December 2024 arXiv papers — page 63

Guwen Lyu, Masahiro Sato

Back-stepping experience replay (BER) is a reinforcement learning technique that can accelerate learning efficiency in reversible environments. BER trains an agent with generated back-stepping transitions of collected experiences and normal forward transitions. However, the original algorithm is designed for a dense-reward environment that does not require c

Xinxi Lyu, Yizhong Wang, Hannaneh Hajishirzi, Pradeep Dasigi

Evaluating the capability of Large Language Models (LLMs) in following instructions has heavily relied on a powerful LLM as the judge, introducing unresolved biases that deviate the judgments from human judges. In this work, we reevaluate various choices for automatic evaluation on a wide range of instruction-following tasks. We experiment with methods that

Chen Duan, Qianyi Jiang, Pei Fu, Jiamin Chen

In the field of scene text spotting, previous OCR methods primarily relied on image encoders and pre-trained text information, but they often overlooked the advantages of incorporating human language instructions. To address this gap, we propose InstructOCR, an innovative instruction-based scene text spotting model that leverages human language instructions

Makoto Nakamura, Takuma Yoshizumi

Blowing-up solutions of Klein-Gordon equations with gauge variant semilinear terms are considered in Friedmann-Lema\'itre-Robertson-Walker spacetimes. Effects of spatial expansion or contraction on the solutions are studied through the scale-function and the curved mass.

Sirwan A. Aula, Tarik A. Rashid

Optimization techniques are pivotal in neural network training, shaping both predictive performance and convergence efficiency. This study introduces Foxtsage, a novel hybrid optimisation approach that integrates the Hybrid FOX-TSA with Stochastic Gradient Descent for training Multi-Layer Perceptron models. The proposed Foxtsage method is benchmarked against

Camille Liotine, Vicky Kalogera, Jeff J. Andrews, Simone S. Bavera

Binaries harboring a millisecond pulsar (MSP) and a black hole (BH) are a key observing target for current and upcoming pulsar surveys. We model the formation and evolution of such binaries in isolation at solar metallicity using the next-generation binary population synthesis code POSYDON. We examine neutron star (NS)-BH binaries where the NS forms first (l

Linh H Nghiem, Aidong A. Ding, Samuel Wu

A recently proposed scheme utilizing local noise addition and matrix masking enables data collection while protecting individual privacy from all parties, including the central data manager. Statistical analysis of such privacy-preserved data is particularly challenging for nonlinear models like logistic regression. By leveraging a relationship between logis

Alireza Pourali, Arian Boukani, Hamzeh Khazaei

Training deep learning models, particularly Transformer-based architectures such as Large Language Models (LLMs), demands substantial computational resources and extended training periods. While optimal configuration and infrastructure selection can significantly reduce associated costs, this optimization requires preliminary analysis tools. This paper intro

Gregor Daiß, Patrick Diehl, Jiakun Yan, John K. Holmen

Dynamic and adaptive mesh refinement is pivotal in high-resolution, multi-physics, multi-model simulations, necessitating precise physics resolution in localized areas across expansive domains. Today's supercomputers' extreme heterogeneity presents a significant challenge for dynamically adaptive codes, highlighting the importance of achieving performance po

Yangkun Chen, Kai Yang, Jian Tao, Jiafei Lyu

Recently, deep Multi-Agent Reinforcement Learning (MARL) has demonstrated its potential to tackle complex cooperative tasks, pushing the boundaries of AI in collaborative environments. However, the efficiency of these systems is often compromised by inadequate sample utilization and a lack of diversity in learning strategies. To enhance MARL performance, we

Xuan Zhao, Xike Xie, Christian S. Jensen

Graph stream summarization refers to the process of processing a continuous stream of edges that form a rapidly evolving graph. The primary challenges in handling graph streams include the impracticality of fully storing the ever-growing datasets and the complexity of supporting graph queries that involve both topological and temporal information. Recent adv

Abdullah Al Abrar Chowdhury, Azizul Hakim Rafi, Adita Sultana, Abdulla All Noman

The escalating challenge of climate change necessitates an urgent exploration of factors influencing carbon emissions. This study contributes to the discourse by examining the interplay of technological, economic, and demographic factors on environmental sustainability. This study investigates the impact of artificial intelligence (AI) innovation, economic g

Aroj Subedi, Pradip Ganesh, Sandip Mishra

Contour map has contour lines that are significant in building a Digital Elevation Model (DEM). During the digitization and pre-processing of contour maps, the contour line intersects with each other or break apart resulting in broken contour segments. These broken segments impose a greater risk while building DEM leading to a faulty model. In this project,

Jiaxin Wu, Yiyang Jiang, Xiao-Yong Wei, Qing Li

Video Corpus Visual Answer Localization (VCVAL) includes question-related video retrieval and visual answer localization in the videos. Specifically, we use text-to-text retrieval to find relevant videos for a medical question based on the similarity of video transcript and answers generated by GPT4. For the visual answer localization, the start and end time

Samantha J Alloo, Kaye S Morgan

X-ray attenuation, phase, and dark-field images provide complementary information. Different experimental techniques can capture these contrast mechanisms, and the corresponding images can be retrieved using various theoretical algorithms. Our previous works developed the Multimodal Intrinsic Speckle-Tracking (MIST) algorithm, which is suitable for multimoda

A. Łusakowski, P. Bogusławski, T. Story

Doping properties of Bi, Cr and Ag dopants in thermoelectric and topological materials PbTe and SnTe are analyzed based on density functional theory calculations in the local density approximations and the large supercell method. In agreement with experiment, in both PbTe and SnTe, Bi is a donor and Ag is an acceptor with a vanishing magnetic moment. In cont

Zhuoyi Shang, Yanwei Liu, Jinxia Liu, Xiaoyan Gu

For general users, training a neural network from scratch is usually challenging and labor-intensive. Fortunately, neural network zoos enable them to find a well-performing model for directly use or fine-tuning it in their local environments. Although current model retrieval solutions attempt to convert neural network models into vectors to avoid complex mul

Vishwesh Sangarya, Jung-Eun Kim

As a strategy for sustainability of deep learning, reusing an existing model by retraining it rather than training a new model from scratch is critical. In this paper, we propose REpresentation Shift QUantifying Estimator (RESQUE), a predictive quantifier to estimate the retraining cost of a model to distributional shifts or change of tasks. It provides a si

Yuxuan Liang, Wentao Zhang, Xinyi Gao, Ling Yang

Efficient training of large-scale heterogeneous graphs is of paramount importance in real-world applications. However, existing approaches typically explore simplified models to mitigate resource and time overhead, neglecting the crucial aspect of simplifying large-scale heterogeneous graphs from the data-centric perspective. Addressing this gap, HGCond intr

Vinayak Arannil, Tomal Deb, Atanu Roy

Early identification of Adverse Drug Events (ADE) is critical for taking prompt actions while introducing new drugs into the market. These ADEs information are available through various unstructured data sources like clinical study reports, patient health records, social media posts, etc. Extracting ADEs and the related suspect drugs using machine learning i

Md Rownak Hossain Chowdhury, Mostafizur Rahman

Addressing the growing demands of artificial intelligence (AI) and data analytics requires new computing approaches. In this paper, we propose a reconfigurable hardware accelerator designed specifically for AI and data-intensive applications. Our architecture features a messaging-based intelligent computing scheme that allows for dynamic programming at runti

Jiaxin Wu, Wengyu Zhang, Xiao-Yong Wei, Qing Li

In this paper, we present our methods and results for the Video-To-Text (VTT) task at TRECVid 2024, exploring the capabilities of Vision-Language Models (VLMs) like LLaVA and LLaVA-NeXT-Video in generating natural language descriptions for video content. We investigate the impact of fine-tuning VLMs on VTT datasets to enhance description accuracy, contextual

Iftekharul Islam, Weizi Li

This report examines the effect of mixed traffic, specifically the variation in robot vehicle (RV) penetration rates, on the fundamental diagrams at unsignalized intersections. Through a series of simulations across four distinct intersections, the relationship between traffic flow characteristics were analyzed. The RV penetration rates were varied from 0% t

Yan-Ying Chen, Nikos Arechiga, Chenyang Yuan, Matthew Hong

Generative AI is revolutionizing engineering design practices by enabling rapid prototyping and manipulation of designs. One example of design manipulation involves taking two reference design images and using them as prompts to generate a design image that combines aspects of both. Real engineering designs have physical constraints and functional requiremen

XueGuang Zhang

In this manuscript, optical quasi-periodic oscillations (QPOs) with 550 day periodicity related to a candidate of sub-pc binary black hole (BBH) system are reported in the reverberation mapped broad line quasar PG 1411+442 but with different line profile of broad H$\alpha$ from that of broad H$\beta$ in its rms spectrum. First, considering sine function to d

Zhenjie Xu, Wenqing Chen, Yi Tang, Xuanying Li

Natural language processing (NLP) has seen remarkable advancements with the development of large language models (LLMs). Despite these advancements, LLMs often produce socially biased outputs. Recent studies have mainly addressed this problem by prompting LLMs to behave ethically, but this approach results in unacceptable performance degradation. In this pap

Ruofei Wang, Hongzhan Lin, Ziyuan Luo, Ka Chun Cheung

Hateful meme detection aims to prevent the proliferation of hateful memes on various social media platforms. Considering its impact on social environments, this paper introduces a previously ignored but significant threat to hateful meme detection: backdoor attacks. By injecting specific triggers into meme samples, backdoor attackers can manipulate the detec

Julian De Vuyst, Stefan Eccles, Philipp A. Hoehn, Josh Kirklin

A significant step towards a rigorous understanding of perturbative gravitational entropy was recently achieved by a series of works showing that a proper accounting of gauge invariance and observer degrees of freedom converts the Type III algebra of QFT observables in a gravitational subregion to a Type II crossed product, whose entropy reduces to the gener

Zhisheng Tang, Mayank Kejriwal

Research on emergent patterns in Large Language Models (LLMs) has gained significant traction in both psychology and artificial intelligence, motivating the need for a comprehensive review that offers a synthesis of this complex landscape. In this article, we systematically review LLMs' capabilities across three important cognitive domains: decision-making b

Ronnie de Souza Santos, Italo Santos, Robson Santos, Cleyton Magalhaes

Technology is a cornerstone of modern life, yet the software engineering field struggles to reflect the diversity of contemporary society. This lack of diversity and inclusivity within the software industry can be traced back to limited representation in software engineering academic settings, where students from underrepresented groups are often stigmatized

Sascha Saralajew, Ashish Rana, Thomas Villmann, Ammar Shaker

Prototype-based classification learning methods are known to be inherently interpretable. However, this paradigm suffers from major limitations compared to deep models, such as lower performance. This led to the development of the so-called deep Prototype-Based Networks (PBNs), also known as prototypical parts models. In this work, we analyze these models wi

Rodolfo Zevallos, Annika Schoene, John E. Ortega

Suicidal ideation is a serious health problem affecting millions of people worldwide. Social networks provide information about these mental health problems through users' emotional expressions. We propose a multilingual model leveraging transformer architectures like mBERT, XML-R, and mT5 to detect suicidal text across posts in six languages - Spanish, Engl

Annika Marie Schoene, John E. Ortega, Rodolfo Joel Zevallos, Laura Haaber Ihle

Recent years have seen a marked increase in research that aims to identify or predict risk, intention or ideation of suicide. The majority of new tasks, datasets, language models and other resources focus on English and on suicide in the context of Western culture. However, suicide is global issue and reducing suicide rate by 2030 is one of the key goals of

Zhongtian Ma, Qiaosheng Zhang, Bocheng Zhou, Yexin Zhang

Despite the growing popularity of graph attention mechanisms, their theoretical understanding remains limited. This paper aims to explore the conditions under which these mechanisms are effective in node classification tasks through the lens of Contextual Stochastic Block Models (CSBMs). Our theoretical analysis reveals that incorporating graph attention mec

Junjie Ye, Yilong Wu, Sixian Li, Yuming Yang

Large language models (LLMs) achieve remarkable advancements by leveraging tools to interact with environments, a critical step toward generalized AI. However, the standard supervised fine-tuning (SFT) approach, which relies on large-scale datasets, often overlooks task-specific characteristics in tool use, leading to performance bottlenecks. To address this

Jiaxin Wu, Chong-Wah Ngo, Xiao-Yong Wei, Qing Li

This year, we explore generation-augmented retrieval for the TRECVid AVS task. Specifically, the understanding of textual query is enhanced by three generations, including Text2Text, Text2Image, and Image2Text, to address the out-of-vocabulary problem. Using different combinations of them and the rank list retrieved by the original query, we submitted four a

Filippo Caleca, Saverio Bocini, Fabio Mezzacapo, Tommaso Roscilde

Spontaneous symmetry breaking (SSB) occurs when a many-body system governed by a symmetric Hamiltonian, and prepared in a symmetry-broken state by the application of a field coupling to its order parameter $O$, retains a finite $O$ value even after the field is switched off. SSB is generally thought to occur only in the thermodynamic limit $N\to \infty$ (for

Xiaobing Chen, Xiangwei Zhou, Songyang Zhang, Mingxuan Sun

Despite some promising results in federated learning using game-theoretical methods, most existing studies mainly employ a one-level game in either a cooperative or competitive environment, failing to capture the complex dynamics among participants in practice. To address this issue, we propose DualGFL, a novel Federated Learning framework with a Dual-level

Hengjia Li, Yang Liu, Yibo Zhao, Haoran Cheng

Recently, 3D generative domain adaptation has emerged to adapt the pre-trained generator to other domains without collecting massive datasets and camera pose distributions. Typically, they leverage large-scale pre-trained text-to-image diffusion models to synthesize images for the target domain and then fine-tune the 3D model. However, they suffer from the t

Trung Hieu Giang, Nguyen Minh Tri, Dang Anh Tuan

We are motivated by studying a boundary-value problem for a class of semilinear degenerate elliptic equations \begin{align}\tag{P}\label{P} \begin{cases} - \Delta_x u - |x|^{2\alpha} \dfrac{\partial^2 u}{\partial y^2} = f(x,y,u) & \textrm{in } \Omega, u = 0 & \textrm{on } \partial \Omega, \end{cases} \end{align} where $x = (x_1, x_2) \in \mathbb{R}^2$, $\Ome

Abdul Wahab, Muqaddar Abbas, Xiaosen Yang, Yuanping Chen

In this work, we theoretically investigate the controllable output probe transmission and group delay in a hybrid cavity magnomechanics (CMM) system. The setup comprises a gain (active) cavity and a passive (loss) cavity, which incorporates an optical parametric amplifier (OPA) and two yttrium iron garnet spheres to facilitate magnon-photon coupling. Unlike

Bin Li, Cong Zhu, Junjie Zhai, Chuanhui Yin

We present a comprehensive computational investigation of electron-phonon interactions in MXH$_3$ hydride compounds, where $M$ represents alkali and post-transition metals, and $X$ denotes 3$d$, 4$d$, and 5$d$ transition metals. Our density functional theory calculations identify 17 dynamically stable compounds. Notably, SrAuH$_3$ and SrZnH$_3$ emerge as the

Jiangnan Fang, Cheng-Tse Liu, Jieun Kim, Yash Bhedaru

In this work, we propose a Multi-LLM summarization framework, and investigate two different multi-LLM strategies including centralized and decentralized. Our multi-LLM summarization framework has two fundamentally important steps at each round of conversation: generation and evaluation. These steps are different depending on whether our multi-LLM decentraliz

Joshua Springer, Gylfi Þór Guðmundsson, Marcel Kyas

A remaining challenge in multirotor drone flight is the autonomous identification of viable landing sites in unstructured environments. One approach to solve this problem is to create lightweight, appearance-based terrain classifiers that can segment a drone's RGB images into safe and unsafe regions. However, such classifiers require data sets of images and

Mohammad Daneshvar, Mandana Delavari

In this paper, we analyze the behavior of a multi-agent system driven by the interactions of agents within a competitive environment. To achieve this, we describe the transition probabilities that underlie the system's stochastic nature. We also derive the Fokker-Planck equations for the density distribution of the number of agents in the system and solve th

Saehyung Lee, Seunghyun Yoon, Trung Bui, Jing Shi

Multimodal large language models (MLLMs) excel at generating highly detailed captions but often produce hallucinations. Our analysis reveals that existing hallucination detection methods struggle with detailed captions. We attribute this to the increasing reliance of MLLMs on their generated text, rather than the input image, as the sequence length grows. To

Suhyun Kang, Jungwon Park, Wonseok Lee, Wonjong Rhee

Cross-Domain Few-Shot Learning~(CDFSL) methods typically parameterize models with task-agnostic and task-specific parameters. To adapt task-specific parameters, recent approaches have utilized fixed optimization strategies, despite their potential sub-optimality across varying domains or target tasks. To address this issue, we propose a novel adaptation mech

Guozhong Zheng, Jiqiang Zhang, Xin Ou, Shengfeng Deng

Behavioral experiments on the ultimatum game (UG) reveal that we humans prefer fair acts, which contradicts the prediction made in orthodox Economics. Existing explanations, however, are mostly attributed to exogenous factors within the imitation learning framework. Here, we adopt the reinforcement learning paradigm, where individuals make their moves aiming

Tiandao Chen, Zhiyuan Huang, Jinyu Pan, Donghan Liu

We demonstrate that by using 1-m-long gas-filled hollow capillary fiber (HCF) with a core diameter of 100 {\mu}m, tunable ultraviolet (UV) dispersive-wave (DW) pulses can be generated in a compact, single-stage set-up driven directly by 40-fs Ti: sapphire laser pulses. By adjusting the gas type and pressure inside the HCF, the central wavelength of the UV DW

Steven M. Gonek, Anurag Sahay

Let $0<\gamma_1\leq \gamma_2 \leq \cdots $ denote the ordinates of nontrivial zeros of the Riemann zeta function with positive imaginary parts. For $c>0$ fixed (but possibly small), $T$ large, and $\gamma_n\leq T$, we call a gap $\gamma_{n+1}-\gamma_n$ between consecutive ordinates ``moderate'' if $\gamma_{n+1}-\gamma_n \geq 2\pi c/\log T$. We investigate wh

J. Ospino, J. L. Hernández-Pastora, A. V. Araujo-Salcedo, L. A. Núñez

This work presents a novel methodology for deriving stationary and axially symmetric solutions to Einstein field equations using the 1+3 tetrad formalism. This approach reformulates the Einstein equations into first order scalar equations, enabling systematic resolution in vacuum scenarios. We derive two distinct solutions in polar and hyperbolic geometries

Jiabao Qiu, Zixuan Ke, Bing Liu

We introduce CLOB, a novel continual learning (CL) paradigm wherein a large language model (LLM) is regarded as a black box. Learning is done incrementally via only verbal prompting. CLOB does not fine-tune any part of the LLM or add any trainable parameters to it. It is particularly suitable for LLMs that are accessible via APIs. We also propose a new CL te

Anas S. Mohammed, Hussein A. Ammar, Krishnendu S. Tharakan, Hesham ElSawy

The cost and limited capacity of fronthaul links pose significant challenges for the deployment of ultra-dense networks (UDNs), specifically for cell-free massive MIMO systems. Hence, cost-effective planning of reliable fronthaul networks is crucial for the future deployment of UDNs. We propose an optimization framework for traffic-aware hybrid fronthaul net

Minseok Son, Inyong Koo, Jinyoung Park, Changick Kim

When trained with severely imbalanced data, deep neural networks often struggle to accurately recognize classes with only a few samples. Previous studies in long-tailed recognition have attempted to rebalance biased learning using known sample distributions, primarily addressing different classification difficulties at the class level. However, these approac

Iiro Kumpulainen, Sebastian Dalleiger, Jilles Vreeken, Nikolaj Tatti

Stochastic Block Models (SBMs) are a popular approach to modeling single real-world graphs. The key idea of SBMs is to partition the vertices of the graph into blocks with similar edge densities within, as well as between different blocks. However, what if we are given not one but multiple graphs that are unaligned and of different sizes? How can we find out

Phu Lai, Wei Xiang, William Damario Lukito, Khoa Tran Phan

Cell-free massive multiple-input multiple-output (CFmMIMO) coordinates a great number of distributed access points (APs) with central processing units (CPUs), effectively reducing interference and ensuring uniform service quality for user equipment (UEs). However, its cooperative nature can result in intense fronthaul signaling between CPUs in large-scale ne

Benny Bao-Sheng Li, Elena Wu, Hins Shao-Xuan Yang, Nicky Yao-Jin Liang

Autonomous driving has garnered significant attention in recent years, especially in optimizing vehicle performance under varying conditions. This paper addresses the challenge of maintaining maximum speed stability in low-speed autonomous driving while following a predefined route. Leveraging reinforcement learning (RL), we propose a novel approach to optim

Chi-Huan Tung, Lijie Ding, Ming-Ching Chang, Guan-Rong Huang

Small-angle scattering (SAS) techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in structural inversion for complex systems, primarily due to the absence of closed-form expressions of scattering functions. To address these challenges, we present a machine learning fram

Ge Gao, Amelia Leon, Andrea Jetten, Jasmine Turner

Educational stakeholders are often particularly interested in sparse, delayed student outcomes, like end-of-year statewide exams. The rare occurrence of such assessments makes it harder to identify students likely to fail such assessments, as well as making it slow for researchers and educators to be able to assess the effectiveness of particular educational

Asang Dani, Shailesh R Sathe

Marathi is one of the most widely used languages in the world. One might expect that the latest advances in NLP research in languages like English reach such a large community. However, NLP advancements in English didn't immediately reach Indian languages like Marathi. There were several reasons for this. They included diversity of scripts used, lack of (pub

Tytti Rintamaki, Harshvardhan J. Pandit

The recently published EU Artificial Intelligence Act (AI Act) is a landmark regulation that regulates the use of AI technologies. One of its novel requirements is the obligation to conduct a Fundamental Rights Impact Assessment (FRIA), where organisations in the role of deployers must assess the risks of their AI system regarding health, safety, and fundame

Chiara Bellotti, Peng-Jie Wong

In this article, we improve the recent work of Hasanalizade, Shen, and Wong by establishing \[ \left| N (T) - \frac{T}{ 2 \pi} \log \left( \frac{T}{2\pi e}\right) \right|\le 0.10076\log T+0.24460\log\log T+8.08344, \] for every $T\ge e$, where $N(T)$ is the number of non-trivial zeros $\rho=\beta+i\gamma$, with $0<\gamma \le T$, of the Riemann zeta-function

Hayder Tirmazi, Ali Tirmazi, Tien Phuoc Tran

We analyze the computational complexity of several popular video games released for the Nintendo Game Boy video game console. We analyze the complexity of generalized versions of four popular Game Boy games: Donkey Kong, Wario Land, Harvest Moon GB, and Mole Mania. We provide original proofs showing that these games are \textbf{NP}-hard. Our proofs rely on K

Davide Martinenghi

In today's data-driven world, algorithms operating with vertically distributed datasets are crucial due to the increasing prevalence of large-scale, decentralized data storage. These algorithms enhance data privacy by processing data locally, reducing the need for data transfer and minimizing exposure to breaches. They also improve scalability, as they can h

Albert Manuel Orozco Camacho, Stefan Horoi, Guy Wolf, Eugene Belilovsky

Combining multiple machine learning models has long been a technique for enhancing performance, particularly in distributed settings. Traditional approaches, such as model ensembles, work well, but are expensive in terms of memory and compute. Recently, methods based on averaging model parameters have achieved good results in some settings and have gained po

Konstantin Donhauser, Kristina Ulicna, Gemma Elyse Moran, Aditya Ravuri

Sparse dictionary learning (DL) has emerged as a powerful approach to extract semantically meaningful concepts from the internals of large language models (LLMs) trained mainly in the text domain. In this work, we explore whether DL can extract meaningful concepts from less human-interpretable scientific data, such as vision foundation models trained on cell

Amitayus Banik, Yanou Cui, Yu-Dai Tsai, Yuhsin Tsai

A phase transition in the dark sector (DS) presents a promising explanation for the stochastic gravitational wave (GW) signals detected in recent observations by Pulsar Timing Arrays (PTAs). Instead of focusing solely on fitting data with phenomenological parameters, we systematically delineate simple, underlying dark sector (DS) models at the microscopic, L

F Sgaramella, D Sirghi, K Toho, F Clozza

The high-precision kaonic neon X-ray transitions measurement performed by the SIDDHARTA-2 collaboration at the DA$Φ$NE collider is reported. Both the X-ray energies and yields for high-n transitions were measured, demonstrating the feasibility of sub-eV Xray spectroscopy for kaonic atoms using low-Z gaseous targets. The measurement provides valuable insights

Yasuo Oda, Kevin Schultz, Leigh Norris, Omar Shehab

The influence of noise on quantum dynamics is one of the main factors preventing current quantum processors from performing accurate quantum computations. Sufficient noise characterization and modeling can provide key insights into the effect of noise on quantum algorithms and inform the design of targeted error protection protocols. However, constructing ef

Jiahe Wan

Compositional understanding allows visual language models to interpret complex relationships between objects, attributes, and relations in images and text. However, most existing methods often rely on hard negative examples and fine-tuning, which can overestimate improvements and are limited by the difficulty of obtaining hard negatives. In this work, we int

Henrique Oyama, Jun Tani

The current study investigates possible neural mechanisms underling autonomous shifts between focus state and mind-wandering by conducting model simulation experiments. On this purpose, we modeled perception processes of continuous sensory sequences using our previous proposed variational RNN model which was developed based on the free energy principle. The

Katarzyna Sadecka, Maciej Bieniek, Paulo E. Faria Junior, Arkadiusz Wójs

We describe here a theory of a quantum dot in an electrically tunable MoSe$_2$/WSe$_2$ heterostructure. Van der Waals heterostructures allow for tuning their electronic properties beyond their monolayer counterparts. We start by determining their electronic structure using density functional theory. We obtain the type-II band alignment and close in energy co

David Amaro-Alcalá

We present a method to characterise qubit gates. Utilising the supermap formalism, we create a scheme for deterministic single-qubit gate analysis. Our approach introduces a new twirling process that is applied directly through fixed circuits. This method removes the requirement to average over random gates. The results enhance randomised benchmarking techni

Curicaveri Palomares-Chavez, Ivan Alvarez-Rios, Francisco S. Guzman

We simulate the formation of Fuzzy Dark Matter (FDM) cores in the presence of a Black Hole (BH) to explore whether BHs can serve as seeds for FDM core condensation. Our analysis is based on the core-condensation via the kinetic relaxation process for random initial conditions of the FDM. In a generic scenario the BH merges with a pre-collapsed mini-cluster f

Qingbo Chu, Youjun Lu, Shenghua Yu

Binary neutron stars (BNSs) detected in the Milky Way have the total masses distributing narrowly around $\sim2.6-2.7M_\odot$, while the BNS merger GW190425 detected via gravitational wave has a significantly larger mass ($\sim3.4M_\odot$). This difference is not well understood, yet. In this paper, we investigate the BNS spin evolution via an improved binar

Mariam Ayman, Sohaila Kandil, Alaa Moheb, Ahmed Abdelkader

The current research conducts a comprehensive analysis of citation networks focusing on publications by authors affiliated with Egyptian institutions. Leveraging the Semantic Scholar platform and its API, a citation network and a co-authorship network graphs are constructed to visualize the interconnections among these publications and their authors. This is

Delaram Golpayegani, Harshvardhan J. Pandit, Dave Lewis

The European Union's Artificial Intelligence Act (AI Act) requires providers and deployers of high-risk AI applications to register their systems into the EU database, wherein the information should be represented and maintained in an easily-navigable and machine-readable manner. Given the uptake of open data and Semantic Web-based approaches for other EU re

Giuseppe Bargagnati, Elena Bogliolo

We prove the vanishing of bounded cohomology of the groups acting on trees with almost prescribed local actions $G(F, F')$, where $F<F'$ are finite permutation groups such that $F'$ is 2-transitive. By contrast, when $F'$ is not 2-transitive, we prove that the second bounded cohomology with real coefficients of the groups $G(F, F')$ is infinite dimensional.

Ammar N. Abbas, Csaba Beleznai

TalkWithMachines aims to enhance human-robot interaction by contributing to interpretable industrial robotic systems, especially for safety-critical applications. The presented paper investigates recent advancements in Large Language Models (LLMs) and Vision Language Models (VLMs), in combination with robotic perception and control. This integration allows r

Anindya Sarkar, Alex DiChristofano, Sanmay Das, Patrick J. Fowler

Tenant evictions threaten housing stability and are a major concern for many cities. An open question concerns whether data-driven methods enhance outreach programs that target at-risk tenants to mitigate their risk of eviction. We propose a novel active geospatial search (AGS) modeling framework for this problem. AGS integrates property-level information in

Desmond Chan, Bart De Keijzer, Tobias Galla, Stefanos Leonardos

We study the exploration-exploitation trade-off for large multiplayer coordination games where players strategise via Q-Learning, a common learning framework in multi-agent reinforcement learning. Q-Learning is known to have two shortcomings, namely non-convergence and potential equilibrium selection problems, when there are multiple fixed points, called Qua

Luíze D'Urso

In this paper, we study the Cremona action on the nef cone of $S_n$, the blowup of $\P^2$ in $n$ very general points, with $n\ge9$. We construct and describe a rational polyhedral fundamental domain of the nef cone for $n=9$ with respect to this action. In the case $n\ge10$, we give a rational polyhedral fundamental domain of the $K_{S_n}$-negative part of t

Alejandro Saavedra, Octavio Fierro, Michael Gammon, Robert B. Mann

Since the derivation of a well-defined $D\to4$ limit for 4D Einstein-Gauss-Bonnet (4DEGB) gravity coupled to a scalar field, there has been considerable interest in testing it as an alternative to Einstein's general theory of relativity. Past work has shown that this theory hosts interesting compact star solutions which are smaller in radius than a Schwarzsc

Paul W. Oxby

The Savitzky-Golay FIR digital filter is based on a least-squares polynomial fit to a sample of equally spaced data. The polynomial fit gives the filter the ability to preserve moments of features in the data like peak width. However the S-G filter is not generally regarded as having a sound statistical basis. This puts the filter in the category of smoothin

Kristóf Bérczi, Tamás Király, Yutaro Yamaguchi, Yu Yokoi

The famous Ryser--Brualdi--Stein conjecture asserts that every $k \times k$ Latin square contains a partial transversal of size $k-1$. Since its appearance, the conjecture has attracted significant interest, leading to several proposed generalizations. One of the most notable of these, by Aharoni, Kotlar, and Ziv, conjectures that $k$ disjoint common bases o

Lorena Velazquez-Ibarra, Juan Barranco

The diverse applications of whispering gallery modes in spherical microresonators are strongly related to the sphere size and material composition. Their design should therefore be optimized to ensure that parameters such as the quality factor and the free spectral range are maximized. Because of the imminent capacity crisis of the optical communication syst

Ido Sivan-Sevilla, Parthav Poudel

The collapse of social contexts has been amplified by digital infrastructures but surprisingly received insufficient attention from Web privacy scholars. Users are persistently identified within and across distinct Web contexts, in varying degrees, through and by different websites and trackers, losing the ability to maintain a fragmented identity. To system

Dongjin Kim, Daniel S. King, Peichen Zhong, Bingqing Cheng

Accurate modeling of long-range forces is critical in atomistic simulations, as they play a central role in determining the properties of materials and chemical systems. However, standard machine learning interatomic potentials (MLIPs) often rely on short-range approximations, limiting their applicability to systems with significant electrostatics and disper

Tobias Ekholm, Pietro Longhi, Vivek Shende

We count holomorphic curves in complex 3-space with boundaries on three special Lagrangian solid tori. The count is valued in the HOMFLYPT skein module of the union of the tori. Using 1-parameter families of curves at infinity, we derive three skein valued operator equations which must annihilate the count, and which dequantize to a mirror of the geometry. W

Sahil Wadhwa, Chengtian Xu, Haoming Chen, Aakash Mahalingam

The automatic generation of counter-speech (CS) is a critical strategy for addressing hate speech by providing constructive and informed responses. However, existing methods often fail to generate high-quality, impactful, and scalable CS, particularly across diverse linguistic contexts. In this paper, we propose a novel methodology to enhance CS generation b

Kajjana Boonpalit, Nongnuch Artrith

Electrocatalytic water splitting, which produces hydrogen and oxygen through water electrolysis, is a promising method for generating renewable, carbon-free alternative fuels. However, its widespread adoption is hindered by the high costs of Pt cathodes and IrO$_{x}$/RuO$_{x}$ anode catalysts. In the search for cost-effective alternatives, barium titanate (B

Beatriz Esteves, Harshvardhan J. Pandit, Georg P. Krog, Paul Ryan

The EU GDPR is a landmark regulation that introduced several rights for individuals to obtain information and control how their personal data is being processed, as well as receive a copy of it. However, there are gaps in the effective use of rights due to each organisation developing custom methods for rights declaration and management. Simultaneously, ther

Bram Vanroy

This paper introduces Fietje, a family of small language models (SLMs) specifically designed for the Dutch language. The model is based on Phi 2, an English-centric model of 2.7 billion parameters. Fietje demonstrated competitive results with larger language models upon its release. A core emphasis of this work is transparency and reproducibility: Fietje is

Sushobhan Chatterjee, Sijia Geng

This paper studies the parameter sensitivity of grid-forming inverters to Hopf bifurcations to address oscillatory instability. An analytical expression for the sensitivity of the stability margin is derived based on the normal vector to the bifurcation hypersurface. We identify the most effective control parameters through comprehensive analysis. In particu

Akash Deep, Abootaleb Shirvani, Chris Monico, Svetlozar Rachev

Because of the theoretical challenges posed by the Efficient Market Hypothesis to technical analysis, the effectiveness of technical indicators in high-frequency trading remains inadequately explored, particularly at the minute-level frequency, where effects of the microstructure of the market dominate. This study evaluates the integration of traditional tec

Pou-Chun Kung, Xianling Zhang, Katherine A. Skinner, Nikita Jaipuria

Photorealistic 3D scene reconstruction plays an important role in autonomous driving, enabling the generation of novel data from existing datasets to simulate safety-critical scenarios and expand training data without additional acquisition costs. Gaussian Splatting (GS) facilitates real-time, photorealistic rendering with an explicit 3D Gaussian representat

Sushobhan Chatterjee, Sijia Geng

With the rapid adoption of emerging inverter-based resources, it is crucial to understand their dynamic interactions across the network and ensure stability. This paper proposes a systematic and efficient method to determine the optimal allocation of grid-forming and grid-following inverters in power networks. The approach leverages a novel unified grid-form

Yuqing Wang, Mika V. Mäntylä, Jesse Nyyssölä, Ke Ping

Modern software systems produce vast amounts of logs, serving as an essential resource for anomaly detection. Artificial Intelligence for IT Operations (AIOps) tools have been developed to automate the process of log-based anomaly detection for software systems. Three practical challenges are widely recognized in this field: high data labeling costs, evolvin

Angela Mastrianni, Hope Twede, Aleksandra Sarcevic, Jeremiah Wander

Generative AI has the potential to transform knowledge work, but further research is needed to understand how knowledge workers envision using and interacting with generative AI. We investigate the development of generative AI tools to support domain experts in knowledge work, examining task delegation and the design of human-AI interactions. Our research fo

Aakash Mahalingam, Vinesh Kumar Gande, Aman Chadha, Vinija Jain

Retrieval-Augmented Generation (RAG) systems have become pivotal in leveraging vast corpora to generate informed and contextually relevant responses, notably reducing hallucinations in Large Language Models. Despite significant advancements, these systems struggle to efficiently process and retrieve information from large datasets while maintaining a compreh