October 2020 arXiv papers — page 94

Y. Yang, P. Xiao, B. Liao, N. Deligiannis

We propose a novel deep neural network, coined DeepFPC-$\ell_2$, for solving the 1-bit compressed sensing problem. The network is designed by unfolding the iterations of the fixed-point continuation (FPC) algorithm with one-sided $\ell_2$-norm (FPC-$\ell_2$). The DeepFPC-$\ell_2$ method shows higher signal reconstruction accuracy and convergence speed than t

Evgenii Sovetkin, Elbert Jan Achterberg, Thomas Weber, Bart E. Pieters

We consider a series of image segmentation methods based on the deep neural networks in order to perform semantic segmentation of electroluminescence (EL) images of thin-film modules. We utilize the encoder-decoder deep neural network architecture. The framework is general such that it can easily be extended to other types of images (e.g. thermography) or so

Yongjie Ye, Weigang Wu

To mitigate the scalability problem of decentralized cryptocurrencies such as Bitcoin and Ethereum, the payment channel, which allows two parties to perform secure coin transfers without involving the blockchain, has been proposed. The payment channel increases the transaction throughput of two parties to a level that is only limited by their network bandwid

Berthold Jäck, Fabian Zinser, Elio J. K\" onig, Sune N. P. Wissing

The wavefunctions of a disordered two-dimensional electron gas at the quantum-critical Anderson transition are predicted to exhibit multifractal scaling in their real space amplitude. We experimentally investigate the appearance of these characteristics in the spatially resolved local density of states of a two-dimensional mixed surface alloy Bi_xPb_{1-x}/Ag

Torsten Auerswald, Maarten H. P. Ambaum

Simulating the collision behaviour of cloud drops in a turbulent environment is numerically expensive. Because of the typical sizes of cloud drops, their motion is predominantly influenced by the smallest turbulent scales in the flow. We can exploit this property by using an Arnold--Beltrami--Childress (ABC) flow instead of a full direct numerical simulation

Krzysztof Kȩpczyński

This paper investigates $π_T(a_1,a_2) = \mathbb{P}\left(\sup\limits_{t\in[0,T]} (σ_1B(t)-c_1t)>a_1, \sup\limits_{t\in[0,T]}( σ_2 B(t)-c_2t)>a_2\right),$ where $\{B(t) : t \geq 0\}$ is a standard Brownian motion, with $T >0, σ_1,σ_2>0, c_1, c_2\in\mathbb{R}.$ We derive explicit formula for the probability $π_T\left(a_1,a_2\right)$ and find its asymptotic beha

Fatemeh Koohestani, Neda Ebrahimi, Mehdi Vatandoost, Yousef Bahrampour

It is proved that all discontinuity points of a finite cosmological time function, $τ$, are on past lightlike rays. As a result, it is proved that if $(M,g)$ is a chronological space-time without past lightlike rays then there is a representation of $g$ such that its cosmological time function is regular. In addition, by reducing conditions of regularity suf

Yiqiang Han, Wenjian Hao, Umesh Vaidya

We develop a data-driven, model-free approach for the optimal control of the dynamical system. The proposed approach relies on the Deep Neural Network (DNN) based learning of Koopman operator for the purpose of control. In particular, DNN is employed for the data-driven identification of basis function used in the linear lifting of nonlinear control system d

Masato Onodera, Rhythm Shimakawa, Tomoko L. Suzuki, Ichi Tanaka

We present the selection, spectroscopic identification, and physical properties of extreme emission line galaxies (EELGs) at $3<z<3.7$ aiming at studying physical properties of an analog population of star-forming galaxies (SFGs) at the epoch of reionization. The sample is selected based on the excess in the observed Ks broad band flux relative to the best-f

Yuanhe Tian, Yan Song, Fei Xia, Tong Zhang

Constituency parsing is a fundamental and important task for natural language understanding, where a good representation of contextual information can help this task. N-grams, which is a conventional type of feature for contextual information, have been demonstrated to be useful in many tasks, and thus could also be beneficial for constituency parsing if the

Benoît Bonnet, Teddy Furon, Patrick Bas

This paper explores the connection between steganography and adversarial images. On the one hand, ste-ganalysis helps in detecting adversarial perturbations. On the other hand, steganography helps in forging adversarial perturbations that are not only invisible to the human eye but also statistically undetectable. This work explains how to use these informat

Hamed Haggi, Wei Sun, Junjian Qi

Phasor measurement units (PMUs) enable better system monitoring and security enhancement in smart grids. In order to enhance power system resilience against outages and blackouts caused by extreme weather events or man-made attacks, it remains a major challenge to determine the optimal number and location of PMUs. In this paper, a multi-objective resilient P

L. Xiao, J. Xu, D. Zhao, Z. Wang

We consider the problem of unsupervised domain adaptation for image classification. To learn target-domain-aware features from the unlabeled data, we create a self-supervised pretext task by augmenting the unlabeled data with a certain type of transformation (specifically, image rotation) and ask the learner to predict the properties of the transformation. H

V. G. Klochkova, V. E. Panchuk, N. S. Tavolzhanskaya, M. V. Yushkin

Some evidences of wind variability and velocity stratification in the extended atmosphere has been found in the spectra of the supergiant V340 Ser (=IRAS 17279$-$1119) taken at the 6-m BTA telescope with a spectral resolution R$\ge$60000. The H$α$ line has a P Cyg profile whose absorption component (V=+34 km/s) is formed in the upper layers of the expanding

Dmitry Kireev, Emmanuel Okogbue, Jayanth RT, Tae-Jun Ko

Wearable bioelectronics with emphasis on the research and development of advanced person-oriented biomedical devices have attracted immense interest in the last decade. Scientists and clinicians find it essential to utilize skin-worn smart tattoos for on-demand and ambulatory monitoring of an individual&#39;s vital signs. Here we report on the development of

Bin-Bin Zhao, Liang Li, Hui-Dong Zhang

Extracting entity pairs along with relation types from unstructured texts is a fundamental subtask of information extraction. Most existing joint models rely on fine-grained labeling scheme or focus on shared embedding parameters. These methods directly model the joint probability of multi-labeled triplets, which suffer from extracting redundant triplets wit

Farzin Ahmadi, Fardin Ganjkhanloo, Kimia Ghobadi

Linear constrained optimization techniques have been applied to many real-world settings. In recent years, inferring the unknown parameters and functions inside an optimization model has also gained traction. This inference is often based on existing observations and/or known parameters. Consequently, such models require reliable, easily accessed, and easily

Meghna Khaturia, Pranav Jha, Abhay Karandikar

Convergence of multiple access technologies is one of the key enablers in providing a diverse set of services to the Fifth Generation (5G) users. Though the 3rd Generation Partnership Project (3GPP) 5G standard defines a common core supporting multiple Radio Access Technologies (RATs), Radio Access Network (RAN) level decisions are taken separately across in

Yoon-Hyun Ryu, Przemek Mróz, Andrew Gould, Kyu-Ha Hwang

We report a new free-floating planet (FFP) candidate, KMT-2017-BLG-2820, with Einstein radius $θ_\e\simeq 6\,\muas$, lens-source relative proper motion $μ_\rel \simeq 8\,\masyr$, and Einstein timescale $t_\e=6.5\,$hr. It is the third FFP candidate found in an ongoing study of giant-source finite-source point-lens (FSPL) events in the KMTNet data base, and th

Ana Marasović, Chandra Bhagavatula, Jae Sung Park, Ronan Le Bras

Natural language rationales could provide intuitive, higher-level explanations that are easily understandable by humans, complementing the more broadly studied lower-level explanations based on gradients or attention weights. We present the first study focused on generating natural language rationales across several complex visual reasoning tasks: visual com

Reza Naserasr, Zhouningxin Wang, Xuding Zhu

A signed graph is a pair $(G, σ)$, where $G$ is a graph and $σ: E(G) \to \{+, -\}$ is a signature which assigns to each edge of $G$ a sign. Various notions of coloring of signed graphs have been studied. In this paper, we extend circular coloring of graphs to signed graphs. Given a signed graph $(G, σ)$ a circular $r$-coloring of $(G, σ)$ is an assignment $ψ

Adam Afandi

Using Atiyah-Bott localization on the space of stable maps to the stack quotient $[\mathbb{P}^1/\mathbb{Z}_2]$, we find recursions that determine all Hodge integrals with descendent insertions at one marked point on the hyperelliptic locus $\overline{\mathcal{H}}_{g, 2g + 2} \subseteq \overline{\mathcal{M}}_{g, 2g + 2}$. The initial conditions required for o

Yong Zhang, Fei Xu, Fengquan Li

In this paper, we consider the Fornberg-Whitham equation and a family of solitary wave solutions is found by using minimization principle, where a related penalization function and the concentration-compactness lemma play a key role in our proof. Besides, we also prove that the family of solitary solutions is orbital stable and decay exponentially when speed

Antoine Fleurence, Yukiko Yamada-Takamura

The transformation of the stripe domain structure of spontaneously-formed epitaxial silicene on ZrB$_2$ thin film into a single-domain driven by the adsorption of a fraction of a monolayer of silicon was used to investigate how dislocations react and eventually annihilate in a two-dimensional honeycomb structure. The in-situ real time STM monitoring of the e

Shuqi Dai, Huan Zhang, Roger B. Dannenberg

Repetition is a basic indicator of musical structure. This study introduces new algorithms for identifying musical phrases based on repetition. Phrases combine to form sections yielding a two-level hierarchical structure. Automatically detected hierarchical repetition structures reveal significant interactions between structure and chord progressions, melody

John Hewitt, Michael Hahn, Surya Ganguli, Percy Liang

Recurrent neural networks empirically generate natural language with high syntactic fidelity. However, their success is not well-understood theoretically. We provide theoretical insight into this success, proving in a finite-precision setting that RNNs can efficiently generate bounded hierarchical languages that reflect the scaffolding of natural language sy

Chi Chen, Xin Peng, Zhenchang Xing, Jun Sun

Context based API recommendation is an important way to help developers find the needed APIs effectively and efficiently. For effective API recommendation, we need not only a joint view of both structural and textual code information, but also a holistic view of correlated API usage in control and data flow graph as a whole. Unfortunately, existing API recom

Cheng Hua, Tauhid Zaman

In the United States, medical responses by fire departments over the last four decades increased by 367%. This had made it critical to decision makers in emergency response departments that existing resources are efficiently used. In this paper, we model the ambulance dispatch problem as an average-cost Markov decision process and present a policy iteration

Antonio Alfieri

We introduce deformations of lattice cohomology corresponding to the knot homologies found by Ozsv\&#39; ath, Stipsicz and Szab\&#39; o in \cite{OSS4}. By means of holomorphic triangles counting, we prove equivalence with the analytic theory for a wide class of knots. This yields combinatorial formulae for the upsilon invariant.

Megumi Nakao, Kotaro Kobayashi, Junko Tokuno, Toyofumi Chen-Yoshikawa

Recent advances in imaging techniques have enabled us to visualize lung tumors or nodules in early-stage cancer. However, the positions of nodules can change because of intraoperative lung deflation, and the modeling of pneumothorax-associated deformation remains a challenging issue for intraoperative tumor localization. In this study, we introduce spatial a

Dong An, Lin Lin, Michael Lindsey

The extended Lagrangian molecular dynamics (XLMD) method provides a useful framework for reducing the computational cost of a class of molecular dynamics simulations with constrained latent variables. The XLMD method relaxes the constraints by introducing a fictitious mass $\varepsilon$ for the latent variables, solving a set of singularly perturbed ordinary

Zachary M. Laubach, Eleanor J. Murray, Kim L. Hoke, Rebecca J. Safran

1.A goal of many research programs in biology is to extract meaningful insights from large, complex data sets. Researchers in Ecology, Evolution and Behavior (EEB) often grapple with long-term, observational data sets from which they construct models to address fundamental questions about biology. Similarly, epidemiologists analyze large, complex observation

Tekin Karadağ

We calculate the Gerstenhaber bracket on Hopf algebra and Hochschild cohomologies of the Taft algebra $T_p$ for any integer $p>2$ which is a nonquasi-triangular Hopf algebra. We show that the bracket is indeed zero on Hopf algebra cohomology of $T_p$, as in all known quasi-triangular Hopf algebras. This example is the first known bracket computation for a no

Ayan Mukhopadhyay, Geoffrey Pettet, Mykel Kochenderfer, Abhishek Dubey

Emergency response to incidents such as accidents, crimes, and fires is a major problem faced by communities. Emergency response management comprises of several stages and sub-problems like forecasting, resource allocation, and dispatch. The design of principled approaches to tackle each problem is necessary to create efficient emergency response management

Sho Takase, Naoaki Okazaki

We present a multi-task learning framework for cross-lingual abstractive summarization to augment training data. Recent studies constructed pseudo cross-lingual abstractive summarization data to train their neural encoder-decoders. Meanwhile, we introduce existing genuine data such as translation pairs and monolingual abstractive summarization data into trai

Stephen E. McKeown

This paper defines two new extrinsic curvature quantities on the corner of a four-dimensional Riemannian manifold with corner. One of these is a pointwise conformal invariant, and the conformal transformation of the other is governed by a new linear second-order pointwise conformally invariant partial differential operator. The Gauss-Bonnet theorem is then s

Mingzhou Xu, Kun Cheng

We prove that moderate deviations for empirical measures for countable nonhomogeneous Markov chains hold under the assumption of uniform convergence of transition probability matrices for countable nonhomogeneous Markov chains in Cesàro sense.

Adrian P. Bustamante, Renato C. Calleja

We correct some tables and figures in [A.P. Bustamante and R.C. Calleja, Physica D: Nonlinear Phenomena, 395 (2019), pp. 15-23, arXiv:1712.05476]. We also report on the new computations that verify the accuracy of the data and extend the results. The new computations have led us to find new patterns in the data that were not noticed before. We formulate some

Jun Fu, Wei Zhou, Zhibo Chen

In traffic forecasting, graph convolutional networks (GCNs), which model traffic flows as spatio-temporal graphs, have achieved remarkable performance. However, existing GCN-based methods heuristically define the graph structure as the physical topology of the road network, ignoring potential dependence of the graph structure over traffic data. And the defin

Rebecca Hicks, Christian W. Bauer, Benjamin Nachman

Readout errors are a significant source of noise for near term intermediate scale quantum computers. Mismeasuring a qubit as a 1 when it should be 0 occurs much less often than mismeasuring a qubit as a 0 when it should have been 1. We make the simple observation that one can improve the readout fidelity of quantum computers by applying targeted X gates prio

Krishnashis Chatterjee, Philip M. Graybill, John J. Socha, Rafael V. Davalos

Inexpensive, portable lab-on-a-chip devices would revolutionize fields like environmental monitoring and global health, but current microfluidic chips are tethered to extensive off-chip hardware. Insects, however, are self-contained and expertly manipulate fluids at the microscale using largely unexplored methods. We fabricated a series of microfluidic devic

Zahra Jadidi, Ali Dorri, Raja Jurdak, Colin Fidge

Due to the rise of Industrial Control Systems (ICSs) cyber-attacks in the recent decade, various security frameworks have been designed for anomaly detection. While advanced ICS attacks use sequential phases to launch their final attacks, existing anomaly detection methods can only monitor a single source of data. Therefore, analysis of multiple security dat

The Dang Vu, Hiroaki Shishido, Kenji M. Kojima, Tomio Koyama

We previously proposed a method to detect neutrons by using a current-biased kinetic inductance detector (CB-KID), where neutrons are converted into charged particles using a 10B conversion layer. The charged particles are detected based on local changes in kinetic inductance of X and Y superconducting meanderlines under a modest DC bias current. The system

M. R. Davoodi, B. Navayi neya, S. A. Mostafavian, S. R. Nabavian

Operational modal analysis (OMA) aims at identifying the modal properties of a structure based on response data of the structure excited by ambient sources. Modal parameters of the ambient vibration structures consist of natural frequencies, mode shapes, and modal damping ratios. In this paper, a typical frame with arbitrary loading has been modeled in finit

Zhen Xiang, David J. Miller, George Kesidis

Backdoor data poisoning is an emerging form of adversarial attack usually against deep neural network image classifiers. The attacker poisons the training set with a relatively small set of images from one (or several) source class(es), embedded with a backdoor pattern and labeled to a target class. For a successful attack, during operation, the trained clas

Matthew Tivnan, Wenying Wang, Grace Gang, J. Webster Stayman

Spectral CT has shown promise for high-sensitivity quantitative imaging and material decomposition. This work presents a new device called a spatial-spectral filter (SSF) which consists of a tiled array of filter materials positioned near the x-ray source that is used to modulate the spectral shape of the x-ray beam. The filter is moved to obtain projection

E. Aydi, L. Chomiuk, L. Izzo, E. J. Harvey

The physical mechanism driving mass ejection during a nova eruption is still poorly understood. Possibilities include ejection in a single ballistic event, a common envelope interaction, a continuous wind, or some combination of these processes. Here we present a study of 12 Galactic novae, for which we have pre-maximum high-resolution spectroscopy. All 12 n

R. Terada, Y. Hasegawa, T. Takeshita

We performed a Geant4 simulation study on showers generated by electrons and hadrons in a large homogeneous calorimeter. We found that the energy deposit can be expressed as a linear function of the track length. The line does not pass through the origin, and the energy deposit at the intercept is proportional to the incident energy. Moreover, for both elect

Alvaro Bátrez, Gustavo Rodriguez-Gomez, Angélica Muñoz-Meléndez

The exploration of asteroids and comets is important in the quest for the formation of the Solar System and it is an important step for human space travel. Moving on the surface of asteroids is challenging for future robotic explorers due to the weak gravity force. In this research, an approach that is based on a new kind of jumping rovers is presented. This

Wanjun Zhong, Duyu Tang, Zenan Xu, Ruize Wang

Deepfake detection, the task of automatically discriminating machine-generated text, is increasingly critical with recent advances in natural language generative models. Existing approaches to deepfake detection typically represent documents with coarse-grained representations. However, they struggle to capture factual structures of documents, which is a dis

Chunnan Wang, Kaixin Zhang, Hongzhi Wang, Bozhou Chen

In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem. These STGCN models have their own advantages, i.e., each of them puts forward many effective operations and achieves good prediction results in the real applications. If users can effectively utili

Sara Amato, Andrea Arnold

Neural inflammation immediately follows the onset of ischemic stroke. During this process, microglial cells can be activated into two different phenotypes: the M1 phenotype, which can worsen brain injury by producing pro-inflammatory cytokines; or the M2 phenotype, which can aid in long term recovery by producing anti-inflammatory cytokines. In this study, w

A. Stepanian, Sh. Khlghatyan, V. G. Gurzadyan

The geodesics of bound spherical orbits i.e. of orbits performing Lense-Thirring precession, are obtained in the case of the $Λ$-term within gravito-electromagnetic formalism. It is shown that the presence of the $Λ$-term in the equations of gravity leads to both relativistic and non-relativistic corrections in the equations of motion. The contribution of th

Ke Yang, Ning-Hua Tong

We use the full-density matrix (FDM) numerical renormalization group (NRG) method to calculate the equilibrium dynamical correlation function $C(ω)$ of the spin operator $σ_z$ at finite temperature for the sub-Ohmic spin-boson model. A peak is observed at the frequency $ω_{T}\sim T$ in the curve of $C(ω)$. The curve merges with the zero temperature $C(ω)$ in

Jinhua Zhu, Yingce Xia, Lijun Wu, Jiajun Deng

Improving sample efficiency is a key research problem in reinforcement learning (RL), and CURL, which uses contrastive learning to extract high-level features from raw pixels of individual video frames, is an efficient algorithm~\citep{srinivas2020curl}. We observe that consecutive video frames in a game are highly correlated but CURL deals with them indepen

Huixin Zhan, Feng Tao, Yongcan Cao

Human demonstrations can provide trustful samples to train reinforcement learning algorithms for robots to learn complex behaviors in real-world environments. However, obtaining sufficient demonstrations may be impractical because many behaviors are difficult for humans to demonstrate. A more practical approach is to replace human demonstrations by human que

Hoang Van, Ahmad Musa, Mihai Surdeanu, Stephen Kobourov

We study the language of food on Twitter during the pandemic lockdown in the United States, focusing on the two month period of March 15 to May 15, 2020. Specifically, we analyze over770,000 tweets published during the lockdown and the equivalent period in the five previous years and highlight several worrying trends. First, we observe that during the lockdo

Yinan Mao, Xueou Wang, David J. Nott, Michael Evans

Bayesian likelihood-free methods implement Bayesian inference using simulation of data from the model to substitute for intractable likelihood evaluations. Most likelihood-free inference methods replace the full data set with a summary statistic before performing Bayesian inference, and the choice of this statistic is often difficult. The summary statistic s

M. V. Klymenko, J. A. Vaitkus, J. S. Smith, J. H. Cole

We present a novel open-source Python framework called NanoNET (Nanoscale Non-equilibrium Electron Transport) for modelling electronic structure and transport. Our method is based on the tight-binding method and non-equilibrium Green&#39;s function theory. The core functionality of the framework is providing facilities for efficient construction of tight-bin

Wookyeong Song, Hee-Seok Oh, Yaeji Lim, Ying Kuen Cheung

This paper presents a new statistical method for clustering step data, a popular form of health record data easily obtained from wearable devices. Since step data are high-dimensional and zero-inflated, classical methods such as K-means and partitioning around medoid (PAM) cannot be applied directly. The proposed method is a novel combination of newly constr

Hongdan Li, Xun Li, Huanshui Zhang

This paper is concerned with the linear quadratic optimal control problem for networked system simultaneously with input delay and Markovian dropout. Different from the results in the literature, we consider the hold-input strategy, which is much more computationally complicated than zero-input strategy, but much better in most cases especially in the transi

Valentin Sulzer, Peyman Mohtat, Suhak Lee, Jason B. Siegel

Recent data-driven approaches have shown great potential in early prediction of battery cycle life by utilizing features from the discharge voltage curve. However, these studies caution that data-driven approaches must be combined with specific design of experiments in order to limit the range of aging conditions, since the expected life of Li-ion batteries

Jueqing Lu, Lan Du, Ming Liu, Joanna Dipnall

Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. It becomes more difficult in multi-label classification, where each instance is labelled with more than one class. In this paper, we present a simple multi-graph aggregation

Razieh Nabi, Joel Pfeiffer, Murat Ali Bayir, Denis Charles

In classical causal inference, inferring cause-effect relations from data relies on the assumption that units are independent and identically distributed. This assumption is violated in settings where units are related through a network of dependencies. An example of such a setting is ad placement in sponsored search advertising, where the clickability of a

Ameya Harmalkar, Jeffrey J. Gray

Computational docking methods can provide structural models of protein-protein complexes, but protein backbone flexibility upon association often thwarts accurate predictions. In recent blind challenges, medium or high accuracy models were submitted in less than 20% of the &#34;difficult&#34; targets (with significant backbone change or uncertainty). Here, w

Jing-Yang You, Bo Gu, Sadamichi Maekawa, Gang Su

In recent experiments, high Curie temperatures Tc above room temperature were reported in ferromagnetic semiconductors Fe-doped GaSb and InSb, while low Tc between 20 K to 90 K were observed in some other semiconductors with the same crystal structure, including Fe-doped InAs and Mn-doped GaSb, InSb, and InAs. Here we study systematically the origin of high

Jenna Reher, Aaron D. Ames

Dynamic walking on bipedal robots has evolved from an idea in science fiction to a practical reality. This is due to continued progress in three key areas: a mathematical understanding of locomotion, the computational ability to encode this mathematics through optimization, and the hardware capable of realizing this understanding in practice. In this context

Marianne Boyer, Antoine Frasie, Laurent Bouyer, Jean-Sébastien Roy

Work-related upper extremity musculoskeletal disorders (WRUED) are a major problem in modern societies as they affect the quality of life of workers and lead to absenteeism and productivity loss. According to studies performed in North America and Western Europe, their prevalence has increased in the last few decades. This challenge calls for improvements in

Frédéric Schweitzer, Alexandre Campeau-Lecours

This paper presents the development and preliminary validation of a control interface based on a sequence matching algorithm. An important challenge in the field of assistive technology is for users to control high dimensionality devices (e.g., assistive robot with several degrees of freedom, or computer) with low dimensionality control interfaces (e.g., a f

Michal Lukasik, Himanshu Jain, Aditya Krishna Menon, Seungyeon Kim

Label smoothing has been shown to be an effective regularization strategy in classification, that prevents overfitting and helps in label de-noising. However, extending such methods directly to seq2seq settings, such as Machine Translation, is challenging: the large target output space of such problems makes it intractable to apply label smoothing over all p

Sam Greydanus

The purpose of this work is to explain how wings work and how they were invented. We use the lens of history, looking at the individual people who wanted to fly, the lens of technology, looking at the key inventions leading up to modern airplanes, and the lens of physics, looking at the equations of airflow that made it all possible. Finally, we derive our o

G. Vietri, V. Mainieri, D. Kakkad, H. Netzer

The SINFONI survey for Unveiling the Physics and Effect of Radiative feedback (SUPER) was designed to conduct a blind search for AGN-driven outflows on X-ray selected AGN at redshift z$\sim$2 with high ($\sim$2 kpc) spatial resolution, and correlate them to the properties of the host galaxy and central black hole. The main aims of this paper are: a) to deriv

Hera Siddiqui, Ajita Rattani, Dakshina Ranjan Kisku, Tanner Dean

Self-diagnostic image-based methods for healthy weight monitoring is gaining increased interest following the alarming trend of obesity. Only a handful of academic studies exist that investigate AI-based methods for Body Mass Index (BMI) inference from facial images as a solution to healthy weight monitoring and management. To promote further research and de

Yunhai Han, Yuhan Liu, David Paz, Henrik Christensen

Calibration of sensors is fundamental to robust performance for intelligent vehicles. In natural environments, disturbances can easily challenge calibration. One possibility is to use natural objects of known shape to recalibrate sensors. An approach based on recognition of traffic signs, such as stop signs, and use of them for recalibration of cameras is pr

Alex Tamkin, Mike Wu, Noah Goodman

Many recent methods for unsupervised representation learning train models to be invariant to different &#34;views,&#34; or distorted versions of an input. However, designing these views requires considerable trial and error by human experts, hindering widespread adoption of unsupervised representation learning methods across domains and modalities. To addres

Marwa El Halabi, Slobodan Mitrović, Ashkan Norouzi-Fard, Jakab Tardos

Submodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data. However, if datapoints have sensitive attributes such as gender or age, such machine learning algorithms, left unchecked, are known to exhibit bias: under- or over-representation of particular groups. This has made th

Akshit Kumar, Parikshit Hegde, Rahul Vaze, Amira Alloum

A multi-level random power transmit strategy that is used in conjunction with a random access protocol (RAP) (e.g. ALOHA, IRSA) is proposed to fundamentally increase the throughput in a distributed communication network. A SIR model is considered, where a packet is decodable as long as its SIR is above a certain threshold. In a slot chosen for transmission b

Zhefan Xu, Di Deng, Kenji Shimada

Autonomous exploration requires robots to generate informative trajectories iteratively. Although sampling-based methods are highly efficient in unmanned aerial vehicle exploration, many of these methods do not effectively utilize the sampled information from the previous planning iterations, leading to redundant computation and longer exploration time. Also

Anthony Thomas, Sanjoy Dasgupta, Tajana Rosing

Hyperdimensional (HD) computing is a set of neurally inspired methods for obtaining high-dimensional, low-precision, distributed representations of data. These representations can be combined with simple, neurally plausible algorithms to effect a variety of information processing tasks. HD computing has recently garnered significant interest from the compute

Samya Sen, Anthony G. Morales, Randy H. Ewoldt

We use high-speed imaging to study the effect of thixotropic aging in drop impact of yield-stress fluids on pre-coated substrates. Our results reveal that drop splashing is suppressed for "aged" compared to "unaged" samples, indicating that thixotropic breakdown timescales during impact are long enough to affect the dynamics. We propose and test several hypo

Lydia Bieri

We find new effects for gravitational waves and memory in asymptotically-flat spacetimes of slow decay. In particular, we derive growing magnetic memory for these general systems. These effects do not arise in spacetimes resulting from data with fast decay towards infinity, including data that is stationary outside a compact set. The new results are derived

Isaac H. Kim

Quantum many-body states that frequently appear in physics often obey an entropy scaling law, meaning that an entanglement entropy of a subsystem can be expressed as a sum of terms that scale linearly with its volume and area, plus a correction term that is independent of its size. We conjecture that these states have an efficient dual description in terms o

HanQin Cai, Keaton Hamm, Longxiu Huang, Jiaqi Li

Robust principal component analysis (RPCA) is a widely used tool for dimension reduction. In this work, we propose a novel non-convex algorithm, coined Iterated Robust CUR (IRCUR), for solving RPCA problems, which dramatically improves the computational efficiency in comparison with the existing algorithms. IRCUR achieves this acceleration by employing CUR d

Lee DeVille

We consider a nonlinear flow on simplicial complexes related to the simplicial Laplacian, and show that it is a generalization of various consensus and synchronization models commonly studied on networks. In particular, our model allows us to formulate flows on simplices of any dimension, so that it includes edge flows, triangle flows, etc. We show that the

Lydia Bieri

We investigate the Einstein vacuum equations as well as the Einstein-null fluid equations describing neutrino radiation. We find new structures in gravitational waves and memory for asymptotically-flat spacetimes of slow decay. These structures do not arise in spacetimes resulting from data that is stationary outside a compact set. Rather the more general si

Adriaan-Alexander Ludl, Tom Michoel

Causal gene networks model the flow of information within a cell, but reconstructing them from omics data is challenging because correlation does not imply causation. Combining genomics and transcriptomics data from a segregating population allows to orient the direction of causality between gene expression traits using genomic variants. Instrumental-variabl

Tobias Fritz, Tomáš Gonda, Paolo Perrone, Eigil Fjeldgren Rischel

Markov categories are a recent categorical approach to the mathematical foundations of probability and statistics. Here, this approach is advanced by stating and proving equivalent conditions for second-order stochastic dominance, a widely used way of comparing probability distributions by their spread. Furthermore, we lay foundation for the theory of compar

Isar Nejadgholi, Svetlana Kiritchenko

NLP research has attained high performances in abusive language detection as a supervised classification task. While in research settings, training and test datasets are usually obtained from similar data samples, in practice systems are often applied on data that are different from the training set in topic and class distributions. Also, the ambiguity in cl

Mrityunjay Ghosh, Nivedita Dey, Debdeep Mitra, Amlan Chakrabarti

Ant colony optimization (ACO) is a commonly used meta-heuristic to solve complex combinatorial optimization problems like traveling salesman problem (TSP), vehicle routing problem (VRP), etc. However, classical ACO algorithms provide better optimal solutions but do not reduce computation time overhead to a significant extent. Algorithmic speed-up can be achi

Alex Degtyarev

We prove that the maximal number of conics in a smooth sextic $K3$-surface $X\subset\mathbb{P}^4$ is 285, whereas the maximal number of real conics in a real sextic is 261. In both extremal configurations, all conics are irreducible.

Hancheng Cao, Vivian Yang, Victor Chen, Yu Jin Lee

Understanding team viability -- a team&#39;s capacity for sustained and future success -- is essential for building effective teams. In this study, we aggregate features drawn from the organizational behavior literature to train a viability classification model over a dataset of 669 10-minute text conversations of online teams. We train classifiers to identi

Eleni Chiou, Francesco Giganti, Shonit Punwani, Iasonas Kokkinos

The need for training data can impede the adoption of novel imaging modalities for learning-based medical image analysis. Domain adaptation methods partially mitigate this problem by translating training data from a related source domain to a novel target domain, but typically assume that a one-to-one translation is possible. Our work addresses the challenge

Nima Hoda

We prove that asymptotic cones of Helly graphs are countably hyperconvex. We use this to show that virtually nilpotent Helly groups are virtually abelian and to characterize virtually abelian Helly groups via their point groups. In fact, we do this for the more general class of coarsely injective spaces and groups. We apply this to prove that the $3$-$3$-$3$

Tobias Damm, Birgit Jacob

We introduce a coercivity condition as a time domain analogue of the frequency criterion provided by the famous Kalman-Yakubovich-Popov lemma. For a simple stochastic linear quadratic control problem we show how the coercivity condition characterizes the solvability of Riccati equations.

Nima Hoda

We define the strong shortcut property for rough geodesic metric spaces, generalizing the notion of strongly shortcut graphs. We show that the strong shortcut property is a rough similarity invariant. We give several new characterizations of the strong shortcut property, including an asymptotic cone characterization. We use this characterization to prove tha

Francesca De Marchis, Andrea Malchiodi, Luca Martinazzi, Pierre-Damien Thizy

Given a closed Riemann surface $(Σ,g)$ and any positive smooth weight, we use a minmax scheme together with compactness, quantization results and with sharp energy estimates to prove the existence of positive critical points of the functional $$J_{p,β}(u)=\frac{2-p}{2}\left(\frac{p\|u\|_{H^1}^2}{2β} \right)^{\frac{p}{2-p}}-\ln \int_Σ(e^{u_+^p}-1) f dv_g,$$ f

Marcelo A. Colominas, Hau-Tieng Wu

Modern time series are usually composed of multiple oscillatory components, with time-varying frequency and amplitude contaminated by noise. The signal processing mission is further challenged if each component has an oscillatory pattern, or the wave-shape function, far from a sinusoidal function, and the oscillatory pattern is even changing from time to tim

Adam Ivankay, Ivan Girardi, Chiara Marchiori, Pascal Frossard

Attribution maps are popular tools for explaining neural networks predictions. By assigning an importance value to each input dimension that represents its impact towards the outcome, they give an intuitive explanation of the decision process. However, recent work has discovered vulnerability of these maps to imperceptible adversarial changes, which can prov

Christos Koutras, George Siachamis, Andra Ionescu, Kyriakos Psarakis

Data scientists today search large data lakes to discover and integrate datasets. In order to bring together disparate data sources, dataset discovery methods rely on some form of schema matching: the process of establishing correspondences between datasets. Traditionally, schema matching has been used to find matching pairs of columns between a source and a

Xuecheng Shao, Wenhui Mi, Michele Pavanello

We present the One-orbital Ensemble Self-Consistent Field (OE-SCF) method, an {alternative} orbital-free DFT solver that extends the applicability of DFT to system sizes beyond the nanoscale while retaining the accuracy required to be predictive. OE-SCF is an iterative solver where the (typically computationally expensive) Pauli potential is treated as an ex