October 2020 arXiv papers — page 114

Silke Auchter, Alexander Schlager, Hannah Thiel, Kaisa Laiho

Compared to traditional nonlinear optical crystals, like BaB$_2$O$_4$, KTiOPO$_4$ or LiNbO$_3$, semiconductor integrated sources of photon pairs may operate at pump wavelengths much closer to the bandgap of the materials. This is also the case for Bragg-reflection waveguides (BRW) targeting parametric down-conversion (PDC) to the telecom C-band. The large no

Sha Wang, Xingwen Hu, Zhicheng Zhang, Shutong Wang

We demonstrated an all polarization-maintaining fiber (PMF) laser which could offer selective transverse mode emission. Offset and cross splicing between PMF sections was used to generate higher order mode and to yield a birefringence induced mode filter. Polarization rotation technique (PRT) was used to select the output beam profile. Both linear cavity and

Zhen Wang, Qiansheng Wang, Chengguo Lv, Xue Cao

Although stance detection has made great progress in the past few years, it is still facing the problem of unseen targets. In this study, we investigate the domain difference between targets and thus incorporate attention-based conditional encoding with adversarial domain generalization to perform unseen target stance detection. Experimental results show tha

Gabriele Oligeri, Simone Raponi, Savio Sciancalepore, Roberto Di Pietro

Physical-layer security is regaining traction in the research community, due to the performance boost introduced by deep learning classification algorithms. This is particularly true for sender authentication in wireless communications via radio fingerprinting. However, previous research efforts mainly focused on terrestrial wireless devices while, to the be

Zeyu Song, Dongliang Chang, Zhanyu Ma, Xiaoxu Li

The loss function is a key component in deep learning models. A commonly used loss function for classification is the cross entropy loss, which is a simple yet effective application of information theory for classification problems. Based on this loss, many other loss functions have been proposed,~\emph{e.g.}, by adding intra-class and inter-class constraint

Dongxu Li, Chenchen Xu, Xin Yu, Kaihao Zhang

Sign language translation (SLT) aims to interpret sign video sequences into text-based natural language sentences. Sign videos consist of continuous sequences of sign gestures with no clear boundaries in between. Existing SLT models usually represent sign visual features in a frame-wise manner so as to avoid needing to explicitly segmenting the videos into i

K. K. Pandey, P. Viswanathan

We provide a general framework to construct fractal interpolation surfaces (FISs) for a prescribed countably infinite data set on a rectangular grid. Using this as a crucial tool, we obtain a parameterized family of bivariate fractal functions simultaneously interpolating and approximating a prescribed bivariate continuous function. Some elementary propertie

Di Hu, Rui Qian, Minyue Jiang, Xiao Tan

Discriminatively localizing sounding objects in cocktail-party, i.e., mixed sound scenes, is commonplace for humans, but still challenging for machines. In this paper, we propose a two-stage learning framework to perform self-supervised class-aware sounding object localization. First, we propose to learn robust object representations by aggregating the candi

Najoung Kim, Tal Linzen

Natural language is characterized by compositionality: the meaning of a complex expression is constructed from the meanings of its constituent parts. To facilitate the evaluation of the compositional abilities of language processing architectures, we introduce COGS, a semantic parsing dataset based on a fragment of English. The evaluation portion of COGS con

Maksim Belyaev, Andrei Velichko, Vadim Putrolaynen, Valentin Perminov

The paper presents the results of a study of electron-beam modification (EBM) of VO2-switch I-V curve threshold parameters and the self-oscillation frequency of a circuit containing such a switching device. EBM in vacuum is reversible and the parameters are restored when exposed to air at pressure of 150 Pa. At EBM with a dose of 3 C/cm2, the voltages of swi

David Rosser, Dario Gerace, Yueyang Chen, Yifan Liu

Establishing a coherent interaction between a material resonance and an optical cavity is a necessary first step for the development of semiconductor quantum optics. Here we demonstrate a coherent interaction between the neutral exciton in monolayer MoSe2 and a zero-dimensional, small mode volume nanocavity. This is observed through a dispersive shift of the

Adam Rupe, James P. Crutchfield

Local causal states are latent representations that capture organized pattern and structure in complex spatiotemporal systems. We expand their functionality, framing them as spacetime autoencoders. Previously, they were only considered as maps from observable spacetime fields to latent local causal state fields. Here, we show that there is a stochastic decod

David Harvey

Hittmeir recently presented a deterministic algorithm that provably computes the prime factorisation of a positive integer $N$ in $N^{2/9+o(1)}$ bit operations. Prior to this breakthrough, the best known complexity bound for this problem was $N^{1/4+o(1)}$, a result going back to the 1970s. In this paper we push Hittmeir's techniques further, obtaining a

Hao-Hsuan Chang, Lingjia Liu, Yang Yi

Deep reinforcement learning (DRL) has been shown to be successful in many application domains. Combining recurrent neural networks (RNNs) and DRL further enables DRL to be applicable in non-Markovian environments by capturing temporal information. However, training of both DRL and RNNs is known to be challenging requiring a large amount of training data to a

Swaroopa Reddy B

In this paper, we propose a scheme called securePrune for reducing the storage space of a full node and synchronization time of bootstrapping nodes joining the Peer-to-Peer (P2P) network in an Unspent Transaction Outputs (UTXO) based blockchain like bitcoin using RSA accumulators. The size of the bitcoin blockchain is growing linearly with transactions. We p

Fahimeh Saleh, Wray Buntine, Gholamreza Haffari

Scarcity of parallel sentence-pairs poses a significant hurdle for training high-quality Neural Machine Translation (NMT) models in bilingually low-resource scenarios. A standard approach is transfer learning, which involves taking a model trained on a high-resource language-pair and fine-tuning it on the data of the low-resource MT condition of interest. Ho

Hai Hu, Kyle Richardson, Liang Xu, Lu Li

Despite the tremendous recent progress on natural language inference (NLI), driven largely by large-scale investment in new datasets (e.g., SNLI, MNLI) and advances in modeling, most progress has been limited to English due to a lack of reliable datasets for most of the world's languages. In this paper, we present the first large-scale NLI dataset (consi

Tetsufumi Tanamoto

All-to-all connections are required in general quantum annealing machines to solve various combinatorial optimization problems. The Lechner, Hauke, and Zoller (LHZ) method, which is used to realize the all-to-all connections, requires many-body interactions in locally connected qubits. Because most of the qubit interactions are two-body interactions, Lechner

Sujin Kim, Jinhwan Lee, Changgu Lee, Sunmin Ryu

Chromium thiophosphate (CrPS4), a monoclinic crystal of C_2^3 space group, is a ternary layered semiconductor with an optical bandgap of 1.4 eV and exhibits antiferromagnetism below 36 K. Despite its potential in optoelectronic and magnetic applications, the symmetry of its lattice vibrations has not been systematically studied. In this work, we performed a

Yujie Li, Sikai Chen, Runjia Du, Paul Young Joun Ha

Emerging transportation technologies offer unprecedented opportunities to improve the efficiency of the transportation system from the perspectives of energy consumption, congestion, and emissions. One of these technologies is connected and autonomous vehicles (CAVs). With the prospective duality of operations of CAVs and human driven vehicles in the same ro

Runjia Du, Sikai Chen, Yujie Li, Jiqian Dong

In preparing for connected and autonomous vehicles (CAVs), a worrisome aspect is the transition era which will be characterized by mixed traffic (where CAVs and human-driven vehicles (HDVs) share the roadway). Consistent with expectations that CAVs will improve road safety, on-road CAVs may adopt rather conservative control policies, and this will likely cau

Kazuya Niizeki, Daisuke Yoshida, Ko Ito, Ippei Nakamura

Quantum computing and quantum communication, have been greatly developed in recent years and expected to contribute to quantum internet technologies, including cloud quantum computing and unconditionally secure communication. However, long-distance quantum communication is challenging mainly because of optical fiber losses; quantum repeaters are indispensabl

Jiqian Dong, Sikai Chen, Paul Young Joun Ha, Yujie Li

Connected Autonomous Vehicle (CAV) Network can be defined as a collection of CAVs operating at different locations on a multilane corridor, which provides a platform to facilitate the dissemination of operational information as well as control instructions. Cooperation is crucial in CAV operating systems since it can greatly enhance operation in terms of saf

Paul Young Joun Ha, Sikai Chen, Jiqian Dong, Runjia Du

Active Traffic Management strategies are often adopted in real-time to address such sudden flow breakdowns. When queuing is imminent, Speed Harmonization (SH), which adjusts speeds in upstream traffic to mitigate traffic showckwaves downstream, can be applied. However, because SH depends on driver awareness and compliance, it may not always be effective in m

Rodolfo Quispe, Helio Pedrini

Person Re-Identification is a challenging task that aims to retrieve all instances of a query image across a system of non-overlapping cameras. Due to the various extreme changes of view, it is common that local regions that could be used to match people are suppressed, which leads to a scenario where approaches have to evaluate the similarity of images base

Md Mosharaf Hossain, Antonios Anastasopoulos, Eduardo Blanco, Alexis Palmer

As machine translation (MT) systems progress at a rapid pace, questions of their adequacy linger. In this study we focus on negation, a universal, core property of human language that significantly affects the semantics of an utterance. We investigate whether translating negation is an issue for modern MT systems using 17 translation directions as test bed.

Douglas Hardin, Edward B. Saff, Ruiwen Shu, Eitan Tadmor

We investigate the large time behavior of $N$ particles restricted to a smooth closed curve in $\mathbb{R}^d$ and subject to a gradient flow with respect to Euclidean hyper-singular repulsive Riesz $s$-energy with $s>1.$ We show that regardless of their initial positions, for all $N$ and time $t$ large, their normalized Riesz $s$-energy will be close to the

Jian Liang, Kun Chen, Ming Lin, Changshui Zhang

Finite Mixture Regression (FMR) refers to the mixture modeling scheme which learns multiple regression models from the training data set. Each of them is in charge of a subset. FMR is an effective scheme for handling sample heterogeneity, where a single regression model is not enough for capturing the complexities of the conditional distribution of the obser

Chengsheng Mao, Liang Yao, Yuan Luo

Graph Neural Network (GNN) aggregates the neighborhood of each node into the node embedding and shows its powerful capability for graph representation learning. However, most existing GNN variants aggregate the neighborhood information in a fixed non-injective fashion, which may map different graphs or nodes to the same embedding, reducing the model expressi

Meiyan Song, Hangguan Shan, Howard H. Yang, Tony Q. S. Quek

Dynamic time-division duplex (D-TDD) has emerged as an effective solution to accommodate the unaligned downlink and uplink traffic in small cell networks. However, the flexibility of traffic configuration also introduces additional inter-cell interference. In this letter, we study the effectiveness of applying fractional frequency reuse (FFR) as an interfere

Jin-Yi Cai, Tianyu Liu

Is Fully Polynomial-time Randomized Approximation Scheme (FPRAS) for a problem via an MCMC algorithm possible when it is known that rapid mixing provably fails? We introduce several weight-preserving maps for the eight-vertex model on planar and on bipartite graphs, respectively. Some are one-to-one, while others are holographic which map superpositions of e

Tongshuai Zhu, Huaiqiang Wang, Haijun Zhang, Dingyu Xing

More than forty years ago, axion was postulated as an elementary particle with a low mass and weak interaction in particle physics to solve the strong $\mathcal{CP}$ (charge conjugation and parity) puzzle. Axions are also considered as a possible component of dark matter of the universe. However, the existence of axions in nature has not been confirmed. Inte

Wei-Jie Li, Zi Li, Yan-Bo Shi, Xian-Tu He

Earth's magnetic field is generated by the liquid outer core and sensitively depends on the thermal conductivity of the core. The dominant component of the Earth's core is Fe and Ni. However, current estimates on FeNi mixtures have not been previously tested at high pressures. In this paper, ab initio simulations were first applied to calculations of

Adam B. Watts, Chris Power, Barbara Catinella, Luca Cortese

Observations of the cold neutral atomic hydrogen (HI) in and around disc galaxies have revealed that spatial and kinematic asymmetries are commonplace, and are reflected in the global HI spectra. We use the TNG100 box from the IllustrisTNG suite of cosmological simulations to study the conditions under which these asymmetries may arise in current theoretical

Yiding Yang, Zunlei Feng, Mingli Song, Xinchao Wang

Graphs have been widely adopted to denote structural connections between entities. The relations are in many cases heterogeneous, but entangled together and denoted merely as a single edge between a pair of nodes. For example, in a social network graph, users in different latent relationships like friends and colleagues, are usually connected via a bare edge

Khodakhast Bibak, Bruce M. Kapron, Venkatesh Srinivasan

Universal hash functions, discovered by Carter and Wegman in 1979, are of great importance in computer science with many applications. MMH$^*$ is a well-known $\triangle$-universal hash function family, based on the evaluation of a dot product modulo a prime. In this paper, we introduce a generalization of MMH$^*$, that we call GMMH$^*$, using the same const

Junlin Wang, Jens Tuyls, Eric Wallace, Sameer Singh

Gradient-based analysis methods, such as saliency map visualizations and adversarial input perturbations, have found widespread use in interpreting neural NLP models due to their simplicity, flexibility, and most importantly, their faithfulness. In this paper, however, we demonstrate that the gradients of a model are easily manipulable, and thus bring into q

Rupert L. Frank, Simon Larson

Let $Ω\subset \mathbb{R}^d$ be bounded with $C^1$ boundary. In this paper we consider Schrödinger operators $-Δ+ W$ on $Ω$ with $W(x)\approx\mathrm{dist}(x, \partialΩ)^{-2}$ as $\mathrm{dist}(x, \partialΩ)\to 0$. Under weak assumptions on $W$ we derive a two-term asymptotic formula for the sum of the eigenvalues of such operators.

Xi Lin, Meng Li, Zuo-jun Max Shen, Yafeng Yin

Connected and automated vehicle (CAV) technology is providing urban transportation managers tremendous opportunities for better operation of urban mobility systems. However, there are significant challenges in real-time implementation, as the computational time of the corresponding operations optimization model increases exponentially with increasing vehicle

Khodakhast Bibak, Bruce M. Kapron, Venkatesh Srinivasan

In this paper, we first give explicit formulas for the number of solutions of unweighted linear congruences with distinct coordinates. Our main tools are properties of Ramanujan sums and of the discrete Fourier transform of arithmetic functions. Then, as an application, we derive an explicit formula for the number of codewords in the Varshamov--Tenengolts co

Zhen-Qing Chen, Panki Kim, Takashi Kumagai, Jian Wang

It is well known that Nash-type inequalities for symmetric Dirichlet forms are equivalent to on-diagonal heat kernel upper bounds for the associated symmetric Markov semigroups. In this paper, we show that both imply (and hence are equivalent to) off-diagonal heat kernel upper bounds under some mild assumptions. Our approach is based on a new generalized Dav

V. Reynaldi, M. Guainazzi, S. Bianchi, I. Andruchow

We present the Catalog of High REsolution Spectra of Obscured Sources (CHRESOS) from the XMM-Newton Science Archive. It comprises the emission-line luminosities of H- and He-like transitions from C to Si, and the Fe 3C and Fe 3G L-shell ones. Here, we concentrate on the soft X-ray OVII(f) and OVIII Ly_alpha emission lines to shed light onto the physical proc

Attayeb Mohsen, Lokesh P. Tripathi, Kenji Mizuguchi

With the advancements in Artificial intelligence (AI) and the accumulation of healthrelated big data, it has become increasingly feasible and commonplace to leverage machine learning technologies to analyze clinical and omics metadata to assess the possibility of adverse drug reactions or events (ADRs) in the course of drug discovery. Here, we have described

Yu Li, Xin Gui, Mojammel A Khan, Weiwei Xie

We have investigated the magnetic and charge transport properties of single crystals of Nowotney Chimney Ladder compound Cr$_{11}$Ge$_{19}$ and mapped out a comprehensive phase diagram reflecting the complicated interplay between the Dzyaloshinskii-Moriya (DM) interaction, the dipolar interaction, and the magnetic anisotropy. We have identified a set of inte

A. Camacho de la Rosa, D. Becerril, G. Gómez-Farfán, R. Esquivel-Sirvent

In this paper we solve the Cattaneo-Vernotte Equation for a periodic heterostructure made of alternate layers of different materials. The solutions describe thermal waves traveling in a periodic system, and it allows us to introduce the concept of thermal crystals. We show that the dispersion relation shows the characteristics of a band-structure, however th

Wange Song, Hanmeng Li, Shenglun Gao, Shengjie Wu

Tremendous efforts have been devoted to the search for exotic topological states, which usually exist at an interface between lattices with differing topological invariants according to the bulk-edge correspondence. Here, we show a new finding of topological states localized at the interface between two gauge-shifted Floquet photonic lattices, despite the sa

Khodakhast Bibak, Bruce M. Kapron, Venkatesh Srinivasan

Let $\Z_n[i]$ be the ring of Gaussian integers modulo a positive integer $n$. Very recently, Camarero and Martínez [IEEE Trans. Inform. Theory, {\bf 62} (2016), 1183--1192], showed that for every prime number $p>5$ such that $p\equiv \pm 5 \pmod{12}$, the Cayley graph $\mathcal{G}_p=\textnormal{Cay}(\Z_p[i], S_2)$, where $S_2$ is the set of units of $\Z_p[i]

Mingzhe Li, Xiuying Chen, Shen Gao, Zhangming Chan

A popular multimedia news format nowadays is providing users with a lively video and a corresponding news article, which is employed by influential news media including CNN, BBC, and social media including Twitter and Weibo. In such a case, automatically choosing a proper cover frame of the video and generating an appropriate textual summary of the article c

Domna G. Kotsifaki, Viet Giang Truong, Sile Nic Chormaic

Optical manipulation has attracted remarkable interest owing to its versatile and non-invasive nature. However, conventional optical trapping remains inefficient for the nanoscopic world. The emergence of plasmonics in recent years has brought a revolutionary change in overcoming limitations due to diffraction and the requirements for high trapping laser pow

Wenfeng Liu, Maoguo Gong, Zedong Tang, A. K. Qin

Graph convolutional networks (GCNs) have been widely used for representation learning on graph data, which can capture structural patterns on a graph via specifically designed convolution and readout operations. In many graph classification applications, GCN-based approaches have outperformed traditional methods. However, most of the existing GCNs are ineffi

Keto D. Zhang, WeiKang Zheng, Thomas de Jaeger, Benjamin E. Stahl

The ejecta velocity is a very important parameter in studying the structure and properties of Type Ia supernovae (SNe Ia). It is also a candidate key parameter in improving the utility of SNe Ia for cosmological distance determinations. Here we study the velocity distribution of a sample of 311 SNe Ia from the kaepora database. The velocities are derived fro

Gerard McCaul, Kurt Jacobs, Denys I. Bondar

We introduce a new technique for the simulation of dissipative quantum systems. This method is composed of an approximate decomposition of the Lindblad equation into a Kraus map, from which one can define an ensemble of wavefunctions. Using principal component analysis, this ensemble can be truncated to a manageable size without sacrificing numerical accurac

Yanan Wu, Nian Li, Xiangyong Zeng

Functions with low $c$-differential uniformity were proposed in $2020$ and attracted lots of attention, especially the P$c$N and AP$c$N functions, due to their applications in cryptography. The objective of this paper is to study P$c$N and AP$c$N functions. As a consequence, we propose a class of P$c$N functions and four classes of AP$c$N functions by using

Xionggui Tang, Yanhua Xu

Brownian ratchet has emerged as a promising tool for understanding motion mechanism of molecules and proteins, and dynamically manipulating particles in non-equilibrium thermodynamics state. Here, we propose and experimentally demonstrate a new type of optical Brownian ratchets, which is generated by using controllable phase profiles in holographic optical t

Hauyu Baobab Liu

We have carried out polarization calibration for archival JVLA ($\sim$9 mm) full polarization observations towards the Class 0 young stellar object (YSO) OMC-3/MMS 6 (also known as HOPS-87), and then compared with the archival ALMA 1.2 mm observations. We found that the innermost $\sim$100 au region of OMC-3/MMS 6 is likely very optically thick (e.g., $τ\gg$

Mohammad Samiul Arshad, William J. Beksi

In this paper, we introduce a novel conditional generative adversarial network that creates dense 3D point clouds, with color, for assorted classes of objects in an unsupervised manner. To overcome the difficulty of capturing intricate details at high resolutions, we propose a point transformer that progressively grows the network through the use of graph co

Binh-Minh Nguyen, Ngoc Tran-Huynh, Michihiro Kawanishi, Tatsuo Narikiyo

How to practically maintain the frequency stability of large-scale power systems by a decentralized way is a simple but non-trivial question. In other words, is it possible to design any local controller without understanding the other controlled areas and with less understanding of network structure? With respect to the special properties of physical intera

Cheng-Zhi Anna Huang, Hendrik Vincent Koops, Ed Newton-Rex, Monica Dinculescu

Machine learning is challenging the way we make music. Although research in deep generative models has dramatically improved the capability and fluency of music models, recent work has shown that it can be challenging for humans to partner with this new class of algorithms. In this paper, we present findings on what 13 musician/developer teams, a total of 61

Tomoharu Iwata

Meta-learning is an important approach to improve machine learning performance with a limited number of observations for target tasks. However, when observations are unbalancedly obtained, it is difficult to improve the performance even with meta-learning methods. In this paper, we propose an active learning method for meta-learning on node response predicti

Sebastian Haan, Fabio Ramos, Dietmar Müller

A critical decision process in data acquisition for mineral and energy resource exploration is how to efficiently combine a variety of sensor types and to minimize total cost. We propose a probabilistic framework for multi-objective optimisation and inverse problems given an expensive cost function for allocating new measurements. This new method is devised

Ho-Lam Chung, Ying-Hong Chan, Yao-Chung Fan

In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods. First, the quality of the existing DG methods are still far from practical use. There is still room for DG quality improvement. Second, the existing DG designs are mainly for single distractor generation. However, for practical MCQ preparation, mu

Kyrollos Yanny, Nick Antipa, William Liberti, Sam Dehaeck

Miniature fluorescence microscopes are a standard tool in systems biology. However, widefield miniature microscopes capture only 2D information, and modifications that enable 3D capabilities increase the size and weight and have poor resolution outside a narrow depth range. Here, we achieve the 3D capability by replacing the tube lens of a conventional 2D Mi

Francis Wagner

We construct the first examples of finitely presented groups with quadratic Dehn function containing a finitely generated infinite torsion subgroup. These examples are "optimal" in the sense that the Dehn function of any such finitely presented group must be at least quadratic. Moreover, we show that for any $n\geq2^{48}$ such that $n$ is either odd

Qinxin Wang, Hao Tan, Sheng Shen, Michael W. Mahoney

Phrase localization is a task that studies the mapping from textual phrases to regions of an image. Given difficulties in annotating phrase-to-object datasets at scale, we develop a Multimodal Alignment Framework (MAF) to leverage more widely-available caption-image datasets, which can then be used as a form of weak supervision. We first present algorithms t

Igor Mezic

We provide a framework for learning of dynamical systems rooted in the concept of representations and Koopman operators. The interplay between the two leads to the full description of systems that can be represented linearly in a finite dimension, based on the properties of the Koopman operator spectrum. The geometry of state space is connected to the notion

Daniel Chicharro, Michel Besserve, Stefano Panzeri

The inference of causal relationships using observational data from partially observed multivariate systems with hidden variables is a fundamental question in many scientific domains. Methods extracting causal information from conditional independencies between variables of a system are common tools for this purpose, but are limited in the lack of independen

Junyao Pan

Given a finite group $G$. The generating pair $(H,a)$ of $G$, that is, $H<G$ and $a\in G$ such that $\langle a,H\rangle=G$. In this paper, we introduce the definition of FF-subgroup to characterize the generating pairs of the symmetric groups, alternating groups and projective groups $PSL(2,q)$. This gives a partial answer to an open problem of J. André and

Viet Anh Nguyen, Fan Zhang, Jose Blanchet, Erick Delage

Conditional estimation given specific covariate values (i.e., local conditional estimation or functional estimation) is ubiquitously useful with applications in engineering, social and natural sciences. Existing data-driven non-parametric estimators mostly focus on structured homogeneous data (e.g., weakly independent and stationary data), thus they are sens

Christopher Iliffe Sprague, Dario Izzo, Petter Ögren

Optimal state-feedback controllers, capable of changing between different objective functions, are advantageous to systems in which unexpected situations may arise. However, synthesising such controllers, even for a single objective, is a demanding process. In this paper, we present a novel and straightforward approach to synthesising these policies through

Kosuke Toda, Naomi Kuze, Toshimitsu Ushio

It is an important decision-making problem for a miner in the blockchain networks if he/she participates in the mining so that he/she earns a reward by creating a new block earlier than other miners. We formulate this decision-making problem as a noncooperative game, because the probability of creating a block depends not only on one&#39;s own available comp

David Salas, Anton Svensson

In 1996, Mallozzi and Morgan [33] proposed a new model for Stackelberg games which we refer here to as the Bayesian approach. The leader has only partial information about how followers select their reaction among possibly multiple optimal ones. This partial information is modeled as a decision-dependent distribution, the so-called belief of the leader. In t

Tom Banks, Bingnan Zhang

We provide semi-rigorous arguments that the Homogeneous Electron Fluid (HEF) has a colloidal phase separating the Wigner Crystal from the high density fluid phase. Near the crossover between crystal and fluid ground state energies, the argument is quite general and valid for practically any quantum transition between a crystal and a more amorphous phase. In

Erlend Grong

As a tool to address the equivalence problem in sub-Riemannian geometry, we introduce a canonical choice of grading and compatible affine connection, available on any sub-Riemannian manifold with constant symbol. We completely compute these structures for contact manifolds of constant symbol, including the cases where the connections of Tanaka-Webster-Tanno

Gabriel Araújo, Igor A. Ferra, Luis F. Ragognette

We investigate global solvability, in the framework of smooth functions and Schwartz distributions, of certain sums of squares of vector fields defined on a product of compact Riemannian manifolds $T \times G$, where $G$ is further assumed to be a Lie group. As in a recent article due to the authors, our analysis is carried out in terms of a system of left-i

Emily Shinkle

We show that for most pairs of surfaces, there exists a finite subgraph of the flip graph of the first surface so that any injective homomorphism of this finite subgraph into the flip graph of the second surface can be extended uniquely to an injective homomorphism between the two flip graphs. Combined with a result of Aramayona-Koberda-Parlier, this implies

Chao Xu, Wang Yang, Congjun Wu

We study a superconducting hetro-junction with one side characterized by the unconventional chiral $p$-wave gap function $p_x\pm ip_y$ and the other side the conventional $s$-wave one. Though a relative phase of $\pm \fracπ{2}$ between any two components of gap functions is favored in the junction region, mutual phase differences cannot achieve $\pm \fracπ{2

Esmail Arasteh Rad

This article provides a ``local'' complementary to the previous results concerning the local models for the moduli stacks of ``global'' $G$-shtukas. Here we study the geometry of Rapoport-Zink spaces for local $P$-shtukas by constructing local models for them. We further discuss certain applications, including some results related to the theory of formal nea

Weilin Li, Kui Ren, Donsub Rim

This work characterizes the range of the single-quadrant approximate discrete Radon transform (ADRT) of square images. The characterization follows from a set of linear constraints on the codomain. We show that for data satisfying these constraints, the exact and fast inversion formula [Rim, Appl. Math. Lett. 102 106159, 2020] yields a square image in a stab

Henrique Ferrolho, Vladimir Ivan, Wolfgang Merkt, Ioannis Havoutis

Benchmarks of state-of-the-art rigid-body dynamics libraries report better performance solving the inverse dynamics problem than the forward alternative. Those benchmarks encouraged us to question whether that computational advantage would translate to direct transcription, where calculating rigid-body dynamics and their derivatives accounts for a significan

Jiahua Chen, Shuai Wang, Sahisnu Mazumder, Bing Liu

Classifying and resolving coreferences of objects (e.g., product names) and attributes (e.g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance. However, the task is challenging as one often needs to consider domain-specific knowledge (e.g., iPad is a tablet and has aspect resolution) to identify coreferences in

Ioannis Antoniou

The existence of cosmological compact objects with very strong gravity is a prediction of General Relativity and an exact solution of the Einstein equations. These objects are called black holes and recently we had the first observations of them. However, the theory of black hole formation has some disadvantages. In order to avoid these, some scientists sugg

Hari Sowrirajan, Jingbo Yang, Andrew Y. Ng, Pranav Rajpurkar

Contrastive learning is a form of self-supervision that can leverage unlabeled data to produce pretrained models. While contrastive learning has demonstrated promising results on natural image classification tasks, its application to medical imaging tasks like chest X-ray interpretation has been limited. In this work, we propose MoCo-CXR, which is an adaptat

Manfred Bucher

Charge-order stripes of different types occur when copper oxides are doped with either heterovalent metal, like $La_{2-x}Sr_xCuO_4$, or oxygen, like $YBa_2Cu_3O_{6+y}$. The difference shows up in the doping dependence of their incommensurability: $q_c(x) \propto \sqrt{x-\check{p}}$ but $q_c(y) \approx 0.3$. The square-root dependence in the former compound f

Jiawei Yang, Yu Chen, Sylwan Rahardja

Over the decades, traditional outlier detectors have ignored the group-level factor when calculating outlier scores for objects in data by evaluating only the object-level factor, failing to capture the collective outliers. To mitigate this issue, we present a method called neighborhood representative (NR), which empowers all the existing outlier detectors t

Ali Najafi, Araz Gholipour-Shilabin, Rahim Dehkharghani, Ali Mohammadpur-Fard

Topic detection is the task of determining and tracking hot topics in social media. Twitter is arguably the most popular platform for people to share their ideas with others about different issues. One such prevalent issue is the COVID-19 pandemic. Detecting and tracking topics on these kinds of issues would help governments and healthcare companies deal wit

Russell Lyons, Avinoam Mann, Romain Tessera, Matthew Tointon

Shalom and Tao showed that a polynomial upper bound on the size of a single, large enough ball in a Cayley graph implies that the underlying group has a nilpotent subgroup with index and degree of polynomial growth both bounded effectively. The third and fourth authors proved the optimal bound on the degree of polynomial growth of this subgroup, at the expen

Xikun Zhang, Deepak Ramachandran, Ian Tenney, Yanai Elazar

Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense, and factual knowledge. One form of knowledge that has not been studied yet in this context is information about the scalar magnitudes of objects. We show that pretrained language models capture a significant amount of this information but are short of the capabi

Christian Pilato, Animesh Basak Chowdhury, Donatella Sciuto, Siddharth Garg

Semiconductor design companies are integrating proprietary intellectual property (IP) blocks to build custom integrated circuits (IC) and fabricate them in a third-party foundry. Unauthorized IC copies cost these companies billions of dollars annually. While several methods have been proposed for hardware IP obfuscation, they operate on the gate-level netlis

Wenhao Li

I characterize the consumer-optimal market segmentation in competitive markets where multiple firms selling differentiated products to consumers with unit demand. This segmentation is public---in that each firm observes the same market segments---and takes a simple form: in each market segment, there is a dominant firm favored by all consumers in that segmen

Amber Srivastava, Raj K. Velicheti, Srinivasa M. Salapaka

Many studies involving large Markov chains require determining a smaller representative (aggregated) chains. Each {\em superstate} in the representative chain represents a {\em group of related} states in the original Markov chain. Typically, the choice of number of superstates in the aggregated chain is ambiguous, and based on the limited prior know-how. In

Da Zheng, Chao Ma, Minjie Wang, Jinjing Zhou

Graph neural networks (GNN) have shown great success in learning from graph-structured data. They are widely used in various applications, such as recommendation, fraud detection, and search. In these domains, the graphs are typically large, containing hundreds of millions of nodes and several billions of edges. To tackle this challenge, we develop DistDGL,

Roberto Violi

I present two independent proofs of the Riemann Hypothesis considered by many the greatest unsolved problem in mathematics. I find that the admissible domain of complex zeros of the Riemann Zeta Function is the critical line. The methods and results of this paper are based on well-known theorems on the number of zeros for complex value functions (Jensen, Tit

Justin N. M. Pinkney, Doron Adler

GANs can generate photo-realistic images from the domain of their training data. However, those wanting to use them for creative purposes often want to generate imagery from a truly novel domain, a task which GANs are inherently unable to do. It is also desirable to have a level of control so that there is a degree of artistic direction rather than purely cu

Danielle Saunders, Rosie Sallis, Bill Byrne

Neural Machine Translation (NMT) has been shown to struggle with grammatical gender that is dependent on the gender of human referents, which can cause gender bias effects. Many existing approaches to this problem seek to control gender inflection in the target language by explicitly or implicitly adding a gender feature to the source sentence, usually at th

Da Wu

Let $C\geq 2$ be a positive integer. Consider the set of $n\times n$ non-negative integer matrices whose row sums and column sums are all equal to $Cn$ and let $X=(X_{ij})_{1\leq i,j\leq n}$ be uniformly distributed on this set. This $X$ is called the random contingency table with uniform margin. In this paper, we study various asymptotic properties of $X=(X

Brielen Madureira, David Schlangen

While humans process language incrementally, the best language encoders currently used in NLP do not. Both bidirectional LSTMs and Transformers assume that the sequence that is to be encoded is available in full, to be processed either forwards and backwards (BiLSTMs) or as a whole (Transformers). We investigate how they behave under incremental interfaces,

Oleg G. Kharlanov, Pavel I. Shustov

We derive the effective Hamiltonian describing collective oscillations of Majorana neutrinos with a transition magnetic moment, allowing for the presence of scalar and pseudoscalar nonstandard neutrino self-interactions (NSSIs). Using this Hamiltonian, we analyze new flavor instability channels of collective oscillations in a core-collapse supernova environm

Carsten H. Botts

In this paper we simulate an ensemble of cooperating, mobile sensing agents that implement the cyclic stochastic optimization (CSO) algorithm in an attempt to survey and track multiple targets. In the CSO algorithm proposed, each agent uses its sensed measurements, its shared information, and its predictions of others&#39; future motion to decide on its next

Connor L. Box, Yaolong Zhang, Rongrong Yin, Bin Jiang

Nonadiabatic effects that arise from the concerted motion of electrons and atoms at comparable energy and time scales are omnipresent in thermal and light-driven chemistry at metal surfaces. Excited (hot) electrons can measurably affect molecule-metal reactions by contributing to state-dependent reaction probabilities. Vibrational state-to-state scattering o

Alexandre R. Berger, Edward B. White

Boundary layer transition triggered by a discrete roughness element generates a turbulent wedge that spreads laterally as it proceeds downstream. Historical literature reports the spreading half angle is approximately 6$^{\circ}$ in zero-pressure gradient flows regardless of Reynolds number and roughness shape. Recent simulations and experiments have sought