December 2023 arXiv papers — page 119

Peter Bella, Roberta Marziani

We consider a variational problem modeling transition between flat and wrinkled region in a thin elastic sheet, and identify the $\Gamma$-limit as the sheet thickness goes to 0, thus extending the previous work of the first author [Bella, ARMA 2015]. The limiting problem is scalar and convex, but constrained and posed for measures. For the $\Gamma$-liminf in

Lipei Du, Chun Shen, Sangyong Jeon, Charles Gale

Our investigation focuses on the rapidity-dependent directed flow, $v_1(y)$, of identified hadrons in Au+Au collisions across a broad range of $\sqrt{s_{\rm NN}}$ from 7.7 to 200 GeV. Employing a (3+1)-dimensional hybrid framework, our study successfully reproduces the characteristic features of the measured $v_1(y)$ for both mesons and baryons across the co

Alexis Thual, Yohann Benchetrit, Felix Geilert, Jérémy Rapin

Deep learning is leading to major advances in the realm of brain decoding from functional Magnetic Resonance Imaging (fMRI). However, the large inter-subject variability in brain characteristics has limited most studies to train models on one subject at a time. Consequently, this approach hampers the training of deep learning models, which typically requires

Yan Zhao, Yuan Zong, Hailun Lian, Cheng Lu

Cross-corpus speech emotion recognition (SER) poses a challenge due to feature distribution mismatch, potentially degrading the performance of established SER methods. In this paper, we tackle this challenge by proposing a novel transfer subspace learning method called acoustic knowledgeguided transfer linear regression (AKTLR). Unlike existing approaches, w

Yajie Duan, Javier Cabrera, Birol Emir

Visualization of extremely large datasets in static or dynamic form is a huge challenge because most traditional methods cannot deal with big data problems. A new visualization method for big data is proposed based on Projection Pursuit, Guided Tour and Data Nuggets methods, that will help display interesting hidden structures such as clusters, outliers, and

Simran Arora, P. K. Sahoo

We reexamine the kink-like parameterization of the deceleration parameter to derive constraints on the transition redshift from cosmic deceleration to acceleration. This is achieved using observational Hubble data, Type Ia Supernovae Pantheon+ samples and Baryon acoustic oscillations. In this parametrization, the value of the initial $q$ parameter is $q_{i}$

Fabian Zierler, Reinhard Alkofer

The gauge-boson, ghost and fermion propagators as well as the gauge-boson--ghost vertex function are studied for SU(N), Sp(2N) and SO(N) gauge groups. We solve a set of coupled Dyson-Schwinger equations in Landau gauge for a variable, fractional number $N_f$ of massless fermions in the fundamental representation. For large $N_f$ we find a phase transition fr

Qi Yang, Xing Nie, Tong Li, Pengfei Gao

Recently, an audio-visual segmentation (AVS) task has been introduced, aiming to group pixels with sounding objects within a given video. This task necessitates a first-ever audio-driven pixel-level understanding of the scene, posing significant challenges. In this paper, we propose an innovative audio-visual transformer framework, termed COMBO, an acronym f

Shashank Yellapantula

In this study, a deep learning based conditional density estimation technique known as conditional variational auto-encoder (CVAE) is used to fill gaps typically observed in particle image velocimetry (PIV) measurements in combustion systems. The proposed CVAE technique is trained using time resolved gappy PIV fields, typically observed in industrially relev

Matei Hanu, Jürgen Hesser, Guido Kanschat, Javier Moviglia

This paper addresses the challenging task of guide wire navigation in cardiovascular interventions, focusing on the parameter estimation of a guide wire system using Ensemble Kalman Inversion (EKI) with a subsampling technique. The EKI uses an ensemble of particles to estimate the unknown quantities. However since the data misfit has to be computed for each

Orestis Lignos

This is the first part of a series of papers aiming to show how trigonometry and analytic tools can help into tackling demanding Olympiad geometry problems. We present several novel techniques for tackling hard problems from various international and national mathematical competitions, and develop the appropriate theoretical background. Several insightful ex

Ranjan Kumar Patel, Yifan Yuan, Ravindra Singh Bisht, Ivan Seskar

RF radiation spectrum is central to wireless and radar systems among numerous high-frequency device technologies. Here, we demonstrate sensing of RF signals in the technologically relevant 2.4 GHz range utilizing vanadium dioxide (VO2), a quantum material that has garnered significant interest for its insulator-to-metal transition. We find the electrical res

Ming Kang, Chee-Ming Ting, Fung Fung Ting, Raphaël C. -W. Phan

We propose a novel Attentional Scale Sequence Fusion based You Only Look Once (YOLO) framework (ASF-YOLO) which combines spatial and scale features for accurate and fast cell instance segmentation. Built on the YOLO segmentation framework, we employ the Scale Sequence Feature Fusion (SSFF) module to enhance the multi-scale information extraction capability o

Will E. Thompson, David M. Vidmar, Jessica K. De Freitas, John M. Pfeifer

Identifying disease phenotypes from electronic health records (EHRs) is critical for numerous secondary uses. Manually encoding physician knowledge into rules is particularly challenging for rare diseases due to inadequate EHR coding, necessitating review of clinical notes. Large language models (LLMs) offer promise in text understanding but may not efficien

Izumi Tanaka, Ken Sakayori, Naoki Kobayashi

Toman et al. have proposed a type system for automatic verification of low-level programs, which combines ownership types and refinement types to enable strong updates of refinement types in the presence of pointer aliases. We extend their type system to support pointer arithmetic, and prove its soundness. Based on the proposed type system, we have implement

Bao Li, Zhenyu Liu, Lizhi Shao, Bensheng Qiu

Directly predicting human epidermal growth factor receptor 2 (HER2) status from widely available hematoxylin and eosin (HE)-stained whole slide images (WSIs) can reduce technical costs and expedite treatment selection. Accurately predicting HER2 requires large collections of multi-site WSIs. Federated learning enables collaborative training of these WSIs wit

Yan Zhuang, Benjamin Hou, Tejas Sudharshan Mathai, Pritam Mukherjee

As a new emerging and promising type of generative models, diffusion models have proven to outperform Generative Adversarial Networks (GANs) in multiple tasks, including image synthesis. In this work, we explore semantic image synthesis for abdominal CT using conditional diffusion models, which can be used for downstream applications such as data augmentatio

A. Nardin, R. Lopes, M. Rizzi, L. Mazza

We study the Bisognano-Wichmann Hamiltonian for fractional quantum Hall states defined on a sphere and explore its relationship with the entanglement Hamiltonian associated to the state. We present results for several examples, namely the bosonic Laughlin state stabilized by contact two-body interactions and the bosonic Moore-Read state stabilized by either

Nikos K. Kollas, Dimitris Moustos

We revisit the mathematics of exactly solvable Unruh-DeWitt detector models, interacting with massless scalar fields under instantaneous interactions, to construct a relativistic quantum Otto heat engine. By deriving the conditions under which the thermodynamic cycle is closed we study the effects of motion on the amount of work that can be extracted from th

John Golden, Andreas Bärtschi, Daniel O'Malley, Elijah Pelofske

We introduce JuliQAOA, a simulation package specifically built for the Quantum Alternating Operator Ansatz (QAOA). JuliQAOA does not require a circuit-level description of QAOA problems, or another package to simulate such circuits, instead relying on a more direct linear algebra implementation. This allows for increased QAOA-specific performance improvement

Evgeny Kuzmin, Maksim Vlasov, Wadim Strielkowski, Marina Faminskaya

This study examines the role of human capital investment in driving sustainable socio-economic growth within the energy industry. The fuel and energy sector undeniably forms the backbone of contemporary economies, supplying vital resources that underpin industrial activities, transportation, and broader societal operations. In the context of the global shift

L. Beddrich, J. K. Jochum, P. Bender, L. Spitz

Here, we discuss the comparability of data acquisitioned with the Modulation of IntEnsity with Zero Effort data, a neutron spin-echo (NSE) technique to neutron Time-of-Flight (ToF) spectroscopy data. As a NSE technique MIEZE records the intermediate scattering function $\mathcal{I}(Q, \tau)$ making it necessary to perform a Fourier transform to directly comp

Yogev Bar-On, Yishay Mansour

A growing number of products use layer 2 solutions to expand the capabilities of primary blockchains like Ethereum, where computation is off-loaded from the root chain, and the results are published to it in bulk. Those include optimistic and zero-knowledge rollups, information oracles, and app-specific chains. This work presents an analysis of layer 2 block

P. H. Souza, J. E. Padilha, R. H. Miwa

Two-dimensional (2D) materials combined with the presence of surface nearly-free electrons (NFE) have been considered quite interesting platforms to be exploited for the development of 2D electronic devices. Further incorporation of foreign elements adds a new degree of freedom to engineer the electronic as well as the magnetic properties of 2D materials. He

William A. Borders, Advait Madhavan, Matthew W. Daniels, Vasileia Georgiou

The increasing scale of neural networks needed to support more complex applications has led to an increasing requirement for area- and energy-efficient hardware. One route to meeting the budget for these applications is to circumvent the von Neumann bottleneck by performing computation in or near memory. An inevitability of transferring neural networks onto

Jibril Frej, Neel Shah, Marta Knežević, Tanya Nazaretsky

The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networks with Knowledge Graphs (KGs) to achieve richer representations of learners and courses. While these enriched representations allow more accurate and personalized recommendations,

Haoqiang Huang, Zihe Wang, Zhide Wei, Jie Zhang

In this paper, we delve into the problem of using monetary incentives to encourage players to shift from an initial Nash equilibrium to a more favorable one within a game. Our main focus revolves around computing the minimum reward required to facilitate this equilibrium transition. The game involves a single row player who possesses $m$ strategies and $k$ c

Dominic van der Zypen

Donald Knuth introduced in The Art of Computer Programming (Vol 4a) a fast approximation to the addition of integers (given in binary) in terms of bit-wise operations by $a + b \; \approx \; a \oplus b \oplus ((a\land b) \ll 1).$ Generalizing this to infinite bit-strings we get a binary operation on ${\mathcal P}(\mathbb{N})$, the power-set of $\mathbb{N}$ (

Trevor Exley, Emilly Hays, Daniel Johnson, Arian Moridani

Soft robotics is a rapidly growing field that spans the fields of chemistry, materials science, and engineering. Due to the diverse background of the field, there have been contrasting naming schemes such as 'intelligent', 'smart' and 'adaptive' materials which add vagueness to the broad innovation among literature. Therefore, a clear, functional and descrip

Parthapratim Mahapatra, Shilpa Kastha, Anuradha Gupta, B. S. Sathyaprakash

Amplitude and phase of the gravitational waveform from compact binary systems can be decomposed in terms of their mass- and current-type multipole moments. In a modified theory of gravity, one or more of these multipole moments could deviate from general theory of relativity. In this work, we show that a waveform model that parametrizes the amplitude and pha

Hao Cheng, Shufeng Kong, Yanchen Deng, Caihua Liu

Core-selecting combinatorial auctions (CAs) restrict the auction result in the core such that no coalitions could improve their utilities by engaging in collusion. The minimum-revenue-core (MRC) rule is a widely used core-selecting payment rule to maximize the total utilities of all bidders. However, the MRC rule can suffer from severe unfairness since it ig

Daniel Bartl, Shahar Mendelson

We show that under minimal assumptions on a class of functions $\mathcal{H}$ defined on a probability space $(\mathcal{X},\mu)$, there is a threshold $\Delta_0$ satisfying the following: for every $\Delta\geq\Delta_0$, with probability at least $1-2\exp(-c\Delta m)$ with respect to $\mu^{\otimes m}$, \[ \sup_{h\in\mathcal{H}} \sup_{t\in\mathbb{R}} \left| \ma

Fan Xu, Nan Wang, Hao Wu, Xuezhi Wen

Graph-based fraud detection (GFD) can be regarded as a challenging semi-supervised node binary classification task. In recent years, Graph Neural Networks (GNN) have been widely applied to GFD, characterizing the anomalous possibility of a node by aggregating neighbor information. However, fraud graphs are inherently heterophilic, thus most of GNNs perform p

Jingran Shen, Nikos Tziritas, Georgios Theodoropoulos

With the rapid development of Deep Learning, more and more applications on the cloud and edge tend to utilize large DNN (Deep Neural Network) models for improved task execution efficiency as well as decision-making quality. Due to memory constraints, models are commonly optimized using compression, pruning, and partitioning algorithms to become deployable on

Soheil Hor, Amin Arbabian

As Moore's Law loses momentum, improving size, performance, and efficiency of processors has become increasingly challenging, ending the era of predictable improvements in hardware performance. Meanwhile, the widespread incorporation of high-definition sensors in consumer devices and autonomous technologies has fueled a significant upsurge in sensory data. C

Tianyu Huang, Yihan Zeng, Zhilu Zhang, Wan Xu

3D generation has raised great attention in recent years. With the success of text-to-image diffusion models, the 2D-lifting technique becomes a promising route to controllable 3D generation. However, these methods tend to present inconsistent geometry, which is also known as the Janus problem. We observe that the problem is caused mainly by two aspects, i.e

Askold Duviryak

Rotary dynamics of polarized composite particles as dipole rigid bodies is considered. It is described the Euler equations singularly perturbed by the radiation reaction torque. The Schott term is taken into account, and the reduction procedure lowering higher derivatives is applied. Asymptotic methods of nonlinear mechanics are used to analyze the rotary dy

Chang Hoong Chow, Boon Long Ng, Vindhiya Prakash, Christian Kurtsiefer

Electromagnetically induced transparency (EIT) can be used to cool an atom in a harmonic potential close to the ground state by addressing several vibrational modes simultaneously. Previous experimental efforts focus on trapped ions and neutral atoms in a standing wave trap. In this work, we demonstrate EIT cooling of an optically trapped single neutral atom

Luke Hagar, Nathaniel T. Stevens

Prior elicitation methods for Bayesian analyses transfigure prior information into quantifiable prior distributions. Recently, methods that leverage copulas have been proposed to accommodate more flexible dependence structures when eliciting multivariate priors. We show that the posterior cannot retain many of these flexible prior dependence structures in la

Ronghui Mu, Leandro Soriano Marcolino, Tianle Zhang, Yanghao Zhang

Reinforcement Learning (RL) has achieved remarkable success in safety-critical areas, but it can be weakened by adversarial attacks. Recent studies have introduced "smoothed policies" in order to enhance its robustness. Yet, it is still challenging to establish a provable guarantee to certify the bound of its total reward. Prior methods relied primarily on c

CMS Collaboration

A search for the production of a top quark in association with a photon and additional jets via flavor changing neutral current interactions is presented. The analysis uses proton-proton collision data recorded by the CMS detector at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$. The search is performed by look

N. Mandarakas, G. V. Panopoulou, V. Pelgrims, S. B. Potter

Context. Calibration of optical polarimeters relies on the use of stars with negligible polarization (unpolarized standard stars) for determining the instrumental polarization zero-point. For wide-field polarimeters, calibration is often done by imaging the same star over multiple positions in the field of view - a process which is time-consuming. A more eff

Isaac Tutusaus, Camille Bonvin, Nastassia Grimm

The Weyl potential, which is the sum of the spatial and temporal distortions of the Universe's geometry, provides a direct way of testing the theory of gravity and the validity of the $\Lambda$CDM (Lambda Cold Dark Matter) model. Here we present measurement of the Weyl potential at four redshifts bins using data from the first three years of observations of

C. H. Namitha, S. Sundar

Let $P$ be a pointed, closed convex cone in $\mathbb{R}^d$. We prove that for two pure isometric representations $V^{(1)}$ and $V^{(2)}$ of $P$, the associated CAR flows $\beta^{V^{(1)}}$ and $\beta^{V^{(2)}}$ are cocycle conjugate if and only if $V^{(1)}$ and $V^{(2)}$ are unitarily equivalent. We also give a complete description of pure isometric represent

Chengjiang Yin, Jian-hua He

We investigate the detectability of gravitational waves that have been lensed by a spinless stellar-mass black hole, with respect to the advanced LIGO. By solving the full relativistic linear wave equations in the spacetime of a Schwarzschild black hole, we find that the strong gravity can create unique signals in the lensed waveform, particularly during the

Le Ngu Nguyen, Praneeth Susarla, Anirban Mukherjee, Manuel Lage Cañellas

Indoor human monitoring systems leverage a wide range of sensors, including cameras, radio devices, and inertial measurement units, to collect extensive data from users and the environment. These sensors contribute diverse data modalities, such as video feeds from cameras, received signal strength indicators and channel state information from WiFi devices, a

Tao Yu, Zongdian Li, Kei Sakaguchi, Omar Hashash

The concept of digital twin (DT), which enables the creation of a programmable, digital representation of physical systems, is expected to revolutionize future industries and will lie at the heart of the vision of a future smart society, namely, Society 5.0, in which high integration between cyber (digital) and physical spaces is exploited to bring economic

Laura Orphal-Kobin, Cem Güney Torun, Julian M. Bopp, Gregor Pieplow

Diamond has emerged as a highly promising platform for quantum network applications. Color centers in diamond fulfill the fundamental requirements for quantum nodes: they constitute optically accessible quantum systems with long-lived spin qubits. Furthermore, they provide access to a quantum register of electronic and nuclear spin qubits and they mediate en

László M. Fehér, András P. Juhász

We give a new method to calculate the universal cohomology classes of coincident root loci. We show a polynomial behavior of them and apply this result to prove that generalized Pl\"ucker formulas are polynomials in the degree, just as the classical Pl\"ucker formulas counting the bitangents and flexes of a degree $d$ generic plane curve. We establish an upp

Zhengdi Liu, Wenwen Sun

This study presents a novel approach for analyzing and establishing Large CALPHAD model (LCM) in complex alloy systems. Through the introduction of "composition space volume", a multi-dimensional metric which allows to quatitatively define alloy composition variations. Utilizing stochastic methods, the study quantifies phase space complexity through phase de

Zhishuai Li, Ziyue Li, Xiaoru Hu, Guoqing Du

Trajectory recovery based on the snapshots from the city-wide multi-camera network facilitates urban mobility sensing and driveway optimization. The state-of-the-art solutions devoted to such a vision-based scheme typically incorporate predefined rules or unsupervised iterative feedback, struggling with multi-fold challenges such as lack of open-source datas

Edan Lerner

Many fibrous materials are modeled as elastic networks featuring a substantial separation between the stiffness scales that characterize different microscopic deformation modes of the network's constituents. This scale separation has been shown to give rise to emergent complexity in these systems' linear and nonlinear mechanical response. Here we study numer

Zhiyuan Sun, Yuta Murakami, Tatsuya Kaneko, Denis Golež

Bilayer materials may support interlayer excitons comprised of electrons in one layer and holes in the other. In experiments, a non-zero exciton density is typically sustained by a bias chemical potential, implemented either by optical pumping or by electrical contacts connected to the two layers. We show that if charge can tunnel between the layers, the che

Constantin Bacuta, Daniel Hayes, Tyler O'Grady

We consider a model convection-diffusion problem and present our recent numerical and analysis results regarding mixed finite element formulation and discretization in the singular perturbed case when the convection term dominates the problem. Using the concepts of optimal norm and saddle point reformulation, we found new error estimates for the case of unif

Timofey Mezhuev, Ilay Kobrin, Alexey Vishnyakov, Daniil Kuts

Numeric truncation is a widely spread error in software written in languages with static data typing, such as C/C++ or Java. It occurs when the significant bits of the value with a bigger type size are truncated during value conversion to the smaller type. Utilizing one of the most powerful methods for path exploration and automated bug detection called dyna

Ruijie Hou, Zhaoyang Yang, Yu Ming, Hongyu Lu

Deep neural networks (DNNs) that incorporated lifelong sequential modeling (LSM) have brought great success to recommendation systems in various social media platforms. While continuous improvements have been made in domain-specific LSM, limited work has been done in cross-domain LSM, which considers modeling of lifelong sequences of both target domain and s

Yuyang Zhou, Guang Cheng, Zongyao Chen, Shui Yu

Machine learning (ML) has gained significant adoption in Android malware detection to address the escalating threats posed by the rapid proliferation of malware attacks. However, recent studies have revealed the inherent vulnerabilities of ML-based detection systems to evasion attacks. While efforts have been made to address this critical issue, many of the

Christian Fiedler, Michael Herty, Sebastian Trimpe

Mean field limits are an important tool in the context of large-scale dynamical systems, in particular, when studying multiagent and interacting particle systems. While the continuous-time theory is well-developed, few works have considered mean field limits for deterministic discrete-time systems, which are relevant for the analysis and control of large-sca

Joseph D. Adelinia, Eryin Wang, Mariana Chavez-Cervantes, Toru Matsuyama

The physics of optically-induced superconductivity remains poorly understood, with questions that range from the underlying microscopic mechanism to the macroscopic electrical response of the non-equilibrium phase. In this paper, we study optically-induced superconductivity in K$_{3}$C$_{60}$ thin films, which display signatures of granularity both in the eq

Adam Lilja, Junsheng Fu, Erik Stenborg, Lars Hammarstrand

The task of online mapping is to predict a local map using current sensor observations, e.g. from lidar and camera, without relying on a pre-built map. State-of-the-art methods are based on supervised learning and are trained predominantly using two datasets: nuScenes and Argoverse 2. However, these datasets revisit the same geographic locations across train

Hui Liu

In this thesis, we first review the linearized soliton perturbation theory developed in recent years, which is particularly simple in the one-kink sector. Using it, the amplitude and probability of kink-meson inelastic scattering can be simplified into a perturbative problem in the kink frame. Although the Sine-Gordon soliton and $\phi^4$ kink, which people

Anish Agashe

The Raychaudhuri equation for a congruence of curves in a general non-Riemannian geometry is derived. A formal connection is established between the expansion scalar and the cross-sectional volume of the congruence. It is found that the expansion scalar is equal to the fractional rate of change of volume, weighted by a scalar factor that depends on the non-R

Andreas A. Bock, Martin S. Andersen

In this experimental work, we present a general framework based on the Bregman log determinant divergence for preconditioning Hermitian positive definite linear systems. We explore this divergence as a measure of discrepancy between a preconditioner and a matrix. Given an approximate factorisation of a given matrix, the proposed framework informs the constru

Gerard Pareras, Victoria Cabedo, Martin McCoustra, Albert Rimola

Gas-phase chemistry at extreme conditions (low densities and temperatures) is difficult, so the presence of interstellar grains is especially important for the synthesis of molecules that cannot form in the gas phase. Interstellar grains are advocated to enhance the encounter rate of the reactive species on their surfaces and to dissipate the energy excess o

Luke Hagar, Nathaniel T. Stevens

In bioequivalence design, power analyses dictate how much data must be collected to detect the absence of clinically important effects. Power is computed as a tail probability in the sampling distribution of the pertinent test statistics. When these test statistics cannot be constructed from pivotal quantities, their sampling distributions are approximated v

Spyridon Kakaroumpas, Odí Soler i Gibert

In this paper we set up a theory of two-matrix weighted little BMO in two parameters. We prove that being a member of this class is equivalent to belonging uniformly in each variable to two-matrix weighted (one-parameter) BMO, a class studied extensively by J. Isralowitz, S. Pott, S. Treil and others. Using this equivalence, we deduce lower and upper bounds

Ugur G. Abdulla

We prove the necessary and sufficient condition for the removability of the fundamental singularity, and equivalently for the unique solvability of the singular Dirichlet problem for the heat equation. In the measure-theoretical context the criterion determines whether the $h$-parabolic measure of the singularity point is null or positive. From the probabili

A. Franco, A. A. Nucita, F. De Paolis, F. Strafella

Gravitational microlensing is known to be an impressive tool for searching dark, small, and compact objects that are missed by the usual astronomical observations. In this paper, by analysing multiple images acquired by DECam, we present the detection and a complete description of the microlensing event LMC J05074558-65574990 which is most likely due to a su

Han Zheng, Luk Yi Cheung, Nikunj Sangwan, Artem Kononov

Gatemon qubits are the electrically tunable cousins of superconducting transmon qubits. In this work, we demonstrate the full coherent control of a gatemon qubit based on hole carriers in a Ge/Si core/shell nanowire, with the longest coherence times in group IV material gatemons to date. The key to these results is a high-quality Josephson junction obtained

L. Ricci, M. Perucho, J. López-Miralles, J. M. Martí

Aims. Relativistic jets launched from active galactic nuclei accelerate up to highly relativistic velocities within a few parsecs to tens of parsecs. The precise way in which this process takes place is still under study. While magnetic acceleration is known to be able to accelerate relativistic outflows, little attention has been paid to the role of thermal

Zhiyu Pan, Zhicheng Zhong, Wenxuan Guo, Yifan Chen

Several methods have been proposed to estimate 3D human pose from multi-view images, achieving satisfactory performance on public datasets collected under relatively simple conditions. However, there are limited approaches studying extracting 3D human skeletons from multimodal inputs, such as RGB and point cloud data. To address this gap, we introduce LiCamP

Zhiao Huang, Feng Chen, Yewen Pu, Chunru Lin

Combining gradient-based trajectory optimization with differentiable physics simulation is an efficient technique for solving soft-body manipulation problems. Using a well-crafted optimization objective, the solver can quickly converge onto a valid trajectory. However, writing the appropriate objective functions requires expert knowledge, making it difficult

Yidi Wang, Hong Li, Siyu Cheng, He Zhao

Exotic quantum solids can host electronic states that spontaneously break rotational symmetry of the electronic structure, such as electronic nematic phases and unidirectional charge density waves (CDWs). When electrons couple to the lattice, uniaxial strain can be used to anchor and control this electronic directionality. Here we reveal an unusual impact of

Andrew Murdoch, Johannes Cornelius Schoeman, Hendrik Willem Jordaan

In this paper, we address the issue of increasing the performance of reinforcement learning (RL) solutions for autonomous racing cars when navigating under conditions where practical vehicle modelling errors (commonly known as \emph{model mismatches}) are present. To address this challenge, we propose a partial end-to-end algorithm that decouples the plannin

W. M. de Muynck

The present paper is a sequel to papers dealing with recent developments on the issue of `quantum measurement'. In this paper `measurement within the domain of application of quantum mechanics' is treated as a \emph{quantum mechanical} \emph{interaction} of a `(sub)microscopic object $(o)$' and an `equally (sub)microscopic part of the measuring instrument $(

Yuan Li, Tianhui Wang, Jingjing Hu, Dengfeng Li

In multi-qubit superconducting systems utilizing flip-chip technology, achieving high accuracy in resonator frequencies is of paramount importance, particularly when multiple resonators share a common Purcell filter with restricted bandwidth. Nevertheless, variations in inter-chip spacing can considerably influence these frequencies. To tackle this issue, we

Xinyue Cheng, Yalu Feng

In this paper, we carry out in-depth research centering around the Harnack inequality for positive solutions to nonlinear heat equation on Finsler metric measure manifolds with weighted Ricci curvature ${\rm Ric}_{\infty}$ bounded below. Aim on this topic, we first give a volume comparison theorem of Bishop-Gromov type. Then we prove a weighted Poincar\'{e}

Easton K. Huch, Jieru Shi, Madeline R. Abbott, Jessica R. Golbus

Mobile health leverages personalized and contextually tailored interventions optimized through bandit and reinforcement learning algorithms. In practice, however, challenges such as participant heterogeneity, nonstationarity, and nonlinear relationships hinder algorithm performance. We propose RoME, a Robust Mixed-Effects contextual bandit algorithm that sim

Christis Katsouris

This set of lecture notes discuss key concepts for the Structural Analysis of Vector Autoregressive models for the teaching of a course on Applied Macroeconometrics with Advanced Topics.

Hao Tan, Jun Li, Yizhuang Zhou, Jun Wan

Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable generalization capabilities to downstream tasks. However, existing prompt tuning based frameworks need to parallelize learnable textual inputs for all categories, suffering from massive GPU memory consumption when there is a large number of categories in the target dataset. Moreover, pre

Aaron Mir, Eduardo Alonso, Esther Mondragón

We propose a novel talking head synthesis pipeline called "DiT-Head", which is based on diffusion transformers and uses audio as a condition to drive the denoising process of a diffusion model. Our method is scalable and can generalise to multiple identities while producing high-quality results. We train and evaluate our proposed approach and compare it agai

Duc V. Dinh, Oliver Brandt

We investigate the electrical properties of nominally undoped, 10-40-nm-thick ScN(111) layers grown on nearly lattice-matched GaN:Fe/Al$_2$O$_3$(0001) templates by plasma-assisted molecular beam epitaxy. Hall-effect measurements yield electron concentrations of 0.7-3.1$\times 10^{19}$ $\text{cm}^{-3}$ and mobilities of 50-160 cm$^{2}$V$^{-1}$s$^{-1}$ at room

Ashraf Maleki, Kim Holmberg

Understanding who shares academic publications on Twitter is critical to interpreting altmetrics as signals of scholarly or societal impact. Prior studies have used diverse and often incompatible user classification schemes, making synthesis difficult. This study presents a systematic review and meta-analysis of 23 empirical studies (covering 79,014 Twitter

Jinxi Li, Ziyang Song, Bo Yang

In this paper, we aim to model 3D scene dynamics from multi-view videos. Unlike the majority of existing works which usually focus on the common task of novel view synthesis within the training time period, we propose to simultaneously learn the geometry, appearance, and physical velocity of 3D scenes only from video frames, such that multiple desirable appl

Mengzhao Wang, Xiangyu Ke, Xiaoliang Xu, Lu Chen

We investigate the problem of multimodal search of target modality, where the task involves enhancing a query in a specific target modality by integrating information from auxiliary modalities. The goal is to retrieve relevant objects whose contents in the target modality match the specified multimodal query. The paper first introduces two baseline approache

Petr Prucha, Peter Madzik

Robotic Process Automation (RPA) has gained widespread adoption in corporate organizations, streamlining work processes while also introducing additional maintenance tasks. Effective governance of RPA can be achieved through the reusability of RPA components. However, refactoring RPA processes poses challenges when dealing with larger development teams, outs

Giovanna Amorim, María Santos, Shinkyu Park, Alessio Franci

We propose a threshold decision-making framework for controlling the physical dynamics of an agent switching between two spatial tasks. Our framework couples a nonlinear opinion dynamics model that represents the evolution of an agent's preference for a particular task with the physical dynamics of the agent. We prove the bifurcation that governs the behavio

Enesio Marinho, Cesar E. P. Villegas, Pedro Venezuela, Alexandre R. Rocha

We present a comprehensive first-principles study on the optoelectronic properties of the single-layer nickel diazenide (penta-NiN$_2$), a recently synthesized Cairo pentagonal 2D semiconductor. We carry out $ab$ $initio$ calculations based on the density-functional theory (DFT) and many-body perturbation theory, within the framework of Green's functions, to

Ivan Bliznets, Jesper Nederlof, Krisztina Szilágyi

An arithmetic progression is a sequence of integers in which the difference between any two consecutive elements is the same. We investigate the parameterized complexity of two problems related to arithmetic progressions, called Cover by Arithmetic Progressions (CAP) and Exact Cover by Arithmetic Progressions (XCAP). In both problems, we are given a set $X$

Shalika Kumbham, Abhijit Debnath, Krothapalli Sreenivasa Rao

Video lectures are becoming more popular and in demand as online classroom teaching is becoming more prevalent. Massive Open Online Courses (MOOCs), such as NPTEL, have been creating high-quality educational content that is freely accessible to students online. A large number of colleges across the country are now using NPTEL videos in their classrooms. So m

M. Lepore, L. Di Mascolo, P. Tozzi, E. Churazov

We present the detailed analysis of the thermal, diffuse emission of the proto-intracluster medium (ICM) detected in the halo of the Spiderweb Galaxy at z=2.16, within a radius of $\sim$ 150 kpc. We combined deep X-ray data from Chandra and millimeter observations of the Sunyaev-Zeldovich (SZ) effect obtained by ALMA. Thanks to independent measurements of th

K. Dolgikh, A. Korochkin, G. Rubtsov, D. Semikoz

Our latest paper investigates the effects of UHECR propagation in a turbulent intergalactic magnetic field in the small-angle scattering regime, specifically focusing on the non-trivial caustic-like pattern that arises with strong deviation from isotropy. In this paper, we explore the effect of the observer's position on the measurement of source flux at a g

Maryam Hadipour, Soroush Haseli, Dong Wang, Saeed Haddadi

We propose a practical scheme for a quantum battery consisting of an atom-cavity interacting system under a structured reservoir in the non-Markovian regime. We investigate a multi-parameter regime for the cavity-reservoir coupling and reveal how these parameters affect the performance of the quantum battery. Our proposed scheme is simple and may be achievab

Yubing Du, Guoshuai Du, Hongliang Dong, Jiayin Li

The modern very large-scale integration systems based on silicon semiconductor are facing the unprecedented challenges especially when transistor feature size lowers further, due to the excruciating tunneling effect and thermal management. Besides the common diamond cubic silicon, numerous exotic silicon allotropes with outstanding properties can emerge unde

David Rupke, Alison Coil, Kelly Whalen, John Moustakas

A new class of radio source, the so-called Odd Radio Circles (ORCs), have been discovered by recent sensitive, large-area radio continuum surveys. The distances of these sources have so far relied on photometric redshifts of optical galaxies found at the centers of or near the ORCs. Here we present Gemini rest-frame optical spectroscopy of six galaxies at th

Cédric Rommel, Victor Letzelter, Nermin Samet, Renaud Marlet

We propose ManiPose, a manifold-constrained multi-hypothesis model for human-pose 2D-to-3D lifting. We provide theoretical and empirical evidence that, due to the depth ambiguity inherent to monocular 3D human pose estimation, traditional regression models suffer from pose-topology consistency issues, which standard evaluation metrics (MPJPE, P-MPJPE and PCK

Yao Zhou, Zhen-Qiang Yin, Yang-Guang Shan, Ze-Hao Wang

Quantum key distribution (QKD) stands as a pioneering method for establishing information-theoretically secure communication channels by utilizing the principles of quantum mechanics. In the security proof of QKD, the phase error rate serves as a critical indicator of information leakage and directly influences the security of the shared key bits between com

Hao Yin, Bayu Jayawardhana, Stephan Trenn

This paper introduce the notion of output contraction that expands the contraction notion to the time-varying nonlinear systems with output. It pertains to the systems' property that any pair of outputs from the system converge to each other exponentially. This concept exhibits a more expansive nature when contrasted with another generalized contraction fram

Gourab Kumar Sar, Dibakar Ghosh

Over the past few decades, the research community has been interested in the study of multi-agent systems and their emerging collective dynamics. These systems are all around us in nature, like bacterial colonies, fish schools, bird flocks, as well as in technology, such as microswimmers and robotics, to name a few. Flocking and swarming are two key componen

Addi Malviya-Thakur, Reed Milewicz, Lavinia Paganini, Ahmed Samir Imam Mahmoud

The proliferation of open-source scientific software for science and research presents opportunities and challenges. In this paper, we introduce the SciCat dataset -- a comprehensive collection of Free-Libre Open Source Software (FLOSS) projects, designed to address the need for a curated repository of scientific and research software. This collection is cru