April 2023 arXiv papers — page 95

Henning Schlömer, Annabelle Bohrdt

Microscopically understanding and classifying phases of matter is at the heart of strongly-correlated quantum physics. With quantum simulations, genuine projective measurements (snapshots) of the many-body state can be taken, which include the full information of correlations in the system. The rise of deep neural networks has made it possible to routinely s

Runze Li, Dahun Kim, Bir Bhanu, Weicheng Kuo

We present RECLIP (Resource-efficient CLIP), a simple method that minimizes computational resource footprint for CLIP (Contrastive Language Image Pretraining). Inspired by the notion of coarse-to-fine in computer vision, we leverage small images to learn from large-scale language supervision efficiently, and finetune the model with high-resolution data in th

James Seale Smith, Yen-Chang Hsu, Lingyu Zhang, Ting Hua

Recent works demonstrate a remarkable ability to customize text-to-image diffusion models while only providing a few example images. What happens if you try to customize such models using multiple, fine-grained concepts in a sequential (i.e., continual) manner? In our work, we show that recent state-of-the-art customization of text-to-image models suffer fro

Tuila Ziliotto, Antonino P. Milone, Anna F. Marino, Aaron L. Dotter

Recent work on metal-intermediate globular clusters (GCs) with [Fe/H]=$-1.5$ and $-0.75$ has illustrated the theoretical behavior of multiple populations in photometric diagrams obtained with the James Webb Space Telescope (JWST). These results are confirmed by observations of multiple populations among M-dwarfs of 47 Tucanae. Here, we explore the multiple p

Johanna Karras, Aleksander Holynski, Ting-Chun Wang, Ira Kemelmacher-Shlizerman

We present DreamPose, a diffusion-based method for generating animated fashion videos from still images. Given an image and a sequence of human body poses, our method synthesizes a video containing both human and fabric motion. To achieve this, we transform a pretrained text-to-image model (Stable Diffusion) into a pose-and-image guided video synthesis model

Siwei Zhang, Qianli Ma, Yan Zhang, Sadegh Aliakbarian

Automatic perception of human behaviors during social interactions is crucial for AR/VR applications, and an essential component is estimation of plausible 3D human pose and shape of our social partners from the egocentric view. One of the biggest challenges of this task is severe body truncation due to close social distances in egocentric scenarios, which b

Adam G. Frim, Michael R. DeWeese

Shortcut engineering consists of a class of approaches to rapidly manipulate physical systems by means of specially designed external controls. In this Letter, we apply these approaches to run-and-tumble particles, which are designed to mimic the chemotactic behavior of bacteria and therefore exhibit complex dynamics due to their self-propulsion and random r

Ge-Peng Ji, Deng-Ping Fan, Peng Xu, Ming-Ming Cheng

Segmenting anything is a ground-breaking step toward artificial general intelligence, and the Segment Anything Model (SAM) greatly fosters the foundation models for computer vision. We could not be more excited to probe the performance traits of SAM. In particular, exploring situations in which SAM does not perform well is interesting. In this report, we cho

Shiwei Zhang, Zhengzheng Wang, Qing Liu, Fei Wang

This paper presents a new annotation method called Sparse Annotation (SA) for crowd counting, which reduces human labeling efforts by sparsely labeling individuals in an image. We argue that sparse labeling can reduce the redundancy of full annotation and capture more diverse information from distant individuals that is not fully captured by Partial Annotati

Moayed Haji Ali, Andrew Bond, Tolga Birdal, Duygu Ceylan

We propose $\textbf{VidStyleODE}$, a spatiotemporally continuous disentangled $\textbf{Vid}$eo representation based upon $\textbf{Style}$GAN and Neural-$\textbf{ODE}$s. Effective traversal of the latent space learned by Generative Adversarial Networks (GANs) has been the basis for recent breakthroughs in image editing. However, the applicability of such adva

Ruicheng Feng, Chongyi Li, Huaijin Chen, Shuai Li

Due to the difficulty in collecting large-scale and perfectly aligned paired training data for Under-Display Camera (UDC) image restoration, previous methods resort to monitor-based image systems or simulation-based methods, sacrificing the realness of the data and introducing domain gaps. In this work, we revisit the classic stereo setup for training data c

Chung-Ching Lin, Jiang Wang, Kun Luo, Kevin Lin

The most recent efforts in video matting have focused on eliminating trimap dependency since trimap annotations are expensive and trimap-based methods are less adaptable for real-time applications. Despite the latest tripmap-free methods showing promising results, their performance often degrades when dealing with highly diverse and unstructured videos. We a

Hongye Xu, Dongfang Liu, Cory Merkel, Michael Zuzak

Logic locking has been proposed to safeguard intellectual property (IP) during chip fabrication. Logic locking techniques protect hardware IP by making a subset of combinational modules in a design dependent on a secret key that is withheld from untrusted parties. If an incorrect secret key is used, a set of deterministic errors is produced in locked modules

Md. Maidul Islam, Tanzina Nasrin Tania, Sharmin Akter, Kazi Hassan Shakib

Heart disorder has just overtaken cancer as the world's biggest cause of mortality. Several cardiac failures, heart disease mortality, and diagnostic costs can all be reduced with early identification and treatment. Medical data is collected in large quantities by the healthcare industry, but it is not well mined. The discovery of previously unknown patterns

Aggelos Kiayias, Elias Koutsoupias, Philip Lazos, Giorgos Panagiotakos

Blockchain systems come with the promise of being inclusive for a variety of decentralized applications (DApps) that can serve different purposes and have different urgency requirements. Despite this, the transaction fee mechanisms currently deployed in popular platforms as well as previous modeling attempts for the associated mechanism design problem focus

Ertugrul Basar

Reconfigurable intelligent surface (RIS)-empowered communication stands out as a solid candidate for future wireless networks due to its flexibility, ease of deployment, and attractive advantages to control the wireless propagation environment. In this perspective article, a brief overview is presented considering the application of reconfigurable intelligen

Eve Shaw, Vyron Vellis

An infinite iterated function system (IIFS) is a countable collection of contraction maps on a compact metric space. In this paper we study the conditions under which the attractor of a such system admits a parameterization by a continuous or H\"older continuous map of the unit interval.

Aravind Venugopal, Stephanie Milani, Fei Fang, Balaraman Ravindran

Multi-agent reinforcement learning (MARL) methods often suffer from high sample complexity, limiting their use in real-world problems where data is sparse or expensive to collect. Although latent-variable world models have been employed to address this issue by generating abundant synthetic data for MARL training, most of these models cannot encode vital glo

Mikael Mieskolainen

We introduce GRANIITTI, a new Monte Carlo event generator designed especially to solve the enigma of glueballs at the LHC. We discuss the available physics processes, compare the simulations against STAR data from RHIC and span ambitious future directions towards the first diffractive event generator with a deep learning-enhanced computational engine.

Alexander Naumann, Felix Hertlein, Laura Dörr, Steffen Thoma

Computer vision applications in transportation logistics and warehousing have a huge potential for process automation. We present a structured literature review on research in the field to help leverage this potential. The literature is categorized w.r.t. the application, i.e. the task it tackles and w.r.t. the computer vision techniques that are used. Regar

Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen

Large Language Models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify and explain social phenomena like persuasiveness and political ideology, then LLMs could augment the Computational Social Science (CSS) pipeline in important ways. This work provide

Aristomenis Donos, Polydoros Kailidis

We examine the effective theory of critical dynamics near superfluid phase transitions in the framework of the Keldysh-Schwinger formalism. We focus on the sector capturing the dynamics of the complex order parameter and the conserved current corresponding to the broken global symmetry. After constructing the theory up to quadratic order in the $a$-fields, w

Tejas Anvekar, Dena Bazazian

In the realm of 3D-computer vision applications, point cloud few-shot learning plays a critical role. However, it poses an arduous challenge due to the sparsity, irregularity, and unordered nature of the data. Current methods rely on complex local geometric extraction techniques such as convolution, graph, and attention mechanisms, along with extensive data-

Anne-Florence Bitbol, Hélène Berthoumieux, Benjamin Spreng, Paulo A. Maia Neto

Actin filaments and microtubules both often form bundles of parallel filaments within cells. Here, we shed light on a universal attractive interaction between two such parallel filaments. Indeed, the electrodynamic Casimir interaction between dielectric objects immersed in salted water at room or body temperature includes a universal contribution that is uns

Ricardo J. Alonso, Irene M. Gamba, Milana Pavic-Colic

From a unified vision of vector valued solutions in weighted Banach spaces, this manuscript establishes the existence and uniqueness for space homogeneous Boltzmann bi-linear systems with conservative collisional forms arising in complex gas dynamical structures. This broader vision is directly applied to dilute multi-component gas mixtures composed of both

Valentin Würzbauer, Kerstin Lenk, Matin Jafarian

The brain consists not only of neurons but also of non-neuronal cells, including astrocytes. Recent discoveries in neuroscience suggest that astrocytes directly regulate neuronal activity by releasing gliotransmitters such as glutamate. In this paper, we consider a biologically plausible mathematical model of a tripartite neuron-astrocyte network. We study t

Bo Li, YiHua Chen, Hao Xu, Fei Zhong

The swift and precise detection of vehicles plays a significant role in intelligent transportation systems. Current vehicle detection algorithms encounter challenges of high computational complexity, low detection rate, and limited feasibility on mobile devices. To address these issues, this paper proposes a lightweight vehicle detection algorithm based on Y

George Zhang, Ivan C. Christov, Aminur Rahman

Discrete dynamical models of walking droplets ("walkers") have allowed swift numerical experiments revealing heretofore unobserved quantum statistics and related behaviors in a classical hydrodynamic system. We present evidence that one such model of walking droplets exhibits the empirically elusive $n$-bounce resonances that are traditionally seen in the sc

Graham Manuell

The constructive approach to mathematics has the advantage that witnesses can be extracted from statements of existence and theorems can be unwound to give algorithms. Even better, constructive theorems can be interpreted in any topos, giving many different results for the price of one. On the other hand, you might have heard that fundamental results from to

Lori E. Porter, Benne W. Holwerda, Sandor Kruk, Maritza Lara-López

The large-scale structure (LSS) of the Universe is comprised of galaxy filaments, tendrils, and voids. The majority of the Universe's volume is taken up by these voids, which exist as underdense, but not empty, regions. The galaxies found inside these voids are expected to be some of the most isolated objects in the Universe. This study, using the Galaxy and

Victor E. Ambruş, Maxim N. Chernodub

The thermodynamics of rigidly rotating systems experience divergences when the system dimensions transverse to the rotation axis exceed the critical size imposed by the causality constraint. The rotation with imaginary angular frequency, suitable for numerical lattice simulations in Euclidean imaginary-time formalism, experiences fractalization of thermodyna

Anton Cheshkov, Pavel Zadorozhny, Rodion Levichev

In this technical report, we evaluated the performance of the ChatGPT and GPT-3 models for the task of vulnerability detection in code. Our evaluation was conducted on our real-world dataset, using binary and multi-label classification tasks on CWE vulnerabilities. We decided to evaluate the model because it has shown good performance on other code-based tas

Hokuto Konno, Abhishek Mallick, Masaki Taniguchi

We provide an approach to study exotic phenomena in relatively small 4-manifolds that captures many different exotic behaviors under one umbrella. These phenomena include exotic smooth structures on 4-manifolds with $b_2=1$, examples of strong corks, and exotic codimension-$1$ embeddings into $\mathbb{C} P^2 \# - \mathbb{C} P^2$ that survive external stabili

Andres Meza, Ryan Kastner

Security graphs model attacks, defenses, mitigations, and vulnerabilities on computer networks and systems. With proper attributes, they provide security metrics using standard graph algorithms. A hyperflow graph is a register-transfer level (RTL) hardware security graph that facilitates security verification. A hyperflow graph models information flows and i

Jasmine Bhullar

For full shifts on finite alphabets, Coelho and Quas showed that the map that sends a H\"older continuous potential $\phi$ to its equilibrium state $\mu_\phi$ is $\overline{d}$-continuous. We extend this result to the setting of full shifts on countable (infinite) alphabets. As part of the proof, we show that the map that sends a strongly positive recurrent

Mainak Singha, Ankit Jha, Bhupendra Solanki, Shirsha Bose

In recent years, the success of large-scale vision-language models (VLMs) such as CLIP has led to their increased usage in various computer vision tasks. These models enable zero-shot inference through carefully crafted instructional text prompts without task-specific supervision. However, the potential of VLMs for generalization tasks in remote sensing (RS)

Alberto García Martín-Caro, Yoshitaka Hatta, Miguel Huidobro

We compute the gravitational form factor $D(t)$ of various nuclei in the generalized Skyrme model where nuclei are described as solitonic field configurations each with a definite baryon number $B$. We separately discuss the cases $B=1$ (nucleons), $B=2$ (deuteron), $B=3$ (helium-3 and tritium) and extrapolate to larger $B$-values. Configurations with $B>1$

Michał Czakon, Zahari Kassabov, Alexander Mitov, Rene Poncelet

We introduce HighTEA, a new paradigm for deploying fully-differential next-to-next-to leading order (NNLO) calculations for collider observables. In principle, any infrared safe observable can be computed and, with very few restrictions, the user has complete freedom in defining their calculation's setup. For example, one can compute generic n-dimensional di

Federico Armata, Salvatore Butera, Federico Montalbano, Roberto Passante

We review several aspects related to the confinement of a massless scalar field in a cavity with a movable conducting wall of finite mass, free to move around its equilibrium position to which it is bound by a harmonic potential, and whose mechanical degrees of freedom are described quantum mechanically. This system, for small displacements of the movable wa

Gabriel S. Gusmão, Andrew J. Medford

Physics-informed neural networks (PINNs) have proven a suitable mathematical scaffold for solving inverse ordinary (ODE) and partial differential equations (PDE). Typical inverse PINNs are formulated as soft-constrained multi-objective optimization problems with several hyperparameters. In this work, we demonstrate that inverse PINNs can be framed in terms o

Filipa Valdeira, Cláudia Soares, João Gomes

Localization is a fundamental enabler technology for many applications, like vehicular networks, IoT, and even medicine. While Global Navigation Satellite Systems solutions offer great performance, they are unavailable in scenarios like indoor or underwater environments, and, for large networks, the instrumentation cost is prohibitive. We develop a localizat

Andriani Keliri, Benoît Douçot

We consider a Josephson bijunction consisting of three superconducting reservoirs connected through two quantum dots. In equilibrium, the interdot coupling is sizable only for distances smaller than the superconducting coherence length. Application of commensurate dc voltages results in a time-periodic Hamiltonian and induces an interdot coupling at large di

Shaina Raza

This study presents a machine learning (ML) pipeline for clinical data classification in the context of a 30-day readmission problem, along with a fairness audit on subgroups based on sensitive attributes. A range of ML models are used for classification and the fairness audit is conducted on the model predictions. The fairness audit uncovers disparities in

Danyun He, Gautam Reddy, Chris H. Rycroft

Soaring birds gain energy from stable ascending currents or shear. However, it remains unclear whether energy loss due to drag can be overcome by extracting work from transient turbulent fluctuations. We designed numerical simulations of gliders navigating in a kinematic model that captures the spatio-temporal correlations of atmospheric turbulence. Energy e

Vojtěch Horný, Sophia N. Chen, Xavier Davoine, Laurent Gremillet

Deciphering the conditions under which neutron captures occur in the Universe to synthesize heavy elements is an endeavour pursued since the 1950s, but that has proven elusive up to now due to the experimental difficulty of generating the extreme neutron fluxes required. It has been evoked that laser-driven (pulsed) neutron sources could produce neutron beam

Andrei V. Konstantinov, Lev V. Utkin, Alexey A. Lukashin, Vladimir A. Muliukha

A new approach called NAF (the Neural Attention Forest) for solving regression and classification tasks under tabular training data is proposed. The main idea behind the proposed NAF model is to introduce the attention mechanism into the random forest by assigning attention weights calculated by neural networks of a specific form to data in leaves of decisio

Weizheng Wang, Ruiqi Wang, Le Mao, Byung-Cheol Min

Developing robotic technologies for use in human society requires ensuring the safety of robots' navigation behaviors while adhering to pedestrians' expectations and social norms. However, maintaining real-time communication between robots and pedestrians to avoid collisions can be challenging. To address these challenges, we propose a novel socially-aware n

Guillermo Ballesteros, Alejandro Pérez Rodríguez, Mathias Pierre

We revisit the idea that the inflaton may have dissipated part of its energy into a thermal bath during inflation, considering monomial inflationary potentials and three different forms of dissipation rate. Using a numerical Fokker-Planck approach to describe the stochastic dynamics of inflationary fluctuations, we confront this scenario with current bounds

Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong

We present a comprehensive solution to learn and improve text-to-image models from human preference feedback. To begin with, we build ImageReward -- the first general-purpose text-to-image human preference reward model -- to effectively encode human preferences. Its training is based on our systematic annotation pipeline including rating and ranking, which c

Rasool Tahmasbi, Keyvan Tahmasbi

We consider modeling a binary response variable together with a set of covariates for two groups under observational data. The grouping variable can be the confounding variable (the common cause of treatment and outcome), gender, case/control, ethnicity, etc. Given the covariates and a binary latent variable, the goal is to construct two directed acyclic gra

Udit Rawat, Jackson Anderson, Dana Weinstein

CMOS-MEMS resonators seamlessly integrated in advanced integrated circuit (IC) technology have the unique capability to enable unprecedented integration of stable frequency references, acoustic spectral processors, and physical sensors. Demonstrations of transducers leveraging piezoelectric properties of emerging ferroelectric materials such as Hafnium Zirco

Saurabhchand Bhati, Jesús Villalba, Piotr Żelasko, Laureano Moro-Velazquez

Self-supervised methods such as Contrastive predictive Coding (CPC) have greatly improved the quality of the unsupervised representations. These representations significantly reduce the amount of labeled data needed for downstream task performance, such as automatic speech recognition. CPC learns representations by learning to predict future frames given cur

Jiaying Lu, Jiaming Shen, Bo Xiong, Wenjing Ma

Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a uniform index system. The index system often organizes biomedical terms in a hierarchy to provide the aligned entities with fine-grained granularity. To address the challenge of scarce s

Maria Parelli, Alexandros Delitzas, Nikolas Hars, Georgios Vlassis

Training models to apply linguistic knowledge and visual concepts from 2D images to 3D world understanding is a promising direction that researchers have only recently started to explore. In this work, we design a novel 3D pre-training Vision-Language method that helps a model learn semantically meaningful and transferable 3D scene point cloud representation

Xiao Hu, Zhi-Yun Li, Lile Wang, Zhaohuan Zhu

Recent high angular resolution ALMA observations have revealed numerous gaps in protoplanetary disks. A popular interpretation has been that planets open them. Most previous investigations of planet gap-opening have concentrated on viscous disks. Here, we carry out 2D (axisymmetric) global simulations of gap opening by a planet in a wind-launching non-ideal

V. D. Esin, D. Yu. Kazmin, Yu. S. Barash, A. V. Timonina

We experimentally investigate charge transport in In-GeTe and In-GeTe-In proximity devices, which are formed as junctions between superconducting indium leads and thick single crystal flakes of $\alpha$-GeTe topological semimetal. We observe nonmonotonic effects of the applied external magnetic field, including reentrant superconductivity in In-GeTe-In Josep

Silviu Pitis, Michael R. Zhang, Andrew Wang, Jimmy Ba

Methods such as chain-of-thought prompting and self-consistency have pushed the frontier of language model reasoning performance with no additional training. To further improve performance, we propose a prompt ensembling method for large language models, which uses a small dataset to construct a set of few shot prompts that together comprise a ``boosted prom

Nicholas Goldowsky-Dill, Chris MacLeod, Lucas Sato, Aryaman Arora

Localizing behaviors of neural networks to a subset of the network's components or a subset of interactions between components is a natural first step towards analyzing network mechanisms and possible failure modes. Existing work is often qualitative and ad-hoc, and there is no consensus on the appropriate way to evaluate localization claims. We introduce pa

Alessandro Munafo', Sanjeev Kumar, Marco Panesi

The purpose of this work is the development of a self-consistent multi-physics modeling framework for ICP discharges. Unlike a monolithic approach, the hydrodynamics and electromagnetic field are handled by separate solvers, all developed within the Center for Hypersonics and Entry Systems Studies (CHESS) at the University of Illinois. Hydrodynamics is model

Samantha Robertson, Zijie J. Wang, Dominik Moritz, Mary Beth Kery

Machine learning (ML) models can fail in unexpected ways in the real world, but not all model failures are equal. With finite time and resources, ML practitioners are forced to prioritize their model debugging and improvement efforts. Through interviews with 13 ML practitioners at Apple, we found that practitioners construct small targeted test sets to estim

Ioannis Kalogeropoulos, Maria Eleftheria Vlontzou, Nikos Psaromanolakis, Eleni Zarogianni

The potential of Edge Computing technologies is yet to be exploited for multi-domain, multi-party data-driven systems. One aspect that needs to be tackled for the realization of envisioned open edge Ecosystems, is the secure and trusted exchange of data services among diverse stakeholders. In this work, we present a novel approach for integrating mechanisms

Timothy Campion, Yuki Maehara

We construct a (lax) Gray tensor product of $(\infty,2)$-categories and characterize it via a model-independent universal property. Namely, it is the unique monoidal biclosed structure on the $\infty$-category of $(\infty,2)$-categories which agrees with the classical Gray tensor product of strict 2-categories when restricted to the Gray cubes (i.e. the Gray

Benjamin Sims, Sergey V. Baryshev

This paper discusses the trends and trade-offs between transverse sigma x and longitudinal sigma z bunch dimensions, rf injector gradient, bunch charge, and intrinsic electron mean transverse energy (MTE), where all can be chosen to be independent, and the resulting effects on emittance and transverse brightness. Using a practical example of a quarter wave n

Arnaud Berny

We propose a novel evolutionary algorithm on bit vectors which derives from the principles of information theory. The information-theoretic evolutionary algorithm (it-EA) iteratively updates a search distribution with two parameters, the center, that is the bit vector at which standard bit mutation is applied, and the mutation rate. The mutation rate is upda

Shuchao Deng, Yanan Sun, Edgar Galvan

Facial expression is one of the most powerful, natural, and universal signals for human beings to express emotional states and intentions. Thus, it is evident the importance of correct and innovative facial expression recognition (FER) approaches in Artificial Intelligence. The current common practice for FER is to correctly design convolutional neural netwo

Haoxiang Ma, Shuo Han, Charles A. Kamhoua, Jie Fu

This paper is concerned with the optimal allocation of detection resources (sensors) to mitigate multi-stage attacks, in the presence of the defender's uncertainty in the attacker's intention. We model the attack planning problem using a Markov decision process and characterize the uncertainty in the attacker's intention using a finite set of reward function

Ning Chen, Jun Yue, Leyuan Fang, Shaobo Xia

Hyperspectral Image (HSI) classification is an important issue in remote sensing field with extensive applications in earth science. In recent years, a large number of deep learning-based HSI classification methods have been proposed. However, existing methods have limited ability to handle high-dimensional, highly redundant, and complex data, making it chal

Yoshiyuki Saito, Xinwei Lee, Dongsheng Cai, Nobuyoshi Asai

Quantum computation consists of a quantum state corresponding to a solution, and measurements with some observables. To obtain a solution with an accuracy $\epsilon$, measurements $O(n/\epsilon^2)$ are required, where $n$ is the size of a problem. The cost of these measurements requires a large computing time for an accurate solution. In this paper, we propo

Francisco J. de Anda, Stephen F. King

We develop a bottom-up approach to flavour models which combine modular symmetry with orbifold constructions. We first consider a 6d orbifold $\mathbb{T}^2/\mathbb{Z}_N$, with a single torus defined by one complex coordinate $z$ and a single modulus field $\tau$, playing the role of a flavon transforming under a finite modular symmetry. We then consider 10d

Marcel Binz, Ishita Dasgupta, Akshay Jagadish, Matthew Botvinick

Meta-learning is a framework for learning learning algorithms through repeated interactions with an environment as opposed to designing them by hand. In recent years, this framework has established itself as a promising tool for building models of human cognition. Yet, a coherent research program around meta-learned models of cognition is still missing. The

Luca Sortino, Merve Gülmüs, Benjamin Tilmann, Leonardo de S. Menezes

Two-dimensional (2D) semiconductors possess strongly bound excitons, opening novel opportunities for engineering light-matter interaction at the nanoscale. However, their in-plane confinement leads to large non-radiative exciton-exciton annihilation (EEA) processes, setting a fundamental limit for their photonic applications. In this work, we demonstrate sup

Federico Cunico, Federico Girella, Andrea Avogaro, Marco Emporio

Continuous mid-air hand gesture recognition based on captured hand pose streams is fundamental for human-computer interaction, particularly in AR / VR. However, many of the methods proposed to recognize heterogeneous hand gestures are tested only on the classification task, and the real-time low-latency gesture segmentation in a continuous stream is not well

CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard

Compact user representations (such as embeddings) form the backbone of personalization services. In this work, we present a new theoretical framework to measure re-identification risk in such user representations. Our framework, based on hypothesis testing, formally bounds the probability that an attacker may be able to obtain the identity of a user from the

Georgios Tzounas, Gabriela Hug

This paper focuses on the Partitioned-Solution Approach (PSA) employed for the Time-Domain Simulation (TDS) of dynamic power system models. In PSA, differential equations are solved at each step of the TDS for state variables, whereas algebraic equations are solved separately. The goal of this paper is to propose a novel, matrix-pencil based technique to stu

B. Tabone, G. Bettoni, E. F. van Dishoeck, A. M. Arabhavi

Carbon is an essential element for life but how much can be delivered to young planets is still an open question. The chemical characterization of planet-forming disks is a crucial step in our understanding of the diversity and habitability of exoplanets. Very low-mass stars ($<0.2~M_{\odot}$) are interesting targets because they host a rich population of te

Theodore Kareta, John W. Noonan, Walter M. Harris, Alessondra Springmann

The release of volatiles from comets is usually from direct sublimation of ices on the nucleus, but for very or hyper-active comets other sources have to be considered to account for the total production rates. In this work, we present new near-infrared imaging and spectroscopic observations of 46P/Wirtanen taken during its close approach to the Earth on 201

A. Romero-Ros, G. C. Katsimiga, S. I. Mistakidis, S. Mossman

We experimentally realize the Peregrine soliton in a highly particle-imbalanced two-component repulsive Bose-Einstein condensate in the immiscible regime. The effective focusing dynamics and resulting modulational instability of the minority component provide the opportunity to dynamically create a Peregrine soliton with the aid of an attractive potential we

Yuki Kudoh, Keiichi Wada, Nozomu Kawakatu, Mariko Nomura

We investigated dusty and dust-free gas dynamics for a radiation-driven sub-pc scale outflow in an active galactic nucleus (AGN) associated with a supermassive black hole $10^7 M_\odot$ and bolometric luminosity $10^{44}$ erg s$^{-1}$ based on the two-dimensional radiation-hydrodynamic simulations. A radiation-driven ``lotus-like'' multi-shell outflow is lau

Nihal Sanjay Singh, Keito Kobayashi, Qixuan Cao, Kemal Selcuk

Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important class of problems involve sampling-based Monte Carlo algorithms used in probabilistic machine learning, optimization, and quantum simulation. Here, we combine stochastic magnetic tu

Ichiro Inoue, Jumpei Yamada, Konrad J. Kapcia, Michal Stransky

X-ray diffraction of silicon irradiated with tightly focused femtosecond x-ray pulses (photon energy: 11.5 keV, pulse duration: 6 fs) was measured at various x-ray intensities up to $4.6\times10^{19}$ W/cm$^2$. The measurement reveals that the diffraction intensity is highly suppressed when the x-ray intensity reaches of the order of $10^{19}$ W/cm$^2$. With

A. H. Nzokem

The paper investigates the performance of the European option price when the log asset price follows a rich class of Generalized Tempered Stable (GTS) distribution. The GTS distribution is an alternative to Normal distribution and $\alpha$-stable distribution for modeling asset return and many physical and economic systems. The data used in the option pricin

Vojtech Panek, Zuzana Kukelova, Torsten Sattler

Visual localization is a core component in many applications, including augmented reality (AR). Localization algorithms compute the camera pose of a query image w.r.t. a scene representation, which is typically built from images. This often requires capturing and storing large amounts of data, followed by running Structure-from-Motion (SfM) algorithms. An in

Julio Ureña, Antonio Sojo, Juani Bermejo, Daniel Manzano

In this study, we introduce an autonomous method for addressing the detection and classification of quantum entanglement, a core element of quantum mechanics that has yet to be fully understood. We employ a multi-layer perceptron to effectively identify entanglement in both two- and three-qubit systems. Our technique yields impressive detection results, achi

I. L. Buchbinder, A. P. Isaev, M. A. Podoinitsyn, S. A. Fedoruk

We introduce and study the generalized Wigner operator. By definition, such an operator transforms the Wigner wave function into a local relativistic field corresponding to an irreducible representation of the Poincar\'e group by extended discrete transformations, with integer helicities $\lambda$ and $-\lambda$. It is shown that the relativistic fields cons

Mark Roantree, Branislava Lalic, Stevan Savic, Dragan Milosevic

The development of a knowledge repository for climate science data is a multidisciplinary effort between the domain experts (climate scientists), data engineers whos skills include design and building a knowledge repository, and machine learning researchers who provide expertise on data preparation tasks such as gap filling and advise on different machine le

Nicolas Delfosse, Adam Paetznick

We propose a scheme for detecting and correcting faults in any Clifford circuit. The scheme is based on the observation that the set of all possible outcome bit-strings of a Clifford circuit is a linear code, which we call the outcome code. From the outcome code we construct a corresponding stabilizer code, the spacetime code. Our construction extends the ci

Hyun Min Lee

We present a review on the self-resonant dark matter scenarios where multiple components of dark matter give rise to a resonant condition in the $u$-channel diagrams for their comparable masses. In this case, there is no need of lighter mediators for enhancing the self-scattering and annihilation cross sections for dark matter. We discuss the velocity-depend

Barnali Das, H. V. Ragavendra

Primordial non-Gaussianity arising from inflationary models is a unique probe of non-trivial dynamics of the inflaton field and its interactions with other fields. Often when examining and constraining the scalar non-Gaussianity arising from inflation, certain templates are adopted for the scalar non-Gaussianity parameter $f_{_{\rm NL}}$, in classifying thei

Michael Gaida, Stefan Nimmrichter

We present and discuss a master equation blueprint for a generic class of quantum measurement feedback based models of friction. A desired velocity-dependent friction force is realized on average by random repeated applications of unsharp momentum measurements followed by immediate outcome-dependent momentum displacements. The master equations can describe a

Gustav Bredell, Kyriakos Flouris, Krishna Chaitanya, Ertunc Erdil

Variational autoencoders (VAEs) are powerful generative modelling methods, however they suffer from blurry generated samples and reconstructions compared to the images they have been trained on. Significant research effort has been spent to increase the generative capabilities by creating more flexible models but often flexibility comes at the cost of higher

Jan Mohring, Michael Burger, Robert Feßler, Jochen Fiedler

This article is a reproduction of a Fraunhofer ITWM report from 28 June 2021 on the contribution of various non-pharmaceutical measures in breaking the 3rd Corona wave in Germany. The main finding is that testing contributed more to the containment of the pandemic in this phase than vaccination or contact restrictions. The analysis is based on a new epidemio

Simon Crawford

We describe all possible coactions of finite groups (equivalently, all group gradings) on two-dimensional Artin-Schelter regular algebras. We give necessary and sufficient conditions for the associated Auslander map to be an isomorphism, and determine precisely when the invariant ring for the coaction is Artin-Schelter regular. The proofs of our results are

Ian Ball, Deniz Kattwinkel

We correct a gap in the proof of Theorem 2 in Matsushima et al. (2010).

Aashaka Desai, Lauren Berger, Fyodor O. Minakov, Vanessa Milan

Sign languages are used as a primary language by approximately 70 million D/deaf people world-wide. However, most communication technologies operate in spoken and written languages, creating inequities in access. To help tackle this problem, we release ASL Citizen, the first crowdsourced Isolated Sign Language Recognition (ISLR) dataset, collected with conse

Álvaro Briz-Redón

From a statistical point of view, crime data present certain peculiarities that have led to a growing interest in their analysis. In particular, a characteristic that some property crimes frequently present is the existence of uncertainty about their exact location in time, being usual to only have a time window that delimits the occurrence of the event. The

Ronaldo S. S. Vieira, Włodek Kluźniak

A massive naked singularity would be cloaked by accreted matter, and thus may appear to a distant observer as an opaque \mbox{(quasi-)}spherical surface of a fluid, not unlike that of a star or planet. We present here analytical solutions for levitating atmospheres around a wide class of spherically symmetric naked singularities. Such an atmosphere can be co

Radoslaw Kolkowski, Andriy Shevchenko

Resonant optical systems have widespread applications in science and technology. However, their quality ($Q$) factors can be significantly deteriorated, if some of their parts exhibit optical absorption. Here, we show that by coupling a lossy mode of such a structure to two independent lossless modes, one can create a nonradiating and absorption-free bound s

Rezaul Karim, He Zhao, Richard P. Wildes, Mennatullah Siam

In this paper, we present an end-to-end trainable unified multiscale encoder-decoder transformer that is focused on dense prediction tasks in video. The presented Multiscale Encoder-Decoder Video Transformer (MED-VT) uses multiscale representation throughout and employs an optional input beyond video (e.g., audio), when available, for multimodal processing (

David Oniani, Bambang Parmanto, Andi Saptono, Allyn Bove

Rehabilitation research focuses on determining the components of a treatment intervention, the mechanism of how these components lead to recovery and rehabilitation, and ultimately the optimal intervention strategies to maximize patients' physical, psychologic, and social functioning. Traditional randomized clinical trials that study and establish new interv

Leander Thiele, Gabriela A. Marques, Jia Liu, Masato Shirasaki

We utilize the probability distribution function (PDF) of normalized convergence maps reconstructed from the Subaru Hyper Suprime-Cam (HSC) Y1 shear catalogue, in combination with the power spectrum, to measure the matter clustering amplitude $S_8=\sigma_8\sqrt{\Omega_m/0.3}$. The large-scale structure's statistical properties are incompletely described by t