December 2023 arXiv papers — page 120

Yakir Aharonov, Tomer Shushi

In this short paper, we show how a quantum nonlocal effect of far-apart wavepackets in the Schrodinger picture of wavefunctions is replaced by a local instability problem when considering the hydrodynamical formulation of quantum mechanics, known as the Madelung picture. As a second result, we show how the Madelung equations describe quantized energies witho

Peisi Huang, Tao Xu

We propose a novel leptogenesis mechanism with a temperature-dependent coupling between the right-handed neutrino and Standard Model particles. This coupling experiences suppression at high temperatures and becomes sizable when the lepton asymmetry washout processes are Boltzmann-suppressed. Such a feature ensures that the washout rates remain consistently b

Tim Davis, Huw Williams

We apply dimensional analysis with Buckinghams "Pi" theorem to estimate the volume of wood in a tree stem, given the tree's height and diameter. We use Meyer's (1953) data on 31 cherry trees from the Allegheny National forest as the main example, and extend our model to look at other forest mensuration data sets.

Jan Piotrowski, Marek Wachnicki, Mateusz Perlik, Jakub Podolak

X (formerly Twitter) has evolved into a contemporary agora, offering a platform for individuals to express opinions and viewpoints on current events. The majority of the topics discussed on Twitter are directly related to ongoing events, making it an important source for monitoring public discourse. However, linking tweets to specific news presents a signifi

Maria Dolores Gadea, Jesus Gonzalo, Andrey Ramos

Determining whether Global Average Temperature (GAT) is an integrated process of order 1, I(1), or is a stationary process around a trend function is crucial for detection, attribution, impact and forecasting studies of climate change. In this paper, we investigate the nature of trends in GAT building on the analysis of individual temperature grids. Our 'mic

Qiong Pan, Xiaoya Zhai, Falai Chen

Shell structures with a high stiffness-to-weight ratio are desirable in various engineering applications. In such scenarios, topology optimization serves as a popular and effective tool for shell structures design. Among the topology optimization methods, solid isotropic material with penalization method(SIMP) is often chosen due to its simplicity and conven

Jesper Nederlof, Krisztina Szilágyi

In this paper we investigate the parameterized complexity of the task of counting and detecting occurrences of small patterns in unit disk graphs: Given an $n$-vertex unit disk graph $G$ with an embedding of ply $p$ (that is, the graph is represented as intersection graph with closed disks of unit size, and each point is contained in at most $p$ disks) and a

Jiahui Li, Rosario Fazio, Yingdan Wang, Stefano Chesi

We investigate the dissipative phase transitions of the anisotropic quantum Rabi model with cavity decay and demonstrate that large spin fluctuations persist in the stationary state, having important consequences on the phase diagram and the critical properties. In the second-order phase transition to the superradiant phase, there is a significant suppressio

Nisarg Chadha, Subroto Mukerjee

We investigate the hydrodynamic regime in metals with momentum-conserving electron-electron scattering. The conservation of momentum results in well-defined dynamics whose effects we investigate via the relevant continuity equations. We find anomalous contributions to the charge and heat transport currents arising from gradients of the velocity field in a se

Camila Pereira, Matheus Pavan, Sungwon Yoon, Ricelli Ramos

This work introduces UstanceBR, a multimodal corpus in the Brazilian Portuguese Twitter domain for target-based stance prediction. The corpus comprises 86.8 k labelled stances towards selected target topics, and extensive network information about the users who published these stances on social media. In this article we describe the corpus multimodal data, a

Thomas Perrin

On a Riemannian manifold with or without boundary, and whether bounded or unbounded, we consider a semilinear wave (or Klein-Gordon) equation with a subcritical nonlinearity (either defocusing or focusing). We establish local controllability around a partially analytic solution, under the Geometric Control Condition. Specifically, some blow-up solutions can

Yufei Guo, Yuanpei Chen, Xiaode Liu, Weihang Peng

The Spiking Neural Network (SNN), as one of the biologically inspired neural network infrastructures, has drawn increasing attention recently. It adopts binary spike activations to transmit information, thus the multiplications of activations and weights can be substituted by additions, which brings high energy efficiency. However, in the paper, we theoretic

Semih Kara, Nuno C. Martins

Population games model the evolution of strategic interactions among a large number of uniform agents. Due to the agents' uniformity and quantity, their aggregate strategic choices can be approximated by the solutions of a class of ordinary differential equations. This mean-field approach has found to be an effective tool of analysis. However its current pro

Haicheng Liao, Zhenning Li, Huanming Shen, Wenxuan Zeng

The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles. To address this challenge, we pioneer a novel behavior-aware trajectory prediction model (BAT) that incorporates insights and findings from traffic psychology, human behavior, and decision-making. Our model co

Hou Tin Chau, David Ellis, Ehud Friedgut, Noam Lifshitz

For integers $n \geq k \geq 1$, the {\em Kneser graph} $K(n, k)$ is the graph with vertex-set consisting of all the $k$-element subsets of $\{1,2,\ldots,n\}$, where two $k$-element sets are adjacent in $K(n,k)$ if they are disjoint. We show that if $(n,k,s) \in \mathbb{N}^3$ with $n > 10000 k s^5$ and $\mathcal{F}$ is set of vertices of $K(n,k)$ of size larg

Sudha, Usha Devi A R, Akshata Shenoy H, Karthik H S

We explore the entanglement features of pure symmetric N-qubit states characterized by N-distinct spinors with a particular focus on the Greenberger-Horne-Zeilinger(GHZ) states and WWbar, an equal superposition of W and obverse W states. Along with a comparison of pairwise entanglement and monogamy properties, we explore the geometric information contained i

Sebastián Carruitero, Alejo Costa Duran, Giulia Pisegna, Mauricio B. Sturla

We propose an extension to the ISM of flocking and swarming. The model has been introduced to explain certain dynamic features of swarming (second sound, a lower than expected dynamic critical exponent) while preserving the mechanism for onset of order provided by the Vicsek model. The ISM has only been formulated with an imitation (``ferromagnetic'') intera

Van Chien Le, Pierrick Cordel, Francesco P. Andriulli, Kristof Cools

This paper introduces a time-domain combined field integral equation for electromagnetic scattering by a perfect electric conductor. The new equation is obtained by leveraging the quasi-Helmholtz projectors, which separate both the unknown and the source fields into solenoidal and irrotational components. These two components are then appropriately rescaled

Tejas Natu, Camille Castera, Jalal Fadili, Peter Ochs

In order to minimize a differentiable geodesically convex function, we study a second-order dynamical system on Riemannian manifolds with an asymptotically vanishing damping term of the form $\alpha/t$. For positive values of $\alpha$, convergence rates for the objective values and convergence of trajectory is derived. We emphasize the crucial role of the cu

Abid Shahriar

A multi-joint enabled robot requires extensive mathematical calculations to determine the end effector's position with respect to the other connective joints involved and their corresponding frames in a specific coordinate system. If a control architecture employs fewer positional constraints which cannot precisely determine the end effector's position in al

Xiaoyang Zhang, Yijie Yang, Dan Wang

Embodied carbon is the total carbon released from the processes associated with a product from cradle to gate. In many industry sectors, embodied carbon dominates the overall carbon footprint. Embodied carbon accounting, i.e., to estimate the embodied carbon of a product, has become an important research topic. Existing studies derive the embodied carbon thr

Tao Chen, Enwei Zhang, Yuting Gao, Ke Li

Although In-Context Learning (ICL) brings remarkable performance gains to Large Language Models (LLMs), the improvements remain lower than fine-tuning on downstream tasks. This paper introduces Multi-Modal In-Context Tuning (MMICT), a novel multi-modal fine-tuning paradigm that boosts multi-modal fine-tuning by fully leveraging the promising ICL capability o

Sandor Beregi, David A. W. Barton, Djamel Rezgui, Simon A. Neild

Real-time hybrid testing is a method in which a substructure of the system is realised experimentally and the rest numerically. The two parts interact in real time to emulate the dynamics of the full system. Such experiments however are often difficult to realise as the actuators and sensors, needed to ensure compatibility and force-equilibrium conditions at

Dylon Chow

We give a description of the Picard group of a reductive group over a number field as an abelianized Galois cohomology group. It gives another approach of a result due to Labesse.

Alexander I. Magunov, Vasily V. Strelkov

High-order harmonic generation in multiphoton ionization regime is studied theoretically to examine the resonant effects of the generating particle. We solve the time-dependent Schr\"odinger equation for In+ in the laser field by expanding the solution in terms of unperturbed eigenstates obtained in the Hartree-Fock approximation. The intensity enhancement o

Tribhuban Parida, Sandeep Chatterjee, Md. Nasim

We explore the implications of the late stage hadronic rescattering phase on the flow of $K^{*0}$. The model calculations are done using a (3+1)-dimensional hybrid framework, incorporating both hydrodynamic evolution and hadronic transport that is calibrated to agree with bulk observables including the elusive rapidity differential $v_1$ of light-flavor hadr

Vivek Gopalakrishnan, Neel Dey, Polina Golland

Surgical decisions are informed by aligning rapid portable 2D intraoperative images (e.g., X-rays) to a high-fidelity 3D preoperative reference scan (e.g., CT). 2D/3D image registration often fails in practice: conventional optimization methods are prohibitively slow and susceptible to local minima, while neural networks trained on small datasets fail on new

Kai Zhong, Luming Sun, Tao Ji, Cuiping Li

Various works have utilized deep learning to address the query optimization problem in database system. They either learn to construct plans from scratch in a bottom-up manner or steer the plan generation behavior of traditional optimizer using hints. While these methods have achieved some success, they face challenges in either low training efficiency or li

Shota Maehara, Yasuhide Numata

In this article, we consider the multiarrangements whose underlying arrangements are the Coxeter arrangement of type $B_2$. For some special multiplicities, we give an explicit description of bases for the derivation modules. As an application, we also describe the lower derivations of bases for the derivation modules of some Coxeter multiarrangements of typ

L. Siddharth, Jianxi Luo

Natural language artefact descriptions are primary carriers of engineering design knowledge, whose retrieval, representation, and reuse are fundamental to supporting knowledge-intensive tasks in the design process. In this paper, we explicate design knowledge from patented artefact descriptions as knowledge graphs and examine these to understand the linguist

Xu Peng, Junwei Zhu, Boyuan Jiang, Ying Tai

Recent advancements in personalized image generation using diffusion models have been noteworthy. However, existing methods suffer from inefficiencies due to the requirement for subject-specific fine-tuning. This computationally intensive process hinders efficient deployment, limiting practical usability. Moreover, these methods often grapple with identity d

Zhen Qin, Daoyuan Chen, Bingchen Qian, Bolin Ding

Pre-trained large language models (LLMs) need fine-tuning to improve their responsiveness to natural language instructions. Federated learning offers a way to fine-tune LLMs using the abundant data on end devices without compromising data privacy. Most existing federated fine-tuning methods for LLMs rely on parameter-efficient fine-tuning techniques, which m

Yuichi Inoue, Yuki Yada, Kotaro Tanahashi, Yu Yamaguchi

Visual Question Answering (VQA) is one of the most important tasks in autonomous driving, which requires accurate recognition and complex situation evaluations. However, datasets annotated in a QA format, which guarantees precise language generation and scene recognition from driving scenes, have not been established yet. In this work, we introduce Markup-QA

Kotaro Tanahashi, Yuichi Inoue, Yu Yamaguchi, Hidetatsu Yaginuma

Various methods have been proposed for utilizing Large Language Models (LLMs) in autonomous driving. One strategy of using LLMs for autonomous driving involves inputting surrounding objects as text prompts to the LLMs, along with their coordinate and velocity information, and then outputting the subsequent movements of the vehicle. When using LLMs for such p

Lara Koehler, Pierre Ronceray, Martin Lenz

In living cells, proteins self-assemble into large functional structures based on specific interactions between molecularly complex patches. Due to this complexity, protein self-assembly results from a competition between a large number of distinct interaction energies, of the order of one per pair of patches. Current self-assembly models however typically i

Franz Weißer, Nurettin Turan, Wolfgang Utschick

This paper investigates the combination of parametric channel estimation with minimum mean square error (MMSE) estimation. We propose a direction-of-arrival (DoA)-aided two-stage channel estimation technique that utilizes the decomposition of wireless communication channels into a line-of-sight (LoS) path and its orthogonal subspace. After estimating the cha

Bingzheng Wang, Guoqiang Wu, Teng Pang, Yan Zhang

Imitation learning aims to solve the problem of defining reward functions in real-world decision-making tasks. The current popular approach is the Adversarial Imitation Learning (AIL) framework, which matches expert state-action occupancy measures to obtain a surrogate reward for forward reinforcement learning. However, the traditional discriminator is a sim

Rolf Sören Kraußhar, Dmitrii Legatiuk

Octonions are 8-dimensional hypercomplex numbers which form the biggest normed division algebras over the real numbers. Motivated by applications in theoretical physics, continuous octonionic analysis has become an area of active research in recent year. Looking at possible practical applications, it is beneficial to work directly with discrete structures, r

Shamanth S, Aditya Kumar Chari, Harshitha S

The concepts of stability and balance represent many critical problems faced by engineering today. The inverted pendulum on a cart is one such non-linear, unstable, multivariate system whose goal is to determine a suitable control action given to the cart such that it stabilizes the pendulum in an upright vertical position. This paper therefore, aims to desi

Shuairu Zhu, Zhen-Ya Zheng, James Rhoads, Junxian Wang

We present the first results of the Hubble Deep Hydrogen Alpha (HDH$\alpha$) project, which analyzes the space-borne deep H$\alpha$ narrowband imaging data in the GOODS-S region. The HDH$\alpha$ data comprises 72 orbits' images taken with the HST ACS/WFC F658N filter. The exposure time varies across a total area of $\sim$76.1 $\rm{arcmin}^2$, adding up to a

Luke Rickard, Alessandro Abate, Kostas Margellos

Synthesising verifiably correct controllers for dynamical systems is crucial for safety-critical problems. To achieve this, it is important to account for uncertainty in a robust manner, while at the same time it is often of interest to avoid being overly conservative with the view of achieving a better cost. We propose a method for verifiably safe policy sy

Kouzhiqiang Yucheng Xie, Jing Wang, Yuheng Jia, Boyu Shi

This paper introduces RankMatch, an innovative approach for Semi-Supervised Label Distribution Learning (SSLDL). Addressing the challenge of limited labeled data, RankMatch effectively utilizes a small number of labeled examples in conjunction with a larger quantity of unlabeled data, reducing the need for extensive manual labeling in Deep Neural Network (DN

Hamid Latif-Martínez, José Suárez-Varela, Albert Cabellos-Aparicio, Pere Barlet-Ros

Detecting anomalies on network traffic is a complex task due to the massive amount of traffic flows in today's networks, as well as the highly-dynamic nature of traffic over time. In this paper, we propose the use of Graph Neural Networks (GNN) for network traffic anomaly detection. We formulate the problem as contextual anomaly detection on network traffic

César E. Torres Ledesma, Gastao F. Frederico, Manuel M. Bonilla, J. Ávalos Rodríguez

In this paper, we derive sufficient conditions ensuring the existence of a weak solution $u$ for a tempered fractional Euler-Lagrange equations $$ \frac{\partial L}{\partial x}(u,{^C}\mathbb{D}_{a^+}^{\alpha, \sigma} u, t) + \mathbb{D}_{b^-}^{\alpha, \sigma}\left(\frac{\partial L}{\partial y}(u, {^C}\mathbb{D}_{a^+}^{\alpha, \sigma}u, t) \right) = 0 $$ on a

Fangqing Chen

This paper introduces a manipulation framework for the elastic rod, including shape representation, sensorimotor-model estimation, and shape controller. Until now, the manipulation of the elastic rod has faced several challenges: 1) shape learning from high-dimensional to low-space dimensional; 2) the modeling of robot manipulation of the elastic rod; 3) the

Shuyi Liu, Franco P. Bonafe, Heiko Appel, Angel Rubio

Here, using low-temperature optical scanning tunneling microscopy (STM), we investigate inelastic light scattering (ILS) in the vicinity of a single-atom quantum point contact (QPC). A vibration mode localized at the single Ag adatom on the Ag(111) surface is resolved in the ILS spectrum, resulting from tip-enhanced Raman scattering (TERS) by the atomically-

Timo Pierre Schrader, Simon Razniewski, Lukas Lange, Annemarie Friedrich

Understanding causality is a core aspect of intelligence. The Event Causality Identification with Causal News Corpus Shared Task addresses two aspects of this challenge: Subtask 1 aims at detecting causal relationships in texts, and Subtask 2 requires identifying signal words and the spans that refer to the cause or effect, respectively. Our system, which is

Tao Meng, Yuntao Shou, Wei Ai, Nan Yin

The main task of Multimodal Emotion Recognition in Conversations (MERC) is to identify the emotions in modalities, e.g., text, audio, image and video, which is a significant development direction for realizing machine intelligence. However, many data in MERC naturally exhibit an imbalanced distribution of emotion categories, and researchers ignore the negati

M. Manzour, A. Ballardini, R. Izquierdo, M. A. Sotelo

Prediction of vehicle lane change maneuvers has gained a lot of momentum in the last few years. Some recent works focus on predicting a vehicle's intention by predicting its trajectory first. This is not enough, as it ignores the context of the scene and the state of the surrounding vehicles (as they might be risky to the target vehicle). Other works assesse

Tian-Niu Xu, Yongcheng Ding, José D. Martín-Guerrero, Xi Chen

In the realm of quantum control, reinforcement learning, a prominent branch of machine learning, emerges as a competitive candidate for computer-assisted optimal design for experiments. This study investigates the extent to which guidance from human experts is necessary for the effective implementation of reinforcement learning in designing quantum control p

Lauren Kennedy, Aki Vehtari, Andrew Gelman

Generalization to new samples is a fundamental rationale for statistical modeling. For this purpose, model validation is particularly important, but recent work in survey inference has suggested that simple aggregation of individual prediction scores does not give a good measure of the score for population aggregate estimates. In this manuscript we explain w

Robert Schippa

We show trilinear Strichartz estimates in one and two dimensions on frequency-dependent time intervals. These improve on the corresponding linear estimates of periodic solutions to the Schr\"odinger equation. The proof combines decoupling iterations with bilinear short-time Strichartz estimates. Secondly, we use decoupling to show new linear Strichartz estim

Xiao-Feng Shi

Nuclear spin memories of divalent neutral atoms can allow spin-preserving resolved-sideband cooling in a strong magnetic field [Phys. Rev. Lett. 99, 123001 (2007)]. We present a theory for cooling $^{87}$Sr nuclear-spin qubits in a weak magnetic field. The theory depends on laser excitation of $5s5p~^1P_1$ to a nearby state which results in $m_J$-dependent A

Dong Zhao, Ruizhi Yang, Shuang Wang, Qi Zang

Presently, self-training stands as a prevailing approach in cross-domain semantic segmentation, enhancing model efficacy by training with pixels assigned with reliable pseudo-labels. However, we find two critical limitations in this paradigm. (1) The majority of reliable pixels exhibit a speckle-shaped pattern and are primarily located in the central semanti

Kunyu Peng, Cheng Yin, Junwei Zheng, Ruiping Liu

In real-world scenarios, human actions often fall outside the distribution of training data, making it crucial for models to recognize known actions and reject unknown ones. However, using pure skeleton data in such open-set conditions poses challenges due to the lack of visual background cues and the distinct sparse structure of body pose sequences. In this

A. Gutiérrez-Rodríguez, V. Cetinkaya, M. Köksal, E. Gurkanli

In the post-LHC era, the muon collider represents a frontier project capable of providing high-energy and high-luminosity leptonic collisions among future lepton-lepton particle accelerators. In addition, it provides significantly cleaner final states than those produced in hadron collisions. With this expectation in mind, in this article, we research the se

Ling Chen, Jiahua Cui

Time series refer to a series of data points indexed in time order, which can be found in various fields, e.g., transportation, healthcare, and finance. Accurate time series forecasting can enhance optimization planning and decision-making support. Time series have multi-scale characteristics, i.e., different temporal patterns at different scales, which pres

András Juhász, Mark Powell

We show that certain smooth tori with group $\mathbb{Z}$ in $S^4$ have exteriors with standard equivariant intersection forms, and so are topologically unknotted. These include the turned 1-twist-spun tori in the 4-sphere constructed by Boyle, the union of the genus one Seifert surface of Cochran and Davis that has no slice derivative with a ribbon disc, and

Santanu Tantubay, Priyanshu Chakraborty

Toroidal Lie algebras are $n$ variable generalizations of affine Kac-Moody Lie algebras. Full toroidal Lie algebra is the semidirect product of derived Lie algebra of toroidal Lie algebra and Witt algebra, also it can be thought of $n$-variable generalization of Affine-Virasoro algebras. Let $\tilde{\mathfrak{h}}$ be a Cartan subalgebra of a toroidal Lie alg

Bidyut Hazarika, Prabwal Phukon

We study the thermodynamic topology of four and five dimensional Horava Lifshitz (HL) black holes in Horava gravity. These exotic black hole solutions belong to a special class of of black holes whose thermodynamics exhibit a line of (continuous) second order phase transitions known as $\lambda$ phase transitions akin to those observed in the superfluidity o

Yubin Wang, Xinyang Jiang, De Cheng, Dongsheng Li

Prompt learning has become a prevalent strategy for adapting vision-language foundation models to downstream tasks. As large language models (LLMs) have emerged, recent studies have explored the use of category-related descriptions as input to enhance prompt effectiveness. Nevertheless, conventional descriptions fall short of structured information that effe

Arsen Khvedelidze, Astghik Torosyan

The interrelation between classicality/quantumness and symmetry of states is discussed within the phase-space formulation of finite-dimensional quantum systems. We derive representations for classicality measures $\mathcal{Q}_N[H_{\varrho}]$ of states from the stratum of given symmetry type $[H_{\varrho}]$ for the Hilbert-Schmidt ensemble of qudits. The expr

Yunseok Seo, Sejin Kim, Kyung Kiu Kim

In this work, we investigate an extended model of holographic superconductor by a non-linear electrodynamic interaction coupled to a complex scalar field. This non-linear interaction term can make a quantum phase transition at zero temperature with finite charge carrier density. By solving full equations of motion, we can construct various shapes of the supe

Kai Mason, Florencia Maurino-Alperovich, David Holder, Kirill Aristovich

Introduction. Noisy measurements frequently cause noisy and inaccurate images in impedance imaging. No post-processing technique exists to calculate the propagation of measurement noise and use this to suppress noise in the image. Objectives. The objectives of this work were (1) to develop a post-processing method for noise-based correction (NBC) in impedanc

M. Roldão, J. L. Figueiredo, P. Monteiro, J. T. Mendonça

The quantum diffusion of a vortex in a two-component quantum fluid of light is investigated. In these systems, the Kerr nonlinearity promotes interactions between the photons, displaying features that are analogue of a Bose-Einstein condensates. Quantum fluids of light have the advantage of simulating matter-wave phenomena at room temperatures. While the ana

Shoyu Nagaoka

We describe the $p$-divisibility transposition for the Fourier coefficients of Hermitian modular forms. The results show that the same phenomenon as that for Siegel modular forms holds for Hermitian modular forms.

Aalok Gangopadhyay, Dwip Dalal, Progyan Das, Shanmuganathan Raman

Diffeomorphisms play a crucial role while searching for shapes with fixed topological properties, allowing for smooth deformation of template shapes. Several approaches use diffeomorphism for shape search. However, these approaches employ only unconstrained diffeomorphisms. In this work, we develop Flow Symmetrization - a method to represent a parametric fam

Yichi Zhang, Jin Yang, Yuchen Liu, Yuan Cheng

Semi-supervised learning has attracted much attention due to its less dependence on acquiring abundant annotations from experts compared to fully supervised methods, which is especially important for medical image segmentation which typically requires intensive pixel/voxel-wise labeling by domain experts. Although semi-supervised methods can improve the perf

Jiaxu Zhao, Meng Fang, Shirui Pan, Wenpeng Yin

Warning: This paper contains content that may be offensive or upsetting. There has been a significant increase in the usage of large language models (LLMs) in various applications, both in their original form and through fine-tuned adaptations. As a result, LLMs have gained popularity and are being widely adopted by a large user community. However, one of th

Zhongqiang Ren, Anushtup Nandy, Sivakumar Rathinam, Howie Choset

Multi-Agent Combinatorial Path Finding (MCPF) seeks collision-free paths for multiple agents from their initial to goal locations, while visiting a set of intermediate target locations in the middle of the paths. MCPF is challenging as it involves both planning collision-free paths for multiple agents and target sequencing, i.e., solving traveling salesman p

Dmitry S. Muratov, Lev Luchnikov, Danila Saranin, Artur Ishteev

Metal halide perovskite solar cells being one of the fastest emerging technologies for renewable energy still has to become more industry friendly in a way that will allow using it for thin film modules or tandems with conventional silicon devices. The simplest way to achieve this is to use chemical vapor deposition (CVD) technique for tandem production. In

T. E. Gureyev, D. M. Paganin, H. M. Quiney

Signal-to-noise ratio and spatial resolution are quantitatively analysed in the context of in-line (propagation based) X-ray phase-contrast imaging. It is known that free-space propagation of a coherent X-ray beam from the imaged object to the detector plane, followed by phase retrieval in accordance with Paganin's method, can increase the signal-to-noise in

Santanu Das

The general theory of relativity (GR) has excelled in explaining gravitational phenomena at the scale of the solar system with remarkable precision. However, when extended to the galactic or cosmological scale, it requires dark matter and dark energy to explain observations. In our previous article (arXiv:2308.04503), we've formulated a gravity theory based

Thomas Perrin

Solutions of a system of wave equations are constructed for both homogeneous and inhomogeneous Dirichlet boundary conditions at every regularity level. We prove that boundary observability, and thus boundary exact controllability, at some regularity level is equivalent to boundary observability at all levels. The main ingredient is the ellipticity of a time-

Mizuki Nakajima, Kaoruko Shinkawa, Yoshihiro Nakata

In the context of avatar-mediated communication, it is crucial for the face-to-face interlocutor to sense the operator's presence and emotions via the avatar. Although androids resembling humans have been developed to convey presence through appearance and movement, few studies have prioritized deepening the communication experience for both operator and int

Max Hahn-Klimroth, Paul W. Dierkes, Matthias W. Kleespies

In several branches of the social sciences and humanities, surveys based on standardized questionnaires are a prominent research tool. While there are a variety of ways to analyze the data, some standard procedures have become established. When those surveys want to analyze differences in the answer patterns of different groups (e.g., countries, gender, age,

Georg Bergner, Antonio González-Arroyo, Ivan Soler

We report on how adjoint zero modes can be used to filter out the topological structures of gauge configurations from the UV fluctuations. We will use the Adjoint Filtering Method (AFM) which relies on the existence of a particular Supersymmetric Zero Mode (SZM) that follows closely the (anti)self-dual part of the action density. In contrast, it is not guara

Petr Satunin, Andrey Sharofeev

We present shower formation constraints on the Lorentz Invariance Violation (LIV) energy scale for photons with cubic dispersion relation from recent gamma ray observations in $100$ TeV -- PeV energy range by LHAASO observatory. We assume Myers-Pospelov effective field theory framework, and calculate the suppression for the Bethe-Heitler process which is mai

David Fernández Llorca, Pedro Frau, Ignacio Parra, Rubén Izquierdo

This paper addresses the often overlooked issue of fairness in the autonomous driving domain, particularly in vision-based perception and prediction systems, which play a pivotal role in the overall functioning of Autonomous Vehicles (AVs). We focus our analysis on biases present in some of the most commonly used visual datasets for training person and vehic

Giorgos Borboudakis, Paulos Charonyktakis, Konstantinos Paraschakis, Ioannis Tsamardinos

AutoML platforms have numerous options for the algorithms to try for each step of the analysis, i.e., different possible algorithms for imputation, transformations, feature selection, and modelling. Finding the optimal combination of algorithms and hyper-parameter values is computationally expensive, as the number of combinations to explore leads to an expon

Demin Yu, Xutao Li, Yunming Ye, Baoquan Zhang

Precipitation nowcasting is an important spatio-temporal prediction task to predict the radar echoes sequences based on current observations, which can serve both meteorological science and smart city applications. Due to the chaotic evolution nature of the precipitation systems, it is a very challenging problem. Previous studies address the problem either f

Joao Magueijo

In this paper we entertain a Machian setting where local physics is non-locally affected by the whole Universe, taking the liberty to identify the local (``Newton's bucket'') with our visible Universe, and the whole Universe (Mach's ``fixed stars'') with the global Universe beyond our horizon. Crucially, we allow for the two to have different properties, so

Mahdi Hejrati, Jouni Mattila

Vast industrial investment along with increased academic research on heavy-duty hydraulic manipulators has unavoidably paved the way for their automatization, necessitating the design of robust and high-precision controllers. In this study, an orchestrated robust controller is designed to address the mentioned issue for generic manipulators with an anthropom

Hasan Oguz, Zekeriya Mehmet Yuksel, Ozgur Onder Karakilinc, Halil Berberoglu

In this study, we explore the effect of integrated auxiliary rods at varying angles to the primary cavity rod on the dispersion characteristics of the photonic crystal coupled cavity waveguide (PC CCW). Here, it is intended to break the symmetry of the cavity region by introducing auxiliary rods which gives the degree of freedom for tuning effective index of

Naike Du, Tiantian Yin, Jing Wang, Rencheng Song

A deep learning-assisted inversion method is proposed to solve the inhomogeneous background imaging problem. Three non-iterative methods, namely the distorted-Born (DB) major current coefficients method, the DB modified Born approximation method, and the DB connection method, are introduced to address the inhomogeneous background inverse scattering problem.

Petr Akhmet'ev, Maxim Dvornikov

We construct a new Yang-Mills 3D-solution on the space of negative scalar curvarure. We discuss a problem of non-abelian gauge symmetry is broken with the assumption that a scalar curvature of the domain is a negative small parameter. In this case we use the following fact: a geometrical scale related with Vassiliev's discriminant of magnetic lines coincids

Mohit Panwar, Pankaj Jain

We study the dipole signal in the spectral index (x) of the differential number counts using quasars in the CatWISE2020 catalog of infrared sources. The index is extracted by using the log-likelihood method. We obtain the value $x=1.579 \pm 0.001$ for a quasar sample of 1355352 sources. We extract the dipole signal in this parameter by employing $\chi^{2}$ m

J. Tokimoto, S. Ohmura, A. Takahashi, K. Iwano

We propose a new approach to extract the important degrees of freedom in quantum dynamics induced by an external stimulus. We calculate the coefficient matrix numerically, where the $i-l$ element of the matrix is the coefficient of the lth basis state at the ith discretized time in the solution of the time-dependent Schr\"odinger equation induced by the exte

Jiashuo Fan, Yaoyuan Liang, Leyao Liu, Shaolun Huang

In this paper, we introduce a novel approach to novel object captioning which employs relative contrastive learning to learn visual and semantic alignment. Our approach maximizes compatibility between regions and object tags in a contrastive manner. To set up a proper contrastive learning objective, for each image, we augment tags by leveraging the relative

Andrew Danso

arXiv admin comment: This version has been removed by arXiv administrators as the submitter did not have the rights to agree to the license at the time of submission

Jiangbin Zheng, Stan Z. Li

While deep generative models show promise for learning inverse protein folding directly from data, the lack of publicly available structure-sequence pairings limits their generalization. Previous improvements and data augmentation efforts to overcome this bottleneck have been insufficient. To further address this challenge, we propose a novel protein design

Marcel Parciak, Sebastiaan Weytjens, Niel Hens, Frank Neven

Approximate functional dependencies (AFDs) are functional dependencies (FDs) that "almost" hold in a relation. While various measures have been proposed to quantify the level to which an FD holds approximately, they are difficult to compare and it is unclear which measure is preferable when one needs to discover FDs in real-world data, i.e., data that only a

Negin Ghamsarian, Yosuf El-Shabrawi, Sahar Nasirihaghighi, Doris Putzgruber-Adamitsch

In recent years, the landscape of computer-assisted interventions and post-operative surgical video analysis has been dramatically reshaped by deep-learning techniques, resulting in significant advancements in surgeons' skills, operation room management, and overall surgical outcomes. However, the progression of deep-learning-powered surgical technologies is

Paul C Bressloff

The Dean-Kawasaki (DK) equation is a stochastic partial differential equation (SPDE) for the global density $\rho$ of a gas of $N$ over-damped Brownian particles. In the thermodynamic limit $N\rightarrow \infty$ with weak pairwise interactions, the expectation ${\mathbb E}[\rho]$ converges in distribution to the solution of a McKean-Vlasov (MV) equation. In

Wenhan Yu, Terence Jie Chua, Jun Zhao

The Metaverse is gaining attention among academics as maturing technologies empower the promises and envisagements of a multi-purpose, integrated virtual environment. An interactive and immersive socialization experience between people is one of the promises of the Metaverse. In spite of the rapid advancements in current technologies, the computation require

Lea Steffen, Martin Schulze, Christian Eichmann, Robin Koch

Care robotics as a research field has developed a lot in recent years, driven by the rapidly increasing need for it. However, these technologies are mostly limited to a very concrete and usually relatively simple use case. The bimanual robot House of Living Labs intelligent Escort (HoLLiE) includes an omnidirectional mobile platform. This paper presents how

Daniel Sacco Shaikh, Alberto Giuseppe Catalano, Fabio Cavaliere, Fabio Franchini

Landau theory's implicit assumption that microscopic details cannot affect the system's phases has been challenged only recently in systems such as antiferromagnetic quantum spin chains with periodic boundary conditions, where topological frustration can be induced. In this work, we show that the latter modifies the zero temperature phase diagram of the XY c

Yiqun Diao, Qinbin Li, Bingsheng He

Federated Learning (FL) has emerged as a promising solution to perform deep learning on different data owners without exchanging raw data. However, non-IID data has been a key challenge in FL, which could significantly degrade the accuracy of the final model. Among different non-IID types, label skews have been challenging and common in image classification

Anna Freni Sterrantino, Denis Rustand, Janet van Niekerk, Elias Teixeira Krainski

In this work, we present a new approach for constructing models for correlation matrices with a user-defined graphical structure. The graphical structure makes correlation matrices interpretable and avoids the quadratic increase of parameters as a function of the dimension. We suggest an automatic approach to define a prior using a natural sequence of simple

Bin Yang, Patrick Pfreundschuh, Roland Siegwart, Marco Hutter

LiDAR Upsampling is a challenging task for the perception systems of robots and autonomous vehicles, due to the sparse and irregular structure of large-scale scene contexts. Recent works propose to solve this problem by converting LiDAR data from 3D Euclidean space into an image super-resolution problem in 2D image space. Although their methods can generate