March 2024 arXiv papers — page 143

Tim Räz

Recently, watermarking schemes for large language models (LLMs) have been proposed to distinguish text generated by machines and by humans. The present paper explores philosophical, political, and ethical ramifications of implementing and using watermarking schemes. A definition of authorship that includes both machines (LLMs) and humans is proposed to serve

Jongwook Choi, Taehoon Kim, Yonghyun Jeong, Seungryul Baek

This paper presents a new approach for the detection of fake videos, based on the analysis of style latent vectors and their abnormal behavior in temporal changes in the generated videos. We discovered that the generated facial videos suffer from the temporal distinctiveness in the temporal changes of style latent vectors, which are inevitable during the gen

Ruoxi Xu, Hongyu Lin, Xianpei Han, Le Sun

The academic intelligence of large language models (LLMs) has made remarkable progress in recent times, but their social intelligence performance remains unclear. Inspired by established human social intelligence frameworks, particularly Daniel Goleman's social intelligence theory, we have developed a standardized social intelligence test based on real-world

Jian Shi, Wei Wang, Mingyang Qi, Xin Li

To enhance localization accuracy in urban environments, an innovative LiDAR-Visual-Inertial odometry, named HDA-LVIO, is proposed by employing hybrid data association. The proposed HDA_LVIO system can be divided into two subsystems: the LiDAR-Inertial subsystem (LIS) and the Visual-Inertial subsystem (VIS). In the LIS, the LiDAR pointcloud is utilized to cal

Clemens Heuberger, Daniel Krenn, Tobias Lechner

In the asymptotic analysis of regular sequences as defined by Allouche and Shallit, it is usually advisable to study their summatory function because the original sequence has a too fluctuating behaviour. It might be that the process of taking the summatory function has to be repeated if the sequence is fluctuating too much. In this paper we show that for al

Yohann Genzmer

In this article, we prove that an algorithm introduced by the author in a previous work and giving the generic dimension of the moduli space of a germ of curve in the complex plane that is the union of smooth curves, can be used identically to find this dimension for any kind of germ of plane curve.

Luca Arrotta, Claudio Bettini, Gabriele Civitarese, Michele Fiori

Context-aware Human Activity Recognition (HAR) is a hot research area in mobile computing, and the most effective solutions in the literature are based on supervised deep learning models. However, the actual deployment of these systems is limited by the scarcity of labeled data that is required for training. Neuro-Symbolic AI (NeSy) provides an interesting r

Zhao-Yi Zhou, Jing-Tao Qiu, Da-Jian Zhang

A crucial yet challenging issue in quantum metrology is to ascertain the ultimate precision achievable in estimation strategies. While there are two paradigms of estimations, local and global, current research is largely confined to local estimations, which are useful once the parameter of interest is approximately known. In this Letter we target a paradigm

Andrea Oldofredi

Recent philosophical discussions about metaphysical indeterminacy have been substantiated with the idea that quantum mechanics, one of the most successful physical theories in the history of science, provides explicit instances of worldly indefiniteness. Against this background, several philosophers underline that there are alternative formulations of quantu

Alexandre Pham, Maria Potop-Butucaru, Sébastien Tixeuil, Serge Fdida

Traditional machine learning systems were designed in a centralized manner. In such designs, the central entity maintains both the machine learning model and the data used to adjust the model's parameters. As data centralization yields privacy issues, Federated Learning was introduced to reduce data sharing and have a central server coordinate the learning o

Vladimir A. Yerokhin, Krzysztof Pachucki, Zoltán Harman, Christoph H. Keitel

Ab initio QED calculations of the nuclear magnetic shielding constant in helium-like ions are presented. We combine the nonrelativistic QED approach based on an expansion in powers of the fine-structure constant $\alpha$ and the so-called ``all-order'' QED approach which includes all orders in the parameter $Z\alpha$ but uses a perturbation expansion in the

Jasper Stang, Torsten Krauß, Alexandra Dmitrienko

The surge in popularity of machine learning (ML) has driven significant investments in training Deep Neural Networks (DNNs). However, these models that require resource-intensive training are vulnerable to theft and unauthorized use. This paper addresses this challenge by introducing DNNShield, a novel approach for DNN protection that integrates seamlessly b

P. S. Ardra, Jasine Babu, Kritika Kashyap, R. Krithika

Color-constrained subgraph problems are those where we are given an edge-colored (directed or undirected) graph and the task is to find a specific type of subgraph, like a spanning tree, an arborescence, a single-source shortest path tree, a perfect matching etc., with constraints on the number of edges of each color. Some of these problems, like color-const

Chengleyang Lei, Wei Feng, Peng Wei, Yunfei Chen

An increasing number of field robots would be used for mission-critical tasks in remote or post-disaster areas. Due to the limited individual abilities, these robots usually require an edge information hub (EIH), with not only communication but also sensing and computing functions. Such EIH could be deployed on a flexibly-dispatched unmanned aerial vehicle (

Nassim Athmouni, Marwa Ennaceur, Sylvain Golenia, Amel Jadlaoui

We examine the discrete Laplacian acting on a triangular lattice, introducing long-range perturbations to both the metric and the potential. Our goal is to establish a Limiting Absorption Principle away from possible embedded eigenvalues. Our study relies on a positive commutator technique.

Erkut Akdag, Zeqi Zhu, Egor Bondarev, Peter H. N. De With

Classification and localization of driving actions over time is important for advanced driver-assistance systems and naturalistic driving studies. Temporal localization is challenging because it requires robustness, reliability, and accuracy. In this study, we aim to improve the temporal localization and classification accuracy performance by adapting video

Yang Chen, Dustin J. Kempton, Rafal A. Angryk

The success of deep learning-based generative models in producing realistic images, videos, and audios has led to a crucial consideration: how to effectively assess the quality of synthetic samples. While the Fr\'{e}chet Inception Distance (FID) serves as the standard metric for evaluating generative models in image synthesis, a comparable metric for time se

M. P. Koprowski, J. V. Wijesekera, J. S. Dunlop, D. J. McLeod

We present a new determination of the star-forming main sequence (MS), obtained through stacking 100k K-band-selected galaxies in the far-infrared (FIR) Herschel and James Clerk Maxwell Telescope (JCMT) imaging. By fitting the dust emission curve to the stacked FIR photometry, we derive the IR luminosities (LIR), and hence the star formation rates (SFRs) out

Yuting Wei, Yuanxing Xu, Xinru Wei, Simin Yang

Given the importance of ancient Chinese in capturing the essence of rich historical and cultural heritage, the rapid advancements in Large Language Models (LLMs) necessitate benchmarks that can effectively evaluate their understanding of ancient contexts. To meet this need, we present AC-EVAL, an innovative benchmark designed to assess the advanced knowledge

Sebastián Rojas-Innocenti, Enrique Baeyens, Alejandro Martín-Crespo, Sergio Saludes-Rodil

The growing share of variable renewable energy (VRE) sources in power systems is increasing the need for short term operational flexibility, particularly from large industrial electricity consumers. This study proposes a practical, two stage optimization framework to unlock this flexibility in cement manufacturing and support participation in electricity bal

Robinroy Peter, Lavanya Ratnabala, Demetros Aschu, Aleksey Fedoseev

Mastering autonomous drone landing on dynamic platforms presents formidable challenges due to unpredictable velocities and external disturbances caused by the wind, ground effect, turbines or propellers of the docking platform. This study introduces an advanced Deep Reinforcement Learning (DRL) agent, Lander:AI, designed to navigate and land on platforms in

A. Tolamatti, K. K. Singh, K. K. Yadav

We report on the properties of central engines in the $\gamma$-ray blazars located at high redshifts beyond z~>~0.4, where the extra-galactic background light (EBL) starts affecting their $\gamma$-ray spectra. The physical engine that provides power to the blazars of very high bolometric luminosity is assumed to be a highly collimated jet of matter moving re

Philip Amortila, Dylan J. Foster, Akshay Krishnamurthy

Exploration is a major challenge in reinforcement learning, especially for high-dimensional domains that require function approximation. We propose exploration objectives -- policy optimization objectives that enable downstream maximization of any reward function -- as a conceptual framework to systematize the study of exploration. Within this framework, we

Can Cui, Imran Ahamad Sheikh, Mostafa Sadeghi, Emmanuel Vincent

Past studies on end-to-end meeting transcription have focused on model architecture and have mostly been evaluated on simulated meeting data. We present a novel study aiming to optimize the use of a Speaker-Attributed ASR (SA-ASR) system in real-life scenarios, such as the AMI meeting corpus, for improved speaker assignment of speech segments. First, we prop

Sharmita Dey, Sarath R. Nair

Mobility impairment caused by limb loss is a significant challenge faced by millions of individuals worldwide. The development of advanced assistive technologies, such as prosthetic devices, has the potential to greatly improve the quality of life for amputee patients. A critical component in the design of such technologies is the accurate prediction of refe

Furong Ye, Chuan Luo, Shaowei Cai

Though numerous solvers have been proposed for the MaxSAT problem, and the benchmark environment such as MaxSAT Evaluations provides a platform for the comparison of the state-of-the-art solvers, existing assessments were usually evaluated based on the quality, e.g., fitness, of the best-found solutions obtained within a given running time budget. However, c

Stefan Denner, David Zimmerer, Dimitrios Bounias, Markus Bujotzek

Content-based image retrieval (CBIR) has the potential to significantly improve diagnostic aid and medical research in radiology. However, current CBIR systems face limitations due to their specialization to certain pathologies, limiting their utility. On the other hand, several vision foundation models have been shown to produce general-purpose visual featu

Fabio Paparella, Karni Chauhan, Luc Koenders, Theo Hofman

This paper presents a modeling and design optimization framework for an Electric Autonomous Mobility-on-Demand system that allows for ride-pooling, i.e., multiple users can be transported at the same time towards a similar direction to decrease vehicle hours traveled by the fleet at the cost of additional waiting time and delays caused by detours. In particu

Jing-Xin Liu, Jian-Te Wang, Hai-Tao Ding

In this paper, we present a novel experimental approach for simulating and detecting topological invariants using ultracold fermions confined in two-dimensional hexagonal optical lattices. We propose achieving two-fold degenerate four-band models with non-trivial topologies in both the AII and A classes by introducing additional inertial forces, Raman proces

Hui Su, Zhi Tian, Xiaoyu Shen, Xunliang Cai

Scaling law principles indicate a power-law correlation between loss and variables such as model size, dataset size, and computational resources utilized during training. These principles play a vital role in optimizing various aspects of model pre-training, ultimately contributing to the success of large language models such as GPT-4, Llama and Gemini. Howe

Miltiadis Paschalis

In this paper we establish Hardy and Heisenberg uncertainty-type inequalities for the exterior of a Schwarzschild black hole. The weights that appear in both inequalities are tailored to fit the geometry, and can both be compared to the related Riemannian distance from the event horizon to yield inequalities for that distance. Moreover, in both cases the cla

Qing-Hong Cao, Kun Cheng, Yandong Liu

We propose to identify whether a sterile neutrino is Dirac-type or Majorana-type by counting the peak of the rapidity distribution at lepton colliders. Our method requires only one charged-lepton tagging, and the nature of sterile neutrinos can be pinned down once they are confirmed.

Clément Bonet, Lucas Drumetz, Nicolas Courty

While many Machine Learning methods were developed or transposed on Riemannian manifolds to tackle data with known non Euclidean geometry, Optimal Transport (OT) methods on such spaces have not received much attention. The main OT tool on these spaces is the Wasserstein distance which suffers from a heavy computational burden. On Euclidean spaces, a popular

Gabriel Tiberiu Pană, Paul-Adrian Gogîţă, Alexandru Nicolin-Żaczek

We report here a series of detailed statistical analyses on the sea level variations in the Port of Trieste using one of the largest existing catalogues that covers more than a century of measurements. We show that the distribution of waiting times, which are defined here akin to econophysics, namely the series of shortest time spans between a given sea leve

Conor Heffernan, Amin Chabchoub, Raphael Stuhlmeier

In this manuscript we investigate the Benjamin-Feir (or modulation) instability for the spatial evolution of water waves from the perspective of the discrete, spatial Zakharov equation, which captures cubically nonlinear and resonant wave interactions in deep water without restrictions on spectral bandwidth. Spatial evolution, with measurements at discrete l

Francesco De Lellis, Marco Coraggio, Nathan C. Foster, Riccardo Villa

We present a data-driven control architecture for modifying the kinematics of robots and artificial avatars to encode specific information such as the presence or not of an emotion in the movements of an avatar or robot driven by a human operator. We validate our approach on an experimental dataset obtained during the reach-to-grasp phase of a pick-and-place

Corentin Reiss

The Burns turbulent dispersion force is the most commonly used turbulent dispersion in the twofluid RANS bubbly-flow literature. However, its derivation is based on a series of hypothesis that are difficult to justify in industrial flows. It is shown that in low-void fraction vertical pipe flow, the Burns turbulent dispersion formulation is equivalent to con

Sougata Biswas, Arunava Chakrabarti

We analyze the topological properties of a family of generalized Su-Schrieffer-Heeger (SSH) chains and mesh geometries. In both the geometries the usual staggering in the distribution of the two overlap integrals is delayed (in space) by the inclusion of a third (additional) hopping term. A tight-binding Hamiltonian is used to unravel the topological phases,

Adéla Šterberová, Andreea Dincu, Stijn Oudshoorn, Vincent van Duinen

Tumor angiogenesis concerns the development of new blood vessels supplying the necessary nutrients for the further development of existing tumor cells. The entire process is complex, involving the production and consumption of chemicals, endothelial cell transitions as well as cell interactions, divisions, and migrations. Microfluidic cell culture platform h

Justin Forlano, Guopeng Li, Tengfei Zhao

In this paper, we establish the unconditional deep-water limit of the intermediate long wave equation (ILW) to the Benjamin-Ono equation (BO) in low-regularity Sobolev spaces on both the real line and the circle. Our main tool is new unconditional uniqueness results for ILW in $H^s$ when $s_0<s\leq \frac 14$ on the line and $s_0<s< \frac 12$ on the circle, w

Emil Goh, Maoyang Xiang, I-Chyn Wey, T. Hui Teo

In the realm of ASIC engineering, the landscape has been significantly reshaped by the rapid development of LLM, paralleled by an increase in the complexity of modern digital circuits. This complexity has escalated the requirements for HDL coding, necessitating a higher degree of precision and sophistication. However, challenges have been faced due to the le

Annamaria Defilippo, Pierangelo Veltri, Pietro Lio', Pietro Hiram Guzzi

Patient triage plays a crucial role in emergency departments, ensuring timely and appropriate care based on correctly evaluating the emergency grade of patient conditions. Triage methods are generally performed by human operator based on her own experience and information that are gathered from the patient management process. Thus, it is a process that can g

Yu-Hsueh Chen, Tarun Grover

A topological phase can undergo a phase transition driven by anyon condensation. A potential obstruction to such a mechanism could arise if there exists a symmetry between anyons that have non-trivial mutual statistics. Here we consider toric code subjected to errors that tend to proliferate anyons with non-trivial mutual statistics. Using triangle inequalit

Ivo P. C. Kersten, Erkut Akdag, Egor Bondarev, Peter H. N. De With

Anomalous behavior detection is a challenging research area within computer vision. Progress in this area enables automated detection of dangerous behavior using surveillance camera feeds. A dangerous behavior that is often overlooked in other research is the throwing action in traffic flow, which is one of the unique requirements of our Smart City project t

Yuanhang Zheng, Peng Li, Wei Liu, Yang Liu

Tool learning aims to extend the capabilities of large language models (LLMs) with external tools. A major challenge in tool learning is how to support a large number of tools, including unseen tools. To address this challenge, previous studies have proposed retrieving suitable tools for the LLM based on the user query. However, previously proposed methods d

Simone Ciani, Eurica Henriques, Igor Skrypnik

We study the boundary behavior of solutions to parabolic double-phase equations through the celebrated Wiener's sufficiency criterion. The analysis is conducted for cylindrical domains and the regularity up to the lateral boundary is shown in terms of either its $p$ or $q$ capacity, depending on whether the phase vanishes at the boundary or not. Eventually w

Yoichi Shiota, Tomohiro Taniguchi, Daiju Hayashi, Hideki Narita

Antiferromagnetic magnons possess a distinctive feature absent in their ferromagnetic counterparts: the presence of two distinct handedness modes, the right-handed (RH) and left-handed (LH) precession modes. The magnon handedness determines the sign of spin polarization carried by the propagating magnon, which is indispensable for harnessing the diverse func

Tobias Fritz

It is often noted that many of the basic concepts of differential geometry, such as the definition of connection, are purely algebraic in nature. Here, we review and extend existing work on fully algebraic formulations of differential geometry which eliminate the need for an underlying manifold. While the literature contains various independent approaches to

Jérémy Barbay

Computerized Adaptive Testing (CAT) measures an examinee's ability while adapting to their level. Both too many questions and too many hard questions can make a test frustrating. Are there some CAT algorithms which can be proven to be theoretically better than others, and in which framework? We show that slightly extending the traditional framework yields a

Baran Ozaydin, Tong Zhang, Deblina Bhattacharjee, Sabine Süsstrunk

Unsupervised Semantic Segmentation (USS) involves segmenting images without relying on predefined labels, aiming to alleviate the burden of extensive human labeling. Existing methods utilize features generated by self-supervised models and specific priors for clustering. However, their clustering objectives are not involved in the optimization of the feature

Dominik Winter, Nicolas Triltsch, Philipp Plewa, Marco Rosati

The creation of in-silico datasets can expand the utility of existing annotations to new domains with different staining patterns in computational pathology. As such, it has the potential to significantly lower the cost associated with building large and pixel precise datasets needed to train supervised deep learning models. We propose a novel approach for t

Eleni Demarchou, Zulqarnain Bin Ashraf, Dieff Vital, Besma Smida

Wireless power transfer has been proposed as a key technology for the foreseen machine type networks. A main challenge in the research community lies in acquiring a simple yet accurate model to capture the energy harvesting performance. In this work, we focus on a half-wave rectifier and based on circuit analysis we provide the actual output of the circuit w

Lucas Brivadis, Antoine Chaillet, Andrii Mironchenko, Fabian Wirth

We consider time-delay systems with a finite number of delays in the state space $L^\infty\times\mathbb{R}^n$. In this framework, we show that forward completeness implies the bounded reachability sets property, while this implication was recently shown by J.L. Mancilla-Aguilar and H. Haimovich to fail in the state space of continuous functions. As a consequ

Raphaël Pagès

The solutions of the equation $f^{(p-1)} + f^p = h^p$ in the unknown function $f $over an algebraic function field of characteristic $p$ are very closely linked to the structure and factorisations of linear differential operators with coefficients in function fields of characteristic $p$. However, while being able to solve this equation over general algebrai

Nikola Slepickova Kasalkova, Silvie Rimpelova, Cyril Vacek, Dominik Fajstavr

Here, we present surface analysis and biocompatibility evaluation of novel composite material based on graphene oxide traded as Hastalex. First, the surface morphology and elemental analysis of the pristine material were examined by atomic force and scanning electron microscopies, and by energy-dispersive and X-ray photoelectron spectroscopies, respectively.

Thomas Perrin

For a damped wave (or Klein-Gordon) equation on a bounded domain, with a focusing power-like nonlinearity satisfying some growth conditions, we prove that a global solution is bounded in the energy space, uniformly in time. Our result applies in particular to the case of a cubic equation on a bounded domain of dimension 3.

Joshua T. Y. Tse, Shunsuke Murai, Katsuhisa Tanaka

Surface lattice resonance supported on plasmonic nanoparticle arrays enhances light-matter interactions for applications such as photoluminescence enhancement. The photoluminescence process is enhanced through confining light beyond the diffraction limit and inducing stronger light-matter interaction. In this work, the absorption mechanisms of plasmonic nano

Harry H. Beyel, Marlo Verket, Viki Peeva, Christian Rennert

Process mining in healthcare presents a range of challenges when working with different types of data within the healthcare domain. There is high diversity considering the variety of data collected from healthcare processes: operational processes given by claims data, a collection of events during surgery, data related to pre-operative and post-operative car

Navin Kumar Chandra, Shubham Sharma, Saptarshi Basu, Aloke Kumar

Boger fluids are viscoelastic liquids having constant viscosity for a broad range of shear rates. They are commonly used to separate the effects of liquid elasticity from viscosity in any experiment. We present an experimental study on the shock-induced aerobreakup of a Boger fluid droplet in the Shear-induced entrainment (SIE) and catastrophic breakup regim

Xiting Zhao, Sören Schwertfeger

Reflective surfaces present a persistent challenge for reliable 3D mapping and perception in robotics and autonomous systems. However, existing reflection datasets and benchmarks remain limited to sparse 2D data. This paper introduces the first large-scale 3D reflection detection dataset containing more than 50,000 aligned samples of multi-return Lidar, RGB

Yeeun Kim, Hyunseo Shin, Eunkyung Choi, Hongseok Oh

Open source is a driving force behind scientific advancement.However, this openness is also a double-edged sword, with the inherent risk that innovative technologies can be misused for purposes harmful to society. What is the likelihood that an open source AI model or dataset will be used to commit a real-world crime, and if a criminal does exploit it, will

Jinchen Zhu, Mingjian Zhang, Ling Zheng, Shizhuang Weng

Recently, the methods based on implicit neural representations have shown excellent capabilities for arbitrary-scale super-resolution (ASSR). Although these methods represent the features of an image by generating latent codes, these latent codes are difficult to adapt for different magnification factors of super-resolution, which seriously affects their per

Shuo Tang, Rui Ye, Chenxin Xu, Xiaowen Dong

Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server, with each agent solving varied tasks over time. To achieve efficient collaboration, agents should: i) autonomously identify beneficial collaborative relationships in a decentralized manner; and ii) adapt to dyna

Yuxuan Li, Xiang Li, Weijie Li, Qibin Hou

Synthetic Aperture Radar (SAR) object detection has gained significant attention recently due to its irreplaceable all-weather imaging capabilities. However, this research field suffers from both limited public datasets (mostly comprising <2K images with only mono-category objects) and inaccessible source code. To tackle these challenges, we establish a new

Viet Duong Hoang, Frederik Falk Nyboe, Nicolaj Haarhøj Malle, Emad Ebeid

We present a fully autonomous self-recharging drone system capable of long-duration sustained operations near powerlines. The drone is equipped with a robust onboard perception and navigation system that enables it to locate powerlines and approach them for landing. A passively actuated gripping mechanism grasps the powerline cable during landing after which

Hongguang Pan, Zhuoyi Li, Yunpeng Fu, Xuebin Qin

Reconstructing visual stimulus images is a significant task in neural decoding, and up to now, most studies consider the functional magnetic resonance imaging (fMRI) as the signal source. However, the fMRI-based image reconstruction methods are difficult to widely applied because of the complexity and high cost of the acquisition equipments. Considering the

Jian Wang, Xu Li, Xingyue Ma, Lan Chen

Sliding ferroelectricity is a unique type of polarity recently observed in a properly stacked van der Waals bilayer. However, electric-field control of sliding ferroelectricity is hard and could induce large coercive electric fields and serious leakage currents which corrode the ferroelectricity and electronic properties, which are essential for modern two-d

Alex V. Lukyanov, Hanan Hozan, Tristan Pryer, Georgios Sialounas

We hypothesize that the spread of oil slicks on the water's surface during oil spills is significantly influenced by water wave motion at the initial or intermediate spreading stages, well before emulsification processes have a substantial impact on the oil film's state. We demonstrate that the spreading dynamics of an oil slick on the water surface are faci

Zijian Chen, Mei Wang, Weihong Deng, Hongzhi Shi

2D face recognition encounters challenges in unconstrained environments due to varying illumination, occlusion, and pose. Recent studies focus on RGB-D face recognition to improve robustness by incorporating depth information. However, collecting sufficient paired RGB-D training data is expensive and time-consuming, hindering wide deployment. In this work, w

Chenhao Wang, Zihan Chen, Nikolaos Pappas, Howard H. Yang

We propose a federated version of adaptive gradient methods, particularly AdaGrad and Adam, within the framework of over-the-air model training. This approach capitalizes on the inherent superposition property of wireless channels, facilitating fast and scalable parameter aggregation. Meanwhile, it enhances the robustness of the model training process by dyn

Agathe Herrou, Florent de Dinechin, Stéphane Letz, Yann Orlarey

Modern programmable digital signal processing relies on floating-point numbers for their ease of use. Fixed-point number formats have the potential to save resources and improve execution time, but realising this potential burdens the programmer with the need to define each format, at every step of the computation. This article reviews existing methods to au

Gudrun Wanner, Sweta Shah, Martin Staab, Henry Wegener

We present first-order models for tilt-to-length (TTL) coupling in LISA, both for the individual interferometers as well as in the time-delay interferometry (TDI) Michelson observables. These models include the noise contributions from angular and lateral jitter coupling of the six test masses, six movable optical subassemblies (MOSAs), and three spacecraft.

B. Petrović, A. Bader, J. Nauschütz, T. Sato

We compare InAs-based and GaSb-based interband cascade lasers (ICLs) with the same 12 stages active region designed to emit at a wavelength of 4.6 {\mu}m. They employ a hybrid cladding architecture with the same geometry and inner claddings consisting of InAs/AlSb superlattices but different outer claddings: The InAs-based ICL employs plasmon enhanced n-type

Alexander Nazarov, Yuval Bloom, Boaz Lubotzky, Hamza Abudayyeh

To harness the potential of radially polarized single photons in applications such as high-dimensional quantum key distribution (HD-QKD) and quantum communication, we demonstrate an on-chip, room-temperature device, which generates highly directional radially polarized photons at very high rates. The photons are emitted from a giant CdSe/CdS colloidal quantu

Manish Kumar Mehta, Joseph Thomas Andrews, Pratima Sen

We investigated the entanglement in a diluted magnetic semiconductor quantum dot, crucial for quantum technologies. Despite their potential, these systems exhibit low extraction rates. We explore self-assembled InGaAs quantum dots, focusing on entanglement between them based on spin states. Our analysis involves defining wavefunctions, employing density matr

Tanausú Hernández Yanes, Artur Niezgoda, Emilia Witkowska

Spin-squeezing in systems with single-particle control is a well-established resource of modern quantum technology. Applied in an optical lattice clock can reduce the statistical uncertainty of spectroscopic measurements. Here, we consider dynamic generation of spin-squeezing with ultra-cold bosonic atoms with two internal states loaded into an optical latti

Ning Xu, Yanhui Wang, Tingting Zhang, Hongshuo Tian

News captioning aims to describe an image with its news article body as input. It greatly relies on a set of detected named entities, including real-world people, organizations, and places. This paper exploits commonsense knowledge to understand named entities for news captioning. By ``understand'', we mean correlating the news content with common sense in t

Pratapaditya Bej, Abhishek Banerjee

We study entanglement activation in a generalized entanglement swapping process involving two Bell pairs and generalized measurements. The conventional understanding posits entangled measurements as both necessary and sufficient for establishing entanglement between distant parties. In this study, we reassess the role of measurement operators in entanglement

MoonJeong Park, Jaeseung Heo, Dongwoo Kim

Graph Neural Network (GNN) resembles the diffusion process, leading to the over-smoothing of learned representations when stacking many layers. Hence, the reverse process of message passing can produce the distinguishable node representations by inverting the forward message propagation. The distinguishable representations can help us to better classify neig

Tao Huang, Jiaqi Liu, Shan You, Chang Xu

Recently, the growing capabilities of deep generative models have underscored their potential in enhancing image classification accuracy. However, existing methods often demand the generation of a disproportionately large number of images compared to the original dataset, while having only marginal improvements in accuracy. This computationally expensive and

Woojung Han, Chanyoung Kim, Dayun Ju, Yumin Shim

Recent advances in text-conditioned image generation diffusion models have begun paving the way for new opportunities in modern medical domain, in particular, generating Chest X-rays (CXRs) from diagnostic reports. Nonetheless, to further drive the diffusion models to generate CXRs that faithfully reflect the complexity and diversity of real data, it has bec

Toghrul Karimov, Edon Kelmendi, Joël Ouaknine, James Worrell

We consider reachability decision problems for linear dynamical systems: Given a linear map on $\mathbb{R}^d$ , together with source and target sets, determine whether there is a point in the source set whose orbit, obtained by repeatedly applying the linear map, enters the target set. When the source and target sets are semialgebraic, this problem can be re

Angeliki Dimitriou, Maria Lymperaiou, Giorgos Filandrianos, Konstantinos Thomas

Counterfactual explanations (CEs) based on concepts are explanations that consider alternative scenarios to understand which high-level semantic features contributed to particular model predictions. In this work, we propose CEs based on the semantic graphs accompanying input data to achieve more descriptive, accurate, and human-aligned explanations. Building

Junbin Liu, Ya Liu, Wing-Kin Ma, Mingjie Shao

In the first part of this study, a convex-constrained penalized formulation was studied for a class of constant modulus (CM) problems. In particular, the error bound techniques were shown to play a vital role in providing exact penalization results. In this second part of the study, we continue our error bound analysis for the cases of partial permutation ma

Jan von der Assen, Jamo Sharif, Chao Feng, Christian Killer

Threat modeling is a popular method to securely develop systems by achieving awareness of potential areas of future damage caused by adversaries. However, threat modeling for systems relying on Artificial Intelligence is still not well explored. While conventional threat modeling methods and tools did not address AI-related threats, research on this amalgama

Rayssa Caju, Jesse Ratzkin, Almir Silva Santos

We study constant Q-curvature metrics conformal to the round metric on the sphere with finitely many point singularities. We show that the moduli space of solutions with finitely many punctures in fixed positions, equipped with the Gromov-Hausdorff topology, has the local structure of a real analytic variety with formal dimension equal to the number of the p

Mingyue Zhao, Han Li, Li Fan, Shiyuan Liu

Fully-supervised airway segmentation has accomplished significant triumphs over the years in aiding pre-operative diagnosis and intra-operative navigation. However, full voxel-level annotation constitutes a labor-intensive and time-consuming task, often plagued by issues such as missing branches, branch annotation discontinuity, or erroneous edge delineation

Matteo Beccaria, Alejandro Cabo-Bizet

The flavored superconformal Schur index of $\mathcal N=4$ $U(N)$ SYM has finite $N$ corrections encoded in its giant graviton expansion in terms of D3 branes wrapped in $AdS_{5}\times S^{5}$. The key element of this decomposition is the non-trivial index of the theory living on the wrapped brane system. A remarkable feature of the Schur limit is that the bra

Leon M. Lohse, Petar Andrejić, Sven Velten, Malte Vassholz

Waveguides offer a means to controllably couple atomic ensembles to the electromagnetic field therein. Here, we demonstrate x-ray propagation in planar thin-film waveguides coupled to M\"ossbauer nuclei under collective resonant excitation by short pulses of synchrotron radiation. We record x-ray photons that have been emitted into resonant modes of the wave

Qi Li, Jianan Zeng, Lei Wu

The simulation of rarefied gas flow based on the Boltzmann equation is challenging, especially when the gas mixtures have disparate molecular masses. In this paper, a computationally tractable kinetic model is proposed for monatomic gas mixtures, to mimic the Boltzmann collision operator as closely as possible. The intra- and inter-collisions are modelled se

Junbin Liu, Ya Liu, Wing-Kin Ma, Mingjie Shao

This study develops a framework for a class of constant modulus (CM) optimization problems, which covers binary constraints, discrete phase constraints, semi-orthogonal matrix constraints, non-negative semi-orthogonal matrix constraints, and several types of binary assignment constraints. Capitalizing on the basic principles of concave minimization and error

Chenhao Zhang, Yongyang Zhou, Lei Zhang

The neural radiance fields (NeRF) have emerged as a prominent methodology for synthesizing realistic images of novel views. While neural radiance representations based on voxels or mesh individually offer distinct advantages, excelling in either rendering quality or speed, each has limitations in the other aspect. In response, we propose a hybrid representat

Changyue Liao, Mo Sun, Zihan Yang, Jun Xie

Nowadays, AI researchers become more and more interested in fine-tuning a pre-trained LLM, whose size has grown to up to over 100B parameters, for their downstream tasks. One approach to fine-tune such huge models is to aggregate device memory from many GPUs. However, this approach introduces prohibitive costs for most data scientists with a limited budget f

Kamel Yamani, Marwa Naïr, Riyadh Baghdadi

In recent years, data has emerged as the new gold, serving as a powerful tool for creating intelligent systems. However, procuring high-quality data remains challenging, especially for code. To address this, we developed TinyPy Generator, a tool that generates random Python programs using a context-free grammar. The generated programs are guaranteed to be co

Kananart Kuwaranancharoen, Lei Xin, Shreyas Sundaram

The problem of distributed optimization requires a group of networked agents to compute a parameter that minimizes the average of their local cost functions. While there are a variety of distributed optimization algorithms that can solve this problem, they are typically vulnerable to "Byzantine" agents that do not follow the algorithm. Recent attempts to add

Hayeon O, Chanuk Yang, Kunsoo Huh

In autonomous driving, 3D object detection provides more precise information for downstream tasks, including path planning and motion estimation, compared to 2D object detection. In this paper, we propose SeSame: a method aimed at enhancing semantic information in existing LiDAR-only based 3D object detection. This addresses the limitation of existing 3D det

Tapio Simula, Niels Kjærgaard, Tilman Pfau

Thouless charge pumps are quantum mechanical devices whose operation relies on topology. They provide the means for transporting quantum matter in space lattices with a single quantum precision. Contrasting space crystals that spontaneously break a continuous spatial translation symmetry and form crystals in space,time crystals have emerged as novel states o

Hasanul Mahmud, Peng Kang, Kevin Desai, Palden Lama

Reducing inference time and energy usage while maintaining prediction accuracy has become a significant concern for deep neural networks (DNN) inference on resource-constrained edge devices. To address this problem, we propose a novel approach based on "converting" autoencoder and lightweight DNNs. This improves upon recent work such as early-exiting framewo

Masatoshi Kobayashi, Kohei Miyagichi, Shin Matsushima

Causal discovery in the presence of unobserved common causes from observational data only is a crucial but challenging problem. We categorize all possible causal relationships between two random variables into the following four categories and aim to identify one from observed data: two cases in which either of the direct causality exists, a case that variab

Wenchi Ke

Osteoporosis is a common skeletal disease that seriously affects patients' quality of life. Traditional osteoporosis diagnosis methods are expensive and complex. The semi-supervised model based on diffusion model and class threshold sinusoidal decay proposed in this paper can automatically diagnose osteoporosis based on patient's imaging data, which has the