February 2024 arXiv papers — page 23

Manuel Iñarrea, J. Pablo Salas, R. González-Férez, P. Schmelcher

We explore the energy transfer dynamics in an array of two chains of identical rigid interacting dipoles. A crossover between two different ground state (GS) equilibrium configurations is observed with varying distance between the two chains of the array. Linearizing around the GS configurations, we verify that interactions up to third nearest neighbors shou

Panqi Jia, Jue Mao, Esin Koyuncu, A. Burakhan Koyuncu

Currently, there is a high demand for neural network-based image compression codecs. These codecs employ non-linear transforms to create compact bit representations and facilitate faster coding speeds on devices compared to the hand-crafted transforms used in classical frameworks. The scientific and industrial communities are highly interested in these prope

Alexander P. Antonov, Artem Ryabov, Philipp Maass

Transport processes in crowded periodic structures are often mediated by cooperative movements of particles forming clusters. Recent theoretical and experimental studies of driven Brownian motion of hard spheres showed that cluster-mediated transport in one-dimensional periodic potentials can proceed in form of solitary waves. We here give a comprehensive de

Yong Hu, Tong Zhang

Let $X$ be a smooth irregular $3$-fold of general type over $\mathbb{C}$. We prove that the optimal Noether inequality $$ \mathrm{vol}(X) \ge \frac{4}{3}p_g(X) $$ holds if $p_g(X) \ge 16$ or if $X$ has a Gorenstein minimal model. Moreover, when $X$ attains the equality and $p_g(X) \ge 16$, its canonical model can be explicitly described.

Dinh-Viet-Toan Le, Louis Bigo, Mikaela Keller, Dorien Herremans

Several adaptations of Transformers models have been developed in various domains since its breakthrough in Natural Language Processing (NLP). This trend has spread into the field of Music Information Retrieval (MIR), including studies processing music data. However, the practice of leveraging NLP tools for symbolic music data is not novel in MIR. Music has

Camilla Fioravanti, Evagoras Makridis, Gabriele Oliva, Maria Vrakopoulou

This paper considers a strongly connected network of agents, each capable of partially observing and controlling a discrete-time linear time-invariant (LTI) system that is jointly observable and controllable. Additionally, agents collaborate to achieve a shared estimated state, computed as the average of their local state estimates. Recent studies suggest th

Qizhi Pei, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang

Recent research trends in computational biology have increasingly focused on integrating text and bio-entity modeling, especially in the context of molecules and proteins. However, previous efforts like BioT5 faced challenges in generalizing across diverse tasks and lacked a nuanced understanding of molecular structures, particularly in their textual represe

Yanghao Su, Jie Zhang, Ting Xu, Tianwei Zhang

Backdoor attacks pose a significant security vulnerability for deep neural networks (DNNs), enabling them to operate normally on clean inputs but manipulate predictions when specific trigger patterns occur. Currently, post-training backdoor detection approaches often operate under the assumption that the defender has knowledge of the attack information, logi

Bi'an Du, Xiang Gao, Wei Hu, Renjie Liao

Generative 3D part assembly involves understanding part relationships and predicting their 6-DoF poses for assembling a realistic 3D shape. Prior work often focus on the geometry of individual parts, neglecting part-whole hierarchies of objects. Leveraging two key observations: 1) super-part poses provide strong hints about part poses, and 2) predicting supe

Chenxin An, Fei Huang, Jun Zhang, Shansan Gong

The ability of Large Language Models (LLMs) to process and generate coherent text is markedly weakened when the number of input tokens exceeds their pretraining length. Given the expensive overhead of finetuning large-scale models with longer sequences, we propose Dual Chunk Attention (DCA), which enables Llama2 70B to support context windows of more than 10

Xinpeng Li, Jingxu Niu, Ke Zhou

In this paper, we define the upper (resp. lower) covariance under multiple probabilities via a corresponding max-min-max (resp. min-max-min) optimization problem and the related properties of covariances are obtained. In particular, we propose a fast algorithm of calculation for upper and lower covariances under the finite number of probabilities. As an appl

Hemant Arora, Arup Samanta

Spin filtering is a fundamental operation in spintronics, enabling the generation and detection of spin-polarized carriers. Here, we proposed and theoretically demonstrated that a 3d transition metal (TM) doped silicon quantum dot (SiQD) is a suitable candidate for spin filter device at room temperature. Using density functional theory (DFT), we investigate

Chao Meng, Warwick P. Bowen

We explore the generation of nonclassical mechanical states by combining continuous position measurement and feedback control. We find that feedback-induced spring softening can greatly enhance position squeezing. Conversely, even with a pure position measurement, we find that spring hardening can enable momentum squeezing. Beyond enhanced squeezing, we show

Jonas Ballweg

Leader Election (LE) is crucial in distributed systems and blockchain technology, ensuring one participant acts as the leader. Traditional LE methods often depend on distributed random number generation (RNG), facing issues like vulnerability to manipulation, lack of fairness, and the need for complex procedures such as verifiable delay functions (VDFs) and

Sidi Mohammed Kaddour, Mohamed Lehsaini, Abdelhamid Bouchachia

The primary objective of non-intrusive load monitoring (NILM) techniques is to monitor and track power consumption within residential buildings. This is achieved by approximating the consumption of each individual appliance from the aggregate energy measurements. Event-based NILM solutions are generally more accurate than other methods. Our paper introduces

P. Dhakal, B. D. Khanal, A. Gurevich, G. Ciovati

We report the RF performance of several single-cell superconducting radio-frequency cavities subjected to low temperature heat treatment in nitrogen environment. The cavities were treated at temperature 120 - 165 $^{\circ}$C for an extended period of time (24 - 48 hours) either in high vacuum or in a low partial pressure of ultra-pure nitrogen. The improveme

Lorenzo Noci, Alexandru Meterez, Thomas Hofmann, Antonio Orvieto

Recently, there has been growing evidence that if the width and depth of a neural network are scaled toward the so-called rich feature learning limit (\mup and its depth extension), then some hyperparameters -- such as the learning rate -- exhibit transfer from small to very large models. From an optimization perspective, this phenomenon is puzzling, as it i

Michael A. Hedderich, Natalie N. Bazarova, Wenting Zou, Ryun Shim

Cyberbullying harms teenagers' mental health, and teaching them upstanding intervention is crucial. Wizard-of-Oz studies show chatbots can scale up personalized and interactive cyberbullying education, but implementing such chatbots is a challenging and delicate task. We created a no-code chatbot design tool for K-12 teachers. Using large language models and

Hao Ma, Zhiyuan Peng, Xu Li, Mingjie Shao

Universal sound separation (USS) aims to extract arbitrary types of sounds from real-world recordings. This can be achieved by language-queried target sound extraction (TSE), which typically consists of two components: a query network that converts user queries into conditional embeddings, and a separation network that extracts the target sound accordingly.

Jintao Ren, Mathis Rasmussen, Jasper Nijkamp, Jesper Grau Eriksen

Deep learning presents novel opportunities for the auto-segmentation of gross tumor volume (GTV) in head and neck cancer (HNC), yet fully automatic methods usually necessitate significant manual refinement. This study investigates the Segment Anything Model (SAM), recognized for requiring minimal human prompting and its zero-shot generalization ability acros

Siyuan Guo, Cheng Deng, Ying Wen, Hechang Chen

In this work, we investigate the potential of large language models (LLMs) based agents to automate data science tasks, with the goal of comprehending task requirements, then building and training the best-fit machine learning models. Despite their widespread success, existing LLM agents are hindered by generating unreasonable experiment plans within this sc

Christian Diddens, Pim J. Dekker, Detlef Lohse

The evaporation of water/1,2-hexanediol binary drops shows remarkable segregation dynamics, with hexanediol-rich spots forming at the rim, thus breaking axisymmetry. While the segregation of hexanediol near the rim can be attributed to the preferential evaporation of water, the symmetry-breaking and spot formation could not yet be successfully explained. Wit

Deepak K. Singh, Sang-Wook Cheong, Jiasen Guo

Recently, a new class of magnetic phenomenon, called altermagnetism, was proposed where the underlying spin configuration resembles antiferromagnetic structure, but the system violates \textbf{PT} (PT: Parity times Time reversal) symmetry due to the alternation of crystalline symmetry across magnetic ions. Although the original idea was proposed for the coll

Tailai Wen, Da Ke, Xiang Wang, Zhitao Huang

Deep learning algorithms have become an essential component in the field of cognitive radio, especially playing a pivotal role in automatic modulation classification. However, Deep learning also present risks and vulnerabilities. Despite their outstanding classification performance, they exhibit fragility when confronted with meticulously crafted adversarial

Zebin Zhang, Jie Jiang, Leonid Kitchatinov

Observations show that faster-rotating stars tend to have stronger magnetic activity and shorter magnetic cycles. The cyclical magnetic activity of the Sun and stars is believed to be driven by the dynamo process. The success of the Babcock-Leighton (BL) dynamo in understanding the solar cycle suggests an important role that starspots could play in stellar m

M. Mallorquín, N. Lodieu, V. J. S. Béjar, M. R. Zapatero Osorio

Despite the thousands of planets in orbit around stars known to date, the mechanisms of planetary formation, migration, and atmospheric loss remain unresolved. In this work, we confirm the planetary nature of a young Saturn-size planet transiting a solar-type star every 8.03 d, TOI-1135\,b. The age of the parent star is estimated to be in the interval of 125

Mansi Goel, Ayush Agarwal, Shubham Agrawal, Janak Kapuriya

Food touches our lives through various endeavors, including flavor, nourishment, health, and sustainability. Recipes are cultural capsules transmitted across generations via unstructured text. Automated protocols for recognizing named entities, the building blocks of recipe text, are of immense value for various applications ranging from information extracti

Alejandro Mas, Noel Merchán, Elena de la Rosa

Let $\mathbb{D}$ denote the unit disc in $\mathbb{C}$. We define the generalized Ces\`aro operator as follows $$ C_{\omega}(f)(z)=\int_0^1 f(tz)\left(\frac{1}{z}\int_0^z B^{\omega}_t(u)\,du\right)\,\omega(t)dt,$$ where $\{B^{\omega}_\zeta\}_{\zeta\in\mathbb{D}}$ are the reproducing kernels of the Bergman space $A^2_\omega$ induced by a radial weight $\omega$

Vyoma Muralidhara, Kaustuv Basu

Galaxy clusters that host radio halos indicate the presence of population(s) of non-thermal electrons. These electrons can scatter low-energy photons of the Cosmic Microwave Background, resulting in the non-thermal Sunyaev-Zeldovich (ntSZ) effect. We measure the average ntSZ signal from 62 radio-halo hosting clusters using the $Planck$ multi-frequency all-sk

Yifan Luo, Haigang Zhou

Classifications and representations are two main topics in the theory of quadratic forms. In this paper, we consider these topics of ternary quadratic forms. For a given squarefree integer $N$, first we give the classification of positive definite ternary quadratic forms of level $4N$ explicitly. Second, we give explicit formulas of the weighted sum of repre

Priyam Sahoo, Saurabh Pujar, Ganesh Nalawade, Richard Gebhardt

The availability of Large Language Models (LLMs) which can generate code, has made it possible to create tools that improve developer productivity. Integrated development environments or IDEs which developers use to write software are often used as an interface to interact with LLMs. Although many such tools have been released, almost all of them focus on ge

Xian-Hui Ge, Masataka Matsumoto, Kilar Zhang

The newly established Seiberg-Witten (SW)/Quasinormal Modes (QNM) correspondence offers an efficient analytical approach to calculate the QNM frequencies, which was only available numerically before. This is based on the fact that both sides are characterized by Heun-type equations. We find that a similar duality exists between Seiberg-Witten theory and blac

Wuyang Chen, Junru Wu, Zhangyang Wang, Boris Hanin

Training a high-quality deep neural network requires choosing suitable hyperparameters, which is a non-trivial and expensive process. Current works try to automatically optimize or design principles of hyperparameters, such that they can generalize to diverse unseen scenarios. However, most designs or optimization methods are agnostic to the choice of networ

G. V. Pavan Kumar

C.V. Raman (1888 - 1970) was a creative scientist, enthusiastic teacher and a science celebrity in India. In all these roles, he communicated science effectively. In this essay, I ask how and why did he communicate science. I take a few examples from his research writings and show his ability to explain science lucidly. By looking into his thoughts on teachi

Stéphane Michoulier, Jean-François Gonzalez, Evgeni Grishin, Clément Petetin

Context: Many barriers prevent dust to form planetesimals via coagulation in protoplanetary discs, such as bouncing, collisional fragmentation or aeolian erosion. Modelling dust and the different phenomena that can alter its evolution is therefore needed. Multiple solutions have been proposed, but still need to be confirmed. Aims: In this paper, we explore t

Fabian Bongratz, Jan Fecht, Anne-Marie Rickmann, Christian Wachinger

Reconstructing the cortex from longitudinal magnetic resonance imaging (MRI) is indispensable for analyzing morphological alterations in the human brain. Despite the recent advancement of cortical surface reconstruction with deep learning, challenges arising from longitudinal data are still persistent. Especially the lack of strong spatiotemporal point corre

Zhou Yang, Zhaochun Ren, Yufeng Wang, Xiaofei Zhu

Empathetic response generation aims to generate empathetic responses by understanding the speaker's emotional feelings from the language of dialogue. Recent methods capture emotional words in the language of communicators and construct them as static vectors to perceive nuanced emotions. However, linguistic research has shown that emotional words in language

Łukasz Kułacz, Adrian Kliks

Reconfigurable intelligent surfaces can be successfully used to control the radio environment. Simple control of the reflection angle of the signal from the surface allows maximization or minimization of the received power in specific places. The paper presents simulations where it is possible to receive a signal in a place where it was not possible, to dete

Boris F. Gribakin, Valentina M. Litvyak, Mladen Kotur, Regis André

The nuclear spin systems in CdTe/(Cd,Zn)Te and CdTe/(Cd,Mg)Te quantum wells (QW) are studied using a multistage technique combining optical pumping and Hanle effect-based detection. The samples demonstrate drastically different nuclear spin dynamics in zero and weak magnetic fields. In CdTe/(Cd,Zn)Te, the nuclear spin relaxation time is found to strongly inc

Tong Hui, Eugenio Cuniato, Michael Pantic, Marco Tognon

Recently, the utilization of aerial manipulators for performing pushing tasks in non-destructive testing (NDT) applications has seen significant growth. Such operations entail physical interactions between the aerial robotic system and the environment. End-effectors with multiple contact points are often used for placing NDT sensors in contact with a surface

Jiaqi Wang, Zhenxi Song, Zhengyu Ma, Xipeng Qiu

Reconstructing natural language from non-invasive electroencephalography (EEG) holds great promise as a language decoding technology for brain-computer interfaces (BCIs). However, EEG-based language decoding is still in its nascent stages, facing several technical issues such as: 1) Absence of a hybrid strategy that can effectively integrate cross-modality (

Martín Lagares, Shun-Qing Zhang

In the three-dimensional ${\cal N}=6$ Chern-Simons matter (ABJM) theory, the integrand for the logarithm of the scattering amplitude admits a decomposition in terms of negative geometries, which implies that all the infrared divergences concentrate in the last loop integration. We compute the infrared-finite functions that arise from performing a three-loop

Luca Salvatore Lorello, Marco Lippi, Stefano Melacci

Artificial intelligence is continuously seeking novel challenges and benchmarks to effectively measure performance and to advance the state-of-the-art. In this paper we introduce KANDY, a benchmarking framework that can be used to generate a variety of learning and reasoning tasks inspired by Kandinsky patterns. By creating curricula of binary classification

Zihao Liu, Xiaoyu Zhang, Guangwei Liu, Ji Zhao

In autonomous driving, the high-definition (HD) map plays a crucial role in localization and planning. Recently, several methods have facilitated end-to-end online map construction in DETR-like frameworks. However, little attention has been paid to the potential capabilities of exploring the query mechanism for map elements. This paper introduces MapQR, an e

Jiya A. Enoch, Ilesanmi B. Oluwafemi, Francis A. Ibikunle, Olulope K. Paul

Trapped human detection in search and rescue (SAR) scenarios poses a significant challenge in pervasive computing. This study addresses this issue by leveraging machine learning techniques, given their high accuracy. However, accurate identification of trapped individuals is hindered by the curse of dimensionality and noisy data. Particularly in non-line-of-

Jun-Jin Peng, Hua Li

In this paper, we aim to perform a systematical investigation on the field equations and Noether potentials for the higher-order gravity theories endowed with Lagrangians depending on the metric and the Riemann curvature tensor, together with $i$th ($i=1,2,\cdot\cdot\cdot$) powers of the Beltrami-d'Alembertian operator $\Box$ acting on the latter. We start w

Kazuki Mitani, Takuya Mieno, Kazuhisa Seto, Takashi Horiyama

This paper investigates the (quasi-)periodicity of a string when the string is edited. A string $C$ is called a cover (as known as a quasi-period) of a string $T$ if each character of $T$ lies within some occurrence of $C$. By definition, a cover of $T$ must be a border of $T$; that is, it occurs both as a prefix and as a suffix of $T$. In this paper, we foc

Jiaqi Lin, Zhihao Li, Xiao Tang, Jianzhuang Liu

Existing NeRF-based methods for large scene reconstruction often have limitations in visual quality and rendering speed. While the recent 3D Gaussian Splatting works well on small-scale and object-centric scenes, scaling it up to large scenes poses challenges due to limited video memory, long optimization time, and noticeable appearance variations. To addres

Xingyu Yang, Ye Mou, Bruno Gallas, Sébastien Bidault

Skyrmions are topological structures characterized by a winding vectorial configuration that provides a quantized topological charge. In magnetic materials, skyrmions are localized spin textures that exhibit unique stability and mobility properties, making them highly relevant to the burgeoning field of spintronics. In optics, these structures open new front

Maxime Faymonville, Carsten Jentsch, Christian H. Weiß

Among the various models designed for dependent count data, integer-valued autoregressive (INAR) processes enjoy great popularity. Typically, statistical inference for INAR models uses asymptotic theory that relies on rather stringent (parametric) assumptions on the innovations such as Poisson or negative binomial distributions. In this paper, we present a n

Abhishek Sebastian, Annis Fathima A, Pragna R, Madhan Kumar S

Our paper introduces a robust framework for the automated identification of diseases in plant leaf images. The framework incorporates several key stages to enhance disease recognition accuracy. In the pre-processing phase, a thumbnail resizing technique is employed to resize images, minimizing the loss of critical image details while ensuring computational e

Lei Song, Chenxiao Gao, Ke Xue, Chenyang Wu

Black-Box Optimization (BBO) has found successful applications in many fields of science and engineering. Recently, there has been a growing interest in meta-learning particular components of BBO algorithms to speed up optimization and get rid of tedious hand-crafted heuristics. As an extension, learning the entire algorithm from data requires the least labo

Darío Jaramillo-Garrido, Antonio L. Maroto, Prado Martín-Moruno

We explore the idea of restoring the full diffeomorphism (Diff) invariance in theories with only transverse diffeomorphisms (TDiff) by the introduction of additional fields. In particular, we consider in detail the case of a TDiff invariant scalar field and how Diff symmetry can be restored preserving locality by introducing an additional vector field. We re

Sizhong Zhou, Yuli Zhang, Tao Zhang, Hongxia Liu

Let $\alpha\in[0,1)$, and let $G$ be a connected graph of order $n$ with $n\geq f(\alpha)$, where $f(\alpha)=6$ for $\alpha\in[0,\frac{2}{3}]$ and $f(\alpha)=\frac{4}{1-\alpha}$ for $\alpha\in(\frac{2}{3},1)$. A graph $G$ is said to be $t$-tough if $|S|\geq tc(G-S)$ for each subset $S$ of $V(G)$ with $c(G-S)\geq2$, where $c(G-S)$ is the number of connected c

Abanti Bhattacharjya, Md Manowarul Islam, Md Ashraf Uddin, Md. Alamin Talukder

With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitated the rapid processing and analysis of vast quantities of biological data for human perception. Most studies focus on locating two connected d

Avadhut Sardeshmukh, Sreedhar Reddy, BP Gautham, Pushpak Bhattacharyya

We propose a variational autoencoder (VAE)-based model for building forward and inverse structure-property linkages, a problem of paramount importance in computational materials science. Our model systematically combines VAE with regression, linking the two models through a two-level prior conditioned on the regression variables. The regression loss is optim

Tyler L. Hayes, César R. de Souza, Namil Kim, Jiwon Kim

Object detectors are typically trained once and for all on a fixed set of classes. However, this closed-world assumption is unrealistic in practice, as new classes will inevitably emerge after the detector is deployed in the wild. In this work, we look at ways to extend a detector trained for a set of base classes so it can i) spot the presence of novel clas

Bassano Vacchini

We consider a recently introduced framework for the description of memory effects based on quantum state distinguishability quantifiers, in which entropic quantifiers can be included. After briefly presenting the approach, we validate it considering the performance of different quantifiers in the characterization of the reduced dynamics of a two-level system

Chengchen Li, Huihang Lin, Rong Yu

Motivated by recent progress on the experimental realization of proximate deconfined quantum critical point in a frustrated quantum magnet, we study the low-energy spin dynamics of a related checkerboard $J$-$Q$ model by using quantum Monte Carlo simulations. The ground state of this model undergoes a weakly first-order quantum phase transition with an emerg

Haoran Lai, Qingsong Yao, Zihang Jiang, Rongsheng Wang

The advancement of Zero-Shot Learning in the medical domain has been driven forward by using pre-trained models on large-scale image-text pairs, focusing on image-text alignment. However, existing methods primarily rely on cosine similarity for alignment, which may not fully capture the complex relationship between medical images and reports. To address this

Chengchen Li, Yi Cui, Weiqiang Yu, Rong Yu

Motivated by recent experimental progress in the quasi-one-dimensional quantum magnet NiNb$_2$O$_6$, we study the spin dynamics of an S=1 ferromagnetic Heisenberg chain with single-ion anisotropy by using a semiclassical molecular dynamics approach. This system undergoes a quantum phase transition from a ferromagnetic to a paramagnetic state under a transver

Vibhu Mishra, Salvatore Manmana, Stefan Kehrein

The quantum adiabatic theorem is a fundamental result in quantum mechanics, with a multitude of applications, both theoretical and practical. Here, we investigate the dynamics of adiabatic processes for quantum many-body systems %in detail by analysing the properties of observable-free, intensive quantities. In particular, we study the adiabatic rate functio

Jiahao Li, Bin Li, Yan Lu

The emerging conditional coding-based neural video codec (NVC) shows superiority over commonly-used residual coding-based codec and the latest NVC already claims to outperform the best traditional codec. However, there still exist critical problems blocking the practicality of NVC. In this paper, we propose a powerful conditional coding-based NVC that solves

Rodica Dinu, Nayana Shibu Deepthi

A cactus graph is a connected graph in which every block is either an edge or a cycle. In this paper, we will examine cactus graphs where all the blocks are $3$-cycles, i.e., triangular cactus graphs, of diameter $4$. Our main focus is to prove that the corresponding edge ring of this family of graphs is not normal and satisfies Serre's condition $(S_2)$. We

Shyam Marjit, Harshit Singh, Nityanand Mathur, Sayak Paul

In the realm of subject-driven text-to-image (T2I) generative models, recent developments like DreamBooth and BLIP-Diffusion have led to impressive results yet encounter limitations due to their intensive fine-tuning demands and substantial parameter requirements. While the low-rank adaptation (LoRA) module within DreamBooth offers a reduction in trainable p

Timon Barlag, Vivian Holzapfel, Laura Strieker, Jonni Virtema

We characterize the computational power of neural networks that follow the graph neural network (GNN) architecture, not restricted to aggregate-combine GNNs or other particular types. We establish an exact correspondence between the expressivity of GNNs using diverse activation functions and arithmetic circuits over real numbers. In our results the activatio

Fufangchen Zhao, Guoqiang Jin, Jiaheng Huang, Rui Zhao

Nowadays both commercial and open-source academic LLM have become the mainstream models of NLP. However, there is still a lack of research on LLM consistency, meaning that throughout the various stages of LLM research and deployment, its internal parameters and capabilities should remain unchanged. This issue exists in both the industrial and academic sector

Peter Dawood, Felix Breuer, Istvan Homolya, Maximilian Gram

Purpose: Noise resilience in image reconstructions by scan-specific robust artificial neural networks for k-space interpolation (RAKI) is linked to nonlinear activations in k-space. To gain a deeper understanding of this relationship, an image space formalism of RAKI is introduced for analyzing noise propagation analytically, identifying and characterizing i

Pavel Petrovič, Peter Verčimák

The mainstream of educational robotics platforms orbits the various versions of versatile robotics sets and kits, while interesting outliers add new opportunities and extend the possible learning situations. Examples of such are reconfigurable robots, rolling sphere robots, humanoids, swimming, or underwater robots. Another kind within this category are flyi

Akito Kawasaki, Ryuhoh Ide, Hector Brunel, Takumi Suzuki

Quantum information processors greatly benefit from high clock frequency to fully harnessing the quantum advantages before they get washed out by the decoherence. In this pursuit, all-optical systems offer unique advantages due to their inherent 100 THz carrier frequency, permitting one to develop THz clock frequency processors. In practice, the bandwidth of

Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti

Modern neural network architectures still struggle to learn algorithmic procedures that require to systematically apply compositional rules to solve out-of-distribution problem instances. In this work, we focus on formula simplification problems, a class of synthetic benchmarks used to study the systematic generalization capabilities of neural architectures.

Shentong Mo, Yansen Wang, Xufang Luo, Dongsheng Li

Visual Prompt Tuning (VPT) techniques have gained prominence for their capacity to adapt pre-trained Vision Transformers (ViTs) to downstream visual tasks using specialized learnable tokens termed as prompts. Contemporary VPT methodologies, especially when employed with self-supervised vision transformers, often default to the introduction of new learnable p

Filip Mandić, Nikola Mišković, Ivan Lončar

The research presented in this paper is aimed at developing a control algorithm for an autonomous surface system carrying a two-sensor array consisting of two acoustic receivers, capable of measuring the time-difference-of-arrival (TDOA) of a quasiperiodic underwater acoustic signal and utilizing this value to steer the system toward the acoustic source in t

Nikolaj Rønne, Alán Aspuru-Guzik, Bjørk Hammer

We present a generative diffusion model specifically tailored to the discovery of surface structures. The generative model takes into account substrate registry and periodicity by including masked atoms and $z$-directional confinement. Using a rotational equivariant neural network architecture, we design a method that trains a denoiser-network for diffusion

Navinder Singh

As is well known, Paul Drude put forward the very first quantitative theory of electrical conduction in metals in 1900. He could successfully account for the Wiedemann-Franz law which states that the ratio of thermal to electrical conductivity divided by temperature is a constant called the Lorenz number. As it turns out, in Drude's derivation, there is a lu

Xuanyi Li, Daquan Zhou, Chenxu Zhang, Shaodong Wei

The recently developed Sora model [1] has exhibited remarkable capabilities in video generation, sparking intense discussions regarding its ability to simulate real-world phenomena. Despite its growing popularity, there is a lack of established metrics to evaluate its fidelity to real-world physics quantitatively. In this paper, we introduce a new benchmark

Jonathan Viquerat, Philippe Meliga, Pablo Jeken, Elie Hachem

Recently, the increasing use of deep reinforcement learning for flow control problems has led to a new area of research, focused on the coupling and the adaptation of the existing algorithms to the control of numerical fluid dynamics environments. Although still in its infancy, the field has seen multiple successes in a short time span, and its fast developm

Meng-Yu Xie, Su-Jian Niu, Yin-Hai Li, Zheng Ge

An ellipsometer is a vital precision tool used for measuring optical parameters with wide applications in many fields, including accurate measurements in film thickness, optical constants, structural profiles, etc. However, the precise measurement of photosensitive materials meets huge obstacles because of the excessive input photons, therefore the requireme

Çağatay Yıldız, Nishaanth Kanna Ravichandran, Nitin Sharma, Matthias Bethge

Continual learning (CL) in large language models (LLMs) is an evolving domain that focuses on developing efficient and sustainable training strategies to adapt models to emerging knowledge and achieve robustness in dynamic environments. Our primary emphasis is on continual domain-adaptive pretraining, a process designed to equip LLMs with the ability to inte

Daniel Jezierski, Zoran Mazej, Wojciech Grochala

We present a new compound in the silver cobalt fluoride system, featuring paramagnetic silver (d9) and high-spin cobalt (d6), synthesized by solid state method in an autoclave under F2 overpressure. Based on powder X ray diffraction, we determined that AgIICoIIIF5 crystallizes in a monoclinic system with space group C2/c. The calculated fundamental band gap

Nishikanta Mohanty, Bikash K. Behera, Christopher Ferrie, Pravat Dash

The paper proposes the Quantum-SMOTE method, a novel solution that uses quantum computing techniques to solve the prevalent problem of class imbalance in machine learning datasets. Quantum-SMOTE, inspired by the Synthetic Minority Oversampling Technique (SMOTE), generates synthetic data points using quantum processes such as swap tests and quantum rotation.

Harshit Agrawal, Ari Hietanen, Simo Särkkä

Cone-beam computed tomography (CBCT) has become a vital imaging technique in various medical fields but scatter artifacts are a major limitation in CBCT scanning. This challenge is exacerbated by the use of large flat panel 2D detectors. The scatter-to-primary ratio increases significantly with the increase in the size of FOV being scanned. Several deep lear

Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti

Large Language Models (LLMs) have revolutionized the field of Natural Language Processing thanks to their ability to reuse knowledge acquired on massive text corpora on a wide variety of downstream tasks, with minimal (if any) tuning steps. At the same time, it has been repeatedly shown that LLMs lack systematic generalization, which allows to extrapolate th

Yashwanth Balaji, Narendra Acharya, Robert Armstrong, Kevin G. Crawford

Superconducting qubits are a promising route to achieving large-scale quantum computers. A key challenge in realising large-scale superconducting quantum processors involves mitigating frequency collisions. In this paper, we present an approach to tuning fixed-frequency qubits with the use of an electron beam to locally anneal the Josephson junction. We demo

Shihao Jiang, Yu Zhang, Junqiang Li, Hongfang Yu

As one of the most successful and effective software testing techniques in recent years, fuzz testing has uncovered numerous bugs and vulnerabilities in modern software, including network protocol software. In contrast to other fuzzing targets, network protocol software exhibits its distinct characteristics and challenges, introducing a plethora of research

Rahime Belen Saglam, Jason R. C. Nurse, Lisa Sugiura

Objective: Domestic abuse cases have risen significantly over the last four years, in part due to the COVID-19 pandemic and the challenges for victims and survivors in accessing support. In this study, we investigate the role that chatbots - Artificial Intelligence (AI) and rule-based - may play in supporting victims/survivors in situations such as these or

Vasilii A. Gromov, Alexandra S. Kogan

Nowadays, technology is rapidly advancing: bots are writing comments, articles, and reviews. Due to this fact, it is crucial to know if the text was written by a human or by a bot. This paper focuses on comparing structures of the coarse-grained partitions of semantic paths for human-written and bot-generated texts. We compare the clusterizations of datasets

Alexander Mikhalychev, Alex Ulyanenkov

We propose a method of spatial resolution enhancement in metrology (thermometry, magnetometry, pH estimation, and similar methods) with blinking fluorescent nanosensors by combining sensing with super-resolution optical fluctuation imaging (SOFI). By utilizing the idea of quantum super-resolution imaging by photon statistics (QSIPS), the applicability of the

Daniele Angioni, Luca Demetrio, Maura Pintor, Luca Oneto

Machine-learning models demand periodic updates to improve their average accuracy, exploiting novel architectures and additional data. However, a newly updated model may commit mistakes the previous model did not make. Such misclassifications are referred to as negative flips, experienced by users as a regression of performance. In this work, we show that th

Mattia Setzu, Marta Marchiori Manerba, Pasquale Minervini, Debora Nozza

Language Models (LMs) have been shown to inherit undesired biases that might hurt minorities and underrepresented groups if such systems were integrated into real-world applications without careful fairness auditing. This paper proposes FairBelief, an analytical approach to capture and assess beliefs, i.e., propositions that an LM may embed with different de

Guillem Domènech

PBH formation requires high-density regions in the (random) density field filling the primordial universe. While only the largest (and so rarest) overdensities collapse to form PBHs, the rest cause large anisotropic stresses, which are the source of GWs. We provide an overview of the theoretical aspects of the GW backgrounds associated with PBHs from large p

Khaled A. Mustafa, Daniel Jarne Ornia, Jens Kober, Javier Alonso-Mora

For an autonomous vehicle to operate reliably within real-world traffic scenarios, it is imperative to assess the repercussions of its prospective actions by anticipating the uncertain intentions exhibited by other participants in the traffic environment. Driven by the pronounced multi-modal nature of human driving behavior, this paper presents an approach t

Emanuel Pfeffer, Michael Waßmer, Yee-Ying Cung, Roger Wolf

In high-energy particle collisions, the primary collision products usually decay further resulting in tree-like, hierarchical structures with a priori unknown multiplicity. At the stable-particle level all decay products of a collision form permutation invariant sets of final state objects. The analogy to mathematical graphs gives rise to the idea that graph

Eva Eigner, Thorsten Händler

Decision-making is a fundamental capability in everyday life. Large Language Models (LLMs) provide multifaceted support in enhancing human decision-making processes. However, understanding the influencing factors of LLM-assisted decision-making is crucial for enabling individuals to utilize LLM-provided advantages and minimize associated risks in order to ma

Xue Li, Song Wang, Henggeng Han, Huiqin Yang

M-type stars are crucial for stellar activity studies since they cover two types of magnetic dynamos and particularly intriguing for habitability studies due to their abundance and long lifespans during the main-sequence stage. In this paper, we used the LAMOST DR9 catalog and the GALEX UV archive data to investigate the chromospheric and UV activities of M-

H. V. Ovcharenko, O. B. Zaslavskii

If two particles moving towards a black hole collide in the vicinity of the horizon, the energy $E_{c.m.}$ in the center of mass frame can grow indefinitely if one of particles is fine-tuned. This is the Ba\~{n}ados, Silk and West (BSW) effect. One of objections against this effect consists in that for some types of a horizon fine-tuned particles cannot reac

Kinjal Roy, Rahul Sharma, Hemanth Manikantan, Biswajit Paul

IGR J0607.4+2205 is a transient Be X-ray binary discovered two decades ago. IGR J0607.4+2205 underwent an outburst in 2023 during which it was observed twice with \textit{NuSTAR}. The main goal of this work is to model the broadband X-ray spectrum of IGR J0607.4+2205 during the outburst and to study the variations of the spectral and timing features at diffe

Bo Gao, Shao-Peng Tang, Jingzhi Yan, Yi-Zhong Fan

A hierarchical triple merger (HTM) constitutes a type of event in which two successive black hole (BH) mergers occur sequentially within the observational window of gravitational wave (GW) detectors, which has important role in testing general relativity and studying BH population. In this work, we conduct an analysis to determine the feasibility of identify

Shubh Goyal, Medha Hira, Shubham Mishra, Sukriti Goyal

Although the rise of Large Language Models (LLMs) in enterprise settings brings new opportunities and capabilities, it also brings challenges, such as the risk of generating inappropriate, biased, or misleading content that violates regulations and can have legal concerns. To alleviate this, we present "LLMGuard", a tool that monitors user interactions with

Jhantu Das, Kalyan Halder, Soumendu Roy, Arindam Bhattacharyya

The main goal of this paper is devoted to N(k)-contact metric manifolds admitting $\ast$-conformal Einstein soliton and also $\ast$-conformal gradient Einstein soliton. In this settings the nature of the manifold, and the potential vector field, potential function of solitons are characterized, and conditions for the $\ast$-conformal Einstein soliton to be e