August 2022 arXiv papers — page 40

Tom Alberts, Christopher Janjigian, Firas Rassoul-Agha, Timo Seppäläinen

We build a regular version of the field $Z_β(t,x|s,y)$ which describes the Green&#39;s function, or fundamental solution, of the parabolic Anderson model (PAM) with white noise forcing on $\mathbb{R}^{1+1}$: $\partial_t Z_β(t,x | s,y) =$ $\frac{1}{2}\partial_{xx} Z_β(t,x|s,y) + βZ_β(t,x | s,y)W(t,x)$, $Z_β(s,x | s,y) = δ(x-y)$ for all $-\infty < s \leq t < \

Bulat Nasrulin, Martijn De Vos, Georgy Ishmaev, Johan Pouwelse

The growing number of implementations of blockchain systems stands in stark contrast with still limited research on a systematic comparison of performance characteristics of these solutions. Such research is crucial for evaluating fundamental trade-offs introduced by novel consensus protocols and their implementations. These performance limitations are commo

Min Wang, Ata Mahjoubfar, Anupama Joshi

Humans apprehend the world through various sensory modalities, yet language is their predominant communication channel. Machine learning systems need to draw on the same multimodal richness to have informed discourses with humans in natural language; this is particularly true for systems specialized in visually-dense information, such as dialogue, recommenda

Taoyuanmin Zhu, Gabriel I. Fernandez, Colin Togashi, Yeting Liu

The logistics of transporting a package from a storage facility to the consumer's front door usually employs highly specialized robots often times splitting sub-tasks up to different systems, e.g., manipulator arms to sort and wheeled vehicles to deliver. More recent endeavors attempt to have a unified approach with legged and humanoid robots. These solution

Sungho Chun, Sungbum Park, Ju Yong Chang

Compared to joint position, the accuracy of joint rotation and shape estimation has received relatively little attention in the skinned multi-person linear model (SMPL)-based human mesh reconstruction from multi-view images. The work in this field is broadly classified into two categories. The first approach performs joint estimation and then produces SMPL p

Sean Breckling, Leora E. Dresselhaus-Marais, Eric Machorro, Michael C. Brennan

High-brightness X-ray pulses, as generated at synchrotrons and X-ray free electron lasers (XFEL), are used in a variety of scientific experiments. Many experimental testbeds require optical equipment, e.g Compound Refractive Lenses (CRLs), to be precisely aligned and focused. The lateral alignment of CRLs to a beamline requires precise positioning along four

Robson da Silva, James A. Sellers

Recently, using modular forms and Smoot's {\tt Mathematica} implementation of Radu's algorithm for proving partition congruences, Merca proved the following two congruences: For all $n\geq 0,$ \begin{align*} A(9n+5) & \equiv 0 \pmod{3}, \\ A(27n+26) & \equiv 0 \pmod{3}. \end{align*} Here $A(n)$ is closely related to the function which counts the number of {\

Sidharth Malhotra, Robin Walters

In this paper we experiment with using neural network structures to predict a protein's secondary structure ({\alpha} helix positions) from only its primary structure (amino acid sequence). We implement a fully connected neural network (FCNN) and preform three experiments using that FCNN. Firstly, we do a cross-species comparison of models trained and tested

Dafeng Zhang, Feiyu Huang, Shizhuo Liu, Xiaobing Wang

Transformer-based methods have achieved impressive image restoration performance due to their capacities to model long-range dependency compared to CNN-based methods. However, advances like SwinIR adopts the window-based and local attention strategy to balance the performance and computational overhead, which restricts employing large receptive fields to cap

Gavin Zhang, Hong-Ming Chiu, Richard Y. Zhang

The matrix completion problem seeks to recover a $d\times d$ ground truth matrix of low rank $r\ll d$ from observations of its individual elements. Real-world matrix completion is often a huge-scale optimization problem, with $d$ so large that even the simplest full-dimension vector operations with $O(d)$ time complexity become prohibitively expensive. Stoch

Goran Radunović

In this paper we introduce an interesting family of relative fractal drums (RFDs in short) at infinity and study their complex dimensions which are defined as the poles of their associated Lapidus (distance) fractal zeta functions introduced in a previous work by the author. We define the tube zeta function at infinity and obtain a functional equation connec

Axel Potier, Johan Mazoyer, Zahed Wahhaj, Pierre Baudoz

Context. Direct imaging of exoplanets takes advantage of state-of-the-art adaptive optics (AO) systems, coronagraphy, and post-processing techniques. Coronagraphs attenuate starlight to mitigate the unfavorable flux ratio between an exoplanet and its host star. AO systems provide diffraction-limited images of point sources and minimize optical aberrations th

Hunsoo Song, Jinha Jung

The digital terrain model (DTM) is fundamental geospatial data for various studies in urban, environmental, and Earth science. The reliability of the results obtained from such studies can be considerably affected by the errors and uncertainties of the underlying DTM. Numerous algorithms have been developed to mitigate the errors and uncertainties of DTM. Ho

Gil Bor, Connor Jackman, Serge Tabachnikov

A bicycle path is a pair of trajectories in ${\mathbb R}^n$, the `front' and `back' tracks, traced out by the endpoints of a moving line segment of fixed length (the `bicycle frame') and tangent to the back track. Bicycle geodesics are bicycle paths whose front track's length is critical among all bicycle paths connecting two given placements of the line seg

Wolfgang Jeltsch, Javier Díaz

In a blockchain system, nodes regularly distribute data to other nodes. The ideal perspective taken in the scientific literature is that data is broadcast to all nodes directly, while in practice data is distributed by repeated multicast. Since correctness and security typically have been established for the ideal setting only, it is vital to show that these

Seokchang Hong, Younghun Hong

In this paper, developing a new approach based on Fourier analysis methods for dispersive PDEs, we establish a low regularity NLS approximation for the one-dimensional cubic Klein-Gordon equation. Our main result includes energy class solutions which are formally asymptotically in $L^2(\mathbb{R})$. A precise rate of convergence is also obtained assuming mor

Zhijian Yang, Yongjin Li

In this paper, combined with the P-angle function of Banach spaces and the geometric constants that can characterize Hilbert spaces, the new angular geometric constant is defined. Firstly, this paper explores the basic properties of the new constant and obtains some inequalities with significant geometric constants. Then according to the derived inequalities

Alexander Brudnyi

We study the differential equation $\frac{\partial G}{\partial\bar z}=g$ with an unbounded Banach-valued Bochner measurable function $g$ on the open unit disk $\mathbb D\subset\mathbb C$. We prove that under some conditions on the growth and essential support of $g$ such equation has a bounded solution given by a continuous linear operator. The obtained resu

Samuel Epstein

We prove a Kolmogorov complexity variant of the birthday paradox. Sufficiently sized random subsets of strings are guaranteed to have two members x and y with low K(x/y). To prove this, we first show that the minimum conditional Kolmogorov complexity between members of finite sets is very low if they are not exotic. Exotic sets have high mutual information w

B. N. Kausik

The Weber Fechner Law of psychophysics observes that human perception is logarithmic in the stimulus. We present an algorithm for incorporating the Weber Fechner law into loss functions for machine learning, and use the algorithm to enhance the performance of deep learning networks.

Sergey Matskevich, Colin S. Gordon

Comments are an important part of the source code and are a primary source of documentation. This has driven interest in using large bodies of comments to train or evaluate tools that consume or produce them -- such as generating oracles or even code from comments, or automatically generating code summaries. Most of this work makes strong assumptions about t

Jiyoon Hwang, Denis Krylov, Robertus J. G. Elbertse, Sangwon Yoon

Recent advances in increasing the spectroscopic energy resolution in scanning tunneling microscopy (STM) have been achieved by integrating electron spin resonance (ESR) with STM. Here, we demonstrate the design and performance of a home-built STM capable of ESR at temperatures ranging from 1 K to 10 K. The STM is incorporated with a home-built Joule-Thomson

Carlos Hernandez-Suarez, Osval Montesinos Lopez

The Coefficient of Parentage (COP) between two individuals is the expected inbreeding of their offspring. Originally exploited by animal breeders, is now a routine calculation among plant breeders as part of crop improvement programs. Here we show that the COP between strains requires a different calculation than the used to calculate the COP between individ

Jessica L. Wert, Pooria Dehghanian, Jonathan Snodgrass, Thomas J. Overbye

In order to correctly model the impacts of geomagnetically induced current (GIC) flows, the generator step-up (GSU) transformer status must be properly modeled. In power flow studies, generators are typically removed from service without disconnecting their GSU transformers since the GSU transformer status has little to no impact on the power flow result. In

Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell

We describe our early efforts to red team language models in order to simultaneously discover, measure, and attempt to reduce their potentially harmful outputs. We make three main contributions. First, we investigate scaling behaviors for red teaming across 3 model sizes (2.7B, 13B, and 52B parameters) and 4 model types: a plain language model (LM); an LM pr

Shenglong Zhou, and Geoffrey Ye Li

Federated learning has burgeoned recently in machine learning, giving rise to a variety of research topics. Popular optimization algorithms are based on the frameworks of the (stochastic) gradient descent methods or the alternating direction method of multipliers. In this paper, we deploy an exact penalty method to deal with federated learning and propose an

Philip B. Stark

Many widely used models amount to an elaborate means of making up numbers--but once a number has been produced, it tends to be taken seriously and its source (the model) is rarely examined carefully. Many widely used models have little connection to the real-world phenomena they purport to explain. Common steps in modeling to support policy decisions, such a

Farhad Aghili

This paper presents an adaptive learning method for data fusion in autonomous driving vehicles. The localization is based on the integration of Inertial Measurement Unit (IMU) with two Real-Time Kinematic (RTK) Global Positioning System (GPS) units in an adaptive Kalman filter (KF). The observability analysis reveals that $i$) integration of a single GPS wit

Juyang Weng

This is a theoretical paper, as a companion paper of the keynote talk at the same conference AIEE 2023. In contrast to conscious learning, many projects in AI have employed so-called "deep learning" many of which seemed to give impressive performance. This paper explains that such performance data are deceptively inflated due to two misconducts: "data deleti

Sibendu Paul, Kunal Rao, Giuseppe Coviello, Murugan Sankaradas

It is a common practice to think of a video as a sequence of images (frames), and re-use deep neural network models that are trained only on images for similar analytics tasks on videos. In this paper, we show that this leap of faith that deep learning models that work well on images will also work well on videos is actually flawed. We show that even when a

Sourav Biswas, Anjan Kumar Gupta

Superconducting weak-link (WL), behaving like a Josephson junction (JJ), is fundamental to many superconducting devices such as nanoSQUIDs, single-photon detectors, and bolometers. The interplay between unique nonlinear dynamics and inevitable Joule heating in a JJ leads to new characteristics. Here, we report a time-dependent model incorporating thermal eff

T. M. Rocha Filho, J. F. F. Mendes, M. L. Lucio, M. A. Moret

We study how available data on COVID-19 cases and deaths in different countries are reliable. Our analysis is based on a modification of the law of anomalous numbers, the Newcomb-Benford law, applied to the daily number of deaths and new cases in each country. We first revisit the Newcomb-Benford law and show how to avoid false negative compliance of the dat

Aizaz U. Chaudhry, Halim Yanikomeroglu

Laser inter-satellite links (LISLs) between satellites in a free-space optical satellite network (FSOSN) can be divided into two classes: permanent LISLs (PLs) and temporary LISLs (TLs). TLs are not desirable in next-generation FSOSNs (NG-FSOSNs) due to high LISL setup time, but they may become feasible in next-next-generation FSOSNs (NNG-FSOSNs). Using the

Cristiano Gratton, Naveen K. D. Venkategowda, Reza Arablouei, Stefan Werner

We develop a new consensus-based distributed algorithm for solving learning problems with feature partitioning and non-smooth convex objective functions. Such learning problems are not separable, i.e., the associated objective functions cannot be directly written as a summation of agent-specific objective functions. To overcome this challenge, we redefine th

Lu Yang, Sheng Wang, Russ B. Altman

Objective For the UK Biobank standardized phenotype codes are associated with patients who have been hospitalized but are missing for many patients who have been treated exclusively in an outpatient setting. We describe a method for phenotype recognition that imputes phenotype codes for all UK Biobank participants. Materials and Methods POPDx (Population-bas

S. Nidhan, J. L. Ortiz-Tarin, S. Sarkar

Large-eddy simulations (LES) are performed to study the flow past a 6:1 prolate spheroid placed at an angle of incidence of $\alpha = 10^{\circ}$. The diameter-based Reynolds number ($Re = U_{\infty}D/\nu$) is set to a value of $5\times10^{3}$ and four values of diameter-based Froude numbers ($Fr = U_\infty/ND$) are analyzed: $Fr = \infty, 6, 1.9,$ and $1$.

Kehinde Olobatuyi, Oludare Ariyo

Modeling of high-dimensional data is very important to categorize different classes. We develop a new mixture model called Multinomial cluster-weighted model (MCWM). We derive the identifiability of a general class of MCWM. We estimate the proposed model through Expectation-Maximization (EM) algorithm via an iteratively reweighted least squares (EM-IRLS) and

Alexey Uvarov

Variational quantum algorithms (VQAs) are a modern family of quantum algorithms designed to solve optimization problems using a quantum computer. Typically VQAs rely on a feedback loop between the quantum device and a classical optimization algorithm. The appeal of VQAs lies in their versatility, resistance to noise, and ability to demonstrate some results e

Jingang Li, Rundi Yang, Yoonsoo Rho, Penghong Ci

Carrier distribution and dynamics in semiconductor materials often govern their physical properties that are critical to functionalities and performance in industrial applications. The continued miniaturization of electronic and photonic devices calls for tools to probe carrier behavior in semiconductors simultaneously at the picosecond time and nanometer le

J. L. Ortiz-Tarin, S. Nidhan, S. Sarkar

The high-Reynolds number stratified wake of a slender body is studied using a high-resolution hybrid simulation. The wake generator is a 6:1 prolate spheroid with a tripped boundary layer, the diameter-based body Reynolds number is $Re= U_\infty D/\nu = 10^5$, and the body Froude numbers are $Fr=U_\infty/ND=\{2,10,\infty\}$. The wake defect velocity ($U_d$)

Youngsoo Kim, H. Dharma Kwon

In the game of investment in the common good, the free rider problem can delay the stakeholders' actions in the form of a mixed strategy equilibrium. However, it has been recently shown that the mixed strategy equilibria of the stochastic war of attrition are destabilized by even the slightest degree of asymmetry between the players. Such extreme instability

Ognjen Milatovic

In the setting of the lattice $\mathbb{Z}^n$ we consider a pseudo-differential operator $A$ whose symbol belongs to a class defined on $\mathbb{Z}^n\times \mathbb{T}^n$, where $\mathbb{T}^n$ is the $n$-torus. We realize $A$ as an operator acting between the discrete Sobolev spaces $H^{s_j}(\mathbb{Z}^n)$, $s_j\in\mathbb{R}$, $j=1,2$, with the discrete Schwar

B. E. Zhilyaev, V. N. Petukhov, V. M. Reshetnyk

NASA commissioned a research team to study Unidentified Aerial Phenomena (UAP), observations of events that cannot scientifically be identified as known natural phenomena. The Main Astronomical Observatory of NAS of Ukraine conducts an independent study of UAP also. For UAP observations, we used two meteor stations. Observations were performed with colour vi

Dan Radu Laţcu

Inspired by the concepts of slant distribution and slant submanifold, with their variants of hemi-slant, semi-slant, bi-slant, or almost bi-slant, we introduce the more general concepts of $k$-slant distribution and $k$-slant submanifold in the settings of an almost Hermitian, an almost product Riemannian, an almost contact metric, and an almost paracontact

Nobuyuki Takei, Martin Miranda, Yuki Miyazawa, Mikio Kozuma

The short-term sensitivity of a kilometer-long fiber-optic gyroscope is limited mainly by thermal phase noise and relative intensity noise. Increasing the phase modulation frequency decreases the thermal phase noise but not the relative intensity noise since it behaves as white noise. Here, we propose and experimentally demonstrate that the angular random wa

Michael Bechtel, QiTao Weng, Heechul Yun

Running deep neural networks (DNNs) on tiny Micro-controller Units (MCUs) is challenging due to their limitations in computing, memory, and storage capacity. Fortunately, recent advances in both MCU hardware and machine learning software frameworks make it possible to run fairly complex neural networks on modern MCUs, resulting in a new field of study widely

Alberto S. Cattaneo, Pavel Mnev

In classical field theory, gluing spacetime manifolds along boundary corresponds to taking a fiber product of the corresponding spaces of fields (as differential graded Fr\'echet manifolds) up to homotopy. We construct this homotopy explicitly in several examples in the setting of BV-BFV formalism (Batalin--Vilkovisky formalism with cutting--gluing).

Davide del Bimbo, Andrea Gemelli, Simone Marinai

Tables are widely used in documents because of their compact and structured representation of information. In particular, in scientific papers, tables can sum up novel discoveries and summarize experimental results, making the research comparable and easily understandable by scholars. Since the layout of tables is highly variable, it would be useful to inter

Deno Stelter, Andrew J. Skemer, Renate Kupke, Cyril Bourgenot

SCALES (Slicer Combined with an Array of Lenslets for Exoplanet Spectroscopy) is a 2 to 5 micron high-contrast lenslet-based Integral Field Spectrograph (IFS) designed to characterize exoplanets and their atmospheres. Like other lenslet-based IFSs, SCALES produces a short micro-spectrum of each lenslet's micro-pupil. We have developed an image slicer that si

Yuan Meng, Rajgopal Kannan, Viktor Prasanna

Monte Carlo Tree Search (MCTS) methods have achieved great success in many Artificial Intelligence (AI) benchmarks. The in-tree operations become a critical performance bottleneck in realizing parallel MCTS on CPUs. In this work, we develop a scalable CPU-FPGA system for Tree-Parallel MCTS. We propose a novel decomposition and mapping of MCTS data structure

Adel Albshri, Bakri Awaji, and Ellis Solaiman

The pervasiveness of the Internet of Things (IoT) has enabled the administration of a large number of intelligent devices. However, IoT is based on centralised models, which introduce a number of problems, such as a single point of failure and security risks. Blockchain may offer a viable option for addressing these concerns. Practically, both blockchain and

Domingos S. P. Salazar

The detailed fluctuation theorem (DFT) is a statement about the asymmetry in the statistics of the entropy production. Consequences of the DFT are the second law of thermodynamics and the thermodynamics uncertainty relation (TUR), which translate into lower bounds for the mean and variance of currents, respectively. However, far from equilibrium, mean and va

Andisheh Khedri, Dominic Horn, Oded Zilberberg

The non-Hermitian dynamics of open systems deal with how intricate coherent effects of a closed system intertwine with the impact of coupling to an environment. The system-environment dynamics can then lead to so-called exceptional points, which are the open-system marker of phase transitions, i.e., the closing of spectral gaps in the complex spectrum. Even

Kate Qi Zhou, Yan Qin, Chau Yuen

Accurately estimating a battery's state of health (SOH) helps prevent battery-powered applications from failing unexpectedly. With the superiority of reducing the data requirement of model training for new batteries, transfer learning (TL) emerges as a promising machine learning approach that applies knowledge learned from a source battery, which has a large

Andrea Gemelli, Emanuele Vivoli, Simone Marinai

Tables are widely used in several types of documents since they can bring important information in a structured way. In scientific papers, tables can sum up novel discoveries and summarize experimental results, making the research comparable and easily understandable by scholars. Several methods perform table analysis working on document images, losing usefu

Adel Albshri, Ali Alzubaidi, Bakri Awaji, Ellis Solaiman

Recently, distributed ledger technologies like blockchain have been proliferating and have attracted interest from the academic community, government, and industry. A wide range of blockchain solutions has been introduced, such as Bitcoin, Ethereum, and Hyperledger technologies in the literature. However, tools for evaluating these solutions and their applic

J. Burke, D. A. Howell, D. J. Sand, G. Hosseinzadeh

The early lightcurves of Type Ia supernovae (SNe Ia) can be used to test predictions about their progenitor systems. If the progenitor system consists of a single white dwarf in a binary with a Roche-lobe-overflowing non-degenerate stellar companion, then the SN ejecta should collide with that companion soon after the explosion and get shock-heated, leaving

Farnoosh Hashemi, Ali Behrouz, Laks V. S. Lakshmanan

A key graph mining primitive is extracting dense structures from graphs, and this has led to interesting notions such as $k$-cores which subsequently have been employed as building blocks for capturing the structure of complex networks and for designing efficient approximation algorithms for challenging problems such as finding the densest subgraph. In appli

Andy Eskenazi, Kevin You, Will Vauclain, Robin Murugadoss

Homological algebra is often understood as the translator between the world of topology and algebra. However, this branch of mathematics is worth studying by itself, given that it provides fascinating perspectives about other disciplines, most notably, category theory. In this paper, we seek to provide an introductory guide for advanced students of mathemati

J. J. Wesdorp, F. J. Matute-Caňadas, A. Vaartjes, L. Grünhaupt

Andreev bound states are fermionic states localized in weak links between superconductors which can be occupied with spinful quasiparticles. Microwave experiments using superconducting circuits with InAs/Al nanowire Josephson junctions have recently enabled probing and coherent manipulation of Andreev states but have remained limited to zero or small fields.

Weihao Xia, Yujiu Yang, Jing-Hao Xue

In this paper, we propose to model the video dynamics by learning the trajectory of independently inverted latent codes from GANs. The entire sequence is seen as discrete-time observations of a continuous trajectory of the initial latent code, by considering each latent code as a moving particle and the latent space as a high-dimensional dynamic system. The

Hannah Earley

Conventional computing has many sources of heat dissipation, but one of these--the Landauer limit--poses a fundamental lower bound of 1 bit of entropy per bit erased. 'Reversible Computing' avoids this source of dissipation, but is dissipationless computation possible? In this paper, a general proof is given for open quantum systems showing that a computer t

Michael Crawshaw, Mingrui Liu, Francesco Orabona, Wei Zhang

Traditional analyses in non-convex optimization typically rely on the smoothness assumption, namely requiring the gradients to be Lipschitz. However, recent evidence shows that this smoothness condition does not capture the properties of some deep learning objective functions, including the ones involving Recurrent Neural Networks and LSTMs. Instead, they sa

Weiting Tan, Philipp Koehn

Mining high-quality bitexts for low-resource languages is challenging. This paper shows that sentence representation of language models fine-tuned with multiple negatives ranking loss, a contrastive objective, helps retrieve clean bitexts. Experiments show that parallel data mined from our approach substantially outperform the previous state-of-the-art metho

André Haefliger, Georges Reeb

The original article titled "Vari\'et\'es (non s\'epar\'ees) \`a une dimension et structures feuillet\'ees du plan" was published in 1957 in French in L'Enseignement Math\'ematique. It establishes a beautiful connection between foliations of the plane and non-Hausdorff $1$-dimensional manifolds arising naturally as leaf spaces of the foliations. Since its ap

Komal Bhattacharyya, David Zwicker, Karen Alim

Biological flow networks adapt their network morphology to optimise flow while being exposed to external stimuli from different spatial locations in their environment. These adaptive flow networks retain a memory of the stimulus location in the network morphology. Yet, what limits this memory and how many stimuli can be stored is unknown. Here, we study a nu

David Freire-Obregón, Javier Lorenzo-Navarro, Oliverio J. Santana, Daniel Hernández-Sosa

Predicting an athlete's performance based on short footage is highly challenging. Performance prediction requires high domain knowledge and enough evidence to infer an appropriate quality assessment. Sports pundits can often infer this kind of information in real-time. In this paper, we propose regressing an ultra-distance runner cumulative race time (CRT),

Po-Hsuan Lin

Sequential equilibrium is the conventional approach for analyzing multi-stage games of incomplete information. It relies on mutual consistency of beliefs. To relax mutual consistency, I theoretically and experimentally explore the dynamic cognitive hierarchy (DCH) solution. One property of DCH is that the solution can vary between two different games sharing

M. Cecilia Gimenez, Luis Reinaudi, Federico Vazquez

We study the contrarian voter model for opinion formation in a society under the influence of an external oscillating propaganda and stochastic noise. Each agent of the population can hold one of two possible opinions on a given issue --against or in favor, and interacts with its neighbors following either an imitation dynamics (voter behavior) or an anti-al

Vincent A. Cicirello

In this paper, we explore the theory and expand upon the practice of fitness landscape analysis for optimization problems over the space of permutations. Many of the computational and analytical tools for fitness landscape analysis, such as fitness distance correlation, require identifying a distance metric for measuring the similarity of different solutions

Gelei Xu, Yawen Wu, Jingtong Hu, Yiyu Shi

Dermatological diseases pose a major threat to the global health, affecting almost one-third of the world's population. Various studies have demonstrated that early diagnosis and intervention are often critical to prognosis and outcome. To this end, the past decade has witnessed the rapid evolvement of deep learning based smartphone apps, which allow users t

S. Capozziello, A. Carleo, G. Lambiase

Observations indicate that intergalactic magnetic fields have amplitudes of the order of $\sim 10^{-6}$ G and are uniform on scales of $\sim 10$ kpc. Despite their wide presence in the Universe, their origin remains an open issue. Even by invoking a dynamo mechanism or a compression effect for magnetic field amplification, the existence of seed fields before

Utkarsha Agwan, Costas J. Spanos, Kameshwar Poolla

In order to manage peak-grid events, utilities run incentive-based demand response (DR) programs in which they offer an incentive to assets who promise to curtail power consumption, and impose penalties if they fail to do so. We develop a probabilistic model for the curtailment capability of these assets, and use it to derive analytic expressions for the opt

Ren Yang, Radu Timofte, Xin Li, Qi Zhang

This paper reviews the Challenge on Super-Resolution of Compressed Image and Video at AIM 2022. This challenge includes two tracks. Track 1 aims at the super-resolution of compressed image, and Track~2 targets the super-resolution of compressed video. In Track 1, we use the popular dataset DIV2K as the training, validation and test sets. In Track 2, we propo

Pablo Magni

In this article we study derived (auto)equivalences of generalized Kummer varieties $\mathrm{Kum}^n(A)$. We provide an answer to a question raised by Namikawa by showing that the generalized Kummer varieties $\mathrm{Kum}^n(A)$ and $\mathrm{Kum}^n(A^\vee)$ are derived equivalent as long as $n$ is even and the abelian surface $A$ admits a polarization whose e

H. R. Fazlollahi

The Renyi entropy coprises a group of data estimates that sums up the well-known Shannon entropy, acquiring a considerable lot of its properties. It appears as unqualified and restrictive entropy, relative entropy, or common data, and has found numerous applications in information theory. in the Renyi's argument, the area law of the black hole entropy plays

Yaniv Yacoby, John Girash, David C. Parkes

Doctoral programs often have high rates of depression, anxiety, isolation, and imposter phenomenon. Consequently, graduating students may feel inadequately prepared for research-focused careers, contributing to an attrition of talent. Prior work identifies an important contributing factor to maladjustment: even with prior exposure to research, entering Ph.D.

M. Horana Gamage, R. Bhandari, G. Bollen, N. D. Gamage

Background: Low energy $\beta$ and electron capture (EC) decays are important systems in neutrino mass determination experiments. An isotope with an ultra-low Q value $\beta$-decay to an excited state in the daughter with Qes < 1 keV could provide a promising alternative candidate for future experiments. $^{75}$Se EC and $^{75}$Ge $\beta$-decay represent suc

Charlotte Siegmann, Markus Anderljung

The European Union is likely to introduce among the first, most stringent, and most comprehensive AI regulatory regimes of the world's major jurisdictions. In this report, we ask whether the EU's upcoming regulation for AI will diffuse globally, producing a so-called "Brussels Effect". Building on and extending Anu Bradford's work, we outline the mechanisms

Yuval Wigderson

Given a graph $G$, its Ramsey number $r(G)$ is the minimum $N$ so that every two-coloring of $E(K_N)$ contains a monochromatic copy of $G$. It was conjectured by Conlon, Fox, and Sudakov that if one deletes a single vertex from $G$, the Ramsey number can change by at most a constant factor. We disprove this conjecture, exhibiting an infinite family of graphs

Zheng Li, Yiyong Liu, Xinlei He, Ning Yu

Relying on the fact that not all inputs require the same amount of computation to yield a confident prediction, multi-exit networks are gaining attention as a prominent approach for pushing the limits of efficient deployment. Multi-exit networks endow a backbone model with early exits, allowing to obtain predictions at intermediate layers of the model and th

Darius Saif, Ashraf Matrawy

There has been growing interest in using the QUIC transport protocol for the Internet of Things (IoT). In lossy and high latency networks, QUIC outperforms TCP and TLS. Since IoT greatly differs from traditional networks in terms of architecture and resources, IoT specific parameter tuning has proven to be of significance. While RFC 9006 offers a guideline f

Lukas Hauer, Zhuoyun Cai, Doris Vollmer, Jonathan T. Pham

Drops in contact with swollen, elastomeric substrates can induce a capillary-mediated phase separation in wetting ridges. Using laser scanning confocal microscopy, we visualize phase separation of oligomeric silicone oil from a crosslinked silicone network during steady-state sliding of water drops. We find an inverse relationship between the oil tip height

Emeson Santana, Gustavo Carneiro, Filipe R. Cordeiro

Label noise is common in large real-world datasets, and its presence harms the training process of deep neural networks. Although several works have focused on the training strategies to address this problem, there are few studies that evaluate the impact of data augmentation as a design choice for training deep neural networks. In this work, we analyse the

David Sanchez, Luciano Zunino, Juan De Gregorio, Raul Toral

Words are fundamental linguistic units that connect thoughts and things through meaning. However, words do not appear independently in a text sequence. The existence of syntactic rules induces correlations among neighboring words. Using an ordinal pattern approach, we present an analysis of lexical statistical connections for 11 major languages. We find that

Hamdy Abdelkhalik, Yehia Arafa, Nandakishore Santhi, Abdel-Hameed Badawy

Graphics processing units (GPUs) are now considered the leading hardware to accelerate general-purpose workloads such as AI, data analytics, and HPC. Over the last decade, researchers have focused on demystifying and evaluating the microarchitecture features of various GPU architectures beyond what vendors reveal. This line of work is necessary to understand

Richard S. Sutton, Michael Bowling, Patrick M. Pilarski

Herein we describe our approach to artificial intelligence research, which we call the Alberta Plan. The Alberta Plan is pursued within our research groups in Alberta and by others who are like minded throughout the world. We welcome all who would join us in this pursuit.

Sabah Al-Fedaghi

This paper is about conceptual modeling of aggregates in software engineering. An aggregate is a cluster of domain objects that can be treated as a single unit. In UML, an aggregation is a type of association in which objects are configured together to form a more complex object. It has been described as one of the biggest betes noires in modeling. In spite

Kexin Zhang, Elmira Deldari, Zhicong Lu, Yaxing Yao

In social Virtual Reality (VR), users are embodied in avatars and interact with other users in a face-to-face manner using avatars as the medium. With the advent of social VR, people with disabilities (PWD) have shown an increasing presence on this new social media. With their unique disability identity, it is not clear how PWD perceive their avatars and whe

Leandros Perivolaropoulos, Foteini Skara

We reanalyze the recently released SH0ES data for the determination of $H_0$. We focus on testing the homogeneity of the Cepheid+SnIa sample and the robustness of the results in the presence of new degrees of freedom in the modeling of Cepheids and SnIa. We thus focus on the four modeling parameters of the analysis: the fiducial luminosity of SnIa $M_B$ and

Andrea Gemelli, Sanket Biswas, Enrico Civitelli, Josep Lladós

Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Networks (GNNs) has become crucial in various document-related tasks since they can unravel important structural patterns, fundamental in key information extraction processes. Previous wo

Li Ding, Lee Spector

Recent advancements in quantum computing have shown promising computational advantages in many problem areas. As one of those areas with increasing attention, hybrid quantum-classical machine learning systems have demonstrated the capability to solve various data-driven learning tasks. Recent works show that parameterized quantum circuits (PQCs) can be used

Marco Bravin

In this paper we highlight a set of ad hoc test functions to study the homogenization of viscous compressible fluid in domains with tiny holes. This set of functions allows to improve previous results in dimensions two and three. As an application we show that the presence of a small obstacle does not influence the dynamics of a viscous compressible fluid in

Angel A. Ciarbonetti, Sergio Idelsohn, Ruben D. Spies

This work deals with the problem of determining a non-homogeneous heat conductivity profile in a steady-state heat conduction boundary-value problem with mixed Dirichlet-Neumann boundary conditions over a bounded domain in $\mathbb{R}^n$, from the knowledge of the state over the whole domain. We develop a method based on a variational approach leading to an

Caitlin Rose, Jeyhan S. Kartaltepe, Gregory F. Snyder, Vicente Rodriguez-Gomez

Identifying merging galaxies is an important - but difficult - step in galaxy evolution studies. We present random forest classifications of galaxy mergers from simulated JWST images based on various standard morphological parameters. We describe (a) constructing the simulated images from IllustrisTNG and the Santa Cruz SAM, and modifying them to mimic futur

Tong Huang, Dan Wu, Marija Ilic

In this paper we propose a co-design of the secondary frequency regulation in systems of AC microgrids and its cyber securty solutions. We term the secondary frequency regulator a Micro-Automatic Generation Control (Micro-AGC) for highlighting its same functionality as the AGC in bulk power systems. We identify sensory challenges and cyber threats facing the

Daniel Butter

The Ramond-Ramond sector of double field theory (DFT) can be described either as an O(D,D) spinor or an O(D-1,1) x O(1,D-1) bispinor. Both formulations may be related to the standard polyform expansion in terms of even or odd rank field strengths corresponding to IIA or IIB duality frames. The spinor approach is natural in a (bosonic) metric formulation of D

David Barnes, Danny Sugrue

For G an arbitrary profinite group, we construct an algebraic model for rational G-spectra in terms of G-equivariant sheaves over the space of subgroups of G. This generalises the known case of finite groups to a much wider class of topological groups, and improves upon earlier work of the first author on the case where G is the p-adic integers. As the purpo

Argel Ramírez Reyes, Da Yang

In contrast to prevailing knowledge, Ram\'irez Reyes and Yang (2021) showed that tropical cyclones (TCs) can form spontaneously without moisture-radiation and surface-flux feedbacks in a cloud-resolving model (CRM) simulation. Here we ask, why? Thirteen 3D cloud-resolving simulations show that the moisture-convection (MC) feedback can effectively lead to spo

Xiao Liu

This paper is concerned with the 2-dim two-phase interface Euler equation linearized at a pair of monotone shear flows in both fluids. We extend the Howard's Semicircle Theorem and study the eigenvalue distribution of the linearized Euler system. Under certain conditions, there are exactly two eigenvalues for each fixed wave number $k\in \mathbb{R}$ in the w