October 2024 arXiv papers — page 75

Chusei Kiumi, Bálint Koczor

Simulating time evolution under quantum Hamiltonians is one of the most natural applications of quantum computers. We introduce TE-PAI, which simulates time evolution exactly by sampling random quantum circuits for the purpose of estimating observable expectation values at the cost of an increased circuit repetition. The approach builds on the Probabilistic

Sebastian Kassing, Simon Weissmann

In this work, we analyze the convergence of Polyak's heavy ball method in both continuous and discrete time for non-convex $C^4$-objective functions satisfying the Polyak-Lojasiewicz inequality. Under this weak assumption, we recover the asymptotic convergence rates originally derived by Polyak in [Polyak, U.S.S.R. Comput. Math. and Math. Phys., 1964] for st

Taewhoo Lee, Chanwoong Yoon, Kyochul Jang, Donghyeon Lee

Recent advancements in large language models (LLM) capable of processing extremely long texts highlight the need for a dedicated evaluation benchmark to assess their long-context capabilities. However, existing methods, like the needle-in-a-haystack test, do not effectively assess whether these models fully utilize contextual information, raising concerns ab

Guy Tsamo, Alla G. Nastovjak, Nataliya L. Shwartz, Philip E. Hoggan

In this work, we present conception and study of gallium nitride (GaN) nanostructures on a gallium arsenide (GaAs) substrate with (111)A orientation. The nanostructures were designed by GaN droplet epitaxy and studied in-situ by X-ray photoelectron spectroscopy and ex-situ by atomic force microscopy, scanning electron microscopy and transmission electron mic

Lam Dinh, Pham Tran Anh Quang, Jérémie Leguay

High performance, reliability and safety are crucial properties of any Software-Defined-Networking (SDN) system. Although the use of Deep Reinforcement Learning (DRL) algorithms has been widely studied to improve performance, their practical applications are still limited as they fail to ensure safe operations in exploration and decision-making. To fill this

Yihong Luo, Yuhan Chen, Siya Qiu, Yiwei Wang

Graph Neural Networks (GNNs) have shown superior performance in node classification. However, GNNs perform poorly in the Few-Shot Node Classification (FSNC) task that requires robust generalization to make accurate predictions for unseen classes with limited labels. To tackle the challenge, we propose the integration of Sharpness-Aware Minimization (SAM)--a

Sheryl Paul, Jyotirmoy V. Deshmukh

Reinforcement learning (RL) has been successfully applied to solve the problem of finding obstacle-free paths for autonomous agents operating in stochastic and uncertain environments. However, when the underlying stochastic dynamics of the environment experiences drastic distribution shifts, the optimal policy obtained in the trained environment may be sub-o

Sintayehu Tesfa

The study of spontaneously induced nonclassicality as a result of the interaction of an ensemble of two-level emitters embedded onto crystal structure embodying photonic band gap (PBG) is presented. The method of coherent-state propagator is applied upon expressing collective atomic operators in terms of boson operators in view of Schwinger's representation.

Zongmeng Zhang, Yufeng Shi, Jinhua Zhu, Wengang Zhou

Trustworthiness is an essential prerequisite for the real-world application of large language models. In this paper, we focus on the trustworthiness of language models with respect to retrieval augmentation. Despite being supported with external evidence, retrieval-augmented generation still suffers from hallucinations, one primary cause of which is the conf

Sindhu Nair, Y. S. Rao, Radha Shankarmani

In recent times, extracting valuable information from large text is making significant progress. Especially in the current era of social media, people expect quick bites of information. Automatic text summarization seeks to tackle this by slimming large texts down into more manageable summaries. This important research area can aid in decision-making by digg

Baihong Li, Qi-qi Li, Boxin Yuan, Ruifang Dong

Biphoton frequency comb (BFC), which encompasses multiple discrete frequency modes and represents high-dimensional frequency entanglement, is crucial in quantum information processing due to its high information capacity and error resilience. It also holds significant potential for enhancing timing precision in quantum metrology. Here, we examine quantum met

Meng Xu, Tong Zhang, Fuyun Wang, Yi Lei

Movie posters are vital for captivating audiences, conveying themes, and driving market competition in the film industry. While traditional designs are laborious, intelligent generation technology offers efficiency gains and design enhancements. Despite exciting progress in image generation, current models often fall short in producing satisfactory poster re

Xiao-Min Zhang, Run-Qing Zhao, Zhi-peng Peng, Xi-Bin Li

This study extended noncanonical warm inflation to the nonminimal derivative coupling scenario. The fundamental equations, including the evolution equations and the slow roll equations of this new framework, were derived. The enlarged damping term, which encompasses both gravitationally enhanced friction and thermal damping, resulted in a well overdamped inf

Jesús Bobadilla, Abraham Gutiérrez, Santiago Alonso, Ángel González-Prieto

Neural collaborative filtering is the state of art field in the recommender systems area; it provides some models that obtain accurate predictions and recommendations. These models are regression-based, and they just return rating predictions. This paper proposes the use of a classification-based approach, returning both rating predictions and their reliabil

ATLAS Collaboration

A search for leptons displaced from the primary vertex is performed with the ATLAS detector at the Large Hadron Collider. The search includes the full proton-proton collision dataset collected during Run 2 at $\sqrt{s}=13$ TeV and a partial dataset collected during Run 3 in 2022-2023 at $\sqrt{s}=13.6$ TeV, corresponding to integrated luminosities of 140 fb$

Mingqi Gao, Xinyu Hu, Li Lin, Xiaojun Wan

The correlation between NLG automatic evaluation metrics and human evaluation is often regarded as a critical criterion for assessing the capability of an evaluation metric. However, different grouping methods and correlation coefficients result in various types of correlation measures used in meta-evaluation. In specific evaluation scenarios, prior work oft

Shunyu Yao, Gary P. T. Choi

Density-equalizing map is a shape deformation technique originally developed for cartogram creation and sociological data visualization on planar geographical maps. In recent years, there has been an increasing interest in developing density-equalizing mapping methods for surface and volumetric domains and applying them to various problems in geometry proces

Daniel Chan, Sean B. Lynch

In this manuscript, we give effective methods for computing the zeta function of maximal orders on surfaces.

M. Lepore, C. Pinto, P. Tozzi, M. Gaspari

We present a detailed analysis of the thermal X-ray emission from the intracluster medium (ICM) in the cool-core galaxy cluster Abell 2667 ($z=0.23$). Our goal is to detect low-temperature ($<2$ keV) X-ray emitting gas, potentially associated to a cooling flow that connects the hot ICM reservoir to the cold gas phase responsible for star formation and superm

Panpan Zhou, Sirui Li, Benyun Zhao, Bo Wahlberg

The pursuit-evasion problem is widespread in nature, engineering, and societal applications. It is commonly observed in nature that predators often exhibit faster speeds than their prey but have less agile maneuverability. Over millions of years of evolution, animals have developed effective and efficient strategies for both pursuit and evasion. In this pape

J. M. Conejero, J. C. Preciado, A. E. Prieto, M. C. Bas

In this paper we present a multi-criteria classification of Vocational and Educational Programs in Extremadura (Spain) during the period 2009-2016. This ranking has been carried out through the integration into a complete database of the detailed information of individuals finishing such studies together with their labor data. The multicriteria method used i

Hampus Malmberg, Fredrik Feyling

An amplifier-less continuous-time analog-to-digital converter consisting of only passives, comparators, and inverters is presented. Beyond simplicity, the architecture displays significant robustness properties with respect to component variations and comparator input offsets. We give an analytical design procedure demonstrating how to parameterize the archi

Seongyeol Kim, Philippe Piot, Gonxiaohui Chen, Scott Doran

Magnetized beams beam with significant canonical angular momentum are critical to electron cooling of hadron beams such as contemplated in next-generation hadron and electron-ion colliders. The transport of magnetized electron beams over long distances in a locally non-axisymmetric external field is challenging. An alternative is to transform the beam into a

Francis Sergeraert

The Smith reduction is a basic tool when analyzing integer matrices up to equivalence, and the Kannan-Bachem (KB) algorithm is the first polynomial algorithm computing such a reduction. Using this algorithm in complicated situations where the rank of the studied matrix is not maximal revealed an unexpected obstacle in the algorithm. This difficulty is descri

Paris Giampouras, HanQin Cai, Rene Vidal

In this paper, we focus on a matrix factorization-based approach to recover low-rank {\it asymmetric} matrices from corrupted measurements. We propose an {\it Overparameterized Preconditioned Subgradient Algorithm (OPSA)} and provide, for the first time in the literature, linear convergence rates independent of the rank of the sought asymmetric matrix in the

D. Trevisani, J. G. López-Salas, C. Vázquez, J. A. García-Rodríguez

In this work we rigorously establish mathematical models to obtain the capital valuation adjustment (KVA) as part of the total valuation adjustments (XVAs). For this purpose, we use a semi-replication strategy based on market theory. We formulate single-factor models in terms of expectations and PDEs. For PDEs formulation, we rigorously obtain the existence

Vinh Nguyen

Generating detailed descriptions from multiple cameras and viewpoints is challenging due to the complex and inconsistent nature of visual data. In this paper, we introduce PerspectiveNet, a lightweight yet efficient model for generating long descriptions across multiple camera views. Our approach utilizes a vision encoder, a compact connector module to conve

Gustavo Penha, Ali Vardasbi, Enrico Palumbo, Marco de Nadai

Generative retrieval for search and recommendation is a promising paradigm for retrieving items, offering an alternative to traditional methods that depend on external indexes and nearest-neighbor searches. Instead, generative models directly associate inputs with item IDs. Given the breakthroughs of Large Language Models (LLMs), these generative systems can

Hanqi Duan, Yao Cheng, Jianxiang Yu, Yao Liu

Graph Neural Networks (GNNs) have emerged as powerful models for learning from graph-structured data. However, GNNs lack the inherent semantic understanding capability of rich textual node attributes, limiting their effectiveness in applications. On the other hand, we empirically observe that for existing GNN models, no one can consistently outperforms other

Shuyuan Wang, Jingliang Duan, Nathan P. Lawrence, Philip D. Loewen

Model-free reinforcement learning (RL) is inherently a reactive method, operating under the assumption that it starts with no prior knowledge of the system and entirely depends on trial-and-error for learning. This approach faces several challenges, such as poor sample efficiency, generalization, and the need for well-designed reward functions to guide learn

Yongjian Wu, Yang Zhou, Jiya Saiyin, Bingzheng Wei

Large-scale visual-language pre-trained models (VLPMs) have demonstrated exceptional performance in downstream object detection through text prompts for natural scenes. However, their application to zero-shot nuclei detection on histopathology images remains relatively unexplored, mainly due to the significant gap between the characteristics of medical image

Arvid Siqveland

We prove that for an arbitrary field $k,$ a complete, associative $k^r$-algebra $\hat H$ augmented over $k^r$ has exactly $r$ maximal two-sided ideals and deserves the name $r$-pointed. If $A$ is any $k$-algebra, $M=\{M_i\}_{i=1}^r$ is a family of simple right $A$-modules with a countable $k$-basis, and there is a homomorphism $\rho_A:A\rightarrow\enm_{\hat

Johannes Karwounopoulos, Mateusz Bieniek, Zhiyi Wu, Adam L. Baskerville

The development of machine-learning (ML) potentials offers significant accuracy improvements compared to molecular mechanics (MM) because of the inclusion of quantum-mechanical effects in molecular interactions. However, ML simulations are several times more computationally demanding than MM simulations, so there is a trade-off between speed and accuracy. On

Ziyi Zeng, Yuhao Wang, Dianlin Hu, T. Michael O'Shea

In certain brain volumetric studies, synthetic T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE) contrast, derived from quantitative T1 MRI (T1-qMRI), proves highly valuable due to its clear white/gray matter boundaries for brain segmentation. However, generating synthetic MP-RAGE (syn-MP-RAGE) typically requires pairs of high-quality, artifac

Abdul Wadud, Fatemeh Golpayegani, Nima Afraz

This paper investigates the emerging challenges of conflict detection and mitigation in Open Radio Access Network (O-RAN). Conflicts between xApps can arise that affect network performance and stability due to the disaggregated nature of O-RAN. This work provides a detailed theoretical framework of Extended Application (xApp)-level conflicts, i.e., direct, i

Zhengli Wang, Shunshun Cao, Jiguang Lu, Yulan Liu

We report the detection of an extreme flux decrease accompanied by clear dispersion measure (DM) and rotation measure (RM) variations for pulsar B1929+10 during the 110-minute radio observation with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The radio flux decreases by 2 to 3 orders of magnitude within a rapid time scale of about 20 mi

Ali Hajilou

We consider a twice anisotropic five-dimensional holographic model supported by Einstein-dilaton-three-Maxwell action that was constructed in the paper [arXiv:2011.07023]. Although, that model reproduced some essential features of the ``heavy quarks'', but did not describe the magnetic catalysis (MC) phenomenon expected from lattice results for the Quark-Glu

Lior Bary-Soroker, Roy Shmueli

We study a random polynomial of degree $n$ over the finite field $\mathbb{F}_q$, where the coefficients are independent and identically distributed and uniformly chosen from the squares in $\mathbb{F}_q$. Our main result demonstrates that the likelihood of such a polynomial being irreducible approaches $1/n + O(q^{-1/2})$ as the field size $q$ grows infinite

Yidan Mao, Jing Gu, Marcus C. Werner, Dongmian Zou

Hyperbolic neural networks (HNNs) have been proved effective in modeling complex data structures. However, previous works mainly focused on the Poincar\'e ball model and the hyperboloid model as coordinate representations of the hyperbolic space, often neglecting the Klein model. Despite this, the Klein model offers its distinct advantages thanks to its stra

Yuli Qiu, Jiashu Yao, Heyan Huang, Yuhang Guo

Multi-step reasoning ability of large language models is crucial in tasks such as math and tool utilization. Current researches predominantly focus on enhancing model performance in these multi-step reasoning tasks through fine-tuning with Chain-of-Thought (CoT) steps, yet these methods tend to be heuristic, without exploring nor resolving the bottleneck. In

Nicholas I-Hsien Kuo, Blanca Gallego, Louisa Jorm

Access to real clinical data is often restricted due to privacy obligations, creating significant barriers for healthcare research. Synthetic datasets provide a promising solution, enabling secure data sharing and model development. However, most existing approaches focus on data realism rather than utility -- ensuring that models trained on synthetic data y

Eric O. D., Andriantiana, Xhanti Sinoxolo

The energy $En(G)$ of a graph $G$ is defined as the sum of the absolute values of its eigenvalues. The Hosoya index $Z(G)$ of a graph $G$ is the number of independent edge subsets of $G$, including the empty set. For any given degree sequence $D$, we characterize the caterpillar $\mathcal{S}(D)$ that has the minimum $Z$ and $En$. %and maximum $\sigma$. In $\

Xiaohua Jing, Zhiyuan Li, Masahiro Yamamoto

This article is concerned with an inverse problem of simultaneously determining a spatially varying coefficient and a Robin coefficient for a one-dimensional fractional diffusion equation with a time-fractional derivative of order $\alpha\in(0,1)$. We prove the uniqueness for the inverse problem by observation data at one interior point over a finite time in

Kefan Zhang, Zhili Zhang, Junyang Zhao, Shenhua Lv

Traditional land vehicle gravity measurement heavily rely on high-precision satellite navigation positioning information. However, the operational range of satellite navigation is limited, and it cannot maintain the required level of accuracy in special environments. To address this issue, we propose a novel land vehicle gravity anomaly measurement method ba

Zhibin Xu, Mengmeng Li, Yilong Han

The strength, $\sigma_{\rm y}$, of a polycrystal decreases with mean grain diameter $D$ at $D\gtrsim50$ atoms (i.e. Hall-Petch behaviour) and increases at $D\lesssim50$ (i.e. inverse Hall-Petch behaviour). Our simulations generalise $\sigma_{\rm y}(D)$ to $\sigma_{\rm y}(D,l)$, where $l$ is the mean thickness of grain boundaries. For various particle composi

Thomas Nagler

Vine copula models have become highly popular practical tools for modeling multivariate dependencies. To maintain tractability, a commonly employed simplifying assumption is that conditional copulas remain unchanged by the conditioning variables. This assumption has sparked a somewhat polarizing debate within the copula community. The fact that much of this

Cheng-Han Yeh, Kuanchun Yu, Chun-Shien Lu

Deep learning models are known to be vulnerable to adversarial attacks by injecting sophisticated designed perturbations to input data. Training-time defenses still exhibit a significant performance gap between natural accuracy and robust accuracy. In this paper, we investigate a new test-time adversarial defense method via diffusion-based recovery along opp

Haru Nakajima, Jun Miura

Lifestyle support through robotics is an increasingly promising field, with expectations for robots to take over or assist with chores like floor cleaning, table setting and clearing, and fetching items. The growth of AI, particularly foundation models, such as large language models (LLMs) and visual language models (VLMs), is significantly shaping this sect

Muzhi Li, Cehao Yang, Chengjin Xu, Zixing Song

Inductive knowledge graph completion (KGC) aims to predict missing triples with unseen entities. Recent works focus on modeling reasoning paths between the head and tail entity as direct supporting evidence. However, these methods depend heavily on the existence and quality of reasoning paths, which limits their general applicability in different scenarios.

Laurent Colbois, Sébastien Marcel

Morphing attacks have diversified significantly over the past years, with new methods based on generative adversarial networks (GANs) and diffusion models posing substantial threats to face recognition systems. Recent research has demonstrated the effectiveness of features extracted from large vision models pretrained on bonafide data only (attack-agnostic f

Yuheng Lu, Bingshuo Qian, Caixia Yuan, Huixing Jiang

Large language models (LLMs) exhibit remarkable capabilities in natural language processing but face catastrophic forgetting when learning new tasks, where adaptation to a new domain leads to a substantial decline in performance on previous tasks. In this paper, we propose Controlled LoRA (CLoRA), a sub-space regularization method on LoRA structure. Aiming t

Anna Sakovich, Christina Sormani

We introduce the notion of causally-null-compactifiable space-times which can be canonically converted into a compact timed-metric-spaces using the cosmological time of Andersson-Howard-Galloway and the null distance of Sormani-Vega. We produce a large class of such space-times including future developments of compact initial data sets and regions which exha

Baoyu Tan

Under the premise that quantum gravity becomes non-negligible, higher-order corrections of non-commutative geometry dominate. In this paper, we studied the thermodynamics of high-order corrections for Schwarzschild-AdS black hole with Lorentz distribution in the framework of non-commutative geometry. Our results indicate that when high-order corrections domi

Yu. M. Zinoviev

Here we provide a short review on the so-called Fradkin-Vasiliev formalism for the construction of higher spin cubic interactions. Initially it was formulated for the massless fields only, but later on it was extended to the arbitrary collections of massive and massless fields.

Dmitry K. Efimov, Georgios P. Katsoulis, Tymoteusz Rozpętkowski, Sergiusz Chwałowski

We investigate correlated three-electron escape in Ne when driven by an intense, infrared laser field. We do so by employing a reduced-dimensionality quantum-mechanical model and two three-dimensional semi-classical models. One semi-classical model is a recently developed one that accounts with effective coulomb potentials for the interaction between two bou

Lara Greten, Robert Salzwedel, Diana Schutsch, Andreas Knorr

The common model to describe exciton-plasmon interaction phenomenologically is the coupled oscillator model. Originally developed for atomic systems rather than solid-state matter, this model treats both excitons and plasmons as single harmonic oscillators coupled via a constant which can be fitted to experiments. In this work, we present a modified coupled

Pei Liu, Haipeng Liu, Xingyu Liu, Yiqun Li

Advancements in intelligent technologies have significantly improved navigation in complex traffic environments by enhancing environment perception and trajectory prediction for automated vehicles. However, current research often overlooks the joint reasoning of scenario agents and lacks explainability in trajectory prediction models, limiting their practica

Weijian Luo, Zemin Huang, Zhengyang Geng, J. Zico Kolter

Despite their strong performances on many generative tasks, diffusion models require a large number of sampling steps in order to generate realistic samples. This has motivated the community to develop effective methods to distill pre-trained diffusion models into more efficient models, but these methods still typically require few-step inference or perform

Marta Zoppello, Henry Shum, Marco Morandotti

This paper deals with systems of spherical particles immersed in a viscous fluid. Two aspects are studied, namely the controllability of such systems, with particular attention to the case of one active particle and either one or two passive ones, and the kinetic limit of such systems as the number of particles diverges. The former issue is tackled in the fr

Indira Barros, Sayanth Ramachandran, Indrani Chakraborty

Field-induced assembly of colloidal particles into structures of desired configurations is extremely relevant from the viewpoint of producing field-assembled micro-swimmers and reconfigurable smart materials. However, the behaviour of colloidal particles under the influence of alternating current (AC) electric fields remains a topic of ongoing investigation

Jia-Ming Wang, Jing-Xuan Wang, Rong-Qiang He, Li Huang

In this study, we introduce a novel implementation of density functional theory integrated with single-site dynamical mean-field theory to investigate the complex properties of strongly correlated materials. This comprehensive first-principles many-body computational toolkit, termed $\texttt{Zen}$, utilizes the Vienna $\textit{ab initio}$ simulation package

Kilian Freitag, Kristian Ceder, Rita Laezza, Knut Åkesson

Reinforcement learning (RL) has emerged as a powerful tool for tackling control problems, but its practical application is often hindered by the complexity arising from intricate reward functions with multiple terms. The reward hypothesis posits that any objective can be encapsulated in a scalar reward function, yet balancing individual, potentially adversar

Andrea Crespi

Moving-boundary work, also called pressure-volume work or expansion work, is perhaps the main form of work discussed in introductory courses about Thermodynamics. Here, we take a particular definition of this kind of thermodynamic work. On this basis, we show with simple reasoning that significant phenomena involving dissipation of mechanical energy, often m

Jiayi Lin, Chenyang Zhang, Haibo Tong, Dongyu Zhang

Multi-Span Question Answering (MSQA) requires models to extract one or multiple answer spans from a given context to answer a question. Prior work mainly focuses on designing specific methods or applying heuristic strategies to encourage models to predict more correct predictions. However, these models are trained on gold answers and fail to consider the inc

Simone Di Prima, Ryan D Stewart, Majdi R Abou Najm, Deniz Yilmaz

The heterogeneous distribution of water-repellent materials at the soil surface causes a phenomenon known as fractional wettability. This condition frequently triggers destabilization of the wetting front during water infiltration, resulting in the formation of fingered bypass flow. However, few analytical tools exist to understand and model this behavior. M

Roldán A. Cala, Luis F. Miranda, José F. Gómez, Christophe Morisset

IRAS 22568+6141 has been classified as a low-ionisation planetary nebula (PN) and presents non-thermal radio continuum emission, which could be a signature of nascent PNe. We present intermediate-resolution long-slit spectra obtained in 2021 and 2023, high-resolution long-slit spectra taken in 2023, and a light curve at the $r$-filter between 1953 and 2019,

Osamu Take, Taketo Akama

Recent MIDI-to-audio synthesis methods using deep neural networks have successfully generated high-quality, expressive instrumental tracks. However, these methods require MIDI annotations for supervised training, limiting the diversity of instrument timbres and expression styles in the output. We propose CoSaRef, a MIDI-to-audio synthesis method that does no

Shashwat Kasliwal, Adam Polak, Pratyush Sharma

We revisit the 3SUM problem in the \emph{preprocessed universes} setting. We present an algorithm that, given three sets $A$, $B$, $C$ of $n$ integers, preprocesses them in quadratic time, so that given any subsets $A' \subseteq A$, $B' \subseteq B$, $C' \subseteq C$, it can decide if there exist $a \in A'$, $b \in B'$, $c \in C'$ with $a+b=c$ in time $O(n^{

Joydeep Majhi, Santanu K. Maiti

We present a comprehensive investigation into the charge and spin circular currents in a mesoscopic hybrid system, with a particular focus on the intricate interplay between the Aubry-Andr\'e-Harper (AAH) potential, Aharonov-Bohm (AB) flux, chemical potential ($\mu$), and antiferromagnetic (AF) ordering. The proposed quantum system comprises a composite stru

Sergio Palafox, Luis O. Silva

This work tackles the problem of spectral characterization of a class of infinite matrices arising from the modelling of small oscillations in a system of interacting particles. The class of matrices under discussion corresponds to the infinite Marchenko-Slavin class. The spectral functions of these matrices are completely characterized and an algorithm is p

ATLAS Collaboration

A search is conducted for a new scalar boson $S$, with a mass distinct from that of the Higgs boson, decaying into four leptons ($\ell =$ $e$, $\mu$) via an intermediate state containing two on-shell, promptly decaying new spin-1 bosons $Z_\text{d}$: $S \rightarrow Z_\text{d}Z_\text{d} \rightarrow 4\ell$, where the $Z_\text{d}$ boson has a mass between 15 an

Krishna Sayana, Raghavendra Vasudeva, Yuri Vasilevski, Kun Su

The recent advances in Large Language Model's generation and reasoning capabilities present an opportunity to develop truly conversational recommendation systems. However, effectively integrating recommender system knowledge into LLMs for natural language generation which is tailored towards recommendation tasks remains a challenge. This paper addresses this

L. Bottura, C. Accettura, A. Kolehmainen, J. Lorenzo Gomez

The Muon Collider is one of the options considered as the next step in High Energy Physics. It bears many challenges, last not least in superconducting magnet technology. The target and capture solenoid is one of them, a channel of approximately 18 m length consisting of co-axial solenoid magnets with a 1.2 m free bore and peak field on axis of 20 T. One of

R. L. Edgar, S. Regnier

Since the launch of the Solar Dynamics Observatory (SDO) in 2010 and throughout the solar cycle 24, the Sun has produced few tens of xclass flares, which are the most energetic solar events. Those flares are produced in regions where the magnetic flux/energy is large and the magnetic configurations are complex. To provide more insights into the flaring proce

Vinod Kumar Dwivedi

In recent years, solid state magnetic cooling based on magnetocaloric effect (MCE) have drawn attention worldwide as a promising alternative potential candidate to the conventional gas compression-expansion cooling technique. In this chapter, the current developments of MCE in pyrochlore oxide materials is presented. The interaction between magnetic frustrat

Yusaku Abe, Naoki Tomioka, Yu Matsuda

The diffusion motions of individual polymer aggregates in disordered porous media were visualized using the single particle tracking (SPT) method because the motions inside porous media play important roles in various fields of science and engineering. The aggregates diffused on the surfaces of pores; continuous adsorption and desorption processes were obser

Mingi Sung, Seungmin Lee, Jiwon Kim, Sejoon Kim

Translating conversational text, particularly in customer support contexts, presents unique challenges due to its informal and unstructured nature. We propose a context-aware LLM translation system that leverages conversation summarization and dialogue history to enhance translation quality for the English-Korean language pair. Our approach incorporates the

Shengjie Zhang, Shulin Lyu

We consider the Hankel determinant generated by the moments of the even weight function ${\rm e}^{-x^2}(A+B\theta(x^2-a^2)), x\in(-\infty,+\infty), a>0, A\ge0, A+B\ge0$. It is intimately related to the gap probability of the Gaussian unitary ensembles on $(-a,a)$ or $(-\infty,-a)\cup(a,+\infty)$. We derive the ladder operators for the monic polynomials ortho

Jean-Philippe Chancelier, Michel de Lara

We propose a unified view of the polarity of functions, that encompasses all specific definitions, generalizes several well-known properties and provides new results. We show that bipolar sets and bipolar functions are isomorphic lattices. Also, we explore three possible notions of polar subdifferential associated with a nonnegative function, and we make the

A. -M Daré, C Demarez, J Missirian, F Michelini

Using a formalism adapted to study transport in quantum open systems, that is the nonequilibrium Green's function formalism, we revisit the working principle of the most popular photosynthetic complex, namely the Fenna Matthews-Olson complex. We underline the driving force behind the exciton flow. We also show that in a realistic parameter regime, two mechan

Raymon S. Watson

In this thesis, we explore various aspects of equilibrium and nonequilibrium thermodynamics for ultracold atomic gases, with a focus on the experimentally realisable one-dimensional (1D) Bose gas. This is a paradigmatic example of an interacting many-body system, which is integrable in the uniform limit and near-integrable otherwise. We first investigate a q

Yunzhi Zhang, Zizhang Li, Matt Zhou, Shangzhe Wu

We introduce the Scene Language, a visual scene representation that concisely and precisely describes the structure, semantics, and identity of visual scenes. It represents a scene with three key components: a program that specifies the hierarchical and relational structure of entities in the scene, words in natural language that summarize the semantic class

Arnaud Guillin, Yu Wang, Lihu Xu, Haoran Yang

Stochastic gradient descent with momentum is a popular variant of stochastic gradient descent, which has recently been reported to have a close relationship with the underdamped Langevin diffusion. In this paper, we establish a quantitative error estimate between them in the 1-Wasserstein and total variation distances.

Liam Boyle, Julian Moosmann, Nicolas Baumann, Seonyeong Heo

Advances in lightweight neural networks have revolutionized computer vision in a broad range of IoT applications, encompassing remote monitoring and process automation. However, the detection of small objects, which is crucial for many of these applications, remains an underexplored area in current computer vision research, particularly for low-power embedde

Julien Bichon, Thi Hoa Emilie Nguyen

We show that for a braided Hopf algebra in the category of comodules over a cosemisimple coquasitriangular Hopf algebra, the Hochschild cohomological dimension, the left and right global dimensions and the projective dimensions of the trivial left and right module all coincide. We also provide convenient criteria for smoothness and the twisted Calabi-Yau pro

Marina Moleti, Roberto Valandro

We study families of deformed ADE surfaces by probing them with a D2-brane in Type IIA string theory. The geometry of the total space $X$ of such a family can be encoded in a scalar field $\Phi$, which lives in the corresponding ADE algebra and depends on the deformation parameters. The superpotential of the probe three dimensional (3d) theory incorporates a

Laurine Weibel

Noguchi proved that the set of dominant maps from a fixed variety to a fixed hyperbolic variety is finite. We extend this result to the setting of orbifold pairs, as introduced by Campana, under suitable assumptions. Certain compactness properties also allow us to prove that the set of orbifold pointed maps and the orbifold automorphism group are finite.

Julie Alberge, Vincent Maladière, Olivier Grisel, Judith Abécassis

When dealing with right-censored data, where some outcomes are missing due to a limited observation period, survival analysis -- known as time-to-event analysis -- focuses on predicting the time until an event of interest occurs. Multiple classes of outcomes lead to a classification variant: predicting the most likely event, a less explored area known as com

Gaël Servignat, Jérôme Novak

Next-generation gravitational wave detectors are expected to increase their sensitivity in the kHz band where binary neutron star remnants are expected to emit. In this context, robust predictions of oscillation modes of the post-merger object are desirable. To that end, we present here our code \texttt{ROXAS} that is aimed at simulating isolated rotating ne

Jun-Yi Shen, Yuan-Chuan Zou

We investigate the use of dust-scattering rings from GRB 221009A to measure cosmic curvature. We derive the relationship between scattering angle and time delay in non-flat universes and attempt to constrain cosmological parameters by fitting theoretical predictions to observational data. The results show that this method is limited in its effectiveness due

Yinyi Lai, Jiaqi Shang, Zenghui Liu, Zheyu Jiang

As terrestrial resources become increasingly depleted, the demand for deep-sea resource exploration has intensified. However, the extreme conditions in the deep-sea environment pose significant challenges for underwater operations, necessitating the development of robust detection robots. In this paper, we propose an advanced path planning methodology that i

Per Bäck, Patrik Lundström, Johan Öinert, Johan Richter

Given a set $A$ and an abelian group $B$ with operators in $A$, in the sense of Krull and Noether, we introduce the Ore group extension $B[x; \sigma_B, \delta_B]$ as the additive group $B[x]$, with $A[x]$ as a set of operators. Here, the action of $A[x]$ on $B[x]$ is defined by mimicking the multiplication used in the classical case where $A$ and $B$ are the

Cheima Hammami, Lucas Polo-López, Luc Le Magoarou

This paper introduces an innovative end-to-end model-based deep learning approach for efficient electromagnetic analysis of high-dimensional frequency selective surfaces (FSS). Unlike traditional data-driven methods that require large datasets, this approach combines physical insights from equivalent circuit models with deep learning techniques to significan

Olga Krestinskaya, Mohammed E. Fouda, Ahmed Eltawil, Khaled N. Salama

Designing generalized in-memory computing (IMC) hardware that efficiently supports a variety of workloads requires extensive design space exploration, which is infeasible to perform manually. Optimizing hardware individually for each workload or solely for the largest workload often fails to yield the most efficient generalized solutions. To address this, we

Avichai Cohen, Shaul Zemel

Given a partition $\lambda$ of a number $k$, it is known that by adding a long line of length $n-k$, the dimension of the associated representation of $S_{n}$ is an integer-valued polynomial of degree $k$ in $n$. We show that its expansion in the binomial basis is bounded by the length of $\lambda$, and that the resulting coefficient of index $h$, with alter

Marc Hoyois

We generalize several basic facts about the motivic sphere spectrum in $\mathbb A^1$-homotopy theory to the category $\mathrm{MS}$ of non-$\mathbb A^1$-invariant motivic spectra over a derived scheme. On the one hand, we show that all the Milnor-Witt K-theory relations hold in the graded endomorphism ring of the motivic sphere. On the other hand, we show tha

Yuze Hu, Mingyu Tong, Ziheng Ren, Fujia Chen

The mathematical theory of braids, influential across scientific disciplines, has emerged as a compelling strategy for light manipulation. Existing approaches to creating braids in photonics, whether in momentum-space bandstructures or real-space fields, often face limitations associated with static nature of devices and lack of tunability. Here, we experime

Chang Meng, Chenhao Zhai, Xueliang Wang, Shuchang Liu

With the rise of short video platforms, video recommendation technology faces more complex challenges. Currently, there are multiple non-personalized modules in the video recommendation pipeline that urgently need personalized modeling techniques for improvement. Inspired by the success of uplift modeling in online marketing, we attempt to implement uplift m

Mehran Sharifi

This study presents an efficient algebraic scheme known as MULES for sharp interface advection, verified against various schemes including first-order upwind, second-order central, van Leer flux limiter, and Geometric Volume-of-Fluid (VOF). Two problems involving a droplet in a two-dimensional (2D) vortex and a stationary droplet were examined. The model ass

Mehran Sharifi

The study of Large-Eddy Simulations (LES) in turbulent flows continues to be a critical area of research, particularly in understanding the behavior of small-scale turbulence structures and their impact on resolved scales. In this study, we focus on the LES of turbulent flows, particularly the one-dimensional Stochastic Burgers Equation (SBE), using fully co