November 2022 arXiv papers — page 20

Sven Zschocke

The time delay of a light signal in the quadrupole field of a body at rest is determined in the second post-Newtonian (2PN) approximation in harmonic coordinates. For grazing light rays at Sun, Jupiter, and Saturn the 2PN quadrupole effect in time delay amounts up to 0.004, 0.14, and 0.04 pico-second, respectively. These values are compared with the time del

Yuanbo Li, Chong Han, Yi Chen, Ziming Yu

Investigation of millimeter (mmWave) and Terahertz (THz) channels relies on channel measurements and estimation of multi-path component (MPC) parameters. As a common measurement technique in the mmWave and THz bands, direction-scan sounding (DSS) resolves angular information and increases the measurable distance. Through mechanical rotation, the DSS creates

Stephen Melczer, Tiadora Ruza

The field of analytic combinatorics is dedicated to the creation of effective techniques to study the large-scale behaviour of combinatorial objects. Although classical results in analytic combinatorics are mainly concerned with univariate generating functions, over the last two decades a theory of analytic combinatorics in several variables (ACSV) has been

Vincent Picouet, David Valls-Gabaud, Bruno Milliard, David Schiminovich

FIREBall-2 is a stratospheric balloon-borne 1-m telescope coupled to a UV multi-object slit spectrograph designed to map the faint UV emission surrounding z~0.7 galaxies and quasars through their Lyman-alpha line emission. This spectro-imager had its first launch on September 22nd 2018 out of Ft. Sumner, NM, USA. Because the balloon was punctured, the flight

Dmitrii Pavlov, Bernd Sturmfels, Simon Telen

Gibbs manifolds are images of affine spaces of symmetric matrices under the exponential map. They arise in applications such as optimization, statistics and quantum~physics, where they extend the ubiquitous role of toric geometry. The Gibbs variety is the zero locus of all polynomials that vanish on the Gibbs manifold. We compute these polynomials and show t

Pepijn Roos Hoefgeest, Lucas Slot

Persistent homology has been widely used to study the topology of point clouds in $\mathbb{R}^n$. Standard approaches are very sensitive to outliers, and their computational complexity depends badly on the number of data points. In this paper we introduce a novel persistence module for a point cloud using the theory of Christoffel-Darboux kernels. This modul

Nikolai Nikolov, Ahmed Yekta Ökten

Recently, the visibility property of Kobayashi (almost) geodesics has been used to provide localizations of the Kobayashi distance. In this note, we provide sufficient growth conditions for the Kobayashi distance to obtain new strong multiplicative and additive localization results. Curiously, the conditions we provide are deeply related to the behaviour of

Hamed Bolandi, Gautam Sreekumar, Xuyang Li, Nizar Lajnef

Structural failures are often caused by catastrophic events such as earthquakes and winds. As a result, it is crucial to predict dynamic stress distributions during highly disruptive events in real time. Currently available high-fidelity methods, such as Finite Element Models (FEMs), suffer from their inherent high complexity. Therefore, to reduce computatio

Shengzheng Kang, Zhicheng Song, Xiaolong Yang, Yao Li

As an effective method to deliver external materials into biological cells, microinjection has been widely applied in the biomedical field. However, the cognition of cell mechanical property is still inadequate, which greatly limits the efficiency and success rate of injection. Thus, a new rate-dependent mechanical model based on membrane theory is proposed

Marléne Baumeister, Marc Ditzhaus, Markus Pauly

Multivariate analysis-of-variance (MANOVA) is a well established tool to examine multivariate endpoints. While classical approaches depend on restrictive assumptions like normality and homogeneity, there is a recent trend to more general and flexible proce dures. In this paper, we proceed on this path, but do not follow the typical mean-focused perspective.

A. E. Piatti, D. M. F. Illesca, A. A. Massara, M. Chiarpotti

We report results on the analysis of eleven new Milky Way open cluster candidates, recently discovered from the detection of stellar overdensities in the Vector Point diagram, by employing extreme deconvolution Gaussian mixture models. We treated these objects as real open clusters and derived their fundamental properties with their associated intrinsic disp

Xinyu Chen, Chengyuan Zhang, Xiaoxu Chen, Nicolas Saunier

The problem of broad practical interest in spatiotemporal data analysis, i.e., discovering interpretable dynamic patterns from spatiotemporal data, is studied in this paper. Towards this end, we develop a time-varying reduced-rank vector autoregression (VAR) model whose coefficient matrices are parameterized by low-rank tensor factorization. Benefiting from

Łukasz Bożyk, Oscar Defrain, Karolina Okrasa, Michał Pilipczuk

The $t$-onion star is the digraph obtained from a star with $2t$ leaves by replacing every edge by a triple of arcs, where in $t$ triples we orient two arcs away from the center, and in the remaining $t$ triples we orient two arcs towards the center. Note that the $t$-onion star contains, as an immersion, every digraph on $t$ vertices where each vertex has o

Jay Jojo Cheng, Jared D. Huling, Guanhua Chen

Medical treatments tailored to a patient's baseline characteristics hold the potential of improving patient outcomes while reducing negative side effects. Learning individualized treatment rules (ITRs) often requires aggregation of multiple datasets(sites); however, current ITR methodology does not take between-site heterogeneity into account, which can hurt

Jakob Geusen, Gustav Bredell, Tianfei Zhou, Ender Konukoglu

Partitioning an image into superpixels based on the similarity of pixels with respect to features such as colour or spatial location can significantly reduce data complexity and improve subsequent image processing tasks. Initial algorithms for unsupervised superpixel generation solely relied on local cues without prioritizing significant edges over arbitrary

Parantak Singh, You Li, Ankur Sikarwar, Weixian Lei

Curriculum design is a fundamental component of education. For example, when we learn mathematics at school, we build upon our knowledge of addition to learn multiplication. These and other concepts must be mastered before our first algebra lesson, which also reinforces our addition and multiplication skills. Designing a curriculum for teaching either a huma

Clemens Hutter, Thomas Allard, Helmut Bölcskei

In [1] it is shown that recurrent neural networks (RNNs) can learn - in a metric entropy optimal manner - discrete time, linear time-invariant (LTI) systems. This is effected by comparing the number of bits needed to encode the approximating RNN to the metric entropy of the class of LTI systems under consideration [2, 3]. The purpose of this note is to provi

Daniel Litinski, Naomi Nickerson

In existing general-purpose architectures for surface-code-based fault-tolerant quantum computers, the cost of a quantum computation is determined by the circuit volume, i.e., the number of qubits multiplied by the number of non-Clifford gates. We introduce an architecture using non-2D-local connections in which the cost does not scale with the number of qub

Mathias Müller, Zifan Jiang, Amit Moryossef, Annette Rios

Automatic sign language processing is gaining popularity in Natural Language Processing (NLP) research (Yin et al., 2021). In machine translation (MT) in particular, sign language translation based on glosses is a prominent approach. In this paper, we review recent works on neural gloss translation. We find that limitations of glosses in general and limitati

Valeriano Aiello

We review a constructions of knots from elements of the Thompson groups due to Vaughan Jones, which comes in two flavours: oriented and unoriented.

Francisco Inácio, Jan P. Springer

Hardware-based triangle rasterization is still the prevalent method for generating images at real-time interactive frame rates. With the availability of a programmable graphics pipeline a large variety of techniques are supported for evaluating lighting and material properties of fragments. However, these techniques are usually restricted to evaluating local

Sahand Rezaei-Shoshtari, Charlotte Morissette, Francois Robert Hogan, Gregory Dudek

In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by know

Taishi Kurahashi

We prove the following version of the first incompleteness theorem that simultaneously strengthens Mostowski's theorem and Vaught's theorem: For any c.e. family $\{ T_i \}_{i \in \omega}$ of consistent extensions of Tarski, Mostowski and Robinson's arithmetic $\mathsf{R}$, there exists a sentence $\varphi$ of arithmetic such that $\varphi \vdash \mathsf{R}$

Aleksi Kurkela, Robin Törnkvist, Korinna Zapp

We introduce ALPACA, a Lorentz invariant parton cascade encoding the AMY effective kinetic theory of QCD at high temperatures. It solves the Boltzmann equation by explicitly simulating the evolution of parton ensembles corresponding to single events. We discuss how the effective masses and temperature entering the elastic collision and splitting/merging rate

Abhiroop Satheesh, Christoph P. Schmidt, Wolfgang A. Wall, Christoph Meier

Tensor interpolation is an essential step for tensor data analysis in various fields of application and scientific disciplines. In the present work, novel interpolation schemes for general, i.e., symmetric or non-symmetric, invertible square tensors are proposed. Critically, the proposed schemes rely on a combined polar and spectral decomposition of the tens

Atefeh Gilani, Gowtham R. Kurri, Oliver Kosut, Lalitha Sankar

We introduce a family of information leakage measures called maximal $\alpha,\beta$-leakage, parameterized by real numbers $\alpha$ and $\beta$. The measure is formalized via an operational definition involving an adversary guessing an unknown function of the data given the released data. We obtain a simple, computable expression for the measure and show tha

Renan Spencer Trindade, Claudia D'Ambrosio, Antonio Frangioni, Claudio Gentile

Our study is motivated by the solution of Mixed-Integer Non-Linear Programming (MINLP) problems with separable non-convex functions via the Sequential Convex MINLP technique, an iterative method whose main characteristic is that of solving, for bounding purposes, piecewise-convex MINLP relaxations obtained by identifying the intervals in which each univariat

Yan Cui, Peng Lu, Yi Zhou

In this paper, we investigate the null controllability of nonlinear wave systems. Initially, we employ a combination of the Galerkin method and a fixed point theorem to establish the null controllability for semi-linear wave equations with nonlinear functions that are dependent on velocities, under the geometric control condition. Subsequently, utilizing a n

S. A. K. Leeney, W. J. Handley, E. de Lera Acedo

Interfering signals such as Radio Frequency Interference from ubiquitous satellite constellations are becoming an endemic problem in fields involving physical observations of the electromagnetic spectrum. To address this we propose a novel data cleaning methodology. Contamination is simultaneously flagged and managed at the likelihood level. It is modeled in

Gabriel Gouraud, Pierre Le Doussal, Gregory Schehr

We consider the quench dynamics of non-interacting fermions in one dimension in the presence of a finite-size impurity at the origin. This impurity is characterized by general momentum-dependent reflection and transmission coefficients which are changed from ${\sf r}_0(k), {\sf t}_0(k)$ to ${\sf r}(k), {\sf t}(k)$ at time $t=0$. The initial state is at equil

Wen-Ya Wu, Qi-Ye Shou, Panos Christakoglou, Prottay Das

When searching for anomalous chiral effects in heavy-ion collisions, one of the most crucial points is the relationship between the signal and the background. In this letter, we present a simulation in a modified blast wave model at LHC energy, which can simultaneously characterize the majority of measurable quantities, in particular, the chiral magnetic eff

Sebastian Rickelhoff, Alexander Schnurr

The probabilistic symbol is the right-hand side derivative of the characteristic functions corresponding to the one-dimensional marginals of a stochastic process. This object, as long as the derivative exists, provides crucial information concerning the stochastic process. For a L\'evy process, one obtains the characteristic exponent while the symbol of a (r

Xiangyu Zhang, Zening Wang, Haiyang Zhang, Luxi Yang

Extremely large-scale massive MIMO (XL-MIMO) has been reviewed as a promising technology for future wireless communications. The deployment of XL-MIMO, especially at high-frequency bands, leads to users being located in the near-field region instead of the conventional far-field. This letter proposes efficient model-based deep learning algorithms for estimat

Lukáš Samuel Marták, Rainer Kelz, Gerhard Widmer

We introduce a novel way to incorporate prior information into (semi-) supervised non-negative matrix factorization, which we call differentiable dictionary search. It enables general, highly flexible and principled modelling of mixtures where non-linear sources are linearly mixed. We study its behavior on an audio decomposition task, and conduct an extensiv

Oussama Korichi, Markus Hiekkamäki, Robert Fickler

Multi-mode fibers (MMFs) and single-mode fibers (SMFs) are widely used in optical communication networks. MMFs are the practical choice in terms of cost in applications that require short distances. Beyond that, SMFs are necessary because of the modal dispersion in MMFs. Here, we present a method capable of interfacing an MMF with an SMF using a re-programma

Lukas Gross, Lasse Maywald, Shivesh Kumar, Frank Kirchner

Linear-quadratic regulators (LQR) are a well known and widely used tool in control theory for both linear and nonlinear dynamics. For nonlinear problems, an LQR-based controller is usually only locally viable, thus, raising the problem of estimating the region of attraction (ROA). The need for good ROA estimations becomes especially pressing for underactuate

Nathan Drenkow, Alvin Tan, Chace Ashcraft, Kiran Karra

The deployment of machine learning models in safety-critical applications comes with the expectation that such models will perform well over a range of contexts (e.g., a vision model for classifying street signs should work in rural, city, and highway settings under varying lighting/weather conditions). However, these one-size-fits-all models are typically o

Gabriele Cugno, Timothy D. Pearce, Ralf Launhardt, Markus. J. Bonse

We present the statistical analysis of a subsample of 45 young stars surrounded by protoplanetary disks (PPDs). This is the largest imaging survey uniquely focused on PPDs to date. Our goal is to search for young forming companions embedded in the disk material and to constrain their occurrence rate in relation to the formation mechanism. We used principal c

Alfonso Maiellaro, Jacopo Settino, Claudio Guarcello, Francesco Romeo

We investigate the topological properties of a Josephson junction obtained by constraining a two-dimensional electron gas at oxide interface to form a quasi-1D conductor. We reveal an anomalous critical current behaviour with a magnetic field applied perpendicular to the Rashba spin-orbit one. We relate the observed critical current enhancement at small magn

W. Ronny Huang, Shuo-Yiin Chang, Tara N. Sainath, Yanzhang He

We explore unifying a neural segmenter with two-pass cascaded encoder ASR into a single model. A key challenge is allowing the segmenter (which runs in real-time, synchronously with the decoder) to finalize the 2nd pass (which runs 900 ms behind real-time) without introducing user-perceived latency or deletion errors during inference. We propose a design whe

Zachary Feinstein, Andreas Sojmark

We introduce a dynamic and stochastic interbank model with an endogenous notion of distress contagion, arising from rational worries about future defaults and ensuing losses. This entails a mark-to-market valuation adjustment for interbank claims, leading to a forward-backward approach to the equilibrium dynamics whereby future default probabilities are need

Piermarco Cannarsa, Valerio Lucarini, Patrick Martinez, Cristina Urbani

We study a two-layer energy balance model, that allows for vertical exchanges between a surface layer and the atmosphere. The evolution equations of the surface temperature and the atmospheric temperature are coupled by the emission of infrared radiation by one level, that emission being captured by the other layer, and the effect of all non radiative vertic

Yiteng Zhang, Zixuan Hu, Yuchen Wang, Sabre Kais

The simulation of open quantum dynamics on quantum circuits has attracted wide interests recently with a variety of quantum algorithms developed and demonstrated. Among these, one particular design of a unitary-dilation-based quantum algorithm is capable of simulating general and complex physical systems. In this paper, we apply this quantum algorithm to sim

Tim-Eric Rathjen, Thorsten Naab, Stefanie Walch, Daniel Seifried

We present magnetohydrodynamic (MHD) simulations of the star-forming multiphase interstellar medium (ISM) in stratified galactic patches with gas surface densities $\Sigma_\mathrm{gas} =$ 10, 30, 50, and 100 $\mathrm{M_\odot\,pc^{-2}}$. The SILCC project simulation framework accounts for non-equilibrium thermal and chemical processes in the warm and cold ISM

Naceur Gaaloul, Holger Ahlers, Leonardo Badurina, Angelo Bassi

Space-borne quantum technologies, particularly those based on atom interferometry, are heralding a new era of strategic and robust space exploration. The unique conditions of space, characterized by low noise and low gravity environments, open up diverse possibilities for applications ranging from precise time and frequency transfer to Earth Observation and

Burla Nur Korkmaz, Roee Diamant, Gil Danino, Alberto Testolin

Effective conservation of maritime environments and wildlife management of endangered species require the implementation of efficient, accurate and scalable solutions for environmental monitoring. Ecoacoustics offers the advantages of non-invasive, long-duration sampling of environmental sounds and has the potential to become the reference tool for biodivers

Jiangyong Huang, William Yicheng Zhu, Baoxiong Jia, Zan Wang

Current computer vision models, unlike the human visual system, cannot yet achieve general-purpose visual understanding. Existing efforts to create a general vision model are limited in the scope of assessed tasks and offer no overarching framework to perform them holistically. We present a new comprehensive benchmark, General-purpose Visual Understanding Ev

Francisca F. Fernandes, Jonas L. Olesen, Sune N. Jespersen, Noam Shemesh

MP-PCA denoising has become the method of choice for denoising in MRI since it provides an objective threshold to separate the desired signal from unwanted thermal noise components. In rodents, thermal noise in the coils is an important source of noise that can reduce the accuracy of activation mapping in fMRI. Further confounding this problem, vendor data o

Jeanne Riga, Rebecca Seviour

In this paper, we introduce the theoretical framework underlying our proposed methodology of verification and validation (V&V) for quantum mechanical emission models using analogous macroscopic electromagnetic systems. We derive the correspondence between quantum mechanics and electromagnetism using the transfer matrix approach, and describe the electromagne

A. A. Vidotto

In this contribution, I briefly review the long-term evolution of the solar wind (its mass-loss rate), including the evolution of observed properties that are intimately linked to the solar wind (rotation, magnetism and activity). I also briefly discuss implications of the evolution of the solar wind on the evolving Earth. I argue that studying exoplanetary

J. Vester, V. Despré, A. I. Kuleff

Due to the electron correlation, a fast removal of an electron from a molecule may create a coherent superposition of cationic states and in this way initiate pure electronic dynamics in which the hole-charge left by ionization migrates throughout the system on an ultrashort time scale. The coupling to the nuclear motion introduces a decoherence that eventua

Fang Peng, Xiaoshan Yang, Linhui Xiao, Yaowei Wang

Although significant progress has been made in few-shot learning, most of existing few-shot image classification methods require supervised pre-training on a large amount of samples of base classes, which limits their generalization ability in real world application. Recently, large-scale Vision-Language Pre-trained models (VLPs) have been gaining increasing

A. A. Vidotto

Stellar activity can reveal itself in the form of radiation (eg, enhanced X-ray coronal emission, flares) and particles (eg, winds, coronal mass ejections). Together, these phenomena shape the space weather around (exo)planets. As stars evolve, so do their different forms of activity -- in general, younger solar-like stars have stronger winds, enhanced flare

Benjamin Bode

A link in $S^3$ is called real algebraic if it is the link of an isolated singularity of a polynomial map from $\mathbb{R}^4$ to $\mathbb{R}^2$. It is known that every real algebraic link is fibered and it is conjectured that the converse is also true. We prove this conjecture for a large family of fibered links, which includes closures of T-homogeneous (and

Nate Harman, Andrew Snowden, Noah Snyder

Let $G$ be the group of all order-preserving self-maps of the real line. In previous work, the first two authors constructed a pre-Tannakian category $\underline{\mathrm{Rep}}(G)$ associated to $G$. The present paper is a detailed study of this category, which we name the Delannoy category. We classify the simple objects, determine branching rules to open su

Donghyeok Heo, Jun Seop Lee, Anwei Zhang, Jun-Won Rhim

We investigate the origin of the ubiquitous existence of flat bands in the network superstructures of atomic chains, where one-dimensional(1D) atomic chains array periodically. While there can be many ways to connect those chains, we consider two representative ways of linking them, the dot-type and triangle-type links. Then, we construct a variety of supers

Andreas Frommer, Gustavo Ramirez-Hidalgo

In lattice QCD, the trace of the inverse of the discretized Dirac operator appears in the disconnected fermion loop contribution to an observable. As simulation methods get more and more precise, these contributions become increasingly important. Hence, we consider here the problem of computing the trace $\mathrm{tr}(D^{-1})$, with $D$ the Dirac operator. Th

Xue Fu, Yang Peng, Yuchao Liu, Yun Lin

Specific emitter identification (SEI) plays an increasingly crucial and potential role in both military and civilian scenarios. It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. Deep learning (DL) and deep neural networks (DNNs) can learn the hidden features of data and

Francesco Mori, Kristian Stølevik Olsen, Supriya Krishnamurthy

Stochastic systems that undergo random restarts to their initial state have been widely investigated in recent years, both theoretically and in experiments. Oftentimes, however, resetting to a fixed state is impossible due to thermal noise or other limitations. As a result, the system configuration after a resetting event is random. Here, we consider such a

Calvin Hawkins, Bo Chen, Kasra Yazdani, Matthew Hale

This paper develops a framework for privatizing the spectrum of the graph Laplacian of an undirected graph using differential privacy. We consider two privacy formulations. The first obfuscates the presence of edges in the graph and the second obfuscates the presence of nodes. We compare these two privacy formulations and show that the privacy formulation th

Rajeev Sahay, Minjun Zhang, David J. Love, Christopher G. Brinton

Recent work has advocated for the use of deep learning to perform power allocation in the downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are vulnerable to adversarial attacks. In the context of maMIMO power allocation, adversarial attacks refer to the injection of subtle perturbations into the deep learning model's input, during i

ALICE Collaboration

The pseudorapidity density of charged particles with minimum transverse momentum ($p_{\rm T}$) thresholds of 0.15, 0.5, 1, and 2 GeV$/c$ is measured in pp collisions at the centre of mass energies of $\sqrt{s} =$ 5.02 and 13 TeV with the ALICE detector. The study is carried out for inelastic collisions with at least one primary charged particle having a pseu

Andrew J. S. Hamilton

Spinors are central to physics: all matter (fermions) is made of spinors, and all forces arise from symmetries of spinors. It is common to consider the geometric (Clifford) algebra as the fundamental edifice from which spinors emerge. This paper advocates the alternative view that spinors are more fundamental than the geometric algebra. The algebra consistin

Xutan Peng, Yipeng Zhang, Jingfeng Yang, Mark Stevenson

Although it has been demonstrated that Natural Language Processing (NLP) algorithms are vulnerable to deliberate attacks, the question of whether such weaknesses can lead to software security threats is under-explored. To bridge this gap, we conducted vulnerability tests on Text-to-SQL systems that are commonly used to create natural language interfaces to d

Jie Gui, Tuo Chen, Minjing Dong, Zhengqi Liu

Recently, masked image modeling (MIM), which learns visual representations by reconstructing the masked patches of an image, has dominated self-supervised learning in computer vision. However, the pre-training of MIM always takes massive time due to the large-scale data and large-size backbones. We mainly attribute it to the random patch masking in previous

Zai Yang, Yi-Lin Mo, Gongguo Tang, Zongben Xu

Atomic norm methods have recently been proposed for spectral super-resolution with flexibility in dealing with missing data and miscellaneous noises. A notorious drawback of these convex optimization methods however is their lower resolution in the high signal-to-noise (SNR) regime as compared to conventional methods such as ESPRIT. In this paper, we devise

Simone Borg Bruun, Maria Maistro, Christina Lioma

While personalised recommendations are successful in domains like retail, where large volumes of user feedback on items are available, the generation of automatic recommendations in data-sparse domains, like insurance purchasing, is an open problem. The insurance domain is notoriously data-sparse because the number of products is typically low (compared to r

Wenxuan Zhu, Chao Yu, Qiang Zhang

Deep reinforcement learning (DRL) requires the collection of interventional data, which is sometimes expensive and even unethical in the real world, such as in the autonomous driving and the medical field. Offline reinforcement learning promises to alleviate this issue by exploiting the vast amount of observational data available in the real world. However,

Jin-Cai Kang, Chun-Lei Tang

In present paper, we study the following nonlinear Schr\"{o}dinger equation with combined power nonlinearities \begin{align*} - \Delta u+V(x)u+\lambda u=|u|^{2^*-2}u+\mu |u|^{q-2}u \quad \quad \text{in} \ \mathbb{ R}^N, \ N\geq 3 \end{align*} having prescribed mass \begin{align*} \int_{ \mathbb{ R}^N}u^2dx=a^2, \end{align*} where $\mu, a>0$, $q\in(2, 2^*)$,

Yanhui Zhang

BCH codes are an important class of cyclic codes, and have wide applicantions in communication and storage systems. However, it is difficult to determine the parameters of BCH codes and only a few cases are known. In this paper, we mainly study three classes of BCH codes with $n=q^{m}-1,\frac{q^{2s}-1}{q+1},\frac{q^{m}-1}{q-1}$. On one hand, we accurately gi

Michal Ajdarów, Šimon Brlej, Petr Novotný

We consider partially observable Markov decision processes (POMDPs) modeling an agent that needs a supply of a certain resource (e.g., electricity stored in batteries) to operate correctly. The resource is consumed by agent's actions and can be replenished only in certain states. The agent aims to minimize the expected cost of reaching some goal while preven

Qingrui Zhang

This is a brief tutorial on the least square estimation technique that is straightforward yet effective for parameter estimation. The tutorial is focused on the linear LSEs instead of nonlinear versions, since most nonlinear LSEs can be approximated non-trivially using its linear counterparts. Linear LSEs can also provide insight into the study of the nonlin

Enrica Tricomi, Mirko Mossini, Francesco Missiroli, Nicola Lotti

Just like in humans vision plays a fundamental role in guiding adaptive locomotion, when designing the control strategy for a walking assistive technology, Computer Vision may bring substantial improvements when performing an environment-based assistance modulation. In this work, we developed a hip exosuit controller able to distinguish among three different

Dominik Engl, Stefan Krömer, Martin Kružík

We perform via $\Gamma$-convergence a 2d-1d dimension reduction analysis of a single-slip elastoplastic body in large deformations. Rigid plastic and elastoplastic regimes are considered. In particular, we show that limit deformations can essentially freely bend even if subjected to the most restrictive constraints corresponding to the elastically rigid sing

James Juno, Collin R. Brown, Gregory G. Howes, Colby C. Haggerty

Examining energization of kinetic plasmas in phase space is a growing topic of interest, owing to the wealth of data in phase space compared to traditional bulk energization diagnostics. Via the field-particle correlation (FPC) technique and using multiple means of numerically integrating the plasma kinetic equation, we have studied the energization of ions

I. Bierenbaum, J. Blümlein, A. De Freitas, A. Goedicke

We calculate the quarkonic $O(\alpha_s^2)$ massive operator matrix elements $\Delta A_{Qg}(N), \Delta A_{Qq}^{\rm PS}(N)$ and $\Delta A_{qq,Q}^{\rm NS}(N)$ for the twist--2 operators and the associated heavy flavor Wilson coefficients in polarized deeply inelastic scattering in the region $Q^2 \gg m^2$ to $O(\varepsilon)$ in the case of the inclusive heavy f

Joseph Hall, Simon Malzard, Eva-Maria Graefe

We construct a semiclassical phase-space density of Schur vectors in non-Hermitian quantum systems. Each Schur vector is associated to a single Planck cell. The Schur states are organised according to a classical norm landscape on phase space - a classical manifestation of the lifetimes which are characteristic of non-Hermitian systems. To demonstrate the ge

Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski

Recent works have impressively demonstrated that there exists a subnetwork in randomly initialized convolutional neural networks (CNNs) that can match the performance of the fully trained dense networks at initialization, without any optimization of the weights of the network (i.e., untrained networks). However, the presence of such untrained subnetworks in

Alexander Glavackij, Dimitri Percia David, Alain Mermoud, Angelika Romanou

Because of the considerable heterogeneity and complexity of the technological landscape, building accurate models to forecast is a challenging endeavor. Due to their high prevalence in many complex systems, S-curves are a popular forecasting approach in previous work. However, their forecasting performance has not been directly compared to other technology f

Xiangliang Kong, Bin Chen, Fan Guo, Chengcai Shen

The acceleration and transport of energetic electrons during solar flares is one of the outstanding topics in solar physics. Recent X-ray and radio imaging and spectroscopy observations have provided diagnostics of the distribution of nonthermal electrons and suggested that, in certain flare events, electrons are primarily accelerated in the loop-top and lik

Cecilia Zaza, Germán Chiarelli, Ludovit P. Zweifel, Mauricio Pilo-Pais

FRET-based approaches are a unique tool for sensing the immediate surroundings and interactions of (bio)molecules. FRET imaging and FLIM (Fluorescence Lifetime Imaging Microscopy) enable the visualization of the spatial distribution of molecular interactions and functional states. However, conventional FLIM and FRET imaging provide average information over a

Fabio Berra

We devote this note to correct an estimate concerning mixed inequalities for the generalized maximal function $M_\Phi$, when certain properties of the associated Young function $\Phi$ are assumed. Although the obtained estimates turn out to be slightly different, they are good extensions of mixed inequalities for the classical Hardy-Littlewood maximal functi

Naga Sai Krishna Adatrao, Gowtham Reddy Gadireddy, Jiho Noh

This paper aims to provide a radical rundown on Conversation Search (ConvSearch), an approach to enhance the information retrieval method where users engage in a dialogue for the information-seeking tasks. In this survey, we predominantly focused on the human interactive characteristics of the ConvSearch systems, highlighting the operations of the action mod

Lalithkumar Seenivasan, Mobarakol Islam, Mengya Xu, Chwee Ming Lim

Purpose: Surgery scene understanding with tool-tissue interaction recognition and automatic report generation can play an important role in intra-operative guidance, decision-making and postoperative analysis in robotic surgery. However, domain shifts between different surgeries with inter and intra-patient variation and novel instruments' appearance degrade

ALICE Collaboration

Multiplicity ($N_{\rm ch}$) distributions and transverse momentum ($p_{\rm T}$) spectra of inclusive primary charged particles in the kinematic range of $|\eta| < 0.8$ and 0.15 GeV/$c$ $< p_{T} <$ 10 GeV/$c$ are reported for pp, p-Pb, Xe-Xe and Pb-Pb collisions at centre-of-mass energies per nucleon pair ranging from $\sqrt{s_{\rm NN}} = 2.76$ TeV up to $13$

Eszter Bokányi, Eelke M. Heemskerk, Frank W. Takes

Large-scale human social network structure is typically inferred from digital trace samples of online social media platforms or mobile communication data. Instead, here we investigate the social network structure of a complete population, where people are connected by high-quality links sourced from administrative registers of family, household, work, school

Yinan Liu, Hu Chen, Wei Shen, Jiaoyan Chen

Personal knowledge bases (PKBs) are crucial for a broad range of applications such as personalized recommendation and Web-based chatbots. A critical challenge to build PKBs is extracting personal attribute knowledge from users' conversation data. Given some users of a conversational system, a personal attribute and these users' utterances, our goal is to pre

Linkun Fan, Fazhi He, Qing Guo, Wei Tang

For saving cost, many deep neural networks (DNNs) are trained on third-party datasets downloaded from internet, which enables attacker to implant backdoor into DNNs. In 2D domain, inherent structures of different image formats are similar. Hence, backdoor attack designed for one image format will suite for others. However, when it comes to 3D world, there is

Abu Shohel Ahmed, Aleksi Peltonen, Mohit Sethi, Tuomas Aura

Remote SIM provisioning (RSP) for consumer devices is the protocol specified by the GSM Association for downloading SIM profiles into a secure element in a mobile device. The process is commonly known as eSIM, and it is expected to replace removable SIM cards. The security of the protocol is critical because the profile includes the credentials with which th

Yin-Cong Zhi, Felix L. Opolka, Yin Cheng Ng, Pietro Liò

Kernels on graphs have had limited options for node-level problems. To address this, we present a novel, generalized kernel for graphs with node feature data for semi-supervised learning. The kernel is derived from a regularization framework by treating the graph and feature data as two Hilbert spaces. We also show how numerous kernel-based models on graphs

MAGIC Collaboration, H. Abe, S. Abe, V. A. Acciari

The SNR G106.3+2.7, detected at 1--100 TeV energies by different $\gamma$-ray facilities, is one of the most promising PeVatron candidates. This SNR has a cometary shape which can be divided into a head and a tail region with different physical conditions. However, it is not identified in which region the 100 TeV emission is produced due to the limited posit

Qianyu Guo, Hongtong Gong, Xujun Wei, Yanwei Fu

This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification, ranking relation classification is sample efficient and domain agnostic. Besides, it provides a new perspective on few-shot learning and is complementary to state-of-the-art

Jinbo Chen, Hui Wu, Jie Yang, Mohamad Sawan

A bio-inspired Neuron-ADC with reconfigurable sampling and static power reduction for biomedical applications is proposed in this work. The Neuron-ADC leverages level-crossing sampling and a bio-inspired refractory circuit to compressively converts bio-signal to digital spikes and information-of-interest. The proposed design can not only avoid dissipating AD

Nicola Dondi, Simeon Hellerman, Ioannis Kalogerakis, Rafael Moser

We study the large-charge sector of large-N fermionic CFTs in three dimensions. Depending on the model and the nature of the fixed charge, we find two types of descriptions: in terms of a superfluid or a Fermi sphere. We explicitly compute the conformal dimensions of the lowest operator of fixed charge and in the superfluid case verify the EFT predictions fo

Johann Bouali

We prove, using $p$-adic Hodge theory for open algebraic varieties, that for a smooth projective variety over a subfield $k\subset\mathbb C$ which is of finite type over $\mathbb Q$, the complex abel jacobi map vanishes if the etale abel jacobi map vanishes. This implies that for a smooth projective morphism $f:X\to S$ of smooth complex algebraic varieties o

Xiao-Ping Chen, Chun-Lei Tang

In this paper, we are concerned with the following Schr\"{o}dinger-Poisson system with critical nonlinearity and critical nonlocal term due to the Hardy-Littlewood-Sobolev inequality \begin{equation}\begin{cases} -\Delta u+u+\lambda\phi |u|^3u =|u|^4u+ |u|^{q-2}u,\ \ &\ x \in \mathbb{R}^{3},\\[2mm] -\Delta \phi=|u|^5, \ \ &\ x \in \mathbb{R}^{3}, \end{cases}

Johannes Wüthrich, Christoph Alt, André Rubbia

In the context of particle detectors, low-temperature covalent wafer-wafer bonding allows for integration of high-Z materials as absorbing layers with readout chips produced in standard CMOS processes. This enables for instance the fabrication of novel highly efficient X-ray imaging sensors. In order to investigate the effects of the covalent bonding on the

Zhizhong Wang, Lei Zhao, Zhiwen Zuo, Ailin Li

Arbitrary style transfer (AST) transfers arbitrary artistic styles onto content images. Despite the recent rapid progress, existing AST methods are either incapable or too slow to run at ultra-resolutions (e.g., 4K) with limited resources, which heavily hinders their further applications. In this paper, we tackle this dilemma by learning a straightforward an

Anders J. E. Bjerrum, Jonatan B. Brask, Jonas S. Neergaard-Nielsen, Ulrik L. Andersen

We perform a theoretical investigation into how a two-mode squeezed vacuum state, that has undergone photon loss, can be stored and purified using noiseless amplification with a collection of solid-state qubits. The proposed method may be used to probabilistically increase the entanglement between the two parties sharing the state. The proposed amplification

Athanasios Papaioannou, Konstantin Herbst, Tobias Ramm, Edward W. Cliver

Aims The possible influence of solar superflares on the near-Earth space radiation environment are assessed through the investigation of scaling laws between the peak proton flux and fluence of Solar Energetic Particle (SEP) events with the solar flare soft X-ray peak photon flux. Methods We compiled a catalog of 65 well-connected (W20-90) SEP events during