cssi/SciSciGPT-SciSciNet huggingface dataset:
Secure OFDM System Design and Capacity Analysis Under Disguised Jamming

In this paper, we propose a securely precoded OFDM (SP-OFDM) system for efficient and reliable transmission under disguised jamming, where the jammer intentionally misleads the receiver by mimicking the characteristics of the authorized signal and causes complete communication failure. More specifically, we bring off a dynamic constellation by introducing secure randomness shared between the legitimate transmitter and receiver, and hence, break the symmetricity between the authorized signal and the disguised jamming. We analyze the channel capacities of both the traditional OFDM and SP-OFDM under hostile jamming using the arbitrarily varying channel (AVC) model. It is shown that the deterministic coding capacity of the traditional OFDM is zero under the worst disguised jamming. On the other hand, due to the secure randomness shared between the authorized transmitter and receiver, SP-OFDM can achieve a positive capacity under disguised jamming since the AVC channel corresponding to SP-OFDM is not symmetrizable. A remarkable feature of the proposed SP-OFDM scheme is that while achieving strong jamming resistance, it has roughly the same high spectral efficiency as the traditional OFDM system. The robustness of the proposed SP-OFDM scheme under disguised jamming is demonstrated through both theoretic and numerical analyses.
ON THE VON NEUMANN AND FRANK-WOLFE ALGORITHMS WITH AWAY STEPS ∗

The von Neumann algorithm is a simple coordinate-descent algorithm to determine whether the origin belongs to a polytope generated by a finite set of points. When the origin is in the interior of the polytope, the algorithm generates a sequence of points in the polytope that converges linearly to zero. The algorithm's rate of convergence depends on the radius of the largest ball around the origin contained in the polytope. We show that under the weaker condition that the origin is in the polytope, possibly on its boundary, a variant of the von Neumann algorithm that includes away steps generates a sequence of points in the polytope that converges linearly to zero. The new algorithm's rate of convergence depends on a certain geometric parameter of the polytope that extends the above radius but is always positive. Our linear convergence result and geometric insights also extend to a variant of the Frank--Wolfe algorithm with away steps for minimizing a convex quadratic function over a polytope.
Identifying Active Sites of the Water–Gas Shift Reaction over Titania Supported Platinum Catalysts under Uncertainty

A comprehensive uncertainty quantification framework has been developed for integrating computational and experimental kinetic data and to identify active sites and reaction mechanisms in catalysis. Three hypotheses regarding the active site for the water–gas shift reaction on Pt/TiO2 catalysts, Pt(111), an edge interface site, and a corner interface site, are tested against experimental kinetic data from three research groups. Uncertainties associated with density functional theory (DFT) calculations and model errors of microkinetic models of the active sites are informed and verified using Bayesian inference and predictive validation. Significant evidence is found for the role of the oxide support in the mechanism. Positive evidence is found in support of the edge interface active site over the corner interface site. For the edge interface site, the CO-promoted redox mechanism is found to be the dominant pathway and only at temperatures above 573 K does the classical redox mechanism contribute significa...
Maintaining the Humanity of Our Models.

Artificial intelligence and machine learning have been major research interests in computer science for the better part of the last few decades. However, all too recently, both AI and ML have rapidly grown to be media frenzies, pressuring companies and researchers to claim they use these technologies. As ML continues to percolate into daily life, we, as computer scientists and machine learning researchers, are responsible for ensuring we clearly convey the extent of our work and the humanity of our models. Regularizing ML for mass adoption requires a rigorous standard for model interpretability, a deep consideration for human bias in data, and a transparent understanding of a model's societal effects.
Coverage Embedding Models for Neural Machine Translation.

In this paper, we enhance the attention-based neural machine translation (NMT) by adding explicit coverage embedding models to alleviate issues of repeating and dropping translations in NMT. For each source word, our model starts with a full coverage embedding vector to track the coverage status, and then keeps updating it with neural networks as the translation goes. Experiments on the large-scale Chinese-to-English task show that our enhanced model improves the translation quality significantly on various test sets over the strong large vocabulary NMT system.
Variational Particle Approximations

Approximate inference in high-dimensional, discrete probabilistic models is a central problem in computational statistics and machine learning. This paper describes discrete particle variational inference (DPVI), a new approach that combines key strengths of Monte Carlo, variational and search-based techniques. DPVI is based on a novel family of particle-based variational approximations that can be fit using simple, fast, deterministic search techniques. Like Monte Carlo, DPVI can handle multiple modes, and yields exact results in a well-defined limit. Like unstructured mean-field, DPVI is based on optimizing a lower bound on the partition function; when this quantity is not of intrinsic interest, it facilitates convergence assessment and debugging. Like both Monte Carlo and combinatorial search, DPVI can take advantage of factorization, sequential structure, and custom search operators. This paper defines DPVI particle-based approximation family and partition function lower bounds, along with the sequential DPVI and local DPVI algorithm templates for optimizing them. DPVI is illustrated and evaluated via experiments on lattice Markov Random Fields, nonparametric Bayesian mixtures and block-models, and parametric as well as non-parametric hidden Markov models. Results include applications to real-world spike-sorting and relational modeling problems, and show that DPVI can offer appealing time/accuracy trade-offs as compared to multiple alternatives. ∗First two authors contributed equally.
Graph Algorithms for Topology Identification Using Power Grid Probing

To perform any meaningful optimization task, power distribution operators need to know the topology and line impedances of their electric networks. Nevertheless, distribution grids currently lack a comprehensive metering infrastructure. Although smart inverters are widely used for control purposes, they have been recently advocated as the means for an active data acquisition paradigm: reading the voltage deviations induced by intentionally perturbing inverter injections, the system operator can potentially recover the electric grid topology. Adopting inverter probing for feeder processing, a suite of graph-based topology identification algorithms is developed here. If the grid is probed at all leaf nodes but voltage data are metered at all nodes, the entire feeder topology can be successfully recovered. When voltage data are collected only at probing buses, the operator can find a reduced feeder featuring key properties and similarities to the actual feeder. To handle modeling inaccuracies and load nonstationarity, noisy probing data need to be preprocessed. If the suggested guidelines on the magnitude and duration of probing are followed, the recoverability guarantees carry over from the noiseless to the noisy setup with high probability.
On known and less known relations of Leonhard Euler with Poland

In this work we focus on research contacts of Leonhard Euler with Polish scientists of his era, mainly with those from the city of Gdansk (then Gedanum, Danzig). L. Euler was the most prolific mathematician of all times, the most outstanding mathematician of the 18th century, and one of the best ever. The complete edition of his manuscripts is still in process (Kleinert 2015; Kleinert, Mattmuller 2007). Euler’s contacts with French, German, Russian, and Swiss scientists have been widely known, while relations with Poland, then one of the largest European countries, are still in oblivion. Euler visited Poland only once, in June of 1766, on his way back from Berlin to St. Petersburg. He was hosted for ten days in Warsaw by Stanislaw II August Poniatowski, the last king of Poland. Many Polish scientists were introduced to Euler, not only from mathematical circles, but also astronomers and geographers. The correspondence of Euler with Gdansk scientists and officials, including Carl L. Ehler, Heinrich Kuhn and Nathanael M. von Wolf, originated already in the mid-1730s. We highlight the relations of L. Euler with H. Kuhn, a professor of mathematics at the Danzig Academic Gymnasium and arguably the best Polish mathematician of his era. It was H. Kuhn from whom Euler learned about the Konigsberg Bridge Problem; hence one can argue that the beginning of the graph theory and topology of the plane originated in Gdansk. In addition, H. Kuhn was the first mathematician who proposed a geometric interpretation of complex numbers, the theme very much appreciated by Euler. Findings included in this paper are either unknown or little known to a general mathematical community.
Metric inequalities for polygons

Let A 1 , A 2 ,…, A n be the vertices of a polygon with unit perimeter, that is Σ i | A i A i +1 |=1. We derive various tight estimates on the minimum and maximum values of the sum of pairwise distances, and respectively sum of pairwise squared distances among its vertices. In most cases such estimates on these sums in the literature were known only for convex polygons. In the second part, we turn to a problem of Bras regarding the maximum perimeter of a simple n -gon ( n odd) contained in a disk of unit radius. The problem was recently solved by Audet et al. 2009, who gave an exact formula. Here we present an alternative simpler proof of this formula. We then examine what happens if the simplicity condition is dropped, and obtain an exact formula for the maximum perimeter in this case as well.
Reinforcement learning with partially known world dynamics

Reinforcement learning would enjoy better success on real-world problems if domain knowledge could be imparted to the algorithm by the modelers. Most problems have both hidden state and unknown dynamics. Partially observable Markov decision processes (POMDPs) allow for the modeling of both. Unfortunately, they do not provide a natural framework in which to specify knowledge about the domain dynamics. The designer must either admit to knowing nothing about the dynamics or completely specify the dynamics (thereby turning it into a planning problem). We propose a new framework called a partially known Markov decision process (PKMDP) which allows the designer to specify known dynamics while still leaving portions of the environment's dynamics unknown. The model represents not only the environment dynamics but also the agent's knowledge of the dynamics. We present a reinforcement learning algorithm for this model based on importance sampling. The algorithm incorporates planning based on the known dynamics and learning about the unknown dynamics. Our results clearly demonstrate the ability to add domain knowledge and the resulting benefits for learning.
Jacobi matrices generated by ratios of hypergeometric functions

A problem of determining zeroes of the Gauss hypergeometric function goes back to Klein, Hurwitz, and Van Vleck. In this very short note we show how ratios of hypergeometric functions arise as m-fu...
A dynamical Shafarevich theorem for twists of rational morphisms

Let $K$ denote a number field and a finite set $S$ of places of $K$ and $\phi:\PP^n\rightarrow\PP^n$ be rational morphism defined over $K$. The main result of this paper proves that there are only finitely many twists of $\phi$ defined over $K$ which have good reduction at all places outside $S$. This answers a question of Silverman in the affirmative.
The Galois group of a stable homotopy theory

Abstract To a “stable homotopy theory” (a presentable, symmetric monoidal stable ∞-category), we naturally associate a category of finite etale algebra objects and, using Grothendieck's categorical machine, a profinite group that we call the Galois group. We then calculate the Galois groups in several examples. For instance, we show that the Galois group of the periodic E ∞ -algebra of topological modular forms is trivial and that the Galois group of K ( n ) -local stable homotopy theory is an extended version of the Morava stabilizer group. We also describe the Galois group of the stable module category of a finite group. A fundamental idea throughout is the purely categorical notion of a “descendable” algebra object and an associated analog of faithfully flat descent in this context.
Sanskrit Sandhi Splitting using seq2(seq)^2

In Sanskrit, small words (morphemes) are combined to form compound words through a process known as Sandhi. Sandhi splitting is the process of splitting a given compound word into its constituent morphemes. Although rules governing word splitting exists in the language, it is highly challenging to identify the location of the splits in a compound word. Though existing Sandhi splitting systems incorporate these pre-defined splitting rules, they have a low accuracy as the same compound word might be broken down in multiple ways to provide syntactically correct splits. In this research, we propose a novel deep learning architecture called Double Decoder RNN (DD-RNN), which (i) predicts the location of the split(s) with 95% accuracy, and (ii) predicts the constituent words (learning the Sandhi splitting rules) with 79.5% accuracy, outperforming the state-of-art by 20%. Additionally, we show the generalization capability of our deep learning model, by showing competitive results in the problem of Chinese word segmentation, as well.
Group Membership Prediction

The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a familial relationship. In this context we propose a novel probability model and introduce latent view-specific and view-shared random variables to jointly account for the view-specific appearance and cross-view similarities among data instances. Our model posits that data from each view is independent conditioned on the shared variables. This postulate leads to a parametric probability model that decomposes group membership likelihood into a tensor product of data-independent parameters and data-dependent factors. We propose learning the data-independent parameters in a discriminative way with bilinear classifiers, and test our prediction algorithm on challenging visual recognition tasks such as multi-camera person re-identification and kinship verification. On most benchmark datasets, our method can significantly outperform the current state-of-the-art.
The Two-Dimensional Small Ball Inequality and Binary Nets

In the current paper we present a new proof of the small ball inequality in two dimensions. More importantly, this new argument, based on an approach inspired by lacunary Fourier series, reveals the first formal connection between this inequality and discrepancy theory, namely the construction of two-dimensional binary nets, i.e. finite sets which are perfectly distributed with respect to dyadic rectangles. This relation allows one to generate all possible point distributions of this type. In addition, we outline a potential approach to the higher-dimensional small ball inequality by a dimension reduction argument. In particular this gives yet another proof of the two-dimensional signed (i.e. coefficients $$\pm 1$$ ) small ball inequality by reducing it to a simple one-dimensional estimate. However, we show that an analogous estimate fails to hold for arbitrary coefficients.
Cloaking the clock: emulating clock skew in controller area networks

Automobiles are equipped with Electronic Control Units (ECUs) that communicate via in-vehicle network protocol standards such as the Controller Area Network (CAN). These protocols were designed under the assumption that separating in-vehicle communications from external networks is sufficient for protection against cyber attacks. This assumption, however, has been shown to be invalid by recent attacks in which adversaries were able to infiltrate the in-vehicle network. Motivated by these attacks, intrusion detection systems (IDSs) have been proposed for in-vehicle networks that attempt to detect attacks by exploiting physical properties such as clock skew of an ECU. In this paper, we propose the cloaking attack, an intelligent masquerade attack in which an adversary modifies the timing of transmitted messages to match the clock skew of a targeted ECU. The attack leverages the fact that, while the clock skew is a physical property of each ECU that cannot be changed by the adversary, the estimation of the clock skew by other ECUs is based on the timing of network traffic, which, being a cyber component only, can be modified by an adversary. We implement the proposed cloaking attack and test it on two IDSs, namely, the current state-of-the-art IDS and its adaptation to the widely-used Network Time Protocol (NTP). We implement the cloaking attack on two hardware testbeds, a prototype and a real vehicle, and show that it is able to deceive both IDSs. We also introduce a new metric called the Maximum Slackness Index to quantify the effectiveness of a clock skew-based IDS in detecting masquerade attacks when the adversary is unable to precisely match the clock skew of the targeted ECU.
A Compact Circumstellar Shell as the Source of High--velocity Features in SN 2011fe

High–velocity features (HVF), especially of Ca II, are frequently seen in Type Ia supernovae observed prior to B–band maximum (Bmax). These HVF start at more than 25, 000 km s in the days after first light, and slow to about 18, 000 km s near Bmax. To recreate the Ca II near–infrared triplet (CaNIR) HVF in SN 2011fe, we consider the interaction between a Type Ia supernova and a compact circumstellar shell, employing a hydrodynamic 1–D simulation using FLASH. We generate synthetic spectra from the hydrodynamic results using syn++. We show that the CaNIR HVF and its velocity evolution is better explained by a supernova model interacting with a shell than a model without a shell, and briefly discuss the implications for progenitor models. Subject headings: supernovae: general — supernovae: individual (SN 2011fe) — line: formation — line: profiles
New SOCP relaxation and branching rule for bipartite bilinear programs

A bipartite bilinear program (BBP) is a quadratically constrained quadratic optimization problem where the variables can be partitioned into two sets such that fixing the variables in any one of the sets results in a linear program. We propose a new second order cone representable (SOCP) relaxation for BBP, which we show is stronger than the standard SDP relaxation intersected with the boolean quadratic polytope. We then propose a new branching rule inspired by the construction of the SOCP relaxation. We describe a new application of BBP called as the finite element model updating problem, which is a fundamental problem in structural engineering. Our computational experiments on this problem class show that the new branching rule together with an polyhedral outer approximation of the SOCP relaxation outperforms a state-of-the-art commercial global solver in obtaining dual bounds.
Device-to-device communications in the millimeter wave band: A novel distributed mechanism

In spite of its potential advantages, the large-scale implementation of the device-to-device (D2D) communications has yet to be realized, mainly due to severe interference and lack of enough bandwidth in the microwave (sW) band. Recently, exploiting the millimeter wave (mmW) band for D2D communications has attracted considerable attention as a potential solution to these challenges. However, its severe sensitivity to blockage along with its directional nature make the utilization of the mmW band a challenging task as it requires line-of-sight (LOS) link detection and careful beam alignment between the D2D transceivers. In this paper, we propose a novel distributed mechanism which enables the D2D devices to discover unblocked LOS links for the mmW band communication. Moreover, as such LOS links are not always available, the proposed mechanism allows the D2D devices to switch to the sW band if necessary. In addition, the proposed mechanism detects the direction of the LOS links to perform the beam alignment. We have used tools from stochastic geometry to evaluate the performance of the proposed mechanism in terms of the signal-to-interference-plus-noise ratio (SINR) coverage probability. The performance of the proposed algorithm is then compared to the one of the single band (i.e., sW/mmW) communication. The simulation results show that the proposed mechanism considerably outperforms the single band communication.
Factorization of matrices of quaternions

Abstract We review known factorization results for quaternion matrices. Specifically, we derive the Jordan canonical form, polar decomposition, singular value decomposition, and QR factorization. We prove that there is a Schur factorization for commuting matrices, and from this derive the spectral theorem. We do not consider algorithms, but do point to some of the numerical literature. Rather than work directly with matrices of quaternions, we work with complex matrices with a specific symmetry based on the dual operation. We discuss related results regarding complex matrices that are self-dual or symmetric, but perhaps not Hermitian.
No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models

For decades, context-dependent phonemes have been the dominant sub-word unit for conventional acoustic modeling systems. This status quo has begun to be challenged recently by end-to-end models which seek to combine acoustic, pronunciation, and language model components into a single neural network. Such systems, which typically predict graphemes or words, simplify the recognition process since they remove the need for a separate expert-curated pronunciation lexicon to map from phoneme-based units to words. However, there has been little previous work comparing phoneme-based versus grapheme-based sub-word units in the end-to-end modeling framework, to determine whether the gains from such approaches are primarily due to the new probabilistic model, or from the joint learning of the various components with grapheme-based units. In this work, we conduct detailed experiments which are aimed at quantifying the value of phoneme-based pronunciation lexica in the context of end-to-end models. We examine phoneme-based end-to-end models, which are contrasted against grapheme-based ones on a large vocabulary English Voice-search task, where we find that graphemes do indeed outperform phonemes. We also compare grapheme and phoneme-based approaches on a multi-dialect English task, which once again confirm the superiority of graphemes, greatly simplifying the system for recognizing multiple dialects.
LabelRank: A stabilized label propagation algorithm for community detection in networks

An important challenge in big data analysis nowadays is detection of cohesive groups in large-scale networks, including social networks, genetic networks, communication networks and so. In this paper, we propose LabelRank, an efficient algorithm detecting communities through label propagation. A set of operators is introduced to control and stabilize the propagation dynamics. These operations resolve the randomness issue in traditional label propagation algorithms (LPA), stabilizing the discovered communities in all runs of the same network. Tests on real-world networks demonstrate that LabelRank significantly improves the quality of detected communities compared to LPA, as well as other popular algorithms.
Global existence for semilinear wave equations exterior to nontrapping obstacles

In this paper, we show global existence, in spatial dimensions greater than or equal to four, for semilinear wave equations with quadratic nonlinearities exterior to a nontrapping obstacle. This extends the previous work of Shibata-Tsutsumi and Hayashi. Shibata-Tsutsumi showed the result for spatial dimensions greater than or equal to 6, and Hayashi proved the result exterior to a ball.
Computable decision making on the reals and other spaces: via partiality and nondeterminism

Though many safety-critical software systems use floating point to represent real-world input and output, the mathematical specifications of these systems' behaviors use real numbers. Significant deviations from those specifications can cause errors and jeopardize safety. To ensure system safety, some programming systems offer exact real arithmetic, which often enables a program's computation to match its mathematical specification exactly. However, exact real arithmetic complicates decision-making: in these systems, it is impossible to compute (total and deterministic) discrete decisions based on connected spaces such as R. We present programming-language semantics based on constructive topology with variants allowing nondeterminism and/or partiality. Either nondeterminism or partiality suffices to allow computable decision making on connected spaces such as R. We then introduce pattern matching on spaces, a language construct for creating programs on spaces, generalizing pattern matching in functional programming, where patterns need not represent decidable predicates and also may overlap or be inexhaustive, giving rise to nondeterminism or partiality, respectively. Nondeterminism and/or partiality also yield formal logics for constructing approximate decision procedures. We extended the Marshall language for exact real arithmetic with these constructs and implemented some programs with it.
Weak expansion properties and large deviation principles for expanding Thurston maps

Abstract In this paper, we prove that an expanding Thurston map f : S 2 → S 2 is asymptotically h-expansive if and only if it has no periodic critical points, and that no expanding Thurston map is h-expansive. As a consequence, for each expanding Thurston map without periodic critical points and each real-valued continuous potential on S 2 , there exists at least one equilibrium state. For such maps, we also establish large deviation principles for iterated preimages and periodic points. It follows that iterated preimages and periodic points are equidistributed with respect to the unique equilibrium state for an expanding Thurston map without periodic critical points and a potential that is Holder continuous with respect to a visual metric on S 2 .
Type Ia Supernovae from Merging White Dwarfs. II. Post-merger Detonations

Merging carbon-oxygen (CO) white dwarfs are a promising progenitor system for Type Ia supernovae (SNe Ia), but the underlying physics and timing of the detonation are still debated. If an explosion occurs after the secondary star is fully disrupted, the exploding primary will expand into a dense CO medium that may still have a disk-like structure. This interaction will decelerate and distort the ejecta. Here we carry out multidimensional simulations of 'tamped' SN Ia models, using both particle and grid-based codes to study the merger and explosion dynamics and a radiative transfer code to calculate synthetic spectra and light curves. We find that post-merger explosions exhibit an hourglass-shaped asymmetry, leading to strong variations in the light curves with viewing angle. The two most important factors affecting the outcome are the scale height of the disk, which depends sensitively on the binary mass ratio, and the total {sup 56}Ni yield, which is governed by the central density of the remnant core. The synthetic broadband light curves rise and decline very slowly, and the spectra generally look peculiar, with weak features from intermediate mass elements but relatively strong carbon absorption. We also consider the effects of the viscous evolution of the remnantmore » and show that a longer time delay between merger and explosion probably leads to larger {sup 56}Ni yields and more symmetrical remnants. We discuss the relevance of this class of aspherical 'tamped' SN Ia for explaining the class of 'super-Chandrasekhar' SN Ia.« less
Microwave-to-optical conversion using lithium niobate thin-film acoustic resonators

Acoustic or mechanical resonators have emerged as a promising means to mediate efficient microwave-to-optical conversion. Here, we demonstrate conversion of microwaves up to 4.5 GHz in frequency to 1500 nm wavelength light using optomechanical interactions on suspended thin-film lithium niobate. Our method uses an interdigital transducer that drives a freestanding 100 μm-long thin-film acoustic resonator to modulate light traveling in a Mach–Zehnder interferometer or racetrack cavity. The strong microwave-to-acoustic coupling offered by the transducer in conjunction with the strong photoelastic, piezoelectric, and electro-optic effects of lithium niobate allows us to achieve a half-wave voltage of Vπ=4.6 V and Vπ=0.77 V for the Mach–Zehnder interferometer and racetrack resonator, respectively. The acousto-optic racetrack cavity exhibits an optomechanical single-photon coupling strength of 1.1 kHz. To highlight the versatility of our system, we also demonstrate a microwave photonic link with unitary gain, which refers to a 0 dB microwave power transmission over an optical channel. Our integrated nanophotonic platform, which leverages the compelling properties of lithium niobate, could help enable efficient conversion between microwave and optical fields.
Fast second-order cone programming for safe mission planning

This paper considers the problem of safe mission planning of dynamic systems operating under uncertain environments. Much of the prior work on achieving robust and safe control requires solving second-order cone programs (SOCP). Unfortunately, existing general purpose SOCP methods are often infeasible for real-time robotic tasks due to high memory and computational requirements imposed by existing general optimization methods. The key contribution of this paper is a fast and memory-efficient algorithm for SOCP that would enable robust and safe mission planning on-board robots in realtime. Our algorithm does not have any external dependency, can efficiently utilize warm start provided in safe planning settings, and in fact leads to significant speed up over standard optimization packages (like SDPT3) for even standard SOCP problems. For example, for a standard quadrotor problem, our method leads to speedup of 1000x over SDPT3 without any deterioration in the solution quality. Our method is based on two insights: a) SOCPs can be interpreted as optimizing a function over a polytope with infinite sides, b) a linear function can be efficiently optimized over this polytope. We combine the above observations with a novel utilization of Wolfe's algorithm [1] to obtain an efficient optimization method that can be easily implemented on small embedded devices. In addition to the above mentioned algorithm, we also design a two-level sensing method based on Gaussian Process for complex obstacles with non-linear boundaries such as a cylinder.
Light-induced optical switching in an asymmetric metal-dielectric microcavity with phase-change material

We propose an infrared power switch based on an asymmetric high-Q microcavity incorporating a metallic nanolayer in close proximity to a layer made of a phase-change material (PCM). The microcavity is designed so that when the PCM layer is in the low-temperature phase, the metallic nanolayer coincides with a nodal plane of the resonant electric-field component, to allow a high resonant transmittance. As the light intensity exceeds a certain threshold, light-induced heating of the PCM layer triggers the phase transition accompanied by an abrupt change in its refractive index in the vicinity of the transition temperature. The latter results in a shift of the nodal plane away from the metallic nanolayer, rendering the entire microcavity highly reflective over a broad frequency range. The nearly binary nature of the PCM refractive index allows for the low-intensity resonant transmission over a broad range of ambient temperatures below the transition point.
Sensorimotor transformation elicits systematic patterns of activity along the dorsoventral extent of the superior colliculus in the macaque monkey

The superior colliculus (SC) is an excellent substrate to study sensorimotor transformations. To date, the spatial and temporal properties of population activity along its dorsoventral axis have been inferred from single electrode studies. Here, we recorded SC population activity in non-human primates using a linear multi-contact array during delayed saccade tasks. We show that during the visual epoch, information appeared first in dorsal layers and systematically later in ventral layers. During the delay period, the laminar organization of low-spiking rate activity matched that of the visual epoch. During the pre-saccadic epoch, spiking activity emerged first in a more ventral layer, ~ 100 ms before saccade onset. This buildup of activity appeared later on nearby neurons situated both dorsally and ventrally, culminating in a synchronous burst across the dorsoventral axis, ~ 28 ms before saccade onset. Collectively, these results reveal a principled spatiotemporal organization of SC population activity underlying sensorimotor transformation for the control of gaze. Corentin Massot et al. use a linear multi-contact array to analyze neuronal activity along the superior colliculus of macaque monkeys during a delayed saccade task. They show that activity of superior colliculus neurons during the visual and pre-saccadic epochs follows a systematic spatiotemporal organization.
Mathematics of Smoothed Particle Hydrodynamics: A Study via Nonlocal Stokes Equations

Smoothed particle hydrodynamics (SPH) is a popular numerical technique developed for simulating complex fluid flows. Among its key ingredients is the use of nonlocal integral relaxations to local differentiations. Mathematical analysis of the corresponding nonlocal models on the continuum level can provide further theoretical understanding of SPH. We present, in this part of a series of works on the mathematics of SPH, a nonlocal relaxation to the conventional linear steady-state Stokes system for incompressible viscous flows. The nonlocal continuum model is characterized by a smoothing length $$\delta $$ which measures the range of nonlocal interactions. It serves as a bridge between the discrete approximation schemes that involve a nonlocal integral relaxation and the local continuum models. We show that for a class of carefully chosen nonlocal operators, the resulting nonlocal Stokes equation is well-posed and recovers the original Stokes equation in the local limit when $$\delta $$ approaches zero. For some other commonly used smooth kernels, there are risks in getting ill-posed continuum models that could lead to computational difficulties in practice. This leads us to discuss the implications of our finding on the design of numerical methods.
Predicting the effects of SNPs on transcription factor binding affinity.

Motivation Genome-wide association studies have revealed that 88% of disease-associated single-nucleotide polymorphisms (SNPs) reside in noncoding regions. However, noncoding SNPs remain understudied, partly because they are challenging to prioritize for experimental validation. To address this deficiency, we developed the SNP effect matrix pipeline (SEMpl). Results SEMpl estimates transcription factor-binding affinity by observing differences in chromatin immunoprecipitation followed by deep sequencing signal intensity for SNPs within functional transcription factor-binding sites (TFBSs) genome-wide. By cataloging the effects of every possible mutation within the TFBS motif, SEMpl can predict the consequences of SNPs to transcription factor binding. This knowledge can be used to identify potential disease-causing regulatory loci. Availability and implementation SEMpl is available from https://github.com/Boyle-Lab/SEM_CPP. Supplementary information Supplementary data are available at Bioinformatics online.
Phrase grounding by soft-label chain conditional random field

The phrase grounding task aims to ground each entity mention in a given caption of an image to a corresponding region in that image. Although there are clear dependencies between how different mentions of the same caption should be grounded, previous structured prediction methods that aim to capture such dependencies need to resort to approximate inference or non-differentiable losses. In this paper, we formulate phrase grounding as a sequence labeling task where we treat candidate regions as potential labels, and use neural chain Conditional Random Fields (CRFs) to model dependencies among regions for adjacent mentions. In contrast to standard sequence labeling tasks, the phrase grounding task is defined such that there may be multiple correct candidate regions. To address this multiplicity of gold labels, we define so-called Soft-Label Chain CRFs, and present an algorithm that enables convenient end-to-end training. Our method establishes a new state-of-the-art on phrase grounding on the Flickr30k Entities dataset. Analysis shows that our model benefits both from the entity dependencies captured by the CRF and from the soft-label training regime. Our code is available at github.com/liujch1998/SoftLabelCCRF
Efficient convolutional neural network for FMCW radar based hand gesture recognition

FMCW radar could detect object's range, speed and Angle-of-Arrival, advantages are robust to bad weather, good range resolution, and good speed resolution. In this paper, we consider the FMCW radar as a novel interacting interface on laptop. We merge sequences of object's range, speed, azimuth information into single input, then feed to a convolution neural network to learn spatial and temporal patterns. Our model achieved 96% accuracy on test set and real-time test.
Touchdown on the Cloud: The Impact of the Super Bowl on Cloud

The Super Bowl is the world's biggest televised sporting event. We examine the impact of the increasing online activities during the Super Bowl of year 2015-2017 on various types of Cloud systems, including the Cloud infrastructure service, the Cloud content delivery networks for live streaming, and popular web services such as on-demand video streaming service and social network applications. We probe these systems from agents deployed around the world to compare their load variations and performance changes during and after the game. Through our studies of three consecutive years, we find that Super Bowl events have impacts on the Cloud. However, the current Cloud system is still able to ensure capacity to cope with the challenges brought by traffic changes during such massive events.
Decentralized Pliable Index Coding

This paper introduces the decentralized Pliable Index CODing (PICOD) problem: a variant of the Index Coding (IC) problem, where a central transmitter serves pliable users with message side information; here, pliable refers to the fact that a user is satisfied by decoding any t messages that are not in its side information set. In the decentralized PICOD, a central transmitter with knowledge of all messages is not present, and instead users share among themselves massages that can only depend on their local side information set. This paper characterizes the capacity of two classes of decentralized complete–S PICOD(t) problems with m messages (where the set S ⊂ [m] contains the sizes of the side information sets, and the number of users is $n = \sum\nolimits_{s \in S} {\binom{m}{s}} $, with no two users having the same side information set): (i) the consecutive case S = [s min : s max ] for some 0 ⩽ s min ⩽ s max ⩽ m –t, and (ii) the complement-consecutive case S = [0 : m−t]\ [s min : s max ], for some 0 ⩽ s min ⩽ s max ⩽ m –t. Interestingly, the optimal code-length for the decentralized PICOD in those cases is the same as for the classical (centralized) PICOD counterpart, except when the problem is no longer pliable, that is, it reduces to an IC problem where every user needs to decode all messages not in its side information set. Although the optimal code-length may be the same in both centralized and decentralized settings, the actual optimal codes are not. For the decentralized PICOD, sparse Maximum Distance Separable (MDS) codes and vector linear index codes are used (as opposed to scalar linear codes).
An introduction to a supersymmetric graph algebra

In this paper, we propose a graph superalgebra which is the supersymmetric analogue of Leavitt path algebras. We find a basis for these superalgebras and characterize when they have polynomial growth.
Temperature Shocks and Establishment Sales

Combining granular daily data on temperatures across the continental United States with detailed establishment data from 1990 to 2015, we study the causal impact of temperature shocks on establishment sales and productivity. Using a large sample yielding precise estimates, we do not find evidence that temperature exposures significantly affect establishment-level sales or productivity, including among industries traditionally classified as “heat sensitive.” At the firm level, we find that temperature exposures aggregated across firm establishments are generally unrelated to sales, productivity, and profitability. Our results support existing findings of a tenuous relation between temperature and aggregate economic growth in rich countries.
A Resource-Efficient Embedded Iris Recognition System Using Fully Convolutional Networks

Applications of fully convolutional networks (FCN) in iris segmentation have shown promising advances. For mobile and embedded systems, a significant challenge is that the proposed FCN architectures are extremely computationally demanding. In this article, we propose a resource-efficient, end-to-end iris recognition flow, which consists of FCN-based segmentation and a contour fitting module, followed by Daugman normalization and encoding. To attain accurate and efficient FCN models, we propose a three-step SW/HW co-design methodology consisting of FCN architectural exploration, precision quantization, and hardware acceleration. In our exploration, we propose multiple FCN models, and in comparison to previous works, our best-performing model requires 50× fewer floating-point operations per inference while achieving a new state-of-the-art segmentation accuracy. Next, we select the most efficient set of models and further reduce their computational complexity through weights and activations quantization using an 8-bit dynamic fixed-point format. Each model is then incorporated into an end-to-end flow for true recognition performance evaluation. A few of our end-to-end pipelines outperform the previous state of the art on two datasets evaluated. Finally, we propose a novel dynamic fixed-point accelerator and fully demonstrate the SW/HW co-design realization of our flow on an embedded FPGA platform. In comparison with the embedded CPU, our hardware acceleration achieves up to 8.3× speedup for the overall pipeline while using less than 15% of the available FPGA resources. We also provide comparisons between the FPGA system and an embedded GPU showing different benefits and drawbacks for the two platforms.
Fourier-Mukai transforms of slope stable torsion-free sheaves on a product elliptic threefold

Abstract On the product elliptic threefold X = C × S where C is an elliptic curve and S is a K3 surface of Picard rank 1, we define a notion of limit tilt stability, which satisfies the Harder-Narasimhan property. We show that under the Fourier-Mukai transform Φ on D b ( X ) induced by the classical Fourier-Mukai transform on D b ( C ) , a slope stable torsion-free sheaf satisfying a vanishing condition in codimension 2 (e.g. a reflexive sheaf) is taken to a limit tilt stable object. We also show that a limit tilt semistable object on X is taken by Φ to a slope semistable sheaf, up to modification by the transform of a codimension 2 sheaf.
DID EARLY TWENTIETH‐CENTURY ALCOHOL PROHIBITION AFFECT MORTALITY?

We investigate the contemporaneous mortality consequences of alcohol prohibition laws introduced in America between 1900 and 1920. We improve on existing studies by constructing a time‐varying measure of prohibition at the state level that corrects for the timing of prohibition enforcement and accounts for the presence of dry counties. Using summary indices that aggregate alcohol‐related mortality due to disease and poor decisions, we find that prohibition significantly reduced mortality rates. These findings are corroborated with an area‐level analysis that exploits data on deaths in urban areas that were wet prior to statewide or federal prohibition and nonurban areas that were partially dry. (JEL I18, N4, K2)
Analyzing GDPR Compliance Through the Lens of Privacy Policy

With the arrival of the European Union’s General Data Protection Regulation (GDPR), several companies are making significant changes to their systems to achieve compliance. The changes range from modifying privacy policies to redesigning systems which process personal data. Privacy policy is the main medium of information dissemination between the data controller and the users. This work analyzes the privacy policies of large-scaled cloud services which seek to be GDPR compliant. We show that many services that claim compliance today do not have clear and concise privacy policies. We identify several points in the privacy policies which potentially indicate non-compliance; we term these GDPR dark patterns. We identify GDPR dark patterns in ten large-scale cloud services. Based on our analysis, we propose seven best practices for crafting GDPR privacy policies.
Osteocyte dysfunction promotes osteoarthritis through MMP13-dependent suppression of subchondral bone homeostasis

Osteoarthritis (OA), long considered a primary disorder of articular cartilage, is commonly associated with subchondral bone sclerosis. However, the cellular mechanisms responsible for changes to subchondral bone in OA, and the extent to which these changes are drivers of or a secondary reaction to cartilage degeneration, remain unclear. In knee joints from human patients with end-stage OA, we found evidence of profound defects in osteocyte function. Suppression of osteocyte perilacunar/canalicular remodeling (PLR) was most severe in the medial compartment of OA subchondral bone, with lower protease expression, diminished canalicular networks, and disorganized and hypermineralized extracellular matrix. As a step toward evaluating the causality of PLR suppression in OA, we ablated the PLR enzyme MMP13 in osteocytes while leaving chondrocytic MMP13 intact, using Cre recombinase driven by the 9.6-kb DMP1 promoter. Not only did osteocytic MMP13 deficiency suppress PLR in cortical and subchondral bone, but it also compromised cartilage. Even in the absence of injury, osteocytic MMP13 deficiency was sufficient to reduce cartilage proteoglycan content, change chondrocyte production of collagen II, aggrecan, and MMP13, and increase the incidence of cartilage lesions, consistent with early OA. Thus, in humans and mice, defects in PLR coincide with cartilage defects. Osteocyte-derived MMP13 emerges as a critical regulator of cartilage homeostasis, likely via its effects on PLR. Together, these findings implicate osteocytes in bone-cartilage crosstalk in the joint and suggest a causal role for suppressed perilacunar/canalicular remodeling in osteoarthritis.