May 2022 arXiv papers — page 66

Stuart Kauffman

Substantial grounds exist to doubt the universal validity of the Newtonian Paradigm that requires a pre-stated, fixed phase space. Therefore, the Second Law of Thermodynamics, stated only for fixed phase spaces, is also in doubt. The validity of the Newtonian Paradigm may stop at the onset of evolving life. Living cells and organisms are Kantian Wholes that

Adrián Inés, Andrés Díaz-Pinto, César Domínguez, Jónathan Heras

The development of mobile and on the edge applications that embed deep convolutional neural models has the potential to revolutionise biomedicine. However, most deep learning models require computational resources that are not available in smartphones or edge devices; an issue that can be faced by means of compact models. The problem with such models is that

Aditya Chandra Mandal, Manisha, Abhijeet Phatak, Zeev Zalevsky

The wavefront is scrambled when coherent light propagates through a random scattering medium and which makes direct use of the conventional optical methods ineffective. In this paper, we propose and demonstrate a structured light illumination for imaging through an opaque scattering layer. Proposed technique is reference free and capable to recover the compl

Xiao Wang, Zhe Chen, Bo Jiang, Jin Tang

To track the target in a video, current visual trackers usually adopt greedy search for target object localization in each frame, that is, the candidate region with the maximum response score will be selected as the tracking result of each frame. However, we found that this may be not an optimal choice, especially when encountering challenging tracking scena

Hai-Ming Zhang, Yi-Yun Huang, Jian-He Zheng, Ruo-Yu Liu

It is usually thought that long-duration gamma-ray bursts (GRBs) are associated with massive star core collapse, whereas short-duration GRBs are associated with mergers of compact stellar binaries. The discovery of a kilonova associated with a nearby (350 Mpc) long-duration GRB-GRB 211211A, however, indicates that the progenitor of this long-duration GRB is

Giuseppe Russo, Christoph Gote, Laurence Brandenberger, Sophia Schlosser

In the U.S. Congress, legislators can use active and passive cosponsorship to support bills. We show that these two types of cosponsorship are driven by two different motivations: the backing of political colleagues and the backing of the bill's content. To this end, we develop an Encoder+RGCN based model that learns legislator representations from bill text

Yuanbo Xu, Yongjian Yang, En Wang, Fuzhen Zhuang

In e-commerce, online retailers are usually suffering from professional malicious users (PMUs), who utilize negative reviews and low ratings to their consumed products on purpose to threaten the retailers for illegal profits. Specifically, there are three challenges for PMU detection: 1) professional malicious users do not conduct any abnormal or illegal int

Y. R. Syau, E. B. Lin, C. J. Liau

Using the concepts of category and functor, we provide some insights and prove an intrinsic property of the category ${\bf AprS}$ of approximation spaces and relation-preserving functions, the category ${\bf RCls}$ of rough closure spaces and continuous functions, and the category ${\bf RInt}$ of rough interior spaces and continuous functions. Furthermore, w

Yi Zheng, Rushin H. Gindra, Emily J. Green, Eric J. Burks

Deep learning is a powerful tool for whole slide image (WSI) analysis. Typically, when performing supervised deep learning, a WSI is divided into small patches, trained and the outcomes are aggregated to estimate disease grade. However, patch-based methods introduce label noise during training by assuming that each patch is independent with the same label as

Yuanbo Xu, En Wang, Yongjian Yang, Yi Chang

Most NN-RSs focus on accuracy by building representations from the direct user-item interactions (e.g., user-item rating matrix), while ignoring the underlying relatedness between users and items (e.g., users who rate the same ratings for the same items should be embedded into similar representations), which is an ideological disadvantage. On the other hand,

Zihan Li, Wentao Chen, Zhiqing Wei, Xingqi Luo

Supervised learning has been widely used for attack categorization, requiring high-quality data and labels. However, the data is often imbalanced and it is difficult to obtain sufficient annotations. Moreover, supervised models are subject to real-world deployment issues, such as defending against unseen artificial attacks. To tackle the challenges, we propo

Novi H. Bong, Joshua Carlson, Bryan Curtis, Ruth Haas

An $X$-TAR (token addition/removal) reconfiguration graph has as its vertices sets that satisfy some property $X$, with an edge between two sets if one is obtained from the other by adding or removing one element. This paper considers the $X$-TAR graph for $X-$ sets of vertices of a base graph $G$ where the $X$-sets of $G$ must satisfy certain conditions. Do

Adam M. Terwilliger, Joshua E. Siegel

In a world increasingly dependent on road-based transportation, it is essential to understand vehicles. We introduce the AI mechanic, an acoustic vehicle characterization deep learning system, as an integrated approach using sound captured from mobile devices to enhance transparency and understanding of vehicles and their condition for non-expert users. We d

Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang

Pre-training models have shown their power in sequential recommendation. Recently, prompt has been widely explored and verified for tuning in NLP pre-training, which could help to more effectively and efficiently extract useful knowledge from pre-training models for downstream tasks, especially in cold-start scenarios. However, it is challenging to bring pro

Eric Wallace, Nicholas Tomlin, Albert Xu, Kevin Yang

We present the Berkeley Crossword Solver, a state-of-the-art approach for automatically solving crossword puzzles. Our system works by generating answer candidates for each crossword clue using neural question answering models and then combines loopy belief propagation with local search to find full puzzle solutions. Compared to existing approaches, our syst

Mustafa Jarrar

We present a formal Arabic wordnet built on the basis of a carefully designed ontology hereby referred to as the Arabic Ontology. The ontology provides a formal representation of the concepts that the Arabic terms convey, and its content was built with ontological analysis in mind, and benchmarked to scientific advances and rigorous knowledge sources as much

Louis Montaut, Quentin Le Lidec, Vladimir Petrik, Josef Sivic

Collision detection between two convex shapes is an essential feature of any physics engine or robot motion planner. It has often been tackled as a computational geometry problem, with the Gilbert, Johnson and Keerthi (GJK) algorithm being the most common approach today. In this work we leverage the fact that collision detection is fundamentally a convex opt

H. Dénes, K. M. Hess, E. A. K. Adams, A. Kutkin

Context. Phased Array Feeds (PAFs) are multi element receivers in the focal plane of a telescope that make it possible to form simultaneously multiple beams on the sky by combining the complex gains of the individual antenna elements. Recently the Westerbork Synthesis Radio Telescope (WSRT) was upgraded with PAF receivers and carried out several observing pr

Wanyu Du, Hanjie Chen, Yangfeng Ji

In task-oriented dialogue systems, response generation from meaning representations (MRs) often suffers from limited training examples, due to the high cost of annotating MR-to-Text pairs. Previous works on self-training leverage fine-tuned conversational models to automatically generate pseudo-labeled MR-to-Text pairs for further fine-tuning. However, some

Matteo Barucco

We construct a rational $T^2$-equivariant elliptic cohomology theory for the 2-torus $T^2$, starting from an elliptic curve C over the complex numbers and a coordinate data around the identity. The theory is defined by constructing an object $EC_{T^2}$ in the algebraic model category $dA(T^2)$, which by Greenlees and Shipley is Quillen-equivalent to rational

Ke Wang, Zhifang Zhang

Piggybacking design has been widely applied in distributed storage systems since it can greatly reduce the repair bandwidth with small sub-packetization. Compared with other existing erasure codes, piggybacking is more convenient to operate and the I/O cost is lower. In this paper, we propose a new efficient design which can further reduce the repair bandwid

Bo-Jiun Hsu, Hoang-Giang Cao, I Lee, Chih-Yu Kao

In recent years, deep reinforcement learning has achieved significant results in low-level controlling tasks. However, the problem of control smoothness has less attention. In autonomous driving, unstable control is inevitable since the vehicle might suddenly change its actions. This problem will lower the controlling system's efficiency, induces excessive m

Charles Alexandre Bédard, Geoffroy Bergeron

We suggest a quantitative and objective notion of emergence. Our proposal uses algorithmic information theory as a basis for an objective framework in which a bit string encodes observational data. A plurality of drops in the Kolmogorov structure function of such a string is seen as the hallmark of emergence. Our definition offers some theoretical results, i

Jiamin Li

Let $S=\mathbb{C}[x_{ij}]$ be a polynomial ring of $m\times n$ generic variables (resp. a polynomial ring of $(2n+1) \times (2n+1)$ skew-symmetric variables) over $\mathbb{C}$ and let $I$ (resp. Pf) be the determinantal ideal of maximal minors (resp. sub-maximal pfaffians) of $S$. Using the representation theoretic techniques introduced in the work of Raicu

Rongge Xu, Zhi-Hao Zhang

In this work, we analyze the macroscopic observables in the 1+1D gapped phases with Abelian onsite symmetries and show that the spacetime observables for each gapped phase form a clear structure that can be mathematically described by enriched fusion categories, which uncovers the behavior of nonlocal excitations that were blurry in traditional Landau paradi

Xueying Qin, Liam O'Connor, Michel Steuwer

Context: Container data types are ubiquitous in computer programming, enabling developers to efficiently store and process collections of data with an easy-to-use programming interface. Many programming languages offer a variety of container implementations in their standard libraries based on data structures offering different capabilities and performance c

Dmitry Ponomarev

We sum up two- and three-point amplitudes in the chiral higher-spin theory over helicities and find that these quite manifestly have the form of invariant traces of the flat space chiral higher-spin algebra. We consider invariant traces of products of higher numbers of on-shell higher-spin fields and interpret these as higher-point scattering amplitudes. Thi

Blake Bordelon, Cengiz Pehlevan

We analyze feature learning in infinite-width neural networks trained with gradient flow through a self-consistent dynamical field theory. We construct a collection of deterministic dynamical order parameters which are inner-product kernels for hidden unit activations and gradients in each layer at pairs of time points, providing a reduced description of net

Diego Escribano, José A. Cuesta

Ensemble models of graphs are one of the most important theoretical tools to study complex networks. Among them, exponential random graphs (ERGs) have proven to be very useful in the analysis of social networks. In this paper we develop a technique, borrowed from the statistical mechanics of lattice gases, to solve Strauss's model of transitive networks. Thi

Mustafa Jarrar, Mohammed Khalilia, Sana Ghanem

This paper presents Wojood, a corpus for Arabic nested Named Entity Recognition (NER). Nested entities occur when one entity mention is embedded inside another entity mention. Wojood consists of about 550K Modern Standard Arabic (MSA) and dialect tokens that are manually annotated with 21 entity types including person, organization, location, event and date.

Pierre Sevestre, Marine Neyret

The growing popularity of Graph Representation Learning (GRL) methods has resulted in the development of a large number of models applied to a miscellany of domains. Behind this diversity of domains, there is a strong heterogeneity of graphs, making it difficult to estimate the expected performance of a model on a new graph, especially when the graph has dis

Dmitry Kovalev, Alexander Gasnikov

In this paper, we study the fundamental open question of finding the optimal high-order algorithm for solving smooth convex minimization problems. Arjevani et al. (2019) established the lower bound $\Omega\left(\epsilon^{-2/(3p+1)}\right)$ on the number of the $p$-th order oracle calls required by an algorithm to find an $\epsilon$-accurate solution to the p

Joseph McDonald, Baolin Li, Nathan Frey, Devesh Tiwari

The energy requirements of current natural language processing models continue to grow at a rapid, unsustainable pace. Recent works highlighting this problem conclude there is an urgent need for methods that reduce the energy needs of NLP and machine learning more broadly. In this article, we investigate techniques that can be used to reduce the energy consu

Antonio Capolupo, Aniello Quaranta, Pia Antonella Setaro

We develop a quantum field theory of boson mixing in curved space. We derive new general oscillation probabilities and prove that the formalism correctly reproduces the flat space limit. We explicitly compute the oscillation formulae for two cosmologically relevant Friedmann-Lemaitre-Robertson-Walker metrics.

Georg Götz, Ricardo Falcón Pérez, Sebastian J. Schlecht, Ville Pulkki

An established model for sound energy decay functions (EDFs) is the superposition of multiple exponentials and a noise term. This work proposes a neural-network-based approach for estimating the model parameters from EDFs. The network is trained on synthetic EDFs and evaluated on two large datasets of over 20000 EDF measurements conducted in various acoustic

Elias R. Most, Alexander A. Philippov

The coalescence of two neutron stars is accompanied by the emission of gravitational waves, and can also feature electromagnetic counterparts powered by mass ejecta and the formation of a relativistic jet after the merger. Since neutron stars can feature strong magnetic fields, the non-trivial interaction of the neutron star magnetospheres might fuel potenti

Arnaud Ducrot, Hao Kang, Shigui Ruan

Age-structured models with nonlocal diffusion arise naturally in describing the population dynamics of biological species and the transmission dynamics of infectious diseases in which individuals disperse nonlocally and interact each other and the age structure of individuals matters. In the first part of our series papers, we study the principal spectral th

Tanya Goyal, Junyi Jessy Li, Greg Durrett

Progress in summarizing long texts is inhibited by the lack of appropriate evaluation frameworks. When a long summary must be produced to appropriately cover the facets of that text, that summary needs to present a coherent narrative to be understandable by a reader, but current automatic and human evaluation methods fail to identify gaps in coherence. In th

Antonio Capolupo, Aniello Quaranta

We show that the study of neutrino capture on tritium, for non-relativistic neutrinos, can allow to distinguish among the various neutrino models, eventually prove the quantum field theory condensation effects and permit to test the hypothesis according to which the flavor vacuum energy gives a contribution to the dark matter of the universe. Indeed, we show

Ariel Neufeld, Sizhou Wu

In this paper we introduce a multilevel Picard approximation algorithm for semilinear parabolic partial integro-differential equations (PIDEs). We prove that the numerical approximation scheme converges to the unique viscosity solution of the PIDE under consideration. To that end, we derive a Feynman-Kac representation for the unique viscosity solution of th

Minghan Li, Xinyu Zhang, Ji Xin, Hongyang Zhang

In information retrieval (IR), candidate set pruning has been commonly used to speed up two-stage relevance ranking. However, such an approach lacks accurate error control and often trades accuracy off against computational efficiency in an empirical fashion, lacking theoretical guarantees. In this paper, we propose the concept of certified error control of

Oliver E. Jensen, Christopher K. Revell

The vertex model is widely used to simulate the mechanical properties of confluent epithelia and other multicellular tissues. This inherently discrete framework allows a Cauchy stress to be attributed to each cell, and its symmetric component has been widely reported, at least for planar monolayers. Here we consider the stress attributed to the neighbourhood

Debraj Nath, Amlan K. Roy

Two physically important potentials (Manning-Rosen and P\"oschl-Teller) are considered for the ro-vibrational energy in diatomic molecules. An improved new approximation is invoked for the centrifugal term, which is then used for their solution within the Nikiforov-Uvarov framework. This employs a recently proposed scheme, which combines the two widely used

E. Minguzzi

The theory of screws clarifies many analogies between apparently unrelated notions in mechanics, including the duality between forces and angular velocities. It is known that the real 6-dimensional space of screws can be endowed with an operator E, E^2 = 0, that converts it into a rank 3 free module over the dual numbers. In this paper we prove the converse,

Eric Wagner, Martin Serror, Klaus Wehrle, Martin Henze

Resource-constrained devices increasingly rely on wireless communication for the reliable and low-latency transmission of short messages. However, especially the implementation of adequate integrity protection of time-critical messages places a significant burden on these devices. We address this issue by proposing BP-MAC, a fast and memory-efficient approac

Fahim Faisal, Antonios Anastasopoulos

Large pretrained multilingual models, trained on dozens of languages, have delivered promising results due to cross-lingual learning capabilities on variety of language tasks. Further adapting these models to specific languages, especially ones unseen during pre-training, is an important goal towards expanding the coverage of language technologies. In this s

Xingyu Zhou, Wen Su, Changyu Liu, Yuling Jiao

We propose a deep generative approach to nonparametric estimation of conditional survival and hazard functions with right-censored data. The key idea of the proposed method is to first learn a conditional generator for the joint conditional distribution of the observed time and censoring indicator given the covariates, and then construct the Kaplan-Meier and

Marcel Reginatto, Sebastian Ulbricht

Finding a physically consistent approach to modelling interactions between classical and quantum systems is a highly nontrivial task. While many proposals based on various mathematical formalisms have been made, most of these efforts run into difficulties of one sort or another. One of the first detailed descriptions was given by Sudarshan and his collaborat

Ivan Ivec

Mixed-norm Lebesgue spaces found their place in the study of some questions in the theory of partial differential equations, as can be seen from recent interest in the continuity of certain classes of pseudodifferential operators on these spaces. In this paper, we use some recent advances in the pseudodifferential calculus for nonsmooth symbols to prove the

Daniil Cherniavskii, Eduard Tulchinskii, Vladislav Mikhailov, Irina Proskurina

The role of the attention mechanism in encoding linguistic knowledge has received special interest in NLP. However, the ability of the attention heads to judge the grammatical acceptability of a sentence has been underexplored. This paper approaches the paradigm of acceptability judgments with topological data analysis (TDA), showing that the geometric prope

Sean McBane, Youngsoo Choi, Karen Willcox

Lattice-like structures can provide a combination of high stiffness with light weight that is useful in many applications, but a resolved finite element mesh of such structures results in a computationally expensive discretization. This computational expense may be particularly burdensome in many-query applications, such as optimization. We develop a stress-

Sang-Hyun Park, Soo-Min Kim, Seonghoon Kim, HongIl Yoo

For in-building networks, the potential of transparent antennas, which are used as windows of a building, is presented in this paper. In this scenario, a transparent window antenna communicates with outdoor devices or base stations, and the indoor repeaters act as relay stations of the transparent window antenna for indoor devices. At indoor, back lobe waves

Yishay Mansour, Richard Nock, Robert C. Williamson

A landmark negative result of Long and Servedio established a worst-case spectacular failure of a supervised learning trio (loss, algorithm, model) otherwise praised for its high precision machinery. Hundreds of papers followed up on the two suspected culprits: the loss (for being convex) and/or the algorithm (for fitting a classical boosting blueprint). Her

Misha Padidar, Jeffrey Larson, Stefan M. Wild

We explore novel approaches for solving nonlinear optimization problems with unrelaxable bound constraints, which must be satisfied before the objective function can be evaluated. Our method reformulates the unrelaxable bound-constrained problem as an unconstrained optimization problem that is amenable to existing unconstrained optimization methods. The refo

Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma

The past two decades have witnessed the rapid development of personalized recommendation techniques. Despite significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field. Many existing studies perform model evaluations and comparisons in an ad-hoc manner, f

J. Bétrisey, G. Buldgen

With the rapid development of asteroseismology thanks to space-based photometry missions such as CoRoT, Kepler, TESS, and in the future, PLATO, and the use of inversion techniques, quasi-model-independent constraints on the stellar properties can be extracted from a given stellar oscillation spectrum. In this context, inversions based on frequency separation

Thomas Cilloni, Charles Walter, Charles Fleming

Recent advances in machine learning show that neural models are vulnerable to minimally perturbed inputs, or adversarial examples. Adversarial algorithms are optimization problems that minimize the accuracy of ML models by perturbing inputs, often using a model's loss function to craft such perturbations. State-of-the-art object detection models are characte

Maria Maffei, Bruno O. Goes, Stephen C. Wein, Andrew N. Jordan

Spin-photon interfaces (SPIs) are key devices of quantum technologies, aimed at coherently transferring quantum information between spin qubits and propagating pulses of polarized light. We study the potential of a SPI for quantum non demolition (QND) measurements of a spin state. After being initialized and scattered by the SPI, the state of a light pulse d

Kayman J. Gonçalves, Giorgio Torrieri

In this addendum to [1], we apply the techniques developed in that paper to the $J/\Psi$ and $\Upsilon$ spin alignement measurements in [2]. We argue that while the data points to a maximally impure density matrix, consistent with Cooper-Frye/Statistical model freeze-out, a measurement of the dependence of the $\phi$-sensitive coefficients on the azimuthal a

Ludwig Bothmann, Kristina Peters, Bernd Bischl

A growing body of literature in fairness-aware machine learning (fairML) aims to mitigate machine learning (ML)-related unfairness in automated decision-making (ADM) by defining metrics that measure fairness of an ML model and by proposing methods to ensure that trained ML models achieve low scores on these metrics. However, the underlying concept of fairnes

Julian Fernandez Bonder, Ariel Salort, Hernan Vivas

In this article we consider a homogeneous eigenvalue problem ruled by the fractional $g-$Laplacian operator whose Euler-Lagrange equation is obtained by minimization of a quotient involving Luxemburg norms. We prove existence of an infinite sequence of variational eigenvalues and study its behavior as the fractional parameter $s\uparrow 1$ among other stabil

Nico Potyka, Xiang Yin, Francesca Toni

There is broad agreement in the literature that explanation methods should be faithful to the model that they explain, but faithfulness remains a rather vague term. We revisit faithfulness in the context of continuous data and propose two formal definitions of faithfulness for feature attribution methods. Qualitative faithfulness demands that scores reflect

Leo Schwinn, Leon Bungert, An Nguyen, René Raab

The reliability of neural networks is essential for their use in safety-critical applications. Existing approaches generally aim at improving the robustness of neural networks to either real-world distribution shifts (e.g., common corruptions and perturbations, spatial transformations, and natural adversarial examples) or worst-case distribution shifts (e.g.

Hao Zhen, Sahand Mosharafian, Jidong J. Yang, Javad Mohammadpour Velni

Given the increasing popularity and demand for connected and autonomous vehicles (CAVs), Eco-driving and platooning in highways and urban areas to increase the efficiency of the traffic system is becoming a possibility. This paper presents Eco-driving trajectory planning for a platoon of heterogeneous electric vehicles (EVs) in urban environments. The propos

Adrián Inés, César Domínguez, Jónathan Heras, Gadea Mata

Nowadays, Machine Learning and Deep Learning methods have become the state-of-the-art approach to solve data classification tasks. In order to use those methods, it is necessary to acquire and label a considerable amount of data; however, this is not straightforward in some fields, since data annotation is time consuming and might require expert knowledge. T

Kun Yi, Yixiao Ge, Xiaotong Li, Shusheng Yang

Since the development of self-supervised visual representation learning from contrastive learning to masked image modeling (MIM), there is no significant difference in essence, that is, how to design proper pretext tasks for vision dictionary look-up. MIM recently dominates this line of research with state-of-the-art performance on vision Transformers (ViTs)

Mario I. Molina

We examine the stability domains of a 1D discrete Schr\"{o}dinger equation in the simultaneous presence of parity-time ($\cal{PT}$) symmetry and fractionality. Direct numerical examination of the eigenvalues of the system reveals that, as the fractional exponent is decreased away from unity (the standard case), the instability gain increases abruptly past a

Anahita Hajibabaei, Andrea Schiffauerova, Ashkan Ebadi

Scientific collaboration in almost every discipline is mainly driven by the need of sharing knowledge, expertise, and pooled resources. Science is becoming more complex which has encouraged scientists to involve more in collaborative research projects in order to better address the challenges. As a highly interdisciplinary field with a rapidly evolving scien

Ivan Karpukhin, Stanislav Dereka, Sergey Kolesnikov

Classification tasks are usually evaluated in terms of accuracy. However, accuracy is discontinuous and cannot be directly optimized using gradient ascent. Popular methods minimize cross-entropy, hinge loss, or other surrogate losses, which can lead to suboptimal results. In this paper, we propose a new optimization framework by introducing stochasticity to

Eleanor McSpirit, Ken Ono

Let $\mathcal{C}_n =\left [\chi_{\lambda}(\mu)\right]_{\lambda, \mu}$ be the character table for $S_n,$ where the indices $\lambda$ and $\mu$ run over the $p(n)$ many integer partitions of $n.$ In this note we study $Z_{\ell}(n),$ the number of zero entries $\chi_{\lambda}(\mu)$ in $\mathcal{C}_n,$ where $\lambda$ is an $\ell$-core partition of $n.$ For ever

Feng Liu, Xiaosong Zhang, Zhiliang Peng, Zonghao Guo

Modern object detectors have taken the advantages of backbone networks pre-trained on large scale datasets. Except for the backbone networks, however, other components such as the detector head and the feature pyramid network (FPN) remain trained from scratch, which hinders fully tapping the potential of representation models. In this study, we propose to in

Yao-Ching Yu, Shi-Jinn Horng

In this paper, we propose a Classification Confidence Network (CLCNet) that can determine whether the classification model classifies input samples correctly. It can take a classification result in the form of vector in any dimension, and return a confidence score as output, which represents the probability of an instance being classified correctly. We can u

Matthew T. Scoggins, Zoltán Haiman, John H. Wise

The existence of $10^9\ {\rm M_\odot}$ supermassive black holes (SMBHs) within the first billion years of the universe remains a puzzle in our conventional understanding of black hole formation and growth. Several suggested formation pathways for these SMBHs lead to a heavy seed, with an initial black hole mass of $10^4-10^6~{\rm M_\odot}$. This can lead to

Valentin Boyanov

I argue that an approach which uses an appropriate admixture of both classical and semiclassical effects is essential for understanding the ultimate fate of gravitational collapse and the nature of black holes. I provide an example of a problem which pushes the boundaries of what is known in both the classical and semiclassical approaches: the evolution of t

A. Bhattacharyya, M. R. Lees, K. Panda, P. P. Ferreira

We investigate the superconducting properties of Sc$_{5}$Co$_{4}$Si$_{10}$ using low-temperature resistivity, magnetization, heat capacity, and muon-spin rotation and relaxation ($\mu$SR) measurements. We find that Sc$_{5}$Co$_{4}$Si$_{10}$ {exhibits type-II} superconductivity with a superconducting transition temperature $T_\mathrm{C}= 3.5 (1)$\,K. The temp

Daniel Grimmer

Despite quantum theory's remarkable success, many philosophers worry that it nonetheless lacks some crucial connection between theory and experiment. One under-discussed aspect of the Quantum Measurement Problems is that it is sometimes unclear how to model our measurement processes in order to extract experimental predictions. Without a solution to these pr

Dora Jambor, Dzmitry Bahdanau

Semantic parsing is the task of producing structured meaning representations for natural language sentences. Recent research has pointed out that the commonly-used sequence-to-sequence (seq2seq) semantic parsers struggle to generalize systematically, i.e. to handle examples that require recombining known knowledge in novel settings. In this work, we show tha

Adina D. Feinstein, Kevin France, Allison Youngblood, Girish M. Duvvuri

High energy X-ray and ultraviolet (UV) radiation from young stars impacts planetary atmospheric chemistry and mass loss. The active $\sim 22$ Myr M dwarf AU Mic hosts two exoplanets orbiting interior to its debris disk. Therefore, this system provides a unique opportunity to quantify the effects of stellar XUV irradiation on planetary atmospheres as a functi

Saif Bunni, Heikki J. Nieminen

It has been recently demonstrated that use of ultrasound increases the tissue yield in ultrasound-enhanced fine-needle aspiration biopsy (USeFNAB) as compared to conventional fine-needle aspiration biopsy (FNAB). To date, the association between bevel geometry and needle tip action has not been widely explored. In this study, we studied the needle resonance

Shuoyang Wang, Guanqun Cao

In this paper, we propose a robust estimator for the location function from multi-dimensional functional data. The proposed estimators are based on the deep neural networks with ReLU activation function. At the meanwhile, the estimators are less susceptible to outlying observations and model-misspecification. For any multi-dimensional functional data, we pro

James R. Anglin, Etienne Wamba

Non-relativistic particles that are effectively confined to two dimensions can in general move on curved surfaces, allowing dynamical phenomena beyond what can be described with scalar potentials or even vector gauge fields. Here we consider a simple case of piecewise uniform curvature: a particle moves on a plane with a spherical extrusion. Depending on the

Amélie Piveteau, Jef Pauwels, Emil Håkansson, Sadiq Muhammad

Dense coding is the seminal example of how entanglement can boost qubit communication, from sending one bit to sending two bits. This is made possible by projecting separate particles onto a maximally entangled basis. We investigate more general communication tasks, in both theory and experiment, and show that simpler measurements enable strong and sometimes

Can Taylan Sari, Sila Kurugol, Onur Afacan, Simon K. Warfield

Segmentation of brain magnetic resonance images (MRI) is crucial for the analysis of the human brain and diagnosis of various brain disorders. The drawbacks of time-consuming and error-prone manual delineation procedures are aimed to be alleviated by atlas-based and supervised machine learning methods where the former methods are computationally intense and

H. F. Yang, K. Y. He, J. Koo, S. W. Shen

Chiral materials have attracted significant research interests as they exhibit intriguing physical properties, such as chiral optical response, spin-momentum locking and chiral induced spin selectivity. Recently, layered transition metal dichalcogenide 1T-TaS2 has been found to host a chiral charge density wave (CDW) order. Nevertheless, the physical consequ

H. Y. Lan, W. Luo, Y. Xu, D. L. Balabanski

In the environment of a hot plasma, as achieved in stellar explosions, capture and photodisintegration reactions proceeding on excited states in the nucleus can considerably contribute to the astrophysical reaction rate. Such reaction rates including the excited-state contribution are obtained from theoretical calculations as the direct experimental determin

Samuel Skirvin, Gary Verth, José Juan González-Avilés, Sergiy Shelyag

Studies on small-scale jets' formation, propagation, evolution, and role, such as type I and II spicules, mottles, and fibrils in the lower solar atmosphere's energetic balance, have progressed tremendously thanks to the combination of detailed observations and sophisticated mathematical modelling. This review provides a survey of the current understanding o

CMS Collaboration

This Letter presents a search for direct production of charginos and neutralinos via electroweak interactions. The results are based on data from proton-proton collisions at a center-of-mass energy of 13 TeV collected with the CMS detector at the LHC, corresponding to an integrated luminosity of 137 fb$^{-1}$. The search considers final states with large mis

Dongliang Jing, Yongzhao Li, Andrew W. Eckford

In this paper, we consider a mobile molecular communication (MC) system consisting of two mobile nanomachines, a transmitter and a receiver, propelled by a positive drift velocity and Brownian motion in a realistic blood-vessel-type flow regime. Considering the nonlinear movement of the nanomachines, an extended Kalman filter is employed to estimate the dist

Conor Heins

In this commentary, I expand on the analysis of the recent article "How particular is the physics of the Free Energy Principle?" by Aguilera et al. by studying the flow fields of linear diffusions, and particularly the rotation of their attracting sets in the presence of different types of solenoidal coupling. This analysis sheds new light on previous claims

Qiang Li, Tao Dai, Shu-Tao Xia

Recently, deep learning methods have shown great success in 3D point cloud upsampling. Among these methods, many feature expansion units were proposed to complete point expansion at the end. In this paper, we compare various feature expansion units by both theoretical analysis and quantitative experiments. We show that most of the existing feature expansion

Beibei Li, Beihong Jin, Jiageng Song, Yisong Yu

With the rapid increase of micro-video creators and viewers, how to make personalized recommendations from a large number of candidates to viewers begins to attract more and more attention. However, existing micro-video recommendation models rely on expensive multi-modal information and learn an overall interest embedding that cannot reflect the user's multi

Yu Zhang, Zhiqiang Gong, Yichuang Zhang, YongQian Li

Transferable adversarial attack is always in the spotlight since deep learning models have been demonstrated to be vulnerable to adversarial samples. However, existing physical attack methods do not pay enough attention on transferability to unseen models, thus leading to the poor performance of black-box attack.In this paper, we put forward a novel method o

Peter Fettke, Wolfgang Reisig

Edsger W. Dijkstra has frequently suggested building a "firewall" between the technology- and application-side of computer science. His justification: The methods to attack the computer scientists' formal, mathematical "correctness problem" differ fundamentally from the methods to attack the applicants' informal "pleasantness problem". In this setting, a mod

Debraj Nath, Amlan K. Roy

Accurate solution of the Schr\"odinger equation with Deng-Fan potential is presented by means of Nikiforov-Uvarov method. A modified Pekeris-type approximation is proposed for the centrifugal term, from a linear combination of the $r \to 0$ and $r \to r_e$ limits. It can potentially offer a series of approximations (depending on an adjustable parameter $\lam

Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot

Energy-based models, a.k.a. energy networks, perform inference by optimizing an energy function, typically parametrized by a neural network. This allows one to capture potentially complex relationships between inputs and outputs. To learn the parameters of the energy function, the solution to that optimization problem is typically fed into a loss function. T

Itay Evron, Edward Moroshko, Rachel Ward, Nati Srebro

To better understand catastrophic forgetting, we study fitting an overparameterized linear model to a sequence of tasks with different input distributions. We analyze how much the model forgets the true labels of earlier tasks after training on subsequent tasks, obtaining exact expressions and bounds. We establish connections between continual learning in th

Ling Chen, Zhishen Huang, Yong Long, Saiprasad Ravishankar

Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent application of deep learning methods for image reconstruction provides a successful data-driven approach to addressing the challenges when reconstructing images with measurement undersampling or various types of noise. In this work,

Mo Zhou, Vishal M. Patel

Adversarial attacks pose safety and security concerns to deep learning applications, but their characteristics are under-explored. Yet largely imperceptible, a strong trace could have been left by PGD-like attacks in an adversarial example. Recall that PGD-like attacks trigger the ``local linearity'' of a network, which implies different extents of linearity

Michael Robinson, Zander Memon

This report presents a connection between the physical acoustics of an object and the topology of the space of echoes resulting from a circular synthetic aperture sonar (CSAS) collection of that object. A simple theoretical model is developed that yields a precise, yet qualitative, description of the space of echoes. This theoretical model is validated in si

Andreas van Hameren, Leszek Motyka, Grzegorz Ziarko

In the hybrid kT-factorization formula, one initial-state parton momentum is space-like and carries non-vanishing transverse components, while the other is on-shell. We promote this factorization formula to next-to-leading order. Studying the partonic cross section, we identify all soft and collinear divergencies in the real and virtual contribution, and rec