August 2022 arXiv papers — page 37

Fabien Candelier, Rabah Mehaddi, Bernhard Mehlig, Jacques Magnaudet

We compute the full set of second-order inertial corrections to the instantaneous force and torque acting on a small spherical rigid particle moving unsteadily in a general steady linear flow. This is achieved by using matched asymptotic expansions and formulating the problem in a coordinate system co-moving with the background flow. Effects of the fluid-vel

George Giamougiannis, Apostolos Tsakyridis, Yangjin Ma, Angelina Totovic

Linear optics aim at realizing any real- and/or complex-valued matrix operator via optical elements, addressing a broad field of applications in the areas of quantum photonics, microwave photonics and optical neural networks. The transfer of linear operators into photonic experimental layouts typically relies on Singular Value Decomposition (SVD) techniques

Damaris Meier

We establish the following uniformization result for metric spaces $X$ of finite Hausdorff 2-measure. If $X$ is homeomorphic to a smooth 2-manifold $M$ with non-empty boundary, then we show that $X$ admits a quasiconformal almost parametrization $M\to X$, by only assuming that $X$ is locally geodesic and has rectifiable boundary. In particular, we recover a

Minhaj Nur Alam, Rikiya Yamashita, Vignav Ramesh, Tejas Prabhune

Self supervised contrastive learning based pretraining allows development of robust and generalized deep learning models with small, labeled datasets, reducing the burden of label generation. This paper aims to evaluate the effect of CL based pretraining on the performance of referrable vs non referrable diabetic retinopathy (DR) classification. We have deve

Samuel Epstein

We provide bounds on the compression size of the solutions to 22 problems in computer science. For each problem, we show that solutions exist with high probability, for some simple probability measure. Once this is proven, derandomization can be used to prove the existence of a simple solution.

Alessandro Daniele, Tommaso Campari, Sagar Malhotra, Luciano Serafini

Neuro-Symbolic (NeSy) integration combines symbolic reasoning with Neural Networks (NNs) for tasks requiring perception and reasoning. Most NeSy systems rely on continuous relaxation of logical knowledge, and no discrete decisions are made within the model pipeline. Furthermore, these methods assume that the symbolic rules are given. In this paper, we propos

Bikash R. Dinda, Md. Wali Hossain, Anjan A. Sen

We show the detectability of interacting and non-interacting cubic Galileon models from the $\Lambda$CDM model through the 21 cm power spectrum. We show that the interferometric observations like the upcoming SKA1-mid can detect both the interacting and the non-interacting cubic Galileon model from the $\Lambda$CDM model depending on the parameter values.

Panagiotis Kaklamanos, Christian Kuehn, Nikola Popović, Mattia Sensi

We study delayed loss of stability in a class of fast-slow systems with two fast variables and one slow one, where the linearisation of the fast vector field along a one-dimensional critical manifold has two real eigenvalues which intersect before the accumulated contraction and expansion are balanced along any individual eigendirection. That interplay betwe

Omar Es-Sahli, Adrian Sescu, M. Z. A. Koshuriyan, Yuji Hattori

High-amplitude free stream turbulence and surface roughness elements can excite a laminar boundary layer flow sufficiently to cause streamwise oriented vortices to develop. These vortices resemble elongated streaks having alternate spanwise variations of the streamwise velocity. Downstream, the vortices `wobble' through an inviscid secondary instability mech

Maria Solyanik-Gorgone, Behrouz Movahhed, Volker J Sorger

In the recent years, heterogeneous machine learning accelerators have become of significant interest in science, engineering and industry. The major processing speed bottlenecks in these platforms come from (a) an electronic data interconnect; (b) an electro-optical interface update rate. In this light, information compression implemented in native to incomi

Hasra Dodampegama, Mohan Sridharan

We present an architecture for ad hoc teamwork, which refers to collaboration in a team of agents without prior coordination. State of the art methods for this problem often include a data-driven component that uses a long history of prior observations to model the behaviour of other agents (or agent types) and to determine the ad hoc agent's behaviour. In m

Arul Elango, Ahmed Al-Tahmeesschi, Mikko Saukkoriipi, Titti Malmivirta

The vulnerabilities associated with modern systems relying on Global Navigation Satellite Systems (GNSS) due to intentional and unintentional interference is an increasing threat. Since radio frequency interference (RFI) significantly degrades the performance of a GNSS receiver. Several traditional critical applications such as aviation, maritime and rail tr

Bharat Bhatia, Ashoke De, Dirk Roekaerts, Assaad R. Masri

The present work focuses on the large eddy simulation (LES) and the study of turbulent dilute methanol spray flames in vitiated coflow using the secondary oxidizer Flamelet Generated Model (FGM). The modified FGM model uses an additional secondary oxidizer parameter in addition to the three other parameters previously used for spray flames progress variable,

Avinash Madasu, Estelle Aflalo, Gabriela Ben Melech Stan, Shachar Rosenman

Multi-modal retrieval has seen tremendous progress with the development of vision-language models. However, further improving these models require additional labelled data which is a huge manual effort. In this paper, we propose a framework MuMUR, that utilizes knowledge transfer from a multilingual model to boost the performance of multi-modal (image and vi

Michael Weiss

On complex problems, state of the art prediction accuracy of Deep Neural Networks (DNN) can be achieved using very large-scale models, consisting of billions of parameters. Such models can only be run on dedicated servers, typically provided by a 3rd party service, which leads to a substantial monetary cost for every prediction. We propose a new software arc

Dimitri Konen

We show that in any Euclidean space, an arbitrary probability measure can be reconstructed explicitly by its geometric (or spatial) distribution function. The reconstruction takes the form of a (potentially fractional) linear PDE, where the differential operator is given in closed form. This result implies that, contrary to a common belief in the statistical

Samarjeet Singh, Amitesh Roy, Jayesh M. Dhadphale, Swetaprovo Chaudhuri

Thermoacoustic instabilities observed in turbulent combustion systems have disastrous consequences and are notoriously challenging to model, predict and control. Here, we introduce a mean-field model of thermoacoustic transitions, where the nonlinear flame response is modeled as the amplitude weighted response of an ensemble of phase oscillators constrained

Mayank Verma, Ashoke De

Vortex-Induced Vibrations (VIV) offer a safe, renewable, and environmentally friendly energy source for energy harvesting. To enhance the energy harvesting capability of the circular cylinder-based devices, the authors explore the placement of the normal slit by determining the most effective slit offset location from the cylinder's center. Using the open-so

Michael Xu, Abinash Kumar, James M. LeBeau

Analysis of nanoscale short-range chemical and/or structural order via (scanning) transmission electron microscopy (S/TEM) imaging is fundamentally limited by projection of the three dimensional sample, which averages informational along the beam direction. Extracting statistically significant spatial correlations between the structure and chemistry determin

Shima Shahfar, Charalambos Poullis

The Swapping Autoencoder achieved state-of-the-art performance in deep image manipulation and image-to-image translation. We improve this work by introducing a simple yet effective auxiliary module based on gradient reversal layers. The auxiliary module's loss forces the generator to learn to reconstruct an image with an all-zero texture code, encouraging be

Sherzod M Mirakhmedov

We consider goodness-of-fit tests for uniformity of a multinomial distribution by means of tests based on a class of symmetric statistics, defined as the sum of some function of cell-frequencies. We are dealing with an asymptotic regime, where the number of cells grows with the sample size. Most attention is focused on the class of power divergence statistic

Huiyue Yi, Yan Xu, Wuxiong Zhang, Hui Xu

The least-absolute shrinkage and selection operator (LASSO) is a regularization technique for estimating sparse signals of interest emerging in various applications and can be efficiently solved via the alternating direction method of multipliers (ADMM), which will be termed as LASSO-ADMM algorithm. The choice of the regularization parameter has significant

Divyanshi Dwivedi, Pradeep Kumar Yemula, Mayukha Pal

Electrical Distribution Systems are extensively penetrated with Distributed Energy Resources (DERs) to cater the energy demands with the general perception that it enhances the system's resilience. However, integration of DERs may adversely affect the grid operation and affect the system resilience due to various factors like their intermittent availability,

Jack Noonan, Anatoly Zhigljavsky

We consider global optimization problems, where the feasible region $\X$ is a compact subset of $\mathbb{R}^d$ with $d \geq 10$. For these problems, we demonstrate the following. First: the actual convergence of global random search algorithms is much slower than that given by the classical estimates, based on the asymptotic properties of random points. Seco

Andriani Keliri, Benoît Douçot

We study the three terminal S-QD-S-QD-S Josephson junction biased with commensurate voltages. In the absence of an applied voltage, the Andreev bound states on each quantum dot hybridize forming an `Andreev molecule'. However, understanding of this system in a non-equilibrium setup is lacking. Applying a dc voltage on the bijunction makes the system time-per

Abhishek Gupta, Raunak Joshi, Nandan Kanvinde, Pinky Gerela

Regression branch of Machine Learning purely focuses on prediction of continuous values. The supervised learning branch has many regression based methods with parametric and non-parametric learning models. In this paper we aim to target a very subtle point related to distance based regression model. The distance based model used is K-Nearest Neighbors Regres

Máximo Bañados, Ernesto Bianchi, Iván Muñoz, Kostas Skenderis

We develop a systematic renormalization procedure for QFT in anti-de Sitter spacetime. UV infinities are regulated using a geodesic point-splitting method, which respects AdS isometries, while IR infinities are regulated by cutting off the radial direction (as in holographic renormalization). The renormalized theory is defined by introducing $Z$ factors for

Nicolai Häni, Volkan Isler

We present a general framework for accurate positioning of sensors and end effectors in farm settings using a camera mounted on a robotic manipulator. Our main contribution is a visual servoing approach based on a new and robust feature tracking algorithm. Results from field experiments performed at an apple orchard demonstrate that our approach converges to

Yoonwoo Jeong, Seungjoo Shin, Junha Lee, Christopher Choy

The recent progress in implicit 3D representation, i.e., Neural Radiance Fields (NeRFs), has made accurate and photorealistic 3D reconstruction possible in a differentiable manner. This new representation can effectively convey the information of hundreds of high-resolution images in one compact format and allows photorealistic synthesis of novel views. In t

Eduárd Zsurka, Noel Plaszkó, Péter Rakyta, Andor Kormányos

We propose that a device composed of two vertically stacked monolayer graphene Josephson junctions can be used for Cooper pair splitting. The hybridization of the Andreev bound states of the two Josephson junction can facilitate non-local transport in this normal-superconductor hybrid structure, which we study by calculating the non-local differential conduc

Brieuc Pinon, Jean-Charles Delvenne, Raphaël Jungers

Meta-learning is a line of research that develops the ability to leverage past experiences to efficiently solve new learning problems. Meta-Reinforcement Learning (meta-RL) methods demonstrate a capability to learn behaviors that efficiently acquire and exploit information in several meta-RL problems. In this context, the Alchemy benchmark has been proposed

Agelos Georgakopoulos

We prove that any two countable, compact, subsets of $\mathbb{S}^n, n\geq 2$ that are homeomorphic also have homeomorphic complements. Thus any wild subspace like the classical construction of Antoine must contain a Cantor set.

Hye-Jin Park, Young-Ju Choi, Young-Woon Lee, Byung-Gyu Kim

Feature Pyramid Network (FPN) has been an essential module for object detection models to consider various scales of an object. However, average precision (AP) on small objects is relatively lower than AP on medium and large objects. The reason is why the deeper layer of CNN causes information loss as feature extraction level. We propose a new scale sequence

Wen Yang, Rui Wang, Yanchao Zhang

Data augmentation greatly increases the amount of data obtained based on labeled data to save on expenses and labor for data collection and labeling. We present a new approach for data augmentation called nine-dot MLS (ND-MLS). This approach is proposed based on the idea of image defor-mation. Images are deformed based on control points, which are calculated

Jincheng Lu, Zi Wang, Rongqian Wang, Jiebin Peng

Including phonon-assisted inelastic process in thermoelectric devices is able to enhance the performance of nonequilibrium work extraction. In this work, we demonstrate that inelastic phonon-thermoelectric devices have a fertile functionality diagram, where particle current and phononic heat currents are coupled and fueled by chemical potential difference. S

Mahsa Asadi, Aurélien Bellet, Odalric-Ambrym Maillard, Marc Tommasi

We consider an online estimation problem involving a set of agents. Each agent has access to a (personal) process that generates samples from a real-valued distribution and seeks to estimate its mean. We study the case where some of the distributions have the same mean, and the agents are allowed to actively query information from other agents. The goal is t

Guangqi Xie, Xin Li, Shiqi Lin, Li Zhang

The rapid development of intelligent tasks, e.g., segmentation, detection, classification, etc, has brought an urgent need for semantic compression, which aims to reduce the compression cost while maintaining the original semantic information. However, it is impractical to directly integrate the semantic metric into the traditional codecs since they cannot b

Robin Khanfir

In order to study convergences of looptrees, we construct continuum trees and looptrees from real-valued c\`adl\`ag functions without negative jumps called excursions. We then provide a toolbox to manipulate the two resulting codings of metric spaces by excursions and we formalize the principle that jumps correspond to loops and that continuous growths corre

Darshan Ganganna Ravindra, Laslo Dinges, Al-Hamadi Ayoub, Vasili Baranau

In this paper, we focus on improving binary 2D instance segmentation to assist humans in labeling ground truth datasets with polygons. Humans labeler just have to draw boxes around objects, and polygons are generated automatically. To be useful, our system has to run on CPUs in real-time. The most usual approach for binary instance segmentation involves enco

Erika Michela Dematteis, Jussara Barale, Giovanni Capurso, Stefano Deledda

Hydrogen is an efficient energy carrier that can be produced from renewable sources, enabling the transition towards CO2-free energy. Hydrogen can be stored for a long period in the solid-state, with suitable alloys. Ti-rich TiFe0.90 compound exhibits a mild activation process for the first hydrogenation, and Ti(Fe,Mn)0.90 substituted alloys can lead to the

Marek Nikołajuk, Tomasz Karpiuk, Lorenzo Ducci, Mirosław Brewczyk

We investigate electromagnetic and gravitational radiation generated during the process of the tidal stripping of a white dwarf star circulating a black hole. We go beyond Chandrasekhar's ideas and not only consider a white dwarf itself as a quantum object, but also describe the dynamics of a produced accretion disk in a quantum way. We model the white dwarf

Felipe F. Braz, Tamíris R. Calixto, Pablo L. Saldanha

One of the quantum metrology goals is to improve the precision in the measurement of a small optical phase introduced in one optical mode in an interferometer, i.e., phase sensing. In this paper, we obtain the relative phase distribution introduced by Luis and S\'anchez-Soto (LSS) [Phys. Rev. A $\mathbf{53}$, 495 (1996)] for several two-mode pure quantum lig

Fengji Zhang, Jin Liu, Yao Wan, Xiao Yu

Stack Overflow is one of the most popular programming communities where developers can seek help for their encountered problems. Nevertheless, if inexperienced developers fail to describe their problems clearly, it is hard for them to attract sufficient attention and get the anticipated answers. We propose M$_3$NSCT5, a novel approach to automatically genera

Haoli Yin, Nithin Buduma

Multi-parametric magnetic resonance imaging (mpMRI) has a growing role in detecting prostate cancer lesions. Thus, it is pertinent that medical professionals who interpret these scans reduce the risk of human error by using computer-aided detection systems. The variety of algorithms used in system implementation, however, has yielded mixed results. Here we i

Hamdi Barkaoui, Kang Du, Yimu Chen, Shumin Xiao

Superchiral field is highly important for ultrasensitive detection of chiral objects and has been intensively explored. Despite the progress, the construction of electromagnetic field in achiral nanostructures with giant chirality (C) is fundamentally restricted. Here, we demonstrate a novel approach to construct superchiral field in achiral dielectric metas

Seungyeop Kang, Kyeong Soo Kim

A clock skew compensation algorithm was recently proposed based on the extension of Bresenham's line drawing algorithm (Kim and Kang, IEEE Commun. Lett., vol. 26, no. 4, pp. 902--906, Apr. 2022), which takes into account the discrete nature of clocks in digital communication systems and mitigates the effect of limited floating-point precision on clock skew c

A. Bazzani, F. Capoani, M. Giovannozzi

In a recent paper, the adiabatic theory of Hamiltonian systems was successfully applied to study the crossing of the linear coupling resonance, $Q_x-Q_y=0$. A detailed explanation of the well-known phenomena that occur during the resonance-crossing process, such as emittance exchange and its dependence on the adiabaticity of the process was obtained. In this

Maarten J. van den Broek, Delphine De Tavernier, Benjamin Sanderse, Jan-Willem van Wingerden

Wind farm flow control aims to improve wind turbine performance by reducing aerodynamic wake interaction between turbines. Dynamic, physics-based models of wind farm flows have been essential for exploring control strategies such as wake redirection and dynamic induction control. Free vortex methods can provide a computationally efficient way to model wind t

A. Bazzani, F. Capoani, M. Giovannozzi

In this paper, new results concerning the phenomenon of adiabatic trapping into resonance for a class of quasi-integrable maps with a time-dependent exciter are presented and discussed in detail. The applicability of the results about trapping efficiency for Hamiltonian systems to the maps considered is proven by using perturbation theory. This allows determ

Guanting Dong, Daichi Guo, Liwen Wang, Xuefeng Li

Most existing slot filling models tend to memorize inherent patterns of entities and corresponding contexts from training data. However, these models can lead to system failure or undesirable outputs when being exposed to spoken language perturbation or variation in practice. We propose a perturbed semantic structure awareness transferring method for trainin

Soren Galatius, Oscar Randal-Williams

We show that the topological Pontryagin classes are algebraically independent in the rationalised cohomology of BTop(d) for all $d \geq 4$.

Chien-An Wang, Corentin Déprez, Hanifa Tidjani, William I. L. Lawrie

Simulations using highly tunable quantum systems may enable investigations of condensed matter systems beyond the capabilities of classical computers. Quantum dots and donors in semiconductor technology define a natural approach to implement quantum simulation. Several material platforms have been used to study interacting charge states, while gallium arseni

Matteo Varbaro, Hongmiao Yu

Since the 1974 paper by Peskine and Szpiro, liaison theory via complete intersections, and more generally via Gorenstein varieties, has become a standard tool kit in commutative algebra and algebraic geometry, allowing to compare algebraic features of linked varieties. In this paper we develop a liaison theory via quasi-Gorenstein varieties, a much broader c

Zhengyang Tang, Benyou Wang, Ting Yao

Deep prompt tuning (DPT) has gained great success in most natural language processing~(NLP) tasks. However, it is not well-investigated in dense retrieval where fine-tuning~(FT) still dominates. When deploying multiple retrieval tasks using the same backbone model~(e.g., RoBERTa), FT-based methods are unfriendly in terms of deployment cost: each new retrieva

Eric D'Hoker, Thomas T. Dumitrescu, Emily Nardoni

We consider the Seiberg-Witten solution of pure $\mathcal{N} =2$ gauge theory in four dimensions, with gauge group $SU(N)$. A simple exact series expansion for the dependence of the $2 (N-1)$ Seiberg-Witten periods $a_I(u), a_{DI}(u)$ on the $N-1$ Coulomb-branch moduli $u_n$ is obtained around the $\mathbb{Z}_{2N}$-symmetric point of the Coulomb branch, wher

Nicolas Boulanger, Paul P. Cook, Josh A. O'Connor, Peter West

The non-linear realisation based on $A_1^{+++}$ is known to describe gravity in terms of both the graviton and the dual graviton. We extend this analysis at the linearised level to find the equations of motion for the first higher dual description of gravity that it contains. We also give a systematic method for finding the additional fields beyond those in

Seungwon Song, Hyungtae Lim, Alex Junho Lee, Hyun Myung

Visual inertial odometry and SLAM algorithms are widely used in various fields, such as service robots, drones, and autonomous vehicles. Most of the SLAM algorithms are based on assumption that landmarks are static. However, in the real-world, various dynamic objects exist, and they degrade the pose estimation accuracy. In addition, temporarily static object

Diana Sola, Christian Warmuth, Bernhard Schäfer, Peyman Badakhshan

In this paper, we introduce the SAP Signavio Academic Models (SAP-SAM) dataset, a collection of hundreds of thousands of business models, mainly process models in BPMN notation. The model collection is a subset of the models that were created over the course of roughly a decade on academic.signavio.com, a free-of-charge software-as-a-service platform that re

Jianlong Yuan, Jinchao Ge, Zhibin Wang, Yifan Liu

Consistency regularization has been widely studied in recent semisupervised semantic segmentation methods, and promising performance has been achieved. In this work, we propose a new consistency regularization framework, termed mutual knowledge distillation (MKD), combined with data and feature augmentation. We introduce two auxiliary mean-teacher models bas

Lorenzo Valentini, Marco Chiani, Enrico Paolini

Grant-free access schemes are candidates to support future massive multiple access applications owing to their capability to reduce control signaling and latency. As a promising class of grant-free schemes, coded random access schemes can achieve high reliabilities also with uncoordinated transmissions and therefore in presence packet collisions. In this pap

A. Antognini, P. Crivelli, L. Gerchow, T. D. Hume

A low-emittance, high-intensity atomic beam of muonium ($\mathrm{M}=\mu^+ + \mathrm{e}^-$) using superfluid helium as muon-to-muonium converter is being developed at the Paul Scherrer Institute (PSI). This beam could advance laser spectroscopy of muonium and allow the first atomic interferometry experiments for the direct observation of the M gravitational i

Chayan Patra, Valay Agarawal, Dipanjali Halder, Anish Chakraborty

The coupled cluster iteration scheme for determining the cluster amplitudes involves a set of nonlinearly coupled difference equations. In the space spanned by the amplitudes, the set of equations are analysed as a multivariate time-discrete map where the concept of time appears in an implicit manner. With the observation that the cluster amplitudes have dif

Axel Luukkonen, Amit Tewari, Kim Björkström, Amir Mohammad Ghafari

Organic water-gated thin-film transistors (WG-TFTs) are of great interest in developing low-cost and high-performance biosensors. The device's sensitivity to changes in measurement conditions can impair long-term operation, and care must be taken to ensure that the WG-TFT sensor response is due to an actual biorecognition event occurring on the sensing elect

Jianzheng Wang, Guoqiang Hu

In this work, we consider solving a distributed optimization problem (DOP) in a multi-agent network with multiple agent clusters. In each cluster, the agents manage separable cost functions composed of possibly non-smooth components and aim to achieve an agreement on a common decision of the cluster. The global cost function is considered as the sum of the i

Hongwei Xu, Suncheng Xiang, Dahong Qian

The softmax-based loss functions and its variants (e.g., cosface, sphereface, and arcface) significantly improve the face recognition performance in wild unconstrained scenes. A common practice of these algorithms is to perform optimizations on the multiplication between the embedding features and the linear transformation matrix. However in most cases, the

Zihao Yuan, Holger Dette

Many important dynamic systems, time series models or even algorithms exhibit non-strong mixing properties. In this paper, we introduce the general concept of $\mathcal{C}_{p,\mathcal{F}}$-mixing to cover such cases, where assumptions on the dependence structure become stronger with increasing $p\in [1, \infty].$ We derive a series of sharp exponential-type

Abhay Kumar, Tanmoy Chattopadhyay, Santosh V. Vadawale, A. R. Rao

The CZTI (Cadmium Zinc Telluride Imager) onboard AstroSat is a high energy coded mask imager and spectrometer in the energy range of 20 - 100 keV. Above 100 keV, the dominance of Compton scattering cross-section in CZTI results in a significant number of 2-pixel Compton events and these have been successfully utilized for polarization analysis of Crab pulsar

João Pedro Mendonça, Krzysztof Jachymski

State-of-the-art experiments using Rydberg atoms can now operate with large numbers of trapped particles with tunable geometry and long coherence time. We propose a way to utilize this in a hybrid setup involving neutral ground state atoms to efficiently simulate condensed matter models featuring electron-phonon coupling. Such implementation should allow for

Kyler Larsen, Arghya Pal, Yogesh Rathi

Magnetic Resonance Imaging (MRI) scans are time consuming and precarious, since the patients remain still in a confined space for extended periods of time. To reduce scanning time, some experts have experimented with undersampled k spaces, trying to use deep learning to predict the fully sampled result. These studies report that as many as 20 to 30 minutes c

N. Menci, M. Castellano, P. Santini, E. Merlin

Early observations with JWST have led to the discovery of an unexpected large density (stellar mass density $\rho_*\approx 10^{6}\,M_{\odot}\,Mpc^{-3}$) of massive galaxies (stellar masses $M_*\geq 10^{10.5}M_{\odot}$) at extremely high redshifts $z\approx 10$. We show that - under the most conservative assumptions, and independently of the baryon physics in

Zhiran Zhang, Maxime Joos, Dolev Bluvstein, Yuanqi Lyu

A single spin quantum sensor can quantitatively detect and image fluctuating electromagnetic fields via their effect on the sensor spin's relaxation time, thus revealing important information about the target solid-state or molecular structures. However, the sensitivity and spatial resolution of spin relaxometry are often limited by the distance between the

David Chapela-Campa, Marlon Dumas

Business Process Simulation (BPS) is a common technique to estimate the impact of business process changes, e.g. what would be the cycle time of a process if the number of traces increases? The starting point of BPS is a business process model annotated with simulation parameters (a BPS model). Several studies have proposed methods to automatically discover

Jinge Wu, Rowena Smith, Honghan Wu

Adverse Childhood Experiences (ACEs) are defined as a collection of highly stressful, and potentially traumatic, events or circumstances that occur throughout childhood and/or adolescence. They have been shown to be associated with increased risks of mental health diseases or other abnormal behaviours in later lives. However, the identification of ACEs from

Maciej Besta, Cesare Miglioli, Paolo Sylos Labini, Jakub Tětek

Important graph mining problems such as Clustering are computationally demanding. To significantly accelerate these problems, we propose ProbGraph: a graph representation that enables simple and fast approximate parallel graph mining with strong theoretical guarantees on work, depth, and result accuracy. The key idea is to represent sets of vertices using pr

Ron Keuth, Mattias Heinrich, Martin Eichenlaub, Marian Himstedt

Video bronchoscopy is routinely conducted for biopsies of lung tissue suspected for cancer, monitoring of COPD patients and clarification of acute respiratory problems at intensive care units. The navigation within complex bronchial trees is particularly challenging and physically demanding, requiring long-term experiences of physicians. This paper addresses

Peter Hirsch, Caroline Malin-Mayor, Anthony Santella, Stephan Preibisch

Tracking all nuclei of an embryo in noisy and dense fluorescence microscopy data is a challenging task. We build upon a recent method for nuclei tracking that combines weakly-supervised learning from a small set of nuclei center point annotations with an integer linear program (ILP) for optimal cell lineage extraction. Our work specifically addresses the fol

Jinge Wu, Rowena Smith, Honghan Wu

Adverse Childhood Experiences (ACEs) are defined as a collection of highly stressful, and potentially traumatic, events or circumstances that occur throughout childhood and/or adolescence. They have been shown to be associated with increased risks of mental health diseases or other abnormal behaviours in later lives. However, the identification of ACEs from

Jesse Railo, Philipp Zimmermann

We characterize partial data uniqueness for the inverse fractional conductivity problem with $H^{s,n/s}$ regularity assumptions in all dimensions. This extends the earlier results for $H^{2s,\frac{n}{2s}}\cap H^s$ conductivities by Covi and the authors. We construct counterexamples to uniqueness on domains bounded in one direction whenever measurements are p

Linyi Yang, Lifan Yuan, Leyang Cui, Wenyang Gao

Few-shot Named Entity Recognition (NER) is imperative for entity tagging in limited resource domains and thus received proper attention in recent years. Existing approaches for few-shot NER are evaluated mainly under in-domain settings. In contrast, little is known about how these inherently faithful models perform in cross-domain NER using a few labeled in-

Parijat Barman, Abhik Chakraborty, Denis Akimov, Ankit Kumar Singh

The deployment of plasmonic nanostructures to enhance nonlinear signal generation requires effective far-to-near field coupling and phase matching for frequency conversion. While the latter can be easily achieved at plasmonic hotspots, the former is an antenna problem that requires dedicated structural design and optimization. Plasmonic gratings are a simple

Tarakeshwar Lakshmipathy, Paul Steinmann, Erik Bitzek

Various fields such as mechanical engineering, materials science, etc., have seen a widespread use of linear elastic fracture mechanics (LEFM) at the continuum scale. LEFM is also routinely applied to the atomic scale. However, its applicability at this scale remains less well studied, with most studies focusing on non-linear elastic effects. Using a harmoni

Stan Weixian Lei, Difei Gao, Jay Zhangjie Wu, Yuxuan Wang

VQA is an ambitious task aiming to answer any image-related question. However, in reality, it is hard to build such a system once for all since the needs of users are continuously updated, and the system has to implement new functions. Thus, Continual Learning (CL) ability is a must in developing advanced VQA systems. Recently, a pioneer work split a VQA dat

Marina Yakovleva, Jean-Luc Pelouard, Fabrice Pardo

It is commonly assumed that the most correct description of the electromagnetic world is the abstract one, and that topological constructs such as lines of force are not covariant. In the present paper, we show that for a $y$-invariant system with a $p$-polarized electromagnetic field, it is possible to construct absolute (i.e. Lorentz invariant) lines, whic

Paul Primus, Gerhard Widmer

The absence of large labeled datasets remains a significant challenge in many application areas of deep learning. Researchers and practitioners typically resort to transfer learning and data augmentation to alleviate this issue. We study these strategies in the context of audio retrieval with natural language queries (Task 6b of the DCASE 2022 Challenge). Ou

Taisuke Izumi, Yuval Emek, Tadashi Wadayama, Toshimitsu Masuzawa

The \emph{$f$-fault-tolerant connectivity labeling} ($f$-FTC labeling) is a scheme of assigning each vertex and edge with a small-size label such that one can determine the connectivity of two vertices $s$ and $t$ under the presence of at most $f$ faulty edges only from the labels of $s$, $t$, and the faulty edges. This paper presents a new deterministic $f$

Zonghai Li, Wei Wang, Junji Jia

This paper studies the deflection of charged particles in a dipole magnetic field in Schwarzschild spacetime background in the weak field approximation. To calculate the deflection angle, we use Jacobi metric and Gauss-Bonnet theorem. Since the corresponding Jacobi metric is a Finsler metric of Randers type, we use both the osculating Riemannian metric metho

Yuanliang Zhang, Xiaofeng Wang, Jinxin Hu, Ke Gao

Multi-scenario recommendation is dedicated to retrieve relevant items for users in multiple scenarios, which is ubiquitous in industrial recommendation systems. These scenarios enjoy portions of overlaps in users and items, while the distribution of different scenarios is different. The key point of multi-scenario modeling is to efficiently maximize the use

Brant E. Robertson, Sandro Tacchella, Benjamin D. Johnson, Ryan Hausen

The dramatic first images with James Webb Space Telescope (JWST) demonstrated its power to provide unprecedented spatial detail for galaxies in the high-redshift universe. Here, we leverage the resolution and depth of the JWST Cosmic Evolution Early Release Science Survey (CEERS) data in the Extended Groth Strip (EGS) to perform pixel-level morphological cla

David Breitbach, Michael Schneider, Björn Heinz, Felix Kohl

Spin-wave amplification techniques are key to the realization of magnon-based computing concepts. We introduce a novel mechanism to amplify spin waves in magnonic nanostructures. Using the technique of rapid cooling, we create a non-equilibrium state in excess of high-energy magnons and demonstrate the stimulated amplification of an externally seeded, propag

Fei Qin, Cullan Howlett, Adam R. H. Stevens, David Parkinson

The next generation of galaxy surveys will provide more precise measurements of galaxy clustering than have previously been possible. The 21-cm radio signals that are emitted from neutral atomic hydrogen (HI) gas will be detected by large-area radio surveys such as WALLABY and the SKA, and deliver galaxy positions and velocities that can be used to measure g

Yilun Xu, Tianhong Gu, Jingwei Xian, Finn Giuliani

The mechanical properties of $\beta$-Sn single crystals have been systematically investigated using a combined methodology of micropillar tests and rate-dependent crystal plasticity modelling. The slip strength and rate sensitivity of several key slip systems within $\beta$-Sn single crystals have been determined. Consistency between the numerically predicte

Pengcheng Tang, Xuejun Zhang

Let $\mu$ be a finite Borel measure on $[0,1)$. In this paper, we consider the generalized integral type Hilbert operator $$\mathcal{I}_{\mu_{\alpha+1}}(f)(z)=\int_{0}^{1}\frac{f(t)}{(1-tz)^{\alpha+1}}d\mu(t)\ \ \ (\alpha>-1).$$ The operator $\mathcal{I}_{\mu_{1}}$ has been extensively studied recently. The aim of this paper is to study the boundedness(resp.

Qianqian Shen, Yanan Li, Jiyong Jin, Bin Liu

Deep learning has achieved tremendous success in computer vision, while medical image segmentation (MIS) remains a challenge, due to the scarcity of data annotations. Meta-learning techniques for few-shot segmentation (Meta-FSS) have been widely used to tackle this challenge, while they neglect possible distribution shifts between the query image and the sup

Puneet Kumar, Sarthak Malik, Balasubramanian Raman, Xiaobai Li

This paper proposes a multimodal emotion recognition system, VIsual Spoken Textual Additive Net (VISTANet), to classify emotions reflected by input containing image, speech, and text into discrete classes. A new interpretability technique, K-Average Additive exPlanation (KAAP), has been developed that identifies important visual, spoken, and textual features

Karen Wintersperger, Hila Safi, Wolfgang Mauerer

The use of quantum processing units (QPUs) promises speed-ups for solving computational problems, but the quantum devices currently available possess only a very limited number of qubits and suffer from considerable imperfections. One possibility to progress towards practical utility is to use a co-design approach: Problem formulation and algorithm, but also

Kumar Ayush, Abhishek Seth, Tarak K Patra

Polymer nanocomposites (PNCs) offer a broad range of thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property relation of PNCs due to their enormous composition and chemical space. Here, we address this problem and develop a new method to model the composition-microstructure rel

Stefan Güttel, Marcel Schweitzer

The computation of f(A)b, the action of a matrix function on a vector, is a task arising in many areas of scientific computing. In many applications, the matrix A is sparse but so large that only a rather small number of Krylov basis vectors can be stored. Here we discuss a new approach to overcome these limitations by randomized sketching combined with an i

Mamta Jangra, Raj Bhupen, Gobinda Majumder, Kiran Gothe

A Cosmic Muon Veto Detector (CMVD) is being built around the mini-Iron Calorimeter (mini-ICAL) detector at the transit campus of the India based Neutrino Observatory, Madurai. The CMV detector will be made using extruded plastic scintillators with embedded wavelength shifting (WLS) fibres which propagate re-emitted photons of longer wavelengths to silicon ph

Mirelle Bueno, Carlos Gemmell, Jeffrey Dalton, Roberto Lotufo

The ability to extrapolate, i.e., to make predictions on sequences that are longer than those presented as training examples, is a challenging problem for current deep learning models. Recent work shows that this limitation persists in state-of-the-art Transformer-based models. Most solutions to this problem use specific architectures or training methods tha

Bodo Manthey, Jesse van Rhijn

We analyze simulated annealing (SA) for simple randomized instances of the Traveling Salesperson Problem. Our analysis shows that the theoretically optimal cooling schedule of Hajek explores members of the solution set which are in expectation far from the global optimum. We obtain a lower bound on the expected length of the final tour obtained by SA on thes