October 2024 arXiv papers — page 26

Teng Zhang

We characterize the matrices $U=(u_{ij})$ for which the operator square-sum identity $$\sum_{i=1}^m\Big|\sum_{j=1}^n u_{ij}A_j\Big|^2=\sum_{j=1}^n|A_j|^2$$ holds for all Schatten-class operators $A_1,\ldots,A_n$; this happens exactly when $U$ is an isometry.Using this characterization, we establish Clarkson--McCarthy type inequalities for several classes of

Mohammed Farfour, Rachid Hedjam, Douglas Foster, Said Gaci

The growing number of seismic and elastic attributes poses a challenge, making the full benefit from each attribute in characterizing geological formation very difficult, if not impossible. Various approaches are routinely employed to select the best attributes for specific purposes. Machine learning (ML) algorithms have demonstrated good capabilities in com

Kosmas Alexandridis, Giorgos Dimitrakopoulos

Multi-term floating-point addition appears in vector dot-product computations, matrix multiplications, and other forms of floating-point data aggregation. A critical step in multi-term floating point addition is the alignment of fractions of the floating-point terms before adding them. Alignment is executed serially by identifying first the maximum of all ex

Luca Cultrera, Federico Becattini, Lorenzo Berlincioni, Claudio Ferrari

Face analysis has been studied from different angles to infer emotion, poses, shapes, and landmarks. Traditionally RGB cameras are used, yet for fine-grained tasks standard sensors might not be up to the task due to their latency, making it impossible to record and detect micro-movements that carry a highly informative signal, which is necessary for inferrin

Takashi Nishimura

We show that, under an additional mild assumption, on the class of generic frontals, any involution whose fixed point set is exactly the same as the fixed point set of the Legendre involution must be the Legendre involution (Theorem 2 in \S 1). Moreover, its natural complexification (Corollary 1 in \S 3) is simultaneously shown.

Arindam Das, Jinmian Li, Sanjoy Mandal, Takaaki Nomura

We investigate TeV scale seesaw scenarios at $\mu^+ e^-$ and $\mu^+ \mu^+$ colliders in the $\mu$TRISTAN experiment. In minimal type-I seesaw scenario we consider two generations of Standard Model (SM) singlet heavy Majorana type Right Handed Neutrinos (RHNs) which couples with SM gauge bosons through light-heavy neutrino mixing. We discuss the prospects of

Yuetao Li, Zijia Kuang, Ting Li, Qun Hao

We propose ActiveSplat, an autonomous high-fidelity reconstruction system leveraging Gaussian splatting. Taking advantage of efficient and realistic rendering, the system establishes a unified framework for online mapping, viewpoint selection, and path planning. The key to ActiveSplat is a hybrid map representation that integrates both dense information abou

A. Kallitsopoulou, R. Aleksan, Y. Angelis, S. Aune

The PICOSEC-Micromegas (PICOSEC-MM) detector is a novel gaseous detector designed for precise timing resolution in experimental measurements. It eliminates time jitter from charged particles in ionization gaps by using extreme UV Cherenkov light emitted in a crystal, detected by a Micromegas photodetector with an appropriate photocathode. The first single-ch

Giuseppina Albano, Virginia Giorno, Francisco Torres-Ruiz

We consider a time-inhomogeneous diffusion process able to describe the dynamics of infected people in a susceptible-infectious epidemic model in which the transmission intensity function is time-dependent. Such a model is well suited to describe some classes of micro-parasitic infections in which individuals never acquire lasting immunity and over the cours

Nick Fischer

We study a broad class of algorithmic problems with an "additive flavor" such as computing sumsets, 3SUM, Subset Sum and geometric pattern matching. Our starting point is that these problems can often be solved efficiently for integers, owed to the rich available tool set including bit-tricks, linear hashing, and the Fast Fourier Transform. However, for real

Emanuele Ledda, Giovanni Scodeller, Daniele Angioni, Giorgio Piras

In learning problems, the noise inherent to the task at hand hinders the possibility to infer without a certain degree of uncertainty. Quantifying this uncertainty, regardless of its wide use, assumes high relevance for security-sensitive applications. Within these scenarios, it becomes fundamental to guarantee good (i.e., trustworthy) uncertainty measures,

Bohan Li, Hankun Wang, Situo Zhang, Yiwei Guo

The auto-regressive architecture, like GPTs, is widely used in modern Text-to-Speech (TTS) systems. However, it incurs substantial inference time, particularly due to the challenges in the next-token prediction posed by lengthy sequences of speech tokens. In this work, we introduce VADUSA, one of the first approaches to accelerate auto-regressive TTS through

Yu Li, Wenjia Zhang

In this paper, we prove the uniqueness of K\"ahler Ricci shrinkers on toric orbifolds, extending the corresponding results previously established for toric manifolds.

Piotr Dulian, Adam Sawicki

In this work, we explore the implications of applying the formalism of symplectic geometry to quantum mechanics, particularly focusing on many-particle systems. We extend the concept of a symplectic indicator of entanglement, originally introduced by Sawicki et al. \cite{sawicki2011}, to these complex systems. Specifically, we demonstrate that the restrictio

Nobuyuki Kanematsu

This study conceptualizes electronic dose to water, which is the radiation energy imparted to a unit mass of water by electronic interactions. The new dosimetry framework excludes nuclear interactions and consequently associated corrections and uncertainties from conventional dosimetry. Based on the international code of practice for dosimetry in radiotherap

E. Sarrey, M. Sigallon, M. -C. Clochard, A-L Hamon

There is an important difference between piezoelectric polymer films and solid crystals for the application to piezoelectric generators. In the case of polymers, the optimal piezoelectric response imposes {\it a large bending regime}. Starting from the linear Curie's constitutive equations, we develop an analytical model under the assumption of the large ben

Shaily, J. K. Singh, Mohit Tyagi, Joao R. L. Santos

In this work, we developed a cosmological model in $ f(Q, C) $ gravity within the framework of symmetric teleparallel geometry. In addition to the non-metricity scalar $Q $, our formulation includes the boundary term $ C $, which accounts for its deviation from the standard Levi-Civita Ricci scalar $ R^* $ in the Lagrangian. We derived the field equations fo

Sahil Ali Akbar, Ananya Verma

Noise, an unwanted component in an image, can be the reason for the degradation of Image at the time of transmission or capturing. Noise reduction from images is still a challenging task. Digital Image Processing is a component of Digital signal processing. A wide variety of algorithms can be used in image processing to apply to an image or an input dataset

Rudy De-Xin de Lange, Roemer Martin Bien Bakker, Tanja Johanna Juliana Bos

Extended reality technology has become a useful tool in many applications, but still suffers from visual deviations that can hamper the utility of the technology. This paper discusses the types of persisting visual deviations experienced when observing the natural world through video see-through head-mounted displays. A generalizable method to measure the ef

Monica Riedler, Stefan Langer

Large Language Models (LLMs) have demonstrated impressive capabilities in answering questions, but they lack domain-specific knowledge and are prone to hallucinations. Retrieval Augmented Generation (RAG) is one approach to address these challenges, while multimodal models are emerging as promising AI assistants for processing both text and images. In this p

Karl Bringmann, Nick Fischer, Vasileios Nakos

Bellman's algorithm for Subset Sum is one of the earliest and simplest examples of dynamic programming, dating back to 1957. For a given set of $n$ integers $X$ and a target $t$, it computes the set of subset sums $\mathcal S(X, t)$ (i.e., the set of integers $s \in [0\ldots t]$ for which there is a subset of $X$ summing to $s$) in time $O(|\mathcal S(X, t)|

Amir Burshtein, Moshe Goldstein

Despite being a pillar of quantum mechanics, little attention has been paid to the onset of Fermi's golden rule as a discrete microscopic bath of modes approaches the macroscopic thermodynamic limit and forms a continuum. Motivated by recent experiments in circuit quantum electrodynamics, we tackle this question through the lens of single-photon decay in a f

Senne Deproost, Denis Steckelmacher, Ann Nowé

With Deep Reinforcement Learning (DRL) being increasingly considered for the control of real-world systems, the lack of transparency of the neural network at the core of RL becomes a concern. Programmatic Reinforcement Learning (PRL) is able to to create representations of this black-box in the form of source code, not only increasing the explainability of t

Dan Ristea, Vasilios Mavroudis

We introduce HonestCyberEval, a new benchmark for assessing AI models' capabilities and risks in automated software exploitation, focusing on their ability to detect and exploit vulnerabilities in real-world software systems. Our evaluation leverages the Nginx web server repository augmented with synthetic vulnerabilities. We assess several leading language

Timur Mamedov, Anton Konushin, Vadim Konushin

Modern person re-identification (Re-ID) methods have a weak generalization ability and experience a major accuracy drop when capturing environments change. This is because existing multi-camera Re-ID datasets are limited in size and diversity, since such data is difficult to obtain. At the same time, enormous volumes of unlabeled single-camera records are av

V. N. Potapov

We consider various definitions of degrees of discrete functions and establish relations between the number of relevant (essential) variables and degrees of two- and three-valued functions. Keywords: relevant variable, sensitivity, degree of Boolean function.

Krishna Chandra Roy, Qian Chen

Anomaly-based cyber threat detection using deep learning is on a constant growth in popularity for novel cyber-attack detection and forensics. A robust, efficient, and real-time threat detector in a large-scale operational enterprise network requires high accuracy, high fidelity, and a high throughput model to detect malicious activities. Traditional anomaly

Pei Zhang, Yanli Wang

We consider the neural representation to solve the Boltzmann-BGK equation, especially focusing on the application in microscopic flow problems. A new dimension reduction model of the BGK equation with the flexible auxiliary distribution functions is first deduced to reduce the problem dimension. Then, a network-based ansatz that can approximate the dimension

Felipe Mogollon, Zaloa Fernandez, Angel Martin, Juan Diego Ortega

Vehicles are sophisticated machines equipped with sensors that provide real-time data for onboard driving assistance systems. Due to the wide variety of traffic, road, and weather conditions, continuous system enhancements are essential. Connectivity allows vehicles to transmit previously unknown data, expanding datasets and accelerating the development of n

Badr AlKhamissi, Yingtian Tang, Abdülkadir Gökce, Johannes Mehrer

While today's large language models exhibit impressive abilities in generating human-like text, they require massive amounts of data during training. We here take inspiration from human cognitive development to train models in limited data conditions. Specifically we present a self-synthesis approach that iterates through four phases: Phase 1 sets up fundame

Mario Ayala, Johannes Zimmer

We consider a d-dimensional symmetric inclusion process (SIP), where particles are allowed to jump arbitrarily far apart. We establish both the hydrodynamic limit and non-equilibrium fluctuations for the empirical measure of particles. With the help of self-duality and Mosco convergence of Dirichlet forms, we extend structural parallels between exclusion and

Dac Thai Nguyen, Trung Thanh Nguyen, Huu Tien Nguyen, Thanh Trung Nguyen

Positron Emission Tomography (PET) and Computed Tomography (CT) are essential for diagnosing, staging, and monitoring various diseases, particularly cancer. Despite their importance, the use of PET/CT systems is limited by the necessity for radioactive materials, the scarcity of PET scanners, and the high cost associated with PET imaging. In contrast, CT sca

Alan Chang, Assaf Naor, Kevin Ren

We prove that if $(\mathcal{M},d)$ is an $n$-point metric space that embeds quasisymmetrically into a Hilbert space, then for every $\tau>0$ there is a random subset $\mathcal{Z}$ of $\mathcal{M}$ such that for any pair of points $x,y\in \mathcal{M}$ with $d(x,y)\ge \tau$, the probability that both $x\in \mathcal{Z}$ and $d(y,\mathcal{Z})\ge \beta\tau/\sqrt{

Azim Farghadan, Mohammad Mahdi Masteri Farahani, Mohsen Akbari

The numerical solution of partial differential equations (PDEs) is essential in computational physics. Over the past few decades, various quantum-based methods have been developed to formulate and solve PDEs. Solving PDEs incur high time complexity for real-world problems with high dimensions, and using traditional methods becomes practically inefficient. Th

Keren Duer, Eli Galanti, Yohai Kaspi

Jupiter's equatorial eastward zonal flows reach wind velocities of ~100 m/s, while on Saturn they are three times as strong and extend about twice as wide in latitude, despite the two planets being overall dynamically similar. Recent gravity measurements obtained by the Juno and Cassini spacecraft uncovered that the depth of zonal flows on Saturn is about th

Boris Wolfson, Erman Acar

Current trends in Machine Learning prefer explainability even when it comes at the cost of performance. Therefore, explainable AI methods are particularly important in the field of Fraud Detection. This work investigates the applicability of Differentiable Inductive Logic Programming (DILP) as an explainable AI approach to Fraud Detection. Although the scala

J. M. Mazon, A. Molino, J. Toledo

This paper deals with Gelfand-type problems \begin{equation}\label{Gelfand10} \qquad\qquad\left\{\begin{array}{ll} - \Delta_m u = \lambda f(u), \quad&\hbox{in} \ \Omega, \ \lambda >0, \\[10pt] u =0, \quad&\hbox{on} \ \partial_m\Omega, \end{array} \right. \end{equation} in the framework of Random Walk Spaces, which includes as particular cases: Gelfand-type p

Halil Utku Unlu, Shuaihang Yuan, Congcong Wen, Hao Huang

We introduce an innovative approach to advancing semantic understanding in zero-shot object goal navigation (ZS-OGN), enhancing the autonomy of robots in unfamiliar environments. Traditional reliance on labeled data has been a limitation for robotic adaptability, which we address by employing a dual-component framework that integrates a GLIP Vision Language

Yo Toda, Osamu Seto

We discuss the neutrino mass and Hubble tension solutions and examine their effects on the redshift-space distortion (RSD) observations. An analysis with RSD data indicates smaller amplitude of perturbation. Including RSD data results in a slightly weaker upper limit on the neutrino mass than that derived for data without RSD, which is common in other extend

Tomohiro Hattori, Hirotaka Irie, Tadashi Kadowaki, Shu Tanaka

Quantum annealing can efficiently obtain solutions to combinatorial optimization problems. Size-reduction methods are used to treat large-scale combinatorial optimization problems that cannot be input directly into a quantum annealer because of its size limitation. Various size-reduction methods using fixing spins have been proposed as quantum-classical hybr

Diana Micloiu, Rowdy Chotkan, Jérémie Decouchant

Streamlined Byzantine Fault Tolerant (BFT) protocols, such as HotStuff [PODC'19], and weighted voting represent two possible strategies to improve consensus in the distributed systems world. Several studies have been conducted on both techniques, but the research on combining the two is scarce. To cover this knowledge gap, we introduce a weighted voting appr

Shrey Shah, Alex Lin, Scott Lin, Josh Patel

Accurate damage prediction is crucial for disaster preparedness and response strategies, particularly given the frequent earthquakes in Turkey. Utilizing datasets on earthquake data, infrastructural quality metrics, and contemporary socioeconomic factors, we tested various machine-learning architectures to forecast death tolls and fatalities per affected pop

José Manuel de Frutos, Manuel A. Vázquez, Pablo Olmos, Joaquín Míguez

Traditional implicit generative models are capable of learning highly complex data distributions. However, their training involves distinguishing real data from synthetically generated data using adversarial discriminators, which can lead to unstable training dynamics and mode dropping issues. In this work, we build on the \textit{invariant statistical loss}

Jiawen Li

We introduce Prior Knowledge Acceleration (PKA), a batch-update method for variance that reuses previously computed sufficient statistics to avoid full recomputation. The update identity is algebraically equivalent to the pairwise formula of Chan, Golub, and LeVeque (1983); our contribution is a runtime-cost analysis that derives an explicit acceleration fac

Tianyu Liu, Jun-Hong Li, Xingchuan Zhu, Huaiming Guo

Elastic strain can displace the massless Dirac fermions in monolayer graphene in a space-dependent fashion, similar to the effect of an external magnetic field, thus giving rise to Landau quantization. We here show that the strain-induced Landau quantization can also take place in Bernal-stacked bilayer graphene, where the low-energy excitations are massive

Blanka Balogh, David Saint-Martin, Olivier Geoffroy

In this study, we present the integration of a neural network-based parameterization into the global atmospheric model ARP-GEM1, leveraging the Python interface of the OASIS coupler. This approach facilitates the exchange of fields between the Fortran-based ARP-GEM1 model and a Python component responsible for neural network inference. As a proof-of-concept

Kun Chen, Zhihua Zhang

A large i.i.d. random matrix with deterministic low-rank perturbation has been extensively studied, particularly in the aspects of the ESD (Empirical Spectral Distribution) and the outliers of eigenvalues. In this work, we investigate the analogous scenario where the perturbation is random and extend the previous results from the deterministic perturbation t

Ali Asadian, Florian Gams, Stephan Sponar

We formulate a precise tradeoff relation between correlation and disturbance for sequential $n$-outcome quantum measurements in Hilbert spaces of arbitrary dimension. This relation highlights key symmetry properties useful for robust estimation and characterization of the measurement parameters against unitary noise, or in scenarios where shared reference fr

Yuanyuan Wang, Biwei Huang, Wei Huang, Xi Geng

The identifiability analysis of linear Ordinary Differential Equation (ODE) systems is a necessary prerequisite for making reliable causal inferences about these systems. While identifiability has been well studied in scenarios where the system is fully observable, the conditions for identifiability remain unexplored when latent variables interact with the s

Hong-fu Chou, Vu Nguyen Ha, Prabhu Thiruvasagam, Thanh-Dung Le

Earth observation (EO) systems are essential for mapping, catastrophe monitoring, and resource management, but they have trouble processing and sending large amounts of EO data efficiently, especially for specialized applications like agriculture and real-time disaster response. This paper presents a novel framework for semantic communication in EO satellite

Jamal Drewlo

In this work, we present a self-contained construction that proves the existence of strictly ergodic Toeplitz $\mathbb{Z}^d$-subshifts with arbitrary prescribed entropy. Moreover, any of these constructed subshifts will have the same maximal equicontinuous factor, which is the universal odometer over $\mathbb{Z}^d$.

Yong Jin, Junfang Leng, Lin Zhou, Yu Jiang

In sensor networks characterized by irregular layouts and poor connectivity, anisotropic properties can significantly reduce the accuracy of distance estimation between nodes, consequently impairing the localization precision of unidentified nodes. Since distance estimation is contingent upon the multi-hop paths between anchor node pairs, assigning different

Mahdi Nouraie, Connor Smith, Samuel Muller

Stability selection is a versatile framework for structure estimation and variable selection in high-dimensional setting, primarily grounded in frequentist principles. In this paper, we propose an enhanced methodology that integrates Bayesian analysis to refine the inference of selection probabilities within the stability selection framework. Traditional app

Martín Méndez, Pau Torras, Adrià Molina, Jialuo Chen

Historical ciphered manuscripts are documents that were typically used in sensitive communications within military and diplomatic contexts or among members of secret societies. These secret messages were concealed by inventing a method of writing employing symbols from diverse sources such as digits, alchemy signs and Latin or Greek characters. When studying

Ulf-G. Meißner

In the limit of vanishing up, down and strange quark masses, QCD exhibits a chiral symmetry. This symmetry is broken spontaneously to its vector subgroup, giving rise to Goldstone bosons. These acquire a small mass through the explicit chiral symmetry breaking for non-vanishing quark masses. The consequences of these broken symmetries can be investigated in

Fabrice Pancher, Sebastien Soler, Fabien Malbet, Manon Lizzana

High precision differential astrometry assesses the positions, distances, and motions of celestial objects in relation to the stars. The focal plane of such space telescope must be calibrated with a precision down to the level of 1e-5 pixel in order to be able to detect Earth-like planets in the close vicinity of the Sun. The presented characterization bench

Abhishek Pal Majumder

We consider an infinite server queue where the arrival and the service rates are both modulated by a stochastic environment governed by an $S$-valued stochastic process $X$ that is ergodic with a limiting measure $\pi\in \mathcal{P}(S)$. Under certain conditions when $X$ is semi-Markovian and satisfies the renewal regenerative property, long-term behavior of

An Nghiep Huynh, Thanh Dat Do, Trong Hop Do

Regional discrimination is a persistent social issue in Vietnam. While existing research has explored hate speech in the Vietnamese language, the specific issue of regional discrimination remains under-addressed. Previous studies primarily focused on model development without considering practical system implementation. In this work, we propose a task called

Xiao Xia, Dan Zhang, Zibo Liao, Zhenyu Hou

The modeling of industrial scenes is essential for simulations in industrial manufacturing. While large language models (LLMs) have shown significant progress in generating general 3D scenes from textual descriptions, generating industrial scenes with LLMs poses a unique challenge due to their demand for precise measurements and positioning, requiring comple

Jarosław Buczyński, Hanieh Keneshlou

The $r$-th cactus variety of a subvariety $X$ in a projective space generalizes the $r$-th secant variety of $X$ and it is defined using linear spans of finite subschemes of $X$ of degree $r$. One of its original purposes was to study the vanishing sets of catalecticant minors. In this article, we equip the cactus variety with a scheme structure, via ``relat

Ole Steuernagel, Ray-Kuang Lee

The addition of a single photon to a light field can lead to exactly the same \emph{outcome} as the subtraction of a single photon. We prove that this \cterm is true for pure squeezed vacuum states of light, and in some sense only for those. We show that mixed states can show this \cterm for addition or subtraction of a photon if they are generated from inco

Aaisha Be, Debasisha Mishra

This article introduces the Hartwig-Spindelb\"{o}ck decomposition of dual complex matrices. We provide representations of some generalized inverses using this decomposition. Further, several characterizations are established for a complex matrix to be Hermitian, normal and new dual EP matrix.

Shaun Cooper

This note discusses elliptic functions in Ramanujan's work.

Seyed-Hassan Mirian-Hosseinabadi

Finding test cases to kill the alive mutants in Mutation testing needs to calculate the Reachability, Infection and Propagation(RIP) conditions and full test specification. In this paper, a formal approach to calculate RIP conditions is proposed. The Dijkestra's weakest precondition predicate transformer (wp(_,_)) is used to calculate infection and propagati

Christina Vantaraki, Matías P. Grassi, Kristina Ignatova, Michael Foerster

We investigate the design of magnetic ordering in one-dimensional mesoscopic magnetic Ising chains by modulating long-range interactions. These interactions are affected by geometrical modifications to the chain, which adjust the energy hierarchy and the resulting magnetic ground states. Consequently, the magnetic ordering can be tuned between antiferromagne

De-Xian Wei

We use A Multi-Phase Transport (AMPT) model to simulate the event-by-event p-Pb collisions at $\sqrt{s_{NN}}$=5.02 TeV. To study the space-time volume fluctuations, two different definitions of radius have been introduced in the calculation: One is weigh of event-by-event charged multiplicity (noted as set I), and the other one is weigh of energy density in

Mateusz Żarski, Jarosław Adam Miszczak

Quick and accurate assessment of the damage state of buildings after natural disasters is crucial for undertaking properly targeted rescue and subsequent recovery operations, which can have a major impact on the safety of victims and the cost of disaster recovery. The quality of such a process can be significantly improved by harnessing the potential of mach

Yuanjin Wang, Hao Wu, Qing Zhao

Metastability is ubiquitous in diverse complex systems. In open quantum systems, metastability offers protection against dissipation and decoherence, yet its application in quantum batteries remains unexplored. We propose a solid-state open quantum battery where metastable states enable stable superextensive charging without complicated protocols and energy

Loïs Delande

We consider Witten Laplacians associated to some non-Morse potentials. We prove Eyring-Kramers formulas for the bottom of the spectrum of these operators in the semiclassical regime and quantify the spectral gap separating these eigenvalues from the rest of the spectrum. The main ingredient is the construction of sharp degenerate Gaussian quasimodes through

Hazem Ibrahim, Nouar AlDahoul, Syed Mustafa Ali Abbasi, Fareed Zaffar

The manner in which different racial and gender groups are portrayed in news coverage plays a large role in shaping public opinion. As such, understanding how such groups are portrayed in news media is of notable societal value, and has thus been a significant endeavour in both the computer and social sciences. Yet, the literature still lacks a longitudinal

Yuxuan Gu, Xiaocheng Feng, Lei Huang, Yingsheng Wu

We present an novel framework for efficiently and effectively extending the powerful continuous diffusion processes to discrete modeling. Previous approaches have suffered from the discrepancy between discrete data and continuous modeling. Our study reveals that the absence of guidance from discrete boundaries in learning probability contours is one of the m

Yifu Sun, Xulong Zhang, Monan Zhou, Wei Li

Music emotion recognition (MER) aims to identify the emotions conveyed in a given musical piece. However, currently, in the field of MER, the available public datasets have limited sample sizes. Recently, segment-based methods for emotion-related tasks have been proposed, which train backbone networks on shorter segments instead of entire audio clips, thereb

Soronzonbold Otgonbaatar, Elise Jennings

Modern supercomputers can handle resource-intensive computational and data-driven problems in various industries and academic fields. These supercomputers are primarily made up of traditional classical resources comprising CPUs and GPUs. Integrating quantum processing units with supercomputers offers the potential to accelerate and manage computationally int

Kaustubh Kislay, Shlok Singh, Soham Joshi, Rohan Dutta

Symbolic Regression remains an NP-Hard problem, with extensive research focusing on AI models for this task. Transformer models have shown promise in Symbolic Regression, but performance suffers with smaller datasets. We propose applying k-fold cross-validation to a transformer-based symbolic regression model trained on a significantly reduced dataset (15,00

F. Capoani, A. Bazzani, M. Giovannozzi

Recently, nonlinear effects have been utilized to cool a special beam represented by an annular distribution in a 2D phase space. This outcome was accomplished using an AC dipole combined with amplitude detuning generated by static nonlinear magnets. In this paper, we investigate a more realistic scenario in which a beam distribution in a 4D phase space incl

Enes Yigitbas, Christian Kaltschmidt

Increasing advances in affordable consumer hardware and accessible software frameworks are now bringing Virtual Reality (VR) to the masses. Especially collaborative VR applications where different people can work together are gaining momentum. In this context, human avatars and their representations are a crucial aspect of collaborative VR applications as th

Muskan Gupta, Priyanka Gupta, Lovekesh Vig

Session-based recommendation (SR) models aim to recommend top-K items to a user, based on the user's behaviour during the current session. Several SR models are proposed in the literature, however,concerns have been raised about their susceptibility to inherent biases in the training data (observed data) such as popularity bias. SR models when trained on the

Fang-Zheng Peng, Li-Sheng Geng, Ju-Jun Xie

The Weinberg compositeness criterion dictates that a pure shallow bound state is characterized by a large scattering length $a_0\gg\mathcal{O}(1/\beta)$ and a positive effective range $r_0$ that naturally scales to the size of $\mathcal{O}(1/\beta)$, where $1/\beta$ signifies the interaction range. In constructing the contact-range effective field theory (EF

Michael Herty, Kai Hinzmann, Siegfried Müller, Ferdinand Thein

Existing theoretical stabilization results for linear, hyperbolic multi-dimensional problems are extended to the discretized multi-dimensional problems. In contrast to existing theoretical and numerical analysis in the spatially one-dimensional case the effect of the numerical dissipation is analyzed and explicitly quantified. Further, using dimensional spli

Matteo Capucci, David Jaz Myers

We introduce contextads and the Ctx construction, unifying various structures and constructions in category theory dealing with context and contextful arrows -- comonads and their Kleisli construction, actegories and their Para construction, adequate triples and their Span construction. Contextads are defined in terms of Lack--Street wreaths, suitably catego

Frank a Campo

We investigate connected posets $C$ of height one as retracts of finite posets $P$. We define two multigraphs: a multigraph $\mathfrak{F}(P)$ reflecting the network of so-called improper 4-crown bundles contained in the extremal points of $P$, and a multigraph $\mathfrak{C}(C)$ depending on $C$ but not on $P$. There exists a close interdependence between $C$

Ryan Stocks, Jorge L. Galvez Vallejo, Fiona C. Y. Yu, Calum Snowdon

The accurate simulation of complex biochemical phenomena has historically been hampered by the computational requirements of high-fidelity molecular-modeling techniques. Quantum mechanical methods, such as ab initio wave-function (WF) theory, deliver the desired accuracy, but have impractical scaling for modelling biosystems with thousands of atoms. Combinin

Huiqiu Lin, Zhe You

The definition of Ricci curvature on graphs was given in Lin-Lu-Yau, Tohoku Math., 2011, which is a variation of Ollivier, J. Funct. Math., 2009. Recently, a powerful limit-free formulation of Lin-Lu-Yau curvature using the graph Laplacian has been given in M\"{u}nch-Wojciechowski, Adv. Math., 2019. Let $F_k$ be the friendship graph obtained from $k$ triangl

Gabriele Onorato

This study investigates the application of Bayesian Optimization (BO) for the hyperparameter tuning of neural networks, specifically targeting the enhancement of Convolutional Neural Networks (CNN) for image classification tasks. Bayesian Optimization is a derivative-free global optimization method suitable for expensive black-box functions with continuous i

Shumpei Takezaki, Kiyohito Tanaka, Seiichi Uchida

Severity level estimation is a crucial task in medical image diagnosis. However, accurately assigning severity class labels to individual images is very costly and challenging. Consequently, the attached labels tend to be noisy. In this paper, we propose a new framework for training with ``ordinal'' noisy labels. Since severity levels have an ordinal relatio

Antonis Achilleos, Adrian Francalanza

This volume contains the proceedings of GandALF 2024, the Fifteenth International Symposium on Games, Automata, Logics, and Formal Verification. GandALF 2024 took place on 19-21 June 2024, in Reykjavik, Iceland. The aim of GandALF 2024 is to bring together researchers from academia and industry who are actively working in the fields of Games, Automata, Logic

Frederic Noël, Leon von Detten, Christoph Hanhart, Martin Hoferichter

Pion-kaon ($\pi K$) final states, often appearing in heavy-particle decays at the precision frontier, are important for Standard-Model tests, to describe crossed channels with exotic states, and for spectroscopy of excited kaon resonances. We construct a representation of the $\pi K$ $S$-wave form factor using the elastic $\pi K$ scattering phase shift via d

Feifei Zhao, Hui Feng, Haibo Tong, Zhengqiang Han

As AI closely interacts with human society, it is crucial to ensure that its behavior is safe, altruistic, and aligned with human ethical and moral values. However, existing research on embedding ethical considerations into AI remains insufficient, and previous external constraints based on principles and rules are inadequate to provide AI with long-term sta

Joshua Robinson, Markus Boettcher

There is mounting evidence that blazars are the sources of part of the very-high-energy astrophysical neutrino flux detected by IceCube. In particular, there have been several spatial and temporal coincidences of individual IceCube neutrino events with flaring blazars, the most prominent of them being IceCube-170922A, coincident with a multi-wavelength flare

Ling Lan, Qiang Du, Simon J. L. Billinge

A quantitative measure of symmetry breaking is introduced that allows the quantification of which symmetries are most strongly broken due to the introduction of some kind of defect in a perfect structure. The method uses a statistical approach based on the Jensen-Shannon divergence. The measure is calculated by comparing the transformed atomic density functi

Benedek Dombos

In this paper, we prove a new Rogers-Ramanujan-type identity, involving grounded partitions, by computing a character of the affine Kac-Moody algebra $D_4^{(3)}$ in two different ways. The product side is derived using Lepowsky's product formula, while the sum side is obtained using perfect crystals with a technique of Dousse and Konan.

Lingling Xue, Shasha Wang, Qing Ping Liu

By introducing a Miura transformation, we derive a generalized super modified Korteweg-de Vries (gsmKdV) equation from the generalized super KdV (gsKdV) equation. It is demonstrated that, while the gsKdV equation takes Kupershmidt's super KdV (sKdV) equation and Geng-Wu's sKdV equation as two distinct reductions, there are also two equations, namely Kupershm

Asim kumar Saha, Ritam Mallick

Causality is an essential factor in determining the maximum mass of a neutron star. Previous works study causality for smooth equations of state. The density at the core of neutron stars can be a few times nuclear saturation density, where the occurrence of first-order phase transition has not been ruled out. The causality condition for first-order phase tra

Kemal Altwlkany, Sead Delalić, Adis Alihodžić, Elmedin Selmanović

Audio fingerprinting techniques have seen great advances in recent years, enabling accurate and fast audio retrieval even in conditions when the queried audio sample has been highly deteriorated or recorded in noisy conditions. Expectedly, most of the existing work is centered around music, with popular music identification services such as Apple's Shazam or

Christian Bergfried, Samaneh Abdi Qezeljeh, Ilia V. Roisman, Herbert De Gersem

The need for higher power density in electrical machines require better cooling strategies. Spray cooling is a very promising and relatively simple technology to apply, but involves extremely complicated physics. In this paper, a quasi-3D thermal finite-element model of a stator winding is created, by extrusion of a 2D cross-sectional finite-element model al

Oleg Lebedev, António P. Morais, Vinícius Oliveira, Roman Pasechnik

We study the Higgs boson decay into dark matter (DM) in the framework of freeze-in at stronger coupling. Even though the Higgs-DM coupling is significant, up to order one, DM does not thermalize due to the Boltzmann suppression of its production at low temperatures. We find that this mechanism leads to observable Higgs decay into invisible final states with

Rajana Akter, Shahnure Rabib, Rahul Deb Mohalder, Laboni Paul

Intrusion detection system (IDS) is a piece of hardware or software that looks for malicious activity or policy violations in a network. It looks for malicious activity or security flaws on a network or system. IDS protects hosts or networks by looking for indications of known attacks or deviations from normal behavior (Network-based intrusion detection syst

Yinyi Lai, Anbo Cao, Yuan Gao, Jiaqi Shang

Early and accurate diagnosis of brain tumors is crucial for improving patient survival rates. However, the detection and classification of brain tumors are challenging due to their diverse types and complex morphological characteristics. This study investigates the application of pre-trained models for brain tumor classification, with a particular focus on d

Nikolaos Kyvelos, N. Asger Mortensen, Xuezhi Zheng, Christos Tserkezis

We investigate how the near-field enhancement of self-similar nanolenses, made of three plasmonic nanospheres with decreasing sizes and separations, is affected by quantum corrections in the optical response of the metal, and by fabrication imperfections related to the positioning of the spheres in the nanolens. In particular, we integrate mesoscopic phenome

Ivana Kovacevic, Milan Stojkov, Milos Simic

The abilities of traditional perimeter-based security architectures are rapidly decreasing as more enterprise assets are moved toward the cloud environment. From a security viewpoint, the Zero Trust framework can better track and block external attackers while limiting security breaches resulting from insider attacks in the cloud paradigm. Furthermore, Zero