February 2024 arXiv papers — page 53

Jialun Pei, Diandian Guo, Jingyang Zhang, Manxi Lin

Scene graph generation (SGG) of surgical procedures is crucial in enhancing holistically cognitive intelligence in the operating room (OR). However, previous works have primarily relied on multi-stage learning, where the generated semantic scene graphs depend on intermediate processes with pose estimation and object detection. This pipeline may potentially c

Théophile Champion, Howard Bowman, Dimitrije Marković, Marek Grześ

Active inference is a leading theory of perception, learning and decision making, which can be applied to neuroscience, robotics, psychology, and machine learning. Active inference is based on the expected free energy, which is mostly justified by the intuitive plausibility of its formulations, e.g., the risk plus ambiguity and information gain / pragmatic v

Celio Trois, Luciana Didonet Del Fabro, Vladimir A. Baulin

Posidonia oceanica is a protected endemic seagrass of Mediterranean sea that fosters biodiversity, stores carbon, releases oxygen, and provides habitat to numerous sea organisms. Leveraging augmented research, we collected a comprehensive dataset of 174 features compiled from diverse data sources. Through machine learning analysis, we discovered the existenc

Marcel Ausloos

In this paper, I report some investigation discussing team selection, whence hierarchy, through ranking indicators, for example when measuring professional cyclist team's sportive value, in particular in multistage races. A logical, it seems, constraint is introduced on the riders: they must finish the race. Several new indicators are defined, justified, and

Ramon Ruiz-Dolz, Joaquin Taverner, John Lawrence, Chris Reed

Some of the major limitations identified in the areas of argument mining, argument generation, and natural language argument analysis are related to the complexity of annotating argumentatively rich data, the limited size of these corpora, and the constraints that represent the different languages and domains in which these data is annotated. To address thes

Pietro Giovanni Bizzaro, Elena Della Valentina, Maurizio Napolitano, Nadia Mana

In this paper, we propose a new annotation scheme to classify different types of clauses in Terms-and-Conditions contracts with the ultimate goal of supporting legal experts to quickly identify and assess problematic issues in this type of legal documents. To this end, we built a small corpus of Terms-and-Conditions contracts and finalized an annotation sche

Jingyao Li, Pengguang Chen, Xuan Ju, Hong Xu

Thanks to advances in deep learning techniques, Human Pose Estimation (HPE) has achieved significant progress in natural scenarios. However, these models perform poorly in artificial scenarios such as painting and sculpture due to the domain gap, constraining the development of virtual reality and augmented reality. With the growth of model size, retraining

Xiang Chang, Zihe Chen, Xiaoyan Dong, Yuxin Cai

Interacting with pedestrians understandably and efficiently is one of the toughest challenges faced by autonomous vehicles (AVs) due to the limitations of current algorithms and external human-machine interfaces (eHMIs). In this paper, we design eHMIs based on gestures inspired by the most popular method of interaction between pedestrians and human drivers.

Vuong D. Nguyen, Shishir K. Shah

Long-term Person Re-Identification (LRe-ID) aims at matching an individual across cameras after a long period of time, presenting variations in clothing, pose, and viewpoint. In this work, we propose CCPA: Contrastive Clothing and Pose Augmentation framework for LRe-ID. Beyond appearance, CCPA captures body shape information which is cloth-invariant using a

Seiji Gobara, Hidetaka Kamigaito, Taro Watanabe

Education that suits the individual learning level is necessary to improve students' understanding. The first step in achieving this purpose by using large language models (LLMs) is to adjust the textual difficulty of the response to students. This work analyzes how LLMs can implicitly adjust text difficulty between user input and its generated text. To cond

Sukila khatun, Amar Kumar Banerjee

In this paper, using the concept of natural density, we have introduced the notion of rough statistical convergence which is an extension of the notion of rough convergence in a partial metric space. We have defined the set of rough statistical limit points of a sequence in a partial metric space and proved that this set is closed and bounded. Finally, we ha

Julien Barrier, Minsoo Kim, Roshan Krishna Kumar, Na Xin

Extensive efforts have been undertaken to combine superconductivity and the quantum Hall effect so that Cooper-pair transport between superconducting electrodes in Josephson junctions is mediated by one-dimensional edge states. This interest has been motivated by prospects of finding new physics, including topologically-protected quasiparticles, but also ext

Royi Jacobovic, John Yehuda Levy, Eilon Solan

A Bayesian game is said to have nested information if the players are ordered, and each player knows the types of all players that follow her in that order. We prove that all multiplayer Bayesian games with finite actions spaces, bounded payoffs,Polish type spaces, and nested information admit a Bayesian equilibrium.

Marko Maljkovic, Gustav Nilsson, Nikolas Geroliminis

Motivated by the omnipresence of hierarchical structures in many real-world applications, this study delves into the intricate realm of bi-level games, with a specific focus on exploring local Stackelberg equilibria as a solution concept. While existing literature offers various methods tailored to specific game structures featuring one leader and multiple f

Michiel van den Berg, Dorin Bucur

For an open set $\Om \subset \R^2$ let $\lambda(\Om)$ denote the bottom of the spectrum of the Dirichlet Laplacian acting in $L^2(\Om)$. Let $w_\Om$ be the torsion function for $\Om$, and let $\|.\|_p$ denote the $L^p$ norm. It is shown that there exist {$\eta_1>0,\eta_2>0$} such that { (i) $\|w_{\Om}\|_{\infty} \lambda(\Om)\ge 1+\eta_1$ for any non-empty, o

Cong Li, Yang Wang, Jianfeng Zhang, Hongxiong Liu

For several decades, it was widely believed that a non-interacting disordered electronic system could only undergo an Anderson metal-insulator transition due to Anderson localization. However, numerous recent theoretical works have predicted the existence of a disorder-driven non-Anderson phase transition that differ from Anderson localization. The frustrati

Suma Al-Hunaishi, Anna Blin, Nao Harada, Pauline Perrin

Yttrium orthosilicate (Y2SiO5 - YSO) is one of the most promising crystals to host rare-earth (RE) ions for quantum technologies applications. In this matrix, they indeed exhibit narrow optical and spin linewidths that can be exploited to develop quantum memories or quantum information processing capabilities. In this paper, we propose a new method to grow R

A. Del Giacco, F. Maspero, V. Levati, M. Vitali

The fabrication and integration of high-quality structures of Yttrium Iron Garnet (YIG) is critical for magnonics.Films with excellent properties are obtained only on single crystal Gadolinium Gallium Garnet (GGG) substrates using high-temperature processes. The subsequent realization of magnonic structures via lithography and etching is not straightforward

Álvaro Peralta Conde

This manuscript explores how a seemingly straightforward inquiry, emerging from the widely accepted semiclassical description of laser-matter interaction, concretely from a well-known adiabatic technique as Coherent Population Return (CPR), can pose a challenge to our comprehension of a fundamental principle in Physics like energy conservation. Throughout ou

Michela Rigoselli

Pulsars are rapidly spinning neutron stars, that radiate at the expense of their strong magnetic field and their high surface temperature. Five decades of multi-wavelength observations showed a large variety of physical parameters, such as the spin period, the magnetic field and the age, and of observational properties, especially in the radio and X-ray band

Priyanshul Govil, Hemang Jain, Vamshi Krishna Bonagiri, Aman Chadha

Large Language Models (LLMs) often inherit biases from the web data they are trained on, which contains stereotypes and prejudices. Current methods for evaluating and mitigating these biases rely on bias-benchmark datasets. These benchmarks measure bias by observing an LLM's behavior on biased statements. However, these statements lack contextual considerati

Zhen-hua Zhao, Hong-Yu Shi, Yan Shao

In this paper we have studied the realizations of the popular TM1 neutrino mixing and neutrino $\mu$-$\tau$ reflection symmetry (which are well motivated from the neutrino oscillation data and lead to interesting phenomenological consequences) in the most minimal seesaw model with a pseudo-Dirac pair of right-handed neutrinos, and their consequences for lept

Jiayu Li, Aixin Sun, Weizhi Ma, Peijie Sun

Recommender systems have been widely used for various scenarios, such as e-commerce, news, and music, providing online contents to help and enrich users' daily life. Different scenarios hold distinct and unique characteristics, calling for domain-specific investigations and corresponding designed recommender systems. Therefore, in this paper, we focus on foo

Alexey A. Sharapov

Poisson electrodynamics is the low-energy limit of a rank-one noncommutative gauge theory. It admits a closed formulation in terms of a Poisson structure on the space-time manifold and reproduces ordinary classical electrodynamics in the commutative limit. In this paper, we address and solve the problem of minimal coupling to charged matter fields with a pro

Shanshan Luo, Mengchen Shi, Wei Li, Xueli Wang

In observational studies, covariates with substantial missing data are often omitted, despite their strong predictive capabilities. These excluded covariates are generally believed not to simultaneously affect both treatment and outcome, indicating that they are not genuine confounders and do not impact the identification of the average treatment effect (ATE

Michal Zamboj, Jakub Řada

Illumination of scenes is usually generated in computer graphics using polygonal meshes. In this paper, we present a geometric method using projections. Starting from an implicit polynomial equation of a surface in 3-D or a curve in 2-D, we provide a semi-algebraic representation of each part of the construction. To solve polynomial condition systems and fin

Matthias Blau, Mbambu Kakona, George Thompson

There are very few explicit evaluations of path integrals for topological gauge theories in more than 3 dimensions. Here we provide such a calculation for the path integral representation of the Ray-Singer Torsion of a flat connection on a vector bundle on base manifolds that are themselves $S^{1}$ bundles of any dimension. The calculation relies on a suitab

Qiaoyu Wang, Mingwei Ma, Binbin Ruan, Menghu Zhou

A series of tetragonal Fe$_{1-x}$Co${_x}$Se single crystals with a complete Co doping range (0$\leq$x$\leq$0.52) up to its solid solubility limit in FeSe have been grown by an eutectic AlCl${_3}$/KCl molten salt method. The typical lateral size of as-grown Fe$_{1-x}$Co${_x}$Se single crystals is 1$-$5 mm. The chemical composition and homogeneity of the cryst

Xinying Li, Yaqi Zhang, Shengjun Fan

This study focuses on a multidimensional backward stochastic differential equation (BSDE) with a general random terminal time $\tau$ taking values in $[0,+\infty]$. The generator $g$ satisfies a stochastic monotonicity condition in the first unknown variable $y$ and a stochastic Lipschitz continuity condition in the second unknown variable $z$, and it can ha

Lu Yu, Arnak Dalalyan

We study the problem of sampling from a target probability density function in frameworks where parallel evaluations of the log-density gradient are feasible. Focusing on smooth and strongly log-concave densities, we revisit the parallelized randomized midpoint method and investigate its properties using recently developed techniques for analyzing its sequen

Dimitri von Rütte, Sotiris Anagnostidis, Gregor Bachmann, Thomas Hofmann

Concept guidance has emerged as a cheap and simple way to control the behavior of language models by probing their hidden representations for concept vectors and using them to perturb activations at inference time. While the focus of previous work has largely been on truthfulness, in this paper we extend this framework to a richer set of concepts such as app

Johann Haselberger, Maximilian Böhle, Bernhard Schick, Steffen Müller

Technological advancements focus on developing comfortable and acceptable driving characteristics in autonomous vehicles. Present driving functions predominantly possess predefined parameters, and there is no universally accepted driving style for autonomous vehicles. While driving may be technically safe and the likelihood of road accidents is reduced, pass

Ossama Kullie

The measurement of the tunneling time-delay is hotly debated and remains controversial. In previous works, we showed that a model that accurately describes the time-delay measured by the attoclock experiment in adiabatic and nonadiabatic field calibrations. In the present work, we show that the tunneling time reveals a universal behavior with disentangled co

Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang

Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. Advanced FSSL methods predominantly focus on training a single model on each client. However, this approach could lead to a discrepancy between the objective functions of labeled and u

Desalegn Wolde Feyisa, Salim Abdi, Rene van Veldhoven, Nicola Calabretta

Managing insertion losses, polarizations and device footprint is crucial in developing large-scale photonic integrated circuits (PICs). This paper presents a solution to these critical challenges by designing a semiconductor optical amplifier (SOA) in the O-band with reduced polarization sensitivity, leveraging the ultra-compact InP Membrane on Silicon (IMOS

Yumin Kim, Heejae Suh, Mingi Kim, Dongyeon Won

Sarcasm is a way of verbal irony where someone says the opposite of what they mean, often to ridicule a person, situation, or idea. It is often difficult to detect sarcasm in the dialogue since detecting sarcasm should reflect the context (i.e., dialogue history). In this paper, we introduce a new dataset for the Korean dialogue sarcasm detection task, KoCoS

Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh, Lars Krupp

In human activity recognition (HAR), the availability of substantial ground truth is necessary for training efficient models. However, acquiring ground pressure data through physical sensors itself can be cost-prohibitive, time-consuming. To address this critical need, we introduce Text-to-Pressure (T2P), a framework designed to generate extensive ground pre

Biswajit Das, Ramesh Hariharasubramanian

A graph $G = (V, E)$ is word-representable, if there exists a word w over the alphabet V such that for letters ${x, y} \in V$ , $x$ and $y$ alternate in $w$ if and only if $xy \in E$. In this paper, we prove that any non-empty word-representable graph can be represented by a word containing no non-trivial squares. This result provides a positive answer to th

Song Tong, Kai Mao, Zhen Huang, Yukun Zhao

Leveraging the synergy between causal knowledge graphs and a large language model (LLM), our study introduces a groundbreaking approach for computational hypothesis generation in psychology. We analyzed 43,312 psychology articles using a LLM to extract causal relation pairs. This analysis produced a specialized causal graph for psychology. Applying link pred

Tomer Shushi

At its microscopic level, the universe follows the laws of quantum mechanics. Focusing on the quantum trajectories of particles as followed from the hydrodynamical formulation of quantum mechanics, we propose that under general requirements, quantum systems follow a disrupted version of the gradient descent model, a basic machine learning algorithm, where th

Shunsuke Nishimura, Moeta Tsukamoto, Kento Sasaki, Kensuke Kobayashi

Quantum diamond microscopy (QDM), which employs nitrogen-vacancy (NV) center ensembles, is a promising approach to quantitatively imaging magnetic fields with both high resolution that approaches the diffraction limit and a wide field of view. The commonly adopted setups of QDM capture the photoluminescence through transparent diamonds, which inevitably enta

Rohini Ramadas

We describe an application of tropical moduli spaces to complex dynamics. A post-critically finite branched covering $\varphi$ of $S^2$ induces a pullback map on the Teichm\"uller space of complex structures of $S^2$; this descends to an algebraic correspondence on the moduli space of point-configurations of $\mathbb{C}\mathbb{P}^1$. We make a case for study

Olivia Reade, Jozef Širáň

With the help of the theory of holomorphic and anti-holomorphic differentials, G. A. Jones [Chiral covers of hypermaps, Ars Math. Contemp. 8 (2015), 425-431] proved that every regular hypermap of a non-spherical type is covered by an infinite number of orientably-regular but chiral hypermaps of the same type. We present a different proof of the same result f

Denis Belomestny, Mark Podolskij, Shi-Yuan Zhou

This paper delves into a nonparametric estimation approach for the interaction function within diffusion-type particle system models. We introduce two estimation methods based upon an empirical risk minimization. Our study encompasses an analysis of the stochastic and approximation errors associated with both procedures, along with an examination of certain

Vasily Kostumov, Bulat Nutfullin, Oleg Pilipenko, Eugene Ilyushin

Vision-Language Models like GPT-4, LLaVA, and CogVLM have surged in popularity recently due to their impressive performance in several vision-language tasks. Current evaluation methods, however, overlook an essential component: uncertainty, which is crucial for a comprehensive assessment of VLMs. Addressing this oversight, we present a benchmark incorporatin

Anna Lentz, Daniel Wachsmuth

We investigate time-dependent optimization problems in fractional Sobolev spaces with the sparsity promoting $L^p$-pseudo norm for $0<p<1$ in the objective functional. In order to avoid computing the fractional Laplacian on the time-space cylinder $I\times \Omega$, we introduce an auxiliary function $w$ on $\Omega$ that is an upper bound for the function $u\

Lucas Kook, Anton Rask Lundborg

Valid statistical inference is crucial for decision-making but difficult to obtain in supervised learning with multimodal data, e.g., combinations of clinical features, genomic data, and medical images. Multimodal data often warrants the use of black-box algorithms, for instance, random forests or neural networks, which impede the use of traditional variable

Renyi Mao, Qingshan Xu, Peng Zheng, Ye Wang

Coordinate-based neural implicit representation or implicit fields have been widely studied for 3D geometry representation or novel view synthesis. Recently, a series of efforts have been devoted to accelerating the speed and improving the quality of the coordinate-based implicit field learning. Instead of learning heavy MLPs to predict the neural implicit v

M. Ivette Gomes

The Portuguese School of Extremes and Applications is nowadays well recognised by the international scientific community, and in my opinion, the organisation of a NATO Advanced Study Institute on Statistical Extremes and Applications, which took place at Vimeiro in the summer of 1983, was a landmark for the international recognition of the group. The dynamic

S. Nallamuthu, Andrea Dzubinska, K. Arun, Vimaljith A. R

Gd2GaSbO7 and Gd2InSbO7 pyrochlore compounds exhibit quantum fluctuations in a spin ice-like state. These compounds have not been adequately studied based on the concept of magnetic frustration. Here, we have synthesised and characterised Gd2GaSbO7 and Gd2InSbO7 to investigate their ground-state magnetic properties. We confirmed the cubic pyrochlore structur

Ilan Meltzer, Yoav Sagi

Clock interferometry refers to the coherent splitting of a clock into two different paths and recombining in a way that reveals the proper time difference between them. Unlike the comparison of two separate clocks, this approach allows testing how non-flat spacetime influences quantum coherence. Atomic clocks are currently the most accurate time keeping devi

Kosuke Matsuzaki, Masaya Taniguchi, Kentaro Inui, Keisuke Sakaguchi

We introduce a Japanese Morphology dataset, J-UniMorph, developed based on the UniMorph feature schema. This dataset addresses the unique and rich verb forms characteristic of the language's agglutinative nature. J-UniMorph distinguishes itself from the existing Japanese subset of UniMorph, which is automatically extracted from Wiktionary. On average, the Wi

Milena Tsvetkova, Taha Yasseri, Niccolo Pescetelli, Tobias Werner

From fake social media accounts and generative artificial intelligence chatbots to trading algorithms and self-driving vehicles, robots, bots and algorithms are proliferating and permeating our communication channels, social interactions, economic transactions and transportation arteries. Networks of multiple interdependent and interacting humans and intelli

Anuradha Welivita, Pearl Pu

This paper investigates the empathetic responding capabilities of ChatGPT, particularly its latest iteration, GPT-4, in comparison to human-generated responses to a wide range of emotional scenarios, both positive and negative. We employ a rigorous evaluation methodology, involving a between-groups study with 600 participants, to evaluate the level of empath

Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu

Retrieval-augmented language models (RALMs) have demonstrated significant potential in refining and expanding their internal memory by retrieving evidence from external sources. However, RALMs will inevitably encounter knowledge conflicts when integrating their internal memory with external sources. Knowledge conflicts can ensnare RALMs in a tug-of-war betwe

Piotr Rybak

Pre-trained language models have revolutionized the natural language understanding landscape, most notably BERT (Bidirectional Encoder Representations from Transformers). However, a significant challenge remains for low-resource languages, where limited data hinders the effective training of such models. This work presents a novel approach to bridge this gap

Haoran He, Chenjia Bai, Ling Pan, Weinan Zhang

Learning a generalist embodied agent capable of completing multiple tasks poses challenges, primarily stemming from the scarcity of action-labeled robotic datasets. In contrast, a vast amount of human videos exist, capturing intricate tasks and interactions with the physical world. Promising prospects arise for utilizing actionless human videos for pre-train

Jan Lange

We generalize a recent result of Pavic--Schreieder regarding the surjectivity of the obstruction morphism defined in [PS23]. As a consequence of this result, we show that geometrically (retract) rational varieties over a Laurent field of characteristic 0, which admit a strictly semi-stable model, have trivial Chow group of zero-cycles. Our key new ingredient

Jeovanny de Jesus Muentes Acevedo

It is well known that the presence of horseshoes leads to positive entropy. If our goal is to construct a continuous map with infinite entropy, we can consider an infinite sequence of horseshoes, ensuring an unbounded number of legs. Estimating the exact values of both the metric mean dimension and mean Hausdorff dimension for a homeomorphism is a challengin

Ningyu Xu, Qi Zhang, Menghan Zhang, Peng Qian

Probing and enhancing large language models' reasoning capacity remains a crucial open question. Here we re-purpose the reverse dictionary task as a case study to probe LLMs' capacity for conceptual inference. We use in-context learning to guide the models to generate the term for an object concept implied in a linguistic description. Models robustly achieve

Felix Wilms, Alejandro Bañón Navarro, Thomas Windisch, Sergey Bozhenkov

We present the first nonlinear, gyrokinetic, radially global simulation of a discharge of the Wendelstein 7-X-like stellarator (W7-X), including kinetic electrons, an equilibrium radial electric field, as well as electromagnetic and collisional effects. By comparison against flux-tube and full-flux-surface simulations, we assess the impact of the equilibrium

Roxana Petcu, Subhadeep Maji

Recent developments in natural language processing (NLP) have highlighted the need for substantial amounts of data for models to capture textual information accurately. This raises concerns regarding the computational resources and time required for training such models. This paper introduces Semantics for data SAliency in Model performance Estimation (SeSaM

Cen-You Li, Olaf Duennbier, Marc Toussaint, Barbara Rakitsch

Sequential learning methods, such as active learning and Bayesian optimization, aim to select the most informative data for task learning. In many applications, however, data selection is constrained by unknown safety conditions, motivating the development of safe learning approaches. A promising line of safe learning methods uses Gaussian processes to model

Zhaoyang Wang, Bo Hu, Mingyang Zhang, Jie Li

Existing free-energy guided No-Reference Image Quality Assessment (NR-IQA) methods still suffer from finding a balance between learning feature information at the pixel level of the image and capturing high-level feature information and the efficient utilization of the obtained high-level feature information remains a challenge. As a novel class of state-of-

Daniel Holmberg, Manu Airaksinen, Viviana Marchi, Andrea Guzzetta

Reliable methods for the neurodevelopmental assessment of infants are essential for early detection of problems that may need prompt interventions. Spontaneous motor activity, or 'kinetics', is shown to provide a powerful surrogate measure of upcoming neurodevelopment. However, its assessment is by and large qualitative and subjective, focusing on visually i

Fengqi Liang, Baigong Zheng, Liqin Zhao, Guorui Zhou

Live streaming recommender system is specifically designed to recommend real-time live streaming of interest to users. Due to the dynamic changes of live content, improving the timeliness of the live streaming recommender system is a critical problem. Intuitively, the timeliness of the data determines the upper bound of the timeliness that models can learn.

Hao Li, Mengqi Huang, Lei Zhang, Bo Hu

GAN-based image attribute editing firstly leverages GAN Inversion to project real images into the latent space of GAN and then manipulates corresponding latent codes. Recent inversion methods mainly utilize additional high-bit features to improve image details preservation, as low-bit codes cannot faithfully reconstruct source images, leading to the loss of

Giovanni Cherubin, Boris Köpf, Andrew Paverd, Shruti Tople

Machine learning models trained with differentially-private (DP) algorithms such as DP-SGD enjoy resilience against a wide range of privacy attacks. Although it is possible to derive bounds for some attacks based solely on an $(\varepsilon,\delta)$-DP guarantee, meaningful bounds require a small enough privacy budget (i.e., injecting a large amount of noise)

Francisco J. R. Ruiz, Tuomas Laakkonen, Johannes Bausch, Matej Balog

A key challenge in realizing fault-tolerant quantum computers is circuit optimization. Focusing on the most expensive gates in fault-tolerant quantum computation (namely, the T gates), we address the problem of T-count optimization, i.e., minimizing the number of T gates that are needed to implement a given circuit. To achieve this, we develop AlphaTensor-Qu

Dmitry Logashenko, Alexander Litvinenko, Raul Tempone, Ekaterina Vasilyeva

We investigate the applicability of the well-known multilevel Monte Carlo (MLMC) method to the class of density-driven flow problems, in particular the problem of salinisation of coastal aquifers. As a test case, we solve the uncertain Henry saltwater intrusion problem. Unknown porosity, permeability and recharge parameters are modelled by using random field

Jungwoo Chae, Hyunin Cho, Sooyeon Go, Kyungmook Choi

Semantic image synthesis (SIS) aims to generate realistic images that match given semantic masks. Despite recent advances allowing high-quality results and precise spatial control, they require a massive semantic segmentation dataset for training the models. Instead, we propose to employ a pre-trained unconditional generator and rearrange its feature maps ac

Marcel Reutzel, G. S. Matthijs Jansen, Stefan Mathias

Excitons -- two-particle correlated electron-hole pairs -- are the dominant low-energy optical excitation in the broad class of semiconductor materials, which range from classical silicon to perovskites, and from two-dimensional to organic materials. Recently, the study of excitons has been brought on a new level of detail by the application of photoemission

Yunchong Song, Siyuan Huang, Xinbing Wang, Chenghu Zhou

Graph pooling compresses graph information into a compact representation. State-of-the-art graph pooling methods follow a hierarchical approach, which reduces the graph size step-by-step. These methods must balance memory efficiency with preserving node information, depending on whether they use node dropping or node clustering. Additionally, fixed pooling r

Ruba Abu Khurma, Esraa Alhenawi, Malik Braik, Fatma A. Hashim

It is of paramount importance to enhance medical practices, given how important it is to protect human life. Medical therapy can be accelerated by automating patient prediction using machine learning techniques. To double the efficiency of classifiers, several preprocessing strategies must be adopted for their crucial duty in this field. Feature selection (F

Xinyu Zhou, Pinxue Guo, Lingyi Hong, Jinglun Li

Reference features from a template or historical frames are crucial for visual object tracking. Prior works utilize all features from a fixed template or memory for visual object tracking. However, due to the dynamic nature of videos, the required reference historical information for different search regions at different time steps is also inconsistent. Ther

Lirong Wu, Yijun Tian, Yufei Huang, Siyuan Li

Protein-Protein Interactions (PPIs) are fundamental in various biological processes and play a key role in life activities. The growing demand and cost of experimental PPI assays require computational methods for efficient PPI prediction. While existing methods rely heavily on protein sequence for PPI prediction, it is the protein structure that is the key t

Md. Alamin Talukder, Rakib Hossen, Md Ashraf Uddin, Mohammed Nasir Uddin

Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods. Detecting credit card fraud is crucial for identifying and preventing unauthorized transactions.Timely detection of fraud enables investigators to take swift actions to mitigate further losses. However, the investi

Antoine Lénat, Olivier Cheminat, Damien Chablat, Camilo Charron

The industry of the future, also known as Industry 5.0, aims to modernize production tools, digitize workshops, and cultivate the invaluable human capital within the company. Industry 5.0 can't be done without fostering a workforce that is not only technologically adept but also has enhanced skills and knowledge. Specifically, collaborative robotics plays a

Ferdinando Zullo

Bonini, Borello and Byrne started the study of saturating linear sets in Desarguesian projective spaces, in connection with the covering problem in the rank metric. In this paper we study \emph{$1$-saturating} linear sets in PG$(2,q^4)$, that is $\mathbb{F}_q$-linear sets in PG$(2,q^4)$ with the property that their secant lines cover the entire plane. By mak

Alexandra Thomieres, Dhruva Khanzode, Emilie Duchalais, Ranjan Jha

The integration of medical imaging, computational analysis, and robotic technology has brought about a significant transformation in minimally invasive surgical procedures, particularly in the realm of laparoscopic rectal surgery (LRS). This specialized surgical technique, aimed at addressing rectal cancer, requires an in-depth comprehension of the spatial d

Julie Keisler, Etienne Le Naour

Achieving net zero carbon emissions by 2050 requires the integration of increasing amounts of wind power into power grids. This energy source poses a challenge to system operators due to its variability and uncertainty. Therefore, accurate forecasting of wind power is critical for grid operation and system balancing. This paper presents an innovative approac

Hardik Prabhu, Jayaraman Valadi, Pandarasamy Arjunan

In this paper, we employ a 1D deep convolutional generative adversarial network (DCGAN) for sequential anomaly detection in energy time series data. Anomaly detection involves gradient descent to reconstruct energy sub-sequences, identifying the noise vector that closely generates them through the generator network. Soft-DTW is used as a differentiable alter

Jan Dudák, T. H. Steele

This article consists of two papers: $\textit{Typical dynamics of Newton's method}$ by Steele and $\textit{Erratum to "Typical dynamics of Newton's method"}$ by Dud\'ak and Steele. Let $C^1(M)$ be the space of continuously differentiable real-valued functions defined on $[-M,M]$. We show that for the typical element $f$ in $C^1(M)$, there exists a set $S \su

Yuwei Xia, Ding Wang, Qiang Liu, Liang Wang

Temporal Knowledge Graph (TKG) forecasting aims to predict future facts based on given histories. Most recent graph-based models excel at capturing structural information within TKGs but lack semantic comprehension abilities. Nowadays, with the surge of LLMs, the LLM-based TKG prediction model has emerged. However, the existing LLM-based model exhibits three

Kenjiro Ishizuka

We consider the damped nonlinear Klein-Gordon equation with a delta potential \begin{align*} \partial_{t}^2u-\partial_{x}^2u+2\alpha \partial_{t}u+u-\gamma {\delta}_0u-|u|^{p-1}u=0, \ & (t,x) \in \mathbb{R} \times \mathbb{R}, \end{align*} where $p>2$, $\alpha>0,\ \gamma<2$, and $\delta_0=\delta_0 (x)$ denotes the Dirac delta with the mass at the origin. When

Changsong Pang, Xieyuanli Chen, Yimin Liu, Huimin Lu

Moving object segmentation (MOS) and Ego velocity estimation (EVE) are vital capabilities for mobile systems to achieve full autonomy. Several approaches have attempted to achieve MOSEVE using a LiDAR sensor. However, LiDAR sensors are typically expensive and susceptible to adverse weather conditions. Instead, millimeter-wave radar (MWR) has gained popularit

Mihailo Škorić

The paper will briefly present the development history of transformer-based language models for the Serbian language. Several new models for text generation and vectorization, trained on the resources of the Society for Language Resources and Technologies, will also be presented. Ten selected vectorization models for Serbian, including two new ones, will be

R. Zamanov, K. A. Stoyanov, G. Latev, J. Marti

We performed optical photometry and spectral observations of the symbiotic stars 4U1954+319 and ZZ CMi. For 4U1954+319 using high-resolution spectra we measure the equivalent widths of diffuse interstellar bands and estimate the interstellar reddening E(B-V)=0.83 +/- 0.09. Using GAIA distances and our photometry, we find (1) absolute V band magnitude of 4U19

Junyoung Choi, Sagnik Bhattacharya, Joohyun Lee

Ultra-wideband (UWB) is emerging as a promising solution that can realize proximity services, such as UWB tagless gate (UTG), thanks to centimeter-level localization accuracy based on two different ranging methods such as downlink time-difference of arrival (DL-TDoA) and double-sided two-way ranging (DS-TWR). The UTG is a UWB-based proximity service that pro

Subhradev Sen, Suman K. Ghosh, Hazem Al-Mofleh

In this investigation, the distribution of the ratio of two independently distributed xgamma (Sen et al. 2016) random variables X and Y , with different parameters, is proposed and studied. The related distributional properties such as, moments, entropy measures, are investigated. We have also shown a unique characterization of the proposed distribution base

Ryo Funayama, Yasuaki Kobayashi, Takeaki Uno

We consider the problem of finding ``dissimilar'' $k$ shortest paths from $s$ to $t$ in an edge-weighted directed graph $D$, where the dissimilarity is measured by the minimum pairwise Hamming distances between these paths. More formally, given an edge-weighted directed graph $D = (V, A)$, two specified vertices $s, t \in V$, and integers $d, k$, the goal of

Jingxuan Kang, Rose-Mary Jose, Miriam Oliva, Thomas Auzelle

The dewetting of thin Pt films on different surfaces is investigated as a means to provide the patterning for the top-down fabrication of GaN nanowire ensembles. The transformation from a thin film to an ensemble of nanoislands upon annealing proceeds in good agreement with the void growth model. With increasing annealing duration, the size and shape uniform

Li Tuobang

Metabolic pathways are fundamental maps in biochemistry that detail how molecules are transformed through various reactions. The complexity of metabolic network, where a single compound can play a part in multiple pathways, poses a challenge in inferring metabolic balance changes over time or after different treatments. Isotopic labeling experiment is the st

Rogier Dinkla, Sebastiaan Mulders, Tom Oomen, Jan-Willem van Wingerden

Factors like improved data availability and increasing system complexity have sparked interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive Control (DeePC). However, closed-loop identification bias arises in the presence of noise, which reduces the effectiveness of obtained control policies. In this paper we propose Closed-lo

Xuemei Tang, Jun Wang

Recently, large language models (LLMs) have been successful in relational extraction (RE) tasks, especially in the few-shot learning. An important problem in the field of RE is long-tailed data, while not much attention is paid to this problem using LLM approaches. Therefore, in this paper, we propose SLCoLM, a model collaboration framework, to mitigate the

Daichi Okuno, Takato Yoshida, Ryoko Arita, Takahiro Kashiwazaki

We present a loop-based optical processor enabling time-domain programmable beam-splitter (BS) operations for a phase-sensitive non-Gaussian state. The loop itself is of high quality, allowing for storage of a non-Gaussian state for up to seven round trips while preserving its Wigner negativity and phase coherence. We perform various BS operations on a non-G

Changkun Liu, Shuai Chen, Yukun Zhao, Huajian Huang

Absolute Pose Regressors (APRs) directly estimate camera poses from monocular images, but their accuracy is unstable for different queries. Uncertainty-aware APRs provide uncertainty information on the estimated pose, alleviating the impact of these unreliable predictions. However, existing uncertainty modelling techniques are often coupled with a specific A

Xiang-Ping Jiang, Weilei Zeng, Yayun Hu, Peng Liu

The mobility edge (ME) is a critical energy delineates the boundary between extended and localized states within the energy spectrum, and it plays a crucial role in understanding the metal-insulator transition in disordered or quasiperiodic systems. While there have been extensive studies on MEs in one-dimensional non-Hermitian (NH) quasiperiodic lattices re

Riku Togashi, Kenshi Abe, Yuta Saito

Typical recommendation and ranking methods aim to optimize the satisfaction of users, but they are often oblivious to their impact on the items (e.g., products, jobs, news, video) and their providers. However, there has been a growing understanding that the latter is crucial to consider for a wide range of applications, since it determines the utility of tho

Xing Yan, Yue Zhao, Qi Wu, Wenxuan Ma

The presence of non-Gaussian tails is a prevalent characteristic in many financial modeling scenarios, necessitating the use of complex non-Gaussian distributions such as the generalized beta of the second kind (GB2) and the skewed generalized $t$ (SGT). The approach we propose for modeling heavy-tailed data differs significantly from traditional methods. We