December 2020 arXiv papers — page 121

Qing Gao, Yungui Gong, Zhu Yi

The production of primordial black hole (PBH) dark matter (DM) and the generation of scalar induced secondary gravitational waves by using the enhancement mechanism with a peak function in the non-canonical kinetic term in natural inflation is discussed. We show explicitly that the power spectrum for the primordial curvature perturbation can be enhanced at $

Sabrina Pasterski, Herman Verlinde

We build on the observation by Hawking, Perry and Strominger that a global black hole space-time supports a large number of soft hair degrees of freedom to shed new light on the firewall argument by Almheiri, Marolf, Polchinski, and Sully. We propose that the soft hair Goldstone mode is encoded in a classical transition function that connects the asymptotic

Ying-Cheng Lai

In applications of nonlinear and complex dynamical systems, a common situation is that the system can be measured but its structure and the detailed rules of dynamical evolution are unknown. The inverse problem is to determine the system equations and structure based solely on measured time series. Recently, methods based on sparse optimization have been dev

Simon Syga, Diana David-Rus, Yannik Schälte, Michael Meyer-Hermann

Countries around the world implement nonpharmaceutical interventions (NPIs) to mitigate the spread of COVID-19. Design of efficient NPIs requires identification of the structure of the disease transmission network. We here identify the key parameters of the COVID-19 transmission network for time periods before, during, and after the application of strict NPI

Florian Beiser, Brendan Keith, Simon Urbainczyk, Barbara Wohlmuth

We introduce adaptive sampling methods for stochastic programs with deterministic constraints. First, we propose and analyze a variant of the stochastic projected gradient method where the sample size used to approximate the reduced gradient is determined on-the-fly and updated adaptively. This method is applicable to a broad class of expectation-based risk

Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki

Fooling images are a potential threat to deep neural networks (DNNs). These images are not recognizable to humans as natural objects, such as dogs and cats, but are misclassified by DNNs as natural-object classes with high confidence scores. Despite their original design concept, existing fooling images retain some features that are characteristic of the tar

Daniel S. P. Tanner, Eric Bousquet, Pierre-Eymeric Janolin

In this work we present a new method for the calculation of the electrostrictive properties of materials using density functional theory. The method relies on the thermodynamical equivalence, in a dielectric, of the quadratic mechanical responses (stress or strain) to applied electric stimulus (electric or polarisation fields) to the strain or stress depende

Viswanatha Reddy Gajjala, Sai Prasanna Teja Reddy, Snehasis Mukherjee, Shiv Ram Dubey

Micro-expression has emerged as a promising modality in affective computing due to its high objectivity in emotion detection. Despite the higher recognition accuracy provided by the deep learning models, there are still significant scope for improvements in micro-expression recognition techniques. The presence of micro-expressions in small-local regions of t

Qiantan Hong, Margarita Davydova, Patrick J Ledwith, Leonid Levitov

Electron hydrodynamics gives rise to surprising correlated behaviors in which electrons "cooperate" to quench dissipation and reduce the electric fields needed to sustain the flow. Such collective "free" flows are usually expected at the hydrodynamic lengthscales exceeding the electron-electron scattering mean free path $\ell_{\rm ee}$. Here

Michael Laskin, Luke Metz, Seth Nabarro, Mark Saroufim

Deep learning models trained on large data sets have been widely successful in both vision and language domains. As state-of-the-art deep learning architectures have continued to grow in parameter count so have the compute budgets and times required to train them, increasing the need for compute-efficient methods that parallelize training. Two common approac

Bas Janssens, Leonid Ryvkin, Cornelia Vizman

We construct an $L_\infty$-algebra on the truncated canonical homology complex of a symplectic manifold, which naturally projects to the universal central extension of the Lie algebra of Hamiltonian vector fields.

Chunhe Xiong, Sunho Kim, Daowen Qiu, Asutosh Kumar

We propose a framework to characterize entanglement with quantum discord, both asymmetric and symmetric, over state extensions. In particular, we show that the minimal Bures distance of discord over state extensions is equivalent to Bures distance of entanglement. This equivalence places quantum discord at a more primitive position than entanglement conceptu

Jean-Baptiste Thomazo, Benjamin Le Révérend, Léa-Laetitia Pontani, Alexis Prevost

To mimic the mechanical response of passive biological cilia in complex fluids, we study the bending dynamics of an anchored elastic fiber submitted to a dilute granular suspension under shear. We show that the bending fluctuations of the fiber accurately encode minute variations of the granular suspension concentration. Indeed, besides the stationary bendin

Alexandre Barthelemy, Sandrine Codis, Francis Bernardeau

In the context of tomographic cosmic shear surveys, a theoretical model for the one-point statistics of the aperture mass (Map) is developed. This formalism is based on the application of the large deviation principle to the projected matter density field and more specifically to the angular aperture masses. The latter holds the advantage of being an observa

R. Emamian, A. F. Kord, A. Ghaani, B. Azadegan

In the present work, we investigate the effects of magnetic field on expanding hot and dense nuclear matter as an ideal fluid. We consider QGP, on the particular case of a (1 + 2) dimensional longitudinally boost-invariant fluid expansion, in the background of an inhomogeneous magnetic field that is generated by external sources. We assume the magnetic field

Alexander I. Cowen-Rivers, Wenlong Lyu, Rasul Tutunov, Zhi Wang

In this work we rigorously analyse assumptions inherent to black-box optimisation hyper-parameter tuning tasks. Our results on the Bayesmark benchmark indicate that heteroscedasticity and non-stationarity pose significant challenges for black-box optimisers. Based on these findings, we propose a Heteroscedastic and Evolutionary Bayesian Optimisation solver (

Maryam Gharamah Ali Alshehri, Eugene Lytvynov

Let $X$ be a locally compact Polish space and $\sigma$ a nonatomic reference measure on $X$ (typically $X=\mathbb R^d$ and $\sigma$ is the Lebesgue measure). Let $X^2\ni(x,y)\mapsto\mathbb K(x,y)\in\mathbb C^{2\times 2}$ be a $2\times 2$-matrix-valued kernel that satisfies $\mathbb K^T(x,y)=\mathbb K(y,x)$. We say that a point process $\mu$ in $X$ is hafnian

Mainak Pal, Laetitia Bettmann, Andreas Kreisel, P. J. Hirschfeld

Correlated electron systems, particularly iron-based superconductors, are extremely sensitive to strain, which inevitably occurs in the crystal growth process. Built-in strain of this type has been proposed as a possible explanation for experiments where nematic order has been observed at high temperatures corresponding to the nominally tetragonal phase of i

Mikhail Anikushin

We study asymptotically compact nonautonomous dynamical systems given by abstract cocycles in Banach spaces. Our main assumptions are given by a squeezing property in a quadratic cone field (given by a family of indefinite quadratic Lyapunov-like functionals) and asymptotic compactness. Under such conditions it is possible to reconstruct foliations as in the

Zhengyang Yu, Song Wu, Zhihao Dou, Erwin M. Bakker

Due to its effectivity and efficiency, deep hashing approaches are widely used for large-scale visual search. However, it is still challenging to produce compact and discriminative hash codes for images associated with multiple semantics for two main reasons, 1) similarity constraints designed in most of the existing methods are based upon an oversimplified

Boris Eng

Technically speaking, the transcendental syntax is about designing logics with a computational foundation. It suggests a new framework for proof theory where logic (proofs, formulas, truth, ...) is no more primitive but computation is. All the logical entities and activities will be presented as formatting/structuring on a given model of computation which sh

Shouvanik Chakrabarti, Rajiv Krishnakumar, Guglielmo Mazzola, Nikitas Stamatopoulos

We give an upper bound on the resources required for valuable quantum advantage in pricing derivatives. To do so, we give the first complete resource estimates for useful quantum derivative pricing, using autocallable and Target Accrual Redemption Forward (TARF) derivatives as benchmark use cases. We uncover blocking challenges in known approaches and introd

Yuval Dagan, Gil Kur

We present an asymptotically optimal $(\epsilon,\delta)$ differentially private mechanism for answering multiple, adaptively asked, $\Delta$-sensitive queries, settling the conjecture of Steinke and Ullman [2020]. Our algorithm has a significant advantage that it adds independent bounded noise to each query, thus providing an absolute error bound. Additional

Yuezun Li, Yiming Li, Baoyuan Wu, Longkang Li

Recently, backdoor attacks pose a new security threat to the training process of deep neural networks (DNNs). Attackers intend to inject hidden backdoors into DNNs, such that the attacked model performs well on benign samples, whereas its prediction will be maliciously changed if hidden backdoors are activated by the attacker-defined trigger. Existing backdo

Charline Le Lan, Laurent Dinh

Thanks to the tractability of their likelihood, several deep generative models show promise for seemingly straightforward but important applications like anomaly detection, uncertainty estimation, and active learning. However, the likelihood values empirically attributed to anomalies conflict with the expectations these proposed applications suggest. In this

S. I. Dimitrov

In this paper we show that, for any fixed $1<c<967/805$, every sufficiently large positive number $N$ and a small constant $\varepsilon>0$, the diophantine inequality \begin{equation*} |p_1^c+p_2^c+p_3^c+p_4^c-N|<\varepsilon \end{equation*} has a solution in prime numbers $p_1,\,p_2,\,p_3,\,p_4$, such that $p_1=x^2 + y^2 +1$.

Kamiokande Collaboration, K. Abe, C. Bronner, Y. Hayato

Due to a very low production rate of electron anti-neutrinos ($\bar{\nu}_e$) via nuclear fusion in the Sun, a flux of solar $\bar{\nu}_e$ is unexpected. An appearance of $\bar{\nu}_e$ in solar neutrino flux opens a new window for the new physics beyond the standard model. In particular, a spin-flavor precession process is expected to convert an electron neut

Tsai-Min Chen, Yuan-Hong Tsai, Huan-Hsin Tseng, Kai-Chun Liu

A combination of cloud-based deep learning (DL) algorithms with portable/wearable (P/W) devices has been developed as a smart heath care system to support automatic cardiac arrhythmias (CAs) classification using electrocardiography (ECG). However, long-term and continuous ECG monitoring is challenging because of limitations of batteries and transmission band

Sitender Pratap Kashyap

We provide a prescription for computing two-point tree amplitudes in the pure spinor formalism that are finite and agree with the corresponding expression in the field theories. In [arXiv:1906.06051v1-arXiv:1909.03672v3], same results were presented for bosonic strings and it was mentioned they can be generalized to superstrings. The pure spinor formalism is

Adepu Ravi Sankar, Yash Khasbage, Rahul Vigneswaran, Vineeth N Balasubramanian

Loss landscape analysis is extremely useful for a deeper understanding of the generalization ability of deep neural network models. In this work, we propose a layerwise loss landscape analysis where the loss surface at every layer is studied independently and also on how each correlates to the overall loss surface. We study the layerwise loss landscape by st

B. N. Khabibullin

The maximum of the modulus of a meromorphic function cannot be restricted from above by the Nevanlinna characteristic of this meromorphic function. But integrals from the logarithm of the module of a meromorphic function allow similar restrictions from above. This is illustrated by one of the important theorems of Rolf Nevanlinna in the classical monograph b

Ningyuan Chen, Anran Li, Shuoguang Yang

We consider the revenue maximization problem for an online retailer who plans to display in order a set of products differing in their prices and qualities. Consumers have attention spans, i.e., the maximum number of products they are willing to view, and inspect the products sequentially before purchasing a product or leaving the platform empty-handed when

ATLAS Collaboration

A search for new phenomena with top quark pairs in final states with one isolated electron or muon, multiple jets, and large missing transverse momentum is performed. Signal regions are designed to search for two-, three-, and four-body decays of the directly pair-produced supersymmetric partner of the top quark (stop). Additional signal regions are designed

Manuel Araújo

We introduce a string diagram calculus for strict $4$-categories and use it to prove that given a cofinite inclusion of $4$-categorical presentations, the induced restriction functor on mapping spaces to a fixed target strict $4$-category is a fibration of strict $4$-groupoids.

Jae Shin Yoon, Lingjie Liu, Vladislav Golyanik, Kripasindhu Sarkar

We present a new pose transfer method for synthesizing a human animation from a single image of a person controlled by a sequence of body poses. Existing pose transfer methods exhibit significant visual artifacts when applying to a novel scene, resulting in temporal inconsistency and failures in preserving the identity and textures of the person. To address

Julian Fischer, Katharina Hopf, Michael Kniely, Alexander Mielke

We establish global-in-time existence results for thermodynamically consistent reaction-(cross-)diffusion systems coupled to an equation describing heat transfer. Our main interest is to model species-dependent diffusivities, while at the same time ensuring thermodynamic consistency. A key difficulty of the non-isothermal case lies in the intrinsic presence

Pavel Osipov

Let $(M,\nabla,g)$ be a Hessian manifold. Then the total space of the tangent bundle $TM$ can be endowed with a K\"ahler structure $\left(I,{\cal g}\right)$. We say that a homogeneous Hessian manifold is a Hessian manifold $(M,\nabla,g)$ endowed with a transitive action of a group $G$ preserving $\nabla$ and $g$. If $(M,\nabla,g)$ is a simply connected homog

Yeongwoo Kim, Ezeddin Al Hakim, Johan Haraldson, Henrik Eriksson

In the resource management of wireless networks, Federated Learning has been used to predict handovers. However, non-independent and identically distributed data degrade the accuracy performance of such predictions. To overcome the problem, Federated Learning can leverage data clustering algorithms and build a machine learning model for each cluster. However

Jack Bowden, Bjoern Bornkamp, Ekkehard Glimm, Frank Bretz

Causal inference methods are gaining increasing prominence in pharmaceutical drug development in light of the recently published addendum on estimands and sensitivity analysis in clinical trials to the E9 guideline of the International Council for Harmonisation. The E9 addendum emphasises the need to account for post-randomization or `intercurrent&#39; event

Yuya Kodama

We prove that the braided Thompson group $BV$ has a linear divergence function. By the work of Dru\c{t}u, Mozes, and Sapir, this leads none of asymptotic cones of $BV$ has a cut-point.

Ya-Dong Wu, Ge Bai, Giulio Chiribella, Nana Liu

Continuous-variable quantum information, encoded into infinite-dimensional quantum systems, is a promising platform for the realization of many quantum information protocols, including quantum computation, quantum metrology, quantum cryptography, and quantum communication. To successfully demonstrate these protocols, an essential step is the certification of

Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa

Existing Bluetooth-based Private Contact Tracing (PCT) systems can privately detect whether people have come into direct contact with COVID-19 patients. However, we find that the existing systems lack functionality and flexibility, which may hurt the success of the contact tracing. Specifically, they cannot detect indirect contact (e.g., people may be expose

Théophile Cantelobre, Benjamin Guedj, María Pérez-Ortiz, John Shawe-Taylor

Many practical machine learning tasks can be framed as Structured prediction problems, where several output variables are predicted and considered interdependent. Recent theoretical advances in structured prediction have focused on obtaining fast rates convergence guarantees, especially in the Implicit Loss Embedding (ILE) framework. PAC-Bayes has gained int

Yajun Ma, Nanqing Ding, Yafeng Zhang, Jiangsheng Hu

We give a new characterization of silting subcategories in the stable category of a Frobenius extriangulated category, generalizing the result of Di et al. (J. Algebra 525 (2019) 42-63) about the Auslander-Reiten type correspondence for silting subcategories over triangulated categories. More specifically, for any Frobenius extriangulated category $\mathcal{

Tim Roith, Leon Bungert

Tackling semi-supervised learning problems with graph-based methods has become a trend in recent years since graphs can represent all kinds of data and provide a suitable framework for studying continuum limits, e.g., of differential operators. A popular strategy here is $p$-Laplacian learning, which poses a smoothness condition on the sought inference funct

Paloma Sodhi, Michael Kaess, Mustafa Mukadam, Stuart Anderson

We&#39;re interested in the problem of estimating object states from touch during manipulation under occlusions. In this work, we address the problem of estimating object poses from touch during planar pushing. Vision-based tactile sensors provide rich, local image measurements at the point of contact. A single such measurement, however, contains limited inf

Arthur Soulié, Akihiro Takano

In 1996, Tong, Yang and Ma defined a family of representations of the braid group which have the same dimensions as the (unreduced) Burau representations but are not equivalent. The Burau representation was defined homologically and extended to the string links in several ways. In this paper, using the method of Silver and Williams, we extend the family of t

Jinyuan Jia, Yupei Liu, Xiaoyu Cao, Neil Zhenqiang Gong

Data poisoning attacks and backdoor attacks aim to corrupt a machine learning classifier via modifying, adding, and/or removing some carefully selected training examples, such that the corrupted classifier makes incorrect predictions as the attacker desires. The key idea of state-of-the-art certified defenses against data poisoning attacks and backdoor attac

Stefano Almi, Ulisse Stefanelli

Topology optimization is concerned with the identification of optimal shapes of deformable bodies with respect to given target functionals. The focus of this paper is on a topology optimization problem for a time-evolving elastoplastic medium under kinematic hardening. We adopt a phase-field approach and argue by subsequent approximations, first by discretiz

Madhu Mishra, Bindusar Sahoo

In this paper we derive the most general curvature squared action coupled to an arbitrary number of vector multiplets in four dimensional $N=2$ supergravity using the dilaton Weyl multiplet. The action that we derive is encoded in a single holomorphic prepotential.

Debraj Das

In this article, we are interested in the high dimensional normal approximation of $T_n =\Big(\sum_{i=1}^{n}X_{i1}/\Big(\sqrt{\sum_{i=1}^{n}X_{i1}^2}\Big),\dots,$ $\sum_{i=1}^{n}X_{ip}/\Big(\sqrt{\sum_{i=1}^{n}X_{ip}^2}\Big)\Big)$ in $\mathcal{R}^p$ uniformly over the class of hyper-rectangles $\mathcal{A}^{re}=\{\prod_{j=1}^{p}[a_j,b_j]\cap\mathcal{R}:-\inf

Konstantinos Meichanetzidis, Alexis Toumi, Giovanni de Felice, Bob Coecke

Natural language processing (NLP) is at the forefront of great advances in contemporary AI, and it is arguably one of the most challenging areas of the field. At the same time, in the area of Quantum Computing (QC), with the steady growth of quantum hardware and notable improvements towards implementations of quantum algorithms, we are approaching an era whe

Dengpan Fu, Dongdong Chen, Jianmin Bao, Hao Yang

In this paper, we present a large scale unlabeled person re-identification (Re-ID) dataset &#34;LUPerson&#34; and make the first attempt of performing unsupervised pre-training for improving the generalization ability of the learned person Re-ID feature representation. This is to address the problem that all existing person Re-ID datasets are all of limited

B. V. Ivanov, V. S. Pantuev, A. N. Bukin, A. A. Semenov

The paper describes methods and presents results of the Troitsk Nu-mass experiment spectrometer cleanup, which inner volume (40 m3) and surfaces (160 m2) was contaminated by 4.4 GBq of tritium. The Troitsk Nu-mass experiment of Institute for Nuclear Research of Russian Academy of Sciences, Moscow, is designed to measure the spectrum of electrons from tritium

Alessandro Ferreri, Matteo Santandrea, Michael Stefszky, Kai H. Luo

Nonlinear SU(1,1) interferometers are fruitful and promising tools for spectral engineering and precise measurements with phase sensitivity below the classical bound. Such interferometers have been successfully realized in bulk and fiber-based configurations. However, rapidly developing integrated technologies provide higher efficiencies, smaller footprints,

Ran Gu, Gregory Gutin, Shasha Li, Yongtang Shi

Bang-Jensen, Bessy, Havet and Yeo showed that every digraph of independence number at most 2 and arc-connectivity at least 2 has an out-branching $B^+$ and an in-branching $B^-$ which are arc-disjoint (such two branchings are called a {\it good pair}), which settled a conjecture of Thomassen for digraphs of independence number 2. They also proved that every

Fabio Bonassi, Marcello Farina, Riccardo Scattolini

The idea of using Feed-Forward Neural Networks (FFNNs) as regression functions for Nonlinear AutoRegressive eXogenous (NARX) models, leading to models herein named Neural NARXs (NNARXs), has been quite popular in the early days of machine learning applied to nonlinear system identification, owing to their simple structure and ease of application to control d

Ahcène Boubekki, Michael Kampffmeyer, Robert Jenssen, Ulf Brefeld

Deep embedded clustering has become a dominating approach to unsupervised categorization of objects with deep neural networks. The optimization of the most popular methods alternates between the training of a deep autoencoder and a k-means clustering of the autoencoder&#39;s embedding. The diachronic setting, however, prevents the former to benefit from valu

Valentin Poirot, Olaf Landsiedel

The last decade saw an emergence of Synchronous Transmissions (ST) as an effective communication paradigm in low-power wireless networks. Numerous ST protocols provide high reliability and energy efficiency in normal wireless conditions, for a large variety of traffic requirements. Recently, with the EWSN dependability competitions, the community pushed ST t

Hugo Parlier, Yunhui Wu, Yuhao Xue

In this note we show that the expected value of the separating systole of a random surface of genus $g$ with respect to Weil-Petersson volume behaves like $2\log g $ as the genus goes to infinity. This is in strong contrast to the behavior of the expected value of the systole which, by results of Mirzakhani and Petri, is independent of genus.

David Ginzburg, David Soudry

We consider the residues at the poles in the right half plane of Eisenstein series, on symplectic groups, or their double covers, induced from Speh representations. We show that for each such pole, there is a unique maximal nilpotent orbit, attached to Fourier coefficients admitted by the corresponding residual representation. We find this orbit in each case

Soo-Min Choi, Hyun Min Lee, Bin Zhu

We consider a novel mechanism to realize exothermic dark matter with dark mesons in the limit of approximate flavor symmetry in a dark QCD. We introduce a local dark $U(1)&#39;$ symmetry to communicate between dark mesons and the Standard Model via $Z&#39;$ portal by partially gauging the dark flavor symmetry with flavor-dependent charges for cancelling chir

Yilin Zhao, Zhuosheng Zhang, Hai Zhao

Multi-choice Machine Reading Comprehension (MRC) as a challenge requires models to select the most appropriate answer from a set of candidates with a given passage and question. Most of the existing researches focus on the modeling of specific tasks or complex networks, without explicitly referring to relevant and credible external knowledge sources, which a

Piotr Kicki, Tomasz Gawron, Krzysztof Ćwian, Mete Ozay

Being able to rapidly respond to the changing scenes and traffic situations by generating feasible local paths is of pivotal importance for car autonomy. We propose to train a deep neural network (DNN) to plan feasible and nearly-optimal paths for kinematically constrained vehicles in small constant time. Our DNN model is trained using a novel weakly supervi

Lilia Anguelova

We study rapid-turn trajectories in a class of two-field cosmological models, whose scalar manifold is the Poincaré disk. Background solutions in multi-field inflation, with field-space trajectories exhibiting sharp turns, can seed primordial black hole (PBH) formation. We investigate a class of exact solutions with hidden symmetry and show that they exhibit

Víctor Bucarey, Martine Labbé, Juan M. Morales, Salvador Pineda

This paper proposes a polynomial-time algorithm to construct the monotone stepwise curve that minimizes the sum of squared errors with respect to a given cloud of data points. The fitted curve is also constrained on the maximum number of steps it can be composed of and on the minimum step length. Our algorithm relies on dynamic programming and is built on th

P. P. Kanevska, S. N. Volkov

The theoretical study of deformability of special sequence of DNA double helix TATA-box is presentated. The paper elaborates on the mechanisms of abnormal deformation of DNA TATA-box double helix that cannot be explained using the standard mechanical model of polymer molecules (WLC) and needs more detailed modeling. Analyzing of DNA TATA-box deformation it i

Eef van Beveren, George Rupp

The importance of $S$-matrix unitarity in realistic meson spectroscopy is reviewed, both its historical development and more recent applications. First the effects of imposing $S$-matrix unitarity on meson resonances is demonstrated in both the elastic and the inelastic case. Then, the static quark model is revisited and its theoretical as well as phenomenol

Jean-Pierre Serre

We review the properties of the finite Coxeter groups which are most useful for applications to cohomological invariants, namely their classes of involutions and their "cubes" (abelian subgroups generated by reflections).

Antoine Marie, Hugh G. A. Burton, Pierre-François Loos

We explore the non-Hermitian extension of quantum chemistry in the complex plane and its link with perturbation theory. We observe that the physics of a quantum system is intimately connected to the position of complex-valued energy singularities, known as exceptional points. After presenting the fundamental concepts of non-Hermitian quantum chemistry in the

Marthe Bonamy, Nicolas Bousquet, Michał Pilipczuk, Paweł Rzążewski

A hereditary class of graphs $\mathcal{G}$ is \emph{$\chi$-bounded} if there exists a function $f$ such that every graph $G \in \mathcal{G}$ satisfies $\chi(G) \leq f(\omega(G))$, where $\chi(G)$ and $\omega(G)$ are the chromatic number and the clique number of $G$, respectively. As one of the first results about $\chi$-bounded classes, Gy\'{a}rf\'{a}s prove

Giovanni A. Oakes, Jingyu Duan, John J. L. Morton, Alpha Lee

Spin qubits in quantum dots are a compelling platform for fault-tolerant quantum computing due to the potential to fabricate dense two-dimensional arrays with nearest neighbour couplings, a requirement to implement the surface code. However, due to the proximity of the surface gate electrodes, cross-coupling capacitances can be substantial, making it difficu

Ophelia Frotscher, Roland Herzog, Markus Richter

Decreasing the time required for accurate thermodynamic property measurements is extremely desirable for model development that can respond to the needs of science and industry within a short time frame. Here, we demonstrate the application of optimal experimental design to measurements of thermodynamic properties. The technique is exemplified using the fitt

Minchen Li, Danny M. Kaufman, Chenfanfu Jiang

We extend the incremental potential contact (IPC) model for contacting elastodynamics to resolve systems composed of codimensional DOFs in arbitrary combination. This enables a unified, interpenetration-free, robust, and stable simulation framework that couples codimension-0,1,2, and 3 geometries seamlessly with frictional contact. Extending IPC to thin stru

Zhaokai Wang, Renda Bao, Qi Wu, Si Liu

When describing an image, reading text in the visual scene is crucial to understand the key information. Recent work explores the TextCaps task, i.e. image captioning with reading Optical Character Recognition (OCR) tokens, which requires models to read text and cover them in generated captions. Existing approaches fail to generate accurate descriptions beca

Vera-Maria Enckell, Sami Nurmi, Syksy Rasanen, Eemeli Tomberg

We study Higgs inflation in the Palatini formulation with the renormalisation group improved potential in the case when loop corrections generate a feature similar to an inflection point. Assuming that there is a threshold correction for the Higgs quartic coupling $λ$ and the top Yukawa coupling $y_t$, we scan the three-dimensional parameter space formed by

J. K. Dewhurst, S. Shallcross, P. Elliott, S. Eisebitt

The dynamics of ultrafast demagnetisation in 3$d$ magnets is complicated by the presence of both spin ${\v S}$ and orbital ${\v L}$ angular momentum, with the microscopic mechanism by which the magnetic moment is redistributed to the lattice, and at what time scales, yet to be resolved. Employing state-of-the-art time dependent density function theory we dis

Saibo Geng, Diego Antognini

Multi-document summaritazion is the process of taking multiple texts as input and producing a short summary text based on the content of input texts. Up until recently, multi-document summarizers are mostly supervised extractive. However, supervised methods require datasets of large, paired document-summary examples which are rare and expensive to produce. I

M. Litsgård, K. Nyström

In this paper we develop a potential theory for strongly degenerate parabolic operators of the form \[ \mathcal{L}:=\nabla_X\cdot(A(X,Y,t)\nabla_X)+X\cdot\nabla_{Y}-\partial_t, \] in unbounded domains of the form \[ Ω=\{(X,Y,t)=(x,x_{m},y,y_{m},t)\in\mathbb R^{m-1}\times\mathbb R\times\mathbb R^{m-1}\times\mathbb R\times\mathbb R\mid x_m>ψ(x,y,y_m,t)\}, \] w

Yaniv Shulman

Modern neural network architectures typically have many millions of parameters and can be pruned significantly without substantial loss in effectiveness which demonstrates they are over-parameterized. The contribution of this work is two-fold. The first is a method for approximating a multivariate Bernoulli random variable by means of a deterministic and dif

Samuel Evington

We obtain an improved upper bound for the nuclear dimension of extensions of $\mathcal{O}_\infty$-stable $\rm{C}^*$-algebras. In particular, we prove that the nuclear dimension of a full extension of an $\mathcal{O}_\infty$-stable $\rm{C}^*$-algebra by a stable AF algebra is one.

Shengchen Li, Yinji Jing, György Fazekas

In this paper, the dataset used for the data challenge organised by Conference on Sound and Music Technology (CSMT) is introduced. The CSMT data challenge requires participants to identify whether a given piece of melody is generated by computer or is composed by human. The dataset is formed by two parts: development dataset and evaluation dataset. The devel

Vasileios I. Kiosses

In this work, based on a recently introduced localization scheme for scalar fields, we argue that the geometry of the space-time, where the particle states of a scalar field are localized, is intimately related to the quantum entanglement of these states. More specifically, we show that on curved space-time can only be localized entangled states, while separ

Sigtryggur Hauksson, Sangyong Jeon, Charles Gale

Penetrating probes in heavy-ion collisions, like jets and photons, are sensitive to the transport coefficients of the produced quark-gluon plasma, such as shear and bulk viscosity. Quantifying this sensitivity requires a detailed understanding of photon emission and jet-medium interaction in a non-equilibrium plasma. Up to now, such an understanding has been

Arthur S. de Sena, Pedro H. J. Nardelli, Daniel B. da Costa, F. Rafael M. Lima

A dual-polarized intelligent reflecting surface (IRS) can contribute to a better multiplexing of interfering wireless users. In this paper, we use this feature to improve the performance of dual-polarized massive multiple-input multiple-output (MIMO) with non-orthogonal multiple access (NOMA) under imperfect successive interference cancellation (SIC). By con

Francisco Chaves

The main goal of this paper is the analytic classification of the germs of singular foliations generated, up to an analytic change of coordinates, by the germs of vector fields of form the $x\partial_x+\sum_{i=1}^{n}a_i(x,\mathbf{z})\partial_{z_i}$, where $a_i(x,\mathbf{z})$ is a germ of analytic function with $a_i(x,0)=0$. We focus on the connection with th

Maren Hackenberg, Marlon Grodd, Clemens Kreutz, Martina Fischer

Differentiable programming has recently received much interest as a paradigm that facilitates taking gradients of computer programs. While the corresponding flexible gradient-based optimization approaches so far have been used predominantly for deep learning or enriching the latter with modeling components, we want to demonstrate that they can also be useful

Kangqiao Liu, Liu Ziyin, Masahito Ueda

In the vanishing learning rate regime, stochastic gradient descent (SGD) is now relatively well understood. In this work, we propose to study the basic properties of SGD and its variants in the non-vanishing learning rate regime. The focus is on deriving exactly solvable results and discussing their implications. The main contributions of this work are to de

André Carvalho

We describe the endomorphisms of the direct product of two free groups of finite rank and obtain conditions for which the subgroup of fixed points is finitely generated and we do the same for periodic points. We also describe the automorphisms of such a group and solve the three Whitehead problems for automorphisms, monomorphisms and endomorphisms for groups

Karina González-López, Eran Bouchbinder, Edan Lerner

Quantifying mechanical disorder in solids, either disordered crystals or glassy solids, and understanding its range of variability are of prime importance, e.g.~for discovering structure-properties relations. The bounds on the degree of mechanical fluctuations in disordered solids and how those depend on solids formation history remain unknown. Here, we stud

Debottam Bhattacharjee, Anindita Bhadra

Interaction with its immediate environment determines the ecology of an organism. Species present in any habitat, wild or urban, may face extreme pressure due to sudden perturbations. When such disturbances are unpredictable, it becomes more challenging to tackle. Implementation of specific strategies is therefore essential for different species to overcome

H. Mohseni Sadjadi, H. Yazdani Ahmadabadi

Flavor transitions of Neutrinos with a nonstandard interaction are studied. A scalar field is conformally coupled to matter and neutrinos. This interaction alters the neutrino effective mass and its wavefunction leading to a damping factor, causing deficits in the probability densities and affecting the oscillation phase. As the matter density determines the

Xi Chen, Bishal Deb, Alexander Dyachenko, Tomack Gilmore

We exhibit a lower-triangular matrix of polynomials $T(a,c,d,e,f,g)$ in six indeterminates that appears empirically to be coefficientwise totally positive, and which includes as a special case the Eulerian triangle. We prove the coefficientwise total positivity of $T(a,c,0,e,0,0)$, which includes the reversed Stirling subset triangle.

Arkaitz Bidaurrazaga, Aritz Pérez, Marco Capó

Currently the amount of data produced worldwide is increasing beyond measure, thus a high volume of unsupervised data must be processed continuously. One of the main unsupervised data analysis is clustering. In streaming data scenarios, the data is composed by an increasing sequence of batches of samples where the concept drift phenomenon may happen. In this

Hiroyuki Tajima, Shoichiro Tsutsui, Takahiro M. Doi, Kei Iida

We investigate many-body properties of equally populated three-component fermions with attractive three-body contact interaction in one dimension. A diagrammatic approach suggests the possible occurrence of Cooper triples at low temperature, which are three-body counterparts of Cooper pairs with a two-body attraction. We develop a minimal framework that brid

Andreas Brandsæter, Ingrid K. Glad

This paper proposes a novel approach to explain the predictions made by data-driven methods. Since such predictions rely heavily on the data used for training, explanations that convey information about how the training data affects the predictions are useful. The paper proposes a novel approach to quantify how different data-clusters of the training data af

Geoff Harris

Combinatorial problems stated as Constraint Satisfaction Problems (CSP) are examined. It is shown by example that any algorithm designed for the original CSP, and involving the AllDifferent constraint, has at least the same level of efficacy when simultaneously applied to both the original and its complementary problem. The 1-to-1 mapping employed to transfo

Lingjuan Lyu, Han Yu, Xingjun Ma, Chen Chen

As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (AI) models is facing efficiency and privacy challenges. Recently, federated learning (FL) has emerged as an alternative solution and continue to thrive in this new reality. Existin

Pablo Baladrón Rodríguez, Veronika Chobanova, Xabier Cid Vidal, Vladimir Gligorov

A method for calibrating the momentum scale in a particle physics detector is described. The method relies on the determination of the masses of the final state particles in two-body decays of neutral particles, which can then be used to obtain corrections in the momentum scale. A modified version of the Armenteros-Podolanski plot and the $K_S^0 \to \pi^+ \p

Dennis Aumiller, Satya Almasian, Sebastian Lackner, Michael Gertz

The growing complexity of legal cases has lead to an increasing interest in legal information retrieval systems that can effectively satisfy user-specific information needs. However, such downstream systems typically require documents to be properly formatted and segmented, which is often done with relatively simple pre-processing steps, disregarding topical