January 2022 arXiv papers — page 20

Ren-Hua Wu, Yu-Sheng Zuo, Chen-Yu Wang, Ce Meng

We study the mass spectra of $\bar{Q}Q\bar{Q}Q\ (Q=c,b)$ systems in QCD sum rules with the complete next-to-leading order (NLO) contribution to the perturbative QCD part of the correlation functions. Instead of meson-meson or diquark-antidiquark currents, we use diagonalized currents under operator renormalization. We find that differing from conventional me

Rong Xuan Zang, Alexandros Maragkoudakis, Els Peeters

We present a detailed study on the spatially resolved polycyclic aromatic hydrocarbon (PAH) emission properties in the (circum)nuclear region (NR) and extranuclear regions (ENRs) of M51a using Spitzer-IRS observations. Correlations among PAH intensity ratios are examined with respect to each other, local physical parameters, galactocentric distance ($R_{g}$)

Ritva Hurri-Syrjänen, Javier C. Martínez-Perales, Carlos Pérez, Antti V. Vähäkangas

In this paper we unify and improve some of the results of Bourgain, Brezis and Mironescu and the weighted Poincar\'e-Sobolev estimate by Fabes, Kenig and Serapioni. More precisely, we get weighted counterparts of the Poincar\'e-Sobolev type inequality and also of the Hardy type inequality in the fractional case under some mild natural restrictions. A main fe

Will Leeson, Matthew B Dwyer

The field of software verification has produced a wide array of algorithmic techniques that can prove a variety of properties of a given program. It has been demonstrated that the performance of these techniques can vary up to 4 orders of magnitude on the same verification problem. Even for verification experts, it is difficult to decide which tool will perf

Hengjie Yang, Recep Can Yavas, Victoria Kostina, Richard D. Wesel

In this paper, we are interested in the performance of a variable-length stop-feedback (VLSF) code with $m$ optimal decoding times for the binary-input additive white Gaussian noise channel. We first develop tight approximations on the tail probability of length-$n$ cumulative information density. Building on the work of Yavas \emph{et al.}, for a given info

Md Momen Bhuiyan, Carlos Augusto Bautista Isaza, Tanushree Mitra, Sang Won Lee

To promote engagement, recommendation algorithms on platforms like YouTube increasingly personalize users' feeds, limiting users' exposure to diverse content and depriving them of opportunities to reflect on their interests compared to others'. In this work, we investigate how exchanging recommendations with strangers can help users discover new content and

Gianmaria De Tommasi, Luigi Emanuel di Grazia, Sara Dubbioso, Massimiliano Mattei

In this paper we propose a vertical stabilization~(VS) control system for tokamak plasmas based on the extremum seeking (ES) algorithm. The gist of the proposed strategy is to inject an oscillating term in the control action and exploit a modified ES algorithm in order to bring to zero the average motion of the plasma along the unstable mode. In this way, th

John R. Doyle, Trevor Hyde

In this paper we study two questions related to exceptional behavior of preperiodic points of polynomials in $\mathbb{Q}[x]$. We show that for all $d\geq 2$, there exists a polynomial $f_d(x) \in \mathbb{Q}[x]$ with $2\leq \mathrm{deg}(f_d) \leq d$ such that $f_d(x)$ has at least $d + \lfloor \log_2(d)\rfloor$ rational preperiodic points. Furthermore, we sho

Melissa Hall, Laurens van der Maaten, Laura Gustafson, Maxwell Jones

Recent research suggests that predictions made by machine-learning models can amplify biases present in the training data. When a model amplifies bias, it makes certain predictions at a higher rate for some groups than expected based on training-data statistics. Mitigating such bias amplification requires a deep understanding of the mechanics in modern machi

Luis Daniel Abreu, Zouhair Mouayn, Felix Voigtlaender

Motivated by results of Dyatlov on Fourier uncertainty principles for Cantor sets and by similar results of Knutsen for joint time-frequency representations (i.e., the short-time Fourier transform (STFT) with a Gaussian window, equivalent to Fock spaces), we suggest a general setting relating localization and uncertainty and prove, within this context, an un

D. Bazeia, M. A. Liao, M. A. Marques

Topological vortices in relativistic gauge theories in flat three-dimensional spacetime are investigated. We consider the symmetry $\rm{U(1)}\times...\times \rm{U(1)}$, and for each $\rm{U(1)}$ subgroup, a complex scalar field transforming under its action is introduced, as well as generalized permeabilities through which the subsystems are coupled. We inves

Michele Ronchi, Nanda Rea, Vanessa Graber, Natasha Hurley-Walker

For about half a century the radio pulsar population was observed to spin in the ~0.002-12s range, with different pulsar classes having a spin-period evolution that differs substantially depending on their magnetic fields or past accretion history. The recent detection of several slowly rotating pulsars has re-opened the long-standing question of the exact p

Alexander Dicke, Albrecht Seelmann, Ivan Veselic

We establish a family of uncertainty principles for finite linear combinations of Hermite functions. More precisely, we give a geometric criterion on a subset $S\subset \RR^d$ ensuring that the $L^2$-seminorm associated to $S$ is equivalent to the full $L^2$-norm on $\RR^d$ when restricted to the space of Hermite functions up to a given degree. We give preci

Joseph Early, Christine Evers, Sarvapali Ramchurn

In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handful of key instances within a bag, making it difficult to interpret what information a classifier is using to make decisions. In this work, we establish the key requirements for int

Yuteng Zhu, Graham D. Finlayson

In previous work, it was shown that a camera can theoretically be made more colorimetric - its RGBs become more linearly related to XYZ tristimuli - by placing a specially designed color filter in the optical path. While the prior art demonstrated the principle, the optimal color-correction filters were not actually manufactured. In this paper, we provide a

Yan Liang, Joshua N. Winn, Simon H. Albrecht

DI Herculis is an eclipsing binary famous for a longstanding disagreement between theory and observation of the apsidal precession rate, which was resolved when both stars were found to be severely misaligned with the orbit. We used data from the Transiting Exoplanet Survey Satellite (TESS) to refine our knowledge of the stellar obliquities and sharpen the c

Yigit Sozen, Ugur C. Topkiran, Hasan Sahin

In the present work, the structural, magnetic, and electronic properties of the two- and one-dimensional honeycomb structures of recently synthesized MnO [Zhang et al. Nat. Commun., 20, 1073-1078 (2021)] are investigated by using first principles calculations. Our calculations show that the single layer 2D MnO crystal has a degenerate antiferromagnetic (AFM)

Daqi Liu, Miroslaw Bober, Josef Kittler

As a structured prediction task, scene graph generation aims to build a visually-grounded scene graph to explicitly model objects and their relationships in an input image. Currently, the mean field variational Bayesian framework is the de facto methodology used by the existing methods, in which the unconstrained inference step is often implemented by a mess

A. Liam Fitzpatrick, Emanuel Katz

Strongly-coupled Quantum Field Theories (QFTs) are ubiquitous in high energy physics and many-body physics, yet our ability to do precise computations in such systems remains limited. Hamiltonian Truncation is a method for doing nonperturbative computations of real-time evolution in strongly coupled QFT in the continuum limit, and works by numerically solvin

Yize Zhao, Tianqi Chen, Jiachen Cai, Sarah Lichenstein

Brain functional connectome, the collection of interconnected neural circuits along functional networks, is one of the most cutting edge neuroimaging traits, and has a potential to play a mediating role within the effect pathway between an exposure and an outcome. While existing mediation analytic approaches are capable of providing insight into complex proc

D. Astesiano, D. Ruggeri, M. Trigiante, T. Van Riet

We study supergravity instantons sourced by axion (and saxion) fields in the Euclidean $AdS_3\times S^3 \times CY_2$ vacua of IIB supergravity. Such instantons are described by geodesic curves on the moduli space; the timelike geodesics can describe Euclidean wormholes, the lightlike geodesics describe (generalisations of) D-instantons and spacelike geodesic

Ruo Li, Wei Zhong

A new type of finite volume WENO schemes for hyperbolic problems was devised in [36] by introducing the order-preserving (OP) criterion. In this continuing work, we extend the OP criterion to the WENO-Z-type schemes. We firstly rewrite the formulas of the Z-type weights in a uniform form from a mapping perspective inspired by extensive numerical observations

D. O. Sabulsky, J. Junca, X. Zou, A. Bertoldi

We present a novel atom interferometer configuration that combines large momentum transfer with the enhancement of an optical resonator for the purpose of measuring gravitational strain in the horizontal directions. Using Bragg diffraction and taking advantage of the optical gain provided by the resonator, we achieve momentum transfer up to $8\hbar k$ with m

Tianshuo Cong, Xinlei He, Yang Zhang

Self-supervised learning is an emerging machine learning paradigm. Compared to supervised learning which leverages high-quality labeled datasets, self-supervised learning relies on unlabeled datasets to pre-train powerful encoders which can then be treated as feature extractors for various downstream tasks. The huge amount of data and computational resources

Dmitri A. Rachkovskij, Denis Kleyko

Hyperdimensional computing (HDC), also known as vector symbolic architectures (VSA), is a computing framework used within artificial intelligence and cognitive computing that operates with distributed vector representations of large fixed dimensionality. A critical step for designing the HDC/VSA solutions is to obtain such representations from the input data

Nelly Cerpa-Urra, Markus Kasper, Caroline Kulcsár, Henri-François Raynaud

The contrast performance of current eXtreme Adaptive Optics (XAO) systems can be improved by adding a second AO correction stage featuring its own wavefront sensor, deformable mirror, and real-time controller. We develop a dynamical model for such a cascade AO (CAO) system with two stages each controlled by a standard integrator, and study its control proper

Alexandra Kuznetsova

We study positive entropy birational automorphisms of threefolds. We identify some conditions which imply that such an automorphism is non-regularizable. We show that this criterion applies in the example of a positive entropy birational automorphism of $\mathbb{P}^3$ constructed by J. Blanc, thus showing that for a general choice of parameters it is non-reg

Joscha Diehl, Malte Gerhold, Nicolas Gilliers

One can build an operatorial model for freeness by considering either the right-handed or the left-handed representation of algebras of operators acting on the free product of the underlying pointed Hilbert spaces. Considering both at the same time, that is, computing distributions of operators in the algebra generated by the left- and right-handed represent

Rodolfo Carvalho dos Santos, Elwis Carlos Sartorelli Duarte, Danilo Okimoto, Alice Presotto

Studies involving vortex dynamics and their interaction with pinning centers are an important ingredient to reach higher critical currents in superconducting materials. The vortex distribution around arrays of engineered defects, such as blind and through holes, may help to improve the superconducting properties. Thus, in this work, we used the time-dependen

David Licht, Raimon Luna, Ryotaku Suzuki

We explore the phase space of non-uniform black branes compactified on oblique lattices with a large number of dimensions. We find the phase diagrams for different periodicities and angles, and determine the thermodynamically preferred phases for each lattice configuration. In a range of angles, we observe that some phases become metastable.

Viktor Christiansson, Francesco Petocchi, Philipp Werner

Simple cubic phosphorus exhibits superconductivity with a maximum $T_c$ of up to 12 K under pressure. The pressure dependence of $T_c$ cannot be consistently explained with a simple electron-phonon mechanism, which has stimulated investigations into the role of electronic correlations and plasmonic contributions. Here, we solve the gap equation of density fu

Weijun Hong, Menghui Zhu, Minghuan Liu, Weinan Zhang

Exploration is crucial for training the optimal reinforcement learning (RL) policy, where the key is to discriminate whether a state visiting is novel. Most previous work focuses on designing heuristic rules or distance metrics to check whether a state is novel without considering such a discrimination process that can be learned. In this paper, we propose a

Jianshu Cao

A general rate theory for resonance energy transfer is formulated to incorporate any degrees of freedom (e.g., rotation, vibration, exciton, and polariton) as well as coherently-coupled composite states. The compact rate expression allows us to establish useful relationships: (i) detailed balance condition when the donor and acceptor are at the same temperat

Nicolas Boullé, Patrick E. Farrell, Marie E. Rognes

We introduce a numerical technique for controlling the location and stability properties of Hopf bifurcations in dynamical systems. The algorithm consists of solving an optimization problem constrained by an extended system of nonlinear partial differential equations that characterizes Hopf bifurcation points. The flexibility and robustness of the method all

Georg Maierhofer, Daan Huybrechs

In recent work (Maierhofer & Huybrechs, 2022, Adv. Comput. Math.), the authors showed that least-squares oversampling can improve the convergence properties of collocation methods for boundary integral equations involving operators of certain pseudo-differential form. The underlying principle is that the discrete method approximates a Bubnov$-$Galerkin metho

Quinn Campbell, Kevin J. Dwyer, Sungha Baek, Andrew D. Baczewski

BCl$_3$ is a promising candidate for atomic-precision acceptor doping in Si, but optimizing the electrical properties of structures created with this technique requires a detailed understanding of adsorption and dissociation pathways for this precursor. Here, we use density functional theory and scanning tunneling microscopy (STM) to identify and explore the

Haoyu Guo, Darshan G. Joshi, Subir Sachdev

We present computations of the thermal Hall coefficient of phonons scattering off a defect with multiple energy levels. Using a microscopic formulation based on the Kubo formula, we find that the leading contribution perturbative in the phonon-defect coupling is proportional to the phonon lifetime, and has a `side-jump' interpretation. Consequently, the ther

Dorota Lipowski, Adam Lipowski

Synonyms and homonyms appear in all natural languages. We analyse their evolution within the framework of the signaling game. Agents in our model use reinforcement learning, where probabilities of selection of a communicated word or of its interpretation depend on weights equal to the number of accumulated successful communications. When the probabilities in

Weijun Hong, Guilin Li, Weinan Zhang, Ruiming Tang

Neural architecture search (NAS) has shown encouraging results in automating the architecture design. Recently, DARTS relaxes the search process with a differentiable formulation that leverages weight-sharing and SGD where all candidate operations are trained simultaneously. Our empirical results show that such procedure results in the co-adaption problem an

Abhijith Neil Abraham, Fariz Rahman, Damanpreet Kaur

This paper presents TableQuery, a novel tool for querying tabular data using deep learning models pre-trained to answer questions on free text. Existing deep learning methods for question answering on tabular data have various limitations, such as having to feed the entire table as input into a neural network model, making them unsuitable for most real-world

Sudarshan Adiga, Ravi Tandon

This paper presents DRE-CUSUM, an unsupervised density-ratio estimation (DRE) based approach to determine statistical changes in time-series data when no knowledge of the pre-and post-change distributions are available. The core idea behind the proposed approach is to split the time-series at an arbitrary point and estimate the ratio of densities of distribu

Steve Huntsman

Building on the recently developed theory of magnitude, we introduce the optimization algorithm EXPLO2 and carefully benchmark it. EXPLO2 advances the state of the art for optimizing high-dimensional ($D \gtrapprox 40$) multimodal functions that are expensive to compute and for which derivatives are not available, such as arise in hyperparameter optimization

Carlos Mougan, Dan Saattrup Nielsen

Monitoring machine learning models once they are deployed is challenging. It is even more challenging to decide when to retrain models in real-case scenarios when labeled data is beyond reach, and monitoring performance metrics becomes unfeasible. In this work, we use non-parametric bootstrapped uncertainty estimates and SHAP values to provide explainable un

John Pougué-Biyong, Akshay Gupta, Aria Haghighi, Ahmed El-Kishky

A key challenge in social network analysis is understanding the position, or stance, of people in the graph on a large set of topics. While past work has modeled (dis)agreement in social networks using signed graphs, these approaches have not modeled agreement patterns across a range of correlated topics. For instance, disagreement on one topic may make disa

Nitheesh M. Nair, Debdutta Ray, Parasuraman Swaminathan

A paper-based co-planar waveguide (CPW) fed monopole antenna for Wi-Fi applications is proposed. The antenna is fabricated by printing a commercial silver nanoparticle (Ag NP) based ink on photo paper substrate. The antenna is designed as a single layer for the ease of fabrication, and it is designed to radiate at two frequencies, 2.4 and 5.8 GHz, which are

Xin Du, Benedicte Legastelois, Bhargavi Ganesh, Ajitha Rajan

Using large pre-trained models for image recognition tasks is becoming increasingly common owing to the well acknowledged success of recent models like vision transformers and other CNN-based models like VGG and Resnet. The high accuracy of these models on benchmark tasks has translated into their practical use across many domains including safety-critical a

Timur Aslyamov, Mathijs Janssen

We study the charging dynamics of a long electrolyte-filled slit pore in response to a suddenly applied potential. In particular, we analytically solve the Poisson-Nernst-Planck (PNP) equations for a pore for which $\lambda_D\ll H\ll L$, with $\lambda_D$ the Debye length and $H$ and $L$ the pore's width and length. For small applied potentials, we find the t

Yasutada Oohama, Bagus Santoso

We are interested in investigating the security of source encryption with a symmetric key under side-channel attacks. In this paper, we propose a general framework of source encryption with a symmetric key under the side-channel attacks, which applies to \emph{any} source encryption with a symmetric key and \emph{any} kind of side-channel attacks targeting t

Xiao Liu, Yan-Qing Ma

AMFlow is a Mathematica package to numerically compute dimensionally regularized Feynman integrals via the recently proposed auxiliary mass flow method. In this framework, integrals are treated as functions of an auxiliary mass parameter and their results can be obtained by constructing and solving differential systems with respect to this parameter, in an a

Gourab Das, Rangeet Bhattacharyya

Quantum communications require efficient implementations of quantum state transportation with high fidelity. Here, we consider the transport of entanglement along a chain of qubits. A series of SWAP operations involving successive pairs of qubits can transport entanglement along the chain. We report that the fidelity of the abovementioned gate has a maximum

Giorgia Dellaferrera, Gabriel Kreiman

Supervised learning in artificial neural networks typically relies on backpropagation, where the weights are updated based on the error-function gradients and sequentially propagated from the output layer to the input layer. Although this approach has proven effective in a wide domain of applications, it lacks biological plausibility in many regards, includi

Hassan Al-Zoubi

In this paper, we consider tubes in the Euclidean 3-space whose Gauss map N is of coordinate finite II-type, i.e., the position vector N satisfies the relation $\Delta^{II}N = \Lambda N$, where $\Delta^{II}$ is the Laplace operator with respect to the second fundamental form I of the surface and $\Lambda$ is a square matrix of order 3. We show that circular

N. Muñoz-Elgueta, F. Arrigoni Battaia, G. Kauffmann, C. De Breuck

Cool gas (T$\sim$10$^{4}$~K) traced by hydrogen Ly$\alpha$ emission is now routinely detected around $z\sim3$ quasars, but little is known about their molecular gas reservoirs. Here, we present an APEX spectroscopic survey of the CO(6-5), CO(7-6) and [CI](2-1) emission lines for 9 quasars from the QSO MUSEUM survey which have similar UV luminosities, but ver

Jordan Cotler, Andrew Strominger

Quantum mechanical unitarity in our universe is challenged both by the notion of the big bang, in which nothing transforms into something, and the expansion of space, in which something transforms into more something. This motivates the hypothesis that quantum mechanical time evolution is always isometric, in the sense of preserving inner products, but not n

Emirhan Gürpınar

We study the properties of secret sharing schemes, where a random secret value is transformed into shares distributed among several participants in such a way that only the qualified groups of participants can recover the secret value. We improve the lower bounds on the sizes of shares for several specific problems of secret sharing. To this end, we use the

Thomas Guyet, Wenbin Zhang, Albert Bifet

The need to analyze information from streams arises in a variety of applications. One of its fundamental research directions is to mine sequential patterns over data streams. Current studies mine series of items based on the presence of the pattern in transactions but pay no attention to the series of itemsets and their multiple occurrences. The pattern over

Simon Liebing, Kai Trepte, Sebastian Schwalbe

Recently, Trepte et al. [J. Chem. Phys., vol. 155, 2021] pointed out the importance of analyzing dipole moments in the Fermi-L\"owdin orbital (FLO) self-interaction correction (SIC) for cyclic, planar molecules. In this manuscript, the effect of the molecular and electronic geometries on dipole moments and polarizabilities is discussed for non-cyclic molecul

James Nelson, Luuk Coopmans, Graham Kells, Stefano Sanvito

We investigate the potential of supervised machine learning to propagate a quantum system in time. While Markovian dynamics can be learned easily, given a sufficient amount of data, non-Markovian systems are non-trivial and their description requires the memory knowledge of past states. Here we analyse the feature of such memory by taking a simple 1D Heisenb

Indrani Chakraborty, Dilip Kumar Ghosh, Nivedita Ghosh, Santosh Kumar Rai

In this work, we explore the signals of an $S_3$-symmetric two Higgs doublet model with two generations of vector-like leptons (VLLs) at the proposed International Linear Collider (ILC). The lightest neutral component of the VLL in this model provides a viable dark matter (DM) candidate satisfying the current relic density data as well as circumventing all d

Ross Parker, Yannan Shen, Alejandro Aceves, John Zweck

We consider the existence and spectral stability of nonlinear discrete localized solutions representing light pulses propagating in a twisted multi-core optical fiber. By considering an even number, $N$, of waveguides, we derive asymptotic expressions for solutions in which the bulk of the light intensity is concentrated as a soliton-like pulses confined to

Johannes Voss

A density functional theory (DFT) approach to computing transition metal oxide heat of formation without adjustable parameters is presented. Different degrees of $d$-electron localization in oxides are treated within the DFT+$U$ approach with site-dependent, first-principles Hubbard $U$-parameters obtained from linear response theory, and delocalized states

H. Attouch, J. Fadili

We revisit the Ravine method of Gelfand and Tsetlin from a dynamical system perspective, study its convergence properties, and highlight its similarities and differences with the Nesterov accelerated gradient method. The two methods are closely related. They can be deduced from each other by reversing the order of the extrapolation and gradient operations in

ANTARES Collaboration, A. Albert, S. Alves, M. André

Solar Atmospheric Neutrinos (SA$\nu$s) are produced by the interaction of cosmic rays with the solar medium. The detection of SA$\nu$s would provide useful information on the composition of primary cosmic rays as well as the solar density. These neutrinos represent an irreducible source of background for indirect searches for dark matter towards the Sun and

Pascal Anastasopoulos, Elias Kiritsis

Fermionic bound states (mesinos) of messengers can play the role of right-handed neutrinos and due to their heavy masses they can realize the seesaw mechanism providing light and heavy sterile neutrinos. We study simple models with a single mesino to present the proof of principle. We extend our analysis to three mesino states where after the seesaw mechanis

Petar Bevanda, Max Beier, Shahab Heshmati-Alamdari, Stefan Sosnowski

We propose a novel framework for learning linear time-invariant (LTI) models for a class of continuous-time non-autonomous nonlinear dynamics based on a representation of Koopman operators. In general, the operator is infinite-dimensional but, crucially, linear. To utilize it for efficient LTI control design, we learn a finite representation of the Koopman o

Andrea Grigorescu, Holger Boche, Rafael F. Schaefer, H. Vincent Poor

The capacity of finite state channels (FSCs) with feedback has been shown to be a limit of a sequence of multi-letter expressions. Despite many efforts, a closed-form single-letter capacity characterization is unknown to date. In this paper, the feedback capacity is studied from a fundamental algorithmic point of view by addressing the question of whether or

Zhi-Feng Liu, Yan-Qing Ma

We find that all Feynman integrals (FIs), having any number of loops, can be completely determined once linear relations between FIs are provided. Therefore, FIs computation is conceptually changed to a linear algebraic problem. Examples up to 5 loops are given to verify this observation. As a byproduct, we get a powerful method to calculate perturbative cor

M. S. Alencar, A. G. Tatashev, O. V. Seleznjev, M. V. Yashina

The paper considers a queueing system with limited processor sharing. No more than n jobs may be served simultaneously. This system may be used for modeling bandwidth sharing in wireless communication systems and processes of service in computer networks. If there are n jobs in the considered queueing system and a new job arrives, then the arriving job is lo

Zhi-Feng Liu, Yan-Qing Ma

We proposed a recipe to systematically calculate Feynman integrals containing linear propagators using the auxiliary mass flow method. The key of the recipe is to introduce a quadratic term for each linear propagator and then using differential equations to get rid of their effects. As an application, we calculated all master integrals of vacuum integrals co

Hongxin Dong, Peng Sun, Bin Yan, C. -P. Yuan

We propose to utilize the exclusive $Z$-boson rare decays $Z\to \Upsilon(ns)+\gamma$ to constrain the $Zb\bar{b}$ couplings at the HL-LHC and 100 TeV proton-proton collider. We demonstrate that the event yield of the proposed processes is sensitive to the axial-vector component of the $Zb\bar{b}$ coupling and can provide complementary information to the jet-

Fabio D'Ambrosio, Shaun D. B. Fell, Lavinia Heisenberg, David Maibach

These notes provide a student-friendly introduction to the theory of gravitational waves in full, non-linear general relativity (GR). We aim for a balance between physical intuition and mathematical rigor and cover topics such as the Newman-Penrose formalism, electromagnetic waves, asymptotically Minkowski spacetimes, the peeling theorem, the universal struc

Ning Jiang, Huan Yang, Tinggui Wang, Jiazheng Zhu

Supermassive black hole binaries (SMBHs) are a fascinating byproduct of galaxy mergers in the hierarchical universe. In the last stage of their orbital evolution, gravitational wave radiation drives the binary inspiral and produces the loudest siren awaiting to be detected by gravitational wave observatories. Periodically varying emission from active galacti

Rituraj Kaushik, Karol Arndt, Ville Kyrki

The framework of Simulation-to-real learning, i.e, learning policies in simulation and transferring those policies to the real world is one of the most promising approaches towards data-efficient learning in robotics. However, due to the inevitable reality gap between the simulation and the real world, a policy learned in the simulation may not always genera

Chenyang Lei, Yazhou Xing, Hao Ouyang, Qifeng Chen

Applying an image processing algorithm independently to each video frame often leads to temporal inconsistency in the resulting video. To address this issue, we present a novel and general approach for blind video temporal consistency. Our method is only trained on a pair of original and processed videos directly instead of a large dataset. Unlike most previ

Maja Schlereth, Daniel Stromer, Katharina Breininger, Alexandra Wagner

Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society. They can lead to severe progressive muscle weakness, muscle degeneration, contracture, deformity and progressive disability. The NMDs evaluated in this study often manifest in early childhood. As subtypes of disease, e.g. Duchenne Muscular Dystropy (DMD) and Spi

Donald Kurtz

Asteroseismology has grown from its beginnings three decades ago to a mature field teeming with discoveries and applications. This phenomenal growth has been enabled by space photometry with precision $10-100$ times better than ground-based observations, with nearly continuous light curves for durations of weeks to years, and by large scale ground-based surv

Tanwir Ahmad, Dragos Truscan, Juri Vain, Ivan Porres

The Internet has become a prime subject to security attacks and intrusions by attackers. These attacks can lead to system malfunction, network breakdown, data corruption or theft. A network intrusion detection system (IDS) is a tool used for identifying unauthorized and malicious behavior by observing the network traffic. State-of-the-art intrusion detection

A. Markina, K. -H. Lin, W. Liu, C. Poelking

Efficiencies of organic solar cells have practically doubled since the development of non-fullerene acceptors (NFAs). However, generic chemical design rules for donor-NFA combinations are still needed. Such rules are proposed by analyzing inhomogeneous electrostatic fields at the donor-acceptor interface. It is shown that an acceptor-donor-acceptor molecular

Manh Nguyen-Duc, Anh Le-Tuan, Manfred Hauswirth, David Bowden

Stream processing and reasoning is getting considerable attention in various application domains such as IoT, Industry IoT and Smart Cities. In parallel, reasoning and knowledge-based features have attracted research into many areas of robotics, such as robotic mapping, perception and interaction. To this end, the Semantic Stream Reasoning (SSR) framework ca

Nelly Elsayed, Zag ElSayed, Anthony S. Maida

Long short-term memory (LSTM) is a robust recurrent neural network architecture for learning spatiotemporal sequential data. However, it requires significant computational power for learning and implementing from both software and hardware aspects. This paper proposes a novel LiteLSTM architecture based on reducing the computation components of the LSTM usin

Lavinia Heisenberg, Hector Villarrubia-Rojo, Jann Zosso

In a model independent approach, we derive generic conditions that any late time modification of the $\Lambda$CDM expansion history must satisfy in order to consistently solve both the $H_0$ and the $\sigma_8$ tensions. Our results are fully analytical and the method is merely based on the assumption that the late-time deviations from $\Lambda$CDM remain sma

Tonatiuh Matos-Wiederhold

This work is concerned with an axiom introduced by Todorc\v{e}vi\'{c} in \cite{stevo} that constitutes a Ramsey-like statement regarding the topology of the reals. Our aim is to explain the axiom in detail, give some interesting applications and finally prove that the axiom is indeed consistent with ZFC, so that it makes sense to consider working with it in

Manfried Faber

With the idea to find geometric formulations of particle physics we investigate the predictions of a three dimensional generalisation of the Sine-Gordon model, very close to the Skyrme model and to the Wu-Yang description of Dirac monopoles. With three rotational degrees of freedom of spatial Dreibeins we formulate a Lagrangian and confront the predictions t

Lidia Garrucho, Kaisar Kushibar, Socayna Jouide, Oliver Diaz

Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical environments. In this work, we explore the domain generalization of deep learning methods for mass detection i

Denis Kuperberg

We study FO+, a fragment of first-order logic on finite words, where monadic predicates can only appear positively. We show that there is an FO-definable language that is monotone in monadic predicates but not definable in FO+. This provides a simple proof that Lyndon's preservation theorem fails on finite structures. We lift this example language to finite

Ilijas Farah, Saeed Ghasemi, Andrea Vaccaro, Alessandro Vignati

We give a unified overview of the study of the effects of additional set theoretic axioms on quotient structures. Our focus is on rigidity, measured in terms of existence (or rather non-existence) of suitably non-trivial automorphisms of the quotients in question. A textbook example for the study of this topic is the Boolean algebra $\mathcal{P}(\mathbb{N})/

Chih-Chiang Huang, Hedongliang Liu, Lukas Holzbaur, Sven Puchinger

This paper is concerned with list decoding of $2$-interleaved binary alternant codes. The principle of the proposed algorithm is based on a combination of a list decoding algorithm for (interleaved) Reed-Solomon codes and an algorithm for (non-interleaved) alternant codes. A new upper bound on the decoding radius is derived and the list size is shown to scal

Vasco D. Silva, Anna Finamore, Rui Henriques

Ongoing traffic changes, including those triggered by the COVID-19 pandemic, reveal the necessity to adapt our public transport systems to the ever-changing users' needs. This work shows that single and multi objective stances can be synergistically combined to better answer the transit network design problem (TNDP). Single objective formulations are dynamic

Stefano Pozzorini, Natalie Schär, Max F. Zoller

We present a new and fully general algorithm for the automated construction of the integrands of two-loop scattering amplitudes. This is achieved through a generalisation of the open-loops method to two loops. The core of the algorithm consists of a numerical recursion, where the various building blocks of two-loop diagrams are connected to each other throug

Mats Barkman, Albert Samoilenka, Andrea Benfenati, Egor Babaev

We consider the interface between a Bardeen-Cooper-Schrieffer superconductor and non-superconducting band insulator. We show that under certain conditions, such interfaces can have an elevated superconducting critical temperature, without increasing the strength of the pairing interaction at the interface. We identify the regimes where the interface critical

David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl, Mohamed Kari

Deep learning based electroencephalography (EEG) signal processing methods are known to suffer from poor test-time generalization due to the changes in data distribution. This becomes a more challenging problem when privacy-preserving representation learning is of interest such as in clinical settings. To that end, we propose a multi-source learning architec

Quentin Cormier

We study the long-time behavior of some McKean-Vlasov stochastic differential equations used to model the evolution of large populations of interacting agents. We give conditions ensuring the local stability of an invariant probability measure. Lions derivatives are used in a novel way to obtain our stability criteria. We obtain results for non-local McKean-

Hamidreza Bakhshzad Mahmoodi, MohammadJavad Salehi, Antti Tölli

Efficient usage of in-device storage and computation capabilities are key solutions to support data-intensive applications such as immersive digital experiences. This paper proposes a location-dependent multi-antenna coded caching -based content delivery scheme tailored specifically for wireless immersive viewing applications. First, a novel memory allocatio

Jimmy He, Tobias Müller, Teun Verstraaten

We study cycle counts in permutations of $1,\dots,n$ drawn at random according to the Mallows distribution. Under this distribution, each permutation $\pi \in S_n$ is selected with probability proportional to $q^{\text{inv}(\pi)}$, where $q>0$ is a parameter and $\text{inv}(\pi)$ denotes the number of inversions of $\pi$. For $\ell$ fixed, we study the vecto

Digesh Raut, Qaisar Shafi, Anil Thapa

We study the low energy implications of a trinification model based on the gauge symmetry $G= SU(3)_c \times SU(3)_L \times SU(3)_R$, without imposing gauge coupling unification. A minimal model requires two Higgs multiplets that reside in the bi-fundamental representation of $G$, and this is shown to be adequate for accommodating the Standard Model (SM) fer

Pouria Mehrabi

In this thesis a probabilistic framework is developed and proposed for Dynamic Object Recognition in 3D Environments. A software package is developed using C++ and Python in ROS that performs the detection and tracking task. Furthermore, a novel Gaussian Process Regression (GPR) based method is developed to detect ground points in different urban scenarios o

Gabriele Veneziano, Gregory A. Vilkovisky

Recently, Damour computed the radiation reaction on gravitational scattering as the (linear) response to the angular momentum loss which he found to be of ${\cal O}(G^2)$ in the gravitational constant. This is a puzzle because any amplitude calculation would produce both energy and angular momentum losses starting only at ${\cal O}(G^3)$. Another puzzle is t

Piotr Mironowicz, Paweł Horodecki, Ryszard Horodecki

We study the non-perfect propagation of information to evolving low-dimensional environment that includes self-evolution as well as noisy initial states and analyze interrelations between the degree of objectivization and environment parameters. In particular, we consider an analytical model of three interacting qubits and derive its objectivity parameters.

Anoosheh Heidarzadeh, Alex Sprintson

This paper introduces the problem of Private Information Retrieval with Reusable and Single-use Side Information (PIR-RSSI). In this problem, one or more remote servers store identical copies of a set of $K$ messages, and there is a user that initially knows $M$ of these messages, and wants to privately retrieve one other message from the set of $K$ messages

Yuezhao Qian, Ziqing Zhang, Yuezhou Liu, Jingjun Xu

We reported on a graphical domain engineering technique with the capability to fabricate macroscale domain structures with nanoscale spatial resolution in non-polar-cut lithium niobate thin film on insulators through the biased probe tip of scanning atomic force microscopy. It was found that the domain writing process is asymmetric with respect to the sponta