May 2024 arXiv papers — page 120

Yi Liu, Alexander W. Levis, Sharon-Lise Normand, Larry Han

Recent years have experienced increasing utilization of complex machine learning models across multiple sources of data to inform more generalizable decision-making. However, distribution shifts across data sources and privacy concerns related to sharing individual-level data, coupled with a lack of uncertainty quantification from machine learning prediction

Luan Pham, Huong Ha, Hongyu Zhang

Detecting failures and identifying their root causes promptly and accurately is crucial for ensuring the availability of microservice systems. A typical failure troubleshooting pipeline for microservices consists of two phases: anomaly detection and root cause analysis. While various existing works on root cause analysis require accurate anomaly detection, t

Natasa Przulj, Noel Malod-Dognin

Heterogeneous, interconnected, systems-level, molecular data have become increasingly available and key in precision medicine. We need to utilize them to better stratify patients into risk groups, discover new biomarkers and targets, repurpose known and discover new drugs to personalize medical treatment. Existing methodologies are limited and a paradigm shi

Zaining Wang, Jinhui Chen, Hao-jie Xu, Jie Zhao

Relativistic heavy ion collisions provide a unique opportunity to study the shape of colliding nuclei, even up to higher-order multiple deformations. In this work, several observables that are sensitive to quadrupole and hexadecapole deformations of Uranium-238 in relativistic U+U collisions have been systematically investigated with A Multi-Phase Transport

R. Donat, M. C. Martí, P. Mulet

Column chromatography is a laboratory and industrial technique used to separate different substances mixed in a solution. Mathematically, it can be modelled using non-linear partial differential equations whose main ingredients are the adsorption isotherms, which are non-linear functions modelling the affinity between the different substances in the solution

Benjamin Teo, Paul Bastide, Cécile Ané

The evolution of molecular and phenotypic traits is commonly modelled using Markov processes along a phylogeny. This phylogeny can be a tree, or a network if it includes reticulations, representing events such as hybridization or admixture. Computing the likelihood of data observed at the leaves is costly as the size and complexity of the phylogeny grows. Ef

David Cabo-Almeida

We report on a comprehensive study of the Direct Detection phenomenology of singlet Dark Matter $t$-channel portal models. For that purpose, we present a complete computation of the loop-induced direct detection cross-section for both scalar and fermionic Dark Matter candidates. We complete the study by comparing the results with current and future bounds fr

Julien Roussillon

We propose a series representation for the Virasoro fusion and modular kernels at any irrational central charge. Two distinct, yet closely related formulas are needed for the cases $c\in \mathbb C \backslash (-\infty,1]$ and $c <1$. Our proposal for $c <1$ agrees numerically with the fusion transformation of the four-point spherical conformal blocks, whereas

Yin Yu, John Harlim, Daning Huang, Yan Li

We consider a Graph Neural Network (GNN) non-Markovian modeling framework to identify coarse-grained dynamical systems on graphs. Our main idea is to systematically determine the GNN architecture by inspecting how the leading term of the Mori-Zwanzig memory term depends on the coarse-grained interaction coefficients that encode the graph topology. Based on t

David Andriot, Susha Parameswaran, Dimitrios Tsimpis, Timm Wrase

We explore the possibility that our universe's current accelerated expansion is explained by a quintessence model with an exponential scalar potential, $V =V_0\, e^{-\lambda\, \phi}$, keeping an eye towards $\lambda \geq \sqrt{2}$ and an open universe, favorable to a string theory realisation and with no cosmological horizon. We work out the full cosmology o

István Tomon

We prove that the number of symmetric chain decompositions of the Boolean lattice $2^{[n]}$ is $$\left(\frac{n}{2e}+o(n)\right)^{2^n}.$$ Furthermore, the number of symmetric chain decompositions of the hypergrid $[t]^n$ is $$n^{(1-o_n(1))\cdot t^n}.$$

Cong Hua, Qianqian Xu, Shilong Bao, Zhiyong Yang

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits furth

CMS Collaboration

Results are presented from a search for new physics in high-mass diphoton events from proton-proton collisions at $\sqrt{s}$ = 13 TeV. The data set was collected in 2016-2018 with the CMS detector at the LHC and corresponds to an integrated luminosity of 138 fb$^{-1}$. Events with a diphoton invariant mass greater than 500 GeV are considered. Two different t

Seoyoung Kim, Chi Hoi Yip, Semin Yoo

Paley graphs and Paley sum graphs are classical examples of quasi-random graphs. In this paper, we provide new constructions of families of quasi-random graphs that behave like Paley graphs but are neither Cayley graphs nor Cayley sum graphs. These graphs give a unified perspective of studying various graphs arising from polynomials over finite fields, such

Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Gérôme Bovet, Gregorio Martínez Pérez

In the current cybersecurity landscape, protecting military devices such as communication and battlefield management systems against sophisticated cyber attacks is crucial. Malware exploits vulnerabilities through stealth methods, often evading traditional detection mechanisms such as software signatures. The application of ML/DL in vulnerability detection h

Yujie Yang, Letian Tao, Likun Wang, Shengbo Eben Li

Controllability is a fundamental property of control systems, serving as the prerequisite for controller design. While controllability test is well established in modelic (i.e., model-driven) control systems, extending it to datatic (i.e., data-driven) control systems is still a challenging task due to the absence of system models. In this study, we propose

Luigi C. Berselli, Elisabetta Chiodaroli, Rossano Sannipoli

In this paper we consider the incompressible 3D Euler and Navier-Stokes equations in a smooth bounded domain. First, we study the 3D Euler equations endowed with slip boundary conditions and we prove the same criteria for energy conservation involving the gradient, already known for the Navier-Stokes equations. Subsequently, we utilise this finding, which is

Palle E. T. Jorgensen, James Tian

In the context of kernel optimization, we prove a result that yields new factorizations and realizations. Our initial context is that of general positive operator-valued kernels. We further present implications for Hilbert space-valued Gaussian processes, as they arise in applications to dynamics and to machine learning. Further applications are given in non

Dong Huang, Tsz On Li, Xiaofei Xie, Heming Cui

Deep Learning Systems (DLSs) have been widely applied in safety-critical tasks such as autopilot. However, when a perturbed input is fed into a DLS for inference, the DLS often has incorrect outputs (i.e., faults). DLS testing techniques (e.g., DeepXplore) detect such faults by generating perturbed inputs to explore data flows that induce faults. Since a DLS

Santeri Larnimaa, Markku Vainio

We propose a new Fourier-transform spectroscopy technique based on the rotational Doppler effect. The technique offers an application for optical vortex frequency combs, where each frequency component carries a unique amount of orbital angular momentum (OAM). Here, we emulate a vortex comb using a tunable single frequency laser and a collection of spiral pha

Aarshvi Gajjar, Wai Ming Tai, Xingyu Xu, Chinmay Hegde

We study active learning methods for single index models of the form $F({\mathbf x}) = f(\langle {\mathbf w}, {\mathbf x}\rangle)$, where $f:\mathbb{R} \to \mathbb{R}$ and ${\mathbf x,\mathbf w} \in \mathbb{R}^d$. In addition to their theoretical interest as simple examples of non-linear neural networks, single index models have received significant recent a

Peter Koymans, Alexander Smith

Recently, Alp\"oge-Bhargava-Shnidman determined the average size of the $2$-Selmer group in the cubic twist family of any elliptic curve over $\mathbb{Q}$ with $j$-invariant $0$. We obtain the distribution of the $3$-Selmer groups in the same family. As a consequence, we improve their upper bound on the density of integers expressible as a sum of two rationa

Fuqiang Zhao, Dzmitry Tsetserukou, Qian Liu

One goal of dexterous robotic grasping is to allow robots to handle objects with the same level of flexibility and adaptability as humans. However, it remains a challenging task to generate an optimal grasping strategy for dexterous hands, especially when it comes to delicate manipulation and accurate adjustment the desired grasping poses for objects of vary

Abhishek Sarkar, Bikash Kumar Dey

We study message identification over a q-ary uniform permutation channel, where the transmitted vector is permuted by a permutation chosen uniformly at random. For discrete memoryless channels(DMCs), the number of identifiable messages grows doubly exponentially. Identification capacity, the maximum second-order exponent, is known to be the same as the Shann

Zichuan Liu, Tianchun Wang, Jimeng Shi, Xu Zheng

Explaining deep learning models operating on time series data is crucial in various applications of interest which require interpretable and transparent insights from time series signals. In this work, we investigate this problem from an information theoretic perspective and show that most existing measures of explainability may suffer from trivial solutions

Maria Teresa Baldassarre, Domenico Gigante, Marcos Kalinowski, Azzurra Ragone

Recently, there has been growing attention on behalf of both academic and practice communities towards the ability of Artificial Intelligence (AI) systems to operate responsibly and ethically. As a result, a plethora of frameworks and guidelines have appeared to support practitioners in implementing Trustworthy AI applications (TAI). However, little research

Herve Bergeron, Przemyslaw Malkiewicz, Patrick Peter

We propose a new method to investigate signatures of a quantum gravity phase in the primordial state of cosmological perturbations. We formulate and study a quantum model of a perturbed Friedmann-Lemaitre-Robertson-Walker universe beyond a tensor-product Born-Oppenheimer-like factorization, that is, without restricting the wave function of the universe to th

Francesco Luigi De Faveri, Guglielmo Faggioli, Nicola Ferro

Ensuring the effectiveness of search queries while protecting user privacy remains an open issue. When an Information Retrieval System (IRS) does not protect the privacy of its users, sensitive information may be disclosed through the queries sent to the system. Recent improvements, especially in NLP, have shown the potential of using Differential Privacy to

Jose A. Lopez, Georg Stemmer, Hector A. Cordourier

Gradient boosted decision trees have achieved remarkable success in several domains, particularly those that work with static tabular data. However, the application of gradient boosted models to signal processing is underexplored. In this work, we introduce gradient boosted filters for dynamic data, by employing Hammerstein systems in place of decision trees

Alexander Shen

Kolmogorov complexity is often used as a convenient language for counting and/or probabilistic existence proofs. However, there are some applications where Kolmogorov complexity is used in a more subtle way. We provide one (somehow) surprising example where an existence of a winning strategy in a natural combinatorial game is proven (and no direct proof is k

Đ. V. Savić, D. Hutsemékers, D. Sluse

Lensed quasars are powerful cosmic laboratories; they are used to simultaneously probe various astrophysical phenomena. Microlensing by stars within distant galaxies acts as strong gravitational lenses of multiply imaged quasars, and provides a unique and direct measurement of the lensed quasar internal structure. Microlensing of the continuum emitting regio

Pierre Recho, Lev Truskinovsky

Taking inspiration from the crawling motion of biological cells on a substrate, we consider a physical model of self-propulsion where the spatio-temporal driving can involve both, a mechanical actuation by active force couples, and a chemical actuation through controlled mass turnover. We show that the competition and cooperation between these two modalities

E. Epelbaum, J. Gegelia, Ulf-G. Meißner, L. Neuhaus

A consistency condition of general relativity as an effective field theory in Minkowskian background uniquely fixes the value of the cosmological constant. In two-loop calculations, including the interaction of gravitons with matter fields, it has been shown that this value of the cosmological constant leads to vanishing vacuum energy, under the assumption t

Birger Moell

Background: Rapid advancements in natural language processing have led to the development of large language models with the potential to revolutionize mental health care. These models have shown promise in assisting clinicians and providing support to individuals experiencing various psychological challenges. Objective: This study aims to compare the perform

Stefan Schacht

Charm CP violation is a unique gate to the physics of up-type quarks and allows searches for physics beyond the Standard Model in new and exciting ways, complementary to kaon and $b$ decays. We review recent advances, focusing on symmetry-based methods and providing an outlook on the next challenges at the intensity frontier of charm physics.

Diji Yang, Jinmeng Rao, Kezhen Chen, Xiaoyuan Guo

Although the Retrieval-Augmented Generation (RAG) paradigms can use external knowledge to enhance and ground the outputs of Large Language Models (LLMs) to mitigate generative hallucinations and static knowledge base problems, they still suffer from limited flexibility in adopting Information Retrieval (IR) systems with varying capabilities, constrained inte

Yujie Xiang, Bojing Liu, Mattias Rantalainen

AI-based models for histopathology whole slide image (WSI) analysis are increasingly common, but unsharp or blurred areas within WSI can significantly reduce prediction performance. In this study, we investigated the effect of image blur on deep learning models and introduced a mixture of experts (MoE) strategy that combines predictions from multiple expert

Zuri Gray, Stefano Bagnulo, Hermann Boehnhardt, Galin Borisov

Comet 67P/Churyumov-Gerasimenko (67P) become observable for the first time in 2021 since the Rosetta rendezvous in 2014--16. Here, we present pre-perihelion polarimetric measurements of 67P from 2021 performed with the Very Large Telescope (VLT), as well as post-perihelion polarimetric measurements from 2015--16 obtained with the VLT and the William Herschel

Abhishek Sinha, Rahul Vaze

A well-studied generalization of the standard online convex optimization (OCO) is constrained online convex optimization (COCO). In COCO, on every round, a convex cost function and a convex constraint function are revealed to the learner after the action for that round is chosen. The objective is to design an online policy that simultaneously achieves a smal

Sungkyung Kang, JungHwan Park, Masaki Taniguchi

We prove that the $(2n,1)$-cable of the figure-eight knot is not smoothly slice when $n$ is odd, by using the real Seiberg-Witten Fr{\o}yshov invariant of Konno-Miyazawa-Taniguchi. For the computation, we develop an $O(2)$-equivariant version of the lattice homotopy type, originally introduced by Dai-Sasahira-Stoffregen. This enables us to compute the real S

B. S. Ayhan

The main purpose of this study is to introduce and study two new classes of continuity called eR-continuous functions and weakly eR-continuous functions via e-regular sets. Both of the forms of continuous functions we have described are stronger than the weakly e-continuity. Furthermore, we obtain various characterizations of weakly eR-continuous functions.

Hagyeong Shin, Sean Trott

Markedness in natural language is often associated with non-literal meanings in discourse. Differential Object Marking (DOM) in Korean is one instance of this phenomenon, where post-positional markers are selected based on both the semantic features of the noun phrases and the discourse features that are orthogonal to the semantic features. Previous work has

Xuchang Guo, Houbiao Li

Rough set is one of the important methods for rule acquisition and attribute reduction. The current goal of rough set attribute reduction focuses more on minimizing the number of reduced attributes, but ignores the spatial similarity between reduced and decision attributes, which may lead to problems such as increased number of rules and limited generality.

Li Ma, Yifan Zhao, Peixi Peng, Yonghong Tian

With the benefit of deep learning techniques, recent researches have made significant progress in image compression artifacts reduction. Despite their improved performances, prevailing methods only focus on learning a mapping from the compressed image to the original one but ignore the intrinsic attributes of the given compressed images, which greatly harms

Sameer Khanna, Daniel Michael, Marinka Zitnik, Pranav Rajpurkar

Medical image interpretation using deep learning has shown promise but often requires extensive expert-annotated datasets. To reduce this annotation burden, we develop an Image-Graph Contrastive Learning framework that pairs chest X-rays with structured report knowledge graphs automatically extracted from radiology notes. Our approach uniquely encodes the di

F. Bonanomi, A. Hacar, A. Socci, D. Petry

ALMA's high-resolution images allow to resolve the filamentary structure of the ISM down to few thousand au at kpc distances. We aim to systematically quantify the impact of the interferometric response and the effects of the short-spacing information during the characterization of the ISM structure using ALMA observations. We create a series of continuum AL

Sun Yang, Qiong Su, Zhishuai Li, Ziyue Li

In sophisticated existing Text-to-SQL methods exhibit errors in various proportions, including schema-linking errors (incorrect columns, tables, or extra columns), join errors, nested errors, and group-by errors. Consequently, there is a critical need to filter out unnecessary tables and columns, directing the language models attention to relevant tables and

Andreas Belaey, Francesca Mariani, Thomas G. Mertens

We determine explicit expressions for the continuous two-sided gravitational wavefunctions in supersymmetric versions of JT gravity, focusing mainly on $\mathcal{N}=2$ JT supergravity. Our approach is based on representation theory of the associated supergroup, for which we determine the relevant mixed parabolic matrix elements that implement asymptotic AdS

Nima Fathi, Amar Kumar, Brennan Nichyporuk, Mohammad Havaei

Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated with the target class, leading to poor generalization and biased predictions. Although explainability via counterfactual image generation has been successful at exposing the problem, bias mitigation strategies that permi

Balint Pato, J. Wilson Staples, Kenneth R. Brown

2D compass codes are a family of quantum error-correcting codes that contain the Bacon-Shor codes, the $X$-Shor and $Z$-Shor codes, and the rotated surface codes. Previous numerical results suggest that the surface code has a constant accuracy and coherence threshold under uniform coherent rotation. However, having analytical proof supporting a constant thre

Jiajie Teng, Huiyu Duan, Yucheng Zhu, Sijing Wu

Recent years have witnessed the rapid development of short videos, which usually contain both visual and audio modalities. Background music is important to the short videos, which can significantly influence the emotions of the viewers. However, at present, the background music of short videos is generally chosen by the video producer, and there is a lack of

Junfeng Chen, Kailiang Wu

Operator learning for Partial Differential Equations (PDEs) is rapidly emerging as a promising approach for surrogate modeling of intricate systems. Transformers with the self-attention mechanism$\unicode{x2013}$a powerful tool originally designed for natural language processing$\unicode{x2013}$have recently been adapted for operator learning. However, they

Maxime Maurice, Rajdeep Dasgupta, Pedram Hassanzadeh

A magma ocean (MO) is thought to be a ubiquitous stage in the early evolution of rocky planets and exoplanets. During the lifetime of the MO, exchanges between the interior and exterior envelopes of the planet are very efficient. In particular, volatile elements that initially are contained in the solid part of the planet can be released and form a secondary

Qianru Zhang, Haixin Wang, Cheng Long, Liangcai Su

This paper focuses on the integration of generative techniques into spatial-temporal data mining, considering the significant growth and diverse nature of spatial-temporal data. With the advancements in RNNs, CNNs, and other non-generative techniques, researchers have explored their application in capturing temporal and spatial dependencies within spatial-te

Fredrik Berggren

A sum of lognormal random variables (RVs) appears in many problems of science and engineering. For example, it is invloved in computing the distribution of recevied signal and interference powers for radio channels subject to lognormal shadow fading. Its distribution has no closed-from expression and it is typically characterized by approximations, asymptote

Aleksandra Szpakowska, Piotr Artiemjew

In this paper, we present an innovative technique for the path planning of flying robots in a 3D environment in Rough Mereology terms. The main goal was to construct the algorithm that would generate the mereological potential fields in 3-dimensional space. To avoid falling into the local minimum, we assist with a weighted Euclidean distance. Moreover, a sea

Anne-Kathrin Schmuck, Philippe Heim, Rayna Dimitrova, Satya Prakash Nayak

Infinite-state games are a commonly used model for the synthesis of reactive systems with unbounded data domains. Symbolic methods for solving such games need to be able to construct intricate arguments to establish the existence of winning strategies. Often, large problem instances require prohibitively complex arguments. Therefore, techniques that identify

F. Paonessa, G. Virone, S. Kianoush, A. Nordio

This paper empirically investigates the body induced electromagnetic (EM) effects, namely the human body blockage, by conducting indoor measurement campaigns in the unexplored sub-THz W-band (75-110 GHz) and G-band (170-260 GHz). The proposed analysis focuses on both the alterations of channel frequency response induced by body presence, fully or partially o

Kaizhen Guo, Zeyu Ma, Hongxiong Liu, Ziyang Wu

We report the magnetic properties of newly synthesized, single crystals of $\mathrm{TbTi_3Bi_4}$ whose crystal structure is highlighted by the stacking of terbium-based zigzag chains and titanium-based kagome lattices. This compound demonstrates extreme easy-axis magnetic anisotropy due to the crystalline-electric-field effect which aligns the $\mathrm{Tb^{3

Zaitian Wang, Pengfei Wang, Kunpeng Liu, Pengyang Wang

Data augmentation is a series of techniques that generate high-quality artificial data by manipulating existing data samples. By leveraging data augmentation techniques, AI models can achieve significantly improved applicability in tasks involving scarce or imbalanced datasets, thereby substantially enhancing AI models' generalization capabilities. Existing

Dylan Phelps, Thomas Pickard, Maggie Mi, Edward Gow-Smith

Despite the recent ubiquity of large language models and their high zero-shot prompted performance across a wide range of tasks, it is still not known how well they perform on tasks which require processing of potentially idiomatic language. In particular, how well do such models perform in comparison to encoder-only models fine-tuned specifically for idioma

Martina Karl

We present recent work on the Spectra of IceCube Neutrino (SIN) candidate sources project. We defined a selection of candidate neutrino sources by identifying blazars in the vicinity of IceCube's highest-energy neutrinos. We now want to shed light on these source candidates' nature, starting at their redshift, continuing with their black hole masses, their v

Zhian Jia

Cluster states are crucial resources for measurement-based quantum computation (MBQC). It exhibits symmetry-protected topological (SPT) order, thus also playing a crucial role in studying topological phases. We present the construction of cluster states based on Hopf algebras. By generalizing the finite group valued qudit to a Hopf algebra valued qudit and i

Pengcheng Sun, Erwu Liu, Wei Ni, Kanglei Yu

Non-independent and identically distributed (Non- IID) data adversely affects federated learning (FL) while heterogeneity in communication quality can undermine the reliability of model parameter transmission, potentially degrading wireless FL convergence. This paper proposes a novel dual-segment clustering (DSC) strategy that jointly addresses communication

Fedor Pakhomov, Michael Rathjen, Dino Rossegger

Feferman proved in 1962 that any arithmetical theorem is a consequence of a suitable transfinite iteration of full uniform reflection of $\mathsf{PA}$. This result is commonly known as Feferman's completeness theorem. The purpose of this paper is twofold. On the one hand this is an expository paper, giving two new proofs of Feferman's completeness theorem th

Chi Ma, Mincong Huang, Chao Wang, Yujie Wang

In this work, we systematically investigate the efficacy of dynamic activation mechanisms within the LLaMA family of language models. Despite the potential of dynamic activation methods to reduce computation and increase speed in models using the ReLU activation function, our empirical findings have uncovered several inherent pitfalls in the current dynamic

Jan Pablo Burgard, João Vitor Pamplona

When using machine learning for automated prediction, it is important to account for fairness in the prediction. Fairness in machine learning aims to ensure that biases in the data and model inaccuracies do not lead to discriminatory decisions. E.g., predictions from fair machine learning models should not discriminate against sensitive variables such as sex

Sebastian Zielinski, Maximilian Zorn, Thomas Gabor, Sebastian Feld

A common way of solving satisfiability instances with quantum methods is to transform these instances into instances of QUBO, which in itself is a potentially difficult and expensive task. State-of-the-art transformations from MAX-3SAT to QUBO currently work by mapping clauses of a 3SAT formula associated with the MAX-3SAT instance to an instance of QUBO and

Golfin Ekatria, Andy Octavian Latief, Fiki Taufik Akbar, Bobby Eka Gunara

We consider a spherically symmetric homogeneous perfect fluid undergoing a gravitational collapse to singularity in the framework of higher-dimensional Rastall gravity in the cases of vanishing and nonvanishing cosmological constants. The possible final states of the collapse in any finite dimension are black hole and naked singularity, but the naked singula

Elie Hammou

The interpretation of LHC data, and the assessment of possible hints of new physics, require the precise knowledge of the proton structure in terms of parton distribution functions (PDFs). I present a methodology designed to determine whether and how global PDF fits might inadvertently 'fit away' signs of new physics in the high-energy tails of the distribut

Sabah Al-Fedaghi

According to many researchers, conceptual model (CM) development is a hard task, and system requirements are difficult to collect, causing many miscommunication problems. CMs require more than modeling ability alone - they first require an understanding of the targeted domain that the model attempts to represent. Accordingly, a preconceptual modeling (pre-CM

Handan Borluk, Gulcin M. Muslu, Fábio Natali

In this paper, we present new results regarding the orbital stability of solitary standing waves for the general fourth-order Schr\"odinger equation with mixed dispersion. The existence of solitary waves can be determined both as minimizers of a constrained complex functional and by using a numerical approach. In addition, for specific values of the frequenc

A. Sandrock

The sources of galactic charged cosmic rays are so far unknown, because their arrival directions are randomized in the galactic magnetic field. Objects accelerating hadrons are expected to produce high-energy neutrinos. In addition, a diffuse galactic neutrino flux is predicted from interactions of galactic cosmic rays with matter during propagation through

Xuanchen Wang, Heng Wang, Dongnan Liu, Weidong Cai

Generating dance from music is crucial for advancing automated choreography. Current methods typically produce skeleton keypoint sequences instead of dance videos and lack the capability to make specific individuals dance, which reduces their real-world applicability. These methods also require precise keypoint annotations, complicating data collection and l

Anila Mjeda, Hazel Murray

Quantum computing presents a transformative potential for the world of computing. However, integrating this technology into the curriculum for computer science students who lack prior exposure to quantum mechanics and advanced mathematics remains a challenging task. This paper proposes a scaffolded learning approach aimed at equipping computer science studen

Marcel Kempf, Nikolas Gauder, Benedikt Jaeger, Johannes Zirngibl

QUIC is a new network protocol standardized in 2021. It was designed to replace the TCP/TLS stack and is based on UDP. The most current web standard HTTP/3 is specifically designed to use QUIC as transport protocol. QUIC claims to provide secure and fast transport with low-latency connection establishment, flow and congestion control, reliable delivery, and

Harshkumar Devmurari, Gautham Kuckian, Prajjwal Vishwakarma, Krunali Vartak

Generating images from hand-drawings is a crucial and fundamental task in content creation. The translation is challenging due to the infinite possibilities and the diverse expectations of users. However, traditional methods are often limited by the availability of training data. Therefore, VisioBlend, a unified framework supporting three-dimensional control

Agisilaos Matalliotakis, Martin D. Verweij

Contrast-enhanced ultrasound (CEUS) presents distinct advantages in diagnostic echography. Utilizing microbubbles (MBs) as conventional contrast agents enhances vascular visualization and organ perfusion, facilitating real-time, non-invasive procedures. There is a current tendency to replace the traditional polydisperse MBs by novel monodisperse formulations

Eduard Kim, Emin Nugaev, Yakov Shnir

We propose a general form of the UV-completed Friedberg-Lee-Sirlin (FLS) model. As can be seen from the mechanical interpretation, UV-completion allows thin-wall approximation for non-topological solitons. The 1-loop renormalized effective potential for the UV-completed FLS model is constructed under the assumption of mass hierarchy. By special choice of par

Yuki Ishiguro, Jun Sato

The asymmetric simple exclusion process (ASEP) is a paradigmatic driven-diffusive system that describes the asymmetric diffusion of particles with hardcore interactions in a lattice. Although the ASEP is known as an exactly solvable model, most exact results are limited to one-dimensional systems. Recently, the exact steady state in the multi-dimensional ASE

Roger J. A. Laeven, Emanuela Rosazza Gianin, Marco Zullino

We introduce Geometric Backward Stochastic Differential Equations (GBSDEs) and two-driver BSDEs, which arise naturally in the geometric dynamics of dynamic return risk measures and of recursive portfolio choice. Through a reduction to auxiliary ordinary BSDEs with logarithmic and singular quadratic (LN-Q) growth rate $y|\ln (y)|+|z|^2/y$, we establish existe

Bin Shen, Yuhan Zhu

In this manuscript, we employ the Nash-Moser iteration technique to determine a condition under which the positive solution $u$ of the generalized nonlinear Poisson equation $$\operatorname{div} (\varphi(|\nabla u|^2)\nabla u) + \psi(u^2)u = 0,$$ on a complete Riemannian manifold with Ricci curvature bounded from below can be shown to satisfy a Cheng-Yau-typ

Karol Kozioł, Jacek Rzadkiewicz

The $4d \to 3p$ x-ray transitions in Cu- and Ni-like tungsten ions have been studied theoretically. The multiconfiguration Dirac-Hartree-Fock (MCDHF) method and the large-scale relativistic configuration-interaction (CI) method have been employed in order to take into account electron correlation effects on the wavelengths and transition rates. It was found

Apolline Chappard, José Alberto Rubiño-Martín, Ricardo Tanausú Génova-Santos

The QUIJOTE MFI instrument (2012-2018) observed the sky at four frequency bands, namely 11, 13, 17 and 19GHz, at 1 degree angular resolution. Using around 10000 h of observations in the so-called nominal mode, QUIJOTE MFI produced sky maps covering approximately 29000 deg2. Here we use the full database of MFI wide survey observations to characterize the cor

Alexandre Landry

In this paper, we investigate static spherically symmetric teleparallel F(T) gravity containing a perfect isotropic fluid. We first write the field equations and proceed to find new teleparallel F(T) solutions for perfect isotropic and linear fluids. By using a power-law ansatz for the coframe components, we find several classes of new non-trivial teleparall

Zhaoyu Shi, Seyed Morteza Habibi Khorasani, Heesoo Shin, Jiasheng Yang

Efficient tools for predicting the drag of rough walls in turbulent flows would have a tremendous impact. However, methods for drag prediction rely on experiments or numerical simulations which are costly and time-consuming. Data-driven regression methods have the potential to provide a prediction that is accurate and fast. We assess the performance and limi

Daniel Gaspar-Figueiredo, Marta Fernández-Diego, Ruben Nuredini, Silvia Abrahão

Adapting the user interface (UI) of software systems to meet the needs and preferences of users is a complex task. The main challenge is to provide the appropriate adaptations at the appropriate time to offer value to end-users. Recent advances in Machine Learning (ML) techniques may provide effective means to support the adaptation process. In this paper, w

Samuel Ackerman, Eitan Farchi, Rami Katan, Orna Raz

We introduce a novel LLM based solution design approach that utilizes combinatorial optimization and sampling. Specifically, a set of factors that influence the quality of the solution are identified. They typically include factors that represent prompt types, LLM inputs alternatives, and parameters governing the generation and design alternatives. Identifyi

Aida Abiad, Gianira N. Alfarano, Alberto Ravagnani

In this note we apply a spectral method to the graph of alternating bilinear forms. In this way, we obtain upper bounds on the size of an alternating rank-metric code for given values of the minimum rank distance. We computationally compare our results with Delsarte's linear programming bound, observing that they give the same value. For small values of the

Paul-Iulian Gavriloaea, Jackson L. Ross, Frank Freimuth, Yuriy Mokrousov

Discovering alternative ways to drive domain wall (DW) dynamics is crucial for advancing spintronic applications. Here we demonstrate via atomistic spin dynamics simulations that optical torques can efficiently drive 90^{\circ} DWs in the Mn2Au antiferromagnet but their spatial symmetry forbids the motion of 180^{\circ} walls. In the steady-state regime, the

S. Muthuvel, R. Venkatraman

The objective of the paper is to determine the complete solutions for the Diophantine equation $x^2 + 3^{\alpha}113^{\beta} = y^{\mathfrak{n}}$ in positive integers $x$ and $y$ (where $x, y \geq 1$), non-negative exponents $\alpha$ and $\beta$, and an integer $\mathfrak{n}\geq 3$, subject to the condition $\text{gcd}(x, y) = 1$.

Mihailo Škorić, Nikola Janković

This paper will present textual corpora for Serbian (and Serbo-Croatian), usable for the training of large language models and publicly available at one of the several notable online repositories. Each corpus will be classified using multiple methods and its characteristics will be detailed. Additionally, the paper will introduce three new corpora: a new umb

Ren Zhao

The 2-distance coloring of a graph $G$ is equivalent to the proper coloring of its square graph $G^2$, it is a special distance labeling problem. DP-coloring (or "Correspondence coloring") was introduced by Dvo\v{r}\'ak and Postle in 2018, to answer a conjecture of list coloring proposed by Borodin. In recent years, many researches pay attention to the DP-co

Soufian Abja, Slawomir Dinew

In this note we continue our investigation of geodesics in the space of convex and plurisubharmonic functions. We show optimal regularity for geodesics joining two smooth strictly convex functions. We also investigate the regularity theory in the $C^{1,\alpha}$ realm. Finally we discuss the regularity of geodesics joining two toric strictly plurisubharmonic

Anuj Sharma, Sukhdeep Singh, S Ratna

The graph neural networks has been proved to be an efficient machine learning technique in real life applications. The handwritten recognition is one of the useful area in real life use where both offline and online handwriting recognition are required. The chain code as feature extraction technique has shown significant results in literature and we have bee

Petros Andreas Pantazopoulos, Johannes Feist, Francisco J. García-Vidal, Akashdeep Kamra

Magnetic order typically emerges due to the short-range exchange interaction between the constituent electronic spins. Recent discoveries have found a crucial role for spin-phonon coupling in various phenomena from optical ultrafast magnetization switching to dynamical control of the magnetic state. Here, we demonstrate theoretically the emergence of a biqua

Zexin Fang, Bin Han, Hans D. Schotten

Unmanned aerial vehicles (UAVs) are well-suited to localize jammers, particularly when jammers are at non-terrestrial locations, where conventional detection methods face challenges. In this work we propose a novel localization method, sample pruning gradient descend (SPGD), which offers robust performance against multiple power-modulated jammers with low co

Maayan Ziv, Eli Galanti, Amir Sheffer, Saburo Howard

NASA's Juno mission provided exquisite measurements of Jupiter's gravity field that together with the Galileo entry probe atmospheric measurements constrains the interior structure of the giant planet. Inferring its interior structure range remains a challenging inverse problem requiring a computationally intensive search of combinations of various planetary

G. G. L. Nashed, Salvatore Capozziello

We discuss spherically symmetric dynamical systems in the framework of a general model of $f({\cal R})$ gravity, i.e. $f({\cal R})={\cal R}e^{\zeta {\cal R}}$, where $\zeta$ is a dimensional quantity in squared length units [L$^2$]. We initially assume that the internal structure of such systems is governed by the Krori-Barua ansatz, alongside the presence o

Keisuke Konosu, Jojiro Totsuka-Yoshinaka

We derive the new identity in homotopy algebras which directly corresponds to the Schwinger-Dyson equations in quantum field theory. As an application, we derive the Ward-Takahashi identities. We demonstrate that the Ward-Takahashi identities are reproduced in several examples. In general, our formula contains divergence. We mediate this problem by introduci