October 2020 arXiv papers — page 96

B. Leandro, A. de Melo, H. Pina

In this paper we prove a Minkowski-like inequality for an asymptotically flat static Einstein-Maxwell (electrostatic) space-time using as approach the inverse mean curvature flow (IMCF). Moreover, we discuss the importance of this inequality in the understanding of the photon sphere.

Nigel Hitchin

We first describe the action of the fundamental group of a closed surface of variable negative curvature on the oriented geodesics in its universal covering in terms of a naturally-defined flat connection whose holonomy lies in the group of Hamiltonian diffeomorphisms of S^1 x R. Consideration of the holonomy necessitates an extension from Riemannian to Fins

Cong Hao, Yao Chen, Xiaofan Zhang, Yuhong Li

High quality AI solutions require joint optimization of AI algorithms, such as deep neural networks (DNNs), and their hardware accelerators. To improve the overall solution quality as well as to boost the design productivity, efficient algorithm and accelerator co-design methodologies are indispensable. In this paper, we first discuss the motivations and cha

Zhiwen Cao, Zongcheng Chu, Dongfang Liu, Yingjie Chen

This paper proposes to use the three vectors in a rotation matrix as the representation in head pose estimation and develops a new neural network based on the characteristic of such representation. We address two potential issues existed in current head pose estimation works: 1. Public datasets for head pose estimation use either Euler angles or quaternions

I. Takyi, M. K. Matfunjwa, H. Weigel

We compute the vacuum polarization energy of kink solitons in the $ϕ^{8}$ model in one space and one time dimensions. There are three possible field potentials that have eight powers of $ϕ$ and that possess kink solitons. For these different field potentials we investigate whether the vacuum polarization destabilizes thesolitons. This may particularly be the

Tomasz Klimsiak, Tomasz Komorowski

In the present paper we prove estimates on {subsolutions of the equation $-Av+c(x)v=0$}, $x\in D$, where $D\subset \bbR^d$ is a domain (i.e. an open and connected set) and $A$ is an integro-differential operator of the Waldenfels type, whose differential part satisfies the uniform ellipticity condition on compact sets. In general, the coefficients of the ope

Fabio Caceffo, Giuseppe Clemente

We examine the dual graph representation of simplicial manifolds in Causal Dynamical Triangulations (CDT) as a mean to build observables, and propose a new representation based on the Finite Element Methods (FEM). In particular, with the application of FEM techniques, we extract the (low-lying) spectrum of the Laplace-Beltrami (LB) operator on the Sobolev sp

Anthony G. O'Farrell

We consider formal maps in any finite dimension $d$ with coefficients in an integral domain $K$ with identity. Those invertible under formal composition form a group $\mathcal{G}$. We consider the centraliser $C_g$ of an element $g\in\mathcal{G}$ which is tangent to the identity of $\mathcal{G}$. Elements of finite order always have an uncountable centralise

Zhengyu Hu

Coastal vegetation can dissipate the energy of storm surge and tsunami, which serves a natural barrier for coastal protection. The experimental study of waves-mangroves interaction was carried out to evaluate the efficiency of wave attenuation by vegetation (WDV). In particular, the model mangrove consisted of a flexible canopy and a rigid stem to imitate na

Takeru Yokota, Tomoya Naito

We show an $\textit{ab initio}$ construction of the energy density functional (EDF) for electron systems using the functional renormalization group. The correlation energies of the homogeneous electron gas given in our framework reproduce the exact behavior at high density and agree with the Monte-Carlo data in a wide range of densities. Our analytic techniq

Simon Geirnaert, Tom Francart, Alexander Bertrand

Auditory attention decoding (AAD) algorithms decode the auditory attention from electroencephalography (EEG) signals that capture the listener's neural activity. Such AAD methods are believed to be an important ingredient towards so-called neuro-steered assistive hearing devices. For example, traditional AAD decoders allow detecting to which of multiple

Jens Müller, Robert Schmier, Lynton Ardizzone, Carsten Rother

Standard supervised learning breaks down under data distribution shift. However, the principle of independent causal mechanisms (ICM, Peters et al. (2017)) can turn this weakness into an opportunity: one can take advantage of distribution shift between different environments during training in order to obtain more robust models. We propose a new gradient-bas

Miguel de Carvalho, Soraia Pereira, Paula Pereira, Patrícia de Zea Bermudez

We introduce a novel regression model for the conditional left and right tail of a possibly heavy-tailed response. The proposed model can be used to learn the effect of covariates on an extreme value setting via a Lasso-type specification based on a Lagrangian restriction. Our model can be used to track if some covariates are significant for the lower values

Vincent Caudrelier, Matteo Stoppato

In recent years, new properties of space-time duality in the Hamiltonian formalism of certain integrable classical field theories have been discovered and have led to their reformulation using ideas from covariant Hamiltonian field theory: in this sense, the covariant nature of their classical $r$-matrix structure was unraveled. Here, we solve the open quest

Konstantinos Spiliotis, Jens Starke, Denise Franz, Angelika Richter

A large scale computational model of the basal ganglia (BG) network is proposed to describes movement disorder including deep brain stimulation (DBS). The model of this complex network considers four areas of the basal ganglia network: the subthalamic nucleus (STN) as target area of DBS, globus pallidus, both pars externa and pars interna (GPe-GPi), and the

Aimon Mirza Baig, Luis Badesa, Goran Strbac

In order to decarbonise the electricity sector, the future Great Britain (GB) power system will be largely dominated by non-synchronous renewables. This will cause low levels of inertia, a key parameter that could lead to frequency deterioration. Therefore, the requirement for ancillary services that contain frequency deviations will increase significantly,

Hitesh Raundal, Mahender Singh, Manpreet Singh

The paper develops a general theory of orderability of quandles with a focus on link quandles of tame links and gives some general constructions of orderable quandles. We prove that knot quandles of many fibered prime knots are right-orderable, whereas link quandles of most non-trivial torus links are not right-orderable. As a consequence, we deduce that the

K. Choi, Hoyun Choi, B. Kahng

The Covid-19 pandemic is ongoing worldwide, and the damage it has caused is unprecedented. For prevention, South Korea has adopted a local quarantine strategy rather than a global lockdown. This approach not only minimizes economic damage, but it also efficiently prevents the spread of the disease. In this work, the spread of COVID-19 under local quarantine

P. Svensson, O. Embreus, S. L. Newton, K. Särkimäki

The electron runaway phenomenon in plasmas depends sensitively on the momentum-space dynamics. However, efficient simulation of the global evolution of systems involving runaway electrons typically requires a reduced fluid description. This is needed for example in the design of essential runaway mitigation methods for tokamaks. In this paper, we present a m

Christian Sommer, Michael Reitz, Francesca Mineo, Claudiu Genes

We study the dependence of the vacuum Rabi splitting (VRS) on frequency disorder, vibrations, near-field effects and density in molecular polaritonics. In the mesoscopic limit, static frequency disorder alone can already introduce a loss mechanism from polaritonic states into a dark state reservoir, which we quantitatively describe, providing an analytical s

Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas

Instrumental variable (IV) regression is a standard strategy for learning causal relationships between confounded treatment and outcome variables from observational data by utilizing an instrumental variable, which affects the outcome only through the treatment. In classical IV regression, learning proceeds in two stages: stage 1 performs linear regression f

Karina González-López, Edan Lerner

The systematic identification of temperature scales in supercooled liquids that are key to understanding those liquids' underlying glass properties, and the latter's formation-history dependence, is a challenging task. Here we study the statistics of particles' squared displacements $δr^2$ between equilibrium liquid configurations at temperature

Kai Wang, Yu Liu, Qian Ma, Quan Z. Sheng

Link prediction based on knowledge graph embeddings (KGE) aims to predict new triples to automatically construct knowledge graphs (KGs). However, recent KGE models achieve performance improvements by excessively increasing the embedding dimensions, which may cause enormous training costs and require more storage space. In this paper, instead of training high

Jean-Baptiste Boin, Nat Roth, Jigar Doshi, Pablo Llueca

The task of roof damage classification and segmentation from overhead imagery presents unique challenges. In this work we choose to address the challenge posed due to strong class imbalance. We propose four distinct techniques that aim at mitigating this problem. Through a new scheme that feeds the data to the network by oversampling the minority classes, an

Ansgar Denner, Giovanni Pelliccioli

Accessing the polarization of weak bosons provides an important probe for the mechanism of electroweak symmetry breaking. Relying on the double-pole approximation and on the separation of polarizations at the amplitude level, we study WZ production at the LHC, with both bosons in a definite polarization mode, including NLO QCD effects. We compare results obt

Zhengyu Hu

Laboratory study was set out to investigate the flow structure and turbulent kinetic energy ($TKE$) within mangrove forest considering flexible canopies and rigid stems under various wave conditions. Canopies and stems have different effects on turbulence generation. The flexible leaves in canopy scale can promote the dissipation of $TKE$ and the rigid stem

Xiaoyu Hu, Jing Lei

We consider the problem of testing the equality of conditional distributions of a response variable given a vector of covariates between two populations. Such a hypothesis testing problem can be motivated from various machine learning and statistical inference scenarios, including transfer learning and causal predictive inference. We develop a nonparametric

G. H. S. Camargo, V. A. De Lorenci, A. L. Ferreira Junior, C. C. H. Ribeiro

The fundamental vacuum state of quantum fields, related to Minkowski space, produces divergent fluctuations that must be suppressed in order to bring reality to the description of physical systems. As a consequence, negative vacuum expectation values of classically positive-defined quantities can appear. This has been addressed in the literature as subvacuum

Jacob Madsen, Timothy J. Pennycook, Toma Susi

The simulation of transmission electron microscopy (TEM) images or diffraction patterns is often required to interpret their contrast and extract specimen features. This is especially true for high-resolution phase-contrast imaging of materials, but electron scattering simulations based on atomistic models are widely used in materials science and structural

Lukas Holzbaur, Hedongliang Liu, Alessandro Neri, Sven Puchinger

Interleaved Reed-Solomon codes admit efficient decoding algorithms which correct burst errors far beyond half the minimum distance in the random errors regime, e.g., by computing a common solution to the Key Equation for each Reed-Solomon code, as described by Schmidt et al. If this decoder does not succeed, it may either fail to return a codeword or miscorr

Bin Han, Yao Zhu, Zhiyuan Jiang, Muxia Sun

It is becoming increasingly clear that an important task for wireless networks is to minimize the age of information (AoI), i.e., the timeliness of information delivery. While mainstream approaches generally rely on the real-time observation of user AoI and channel state, there has been little attention to solve the problem in a complete (or partial) absence

Rui Oliveira, Oskar Ljungqvist, Pedro F. Lima, Jonas Mårtensson

Driving heavy-duty vehicles, such as buses and tractor-trailer vehicles, is a difficult task in comparison to passenger cars. Most research on motion planning for autonomous vehicles has focused on passenger vehicles, and many unique challenges associated with heavy-duty vehicles remain open. However, recent works have started to tackle the particular diffic

L. Gu, W. Li, C. Reichhardt, C. J. O. Reichhardt

Langevin dynamical simulations are performed to study the depinning dynamics of two-dimensional dusty plasmas on a one-dimensional periodic substrate. From the diagnostics of the sixfold coordinated particles $P_6$ and the collective drift velocity $V_x$, three different states appear, which are the pinning, disordered plastic flow, and moving ordered states

Matthew Kukla, Rachel Traylor

We construct rings of typed ordered fuzzy numbers whose component functions are of a common form. As this ring also contains improper fuzzy numbers (OFNs whose membership "functions" are actually just relations), we develop a set of operations to convert an improper fuzzy number to a proper one of the same type.

Ya-Wen Teng, Yishuo Shi, Chih-Hua Tai, De-Nian Yang

Viral marketing on social networks, also known as Influence Maximization (IM), aims to select k users for the promotion of a target item by maximizing the total spread of their influence. However, most previous works on IM do not explore the dynamic user perception of promoted items in the process. In this paper, by exploiting the knowledge graph (KG) to cap

Sergei M. Kuzenko, Alec E. Pindur

We describe a five-dimensional analogue of Wigner's operator equation ${\mathbb W}_a = λP_a$, where ${\mathbb W}_a $ is the Pauli-Lubanski vector, $P_a$ the energy-momentum operator, and $λ$ the helicity of a massless particle. Higher dimensional generalisations are also given.

Lan-Lan Gao, Sahal Kaushik, Dmitri E. Kharzeev, Evan John Philip

In Weyl semimetals subjected to torsion, there are two different kinds of chirality: i) the (coordinate-space) shape of the twisted crystal is chiral, and ii) the momentum space contains chiral quasi-particles. Here we construct a general kinetic theory of anomalous transport using the phase space (coordinate and momentum spaces combined) Berry curvature ind

Paolo Cea

P-stars are compact relativistic stars made of deconfined up and down quarks in a chromomagnetic condensate proposed by us long time ago. P-stars do not admit a critical mass thereby they are able to overcome the gravitational collapse to black holes. In this work we discuss in greater details our theoretical proposal for P-stars. We point out that our theor

Takao Tsumuraya, Ikumu Watanabe, Takahiro Sawaguchi

A class of Fe-Mn-Si-based alloys exhibits a reversible martensitic transformation between the $γ$ phase with a face-centered cubic~(fcc) structure and an $ε$ phase with a hexagonal close-packed (hcp) structure. During the deformation-induced $γ$--$ε$ transformation, we identified a phase that is different from the $ε$ phase. In this new phase, the electron d

Nan-Shu Wu, Su Xu, Zuojia Wang, Hong-Bo Sun

Multidimensional modulation was widely studied in the past decades due to the explosive development of modern wireless communication. Here, we propose a spirally reconfigurable slotted antenna inspired by the multidimensional modulation. The amplitude, phase, and frequency-shift modulation are analog by integrating three-dimensional mechanical switching to a

Alberto Fabrizio, Ksenia R. Briling, David D. Girardier, Clemence Corminboeuf

The on-top pair density [$Π(\mathrm{\mathbf{r}})$] is a local quantum-chemical property that reflects the probability of two electrons of any spin to occupy the same position in space. Being the simplest quantity related to the two-particle density matrix, the on-top pair density is a powerful indicator of electron correlation effects, and as such, it has be

Janne Räty, Rasmus Astrup, Johannes Breidenbach

Diameter at breast height (DBH) distributions offer valuable information for operational and strategic forest management decisions. We predicted DBH distributions using Norwegian national forest inventory and airborne laser scanning data and compared the predictive performances of linear mixed-effects (PPM), generalized linear-mixed (GLM) and k nearest neigh

Akihiro Higashitani, Hidefumi Ohsugi

In the present paper, we prove that the toric ideals of certain $s$-block diagonal matching fields have quadratic Gröbner bases. Thus, in particular, those are quadratically generated. By using this result, we provide a new family of toric degenerations of Grassmannians.

Joana Fraxanet, Utso Bhattacharya, Tobias Grass, Debraj Rakshit

We present a detailed study of the topological properties of the Kitaev chain with long-range pairing terms and in the presence of an Aubry-André-Harper on-site potential. Specifically, we consider algebraically decaying superconducting pairing amplitudes; the exponent of this decay is found to determine a critical pairing strength, below which the chain rem

Weile Wei, Arghya Chatterjee, Kevin Huck, Oscar Hernandez

This paper describes how we successfully used the HPX programming model to port the DCA++ application on multiple architectures that include POWER9, x86, ARM v8, and NVIDIA GPUs. We describe the lessons we can learn from this experience as well as the benefits of enabling the HPX in the application to improve the CPU threading part of the code, which led to

Roland Bauerschmidt, Christian Webb

We prove that the truncated correlation functions of the charge and gradient fields associated with the massless sine-Gordon model on $\mathbb{R}^2$ with $\beta=4\pi$ exist for all coupling constants and are equal to those of the chiral densities and vector current of free massive Dirac fermions. This is an instance of Coleman's prediction that the massless

Muhammad Waleed Gondal, Shruti Joshi, Nasim Rahaman, Stefan Bauer

Meta-learning algorithms adapt quickly to new tasks that are drawn from the same task distribution as the training tasks. The mechanism leading to fast adaptation is the conditioning of a downstream predictive model on the inferred representation of the task's underlying data generative process, or \emph{function}. This \emph{meta-representation}, which

Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong

Conventional image classifiers are trained by randomly sampling mini-batches of images. To achieve state-of-the-art performance, practitioners use sophisticated data augmentation schemes to expand the amount of training data available for sampling. In contrast, meta-learning algorithms sample support data, query data, and tasks on each training step. In this

Alessia Ascanelli, Sandro Coriasco, André Suß

We study function-valued solutions of a class of stochastic partial differential equations, involving operators with polynomially bounded coefficients. We consider semilinear equations under suitable parabolicity hypotheses. We provide conditions on the initial data and on the stochastic terms, namely, on the associated spectral measure, so that these mild s

Silvio Ghilardi, Alessandro Gianola, Deepak Kapur

In this paper, the theory of McCarthy's extensional arrays enriched with a maxdiff operation (this operation returns the biggest index where two given arrays differ) is proposed. It is known from the literature that a diff operation is required for the theory of arrays in order to enjoy the Craig interpolation property at the quantifier-free level. Howev

Yufang Cui, Anders Lindquist

Analytic interpolation problems with rationality and derivative constraints are ubiquitous in systems and control. This paper provides a new method for such problems, both in the scalar and matrix case, based on a non-standard Riccati-type equation. The rank of the solution matrix is the same as the degree of the interpolant, thus providing a natural approac

Felix A. Wolf, Malte Schwerhoff, Peter Müller

Modern separation logics allow one to prove rich properties of intricate code, e.g. functional correctness and linearizability of non-blocking concurrent code. However, this expressiveness leads to a complexity that makes these logics difficult to apply. Manual proofs or proofs in interactive theorem provers consist of a large number of steps, often with sub

Jing Xu, Da Ju, Margaret Li, Y-Lan Boureau

Models trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior and unwanted biases. We investigate a variety of methods to mitigate these issues in the context of open-domain generative dialogue models. We introduce a new human-and-model-in-the-loop framewor

Andreas Look, Simona Doneva, Melih Kandemir, Rainer Gemulla

In this paper, we introduce an efficient backpropagation scheme for non-constrained implicit functions. These functions are parametrized by a set of learnable weights and may optionally depend on some input; making them perfectly suitable as a learnable layer in a neural network. We demonstrate our scheme on different applications: (i) neural ODEs with the i

Gonzalo Navarro

The research on indexing repetitive string collections has focused on the same search problems used for regular string collections, though they can make little sense in this scenario. For example, the basic pattern matching query "list all the positions where pattern $P$ appears" can produce huge outputs when $P$ appears in an area shared by many doc

Xiaofei Sun, Zijun Sun, Yuxian Meng, Jiwei Li

The difficulty of generating coherent long texts lies in the fact that existing models overwhelmingly focus on predicting local words, and cannot make high level plans on what to generate or capture the high-level discourse dependencies between chunks of texts. Inspired by human writing processes, where a list of bullet points or a catalog is first outlined,

Halgurd Taher, Alessandro Torcini, Simona Olmi

A synaptic theory of Working Memory (WM) has been developed in the last decade as a possible alternative to the persistent spiking paradigm. In this context, we have developed a neural mass model able to reproduce exactly the dynamics of heterogeneous spiking neural networks encompassing realistic cellular mechanisms for short-term synaptic plasticity. This

Rajaei Khatib, Dror Simon, Michael Elad

The fields of signal and image processing have been deeply influenced by the introduction of deep neural networks. These are successfully deployed in a wide range of real-world applications, obtaining state of the art results and surpassing well-known and well-established classical methods. Despite their impressive success, the architectures used in many of

Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda, Michael Gastegger

In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of ab

Jean-Bernard Lasserre

We consider degree-d forms on the Euclidean unit sphere. We specialize to our setting a genericity result by Nie obtained in a more general framework. We exhibit an homogeneous polynomial Res in the coefficients of f , such that if Res(f) = 0 then all points that satisfy first-and second-order necessary optimality conditions are in fact local minima of f on

Onur Teymur, Jackson Gorham, Marina Riabiz, Chris. J. Oates

Several researchers have proposed minimisation of maximum mean discrepancy (MMD) as a method to quantise probability measures, i.e., to approximate a target distribution by a representative point set. We consider sequential algorithms that greedily minimise MMD over a discrete candidate set. We propose a novel non-myopic algorithm and, in order to both impro

Jian-Rong Li

Denote by $N \subset SL_k$ the subgroup of unipotent upper triangular matrices. In this paper, we show that the dual canonical basis of $\mathbb{C}[N]$ (and base affine spaces) can be parameterized by semi-standard Young tableaux. Moreover, we give an explicit formula for every element in the the dual canonical basis using the data of the corresponding semis

Marc Geiller, Etera R. Livine, Francesco Sartini

We reveal an $\mathfrak{iso}(2,1)$ Poincaré algebra of conserved charges associated with the dynamics of the interior of black holes. The action of these Noether charges integrates to a symmetry of the gravitational system under the Poincaré group ISO$(2,1)$, which allows to describe the evolution of the geometry inside the black hole in terms of geodesics a

Peter G. Casazza, Janet C. Tremain

Edidin [3] proved a fundamental result in phase retrieval: Theorem: A family of orthogonal projections $\{P_i\}_{i=1}^m$ does phase retrieval in $\mathbb{R}^n$ if and only if for every $0\not= x\in \mathbb{R}^n$, the family $\{P_ix\}_{i=1}^m$ spans $\mathbb{R}^n$. The proof of this result relies on Algebraic Geometry and so is inaccessible to many people in

Joosep Jääger, Alisa Pankova

Logic Programming (LP) is a subcategory of declarative programming that is considered to be relatively simple for non-programmers. LP developers focus on describing facts and rules of a logical derivation, and do not need to think about the algorithms actually implementing the derivation. Secure multiparty computation (MPC) is a cryptographic technology that

Jean Lécureux, Mikael de la Salle, Stefan Witzel

We prove that cocompact (and more generally: undistorted) lattices on $\tilde{A}_2$-buildings satisfy Lafforgue's strong property (T), thus exhibiting the first examples that are not related to algebraic groups over local fields. Our methods also give two further results. First, we show that the first $\ell^p$-cohomology of an $\tilde{A}_2$-building vanishes

Dionysios Karagiannis, José Fonseca, Roy Maartens, Stefano Camera

After reionisation, the 21cm emission line of neutral hydrogen within galaxies provides a tracer of dark matter. Next-generation intensity mapping surveys, with the SKA and other radio telescopes, will cover large sky areas and a wide range of redshifts, facilitating their use as probes of primordial non-Gaussianity. {Previous works have shown that the bispe

Andreas Krämer, Jonas Köhler, Frank Noé

Many types of neural network layers rely on matrix properties such as invertibility or orthogonality. Retaining such properties during optimization with gradient-based stochastic optimizers is a challenging task, which is usually addressed by either reparameterization of the affected parameters or by directly optimizing on the manifold. This work presents a

Adam Coogan, Konstantin Karchev, Christoph Weniger

The analysis of optical images of galaxy-galaxy strong gravitational lensing systems can provide important information about the distribution of dark matter at small scales. However, the modeling and statistical analysis of these images is extraordinarily complex, bringing together source image and main lens reconstruction, hyper-parameter optimization, and

Sean Braithwaite, Ethan Buchman, Ismail Khoffi, Igor Konnov

In Tendermint blockchains, the proof-of-stake mechanism and the underlying consensus algorithm entail a dynamic fault model that implies that the active validators (nodes that sign blocks) may change over time, and a quorum of these validators is assumed to be correct only for a limited period of time (called trusting period). The changes of the validator se

L. Margolis, T. Moede

We study the Modular Isomorphism Problem (MIP) for groups of small order based on an improvement of an algorithm described by B. Eick. Our improvement allows to determine quotients $I(kG)/I(kG)^m$ of the augmentation ideal without first computing the full augmentation ideal $I(kG)$. It allows us to verify that the MIP has a positive answer for groups of orde

Jean-Marc Richard

We present a selection of topics with an interplay of hadron and few-body physics. This includes few-nucleon systems, light hypernuclei and quark dynamics for baryons and multiquarks. It is stressed that standard quark models predict very few stable multiquarks.

Hoang Dinh Van, Lander Hermans, Wendy Lowen

We introduce an operad which acts on the Gerstenhaber-Schack complex of a prestack as defined by Dinh Van and Lowen, and which in particular allows us to endow this complex with an underlying $L_{\infty}$-structure. We make use of the operad $\operatorname{Quilt}$ which was used by Hawkins in order to solve the presheaf case. Due to the additional difficulty

Vladimr R. Chechetkin, Vasily V. Lobzin

A method of Transitional Automorphic Mapping of the Genome on Itself (TAMGI) is aimed at combining detection and reconstruction of correlational and quasi-periodic motifs in the viral genomic RNA/DNA sequences. The motifs reconstructed by TAMGI are robust with respect to indels and point mutations and can be tried as putative therapeutic targets. We develope

Gyuwan Kim, Kyunghyun Cho

Despite transformers' impressive accuracy, their computational cost is often prohibitive to use with limited computational resources. Most previous approaches to improve inference efficiency require a separate model for each possible computational budget. In this paper, we extend PoWER-BERT (Goyal et al., 2020) and propose Length-Adaptive Transformer tha

Abraham Gutierrez, Sebastian Müller

Multivariate regression models and ANOVA are probably the most frequently applied methods of all statistical analyses. We study the case where the predictors are qualitative variables, and the response variable is quantitative. In this case, we propose an alternative to the classic approaches that does not assume homoscedasticity but assumes that a Markov ch

Sébastien Ragot

Originality criteria are frequently used to compare assets and, in particular, to assess the validity of intellectual property (IP) rights such as copyright and design rights. In this work, the originality of an asset is formulated as a function of the distances between this asset and its comparands, using concepts of maximum entropy and surprisal analysis.

David Hernandez

We develop the representation theory of shifted quantum affine algebras $\mathcal{U}_q^\mu(\hat{\mathfrak{g}})$ and of their truncations which appeared in the study of quantized K-theoretic Coulomb branches of 3d $N = 4$ SUSY quiver gauge theories. Our approach is based on novel techniques, which are new in the cases of shifted Yangians or ordinary quantum a

Artem Chumachenko, Daniil Gavrilov, Nikita Balagansky, Pavel Kalaidin

In this work, we present a novel approach for simultaneous knowledge transfer and model compression called Weight Squeezing. With this method, we perform knowledge transfer from a teacher model by learning the mapping from its weights to smaller student model weights. We applied Weight Squeezing to a pre-trained text classification model based on BERT-Medium

Rafaela Schroeder, Jiguang He, Markku Juntti

The reconfigurable intelligent surface (RIS) plays an important role in maintaining the connectivity in millimeter wave (mmWave) MIMO systems when the direct channel between the transceivers is blocked. However, it is difficult to acquire the channel state information (CSI), which is essential for the design of RIS phase control matrix and beamforming vector

Radek Janostik, Jan Konecny, Petr Krajča

LCM is an algorithm for enumeration of frequent closed itemsets in transaction databases. It is well known that when we ignore the required frequency, the closed itemsets are exactly intents of formal concepts in Formal Concept Analysis (FCA). We describe LCM in terms of FCA and show that LCM is basically the Close-by-One algorithm with multiple speed-up fea

Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser

The data drawn from biological, economic, and social systems are often confounded due to the presence of unmeasured variables. Prior work in causal discovery has focused on discrete search procedures for selecting acyclic directed mixed graphs (ADMGs), specifically ancestral ADMGs, that encode ordinary conditional independence constraints among the observed

Shaoxiong Ji, Shirui Pan, Pekka Marttinen

Medical code assignment from clinical text is a fundamental task in clinical information system management. As medical notes are typically lengthy and the medical coding system's code space is large, this task is a long-standing challenge. Recent work applies deep neural network models to encode the medical notes and assign medical codes to clinical docu

Florian Lehner, Christian Lindorfer

Let $G$ be a quasi-transitive, locally finite, connected graph rooted at a vertex $o$, and let $c_n(o)$ be the number of self-avoiding walks of length $n$ on $G$ starting at $o$. We show that if $G$ has only thin ends, then the generating function $F_{\mathrm{SAW},o}(z)=\sum_{n \geq 0} c_n(o) z^n$ is an algebraic function. In particular, the connective const

A. Avetisyan, R. Dallakyan, N. Dobrovolski, A. Manukyan

In recent years, the possibility of direct production of the 99mTc isotope (bypassing the parent 99Mo stage) for medical purposes using nuclear reactions on charged particle beams has been actively discussed around the world [1,2]. At A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute), an activity is underway to develop a technology for

Ayesha Hussain

In this paper, we consider the distribution of the continuous paths of Dirichlet character sums modulo prime $q$ on the complex plane. We also find a limiting distribution as $q \rightarrow \infty$ using Steinhaus random multiplicative functions, stating properties of this random process. This is motivated by Kowalski and Sawin's work on Kloosterman path

Xianjing Dong

We generalize the H. Cartan's theory of holomorphic curves for a general open Riemann surface. Besides, a vanishing theorem for jet differentials and a Bloch's theorem for Riemann surfaces are obtained.

Sabrina Kelbij Star, Benjamin Sanderse, Giovanni Stabile, Gianluigi Rozza

A novel reduced order model (ROM) for incompressible flows is developed by performing a Galerkin projection based on a fully (space and time) discrete full order model (FOM) formulation. This 'discretize-then-project' approach requires no pressure stabilization technique (even though the pressure term is present in the ROM) nor a boundary control tec

Zhixin Chen, Mengxiang Lin

The application of reinforcement learning (RL) in robotic control is still limited in the environments with sparse and delayed rewards. In this paper, we propose a practical self-imitation learning method named Self-Imitation Learning with Constant Reward (SILCR). Instead of requiring hand-defined immediate rewards from environments, our method assigns the i

J. Adamczewski-Musch, A. Belyaev, A. Blanco, C. Blume

A feasibility study has been performed in order to investigate the performance of the HADES detector to measure the electromagnetic decays of the hyperon resonances $Σ(1385)^0$, $Λ(1405)$ and $ΛΛ(1520)$ as well as the production of double strange baryon systems $Ξ^-$ and $ΛΛ$ in p+p reactions at a beam kinetic energy of 4.5 GeV. The existing HADES detector w

Alan J. Cain, António Malheiro, Duarte Ribeiro

This paper presents new results on the identities satisfied by the hypoplactic monoid. We show how to embed the hypoplactic monoid of any rank strictly greater than 2 (including infinite rank) into a direct product of copies of the hypoplactic monoid of rank 2. This confirms that all hypoplactic monoids of rank greater than or equal to 2 satisfy exactly the

Hamzeh Keshavarzi

M. J. Beltrán-Meneua et al. \cite{beltran1} and E. Jordá and A. Rodríguez-Arenas \cite{jorda} characterized the (uniformly) mean ergodic composition operators on $H^\infty(\mathbb{D})$ and $H^\infty_ν(\mathbb{D})$, respectively. In this paper, by using the interpolating sequences, we give other necessary and sufficient conditions for the (uniformly) mean erg

Karolis Martinkus, Aurelien Lucchi, Nathanaël Perraudin

Learning system dynamics directly from observations is a promising direction in machine learning due to its potential to significantly enhance our ability to understand physical systems. However, the dynamics of many real-world systems are challenging to learn due to the presence of nonlinear potentials and a number of interactions that scales quadratically

Dhimiter D. Canko, Nikolaos Syrrakos

We present in detail two resummation methods emerging from the application of the Simplified Differential Equations approach to a canonical basis of master integrals. The first one is a method which allows for an easy determination of the boundary conditions, since it finds relations between the boundaries of the basis elements and the second one indicates h

A. Kostenkov, A. Vinokurov, Y. Solovyeva

In current work we continued study of wind parameters of ultraluminous X-ray sources (ULXs) using model grids of extended atmospheres. We present sets of models with temperatures from 18000 up to 56000K as a line equivalent widths (EWs) and their ratios diagrams. The fundamental wind parameters for some spectrally studied ULXs were estimated using EW diagram

Yuxian Meng, Chun Fan, Zijun Sun, Eduard Hovy

Any prediction from a model is made by a combination of learning history and test stimuli. This provides significant insights for improving model interpretability: {\it because of which part(s) of which training example(s), the model attends to which part(s) of a test example}. Unfortunately, existing methods to interpret a model's predictions are only a

Görkem Algan, Ilkay Ulusoy, Şaban Gönül, Banu Turgut

Computer vision systems recently made a big leap thanks to deep neural networks. However, these systems require correctly labeled large datasets in order to be trained properly, which is very difficult to obtain for medical applications. Two main reasons for label noise in medical applications are the high complexity of the data and conflicting opinions of e

Hamzeh Keshavarzi

In this paper, we study (uniformly) mean ergodic composition operators on $H^\infty(\mathbb{B}_n)$. Under some additional assumptions, it is shown that mean ergodic operators have norm convergent iterates in $H^\infty(\mathbb{B}_n)$, and that they are always uniformly mean ergodic.

Martin Tveten, Idris A. Eckley, Paul Fearnhead

Motivated by a condition monitoring application arising from subsea engineering we derive a novel, scalable approach to detecting anomalous mean structure in a subset of correlated multivariate time series. Given the need to analyse such series efficiently we explore a computationally efficient approximation of the maximum likelihood solution to the resultin

Michael T. Roman, Eliza M. -R. Kempton, Emily Rauscher, Caleb K. Harada

Using a general circulation model (GCM), we investigate trends in simulated hot Jupiter atmospheres for a range of irradiation temperatures (1,500 - 4,000 K), surface gravities (10 and 40 m s-2), and cloud conditions. Our models include simplified temperature-dependent clouds with radiative feedback and show how different cloud compositions, vertical thickne