuclear physics and electronic systems. Here we present work in which we use a variance minimisation method to find the full spectrum of energy eigenval

Daria Korobenko, Anastasija Nikiforova, Rajesh Sharma

As artificial intelligence continues its unprecedented global expansion, accompanied by a proliferation of benefits, an increasing apprehension about the privacy and security implications of AI-enabled systems emerges. The pivotal question of effectively controlling AI development at both jurisdictional and organizational levels has become a prominent theme

B. Appiah, P. Dani, W. Ge, C. Hudson

Genevois recently classified which graph braid groups on $\ge 3$ strands are word hyperbolic. In the $3$-strand case, he asked whether all such word hyperbolic groups are actually free; this reduced to checking two infinite classes of graphs: sun and pulsar graphs. We prove that $3$-strand braid groups of sun graphs are free. On the other hand, it was known

Jesse Atuhurra, Seiveright Cargill Dujohn, Hidetaka Kamigaito, Hiroyuki Shindo

Natural language processing (NLP) practitioners are leveraging large language models (LLM) to create structured datasets from semi-structured and unstructured data sources such as patents, papers, and theses, without having domain-specific knowledge. At the same time, ecological experts are searching for a variety of means to preserve biodiversity. To contri

Pengfei Zhang, Zhenhua Yu

In generic closed quantum systems, the complexity of operators increases under time evolution governed by the Heisenberg equation, reflecting the scrambling of local quantum information. However, when systems interact with an external environment, the system-environment coupling allows operators to escape from the system, inducing a dynamical transition betw

Guanjian Hu, Shikuan Wang, Jing Hu, RuiQiang Li

We investigate the noise in spin transport through a single quantum dot (QD) tunnel coupled to ferromagnetic electrodes with noncollinear magnetizations. Based on a spin-resolved quantum master equation, auto- and cross-correlations of spin-resolved currents are analyzed to reveal the underlying spin transport dynamics and characteristics for various polariz

Chung-Ming Pan

We prove a conjecture proposed by Berman-Boucksom-Eyssidieux-Guedj-Zeriahi, affirming that the Demailly-Lelong number can be determined through a combination of intersection numbers given by the divisorial part of the potential and the SNC divisors over a log resolution of the maximal ideal of a given point. Moreover, this result establishes a pointwise comp

H. Kitagawa, T. Tada, K. Abe, C. Bronner

We present the results of the charge ratio ($R$) and polarization ($P^{\mu}_{0}$) measurements using the decay electron events collected from 2008 September to 2022 June by the Super-Kamiokande detector. Because of its underground location and long operation, we performed high precision measurements by accumulating cosmic-ray muons. We measured the muon char

Sangamesh Kodge, Deepak Ravikumar, Gobinda Saha, Kaushik Roy

Label corruption, where training samples are mislabeled due to non-expert annotation or adversarial attacks, significantly degrades model performance. Acquiring large, perfectly labeled datasets is costly, and retraining models from scratch is computationally expensive. To address this, we introduce Scaled Activation Projection (SAP), a novel SVD (Singular V

Shmuel Friedland, Cynthia Vinzant

We give a semidefinite programming characterization of the Crawford number. We show that the computation of the Crawford number within $\varepsilon$ precision is computable in polynomial time in the data and $|\log \varepsilon |$.

M. A. Korolev

If $d$ is not a perfect square, we define $T(d)$ as the length of the minimal period of the simple continued fraction expansion for $\sqrt{d}$. Otherwise, we put $T(d) = 0$. In the recent paper (2024), F.Battistoni, L.Greni\'{e} and G.Molteni established (in particular) an upper bound for the second moment of $T(d)$ over the segment $x<d\leqslant 2x$. As a c

Xinliang Huang, Fawei Zheng, Ning Hao

We present TopoTB, a software package written in the Mathematica language, designed to compute electronic structures, topological properties, and phase diagrams based on tight-binding models. TopoTB is user-friendly, with an interactive user interface that enables the tuning of model parameters for fitting the target energy bands in a WYSIWYG way. In additio

Lyes Smaili, Soulaimane Berkane

We revisit the Safety Velocity Cones (SVCs) obstacle avoidance approach for real-time autonomous navigation in an unknown $n$-dimensional environment. We propose a locally Lipschitz continuous implementation of the SVC controller using the distance-to-the-obstacle function and its gradient. We then show that the proposed implementation guarantees safe naviga

Samarth Khanna, Sree Bhattacharyya, Sudipto Ghosh, Kushagra Agarwal

The exponential growth in scale and relevance of social networks enable them to provide expansive insights. Predicting missing links in social networks efficiently can help in various modern-day business applications ranging from generating recommendations to influence analysis. Several categories of solutions exist for the same. Here, we explore various fea

Florian Beier, Robert Beinert

The Gromov-Wasserstein (GW) transport problem is a relaxation of classic optimal transport, which seeks a transport between two measures while preserving their internal geometry. Due to meeting this theoretical underpinning, it is a valuable tool for the analysis of objects that do not possess a natural embedding or should be studied independently of it. Pri

Alexander Theis, Steffen Hagstotz, Robert Reischke, Jochen Weller

Fast Radio Bursts (FRBs) are a sensitive probe of the electron distribution in both the large-scale structure and their host galaxies through the dispersion measure (DM) of the radio pulse. Baryonic feedback models are crucial for modelling small scales for ongoing cosmological surveys that are expected to change the electron distribution in galaxies in a wa

Diodato Ferraioli, Carmine Ventre

A growing body of work in economics and computation focuses on the trade-off between implementability and simplicity in mechanism design. The goal is to develop a theory that not only allows to design an incentive structure easy to grasp for imperfectly rational agents, but also understand the ensuing limitations on the class of mechanisms that enforce it. I

Tim Rensmeyer, Oliver Niggemann

Achieving robust uncertainty quantification for deep neural networks represents an important requirement in many real-world applications of deep learning such as medical imaging where it is necessary to assess the reliability of a neural network's prediction. Bayesian neural networks are a promising approach for modeling uncertainties in deep neural networks

Viacheslav Tsaran, Marc Vanderhaeghen

In this work, we present an updated model for nuclear $\pi^0$ photoproduction, which incorporates pion second-order rescattering on intermediate excited nuclear states. Our approach is based on the distorted wave impulse approximation in momentum space. The many-body medium effects are incorporated in the complex effective $\Delta$ self-energy, employing the

Subash Neupane, Shaswata Mitra, Sudip Mittal, Noorbakhsh Amiri Golilarz

Large Language Models (LLMs) have shown impressive capabilities in generating human-like responses. However, their lack of domain-specific knowledge limits their applicability in healthcare settings, where contextual and comprehensive responses are vital. To address this challenge and enable the generation of patient-centric responses that are contextually r

Aditya Tamar, Ben Hudson, Daniel Palumbo

Earth-based Very Long Baseline Interferometry (VLBI) has made rapid advances in imaging black holes. However, due to the limitations imposed on terrestrial VLBI by the Earth's finite size and turbulent atmosphere, it is imperative to have a space-based component in future VLBI missions. Herein, this paper investigates the effect of Earth's oblateness, also k

Daniel Honerkamp, Martin Büchner, Fabien Despinoy, Tim Welschehold

To fully leverage the capabilities of mobile manipulation robots, it is imperative that they are able to autonomously execute long-horizon tasks in large unexplored environments. While large language models (LLMs) have shown emergent reasoning skills on arbitrary tasks, existing work primarily concentrates on explored environments, typically focusing on eith

Antony C Chan

The 96-eyes instrument is capable of computational extended depth of focus (eDOF) of up to +/- 30 micrometer in the phase channel, and conventional depth of field (DOF) of +/- 5 micrometer in the fluorescence channel. However, it requires minimal plate-to-plate cover glass depth variation to function. Plate depths are measured using a third-party plate scann

Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi

Recent advancements in large language models (LLMs) have significantly enhanced their coding capabilities. However, existing benchmarks predominantly focused on simplified or isolated aspects of coding, such as single-file code generation or repository issue debugging, falling short of measuring the full spectrum of challenges raised by real-world programmin

Xia Chen, Yaozhong Hu

In this paper, we investigate the hyperbolic Anderson equation generated by a time-independent Gaussian noise with two objectives: The solvability and intermittency. First, we prove that Dalang's condition is necessary and sufficient for existence of the solution. Second, we establish the precise long time and high moment asymptotics for the solution under t

Hai-Chao Li, Wen Huang, Wei Xiong

Superradiant phase transitions play a fundamental role in understanding the mechanism of collective light-matter interaction at the quantum level. Here we investigate multiple superradiant phases and phase transitions with different symmetry-breaking patterns in a two-mode V-type Dicke model. Interestingly, we show that there exists a quadruple point where o

Salvatore Capozziello, Giuseppe Sarracino, Giulia De Somma

A critical discussion on the $H_0$ Hubble constant tension is presented by considering both early and late-type observations. From recent precise measurements, discrepancies emerge when comparing results for some cosmological quantities obtained at different redshifts. We highlight the most relevant measurements of $H_0$ and propose potential ideas to solve

Sophie Beaumont, Alexeï Molin, Nicolas Clerc, Étienne Pointecouteau

Context. Future X-ray observatories with high spectral resolution and imaging capabilities will enable measurements and mappings of emission line shifts in the intracluster medium (ICM). Such direct measurements can serve as unique probes of turbulent motions in the ICM. Determining the level and scales of turbulence will improve our understanding of the gal

Ferlinda Feliana, Ting-Wei Hung, Binbin Chen, Ray-Guang Cheng

The open fronthaul interface defined by O-RAN ALLIANCE aims to support the interoperability between multi-vendor open radio access network (O-RAN) radio units (O-RU) and O-RAN distributed units (O-DU). This paper introduces a new tool that could be used to evaluate Denial-of-Service (DoS) attacks against the open fronthaul interface. We launched an array of

Chang Su, Fang Zhou, Linyuan Lü

Exploring the internal mechanism of information spreading is critical for understanding and controlling the process. Traditional spreading models often assume individuals play the same role in the spreading process. In reality, however, individuals' diverse characteristics contribute differently to the spreading performance, leading to a heterogeneous infect

Simon L. Jeger, Valentin Wüest, Charbel Toumieh, Dario Floreano

Avian-informed drones feature morphing wing and tail surfaces, enhancing agility and adaptability in flight. Despite their large potential, realising their full capabilities remains challenging due to the lack of generalized control strategies accommodating their large degrees of freedom and cross-coupling effects between their control surfaces. Here we prop

Gianmarco Gurioli, Weijie Wang, Xiaoqiang Yan

In this paper, a class of high-order methods to numerically solve Functional Differential Equations with Piecewise Continuous Arguments (FDEPCAs) is discussed. The framework stems from the expansion of the vector field associated with the reference differential equation along the shifted and scaled Legendre polynomial orthonormal basis, working on a suitable

Matteo Masto, Vincent Favre-Nicolin, Steven Leake, Tobias Schülli

We propose a deep learning algorithm for the inpainting of Bragg Coherent Diffraction Imaging (BCDI) patterns affected by detector gaps. These regions of missing intensity can compromise the accuracy of reconstruction algorithms, inducing artifacts in the final result. It is thus desirable to restore the intensity in these regions in order to ensure more rel

Dau Thi Hue, Huynh Viet Khanh, Bui Xuan Hai

In this paper, we investigate subnormal subgroups of the multiplicative group of an almost locally simple artinian algebra with involution. In particular, we show that if either the set of traces or the set of norms of such a subgroup with respect to this involution is central, then the algebra must be either a quaternion division algebra or the matrix ring

Margherita Putti, Nicola Bartolo, Sukannya Bhattacharya, Marco Peloso

Primordial tensor modes can induce Cosmic Microwave Background spectral distortions during horizon re-entry. We investigate a specific mechanism proposed for this purpose, characterized by the coupling of an SU(2) gauge field to an axion undergoing a momentary stage of rapid evolution during inflation. Examining also the scalar perturbations produced by this

Sitao Cheng, Ziyuan Zhuang, Yong Xu, Fangkai Yang

Large Language Models (LLMs) have shown potential in reasoning over structured environments, e.g., knowledge graph and table. Such tasks typically require multi-hop reasoning, i.e., match natural language utterance with instances in the environment. Previous methods leverage LLMs to incrementally build a reasoning path, where the LLMs either invoke tools or

Niklas Grieger, Siamak Mehrkanoon, Stephan Bialonski

Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by r

Lei Shi, Paul Bürkner, Andreas Bulling

We present ActionDiffusion -- a novel diffusion model for procedure planning in instructional videos that is the first to take temporal inter-dependencies between actions into account in a diffusion model for procedure planning. This approach is in stark contrast to existing methods that fail to exploit the rich information content available in the particula

S. Bellotti, J. Morin, L. T. Lehmann, P. Petit

Dynamo models of stellar magnetic fields for partly and fully convective stars are guided by observational constraints. Zeeman-Doppler imaging has revealed a variety of magnetic field geometries and, for fully convective stars in particular, a dichotomy: either strong, mostly axisymmetric, and dipole-dominated or weak, non-axisymmetric, and multipole-dominat

Gianmarco Guglielmo, Andrea Montessori, Jean-Michel Tucny, Michele La Rocca

Application of Neural Networks to river hydraulics is fledgling, despite the field suffering from data scarcity, a challenge for machine learning techniques. Consequently, many purely data-driven Neural Networks proved to lack predictive capabilities. In this work, we propose to mitigate such problem by introducing physical information into the training phas

Zhentao Zhang

We investigate the microscopic origin of the negative pressure produced by the constant energy density of the vacuum. It is shown that the zero-point photons in the quantum vacuum could generate the pressures of this type in confined spaces for the photon field. We find in particular that an anomalous radiation plays a role in the occurrence of a negative pr

L. C. Ugwuoke, T. P. J. Krüger, M. S. Tame

The interaction between the electric dipole moments of a quantum emitter and a metal nanoparticle gives rise to unique optical properties, such as interference-induced photon correlations, that could be useful for enhanced intensity-based sensing. Using the quantum theory of photodetection, we propose a nanosensor system comprising a quantum emitter and a me

Thi Kim Nhung Dang, Milan Lopuhaä-Zwakenberg, Mariëlle Stoelinga

Fault tree analysis is a vital method of assessing safety risks. It helps to identify potential causes of accidents, assess their likelihood and severity, and suggest preventive measures. Quantitative analysis of fault trees is often done via the dependability metrics that compute the system's failure behaviour over time. However, the lack of precise data is

Ze Ruan, Xiu-Cai Jiang, Ze-Yi Song, Yu-Zhong Zhang

An improved particle swarm optimization algorithm is proposed and its superiority over standard particle swarm optimization algorithm is tested on two typical benchmark functions. By employing this algorithm to search for the magnetic ground states of the Hubbard model on the real-space square lattice with finite size based on the mean-field approximation, t

Matteo Taiana, Matteo Toso, Stuart James, Alessio Del Bue

Robustly estimating camera poses from a set of images is a fundamental task which remains challenging for differentiable methods, especially in the case of small and sparse camera pose graphs. To overcome this challenge, we propose Pose-refined Rotation Averaging Graph Optimization (PRAGO). From a set of objectness detections on unordered images, our method

Junwei Su, Difan Zou, Chuan Wu

Stochastic gradient descent (SGD) exhibits strong algorithmic regularization effects in practice and plays an important role in the generalization of modern machine learning. However, prior research has revealed instances where the generalization performance of SGD is worse than ridge regression due to uneven optimization along different dimensions. Precondi

Enrico Zardini, Amer Delilbasic, Enrico Blanzieri, Gabriele Cavallaro

Support vector machines (SVMs) are widely used machine learning models (e.g., in remote sensing), with formulations for both classification and regression tasks. In the last years, with the advent of working quantum annealers, hybrid SVM models characterised by quantum training and classical execution have been introduced. These models have demonstrated comp

Paolo Ciafaloni, Giampaolo Co', Dimitri Colferai, Denis Comelli

In processes taking place at energies much higher than the weak scale, electroweak corrections can be taken into account by using electroweak evolution equations, that are analogous to the DGLAP equations in QCD. We show that weak isospin conservation in these equations imposes to modify the expressions of the splitting functions commonly used in the literat

Sangjun Kang, Maximilian Töllner, Di Wang, Christian Minnert

Achieving a correlative measurement of both magnetic and atomic structures at the nanoscale is imperative to understand the fundamental magnetism of matters and for fostering the development of new magnetic nanomaterials. Conventional microscopy methods fall short in providing the two information simultaneously. Here, we develop a new approach to simultaneou

Alessandra Canetta, Serhii Volosheniuk, Sayooj Satheesh, José Pedro Alvarinhas Batista

Heat-to-charge conversion efficiency of thermoelectric materials is closely linked to the entropy per charge carrier. Thus, magnetic materials are promising building blocks for highly efficient energy harvesters, as their carrier entropy is boosted by a spin degree of freedom. In this work, we investigate how this spin entropy impacts heat-to-charge conversi

Yuxing Han, Yunan Ding, Chen Ye Gan, Jiangtao Wen

Classifying videos into distinct categories, such as Sport and Music Video, is crucial for multimedia understanding and retrieval, especially when an immense volume of video content is being constantly generated. Traditional methods require video decompression to extract pixel-level features like color, texture, and motion, thereby increasing computational a

Hannes Waclawek, Stefan Huber

Piecewise Polynomials (PPs) are utilized in several engineering disciplines, like trajectory planning, to approximate position profiles given in the form of a set of points. While the approximation target along with domain-specific requirements, like Ck -continuity, can be formulated as a system of equations and a result can be computed directly, such closed

Miao-Xiang Liang, Yu-Xiang Qiu, Hai-Chao Li, Wei Xiong

Exploring intermixing and interplay between different frequency-mixing processes has always been one of the interesting subjects at the interface of nonlinear optics with quantum optics. Here we investigate coherent competition and control between three-wave mixing (TWM) and four-wave mixing (FWM) in a cyclic three-level superconducting quantum system. In th

David Adenyo, Jason R. Guertin, Bernard Candas, Caroline Sirois

Marginal structural models have been increasingly used by analysts in recent years to account for confounding bias in studies with time-varying treatments. The parameters of these models are often estimated using inverse probability of treatment weighting. To ensure that the estimated weights adequately control confounding, it is possible to check for residu

Jose A. Carrillo, Gui-Qiang G. Chen, Difan Yuan, Ewelina Zatorska

We are concerned with the global existence of finite-energy entropy solutions of the one-dimensional compressible Euler equations with (possibly) damping, alignment forces, and nonlocal interactions: Newtonian repulsion and quadratic confinement. Both the polytropic gas law and the general gas law are analyzed. This is achieved by constructing a sequence of

Alessandro Audrito, Gabriele Fioravanti, Stefano Vita

In this paper, we complete the analysis initiated in [AFV24] establishing some higher order $C^{k+2,\alpha}$ Schauder estimates ($k \in \mathbb{N}$) for a a class of parabolic equations with weights that are degenerate/singular on a characteristic hyperplane. The $C^{2,\alpha}$-estimates are obtained through a blow-up argument and a Liouville theorem, while

Juan Sanz García, Rosa Maskri, Alexander Mitrushchenkov, Loïc Joubert-Doriol

We present two alternative methods for optimizing minimum energy conical intersection (MECI) molecular geometries without knowledge of the derivative coupling (DC). These methods are based on the utilization of Lagrange multipliers: i) one method uses an approximate calculation of the DC, while the other ii) do not require the DC. Both methods use the fact t

Daniel Feliú, Felipe Barra

The reduced state of a small system strongly coupled to a thermal bath may be athermal and used as a small battery once disconnected. If the disconnecting process is too slow, the coupling between the battery and the bath weakens, and at some point, the battery will be in a thermal state that can not be used as a battery. Thus, the unitarily extractable ener

Kexuan Zhang, Xiaobei Zou, Yang Tang

Time series analysis is a vital task with broad applications in various domains. However, effectively capturing cross-dimension and cross-time dependencies in non-stationary time series poses significant challenges, particularly in the context of environmental factors. The spurious correlation induced by the environment confounds the causal relationships bet

Maksim A. Smirnov, Ilya V. Fedotov, Anastasia M. Smirnova, Albert F. Khairullin

In this Letter, we report a first experimental realization of bright ultra-broadband (180 THz) fiber-based biphoton source with widely spectrally separated signal and idler photons. Such a two-photon source is realized due to the joint use of broadband phase-matching of interacting light waves and high optical nonlinearity of a silica-core photonic crystal f

Asif Newaz, Farhan Shahriyar Haq, Nadim Ahmed

Phishing is an increasingly sophisticated form of cyberattack that is inflicting huge financial damage to corporations throughout the globe while also jeopardizing individuals' privacy. Attackers are constantly devising new methods of launching such assaults and detecting them has become a daunting task. Many different techniques have been suggested, each wi

Tobias Dornheim, Sebastian Schwalbe, Panagiotis Tolias, Maximilan Böhme

We present quasi-exact ab initio path integral Monte Carlo (PIMC) results for the partial static density responses and local field factors of hydrogen in the warm dense matter regime, from solid density conditions to the strongly compressed case. The full dynamic treatment of electrons and protons on the same footing allows us to rigorously quantify both ele

Hang Hu, Sidi Wu, Guoxiong Cai, Na Liu

Physics-informed neural networks (PINNs) have successfully addressed various computational physics problems based on partial differential equations (PDEs). However, while tackling issues related to irregularities like singularities and oscillations, trained solutions usually suffer low accuracy. In addition, most current works only offer the trained solution

Zhanxin Gao, Jun Cen, Xiaobin Chang

Continual learning empowers models to adapt autonomously to the ever-changing environment or data streams without forgetting old knowledge. Prompt-based approaches are built on frozen pre-trained models to learn the task-specific prompts and classifiers efficiently. Existing prompt-based methods are inconsistent between training and testing, limiting their e

Matthias Birkner, Andrej Depperschmidt, Timo Schlüter

We consider random walks in dynamic random environments which arise naturally as spatial embeddings of ancestral lineages in spatial locally regulated population models. In particular, as the main result, we prove the quenched central limit theorem for a random walk in dynamic random environment generated by time reversal of logistic branching random walks i

Armin Sheibanifard, Hongchuan Yu

The storage of medical images is one of the challenges in the medical imaging field. There are variable works that use implicit neural representation (INR) to compress volumetric medical images. However, there is room to improve the compression rate for volumetric medical images. Most of the INR techniques need a huge amount of GPU memory and a long training

Anastasios Foliadis, Mario H. Castañeda, Richard A. Stirling-Gallacher, Reiner S. Thomä

Deep learning (DL) methods have been shown to improve the performance of several use cases for the fifth-generation (5G) New radio (NR) air interface. In this paper we investigate user equipment (UE) positioning using the channel state information (CSI) fingerprints between a UE and multiple base stations (BSs). In such a setup, we consider two different fus

Sara Sterlie, Nina Weng, Aasa Feragen

Generative AI, such as large language models, has undergone rapid development within recent years. As these models become increasingly available to the public, concerns arise about perpetuating and amplifying harmful biases in applications. Gender stereotypes can be harmful and limiting for the individuals they target, whether they consist of misrepresentati

Dieter Verbruggen, Hazem Sallouha, Sofie Pollin

In the evolution of 6th Generation (6G) technology, the emergence of cell-free networking presents a paradigm shift, revolutionizing user experiences within densely deployed networks where distributed access points collaborate. However, the integration of intelligent mechanisms is crucial for optimizing the efficiency, scalability, and adaptability of these

Tuukka Korhonen, Fedor V. Fomin, Pekka Parviainen

Markov networks are probabilistic graphical models that employ undirected graphs to depict conditional independence relationships among variables. Our focus lies in constraint-based structure learning, which entails learning the undirected graph from data through the execution of conditional independence tests. We establish theoretical limits concerning two

Cynthia Meouchi, Sandra Barna, Anatoly Rosenfeld, Linh T. Tran

This paper characterizes the microdosimetric spectra of a single-energy carbon-ion pencil beam at MedAustron using a miniature solid-state silicon microdosimeter to estimate the impact of the lateral distribution of the different fragments on the microdosimetric spectra. The microdosimeter was fixed at one depth and then laterally moved away from the central

G. R. Boroun, Phuoc Ha

Using Laplace transform techniques, we describe the determination of the longitudinal structure function $F_{L}(x,Q^2)$, at the leading-order approximation in momentum space, from the structure function $F_{2}(x,Q^2)$ and its derivative with respect to ${\ln}Q^2$ in a kinematical region of low values of the Bjorken variable $x$. Since the $x$ dependence of $

Lauri Juvela, Eero-Pekka Damskägg, Aleksi Peussa, Jaakko Mäkinen

This paper describes a data-driven approach to creating real-time neural network models of guitar amplifiers, recreating the amplifiers' sonic response to arbitrary inputs at the full range of controls present on the physical device. While the focus on the paper is on the data collection pipeline, we demonstrate the effectiveness of this conditioned black-bo

Fen Lyu, En-Wei Liang, D. Li

The radiation physics of fast radio bursts (FRBs) remains an open question. Current observations have discovered that narrowly-banded bursts of FRB 20201124A are active in 0.4-2 GHz and their spectral peak frequency ($\nu^{\rm obs}_{p}$) are mostly toward $\sim 1$ GHz. Utilizing a sample of 1268 bursts of FRB 20201124A detected with the FAST telescope, we sh

Zhihao Chen, Yiyuan Ge, Yanyan Lv, Ziyang Wang

The study of Cloth-Changing Person Re-identification (CC-ReID) focuses on retrieving specific pedestrians when their clothing has changed, typically under the assumption that the entire pedestrian images are visible. Pedestrian images in real-world scenarios, however, are often partially obscured by obstacles, presenting a significant challenge to existing C

Yihao Liu, Feng Xue, Anlong Ming, Mingshuai Zhao

In the last year, universal monocular metric depth estimation (universal MMDE) has gained considerable attention, serving as the foundation model for various multimedia tasks, such as video and image editing. Nonetheless, current approaches face challenges in maintaining consistent accuracy across diverse scenes without scene-specific parameters and pre-trai

Philip Naveen

A learning rate scheduler is a predefined set of instructions for varying search stepsizes during model training processes. This paper introduces a new logarithmic method using harsh restarting of step sizes through stochastic gradient descent. Cyclical log annealing implements the restart pattern more aggressively to maybe allow the usage of more greedy alg

Noah Ziethen, David Zwicker

Droplets are essential for spatially controlling biomolecules in cells. To work properly, cells need to control the emergence and morphology of droplets. On the one hand, driven chemical reactions can affect droplets profoundly. For instance, reactions can control how droplets nucleate and how large they grow. On the other hand, droplets coexist with various

Bingchen Liu, Yuanyuan Fang

Federated learning (FL) promotes the development and application of artificial intelligence technologies by enabling model sharing and collaboration while safeguarding data privacy. Knowledge graph (KG) embedding representation provides a foundation for knowledge reasoning and applications by mapping entities and relations into vector space. Federated KG emb

Ting-Jui Chang, Shahin Shahrampour

Recent advancement in online optimization and control has provided novel tools to study online linear quadratic regulator (LQR) problems, where cost matrices are time-varying and unknown in advance. In this work, we study the online linear quadratic Gaussian (LQG) problem over the manifold of stabilizing controllers that are linearly constrained to impose ph

M. Brož, P. Vernazza, M. Marsset, F. E. DeMeo

Understanding the origin of bright shooting stars and their meteorite samples is among the most ancient astronomy-related questions that at larger scales has human consequences [1-3]. As of today, only ${\sim}\,6\%$ of meteorite falls have been firmly linked to their sources (Moon, Mars, and asteroid (4) Vesta [4-6]). Here, we show that ${\sim}\,70\%$ of met

Masud Mansuripur

In learning quantum mechanics, an essential question has always been: How does one go about developing a "physical feel" for quantum phenomena? Naturally, one needs a basis or ground zero to start from, and that basis must be unlike anything with which we are already familiar in consequence of our experiences with the world of classical physics. We argue (ch

Xinjie Zhang, Xingtong Ge, Tongda Xu, Dailan He

Implicit neural representations (INRs) recently achieved great success in image representation and compression, offering high visual quality and fast rendering speeds with 10-1000 FPS, assuming sufficient GPU resources are available. However, this requirement often hinders their use on low-end devices with limited memory. In response, we propose a groundbrea

Maik Dannecker, Vanessa Kyriakopoulou, Lucilio Cordero-Grande, Anthony N. Price

We introduce a conditional implicit neural atlas (CINA) for spatio-temporal atlas generation from Magnetic Resonance Images (MRI) of the neurotypical and pathological fetal brain, that is fully independent of affine or non-rigid registration. During training, CINA learns a general representation of the fetal brain and encodes subject specific information int

Samitha Somathilaka, Adrian Ratwatte, Sasitharan Balasubramaniam, Mehmet Can Vuran

In our earlier work, we introduced the concept of Gene Regulatory Neural Network (GRNN), which utilizes natural neural network-like structures inherent in biological cells to perform computing tasks using chemical inputs. We define this form of chemical-based neural network as Wet TinyML. The GRNN structures are based on the gene regulatory network and have

Michaël Marsset, Pierre Vernazza, Miroslav Brož, Cristina A. Thomas

Studies of micrometeorites in mid-Ordovician limestones and Earth's impact craters indicate that our planet witnessed a massive infall of ordinary L chondrite material 466 million years (My) ago (Heck et al. 2017, Schmieder & Kring 2020, Kenkmann 2021) that may have been at the origin of the first major mass extinction event (Schmitz et al. 2019). The breaku

ATLAS Collaboration

A search is performed for localized excesses in the low-mass dijet invariant mass distribution, targeting a hypothetical new particle decaying into two jets and produced in association with either a high transverse momentum photon or a jet. The search uses the full Run 2 data sample from LHC proton-proton collisions collected by the ATLAS experiment at a cen

Pearse C. Murphy, Stéphane Aicardi, Baptiste Cecconi, Carine Briand

Solar radio spikes are short lived, narrow bandwidth features in low frequency solar radio observations. The timing of their occurrence and the number of spikes in a given observation is often unpredictable. The high temporal and frequency of resolution of modern radio telescopes such as NenuFAR mean that manually identifying radio spikes is an arduous task.

Sarah Joiret, Sean N. Raymond, Guillaume Avice, Matthew S. Clement

Recent models of solar system formation suggest that a dynamical instability among the giant planets happened within the first 100 Myr after disk dispersal, perhaps before the Moon-forming impact. As a direct consequence, a bombardment of volatile-rich impactors may have taken place on Earth before internal and atmospheric reservoirs were decoupled. However,

Noam Soker

I point out similarities between point-symmetric X-ray morphologies in cooling flow groups and clusters of galaxies, which are observed to be shaped by jets, and point-symmetric morphologies of eight core-collapse supernova (CCSN) remnants. I identify these similarities by qualitative eye inspection of multiwavelength images. I use these similarities to stre

Kristina Đurović, Ardeshir Hanifi, Philipp Schlatter, Kenzo Sasaki

Under action of free-stream turbulence (FST), elongated streamwise streaky structures are generated inside the boundary layer, and their amplitude and wavelength are crucial for the transition onset. While turbulence intensity is strongly correlated with the transitional Reynolds number, characteristic length scales of the FST are often considered to have a

Yifei Gao, Jiaqi Wang, Zhiyu Lin, Jitao Sang

The evolution of Artificial Intelligence Generated Contents (AIGCs) is advancing towards higher quality. The growing interactions with AIGCs present a new challenge to the data-driven AI community: While AI-generated contents have played a crucial role in a wide range of AI models, the potential hidden risks they introduce have not been thoroughly examined.

Frank Saueressig, Agustín Silva

Asymptotic safety is a powerful mechanism for obtaining a consistent and predictive quantum field theory beyond the realm of perturbation theory. It hinges on an interacting fixed point of the Wilsonian renormalization group flow which controls the microscopic dynamics. Connecting the fixed point to observations requires constructing the set of effective act

Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, Suchin Gururangan

Scaling laws are useful guides for derisking expensive training runs, as they predict performance of large models using cheaper, small-scale experiments. However, there remain gaps between current scaling studies and how language models are ultimately trained and evaluated. For instance, scaling is usually studied in the compute-optimal training regime (i.e.

Xiang Yuan, Hanming Guo, Songlin Zhuang, Jinbing Hu

The generation and focusing properties of higher-order polarized beams have attracted lots of interests due to its significant applications. In this paper,we derived the formula of transforming linear polarization into higher-order polarization, which is applicable to generating arbitrary order polarization. Based on the derived formula, the focusing propert

Dieter Weber, David Landers, Chen Huang, Emanuela Liberti

In four-dimensional scanning transmission electron microscopy (4D STEM) a focused beam is scanned over a specimen and a diffraction pattern is recorded at each position using a pixelated detector. During the experiment, it must be ensured that the scan coordinate system of the beam is correctly calibrated relative to the detector coordinate system. Various s

Yu Jiang

The Terwilliger algebras of association schemes over an arbitrary field $\mathbb{F}$ were called the Terwilliger $\mathbb{F}$-algebras of association schemes in [8]. In this paper, we study the Terwilliger $\mathbb{F}$-algebras of factorial association schemes. We determine the $\mathbb{F}$-dimensions, the centers, the semisimplicity, the Jacobson radicals,

Murat Onur Yildirim, Elif Ceren Gok Yildirim, Decebal Constantin Mocanu, Joaquin Vanschoren

Neural networks often struggle with catastrophic forgetting when learning sequences of tasks or data streams, unlike humans who can continuously learn and consolidate new concepts even in the absence of explicit cues. Online data-incremental learning seeks to emulate this capability by processing each sample only once, without having access to task or stream