May 2023 arXiv papers — page 65

F. Rizzuti, R. Hirschi, W. D. Arnett, C. Georgy

Our knowledge of stellar evolution is driven by one-dimensional (1D) simulations. 1D models, however, are severely limited by uncertainties on the exact behaviour of many multi-dimensional phenomena occurring inside stars, affecting their structure and evolution. Recent advances in computing resources have allowed small sections of a star to be reproduced wi

Yuxiao Li, Santiago Mazuelas, Yuan Shen

Radio frequency (RF)-based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements. Soft range information (SRI) offers a promising alternative for highly accurate localization that gives all probable range values rather than a single estimate of distance. We propose a deep learning

Hasan Nayir, Erhan Karakoca, Güneş Karabulut Kurt, Ali Görçin

Terahertz (THz) frequencies are important for next generation wireless systems due to the advantages in terms of large available bandwidths. On the other hand, the limited range due to high attenuation in these frequencies can be overcome via densely installed heterogeneous networks also utilizing UAVs in a three-dimensional hyperspace. Yet, THz communicatio

Haonan Qiu, Zeyin Song, Yanqi Chen, Munan Ning

Biologically inspired spiking neural networks (SNNs) have garnered considerable attention due to their low-energy consumption and spatio-temporal information processing capabilities. Most existing SNNs training methods first integrate output information across time steps, then adopt the cross-entropy (CE) loss to supervise the prediction of the average repre

Bogdan Damski

We discuss the dynamics of field configurations encoded in the certain class of electric (magnetic) dipole-charged states in the Proca theory of the real massive vector field. We construct such states so as to ensure that the long distance structure of the mean electromagnetic field in them is initially set by the formula describing the electromagnetic field

Martin Moriamé, Timoteo Carletti

Synchronization is an ubiquitous phenomenon in dynamical systems of networked oscillators. While it is often a goal to achieve, in some context one would like to decrease it, e.g., although synchronization is essential to the good functioning of brain dynamics, hyper-synchronization can induce problems like epilepsy seizures. Motivated by this problem, schol

Benjamin Kraner, Nicolò Vallarano, Claudio J. Tessone, Caspar Schwarz-Schilling

This paper presents a study of the Poof-of-Stake (PoW) Ethereum consensus protocol, following the recent switch from Proof-of-Work (PoS) to Proof-of-Stake within Merge upgrade. The new protocol has resulted in reduced energy consumption and a shift in economic incentives, but it has also introduced new threat sources such as chain reorganizations and balanci

Rowel Atienza

State of the art (SOTA) neural text to speech (TTS) models can generate natural-sounding synthetic voices. These models are characterized by large memory footprints and substantial number of operations due to the long-standing focus on speech quality with cloud inference in mind. Neural TTS models are generally not designed to perform standalone speech synth

Yuxiao Li, Santiago Mazuelas, Yuan Shen

Ultra-wideband (UWB)-based techniques, while becoming mainstream approaches for high-accurate positioning, tend to be challenged by ranging bias in harsh environments. The emerging learning-based methods for error mitigation have shown great performance improvement via exploiting high semantic features from raw data. However, these methods rely heavily on fu

Vaishnavi Himakunthala, Andy Ouyang, Daniel Rose, Ryan He

Despite exciting recent results showing vision-language systems' capacity to reason about images using natural language, their capacity for video reasoning remains under-explored. We motivate framing video reasoning as the sequential understanding of a small number of keyframes, thereby leveraging the power and robustness of vision-language while alleviating

Hyunwoo Kang, Jaeho Shin, Jaewook Shin, Youngseok Jang

In this study, a new concept of a wheelchair-towing robot for the facile electrification of manual wheelchairs is introduced. The development of this concept includes the design of towing robot hardware and an autonomous driving algorithm to ensure the safe transportation of patients to their intended destinations inside the hospital. We developed a novel do

Guotao Wang, Chenglizhao Chen, Aimin Hao, Hong Qin

To date, the widely adopted way to perform fixation collection in panoptic video is based on a head-mounted display (HMD), where users' fixations are collected while wearing an HMD to explore the given panoptic scene freely. However, this widely-used data collection method is insufficient for training deep models to accurately predict which regions in a give

Giovanni F. Gronchi, Giulio Baù, Clara Grassi

We consider the Keplerian distance $d$ in the case of two elliptic orbits, i.e. the distance between one point on the first ellipse and one point on the second one, assuming they have a common focus. The absolute minimum $d_{\rm min}$ of this function, called MOID or orbit distance in the literature, is relevant to detect possible impacts between two objects

Umberto Cappellazzo, Muqiao Yang, Daniele Falavigna, Alessio Brutti

The ability to learn new concepts sequentially is a major weakness for modern neural networks, which hinders their use in non-stationary environments. Their propensity to fit the current data distribution to the detriment of the past acquired knowledge leads to the catastrophic forgetting issue. In this work we tackle the problem of Spoken Language Understan

Abel L. Peirson, Michela Negro, Ioannis Liodakis, Riccardo Middei

We report the first $> 99\%$ confidence detection of X-ray polarization in BL Lacertae. During a recent X-ray/$\gamma$-ray outburst, a 287 ksec observation (2022 November 27-30) was taken using the Imaging X-ray Polarimetry Explorer ({\it IXPE}), together with contemporaneous multiwavelength observations from the Neil Gehrels {\it Swift} observatory and {\it

Kangqiang Li, Yuxuan Wang

This paper gives two theoretical results on estimating low-rank parameter matrices for linear models with multivariate responses. We first focus on robust parameter estimation of low-rank multi-task learning with heavy-tailed data and quantization scenarios. It comprises two cases: quantization under heavy-tailed responses and quantization with both heavy-ta

Mounir Bensalem, Francisco Carpio, Admela Jukan

Serverless computing has emerged as a new execution model which gained a lot of attention in cloud computing thanks to the latest advances in containerization technologies. Recently, serverless has been adopted at the edge, where it can help overcome heterogeneity issues, constrained nature and dynamicity of edge devices. Due to the distributed nature of edg

Nicolas Spyratos

We propose a novel database model whose basic structure is a labeled, directed, acyclic graph with a single root, in which the nodes represent the data sets of an application and the edges represent functional relationships among the data sets. We call such a graph an application context or simply context. The query language of a context consists of two type

Jiajie Mei

We propose studying a new representation of on-shell Anti de Sitter (AdS) amplitude in Mellin Momentum space, where it encodes all the dynamical information in Cosmological Correlators. At tree level, we demonstrate that this amplitude has a similar analytic structure as the S-matrix, with residues of poles made up of on-shell lower-point amplitudes. We use

Jasenka Dizdarevic, Marc Michalke, Admela Jukan

The Message Queuing Telemetry Transport (MQTT) protocol is one of the most widely used IoT protocol solutions. In this work, we are especially interested in open-source MQTT Broker implementations (such as Mosquitto, EMQX, RabbitMQ, VerneMQ, and HiveMQ). To this end, we engineer a network testbed to experimentally benchmark the performance of these implement

Balsam Alkouz, Athman Bouguettaya, Abdallah Lakhdari

We propose a novel failure-sentient framework for swarm-based drone delivery services. The framework ensures that those drones that experience a noticeable degradation in their performance (called soft failure) and which are part of a swarm, do not disrupt the successful delivery of packages to a consumer. The framework composes a weighted continual federate

Yuxiao Li, Santiago Mazuelas, Yuan Shen

Received waveforms contain rich information for both range information and environment semantics. However, its full potential is hard to exploit under multipath and non-line-of-sight conditions. This paper proposes a deep generative model (DGM) for simultaneous range error mitigation and environment identification. In particular, we present a Bayesian model

Thejan Rajapakshe, Rajib Rana, Sara Khalifa, Berrak Sisman

Speech Emotion Recognition (SER) is a critical enabler of emotion-aware communication in human-computer interactions. Recent advancements in Deep Learning (DL) have substantially enhanced the performance of SER models through increased model complexity. However, designing optimal DL architectures requires prior experience and experimental evaluations. Encour

Tom Suys, Sunyou Hwang, Guido C. H. E. de Croon, Bart D. W. Remes

We present a novel controller for fixed-wing UAVs that enables autonomous soaring in an orographic wind field, extending flight endurance. Our method identifies soaring regions and addresses position control challenges by introducing a target gradient line (TGL) on which the UAV achieves an equilibrium soaring position, where sink rate and updraft are balanc

A. Deltuva, D. Jurčiukonis

Nonlocal nucleon-nucleus optical potential with rotational quadrupole deformation enabling the excitation of the ${}^{24}\mathrm{Mg}(2^+)$ state is developed; it fits well the proton-${}^{24}\mathrm{Mg}$ elastic and inelastic differential cross section in the beam energy range from 30 to 45 MeV per nucleon. The inelastic deuteron-${}^{24}\mathrm{Mg}$ scatter

Konrad K. Dabrowski, Peter Jonsson, Sebastian Ordyniak, George Osipov

Constraint satisfaction problems form a nicely behaved class of problems that lends itself to complexity classification results. From the point of view of parameterized complexity, a natural task is to classify the parameterized complexity of MinCSP problems parameterized by the number of unsatisfied constraints. In other words, we ask whether we can delete

Xuekai Zhu, Biqing Qi, Kaiyan Zhang, Xinwei Long

While large language models (LLMs) excel in various natural language processing tasks, their huge size and the inaccessibility of parameters present challenges for practical deployment. Previous studies try to distill task-specific ability from LLMs to smaller models, using data synthesis and chain-of-thought (CoT) fine-tuning. However, synthetic CoT data of

Ruiqi Liu, Leyi Zhang, Ruyue Yu-Ngok Li, Marco Di Renzo

With the standardization and commercialization completed at an unforeseen pace for 5th generation (5G) wireless networks, researchers, engineers and executives from the academia and industry have turned their attention to new candidate technologies that can support next generation wireless networks enabling more advanced capabilities in emerging scenarios. E

Shoaib M. Sami, Nasser M. Nasrabadi, Raghuveer Rao

One of the major obstacles in designing an automatic target recognition (ATR) algorithm, is that there are often labeled images in one domain (i.e., infrared source domain) but no annotated images in the other target domains (i.e., visible, SAR, LIDAR). Therefore, automatically annotating these images is essential to build a robust classifier in the target d

Yuxiao Li, Santiago Mazuelas, Yuan Shen

Localization systems based on ultra-wide band (UWB) measurements can have unsatisfactory performance in harsh environments due to the presence of non-line-of-sight (NLOS) errors. Learning-based methods for error mitigation have shown great performance improvement via directly exploiting the wideband waveform instead of handcrafted features. However, these me

N Md Jubair Basha, Gopinath Ganapathy, Mohammed Moulana

Component-based software development (CBD) is a methodology that has been embraced by the software industry to accelerate development, save costs and timelines, minimize testing requirements, and boost quality and output. Compared to the conventional software development approach, this led to the system's development being completed more quickly. By choosing

Truong Giang Nguyen, Thanh Le-Cong, Hong Jin Kang, Ratnadira Widyasari

With the increasing reliance on Open Source Software, users are exposed to third-party library vulnerabilities. Software Composition Analysis (SCA) tools have been created to alert users of such vulnerabilities. SCA requires the identification of vulnerability-fixing commits. Prior works have proposed methods that can automatically identify such vulnerabilit

Jade Garcia Bourrée, Erwan Le Merrer, Gilles Tredan, Benoît Rottembourg

Algorithmic auditing has become central to platform accountability under frameworks such as the AI Act and the Digital Services Act. In practice, this obligation is discharged through dedicated Audit APIs. This architecture creates a paradox: the entity under scrutiny controls the evaluation interface. A platform facing legal sanctions can serve a compliant

Kexin Jin, Chenguang Liu, Jonas Latz

The Stochastic Gradient Langevin Dynamics (SGLD) are popularly used to approximate Bayesian posterior distributions in statistical learning procedures with large-scale data. As opposed to many usual Markov chain Monte Carlo (MCMC) algorithms, SGLD is not stationary with respect to the posterior distribution; two sources of error appear: The first error is in

M. A. Moreno-Frías, J. C. Rosales

In this work we will show that if $F$ is a positive integer, then ${\mathrm{Sat}}(F)=\{S\mid S \mbox{ is a saturated numerical semigroup with Frobenius number } F\}$ is a covariety. As a consequence, we present two algorithms: one that computes ${\mathrm{Sat}}(F),$ and the other which computes all the elements of ${\mathrm{Sat}}(F)$ with a fixed genus. If $X

Yuxiao Li, Zhiming Wang, Yuan Shen

Learning-based methods for blind single image super resolution (SISR) conduct the restoration by a learned mapping between high-resolution (HR) images and their low-resolution (LR) counterparts degraded with arbitrary blur kernels. However, these methods mostly require an independent step to estimate the blur kernel, leading to error accumulation between ste

Kim Jie Koh, Fehmi Cirak

The efficient representation of random fields on geometrically complex domains is crucial for Bayesian modelling in engineering and machine learning. Today's prevalent random field representations are either intended for unbounded domains or are too restrictive in terms of possible field properties. Because of these limitations, techniques leveraging the his

Ayush K. Varshney, Sonakshi Garg, Arka Ghosh, Sargam Gupta

Federated learning (FL) is a distributed machine learning strategy that enables participants to collaborate and train a shared model without sharing their individual datasets. Privacy and fairness are crucial considerations in FL. While FL promotes privacy by minimizing the amount of user data stored on central servers, it still poses privacy risks that need

Arseny Moskvichev, Ky-Vinh Mai

We propose a new large-scale (nearly a million questions) ultra-long-context (more than 50,000 words average document length) reading comprehension dataset. Using GPT 3.5, we summarized each scene in 1,500 hand-curated fiction books from Project Gutenberg, which resulted in approximately 150 scene-level summaries per book. After that, we created a number of

Taiki Miyanishi, Daichi Azuma, Shuhei Kurita, Motoki Kawanabe

We present a novel task for cross-dataset visual grounding in 3D scenes (Cross3DVG), which overcomes limitations of existing 3D visual grounding models, specifically their restricted 3D resources and consequent tendencies of overfitting a specific 3D dataset. We created RIORefer, a large-scale 3D visual grounding dataset, to facilitate Cross3DVG. It includes

Ryosuke Sonoda

Class imbalance and group (e.g., race, gender, and age) imbalance are acknowledged as two reasons in data that hinder the trade-off between fairness and utility of machine learning classifiers. Existing techniques have jointly addressed issues regarding class imbalance and group imbalance by proposing fair over-sampling techniques. Unlike the common oversamp

Zhibin Li, Jingmin Liang, Song He, Li Li

The cumulants of baryon number fluctuations serve as a good probe for experimentally exploring the QCD phase diagram at finite density, giving rise to characteristic fluctuation patterns associated with a possible critical endpoint (CEP). We compute the higher-order baryon number susceptibilities at finite temperature and baryon chemical potential using a ho

Yiting Qu, Xinyue Shen, Xinlei He, Michael Backes

State-of-the-art Text-to-Image models like Stable Diffusion and DALLE$\cdot$2 are revolutionizing how people generate visual content. At the same time, society has serious concerns about how adversaries can exploit such models to generate unsafe images. In this work, we focus on demystifying the generation of unsafe images and hateful memes from Text-to-Imag

Yuxiao Li, Santiago Mazuelas, Yuan Shen

Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for general-purpose generative modeling of data distributions. However, it remains challenging for existing methods to address advanced conditional generative problems without annotations, which can enable multiple applications like image-to-image translation and imag

Anke Tang, Yong Luo, Han Hu, Fengxiang He

This paper studies multiparty learning, aiming to learn a model using the private data of different participants. Model reuse is a promising solution for multiparty learning, assuming that a local model has been trained for each party. Considering the potential sample selection bias among different parties, some heterogeneous model reuse approaches have been

Aleksei Cherkasov, Kirill M. Gerke, Aleksey Khlyupin

Structures are abundant in both natural and human-made environments and usually studied in the form of images or scattering patterns. To characterize structures a huge variety of descriptors is available spanning from porosity to radial and correlation functions. In addition to morphological structural analysis, such descriptors are necessary for stochastic

Inge S. Helland

An alternative approach towards quantum theory is described, and tentative attempts to connect his approach to special and general relativity are discussed. Important concepts are gauge groups and information/entropy connected to some physical systems. Some recent results on information in connection to black holes are touched upon, and it is indicated how e

Xiaolong Chen, Xin Qi, Chunguang Su, Yuan He

The superconducting linear accelerator is a highly flexiable facility for modern scientific discoveries, necessitating weekly reconfiguration and tuning. Accordingly, minimizing setup time proves essential in affording users with ample experimental time. We propose a trend-based soft actor-critic(TBSAC) beam control method with strong robustness, allowing th

Charles Vanwynsberghe, Jiguang He, Chongwen Huang, Merouane Debbah

Holographic multiple-input multiple-output (MIMO) is deemed as a promising technique beyond massive MIMO, unleashing near-field communications, localization, and sensing in the next-generation wireless networks. Semi-continuous surface with densely packed elements brings new opportunities for increased spatial degrees of freedom (DoFs) and spectrum efficienc

Amar Kumar Banerjee, Mahendranath Paul

In this paper we study $I^K$-convergence of functions with respect to probabilistic norm $\nu$ which is a generalization of $I^*_{\nu}$-convergence in probabilistic norm spaces. We also study on $I^K$-Cauchy functions and $I^K$-limit points with respect to probabilistic norm $\nu$ in the same space.

Da Yu, Sivakanth Gopi, Janardhan Kulkarni, Zinan Lin

Text prediction models, when used in applications like email clients or word processors, must protect user data privacy and adhere to model size constraints. These constraints are crucial to meet memory and inference time requirements, as well as to reduce inference costs. Building small, fast, and private domain-specific language models is a thriving area o

Yuanzhen Xie, Tao Xie, Mingxiong Lin, WenTao Wei

In most current research, large language models (LLMs) are able to perform reasoning tasks by generating chains of thought through the guidance of specific prompts. However, there still exists a significant discrepancy between their capability in solving complex reasoning problems and that of humans. At present, most approaches focus on chains of thought (CO

Yong Yang, Qiong Chen, Yuan Feng, Tianlin Huang

Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set and then apply the knowledge to segment target objects in a query set. However, the extracted knowledge is insufficient to cope with the variable intra-class differences since the knowledge is obtained from a few samples in the sup

Alexandre Pasquiou, Yair Lakretz, Bertrand Thirion, Christophe Pallier

Two fundamental questions in neurolinguistics concerns the brain regions that integrate information beyond the lexical level, and the size of their window of integration. To address these questions we introduce a new approach named masked-attention generation. It uses GPT-2 transformers to generate word embeddings that capture a fixed amount of contextual in

Leonardo Ranaldi, Elena Sofia Ruzzetti, Davide Venditti, Dario Onorati

Cheap-to-Build Very Large-Language Models (CtB-LLMs) with affordable training are emerging as the next big revolution in natural language processing and understanding. These CtB-LLMs are democratizing access to trainable Very Large-Language Models (VLLMs) and, thus, may represent the building blocks of many NLP systems solving downstream tasks. Hence, a litt

Yang-Guang Shan, Zhen-Qiang Yin, Shuang Wang, Wei Chen

All kinds of device loopholes give rise to a great obstacle to practical secure quantum key distribution (QKD). In this article, inspired by the original side-channel-secure protocol [Physical Review Applied 12, 054034 (2019)], a new QKD protocol called phase-coding side-channel-secure (PC-SCS) protocol is proposed. This protocol can be immune to all uncorre

Yi Liu, Gelei Deng, Zhengzi Xu, Yuekang Li

Large Language Models (LLMs), like ChatGPT, have demonstrated vast potential but also introduce challenges related to content constraints and potential misuse. Our study investigates three key research questions: (1) the number of different prompt types that can jailbreak LLMs, (2) the effectiveness of jailbreak prompts in circumventing LLM constraints, and

Peitian Zhang, Zheng Liu, Yujia Zhou, Zhicheng Dou

Recently, generative retrieval emerges as a promising alternative to traditional retrieval paradigms. It assigns each document a unique identifier, known as DocID, and employs a generative model to directly generate the relevant DocID for the input query. A common choice for DocID is one or several natural language sequences, e.g. the title or n-grams, so th

Yufei Xie, Shaoman Li, Penghui Lin

This dissertation presents a methodology for recording speed climbing training sessions with multiple cameras and annotating the videos with relevant data, including body position, hand and foot placement, and timing. The annotated data is then analyzed using deep learning techniques to create a standard dataset of speed climbing training videos. The results

Takyoung Kim, Jamin Shin, Young-Ho Kim, Sanghwan Bae

Most task-oriented dialogue (TOD) benchmarks assume users that know exactly how to use the system by constraining the user behaviors within the system's capabilities via strict user goals, namely "user familiarity" bias. This data bias deepens when it combines with data-driven TOD systems, as it is impossible to fathom the effect of it with existing static e

Yi-Rui Yang, Chang-Wei Shi, Wu-Jun Li

Byzantine-robust distributed learning (BRDL), in which computing devices are likely to behave abnormally due to accidental failures or malicious attacks, has recently become a hot research topic. However, even in the independent and identically distributed (i.i.d.) case, existing BRDL methods will suffer from a significant drop on model accuracy due to the l

Chi Yanling, Xu Yuyu, Liu Huiying, Wu Xiaoxiang

This work proposed a novel deep registration pipeline for 3D CT and 2D U/S kidney scans of free breathing, which consists of a feature network, and a 3D-2D CNN-based registration network. The feature network has handcraft texture feature layers to reduce the semantic gap. The registration network is encoder-decoder structure with loss of feature-image-motion

Jana Vatter, Ruben Mayer, Hans-Arno Jacobsen

Graph Neural Networks (GNNs) are an emerging research field. This specialized Deep Neural Network (DNN) architecture is capable of processing graph structured data and bridges the gap between graph processing and Deep Learning (DL). As graphs are everywhere, GNNs can be applied to various domains including recommendation systems, computer vision, natural lan

Clément Erignoux, Linjie Zhao

We derive the stationary fluctuations for the Facilitated Exclusion Process (FEP) in one dimension in the symmetric, weakly asymmetric and asymmetric cases. Our proof relies on the mapping between the FEP and the zero-range process, and extends the strategy in \cite{erignoux2022mapping}, where hydrodynamic limits were derived for the FEP, to its stationary f

Bin Yang, Xingche Guo, Ji Meng Loh, Qinxia Wang

Electroencephalogram (EEG) provides noninvasive measures of brain activity and is found to be valuable for diagnosis of some chronic disorders. Specifically, pre-treatment EEG signals in alpha and theta frequency bands have demonstrated some association with anti-depressant response, which is well-known to have low response rate. We aim to design an integrat

Svjetlana Fajfer, Jernej Fesel Kamenik, Arman Korajac, Nejc Košnik

We present constraints on the left-handed dimension-6 interactions that contribute to semileptonic and leptonic decays of $K$, $D$, pions and to nuclear beta decay. We employ the flavour covariant description of the effective couplings, identify universal CP phases of New Physics and derive constraints from decay rates and CP-odd quantities. As a result, we

Xiangnan Chen, Qian Xiao, Juncheng Li, Duo Dong

Visual Relation Extraction (VRE) is a powerful means of discovering relationships between entities within visually-rich documents. Existing methods often focus on manipulating entity features to find pairwise relations, yet neglect the more fundamental structural information that links disparate entity pairs together. The absence of global structure informat

Aishwarya Venkataramanan, Assia Benbihi, Martin Laviale, Cedric Pradalier

Recent works show that the data distribution in a network's latent space is useful for estimating classification uncertainty and detecting Out-of-distribution (OOD) samples. To obtain a well-regularized latent space that is conducive for uncertainty estimation, existing methods bring in significant changes to model architectures and training procedures. In t

Amir Fernández Ouaridi

We prove that a transposed Poisson algebra is simple if and only if its associated Lie bracket is simple. Consequently, any simple finite-dimensional transposed Poisson algebra over an algebraically closed field of characteristic zero is trivial. Similar results are obtained for transposed Poisson superalgebras. Furthermore, we show that the Kantor double of

Ramachandran S

When G denotes a graph, the unlabeled subgraph obtained by deleting a vertex from G is called a card of G and the collection of all cards of G is the deck of G. A graph having the same deck as G is called a hypomorph of G. A graph is called reconstructible if it is isomorphic to all its hypomorphs. Reconstruction Conjecture claims that all graphs are reconst

Sabine Doppler, Philip L. Lederer, Joachim Schöberl, Henry von Wahl

We present a discontinuous Galerkin method for moist atmospheric dynamics, with and without warm rain. By considering a combined density for water vapour and cloud water, we avoid the need to model and compute a source term for condensation. We recover the vapour and cloud densities by solving a pointwise non-linear problem each time step. Consequently, we e

Francesco Capolupo, Antonio Rinalducci

This paper presents the preliminary design of the descent and landing trajectory of the ESA Argonaut lunar lander. The mission scenario and driving system constraints are presented and accounted for in the design of a fuel-optimal trajectory that includes divert capabilities, as required to achieve a safe landing. A sub-optimal descent and landing trajectory

Anirudh Pradhan, Gopikant Goswami, Syamala Krishnannair

We have developed a Bianchi I cosmological model of the universe in $f(R,T)$ gravity theory which fit good with the present day scenario of accelerating universe. The model displays transition from deceleration in the past to the acceleration at the present. As in the $\Lambda$CDM model, we have defined the three energy parameters $\Omega_m$, $\Omega_{\mu}$

Richard G. Forbes, Sergey V. Filippov, Anatoly G. Kolosko, Eugeni O. Popov

This paper forms part of a long-term project to put field electron emission (FE) onto a better scientific basis, by seeking reliable quantitative agreement between theory and experiment, especially as regards emission-current values. The main paper aims are: (1) to respond to remarks made in recent papers; (2) to restate the thinking behind our 2022 methodol

Shohei Higashiyama, Hiroki Ouchi, Hiroki Teranishi, Hiroyuki Otomo

Geoparsing is a fundamental technique for analyzing geo-entity information in text. We focus on document-level geoparsing, which considers geographic relatedness among geo-entity mentions, and presents a Japanese travelogue dataset designed for evaluating document-level geoparsing systems. Our dataset comprises 200 travelogue documents with rich geo-entity i

Mingzhu Zhang, Ruiping Yin, Zhen Yang, Yipeng Wang

Multi-task learning has been widely applied in computational vision, natural language processing and other fields, which has achieved well performance. In recent years, a lot of work about multi-task learning recommender system has been yielded, but there is no previous literature to summarize these works. To bridge this gap, we provide a systematic literatu

Li-Xin Zhang

Applications of CAR for balancing continuous covariates remain comparatively rare, especially in multi-treatment clinical trials, and the theoretical properties of multi-treatment CAR have remained largely elusive for decades. In this paper, we consider a general framework of CAR procedures for multi-treatment clinal trials which can balance general covariat

Juan Montes Maestre, Yinwei Du, Ronan Hinchet, Stelian Coros

Stripe patterns are ubiquitous in nature and everyday life. While the synthesis of these patterns has been thoroughly studied in the literature, their potential to control the mechanics of structured materials remains largely unexplored. In this work, we introduce Differentiable Stripe Patterns -- a computational approach for automated design of physical sur

Weifeng Chen, Yatai Ji, Jie Wu, Hefeng Wu

Recent advances in text-to-image (T2I) diffusion models have enabled impressive image generation capabilities guided by text prompts. However, extending these techniques to video generation remains challenging, with existing text-to-video (T2V) methods often struggling to produce high-quality and motion-consistent videos. In this work, we introduce Control-A

Mingjin Zhang, Jiamin Xu, Chengyu He, Wenteng Shang

Synthetic aperture radar (SAR) is prevalent in the remote sensing field but is difficult to interpret in human visual perception. Recently, SAR-to-optical (S2O) image conversion methods have provided a prospective solution for interpretation. However, since there is a huge domain difference between optical and SAR images, they suffer from low image quality a

Francesco Pavese

A $4$-general set in ${\rm PG}(n,q)$ is a set of points of ${\rm PG}(n,q)$ spanning the whole ${\rm PG}(n,q)$ and such that no four of them are on a plane. Such a pointset is said to be complete if it is not contained in a larger $4$-general set of ${\rm PG}(n, q)$. In this paper upper and lower bounds for the size of the largest and the smallest complete $4

John Ellis, Natsumi Nagata, Keith A. Olive, Jiaming Zheng

Electroweak loop corrections to the matrix elements for the spin-independent scattering of cold dark matter particles on nuclei are generally small, typically below the uncertainty in the local density of cold dark matter. However, as shown in this paper, there are instances in which the electroweak loop corrections are relatively large, and change significa

Yuanfeng Ji, Yatao Bian, Guoji Fu, Peilin Zhao

The regulation of various cellular processes heavily relies on the protein complexes within a living cell, necessitating a comprehensive understanding of their three-dimensional structures to elucidate the underlying mechanisms. While neural docking techniques have exhibited promising outcomes in binary protein docking, the application of advanced neural arc

L. Bonavera, M. M. Cueli, J. González-Nuevo, J. M. Casas

This paper is the third in a series on submillimeter galaxy magnification bias, focusing on the tomographic scenario. It refines the methodology used to constrain the halo occupation distribution model and cosmological parameters within a flat $\Lambda$CDM model, using updated data. The study aims to optimize CPU time, explore strategies for analyzing differ

Marcos M. Cueli, Joaquín González-Nuevo, Laura Bonavera, Andrea Lapi

The main goal of this work, the second in a three-paper series, is to test the impact of a methodological improvement in measuring the magnification bias signal on a sample of submillimeter galaxies and its implications for constraining cosmological parameters. The analysis considers the angular cross-correlation function between a foreground sample of GAMA

J. González-Nuevo, L. Bonavera, M. M. Cueli, D. Crespo

In this study, we aim to develop a new methodology to estimate the cross-correlation function and uncertainties and apply it to the analysis of magnification bias in galaxy surveys. We adopt a new methodological framework that uses a statistically rigorous approach to obtain more robust measurements for constraining cosmological parameters. This strategy inv

Pin-Jung Chen, Po-Yan Tseng

A primordial black hole (PBH) with mass $10^{-15}\leq M_{\rm PBH}/M_{\odot}\leq 10^{-10}$ is currently beyond the sensitivity of both microlensing and black hole (BH) evaporation methods. A novel scenario has been proposed: When a PBH with mass $10^{-14}\leq M_{\rm PBH}/M_{\odot}\leq 10^{-11}$ transits through a white dwarf (WD) made up of carbon and oxygen,

Liyan Kang, Luyang Huang, Ningxin Peng, Peihao Zhu

We present a large-scale video subtitle translation dataset, BigVideo, to facilitate the study of multi-modality machine translation. Compared with the widely used How2 and VaTeX datasets, BigVideo is more than 10 times larger, consisting of 4.5 million sentence pairs and 9,981 hours of videos. We also introduce two deliberately designed test sets to verify

Qi Jia, Haifeng Tang, Kenny Q. Zhu

Changing speaker names consistently throughout a dialogue should not affect its meaning and corresponding outputs for text generation from dialogues. However, pre-trained language models, serving as the backbone for dialogue-processing tasks, have shown to be sensitive to nuances. This may result in unfairness in real-world applications. No comprehensive ana

V. Guadilla, A. Algora, M. Estienne, M. Fallot

The shape of the electron spectrum emitted in $\beta$ decay carries a wealth of information about nuclear structure and fundamental physics. In spite of that, few dedicated measurements have been made of $\beta$-spectrum shapes. In this work we present a newly developed detector for $\beta$ electrons based on a telescope concept. A thick plastic scintillator

Minki Kang, Wooseok Han, Sung Ju Hwang, Eunho Yang

Emotional Text-To-Speech (TTS) is an important task in the development of systems (e.g., human-like dialogue agents) that require natural and emotional speech. Existing approaches, however, only aim to produce emotional TTS for seen speakers during training, without consideration of the generalization to unseen speakers. In this paper, we propose ZET-Speech,

Daiki Saito, Tomohiro Harada, Yasutaka Koga, Chul-Moon Yoo

We investigate the probability distribution of the spins of primordial black holes (PBHs) formed in the universe dominated by a perfect fluid with the linear equation of state $p=w\rho$, where $p$ and $\rho$ are the pressure and energy density of the fluid, respectively. We particularly focus on the parameter region $0<w\leq 1/3$ since the larger value of th

Danqing Wang, Lei Li

Large language models (LLMs) have demonstrated their potential to refine their generation based on their own feedback. However, the feedback from LLM itself is often inaccurate, thereby limiting its benefits. In this paper, we propose Study Assistant for Large LAnguage Model (SALAM), a novel framework with an auxiliary agent to assist the main LLM in learnin

Lev Vaidman

In a recent paper, Hance, Rarity and Ladyman [Phys. Rev. Res. {\bf 5}, 023048 (2023)] criticized recent proposals connecting weak values and the past of a quantum particle. I argue that their conclusion follows from a conceptual error in understanding the approach to the past of the particle they discuss.

Mohammad Hossein Zarei, Mohsen Rahmani Haghighi

While local unitary transformations are used for identifying quantum states which are in the same topological class, non-local unitary transformations are also important for studying the transition between different topological classes. In particular, it is an important task to find suitable non-local transformations that systematically sweep different topol

Zae Myung Kim, David E. Taylor, Dongyeop Kang

Conversational implicatures are pragmatic inferences that require listeners to deduce the intended meaning conveyed by a speaker from their explicit utterances. Although such inferential reasoning is fundamental to human communication, recent research indicates that large language models struggle to comprehend these implicatures as effectively as the average

Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Senzhang Wang

Many real-world graph learning tasks require handling dynamic graphs where new nodes and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs. To alleviate the problem, continual graph learning methods are proposed. However,

Chengpeng Hu, Ziming Wang, Jialin Liu, Junyi Wen

As one of the core parts of flexible manufacturing systems, material handling involves storage and transportation of materials between workstations with automated vehicles. The improvement in material handling can impulse the overall efficiency of the manufacturing system. However, the occurrence of dynamic events during the optimisation of task arrangements

Zhanwen Zhou, Hankz Hankui Zhuo, Xiaowu Zhang, Qiyuan Deng

Routing is a crucial and time-consuming stage in modern design automation flow for advanced technology nodes. Great progress in the field of reinforcement learning makes it possible to use those approaches to improve the routing quality and efficiency. However, the scale of the routing problems solved by reinforcement learning-based methods in recent studies

Antoine Klauser, Bernhard Strasser, Wolfgang Bogner, Lukas Hingerl

A novel method for fast and high-resolution metabolic imaging, called ECcentric Circle ENcoding TRajectorIes for Compressed sensing (ECCENTRIC), has been developed and implemented at 7 Tesla MRI. ECCENTRIC is a non-Cartesian spatial-spectral encoding method optimized to accelerate magnetic resonance spectroscopic imaging (MRSI) with high signal-to-noise at u