November 2024 arXiv papers — page 93

Zeqing He, Zhibo Wang, Zhixuan Chu, Huiyu Xu

Despite the outstanding performance of Large language Models (LLMs) in diverse tasks, they are vulnerable to jailbreak attacks, wherein adversarial prompts are crafted to bypass their security mechanisms and elicit unexpected responses. Although jailbreak attacks are prevalent, the understanding of their underlying mechanisms remains limited. Recent studies

Patrick Cousot, Jeffery Wang

We design various logics for proving hyper properties of iterative programs by application of abstract interpretation principles. In part I, we design a generic, structural, fixpoint abstract interpreter parameterized by an algebraic abstract domain describing finite and infinite computations that can be instantiated for various operational, denotational, or

Pietro Colombo, Raffaele Mattera, Philipp Otto

In this paper, we study the problem of forecasting the next year's number of Atlantic hurricanes, which is relevant in many fields of applications such as land-use planning, hazard mitigation, reinsurance and long-term weather derivative market. Considering a set of well-known predictors, we compare the forecasting accuracy of both machine learning and simpl

Jason Jorge, Robin Wordsworth, Danica Adams

Abundant geomorphological and geochemical evidence of liquid water on the surface of early Mars during the late Noachian and early Hesperian periods needs to be reconciled with a fainter young Sun. While a dense CO2 atmosphere and related warming mechanisms are potential solutions to the early Mars climate problem, further investigation is warranted. Here, w

Tingting Wu, Ruyi Min, Peixuan Song, Hengtao Guo

The accurate segmentation of retinal blood vessels plays a crucial role in the early diagnosis and treatment of various ophthalmic diseases. Designing a network model for this task requires meticulous tuning and extensive experimentation to handle the tiny and intertwined morphology of retinal blood vessels. To tackle this challenge, Neural Architecture Sear

Marcello Poletti

This work explores a pataphysical approach to the concept of space and time, inspired by Aristotelian philosophy and the insights of John Wheeler. We propose the "Physics of Pears", a theory that conceives space as a graph and time as a measure of change, within a geometrodynamical context. The goal is to stimulate reflections on the nature of physical laws

Hongxi Xiang, Carlo Cenedese, Efe C. Balta, John Lygeros

Congestion on highways has become a significant social problem due to the increasing number of vehicles, leading to considerable waste of time and pollution. Regulating the outflow from the Service Station can help alleviate this congestion. Notably, traffic flows follow recurring patterns over days and weeks, allowing for the application of Iterative Learni

Anatoliy Savchenkov, Jingwei Li, Ruixuan Wang, Andrey B. Matsko

We have demonstrated, for the first time to our knowledge, self-injection locking of a distributed feedback (DFB) diode laser to a multimode 4H-silicon carbide (4H-SiC) microring resonator, and observed resonant opto-mechanical oscillation in the cavity modes. While the fundamental transverse-electric mode family of the silicon carbide microring was opticall

E. T. Akhmedov, V. I. Lapushkin, D. I. Sadekov

We start with the consideration of the loop effects for light fields with non-zero mass in the expanding Poincar\'e patch of de Sitter space-time. We derive the Dyson-Schwinger equation, which sums up the leading infrared (growing with time) loop corrections in certain limit for small initial perturbations above the Bunch-Davies state. The solution of this e

Deepa Anand, Bipul Das, Vyshnav Dangeti, Antony Jerald

In a setting where segmentation models have to be built for multiple datasets, each with its own corresponding label set, a straightforward way is to learn one model for every dataset and its labels. Alternatively, multi-task architectures with shared encoders and multiple segmentation heads or shared weights with compound labels can also be made use of. Thi

Darya Yasinskaya, Yury Panov

This study provides a comprehensive analysis of the ground state and thermodynamic properties of a spin-pseudospin chain representing a model of a one-dimensional dilute magnet with two types of nonmagnetic charged impurities. For this purpose, a method utilizing the transfer-matrix properties is employed. Despite the wide variety of intriguing frustrated ph

Parvathi S Nair, Sudhansu Sekhar Rout

Let $S$ be a fixed set of primes and let $(X_{l})_{l\geq 1}$ be the $X$-coordinates of the positive integer solutions $(X, Y)$ of the Pell equation $X^2-dY^2 = 1$ corresponding to a non-square integer $d>1$. We show that there are only a finite number of non-square integers $d>1$ such that there are at least two different elements of the sequence $(X_{l})_{l

Annalena Bea Aicher, Yuki Matsuda, Keichii Yasumoto, Wolfgang Minker

Engaging in discussions that involve diverse perspectives and exchanging arguments on a controversial issue is a natural way for humans to form opinions. In this process, the way arguments are presented plays a crucial role in determining how engaged users are, whether the interaction takes place solely among humans or within human-agent teams. This is of gr

Prakhar Ganesh, Usman Gohar, Lu Cheng, Golnoosh Farnadi

With fairness concerns gaining significant attention in Machine Learning (ML), several bias mitigation techniques have been proposed, often compared against each other to find the best method. These benchmarking efforts tend to use a common setup for evaluation under the assumption that providing a uniform environment ensures a fair comparison. However, bias

Xianjuan Chen, Shuxiang Cai, Alan F. Smeaton

This study uses data from domestic electricity smart meters to estimate annual electricity bills for a whole year. We develop a method for back-filling data smart meter for up to six missing months for users who have less than one year of smart meter data, ensuring reliable estimates of annual consumption. We identify five distinct electricity consumption us

Wadim Zudilin

The product sides of the Rogers--Ramanujan identities and alike often appear to be "transparently modular" (functions). The old work by Rogers (1894) and recent work by Rosengren make use (somewhat implicitly) of this fact for proving the identities with the help of underlying modular equations$-$the main challenge is verifying the latter for the sum sides.

Ting Zhu, Yue Jin, Jeremie Houssineau, Giovanni Montana

In decentralized multi-agent reinforcement learning, agents learning in isolation can lead to relative over-generalization (RO), where optimal joint actions are undervalued in favor of suboptimal ones. This hinders effective coordination in cooperative tasks, as agents tend to choose actions that are individually rational but collectively suboptimal. To addr

Xi Fang, Jiankun Wang, Xiaochen Cai, Shangqian Chen

In recent decades, chemistry publications and patents have increased rapidly. A significant portion of key information is embedded in molecular structure figures, complicating large-scale literature searches and limiting the application of large language models in fields such as biology, chemistry, and pharmaceuticals. The automatic extraction of precise che

Diego Castaño, Valeria Castaño, José Patricio Díaz Varela, Marcela Muñoz Santis

In this article we characterize the equivalent algebraic semantics for the one-variable monadic fragment of the first-order logic ${\cal G} \forall_{\sim}$ defined by F. Esteva, L. Godo, P. H\'ajek and M. Navara in Residuated fuzzy logics with an involutive negation, Archive for Mathematical Logic 39 (2000). To this end, we first introduce the variety $\math

Swapnamay Mondal

Recent computations in gravity suggest that non-supersymmetric extremal black holes lack any sizeable ground state degeneracy. We confirm this for D-brane description of non-supersymmetric 4-charge extremal black holes in N=8 string theory. The microscopic description comprises four stacks of D-branes wrapping various cycles of the internal six-torus and int

Yin Chen, Runxuan Zhang

The coefficient algebra of a finite-dimensional Lie algebra on a finite-dimensional representation is defined as the subalgebra generated by all coefficients of the corresponding characteristic polynomial. We explore connections between classical invariant theory and the coefficient algebras of finite-dimensional complex Lie algebras on some representations.

Abdelrhman Y. Soliman, Ahmed M. Nor, Octavian Fratu, Simona Halunga

Every year, humanity loses about 1.5 million persons due to diabetic disease. Therefore continuous monitoring of diabetes is highly needed, but the conventional approach, i.e., fingertip pricking, causes mental and physical pain to the patient. This work introduces painless and cheaper non-invasive blood glucose level monitoring, Exploiting the advancement a

Jöbke Janssen, Alessandro Zocca, Bert Zwart, Jalal Kazempour

One of the main responsibilities of a Transmission System Operator (TSO) operating an electric grid is to maintain a designated frequency (e.g., 50 Hz in Europe). To achieve this, TSOs have created several products called frequency-supporting ancillary services. The Frequency Containment Reserve (FCR) is one of these ancillary service products. This article

Ilja Gogić, Mateo Tomašević

Let $M_n$ be the algebra of $n \times n$ complex matrices and $\mathcal{T}_n \subseteq M_n$ the corresponding upper-triangular subalgebra. In their influential work, Petek and \v{S}emrl characterize Jordan automorphisms of $M_n$ and $\mathcal{T}_n$, when $n \geq 3$, as (injective in the case of $\mathcal{T}_n$) continuous commutativity and spectrum preservin

Edward Bortnikov, Michael Azran, Asa Bornstein, Shmuel Dashevsky

We present~\emph{KV-Tandem}, a modular architecture for building LSM-based storage engines on top of simple, non-ordered persistent key-value stores (KVSs). KV-Tandem enables advanced functionalities such as range queries and snapshot reads, while maintaining the native KVS performance for random reads and writes. Its modular design offers better performance

Jingyun Sun, Zhongze Luo

A policy knowledge graph can provide decision support for tasks such as project compliance, policy analysis, and intelligent question answering, and can also serve as an external knowledge base to assist the reasoning process of related large language models. Although there have been many related works on knowledge graphs, there is currently a lack of resear

Christopher Gerling, Stefan Lessmann

This paper explores the growing impact of AI and NLP in bank marketing, highlighting their evolving roles in enhancing marketing strategies, improving customer engagement, and creating value within this sector. While AI and NLP have been widely studied in general marketing, there is a notable gap in understanding their specific applications and potential wit

Alex Beeson, David Ireland, Giovanni Montana

Expanding reinforcement learning (RL) to offline domains generates promising prospects, particularly in sectors where data collection poses substantial challenges or risks. Pivotal to the success of transferring RL offline is mitigating overestimation bias in value estimates for state-action pairs absent from data. Whilst numerous approaches have been propos

Minhee Jang, Juheon Son, Thanaporn Viriyasaranon, Junho Kim

The integration of deep learning technologies in medical imaging aims to enhance the efficiency and accuracy of cancer diagnosis, particularly for pancreatic and breast cancers, which present significant diagnostic challenges due to their high mortality rates and complex imaging characteristics. This paper introduces Diffusion-Driven Diagnosis (D-Cube), a no

Georgios Itsios

We construct a plethora of type-II supergravity solutions featuring AdS factors in their geometries, derived from integrable deformations of coset CFTs. Specifically, we uplift the $\lambda$-deformed models of $SO(4)_k/SO(3)_k$ and $SO(5)_k/SO(4)_k$, including the non-compact version of the latter. Requiring reality for the backgrounds imposes bounds on the

András Mészáros

We prove that for a large class of random block lower triangular matrices, the Sylow $p$-subgroups of their cokernels have the same constant order fluctuations as that of the matrix products studied by Nguyen and Van Peski in arXiv:2409.03099. We also show that the theorem of Nguyen and Van Peski remains true under a weaker assumption on the number of factor

Hui Gao, Tong Liu

We study Nygaard-, conjugate-, and Hodge filtrations on the many variants of Breuil--Kisin modules associated to integral semi-stable Galois representations. This leads to an integral Sen operator satisfying certain ``$1$-degree shrinking" on the increasing conjugate filtration, and (in special cases) a mod $p$ Sen operator satisfying certain ``$p$-degree sh

Haoran Gao, Xichuan Zhou, Yingcheng Lin, Min Tian

The prevailing of artificial intelligence-of-things calls for higher energy-efficient edge computing paradigms, such as neuromorphic agents leveraging brain-inspired spiking neural network (SNN) models based on spatiotemporally sparse binary spikes. However, the lack of efficient and high-accuracy deep SNN learning algorithms prevents them from practical edg

Tomas Horych, Christoph Mandl, Terry Ruas, Andre Greiner-Petter

High annotation costs from hiring or crowdsourcing complicate the creation of large, high-quality datasets needed for training reliable text classifiers. Recent research suggests using Large Language Models (LLMs) to automate the annotation process, reducing these costs while maintaining data quality. LLMs have shown promising results in annotating downstrea

Abdesselam Ferdi, Abdelmalik Taleb-Ahmed, Amir Nakib, Youcef Ferdi

The demand for deploying deep convolutional neural networks (DCNNs) on resource-constrained devices for real-time applications remains substantial. However, existing state-of-the-art structured pruning methods often involve intricate implementations, require modifications to the original network architectures, and necessitate an extensive fine-tuning phase.

A. P. Balachandran

The Palatini action is based on vector-valued one forms or frames and SL(2,C) connections on R^4. Using the spacetime split of R^4 as a direct sum of R^3 and R^1, the Gauss law in this paper is treated on a Hilbert space. This is achieved by noting that quantum operators act on a complex Hilbert space and SL(2,C) is just the complexification of the compact S

Zijun Liu, Kaiming Liu, Yiqi Zhu, Xuanyu Lei

Rapid development of artificial intelligence has drastically accelerated the development of scientific discovery. Trained with large-scale observation data, deep neural networks extract the underlying patterns in an end-to-end manner and assist human researchers with highly-precised predictions in unseen scenarios. The recent rise of Large Language Models (L

Sihong Shao, Chuan Yang, Dong Zhang, Weixi Zhang

We introduce and develop equivalent spectral graph theory for several fundamental graph cut problems including maxcut, mincut, Cheeger cut, anti-Cheeger cut, dual Cheeger problem and their useful variants. A specified strategy for achieving an equivalent eigenproblem is proposed for a general graph cut problem via the set-pair Lov\'asz extension and the Dink

Lu Hong-Peng, Tian Hui, Zhang Li-Yun, Chen He-Chao

We report the detection of an extreme stellar prominence eruption on the M dwarf LAMOST J044431.62+235627.9, observed through time-domain H$\alpha$ spectroscopy with the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). This prominence eruption was accompanied by a superflare lasting over 160.4 minutes. The H$\alpha$ line profile exhibits s

Hong-Bo Li, Lijing Shao, Cheng-Jun Xia, Ren-Xin Xu

As density increases, the shape of nuclei transitions to non-spherical ``nuclear pasta" structures. The physical properties of the nuclear pasta, such as thermal conductivity and elasticity, have implications for detecting continuous gravitational waves from a rapidly rotating neutron star. In this work, we investigate the effect of the nuclear pasta on the

Xiaoyang Lin, Renchi Yang, Haoran Zheng, Xiangyu Ke

Subspace clustering seeks to identify subspaces that segment a set of n data points into k (k<<n) groups, which has emerged as a powerful tool for analyzing data from various domains, especially images and videos. Recently, several studies have demonstrated the great potential of subspace clustering models for partitioning vertices in attributed graphs, refe

Sándor Baran, Julio C. Marín, Omar Cuevas, Mailiu Díaz

By the end of 2023, renewable sources cover 63.4% of the total electric power demand of Chile, and in line with the global trend, photovoltaic (PV) power shows the most dynamic increase. Although Chile's Atacama Desert is considered the sunniest place on Earth, PV power production, even in this area, can be highly volatile. Successful integration of PV energ

Shaolin Zhu, Supryadi, Shaoyang Xu, Haoran Sun

This paper provides a comprehensive survey of the latest research on multilingual large language models (MLLMs). MLLMs not only are able to understand and generate language across linguistic boundaries, but also represent an important advancement in artificial intelligence. We first discuss the architecture and pre-training objectives of MLLMs, highlighting

Bobo Hua, Ruowei Li

We introduce the discrete poly-Laplace operator on a subgraph with Dirichlet boundary condition. We obtain upper and lower bounds for the sum of the first $k$ Dirichlet eigenvalues of the poly-Laplace operators on a finite subgraph of lattice graph $\mathbb{Z}^{d}$ extending classical results of Li-Yau and Kr\"oger. Moreover, we prove that the Dirichlet $2l$

Enyu Shi, Jiayi Zhang, Ziheng Liu, Yiyang Zhu

Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS) are two advanced transceiver technologies for realizing future sixth-generation (6G) networks. In this paper, we investigate the joint precoding and access point (AP) selection for energy efficient RIS-aided CF mMIMO system. To address the associated co

Wenjia Jiang, Xiaoke Zhu, Jiakang Gao, Di Liao

Video-based visible-infrared person re-identification (VVI-ReID) is challenging due to significant modality feature discrepancies. Spatial-temporal information in videos is crucial, but the accuracy of spatial-temporal information is often influenced by issues like low quality and occlusions in videos. Existing methods mainly focus on reducing modality diffe

Yajie Chen, Hardi Peter, Damien Przybylski

Transition region explosive events are characterized by non-Gaussian profiles of the emission lines formed at transition region temperatures, and they are believed to be manifestations of small-scale reconnection events in the transition region. We took a 3D self-consistent quiet-Sun model extending from the upper convection zone to the lower corona calculat

Hong-Hao Fan, Cui-Wen Zhang, Suo Tang, Bai-Song Xie

Vortex information of scalar pair production in circularly polarized field is investigated in the multiphoton regime. We find that vortex orientation is related to the intrinsic orbital angular momentum of created particles associating with the helicity of absorbed photons, while the magnitude of the orbital angular momentum, i.e., the topology charge is det

Tingyu Qu, Mingxiao Li, Tinne Tuytelaars, Marie-Francine Moens

Recent advances in multimodal Large Language Models (LLMs) have shown great success in understanding multi-modal contents. For video understanding tasks, training-based video LLMs are difficult to build due to the scarcity of high-quality, curated video-text paired data. In contrast, paired image-text data are much easier to obtain, and there is substantial

Karin Halupczok, Marvin Ohst

We prove that for all constants $a\in\N$, $b\in\Z$, $c,d\in\R$, $c\neq 0$, the fractions $\phi(an+b)/(cn+d)$ lie dense in the interval $]0,D]$ (respectively $[D,0[$ if $c<0$), where $D=a\phi(\gcd(a,b))/(c\gcd(a,b))$. This interval is the largest possible, since it may happen that isolated fractions lie outside of the interval: we prove a complete determinati

Haobing Liu, Rongxin Fu, Zongliang Guo, Menglei Zhao

Optical tweezers, with their high precision, dynamic control, and non-invasiveness, are increasingly important in scientific research and applications at the micro and nano scales. However, manipulation by optical tweezers is challenged by adsorption forces, including van der Waals forces, capillary forces, and electrostatic forces, which are present between

Joris De Moor, Christian Sadel, Hermann Schulz-Baldes

In both the random hopping model and at topological phase transitions in one-dimensional chiral systems, the Lyapunov exponent vanishes at zero energy, but is here shown to have an inverse logarithmic increase with a coefficient that is computed explicitly. This is the counterpart of the Dyson spike in the density of states. The argument also transposes to t

John Stachurski, Jingni Yang, Ziyue Yang

In the theory of dynamic programming, an optimal policy is a policy whose lifetime value dominates that of all other policies from every possible initial condition in the state space. This raises a natural question: when does optimality from a single state imply optimality from every state? Working in a general setting, we provide sufficient conditions for t

Xiaoliang Luo, Michael Ramscar, Bradley C. Love

The impressive performance of large language models (LLMs) has led to their consideration as models of human language processing. Instead, we suggest that the success of LLMs arises from the flexibility of the transformer learning architecture. To evaluate this conjecture, we trained LLMs on scientific texts that were either in a forward or backward format.

Endres Puschner, Maik Ender, Steffen Becker, Christof Paar

Field Programmable Gate Arrays (FPGAs) are known for their reprogrammability that allows for post-manufacture circuitry changes. Nowadays, they are integral to a variety of systems including high-security applications such as aerospace and military systems. However, this reprogrammability also introduces significant security challenges, as bitstream manipula

Ananya Unnikrishnan

Reinforcement learning (RL) has emerged as a transformative approach for financial trading, enabling dynamic strategy optimization in complex markets. This study explores the integration of sentiment analysis, derived from large language models (LLMs), into RL frameworks to enhance trading performance. Experiments were conducted on single-stock trading with

Eldar Knar

The chronological hierarchy and classification of psychological types of individuals are examined. The anomalous nature of psychological activity in individuals involved in scientific work is highlighted. Certain aspects of the introverted thinking type in scientific activities are analyzed. For the first time, psychological archetypes of scientists with pro

Medant Sharan, Chandranath Adak

This paper investigates the strategy game So Long Sucker (SLS) as a novel benchmark for multi-agent reinforcement learning (MARL). Unlike traditional board or video game testbeds, SLS is distinguished by its coalition formation, strategic deception, and dynamic elimination rules, making it a uniquely challenging environment for autonomous agents. We introduc

Ji-Yu Cheng

We show how to understand CRT symmetry as gauge symmetry in holographic de Sitter universe involving a pair of mirror universes, in which frame there are two times going in two opposite temporal directions each. The CRT symmetry is global with respect to the big bang for these two-sheet universes. In this construction the presence of an observer is equivalen

Ofir Zafrir, Igor Margulis, Dorin Shteyman, Shira Guskin

Speculative Decoding has gained popularity as an effective technique for accelerating the auto-regressive inference process of Large Language Models. However, Speculative Decoding entirely relies on the availability of efficient draft models, which are often lacking for many existing language models due to a stringent constraint of vocabulary compatibility.

Merav Parter, Elad Tzalik

An $f$-edge (or vertex) connectivity certificate is a sparse subgraph that maintains connectivity under the failure of at most $f$ edges (or vertices). It is well known that any $n$-vertex graph admits an $f$-edge (or vertex) connectivity certificate with $\Theta(f n)$ edges (Nagamochi and Ibaraki, Algorithmica 1992). A recent work by (Bodwin, Haeupler and P

Bin Xu, Yiguan Lin, Yinghao Li, Yang Gao

Large language models demonstrate exceptional performance in simple code generation tasks but still face challenges in tackling complex problems. These challenges may stem from insufficient reasoning and problem decomposition capabilities. To address this issue, we propose a reasoning-augmented data generation process, SRA-MCTS, which guides the model to aut

Noa Konforty, Moshe-Ishay Cohen, Ohad Segal, Yonatan Plotnik

We study the nonlinear process of second harmonic generation in photonic time-crystals, materials with refractive index that varies abruptly and periodically in time, and obtain the phase matching condition for this process. We find conditions for which the second harmonic generation is highly enhanced even in the absence of phase matching, governed by the e

Berry Schoenmakers

Leftist heaps and skew heaps are two well-known data structures for mergeable priority queues. Leftist heaps are constructed for efficiency in the worst-case sense whereas skew heaps are self-adjusting, designed for efficiency in the amortized sense. In this paper, we analyze the amortized complexity of leftist heaps to initiate a full performance comparison

Hidde Stoffels

Energy conditions are attempts to summarise the properties of realistic descriptions of matter via constraints on the energy-momentum tensor. This is, for example, useful when one wants to understand the types of spacetime geometry that can be realised in general relativity. However, it is currently unknown what (if any) energy condition a realistic quantum

Asaf Petruschka

Let $T$ be a rooted tree in which a set $M$ of vertices are marked. The lowest common ancestor (LCA) of $M$ is the unique vertex $\ell$ with the following property: after failing (i.e., deleting) any single vertex $x$ from $T$, the root remains connected to $\ell$ if and only if it remains connected to some marked vertex. In this note, we introduce a general

Ortal Ashkenazi, Elad Yom-Tov, Liron Vardi David

Screening questionnaires are used in medicine as a diagnostic aid. Creating them is a long and expensive process, which could potentially be improved through analysis of social media posts related to symptoms and behaviors prior to diagnosis. Here we show a preliminary investigation into the feasibility of generating screening questionnaires for a given medi

Victor Borsevici, Samit Ganguly, Goutam Manna

This study presents a new framework to explore the fundamental relationship between gravity and quantum mechanics, with particular emphasis on the fundamental role of primordial black holes (PBHs) in cosmology. Through the concept of self-gravitating condensed light in the form of the experimentally discovered quantum photon Bose-Einstein condensate, this wo

Shubham Tanaji Kakde, Rony Mitra, Jasashwi Mandal, Manoj Kumar Tiwari

Multivariate Long Sequence Time-series Forecasting (LSTF) has been a critical task across various real-world applications. Recent advancements focus on the application of transformer architectures attributable to their ability to capture temporal patterns effectively over extended periods. However, these approaches often overlook the inherent relationships a

Yakun Niu, Pei Chen, Lei Zhang, Lei Tan

Image Forgery Localization (IFL) technology aims to detect and locate the forged areas in an image, which is very important in the field of digital forensics. However, existing IFL methods suffer from feature degradation during training using multi-layer convolutions or the self-attention mechanism, and perform poorly in detecting small forged regions and in

Chang Liu, Rui Li, Kaidong Zhang, Yunwei Lan

Recent advancements of generative AI have significantly promoted content creation and editing, where prevailing studies further extend this exciting progress to video editing. In doing so, these studies mainly transfer the inherent motion patterns from the source videos to the edited ones, where results with inferior consistency to user prompts are often obs

Yu Jiang, Xindi Tong, Ziyao Liu, Huanyi Ye

Federated unlearning (FU) offers a promising solution to effectively address the need to erase the impact of specific clients' data on the global model in federated learning (FL), thereby granting individuals the ``Right to be Forgotten". The most straightforward approach to achieve unlearning is to train the model from scratch, excluding clients who request

Yoonje Jeong, Sang-Gyun Youn

We prove that a freer quantum group has smaller moments of the self-adjoint main character in the category of finitely generated discrete quantum groups. As a result, the moments are minimized precisely by the unitary free quantum groups $\mathbb{F}U(Q)$. Furthermore, in the spirit of [CC22], we prove that the operator norm of the self-adjoint main character

J. Rivera-Dean, P. Stammer, M. F. Ciappina, M. Lewenstein

High-harmonic generation (HHG) is a nonlinear process in which a strong driving field interacts with a material, resulting in the frequency up-conversion of the driver into its high-order harmonics. This process is highly sensitive to the field's polarization: circular polarization, for instance, inhibits HHG. In this work, we demonstrate that the use of non

R. Drebotiy, H. Shynkarenko

We propose certain approach of solving two-dimensional non-stationary and stationary advection-diffusion-reaction boundary value problems through their reduction to the set of corresponding one-dimensional problems. This method leverages special splitting and interpolation schemes, providing iterative algorithm with a large degree of parallelization possibil

Fang-Jun Cheng, Cong-Cong Lou, Ai-Xi Chen, Li-Xuan Wei

Unconventional superconductors that spontaneously break space-group symmetries of their underlying crystal lattice are distinguished by spatial modulations of the superconducting order parameter. These states have recently captured significant attention in various strongly correlated materials, where the breaking of translational or intra-unit-cell symmetrie

Yu Jiang, Chee Wei Tan, Kwok-Yan Lam

Federated learning facilitates collaborative machine learning, enabling multiple participants to collectively develop a shared model while preserving the privacy of individual data. The growing importance of the "right to be forgotten" calls for effective mechanisms to facilitate data removal upon request. In response, federated unlearning (FU) has been deve

Saleh Ashkboos, Bram Verhoef, Torsten Hoefler, Evangelos Eleftheriou

Quantization-aware training (QAT) schemes have been shown to achieve near-full precision accuracy. They accomplish this by training a quantized model for multiple epochs. This is computationally expensive, mainly because of the full precision backward pass. On the other hand, post-training quantization (PTQ) schemes do not involve training and are therefore

Jianwen Zhou, Puming Yang

In this paper, we study a type of p-Kirchhoff equation $$ -\left( a+b\int_{\mathbb{R} ^3}{\left| \nabla u \right|^pdx} \right) \varDelta _pu=\lambda \left| u \right|^{p-2}u+\left| u \right|^{q-2}u, x \in \mathbb{R}^3 $$ with the prescribed mass $$ \left(\int_{\mathbb{R} ^3}{\left| u \right|^{p}dx}\right)^\frac{1}{p} = c > 0 $$ where $a>0, b > 0,\frac{3}{2} <

Menghan Wang, Yuchen Guo, Duanfeng Zhang, Jianian Jin

How to leverage large language model's superior capability in e-commerce recommendation has been a hot topic. In this paper, we propose LLM-PKG, an efficient approach that distills the knowledge of LLMs into product knowledge graph (PKG) and then applies PKG to provide explainable recommendations. Specifically, we first build PKG by feeding curated prompts t

Gus Henry Smith, Colin Knizek, Daniel Petrisko, Zachary Tatlock

State-of-the-art hardware compilers for FPGAs often fail to find efficient mappings of high-level designs to low-level primitives, especially complex programmable primitives like digital signal processors (DSPs). New approaches apply sketch-guided program synthesis to more optimally map designs. However, this approach has two primary drawbacks. First, sketch

Grzegorz Pastuszak, Alicja Jaworska-Pastuszak, Takeo Kamizawa, Andrzej Jamiołkowski

A one-parameter family of hermiticity-preserving superoperators is a time-dependent family $\{\Phi_{t}\colon\mathbb{M}_{n}(\mathbb{C})\rightarrow\mathbb{M}_{n}(\mathbb{C})\}_{t\in\mathbb{R}}$ of hermiticity-preserving superoperators determined, in a certain sense, by real and complex polynomial functions in the variable $t\in\mathbb{R}$. The paper studies su

Juan Carlos Estrada-Jimenez, Valdemar Ramon Farre-Guijarro, Diana Carolina Alvarez-Paredes, Marie-Laure Watrinet

Operational data in next-generation networks offers a valuable resource for Mobile Network Operators to autonomously manage their systems and predict potential network issues. Machine Learning and Digital Twin can be applied to gain important insights for intelligent decision-making. This paper proposes a framework for Radio Frequency planning and failure de

Jianlei Chi, Xiaotian Wang, Yuhan Huang, Lechen Yu

Synchronizing production and test code, known as PT co-evolution, is critical for software quality in the software development lifecycle. Existing methods for automatic PT co-evolution either utilize predefined heuristic rules or rely on simple application of machine learning techniques. Due to the limitations of underlying techniques, existing methods eithe

Piotr Chlebicki, Maciej Beręsewicz

Population size estimation is a major challenge in official statistics, social sciences, and natural sciences. The problem can be tackled by applying capture-recapture methods, which vary depending on the number of sources used, particularly on whether a single or multiple sources are involved. This paper focuses on the first group of methods and introduces

Francesco Mazza, Caitao Zhan, Joaquin Chung, Rajkumar Kettimuthu

Quantum Local Area Networks (QLANs) represent a promising building block for larger scale quantum networks with the ambitious goal -- in a long time horizon -- of realizing a Quantum Internet. Surprisingly, the physical topology of a QLAN can be enriched by a set of artificial links, enabled by shared multipartite entangled states among the nodes of the netw

Kai Cao, Tao Yu, Jihong Li, Xiaojing Chen

The rapid evolution of communication technologies has spurred a growing demand for energy-efficient network architectures and performance metrics. Active Reconfigurable Intelligent Surfaces (RIS) are emerging as a key component in green network architectures. Compared to passive RIS, active RIS are equipped with amplifiers on each reflecting element, allowin

Yin-Yin Bao, Er-Chao Li, Hong-Qiang Yang, Bin-Bin Jia

In semiconductor manufacturing, wafer defect maps (WDMs) play a crucial role in diagnosing issues and enhancing process yields by revealing critical defect patterns. However, accurately categorizing WDM defects presents significant challenges due to noisy data, unbalanced defect classes, and the complexity of failure modes. To address these challenges, this

Mohammad Soleymani, Ignacio Santamaria, Eduard Jorswieck, Marco Di Renzo

The performance of modern wireless communication systems is typically limited by interference. The impact of interference can be even more severe in ultra-reliable and low-latency communication (URLLC) use cases. A powerful tool for managing interference is rate splitting multiple access (RSMA), which encompasses many multiple-access technologies like non-or

Abdulrahman Alharbi, Diogo Gomes, Giuseppe Di Fazio, Melih Ucer

We establish interior regularity results for first-order, stationary, local mean-field game (MFG) systems. Specifically, we study solutions of the coupled system consisting of a Hamilton-Jacobi-Bellman equation $H(x, Du, m) = 0$ and a transport equation $-\operatorname{div}(m D_pH(x, Du, m)) = 0$ in a domain $\Omega \subset \mathbb{R}^d$. Under suitable stru

Sibo Wei, Xueping Peng, Yi-Fei Wang, Tao Shen

The surge of large language models (LLMs) has driven significant progress in medical applications, including traditional Chinese medicine (TCM). However, current medical LLMs struggle with TCM diagnosis and syndrome differentiation due to substantial differences between TCM and modern medical theory, and the scarcity of specialized, high-quality corpora. To

Laura Gambera, Salvatore A. Marano

In this paper, the existence of positive weak solutions to a Dirichlet problem driven by the fractional $(p,q)$-Laplacian and with reaction both weakly singular and non-locally convective (i.e., depending on the distributional Riesz gradient of solutions) is established. Due to the nature of the right-hand side, we address the problem via sub-super solution

Makoto Naka, Yukitoshi Motome, Hitoshi Seo

Altermagnet is a class of antiferromagnets, which shows a staggered spin ordering with wave vector ${\bm q}=0$, while its net magnetization is canceled out in the limit of zero relativistic spin-orbit coupling. The simplest case is when the up and down spins are ordered on two crystallographically equivalent sublattice sites within the unit cell that are not

Weronika Smolak-Dyżewska, Dawid Malarz, Kornel Howil, Jan Kaczmarczyk

Implicit Neural Representations (INRs) employ neural networks to approximate discrete data as continuous functions. In the context of video data, such models can be utilized to transform the coordinates of pixel locations along with frame occurrence times (or indices) into RGB color values. Although INRs facilitate effective compression, they are unsuitable

Marlies Pirner, Gayrat Toshpulatov

A kinetic model for semiconductor devices is considered on a flat torus. We prove exponential decay to equilibrium for this non-linear kinetic model by hypocoercivity estimates. This seems to be the first hypocoercivity result for this nonlinear kinetic equation for semiconductor devices without smallness assumptions. The analysis benefits from uniform bound

Wenlun Zhang, Shimpei Ando, Yung-Chin Chen, Kentaro Yoshioka

SRAM-based Analog Compute-in-Memory (ACiM) demonstrates promising energy efficiency for deep neural network (DNN) processing. Nevertheless, efforts to optimize efficiency frequently compromise accuracy, and this trade-off remains insufficiently studied due to the difficulty of performing full-system validation. Specifically, existing simulation tools rarely

A. Y. Potekhin, A. I. Chugunov, N. N. Shchechilin, N. Chamel

Parametrized nucleon density distributions are widely employed for the calculation of the properties of atomic nuclei and dense inhomogeneous matter in compact stars within the Thomas-Fermi method and its extensions. We show that the use of insufficiently smooth parametrizations may deteriorate the accuracy of this method. We discuss and clarify the smoothne

Rui Zhao, Bin Shi, Zhiming Liang, Jianfei Ruan

Graph Neural Networks (GNNs) have been widely employed for semi-supervised node classification tasks on graphs. However, the performance of GNNs is significantly affected by label noise, that is, a small amount of incorrectly labeled nodes can substantially misguide model training. Mainstream solutions define node classification with label noise (NCLN) as a

Pui-Wai Ma

In our previous work [Ma and Chan (2023)], we presented a feedforward unitary equivariant neural network. We proposed three distinct activation functions tailored for this network: a softsign function with a small residue, an identity function, and a Leaky ReLU function. While these functions demonstrated the desired equivariance properties, they limited the

Vu Thi Huong, Hong-Kun Xu, Nguyen Dong Yen

By applying some techniques of set-valued and variational analysis, we study solution stability of nonhomogeneous split equality problems and nonhomogeneous split feasibility problems, where the constraint sets need not be convex. Necessary and sufficient conditions for the Lipschitz-likeness of the solution maps of the problems are given and illustrated by