July 2022 arXiv papers — page 46

Daniel Soh, Eric Chatterjee

In this study, we explore the theoretical application of entangled multi-mode squeezed light for label-free optical super-resolution imaging. By generating massively entangled multi-mode squeezed light through an array of balanced beam splitters, using a single-mode squeezed light input, we create a multi-mode quantum light state with exceptional entanglemen

Tong Wu, Tianhao Wang, Vikash Sehwag, Saeed Mahloujifar

Recent works have demonstrated that deep learning models are vulnerable to backdoor poisoning attacks, where these attacks instill spurious correlations to external trigger patterns or objects (e.g., stickers, sunglasses, etc.). We find that such external trigger signals are unnecessary, as highly effective backdoors can be easily inserted using rotation-bas

Pengfei Zhang, Zhongxiang Wang

We report our search for quasi-periodic signals in long-term optical and $\gamma$-ray data for the blazar PKS~1222+216, where the data are from the Steward Observatory blazar monitoring program and the all-sky survey with the Large Area Telescope onboard the {\it Fermi Gamma-ray Space Telescope}, respectively. A quasi-periodic signal, with a period of $\sime

Ehsan Kazemi

The problem of fully supervised classification is that it requires a tremendous amount of annotated data, however, in many datasets a large portion of data is unlabeled. To alleviate this problem semi-supervised learning (SSL) leverages the knowledge of the classifier on the labeled domain and extrapolates it to the unlabeled domain which has a supposedly si

Kota Chin, Keita Emura, Kazumasa Omote, Shingo Sato

In an open-bid auction, a bidder can know the budgets of other bidders. Thus, a sealed-bid auction that hides bidding prices is desirable. However, in previous sealed-bid auction protocols, it has been difficult to provide a ``fund binding'' property, which would guarantee that a bidder has funds more than or equal to the bidding price and that the funds are

Peter J. Oberly

We define an inner product on a vector space of adelic measures over a number field. We find that the norm induced by this inner product governs weak convergence at each place of $K$. The canonical adelic measure associated to a rational map is in this vector space, and the square of the norm of the difference of two such adelic measures is the Arakelov-Zhan

Trevor Campbell, Saifuddin Syed, Chiao-Yu Yang, Michael I. Jordan

Exchangeability -- in which the distribution of an infinite sequence is invariant to reorderings of its elements -- implies the existence of a simple conditional independence structure that may be leveraged in the design of statistical models and inference procedures. In this work, we study a relaxation of exchangeability in which this invariance need not ho

Joanne Truong, Max Rudolph, Naoki Yokoyama, Sonia Chernova

If we want to train robots in simulation before deploying them in reality, it seems natural and almost self-evident to presume that reducing the sim2real gap involves creating simulators of increasing fidelity (since reality is what it is). We challenge this assumption and present a contrary hypothesis -- sim2real transfer of robots may be improved with lowe

Irina Wang, Cole Becker, Bart Van Parys, Bartolomeo Stellato

Robust optimization is a tractable and expressive technique for decision-making under uncertainty, but it can lead to overly conservative decisions when pessimistic assumptions are made on the uncertain parameters. Wasserstein distributionally robust optimization can reduce conservatism by being data-driven, but it often leads to very large problems with pro

David Shlivko

We explore the possibility of a scalar field driving ekpyrotic contraction through a non-canonical kinetic energy density rather than a negative potential. We find that this kinetically-driven ekpyrosis ("k-ekpyrosis") can be achieved in a variety of models, including scalar field theories with power-law, polynomial, or DBI-like kinetic terms in the action.

Adam Freese, Wim Cosyn

The light front densities of momentum, angular momentum, and intrinsic pressure are calculated for the photon, both in the free case and at leading order in quantum electrodynamics. In the latter case, we moreover decompose the form factors into photon and electron contributions. Circularly and linearly polarized photons are both considered, with the latter

Adam Freese, Wim Cosyn

Densities associated with the energy-momentum tensor are calculated for spin-one targets. These calculations are done in a light front formalism, which accounts for relativistic effects due to boosts and allows for arbitrary spatial localization of the target. These densities include the distribution of momentum, angular momentum, and pressures over a two-di

Benjamin Howson, Ciara Pike-Burke, Sarah Filippi

The stochastic generalised linear bandit is a well-understood model for sequential decision-making problems, with many algorithms achieving near-optimal regret guarantees under immediate feedback. However, the stringent requirement for immediate rewards is unmet in many real-world applications where the reward is almost always delayed. We study the phenomeno

Khoi D. Nguyen, Quoc-Huy Tran, Khoi Nguyen, Binh-Son Hua

We present a novel method for few-shot video classification, which performs appearance and temporal alignments. In particular, given a pair of query and support videos, we conduct appearance alignment via frame-level feature matching to achieve the appearance similarity score between the videos, while utilizing temporal order-preserving priors for obtaining

Iani JMB Gayo, Shaheer U. Saeed, Dean C. Barratt, Matthew J. Clarkson

Clinically significant prostate cancer has a better chance to be sampled during ultrasound-guided biopsy procedures, if suspected lesions found in pre-operative magnetic resonance (MR) images are used as targets. However, the diagnostic accuracy of the biopsy procedure is limited by the operator-dependent skills and experience in sampling the targets, a sequ

Andrew Yu

Let $S_n$ be the symmetric group of all permutations of $\{1, \cdots, n\}$ with two generators: the transposition switching $1$ with $2$ and the cyclic permutation sending $k$ to $k+1$ for $1\leq k\leq n-1$ and $n$ to $1$ (denoted by $\sigma$ and $\tau$). In this article, we study quantum complexity of permutations in $S_n$ using $\{\sigma, \tau, \tau^{-1}\}

Konrad Kułakowski, Anna Kędzior, Jacek Szybowski, Jiri Mazurek

In this article, we propose two quantitative methods for calculating weight vectors for incomplete pairwise comparison matrices using reference values. Both procedures are extensions of arithmetic and geometric heuristic estimation (HRE) methods. The proposed solutions allow flexible selection of the number of reference alternatives and the range of comparis

Gadiel Sznaier Camps, Robert Dyro, Marco Pavone, Mac Schwager

We propose an algorithm to (i) learn online a deep signed distance function (SDF) with a LiDAR-equipped robot to represent the 3D environment geometry, and (ii) plan collision-free trajectories given this deep learned map. Our algorithm takes a stream of incoming LiDAR scans and continually optimizes a neural network to represent the SDF of the environment a

Mile Mitrovic, Aleksandr Lukashevich, Petr Vorobev, Vladimir Terzija

In recent years, electricity generation has been responsible for more than a quarter of the greenhouse gas emissions in the US. Integrating a significant amount of renewables into a power grid is probably the most accessible way to reduce carbon emissions from power grids and slow down climate change. Unfortunately, the most accessible renewable power source

Joshua Nevin

Let $G$ be a planar embedding with list-assignment $L$ and outer cycle $C$, and let $P$ be a path of length at most four on $C$, where each vertex of $G\setminus C$ has a list of size at least five and each vertex of $C\setminus P$ has a list of size at least three. This is the final paper in a sequence of three papers in which we prove some results about pa

Leonardo Kanashiro Felizardo, Elia Matsumoto, Emilio Del-Moral-Hernandez

The optimal stopping problem is a category of decision problems with a specific constrained configuration. It is relevant to various real-world applications such as finance and management. To solve the optimal stopping problem, state-of-the-art algorithms in dynamic programming, such as the least-squares Monte Carlo (LSMC), are employed. This type of algorit

Daniele Friolo, Fabio Massacci, Chan Nam Ngo, Daniele Venturi

A recent trend in multi-party computation is to achieve cryptographic fairness via monetary penalties, i.e. each honest player either obtains the output or receives a compensation in the form of a cryptocurrency. We pioneer another type of fairness, financial fairness, that is closer to the real-world valuation of financial transactions. Intuitively, a penal

Joshua Nevin

Let $G$ be a planar embedding with list-assignment $L$ and outer cycle $C$, and let $P$ be a path of length at most four on $C$, where each vertex of $G\setminus C$ has a list of size at least five and each vertex of $C\setminus P$ has a list of size at least three. This is the second paper in a sequence of three papers in which we prove some results about p

Mohammad Alali, Mahdi Imani

A major goal in genomics is to properly capture the complex dynamical behaviors of gene regulatory networks (GRNs). This includes inferring the complex interactions between genes, which can be used for a wide range of genomics analyses, including diagnosis or prognosis of diseases and finding effective treatments for chronic diseases such as cancer. Boolean

Sean Dobbs

The strange quark hadrons sit at an important crossroads between the light and heavy quark hadrons, but their spectrum is comparatively poorly known. The KLF experiment was recently approved to run in Hall D of Jefferson Lab, and will use an intense secondary beam of $K_L$ mesons with the existing GlueX spectrometer to collect data several orders of magnitud

Joshua Nevin

Let $G$ be a planar embedding with list-assignment $L$ and outer cycle $C$, and let $P$ be a path of length at most four on $C$, where each vertex of $G\setminus C$ has a list of size at least five and each vertex of $C\setminus P$ has a list of size at least three. In this paper, we prove some results about partial $L$-colorings $\phi$ of $C$ with the prope

Tara Abrishami

A separation of a graph $G$ is a partition $(A_1, A_2, C)$ of $V(G)$ such that $A_1$ is anticomplete to $A_2$. A classic result from Robertson and Seymour's Graph Minors Project states that there is a correspondence between tree decompositions and laminar collections of separations. A many-sided separation of a graph $G$ is a partition $(A_1, \ldots, A_k, C)

Joshua Nevin

This is the first in a sequence of three papers in which we prove the following generalization of Thomassen's 5-choosability theorem: Let $G$ be a finite graph embedded on a surface of genus $g$. Then $G$ can be $L$-colored, where $L$ is a list-assignment for $G$ in which every vertex has a 5-list except for a collection of pairwise far-apart components, eac

Luke Wood, Francois Chollet

Evaluating the COCO mean average precision (MaP) and COCO recall metrics as part of the static computation graph of modern deep learning frameworks poses a unique set of challenges. These challenges include the need for maintaining a dynamic-sized state to compute mean average precision, reliance on global dataset-level statistics to compute the metrics, and

Khalid Oublal, Xinyi Dai

Fully Automatic License Plate Recognition (ALPR) has been a frequent research topic due to several practical applications. However, many of the current solutions are still not robust enough in real situations, commonly depending on many constraints. This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector and

Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang

Deep generative models have achieved conspicuous progress in realistic image synthesis with multifarious conditional inputs, while generating diverse yet high-fidelity images remains a grand challenge in conditional image generation. This paper presents a versatile framework for conditional image generation which incorporates the inductive bias of CNNs and p

Hiroki Ueda, Elizabeth Skoropata, Cinthia Piamonteze, Nazaret Ortiz Hernandez

Incomplete cancellation of collinear antiparallel spins gives rise to ferrimagnetism. Even if the oppositely polarized spins are owing to the equal number of a single magnetic element having the same valence state, in principle, a ferrimagnetic state can still arise from the crystallographic inequivalence of the host ions. However, experimental identificatio

Bastian Wittmann, Fernando Navarro, Suprosanna Shit, Bjoern Menze

Detection Transformers represent end-to-end object detection approaches based on a Transformer encoder-decoder architecture, exploiting the attention mechanism for global relation modeling. Although Detection Transformers deliver results on par with or even superior to their highly optimized CNN-based counterparts operating on 2D natural images, their succes

Noam Prywes, Naiya R Phillips, Owen T Tuck, Luis E Valentin-Alvarado

Carbon fixation is the process by which CO2 is converted from a gas into biomass. The Calvin Benson Bassham (CBB) cycle is the dominant carbon fixation pathway on earth, driving >99.5% of the ~120 billion tons of carbon that are "fixed" as sugar, by plants, algae and cyanobacteria. The carboxylase enzyme in the CBB, ribulose-1,5-bisphosphate carboxylase/oxyg

Siming Zheng, Yuanyuan Lin, Jian Huang

We propose a mutual information-based sufficient representation learning (MSRL) approach, which uses the variational formulation of the mutual information and leverages the approximation power of deep neural networks. MSRL learns a sufficient representation with the maximum mutual information with the response and a user-selected distribution. It can easily

Arash Badie-Modiri, Mikko Kivelä

In the last decade, temporal networks and static and temporal hypergraphs have enabled modelling connectivity and spreading processes in a wide array of real-world complex systems such as economic transactions, information spreading, brain activity and disease spreading. In this manuscript, we present the Reticula C++ library and Python package: A comprehens

Umut Çalıkyılmaz, Sadi Turgut

Quantum Search Algorithm made a big impact by being able to solve the search problem for a set with $N$ elements using only $O(\sqrt{N})$ steps. Unfortunately, it is impossible to reduce the order of the complexity of this problem, however, it is possible to make improvements by a constant factor. In this paper we pursued such improvements for search problem

Mohamed Ghattassi, Xiaokai Huo, Nader Masmoudi

This paper focuses on the nonlinear Milne problem of the radiative heat transfer system on the half-space. The nonlinear model is described by a second order ODE for temperature coupled to transport equation for radiative intensity. The nonlinearity of the fourth power Stefan-Boltzmann law of black body radiation, bring additional difficulty in mathematical

Andreas Aigner, Andreas Tittl, Juan Wang, Thomas Weber

Plasmon resonances play a pivotal role in enhancing light-matter interactions in nanophotonics, but their low-quality factors have hindered applications demanding high spectral selectivity. Even though symmetry-protected bound states in the continuum with high-quality factors have been realized in dielectric metasurfaces, impinging light is not efficiently c

Prabhat Agarwal, Manisha Srivastava, Vishwakarma Singh, Charles Rosenberg

Spam is a serious problem plaguing web-scale digital platforms which facilitate user content creation and distribution. It compromises platform's integrity, performance of services like recommendation and search, and overall business. Spammers engage in a variety of abusive and evasive behavior which are distinct from non-spammers. Users' complex behavior ca

Manal Almaeen, Jake Grigsby, Joshua Hoskins, Brandon Kriesten

We develop a framework to establish benchmarks for machine learning and deep neural networks analyses of exclusive scattering cross sections (FemtoNet). Within this framework we present an extraction of Compton form factors for deeply virtual Compton scattering from an unpolarized proton target. Critical to this effort is a study of the effects of physics co

Jiezhang Cao, Jingyun Liang, Kai Zhang, Wenguan Wang

In this paper, we study a practical space-time video super-resolution (STVSR) problem which aims at generating a high-framerate high-resolution sharp video from a low-framerate low-resolution blurry video. Such problem often occurs when recording a fast dynamic event with a low-framerate and low-resolution camera, and the captured video would suffer from thr

Richen Xiong, Jacob H. Nie, Samuel L. Brantly, Patrick Hays

A panoply of unconventional electronic states has been observed in moir\'e superlattices. Engineering similar bosonic phases remains, however, largely unexplored. We report the observation of a bosonic correlated insulator in WSe2/WS2 moir\'e superlattices composed of excitons, i.e., tightly bound electron-hole pairs. We develop a pump probe spectroscopy met

Yijiong Lin, John Lloyd, Alex Church, Nathan F. Lepora

High-resolution optical tactile sensors are increasingly used in robotic learning environments due to their ability to capture large amounts of data directly relating to agent-environment interaction. However, there is a high barrier of entry to research in this area due to the high cost of tactile robot platforms, specialised simulation software, and sim-to

Vojtech Panek, Zuzana Kukelova, Torsten Sattler

Visual localization, i.e., the problem of camera pose estimation, is a central component of applications such as autonomous robots and augmented reality systems. A dominant approach in the literature, shown to scale to large scenes and to handle complex illumination and seasonal changes, is based on local features extracted from images. The scene representat

Gabriel Sarch, Zhaoyuan Fang, Adam W. Harley, Paul Schydlo

We introduce TIDEE, an embodied agent that tidies up a disordered scene based on learned commonsense object placement and room arrangement priors. TIDEE explores a home environment, detects objects that are out of their natural place, infers plausible object contexts for them, localizes such contexts in the current scene, and repositions the objects. Commons

Keunwoo Choi, Sangshin Oh, Minsung Kang, Brian McFee

"Foley" refers to sound effects that are added to multimedia during post-production to enhance its perceived acoustic properties, e.g., by simulating the sounds of footsteps, ambient environmental sounds, or visible objects on the screen. While foley is traditionally produced by foley artists, there is increasing interest in automatic or machine-assisted tec

Bartosz Łanucha, Małgorzata Michalska

In this paper we investigate intertwining relations for compressions of $k^{th}$--order slant Toeplitz operators to model spaces. We then ask when a product of two such compressions is a compression itself.

Abhinav Kumar, Garrick Brazil, Enrique Corona, Armin Parchami

Modern neural networks use building blocks such as convolutions that are equivariant to arbitrary 2D translations. However, these vanilla blocks are not equivariant to arbitrary 3D translations in the projective manifold. Even then, all monocular 3D detectors use vanilla blocks to obtain the 3D coordinates, a task for which the vanilla blocks are not designe

Leonardo F. Cavenaghi

In his unpublished notes on fat bundles, W. Ziller poses a compelling question: given a fat principal $G$-bundle $(P, g) \rightarrow (B, h)$ with $\dim G = 3$, and $g$ representing a Riemannian submersion metric ensuring that the $G$-orbits are totally geodesic, can one modify $h$ to render all vertical curvatures equal to $1$? In this note, we establish a r

Lukas Allwicher, Darius. A. Faroughy, Florentin Jaffredo, Olcyr Sumensari

HighPT is a Mathematica package for the analysis of high-energy data of semileptonic transitions at hadron colliders. It allows to compute high-$p_T$ tail observables for semileptonic processes, i.e. Drell-Yan cross sections, for dilepton and monolepton final states at the LHC. These observables can be calculated at tree level within the Standard Model Effec

Michael Heisel, Peter P Sullivan, Gabriel G Katul, Marcelo Chamecki

A persistent spatial organization of eddies is identified in the lowest portion of the stably-stratified planetary boundary layer. The analysis uses flow realizations from published large-eddy simulations (Sullivan et al., J Atmos Sci 73(4):1815-1840, 2016) ranging in stability from neutral to nearly z-less stratification. The coherent turbulent structure is

Konstantinos A. Draziotis

Using elementary number theory we study Diophantine equations over the rational integers of the following form, $y^2=(x+a)(x+a+k)(x+b)(x+b+k)$, $y^2=c^2x^4+ax^2+b$ and $y^2=(x^2-1)(x^2-\alpha^2)(x^2-(\alpha+1)^2).$ We express their integer solutions by means of the divisors of the discriminant of $f(x),$ where $y^2=f(x)$.

Anthony Iarrobino

We show here that codimension three Artinian Gorenstein sequences are log-concave, and that there are codimension four Artinian Gorenstein sequences that are not log-concave. We also show that all level sequences in codimension two, and every compressed level Hilbert function in any codimension is log-concave.

Glòria Montaña

This dissertation investigates exotic hadrons with heavy-quark content that may be understood as being generated dynamically from the hadron-hadron interaction. This interaction is derived from a suitable effective Lagrangian and properly unitarized in a full coupled-channel basis. In particular, we discuss the possible interpretation of some of the {\Omega}

Yankun Huang, Qihang Lin, Nick Street, Stephen Baek

We propose a federated learning method with weighted nodes in which the weights can be modified to optimize the model's performance on a separate validation set. The problem is formulated as a bilevel optimization where the inner problem is a federated learning problem with weighted nodes and the outer problem focuses on optimizing the weights based on the v

Johnathon Barbish, Chaoyang Ti, Kamil Ekinci, Mark Paul

We explore the dynamics of a nanoscale doubly-clamped beam that is under high tension, immersed in a viscous fluid, and driven externally by a spatially varying drive force. We develop a theoretical description that is valid for all possible values of tension, includes the motion of the higher modes of the beam, and accounts for a harmonic force that is appl

Leonardo F. Cavenaghi, Lino Grama, Llohann D. Sperança

Guided by rather counterintuitive properties of \emph{dual holonomy fields}, we introduce different notions of \emph{fatness} for Riemannian submersions. We introduce \emph{integral fatness} as an intrinsic condition implied by positive sectional curvature; \emph{sub-fatness} as a rigidity constraint that naturally arises in \emph{canonical variation} limits

Zhaolin Wang, Xidong Mu, Yuanwei Liu

A simultaneously transmitting and reflecting intelligent surface (STARS) enabled integrated sensing and communications (ISAC) framework is proposed, where the whole space is divided by STARS into a sensing space and a communication space. A novel sensing-at-STARS structure, where dedicated sensors are installed at the STARS, is proposed to address the signif

Chenru Duan, Aditya Nandy, Ralf Meyer, Naveen Arunachalam

Approximate density functional theory (DFT) has become indispensable owing to its cost-accuracy trade-off in comparison to more computationally demanding but accurate correlated wavefunction theory. To date, however, no single density functional approximation (DFA) with universal accuracy has been identified, leading to uncertainty in the quality of data gen

Qizhen Zhang, Jiacheng Wu, Ang Chen, Vincent Liu

Cloud data centers are evolving fast. At the same time, today's large-scale data analytics applications require non-trivial performance tuning that is often specific to the applications, workloads, and data center infrastructure. We propose TeShu, which makes network shuffling an extensible unified service layer common to all data analytics. Since an optimal

N. J. Abdulameer, U. Acharya, C. Aidala, N. N. Ajitanand

The PHENIX experiment reports systematic measurements at the Relativistic Heavy Ion Collider of $\phi$-meson production in asymmetric Cu$+$Au collisions at $\sqrt{s_{_{NN}}}$=200 GeV and in U$+$U collisions at $\sqrt{s_{_{NN}}}$=193 GeV. Measurements were performed via the $\phi\rightarrow K^{+}K^{-}$ decay channel at midrapidity $|\eta|<0.35$. Features of $

L. M. Pirovano, D. Fedele, E. F. van Dishoeck, M. R. Hogerheijde

[Abridged] Far-infrared observations with Herschel revealed a surprisingly low abundance of cold-water reservoirs in protoplanetary discs. On the other hand, a handful of discs show emission of hot water transitions excited at temperatures above a few hundred Kelvin. In particular, the protoplanetary discs around the Herbig Ae stars HD 100546 and HD 163296 s

Massimiliano Giona, Andrea Cairoli, Davide Cocco, Rainer Klages

This article investigates the spectral structure of the evolution operators associated with the statistical description of stochastic processes possessing finite propagation velocity. Generalized Poisson-Kac processes and L\'evy walks are explicitly considered as paradigmatic examples of regular and anomalous dynamics. A generic spectral feature of these pro

Safoura S. Mirkhalaf, Mohammad Mehboudi, Zohre Nafari Qaleh, Saleh Rahimi-Keshari

We provide new operational significance of nonclassicality in nonequilibrium temperature estimation of bosonic baths with Gaussian probe states and Gaussian dynamics. We find a bound on the thermometry performance using classical probe states. Then we show that by using nonclassical probe states, single-mode and two-mode squeezed vacuum states, one can profo

Caleb Tung, Abhinav Goel, Xiao Hu, Nicholas Eliopoulos

Computer vision is often performed using Convolutional Neural Networks (CNNs). CNNs are compute-intensive and challenging to deploy on power-contrained systems such as mobile and Internet-of-Things (IoT) devices. CNNs are compute-intensive because they indiscriminately compute many features on all pixels of the input image. We observe that, given a computer

Nora Breton, Gustavo Gutierrez-Cano, Alberto A. Garcia-Diaz

In this paper the motion of charged and uncharged test particles in the rotating nonlinearly charged black hole is examined. Its asymptotics can be de Sitter or anti-de Sitter, depending on the value of the nonlinear parameter; consequently this BH can present one, two or three horizons, the third one being the cosmological horizon in the de Sitter case. Ang

Paul Kassianik, Erik Nijkamp, Bo Pang, Yingbo Zhou

As machine learning tools progress, the inevitable question arises: How can machine learning help us write better code? With significant progress being achieved in natural language processing with models like GPT-3 and Bert, the applications of natural language processing techniques to code are starting to be explored. Most of the research has been focused o

Eszter Bokányi, Zsolt Vizi, Júlia Koltai, Gergely Röst

Near-real time estimations of the effective reproduction number are among the most important tools to track the progression of a pandemic and to inform policy makers and the general public. However, these estimations rely on reported case numbers, commonly recorded with significant biases. The epidemic outcome is strongly influenced by the dynamics of social

Arkadijs Slobodkins, Johannes Tausch

Node elimination is a numerical approach to obtain cubature rules for the approximation of multivariate integrals. Beginning with a known cubature rule, nodes are selected for elimination, and a new, more efficient rule is constructed by iteratively solving the moment equations. This paper introduces a new criterion for selecting which nodes to eliminate tha

Sami Yamani, Yashasvi Raj, Tamer A. Zaki, Gareth H. McKinley

The interplay between viscoelasticity and inertia in dilute polymer solutions at high deformation rates can result in inertio-elastic instabilities. The nonlinear evolution of these instabilities generates a state of turbulence with significantly different spatio-temporal features compared to Newtonian turbulence, termed elasto-inertial turbulence (EIT). We

J. E. Cieza Montalvo, K. I. Cuba Quispe, R. J. Gil Ramírez, C. A. Morgan Cruz

In this work we present an analysis of production and signature of charged Higgs bosons $H_2^{\pm}$ in the version of the 3-3-1 model containing heavy leptons at the CLIC (Cern Linear Collider). The production rate is found to be significant for the direct production of $e^{-} e^{+} \rightarrow H_{2}^{+} H_{2}^{-}$. We also studied the possibility to identif

Bálint Farkas, Birgit Jacob, Merlin Schmitz

Due to the seminal works of Hochbruck and Ostermann exponential splittings are well established numerical methods utilizing operator semigroup theory for the treatment of semilinear evolution equations whose principal linear part involves a sectorial operator with angle greater than ${\pi}/2$ (meaning essentially the holomorphy of the underlying semigroup).

Sebastian Jäger, Alexander Flick, Jessica Adriana Sanchez Garcia, Kaspar von den Driesch

The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Machine Learning (ML) can help to foster sustainable consumption patterns by accounting for sustainability aspects in product search or recommendations of modern retail platforms. However, the lack of large hig

Oliver Mey, Deniz Neufeld

Analyzing vibration data using deep neural network algorithms is an effective way to detect damages in rotating machinery at an early stage. However, the black-box approach of these methods often does not provide a satisfactory solution because the cause of classifications is not comprehensible to humans. Therefore, this work investigates the application of

Aditya Desai, Anshumali Shrivastava

Embedding tables dominate industrial-scale recommendation model sizes, using up to terabytes of memory. A popular and the largest publicly available machine learning MLPerf benchmark on recommendation data is a Deep Learning Recommendation Model (DLRM) trained on a terabyte of click-through data. It contains 100GB of embedding memory (25+Billion parameters).

Alok Kumar Pandey, Alba de las Heras, Julio San Román, Javier Serrano

Vigorous efforts to harness the topological properties of light have enabled a multitude of novel applications. Translating the applications of structured light to higher spatial and temporal resolutions mandates their controlled generation, manipulation, and thorough characterization in the short-wavelength regime. Here, we resort to high-order harmonic gen

Przemek Mroz, Andrzej Udalski, Andrew Gould

Two independent groups reported the discovery of an isolated dark stellar remnant in the microlensing event OGLE-2011-BLG-0462 based on photometric ground-based observations coupled with astrometric measurements taken with the Hubble Space Telescope. These two analyses yielded discrepant mass measurements, with the first group reporting that the lensing obje

Maryam Khalid, Momin Uppal

In this paper, we consider bidirectional relaying between two diffusion-based molecular transceivers (bio-nodes). As opposed to existing literature, we incorporate the effect of direct diffusion links between the nodes and leverage it to improve performance. Assuming network coding type operation at the relay, we devise a detection strategy, based on the max

Madhureeta Das, Xianhao Chen, Xiaoyong Yuan, Lan Zhang

Given the rapidly changing machine learning environments and expensive data labeling, semi-supervised domain adaptation (SSDA) is imperative when the labeled data from the source domain is statistically different from the partially labeled data from the target domain. Most prior SSDA research is centrally performed, requiring access to both source and target

Yoon Chan Taak, Myungshin Im, Yongjung Kim, Minhee Hyun

Galaxies and their central supermassive black holes are known to coevolve, but the physical background for this is unknown as of yet. The High-$z$ Universe probed via Lensing by QSOs (HULQ) project aims to investigate this coevolution by using quasi-stellar object (QSO) host galaxies acting as gravitational lenses (QSO lenses). We present the results of the

Xingwen Zhu, Xiaozhe Hu, Pengtao Sun

In this paper, a meshfree method using the deep neural network (DNN) approach is developed for solving two kinds of dynamic two-phase interface problems governed by different dynamic partial differential equations on either side of the stationary interface with the jump and high-contrast coefficients. The first type of two-phase interface problem to be studi

Kaberi Goswami, K. Narayan

We study 4-dimensional Schwarzschild de Sitter black holes in the regime where the black hole mass is small compared with the de Sitter scale. Then the de Sitter temperature is very low compared with that of the black hole and we study the black hole, approximating the ambient de Sitter space as a frozen classical background. We consider distant observers in

Azzam Alhussain, Mingjie Lin

Acceleration of Convolutional Neural Network (CNN) on edge devices has recently achieved a remarkable performance in image classification and object detection applications. This paper proposes an efficient and scalable CNN-based SoC-FPGA accelerator design that takes pre-trained weights with a 16-bit fixed-point quantization and target hardware specification

Hongyu Zhou, Raf Guns, Tim C. E. Engels

This study contributes to the recent discussions on indicating interdisciplinarity, i.e., going beyond catch-all metrics of interdisciplinarity. We propose a contextual framework to improve the granularity and usability of the existing methodology for interdisciplinary knowledge flow (IKF) in which scientific disciplines import and export knowledge from/to o

Numan Ahmad, Behram Wali, Asad J. Khattak

A variety of statistical and machine learning methods are used to model crash frequency on specific roadways with machine learning methods generally having a higher prediction accuracy. Recently, heterogeneous ensemble methods (HEM), including stacking, have emerged as more accurate and robust intelligent techniques and are often used to solve pattern recogn

Ashwin Sanjay Lele, Arijit Raychowdhury

Optical flow computation with frame-based cameras provides high accuracy but the speed is limited either by the model size of the algorithm or by the frame rate of the camera. This makes it inadequate for high-speed applications. Event cameras provide continuous asynchronous event streams overcoming the frame-rate limitation. However, the algorithms for proc

Xiruo Liu, Shibani Singh, Cory Cornelius, Colin Busho

Existing adversarial example research focuses on digitally inserted perturbations on top of existing natural image datasets. This construction of adversarial examples is not realistic because it may be difficult, or even impossible, for an attacker to deploy such an attack in the real-world due to sensing and environmental effects. To better understand adver

Maria Galloway-Sprietsma, Yancy L. Shirley, James Di Francesco, Jared Keown

Understanding the chemical processes during starless core and prestellar core evolution is an important step in understanding the initial stages of star and disk formation. This project is a study of deuterated ammonia, o-NH$_2$D, in the L1251 star-forming region toward Cepheus. Twenty-two dense cores (twenty of which are starless or prestellar, and two of w

Shubham Kala, Hemwati Nandan, Prateek Sharma

The null geodesics of the regular and rotating magnetically charged black hole in a non-minimally coupled Einstein-Yang-Mills theory surrounded by a plasma medium is studied. The effect of magnetic charge and Yang-Mills parameter on the effective potential and radius of photon orbits has investigated. We then study the shadow of a regular and rotating magnet

Drew Prinster, Anqi Liu, Suchi Saria

We propose \textbf{JAWS}, a series of wrapper methods for distribution-free uncertainty quantification tasks under covariate shift, centered on the core method \textbf{JAW}, the \textbf{JA}ckknife+ \textbf{W}eighted with data-dependent likelihood-ratio weights. JAWS also includes computationally efficient \textbf{A}pproximations of JAW using higher-order inf

Lukas Allwicher, Nudžeim Selimović

The evidence for Lepton Flavour Universality (LFU) violation in semileptonic $B$-decays has been rising over the past few years. Relying on generic effective field theory (EFT) results, it has been shown that models addressing the $B$-anomalies necessarily lead, at one-loop, to deviations from LFU in $\tau$ decays at the few per-mil level. Once a (renormaliz

Lukas Allwicher, Darius A. Faroughy, Florentin Jaffredo, Olcyr Sumensari

We investigate the high-$p_T$ tails of the $pp\to \ell \nu$ and $pp \to \ell \ell$ Drell-Yan processes as probes of New Physics in semileptonic interactions with an arbitrary flavor structure. For this purpose, we provide a general decomposition of the $2\to2$ scattering amplitudes in terms of form-factors that we match to specific scenarios, such as the Sta

Érica Z. Fornaroli, Mykola Khrypchenko, Ednei A. Santulo

Let $I(X,K)$ be the incidence algebra of a finite connected poset $X$ over a field $K$ and $D(X,K)$ its subalgebra consisting of diagonal elements. We describe the bijective linear maps $\varphi:I(X,K)\to I(X,K)$ that strongly preserve the commutativity and satisfy $\varphi(D(X,K))=D(X,K)$. We prove that such a map $\varphi$ is a composition of a commutativi

Heidar Moradi, Seyed Faroogh Moosavian, Apoorv Tiwari

We outline a holographic framework that attempts to unify Landau and beyond-Landau paradigms of quantum phases and phase transitions. Leveraging a modern understanding of symmetries as topological defects/operators, the framework uses a topological order to organize the space of quantum systems with a global symmetry in one lower dimension. The global symmet

Adrian Martini, Avi Mayorcas

Using the method of paracontrolled distributions, we show the local well-posedness of an additive noise approximation to the fluctuating hydrodynamics of the Keller-Segel model on the two-dimensional torus. Our approximation is a non-linear, non-local, parabolic-elliptic stochastic PDE with an irregular, heterogeneous space-time noise. As a consequence of th

I. J. Arnquist, F. T. Avignone, A. S. Barabash, C. J. Barton

The Majorana Demonstrator is a leading experiment searching for neutrinoless double-beta decay with high purity germanium detectors (HPGe). Machine learning provides a new way to maximize the amount of information provided by these detectors, but the data-driven nature makes it less interpretable compared to traditional analysis. An interpretability study re

Marc Mukendi Mpanda

This paper defines fractional Heston-type (fHt) model as an arbitrage-free financial market model with the infinitesimal return volatility described by the square of a single stochastic equation with respect to fractional Brownian motion with Hurst parameter H in (0, 1). We extend the idea of Alos and [Alos, E., & Ewald, C. O. (2008). Malliavin differentiabi

Anshuman Borgohain, Kanak Saha, Bruce Elmegreen, Rupjyoti Gogoi

Blue Compact Dwarfs (BCDs) are low-luminosity (M$_{K} > -21$ mag), metal-poor ($\frac{1}{50}$ $\le Z/Z_{\odot} \le\frac{1}{2}$), centrally concentrated galaxies with bright clumps of star-formation. Cosmological surface brightness dimming and small size limit their detection at high redshifts, making their formation process difficult to observe. Observations

Adam P. Schmidt, Laura A. Albert

For decades, voting-by-mail and the use of ballot drop boxes has substantially grown, and in response, many election officials have added new drop boxes to their voting infrastructure. However, existing guidance for locating drop boxes is limited. In this paper, we introduce an integer programming model, the drop box location problem (DBLP), to locate drop b