September 2022 arXiv papers — page 4

Paolo Gambino, Shoji Hashimoto, Sandro Mächler, Marco Panero

We present a non-perturbative computation of inclusive rates of semileptonic decays of heavy mesons from lattice QCD simulations. The calculation is based on the extraction of smeared spectral functions obtained from four-point Euclidean correlation functions computed on configuration ensembles of the JLQCD and ETM collaborations. We compare our results for

Ido Goorevitch, Ron Holzman

We prove that a family $\mathcal{T}$ of distinct triangles on $n$ given vertices that does not have a rainbow triangle (that is, three edges, each taken from a different triangle in $\mathcal{T}$, that form together a triangle) must be of size at most $\frac{n^2}{8}$. We also show that this result is sharp and characterize the extremal case. In addition, we

Anne Baanen, Alex J. Best, Nirvana Coppola, Sander R. Dahmen

Diophantine equations are a popular and active area of research in number theory. In this paper we consider Mordell equations, which are of the form $y^2=x^3+d$, where $d$ is a (given) nonzero integer number and all solutions in integers $x$ and $y$ have to be determined. One non-elementary approach for this problem is the resolution via descent and class gr

James C. R. Whittington, Will Dorrell, Surya Ganguli, Timothy E. J. Behrens

Neurons in the brain are often finely tuned for specific task variables. Moreover, such disentangled representations are highly sought after in machine learning. Here we mathematically prove that simple biological constraints on neurons, namely nonnegativity and energy efficiency in both activity and weights, promote such sought after disentangled representa

Peter Gangl, Michael H. Gfrerer

We introduce a unified sensitivity concept for shape and topological perturbations and perform the sensitivity analysis for a discretized PDE-constrained design optimization problem in two space dimensions. We assume that the design is represented by a piecewise linear and globally continuous level set function on a fixed finite element mesh and relate pertu

Jianwei Fei, Yunshu Dai, Peipeng Yu, Tianrun Shen

In this work, we propose a novel method to improve the generalization ability of CNN-based face forgery detectors. Our method considers the feature anomalies of forged faces caused by the prevalent blending operations in face forgery algorithms. Specifically, we propose a weakly supervised Second Order Local Anomaly (SOLA) learning module to mine anomalies i

Carlos Aravena, Diego Pasmino, Juan E. Tapia, Christoph Busch

Non-referential face image quality assessment methods have gained popularity as a pre-filtering step on face recognition systems. In most of them, the quality score is usually designed with face matching in mind. However, a small amount of work has been done on measuring their impact and usefulness on Presentation Attack Detection (PAD). In this paper, we st

Pierre Touboul, Gilles Métris, Manuel Rodrigues, Joel Bergé

The space mission MICROSCOPE dedicated to the test of the Equivalence Principle (EP) operated from April 25, 2016 until the deactivation of the satellite on October 16, 2018. In this analysis we compare the free-fall accelerations ($a_{\rm A}$ and $a_{\rm B}$) of two test masses in terms of the E\"otv\"os parameter $\eta({\rm{A, B}}) = 2 \frac{a_{\rm A}- a_{

Pierre Touboul, Gilles Métris, Manuel Rodrigues, Joel Bergé

The MICROSCOPE mission was designed to test the Weak Equivalence Principle (WEP), stating the equality between the inertial and the gravitational masses, with a precision of $10^{-15}$ in terms of the E\"otv\"os ratio $\eta$. Its experimental test consisted of comparing the accelerations undergone by two collocated test masses of different compositions as th

Enzo Tartaglione

Deep learning models are nowadays broadly deployed to solve an incredibly large variety of tasks. Commonly, leveraging over the availability of "big data", deep neural networks are trained as black-boxes, minimizing an objective function at its output. This however does not allow control over the propagation of some specific features through the model, like

Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca

Many Graph Neural Networks (GNNs) perform poorly compared to simple heuristics on Link Prediction (LP) tasks. This is due to limitations in expressive power such as the inability to count triangles (the backbone of most LP heuristics) and because they can not distinguish automorphic nodes (those having identical structural roles). Both expressiveness issues

Tom Barnowsky, Arkady V. Krasheninnikov, Rico Friedrich

The exfoliation energy - quantifying the energy required to extract a two-dimensional (2D) sheet from the surface of a bulk material - is a key parameter determining the synthesizability of 2D compounds. Here, using ab initio calculations, we present a new group of non-van der Waals 2D materials derived from non-layered crystals which exhibit ultra low exfol

Ji Wang, Jason J. Wang, Jean-Baptiste Ruffio, Geoffrey A. Blake

The formation and evolution pathway for the directly-imaged multi-planetary system HR 8799 remains mysterious. Accurate constraints on the chemical composition of the planetary atmosphere(s) are key to solving the mystery. We perform a detailed atmospheric retrieval on HR 8799~c to infer the chemical abundances and abundance ratios using a combination of pho

Itai Gat, Felix Kreuk, Tu Anh Nguyen, Ann Lee

Generative Spoken Language Modeling research focuses on optimizing speech Language Models (LMs) using raw audio recordings without accessing any textual supervision. Such speech LMs usually operate over discrete units obtained from quantizing internal representations of self-supervised models. Although such units show impressive modeling results, their robus

Revaz Tevzadze

The representation of the solution of some Backward Stochastic Differential Equation as an infinite series is obtained. Some exactly solvable examples are considered.

Alexey V. Verkhovtsev, Yury Erofeev, Andrey V. Solov'yov

Physical mechanisms of structural transformations in deposited metallic clusters exposed to an electron beam of a transmission electron microscope (TEM) are studied theoretically and computationally. Recent TEM experiments with size-selected Au$_{923}$ clusters softly deposited on a carbon substrate showed that the clusters undergo structural transformations

Greg Hamilton, Travis Dore, Christopher Plumberg

We introduce a novel set of observables associated to the rapidly developing field of persistent homology for the quantitative characterization of nuclear collisions and their evolution. Persistent homology allows for the identification of topological and homological characteristics of distributions in multi-dimensional spaces. We demonstrate here how to app

Gohar Hovhannesyan, Maxence Lepers

Laser cooling and trapping of lanthanides has opened the possibility to carry out new experiments with ultracold dipolar gases, for example for quantum simulation of solid state physics. To identify new suitable candidates for laser-cooling, it is important to have a precise spectroscopic knowledge of the atom under consideration. Along this direction, we pr

David Jensen, Sam Payne

We propose a definition of tropical linear series that isolates some of the essential combinatorial properties of tropicalizations of not-necessarily-complete linear series on algebraic curves. The definition combines the Baker-Norine notion of rank with the notion of tropical independence and has the property that the restriction of a tropical linear series

Ben Craps, Juan Hernandez, Mikhail Khramtsov, Maria Knysh

We revisit the model of an AdS$_2$ black hole in JT gravity evaporating into an external bath. We study when, and how much, information about the black hole interior can be accessed through different portions of the Hawking radiation collected in the bath, and we obtain the corresponding full quantitative Page curves. As a refinement of previous results, we

Marco Cattaneo, Gian Luca Giorgi, Roberta Zambrini, Sabrina Maniscalco

The quantum collision models are a useful method to describe the dynamics of an open quantum system by means of repeated interactions between the system and some particles of the environment, which are usually termed "ancillas". In this paper, we review the main collision models for the dynamics of multipartite open quantum systems, which are composed of sev

Zhengyu Wang, Yujie Zhang, Qi Yang, Yiling Xu

Point cloud quality assessment (PCQA) has become an appealing research field in recent days. Considering the importance of saliency detection in quality assessment, we propose an effective full-reference PCQA metric which makes the first attempt to utilize the saliency information to facilitate quality prediction, called point cloud quality assessment using

Jag Mohan Singh, Raghavendra Ramachandra

Face Recognition Systems (FRS) are vulnerable to various attacks performed directly and indirectly. Among these attacks, face morphing attacks are highly potential in deceiving automatic FRS and human observers and indicate a severe security threat, especially in the border control scenario. This work presents a face morphing attack detection, especially in

Alexander C Murph, Jan Hannig, Jonathan P Williams

We introduce a novel approach to inference on parameters that take values in a Riemannian manifold embedded in a Euclidean space. Parameter spaces of this form are ubiquitous across many fields, including chemistry, physics, computer graphics, and geology. This new approach uses generalized fiducial inference to obtain a posterior-like distribution on the ma

Vikas Tokala, Mike Brookes, Patrick A. Naylor

STOI-optimal masking has been previously proposed and developed for single-channel speech enhancement. In this paper, we consider the extension to the task of binaural speech enhancement in which spatial information is known to be important to speech understanding and therefore should be preserved by the enhancement processing. Masks are estimated for each o

Wiktor Lazarski, Maciej Zieba, Tanguy Jeanneau, Tobias Zillig

Iris-based identification systems are among the most popular approaches for person identification. Such systems require good-quality segmentation modules that ideally identify the regions for different eye components. This paper introduces the new two-headed architecture, where the eye components and eyelashes are segmented using two separate decoding module

Jiazheng Zhou, Gisela E Hagberg, Ali Aghaeifar, Jonas Bause

Objective Maps of B0 field inhomogeneities are often used to improve MRI image quality, even in a retrospective fashion. These field inhomogeneities depend on the exact head position within the static field but acquiring field maps (FM) at every position is time consuming. Here we explore different ways to obtain B0 predictions at different head positions. M

Michelle Chen Huebscher, Christian Buck, Massimiliano Ciaramita, Sascha Rothe

Learning to search is the task of building artificial agents that learn to autonomously use a search box to find information. So far, it has been shown that current language models can learn symbolic query reformulation policies, in combination with traditional term-based retrieval, but fall short of outperforming neural retrievers. We extend the previous le

V. Pomjakushin, I. Plokhikh, J. S. White, Y. Fujishiro

From new neutron powder diffraction experiments on the chiral cubic ($P2{_1}3$) magnet manganese germanide MnGe, we analyse all of the possible crystal symmetry-allowed magnetic superstructures that are determined successfully from the data. The incommensurate propagation vectors $k$ of the magnetic structure are found to be aligned with the [100] cubic axes

P. Tozzi, R. Gilli, A. Liu, S. Borgani

We present the X-ray imaging and spectral analysis of the diffuse emission around the Spiderweb galaxy at z=2.16 and of its nuclear emission, based on a deep (700 ks) Chandra observation. We characterize the nuclear emission and computed the contamination in the surrounding regions due to the wings of the instrument PSF. Then, we quantified the extended emis

Tianlin Liu, Joan Puigcerver, Mathieu Blondel

Regularized optimal transport (OT) is now increasingly used as a loss or as a matching layer in neural networks. Entropy-regularized OT can be computed using the Sinkhorn algorithm but it leads to fully-dense transportation plans, meaning that all sources are (fractionally) matched with all targets. To address this issue, several works have investigated quad

N. I. Md. Ashafuddula, Rafiqul Islam

Melanoma skin cancer is one of the most dangerous and life-threatening cancer. Exposure to ultraviolet rays may damage the skin cell's DNA, which causes melanoma skin cancer. However, it is difficult to detect and classify melanoma and nevus mole at the immature stages. In this work, an automatic deep learning system is developed based on the intensity value

Carolina Lüthi, Luis Flacke, Aisha Aqeel, Akashdeep Kamra

We investigate the coupled magnetization dynamics in heterostructures of a single crystal of the chiral magnet $\mathrm{Cu_2OSeO_3}$ (CSO) and a polycrystalline ferromagnet $\mathrm{NiFe}$ (Py) thin film using broadband ferromagnetic resonance (FMR) at cryogenic temperatures. We observe the excitation of a hybrid mode (HM) below the helimagnetic transition t

Florian Lackner, Wolfgang E. Ernst

Besides the use as cold matrix for spectroscopic studies, superfluid helium droplets have served as a cold environment for the synthesis of molecules and clusters. Since vibrational frequencies of molecules in helium droplets exhibit almost no shift compared to the free molecule values, one could assume the solvated particles move frictionless and undergo a

Soumyadeep Saha, Meraj E Mustafa, Manfred Eich, Alexander Yu. Petrov

Significant suppression of radiation in 3D structured media with small refractive index 1.4-1.6, such as of glass or polymers, is a desirable feature yet to be obtained. For periodical structures this is realised at frequencies of the complete photonic band gap (CPBG), which up to now was demonstrated to open for materials with refractive index of at least 1

Mark Edelman, Avigayil Helman

In this paper, we derive analytic expressions for coefficients of the equations that allow calculations of asymptotically periodic points in fractional difference maps. Numerical solution of these equations allows us to draw the bifurcation diagram for the fractional difference logistic map. Based on the numerically calculated bifurcation points, we make a c

June-Young M. Lee, Changki Hong, Tomer Alkalay, Noam Schiller

Correlations of partitioned particles carry essential information about their quantumness. Partitioning full beams of charged particles leads to current fluctuations, with their autocorrelation (namely, shot noise) revealing the particle' charge. This is not the case when the partitioned particle beams are diluted. Bosons or fermions will exhibit particles a

Felix Erben, Vera Gülpers, Maxwell T. Hansen, Raoul Hodgson

We present a strategy for calculating the rare decay of a $\Sigma^+ (uus)$ baryon to a proton $(uud)$ and di-lepton pair using lattice QCD. To determine this observable one needs to numerically evaluate baryonic two-, three-, and four-point correlation functions related to the target process. In particular, the four-point function arises from the insertion o

R. Yao, W. -Q. Lian, Y. -K. Wu, G. -X. Wang

Storage lifetime and capacity are two important factors to characterize the performance of a quantum memory. Here we report the stable trapping of above 200 ions in a cryogenic setup, and demonstrate the combination of the multi-qubit capacity and long storage lifetime by measuring the coherence time of randomly chosen ions to be on the order of hundreds of

Ruben Campos Delgado, Stefan Forste

We study $\mathcal{N}=2$ supersymmetric Jackiw-Teitelboim (JT) gravity at finite temperature coupled to matter. The matter fields are related to superconformal primaries by AdS/CFT duality. Due to broken super reparametrisation invariance in the SCFT dual, there are corrections to superconformal correlators. These are generated by the exchange of super-Schwa

Nicolas Parra-A, Vladimir Vargas-Calderón, Herbert Vinck-Posada, Nicanor Vinck

Road maintenance is an essential process for guaranteeing the quality of transportation in any city. A crucial step towards effective road maintenance is the ability to update the inventory of the road network. We present a proof of concept of a protocol for maintaining said inventory based on the use of unmanned aerial vehicles to quickly collect images whi

Xun Liu, Alex Hay-Man Ng, Fangyuan Lei, Yikuan Zhang

Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-world problems on graph-structured data. However, these models usually have at least one of four fundamental limitations: over-smoothing, over-fitting, difficult to train, and strong homophily assumption. For example, Simple Graph Convolution (SGC) is known to suf

Kolja Knauer, Gil Puig i Surroca

We show that every commutative idempotent monoid (a.k.a lattice) is the endomorphism monoid of a subcubic graph. This solves a problem of Babai and Pultr [J. Comb.~Theory, Ser.~B, 1980] and the degree bound is best-possible. On the other hand, we show that no class excluding a minor can have all commutative idempotent monoids among its endomorphism monoids.

Yoshihiro Okawa, Tomotake Sasaki, Hitoshi Yanami, Toru Namerikawa

Recent rapid developments in reinforcement learning algorithms have been giving us novel possibilities in many fields. However, due to their exploring property, we have to take the risk into consideration when we apply those algorithms to safety-critical problems especially in real environments. In this study, we deal with a safe exploration problem in reinf

Thudiyangal Mithun, Aleksandra Maluckov, Ana Mančić, Avinash Khare

In the present work we revisit the Salerno model as a prototypical system that interpolates between a well-known integrable system (the Ablowitz-Ladik lattice) and an experimentally tractable non-integrable one (the discrete nonlinear Schr\"odinger model). The question we ask is: for "generic" initial data, how close are the integrable to the non-integrable

Wanqin Ma, Huifeng Yao, Yiqun Lin, Jiarong Guo

Developing a deep learning method for medical segmentation tasks heavily relies on a large amount of labeled data. However, the annotations require professional knowledge and are limited in number. Recently, semi-supervised learning has demonstrated great potential in medical segmentation tasks. Most existing methods related to cardiac magnetic resonance ima

Xinxing Wu, Chong Peng, Richard Charnigo, Qiang Cheng

Interpreting critical variables involved in complex biological processes related to survival time can help understand prediction from survival models, evaluate treatment efficacy, and develop new therapies for patients. Currently, the predictive results of deep learning (DL)-based models are better than or as good as standard survival methods, they are often

Navin Raj Prabhu, Nale Lehmann-Willenbrock, Timo Gerkman

To train machine learning algorithms to predict emotional expressions in terms of arousal and valence, annotated datasets are needed. However, as different people perceive others' emotional expressions differently, their annotations are subjective. To account for this, annotations are typically collected from multiple annotators and averaged to obtain ground

Michał Gawełczyk, Michał Zieliński

We utilize Bardeen's tunneling theory to calculate intra- and interorbital hopping integrals between phosphorus donors in silicon using known orbital wave functions. While the two-donor problem can be solved directly, the knowledge of hoppings for various pairs of orbitals is essential for constructing multi-orbital Hubbard models for chains and arrays of do

Inés García-Redondo, Anthea Monod, Anna Song

Within the context of topological data analysis, the problems of identifying topological significance and matching signals across datasets are important and useful inferential tasks in many applications. The limitation of existing solutions to these problems, however, is computational speed. In this paper, we harness the state-of-the-art for persistent homol

G. Amadio, J. Apostolakis, P. Buncic, G. Cosmo

Making general particle transport simulation for high-energy physics (HEP) single-instruction-multiple-thread (SIMT) friendly, to take advantage of accelerator hardware, is an important alternative for boosting the throughput of simulation applications. To date, this challenge is not yet resolved, due to difficulties in mapping the complexity of Geant4 compo

Sven Krippendorf, Valentí Vall Camell

Sampling string flux vacua enables us to study structures in the string landscape. Here we demonstrate that sampling at large number of moduli is possible for the simplified landscape model of ADK. Using dimensional reduction, we identify analytic structures in these samples. In this example, we find that these structures are rather insensitive to the underl

Longjun Xiang, Chao Zhang, Luyang Wang, Jian Wang

The linear intrinsic anomalous Hall effect (IAHE) and second-order IAHE have been intensively investigated in time-reversal broken systems. However, as one of the important members of the nonlinear Hall family, the investigation of third-order IAHE remains absent due to the lack of an appropriate theoretical approach, although the third-order extrinsic AHE h

Alexey Sharapov, Evgeny Skvortsov, Arseny Sukhanov, Richard Van Dongen

Recently, a unique class of local Higher Spin Gravities with propagating massless fields in $4d$ - Chiral Higher Spin Gravity - was given a covariant formulation both in flat and $(A)dS_4$ spacetimes at the level of equations of motion. We unfold the corresponding homological perturbation theory as to explicitly obtain all interaction vertices. The vertices

Jonah Burgess

Denial of Service (DoS) and Distributed Denial of Service of Service (DDoS) attacks are commonly used to disrupt network services. Attack techniques are always improving and due to the structure of the internet and properties of network protocols it is difficult to keep detection and mitigation techniques up to date. A lot of research has been conducted in t

Fiona Mukherjee, Anye Shi, Xin Wang, Fengqi You

Identifying and removing microplastics (MPs) from the environment is a global challenge. This study explores how the colloidal fraction of common MPs behave at aqueous interfaces of liquid crystal (LC) films. We observed polyethylene (PE) and polystyrene (PS) microparticles to be captured at the LC interface and exhibit distinct two-dimensional aggregation p

Jonah Burgess

In recent years, the darknet has become the key location for the distribution of malware and exploits. We have seen scenarios where software vulnerabilities have been disclosed by vendors and shortly after, operational exploits are available on darknet forums and marketplaces. Many marketplace vendors offer zero-day exploits that have not yet been discovered

Juliette Mattioli, Agnes Delaborde, Souhaiel Khalfaoui, Freddy Lecue

This paper reviews the entire engineering process of trustworthy Machine Learning (ML) algorithms designed to equip critical systems with advanced analytics and decision functions. We start from the fundamental principles of ML and describe the core elements conditioning its trust, particularly through its design: namely domain specification, data engineerin

R. Dupuy, J. Filser, C. Richter, T. Buttersack

The determination of depth profiles across interfaces is of primary importance in many scientific and technological areas. Photoemission spectroscopy is in principle well suited for this purpose, yet a quantitative implementation for investigations of liquid-vapor interfaces is hindered by the lack of understanding of electron-scattering processes in liquids

Sohyun Park

We study medium-modifications of the gluon splitting into a quark and anti-quark pair. Applying the Baier-Dokshitzer-Mueller-Peign\'e-Schiff and Zakharov (BDMPS-Z) formalism, we derive a path-integral formula for the in-medium $g\to q \bar{q}$ splitting function in the close-to-eikonal limit. Our analysis shows that there are two qualitatively different medi

Franz W. Kamber, Peter W. Michor

In [4] and [5], we generalized the concept of completion of an infinitesimal group action $\zeta : {\mathfrak g} \to \mathfrak X (M)$ to an actual group action on a (non-compact) manifold $M$, originally introduced by R. Palais [9], and showed by examples that this completion may have quite pathological properties (much like the leaf space of a foliation). I

Shai Feldfogel, Konstantinos Karapiperis, Jose Andrade, David S. Kammer

The recently developed level-set-DEM is able to seamlessly handle arbitrarily shaped grains and their contacts through a discrete level-set representation of grains' volume and a node-based discretization of their bounding surfaces. Heretofore, the convergence properties of LS-DEM with refinement of these discretizations have not been examined. Here, we exam

Luca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli

Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations. Ideally, the distribution of the data points in the latent space should depend only on the task, the data, the loss, and other architecture-specific constraints. However, factors such as the random weights initialization, training h

Pierre Carmier

We derive an upper bound for the expected gain of informed traders in the Glosten-Milgrom model with finite horizon, fully analogous to a generalized second law of thermodynamics. This result extends that obtained by Touzo et al. a couple of years ago. The proof relies on Bayesian inference (exploiting the invariance of the problem under consecutive game seq

Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, Artem Babenko

Denoising diffusion probabilistic models are currently becoming the leading paradigm of generative modeling for many important data modalities. Being the most prevalent in the computer vision community, diffusion models have also recently gained some attention in other domains, including speech, NLP, and graph-like data. In this work, we investigate if the f

Xiao Cao, Yan-Mei Chen, Yong Shi, Min Bao

Frequent accretion of external cold gas is thought to play an important role in galaxy assembly. However, almost all known kinematically misaligned galaxies identify only one gas disk that is misaligned with the stellar disk, implying a single gas acquisition event. Here we report a new configuration in two galaxies where both contain two gas disks misaligne

G. Martinelli, M. Naviglio, S. Simula, L. Vittorio

We present the results of the application of the Dispersion Matrix approach to exclusive semileptonic $B$-meson decays. This method allows to determine the hadronic form factors in a non-perturbative and completely model-independent way. Starting from lattice results available at large values of the momentum transfer, the behaviour of the form factors in the

Peter Benner, Pawan Goyal, Jan Heiland, Igor Pontes

Linear projection schemes like Proper Orthogonal Decomposition can efficiently reduce the dimensions of dynamical systems but are naturally limited, e.g., for convection-dominated problems. Nonlinear approaches have shown to outperform linear methods in terms of dimension reduction versus accuracy but, typically, come with a large computational overhead. In

Mashkoor Ali, Ankik Kumar Giri, Philippe Laurencot

A discrete version of the nonlinear collision-induced breakage equation is studied. Existence of solutions is investigated for a broad class of unbounded collision kernels and daughter distribution functions, the collision kernel $a_{i,j}$ satisfiying $a_{i,j} \leq A i j$ for some $A>0$. More precisely, it is proved that given suitable conditions, there exis

Jian-Gang Tang

In this article, we discuss the question of whether P equals NP, we do not follow the line of research of many researchers, which is to try to find such a problem Q, and the problem Q belongs to the class of NP-complete, if the problem Q is proved to belong to P, then P and NP are the same, if the problem Q is proved not to belong to P, then P and NP are sep

Minkyu Kim, Hyun-Soo Choi, Jinho Kim

Neural Additive Models (NAMs) have recently demonstrated promising predictive performance while maintaining interpretability. However, their capacity is limited to capturing only first-order feature interactions, which restricts their effectiveness on real-world datasets. To address this limitation, we propose Higher-order Neural Additive Models (HONAMs), an

Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li

Inverse molecular design is critical in material science and drug discovery, where the generated molecules should satisfy certain desirable properties. In this paper, we propose equivariant energy-guided stochastic differential equations (EEGSDE), a flexible framework for controllable 3D molecule generation under the guidance of an energy function in diffusi

Zihao Yu, Chengkun Jiang, Yuan He, Xiaolong Zheng

Clock synchronization is a key function in embedded wireless systems and networks. This issue is equally important and more challenging in IoT systems nowadays, which often include heterogeneous wireless devices that follow different wireless standards. Conventional solutions to this problem employ gateway-based indirect synchronization, which suffers low ac

Mohatashem Reyaz Makhdoomi, Vivek Muralidharan, Kuldeep R. Barad, Juan Sandoval

On-orbit operations such as servicing and assembly are considered a priority for the future space industry. Ground-based facilities that emulate on-orbit interactions are key tools for developing and testing space technology. This paper presents a control framework to emulate on-orbit operations using on-ground robotic manipulators. It combines Virtual Forwa

Christian Herglotz, Matthias Kränzler, Robert Schober, André Kaup

In recent years, the global use of online video services has increased rapidly. Today, a manifold of applications, such as video streaming, video conferencing, live broadcasting, and social networks, make use of this technology. A recent study found that the development and the success of these services had as a consequence that, nowadays, more than 1% of th

Ali Younes, Simone Schaub-Meyer, Georgia Chalvatzaki

Extracting informative representations from videos is fundamental for effectively learning various downstream tasks. We present a novel approach for unsupervised learning of meaningful representations from videos, leveraging the concept of image spatial entropy (ISE) that quantifies the per-pixel information in an image. We argue that \textit{local entropy}

Eleonora Alei, Björn S. Konrad, Paul Mollière, Sascha P. Quanz

Atmospheric retrieval studies are essential to determine the science requirements for future generation missions, such as the Large Interferometer for Exoplanets (LIFE). The use of heterogeneous absorption cross-sections might be the cause of systematic effects in retrievals, which could bias a correct characterization of the atmosphere. In this contribution

Weijie Wang, Bo Li, Nicu Sebe, Bruno Lepri

Due to the subjective crowdsourcing annotations and the inherent inter-class similarity of facial expressions, the real-world Facial Expression Recognition (FER) datasets usually exhibit ambiguous annotation. To simplify the learning paradigm, most previous methods convert ambiguous annotation results into precise one-hot annotations and train FER models in

Danjie Dai, Xinyan Wang, Jingnan Yang, Jianchen Dang

Single charge control of localized excitons (LXs) in two-dimensional transition metal dichalcogenides (TMDCs) is crucial for potential applications in quantum information processing and storage. However, traditional electrostatic doping method with applying metallic gates onto TMDCs may cause the inhomogeneous charge distribution, optical quench, and energy

Janine Christine Franz, Stefan Yoshi Buhmann, A. Salam

Resonance energy transfer between chiral molecules can be used to discriminate between different enantiomers. The transfer rate between chiral molecules consists of a non-discriminatory and discriminatory parts. We derive these two rate contributions in the framework of macroscopic quantum electrodynamics. We show that their ratio is usually larger in the re

Bastian Pfeifer, Marcus D. Bloice, Michael G. Schimek

Multi-view clustering methods are essential for the stratification of patients into sub-groups of similar molecular characteristics. In recent years, a wide range of methods has been developed for this purpose. However, due to the high diversity of cancer-related data, a single method may not perform sufficiently well in all cases. We present Parea, a multi-

Lennart Brocki, Wistan Marchadour, Jonas Maison, Bogdan Badic

Deep learning has shown superb performance in detecting objects and classifying images, ensuring a great promise for analyzing medical imaging. Translating the success of deep learning to medical imaging, in which doctors need to understand the underlying process, requires the capability to interpret and explain the prediction of neural networks. Interpretab

Ignacio Vizzo, Tiziano Guadagnino, Benedikt Mersch, Louis Wiesmann

Robust and accurate pose estimation of a robotic platform, so-called sensor-based odometry, is an essential part of many robotic applications. While many sensor odometry systems made progress by adding more complexity to the ego-motion estimation process, we move in the opposite direction. By removing a majority of parts and focusing on the core elements, we

Yan-Jin Wang, Min Zha, Shi-Cong Hu, Chuan-Dong Gao

Background estimation is essential when studying TeV gamma-ray astronomy for extensive air shower arrays. In this work, by applying four applying four different methods including equi-zenith angle method, surrounding window method, direct integration method, and time-swapping method, the number of the background events is calculated. Based on simulation samp

Absos Ali Shaikh, Musavvir Ali, Mohammad Salman, Fusun Ozen Zengin

The paper aims to investigate curvature inheritance symmetry in M-projectively flat spacetimes. It is shown that the curvature inheritance symmetry in M-projectively flat spacetime is a conformal motion. We have proved that M- projective curvature tensor follows the symmetry inheritance property along a vector field $\xi$, when spacetime admits the condition

Emil Junker

In the Group Identification problem, we are given a set of individuals and are asked to identify a socially qualified subset among them. Each individual in the set has an opinion about who should be considered socially qualified. There are several different rules that can be used to determine the socially qualified subset based on these mutual opinions. In a

F. Garzón, M. Balcells, J. Gallego, C. Gry

We present EMIR, a powerful near-infrared (NIR) camera and multi-object spectrograph (MOS) installed at the Nasmyth focus of the 10.4 m GTC. EMIR was commissioned in mid-2016 and is offered as a common-user instrument. It provides spectral coverage of 0.9 to 2.5 $\mu m$ over a field of view (FOV) of 6.67x6.67 squared arcmin in imaging mode, and 6.67x4 square

Klára Karasová, Benjamin Vejnar

We prove that every Peano continuum with uncountably many local cut points is a topological fractal. This extends some recent results and it partially answers a conjecture by Hata. We also discuss the number of mappings which are necessary for witnessing the structure of a topological fractal.

Matthijs Schrage, Mahmoud Medany, Daniel Ahmed

Powered by acoustics, existing therapeutic and diagnostic procedures will become less invasive and new methods will become available that have never been available before. Acoustically driven microrobot navigation based on microbubbles is a promising approach for targeted drug delivery. Previous studies have used acoustic techniques to manipulate microbubble

Balázs Csikós, Tamás Kátay, Anett Kocsis, Máté Pálfy

The set $\mathcal S(G)$ of compact subgroups of a Hausdorff topological group $G$ can be equipped with the Vietoris topology. A compact subgroup $K\in\mathcal S(G)$ is isolated up to conjugacy if there is a neighborhood $\mathcal U\subseteq\mathcal S(G)$ of $K$ such that every $L\in\mathcal U$ is conjugate to $K$. In this paper, we characterize compact subgr

Evis Ieronymou

We use recent advances in the local evaluation of Brauer elements to study the role played by {\it odd} torsion elements of the Brauer group in the arithmetic of diagonal quartic surfaces over {\it arbitrary} number fields. We show that over a local field if the order of the Brauer element is odd and coprime to the residue characteristic then the evaluation

Bent Flyvbjerg, Alexander Budzier, Jong Seok Lee, Mark Keil

If managers assume a normal or near-normal distribution of Information Technology (IT) project cost overruns, as is common, and cost overruns can be shown to follow a power-law distribution, managers may be unwittingly exposing their organizations to extreme risk by severely underestimating the probability of large cost overruns. In this research, we collect

Tomas Reis

In this thesis we explore the physics of renormalons in integrable models under the framework of resurgence. In the first part, we review some background on resurgence, integrability and renormalons, including a discussion of large N renormalons and ring diagrams. In the second part, we start from the Bethe ansatz integral equations and obtain exact trans-se

YanNan Liu, Jian Lu

The dual Minkowski problem in the two-dimensional plane is studied in this paper. By combining the theoretical analysis and numerical estimation of an integral with parameters, we find the number of solutions to this problem for the constant dual curvature case when $0<q\leq4$. An improved nonuniqueness result when $q>4$ is also obtained. As an application,

David Hähnel, Jens Förstner, Viktor Myroshnychenko

Dielectric metasurfaces provide a unique platform for efficient harmonic generation and optical wavefront manipulation at the nanoscale. Tailoring phase and amplitude of a nonlinearly generated wave with a high emission efficiency using resonance-based metasurfaces is a challenging task which often requires state-of-the-art numerical methods. Here, we propos

Abicumaran Uthamacumaran, Felipe S. Abrahão, Narsis A. Kiani, Hector Zenil

We demonstrate that the assembly pathway method underlying assembly theory (AT) is an encoding scheme widely used by popular statistical compression algorithms. We show that in all cases (synthetic or natural) AT performs similarly to other simple coding schemes and underperforms compared to system-related indexes based upon algorithmic probability that take

Zhen Xing, Hengduo Li, Zuxuan Wu, Yu-Gang Jiang

The performance of existing single-view 3D reconstruction methods heavily relies on large-scale 3D annotations. However, such annotations are tedious and expensive to collect. Semi-supervised learning serves as an alternative way to mitigate the need for manual labels, but remains unexplored in 3D reconstruction. Inspired by the recent success of semi-superv

Carlo Alfano, Patrick Rebeschini

We analyze the convergence rate of the unregularized natural policy gradient algorithm with log-linear policy parametrizations in infinite-horizon discounted Markov decision processes. In the deterministic case, when the Q-value is known and can be approximated by a linear combination of a known feature function up to a bias error, we show that a geometrical

Karyn Le Hur

Here, we reveal our recent progress on a geometrical approach of quantum physics and topological crystals linking with Dirac magnetic monopoles and gauge fields through classical electrodynamics. The Bloch sphere of a quantum spin-1/2 particle acquires an integer topological charge in the presence of a radial magnetic field. We show that global topological p

Aitor Martinez Seras, Javier Del Ser, Jesus L. Lobo, Pablo Garcia-Bringas

Research around Spiking Neural Networks has ignited during the last years due to their advantages when compared to traditional neural networks, including their efficient processing and inherent ability to model complex temporal dynamics. Despite these differences, Spiking Neural Networks face similar issues than other neural computation counterparts when dep