cssi/SciSciGPT-SciSciNet huggingface dataset:
Eigenvalue inequalities for Klein–Gordon operators

We consider the pseudodifferential operators Hm,Ω associated by the prescriptions of quantum mechanics to the Klein–Gordon Hamiltonian |P|2+m2 when restricted to a bounded, open domain Ω∈Rd. When the mass m is 0 the operator H0,Ω coincides with the generator of the Cauchy stochastic process with a killing condition on ∂Ω. (The operator H0,Ω is sometimes called the fractional Laplacian with power 12, cf. [R. Banuelos, T. Kulczycki, Eigenvalue gaps for the Cauchy process and a Poincare inequality, J. Funct. Anal. 211 (2) (2004) 355–423; R. Banuelos, T. Kulczycki, The Cauchy process and the Steklov problem, J. Funct. Anal. 234 (2006) 199–225; E. Giere, The fractional Laplacian in applications, http://www.eckhard-giere.de/math/publications/review.pdf].) We prove several universal inequalities for the eigenvalues 0<β1<β2⩽⋯ of Hm,Ω and their means βk¯:=1k∑l=1kβl. Among the inequalities proved are: βk¯⩾cst.(k|Ω|)1/d for an explicit, optimal “semiclassical” constant depending only on the dimension d. For any dimension d⩾2 and any k, βk+1⩽d+1d−1βk¯. Furthermore, when d⩾2 and k⩾2j, β¯kβ¯j⩽d21/d(d−1)(kj)1d. Finally, we present some analogous estimates allowing for an operator including an external potential energy field, i.e., Hm,Ω+V(x), for V(x) in certain function classes.
Plug-in approach to active learning

We present a new active learning algorithm based on nonparametric estimators of the regression function. Our investigation provides probabilistic bounds for the rates of convergence of the generalization error achievable by proposed method over a broad class of underlying distributions. We also prove minimax lower bounds which show that the obtained rates are almost tight.
Minimax Optimum Estimators for Phase Synchronization in IEEE 1588

The IEEE 1588 protocol has received recent interest as a means of delivering sub-microsecond level clock phase synchronization over packet-switched mobile backhaul networks. Due to the randomness of the end-to-end delays in packet networks, the recovery of clock phase from packet timestamps in IEEE 1588 must be treated as a statistical estimation problem. A number of estimators for this problem have been suggested in the literature, but little is known about the best achievable performance. In this paper, we describe new minimax estimators for this problem, that are optimum in terms of minimizing the maximum mean squared error over all possible values of the unknown parameters. Minimax estimators that utilize information from past timestamps to improve accuracy are also introduced. Simulation results indicate that significant performance gains over conventional estimators can be obtained via such optimum processing techniques. These minimax estimators also provide fundamental limits on the performance of phase offset estimation schemes.
Hybrid PS-V Technique: A Novel Sensor Fusion Approach for Fast Mobile Eye-Tracking With Sensor-Shift Aware Correction

This paper introduces and evaluates a novel hybrid technique that fuses two eye-tracking methodologies: photosensor oculography and video oculography. The main concept of the technique is to use a few fast and power-economic photosensors as the core mechanism for performing high speed eye-tracking, while in parallel, operate a video sensor at low sampling rate (snapshot mode) to perform dead-reckoning error correction when sensor shifts occur. We present and evaluate the functional components of the proposed technique using model-based simulation. Our experiments are performed for different scenarios involving combinations of horizontal and vertical eye movements and sensor shifts. Our evaluation shows that the proposed technique can be used to provide robustness to sensor shifts that otherwise could induce error larger than 5°. Our analysis suggests that the technique can be potentially employed to enable high speed eye tracking at low power profiles, making it suitable for use in emerging head-mounted devices, e.g., AR/VR headsets.
Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More

In this paper, we provide faster algorithms for computing variousfundamental quantities associated with random walks on a directedgraph, including the stationary distribution, personalized PageRankvectors, hitting times, and escape probabilities. In particular, ona directed graph with n vertices and m edges, we show how tocompute each quantity in time O(m3/4n + mn2/3), wherethe O notation suppresses polylog factors in n, the desired accuracy, and the appropriate condition number (i.e. themixing time or restart probability). Our result improves upon the previous fastest running times for these problems, previous results either invoke a general purpose linearsystem solver on a n × n matrix with m non-zero entries, or depend polynomially on the desired error or natural condition numberassociated with the problem (i.e. the mixing time or restart probability). For sparse graphs, we obtain a running time of O(n7/4), breaking the O(n2) barrier of the best running time one couldhope to achieve using fast matrix multiplication. We achieve our result by providing a similar running time improvementfor solving directed Laplacian systems, a natural directedor asymmetric analog of the well studied symmetric or undirected Laplaciansystems. We show how to solve such systems in time O(m3/4n + mn2/3), and efficiently reduce a broad range of problems to solving O(1) directed Laplacian systems on Eulerian graphs. We hope these resultsand our analysis open the door for further study into directedspectral graph theory.
THE FREQUENCY OF PATTERN OCCURRENCE IN RANDOM WALKS

In the past decade, the use of ordinal patterns in the analysis of time series and dynamical systems has become an important tool. Ordinal patterns (otherwise known as a permutation patterns) are found in time series by taking $n$ data points at evenly-spaced time intervals and mapping them to a length-$n$ permutation determined by relative ordering. The frequency with which certain patterns occur is a useful statistic for such series. However, the behavior of the frequency of pattern occurrence is unstudied for most models. We look at the frequency of pattern occurrence in random walks in discrete time, and we define a natural equivalence relation on permutations under which equivalent patterns appear with equal frequency, regardless of probability distribution. We characterize these equivalence classes applying combinatorial methods.
Generalizable Adversarial Training via Spectral Normalization.

Deep neural networks (DNNs) have set benchmarks on a wide array of supervised learning tasks. Trained DNNs, however, often lack robustness to minor adversarial perturbations to the input, which undermines their true practicality. Recent works have increased the robustness of DNNs by fitting networks using adversarially-perturbed training samples, but the improved performance can still be far below the performance seen in non-adversarial settings. A significant portion of this gap can be attributed to the decrease in generalization performance due to adversarial training. In this work, we extend the notion of margin loss to adversarial settings and bound the generalization error for DNNs trained under several well-known gradient-based attack schemes, motivating an effective regularization scheme based on spectral normalization of the DNN's weight matrices. We also provide a computationally-efficient method for normalizing the spectral norm of convolutional layers with arbitrary stride and padding schemes in deep convolutional networks. We evaluate the power of spectral normalization extensively on combinations of datasets, network architectures, and adversarial training schemes. The code is available at this https URL.
Constructions of Strongly Regular Cayley Graphs Using Even Index Gauss Sums

In this paper, generalizing the result in [9], I construct strongly regular Cayley graphs by using union of cyclotomic classes of and Gauss sums of index w, where is even. In particular, we obtain three infinite families of strongly regular graphs with new parameters.
Speech Map: a statistical multimodal atlas of 4D tongue motion during speech from tagged and cine MR images

Quantitative measurement of functional and anatomical traits of 4D tongue motion in the course of speech or other lingual behaviors remains a major challenge in scientific research and clinical applications. Here, we introduce a statistical multimodal atlas of 4D tongue motion using healthy subjects, which enables a combined quantitative characterization of tongue motion in a reference anatomical configuration. This atlas framework, termed Speech Map, combines cine- and tagged-MRI in order to provide both the anatomic reference and motion information during speech. Our approach involves a series of steps including (1) construction of a common reference anatomical configuration from cine-MRI, (2) motion estimation from tagged-MRI, (3) transformation of the motion estimations to the reference anatomical configuration, and (4) computation of motion quantities such as Lagrangian strain. Using this framework, the anatomic configuration of the tongue appears motionless, while the motion fields and associated strain measurements change over the time course of speech. In addition, to form a succinct representation of the high-dimensional and complex motion fields, principal component analysis is carried out to characterize the central tendencies and variations of motion fields of our speech tasks. Our proposed method provides a platform to quantitatively and objectively explain the differences and variability of tongue motion by illuminating internal motion and strain that have so far been intractable. The findings are used to understand how tongue function for speech is limited by abnormal internal motion and strain in glossectomy patients.
Learning in Integer Latent Variable Models with Nested Automatic Differentiation

We develop nested automatic differentiation (AD) algorithms for exact inference and learning in integer latent variable models. Recently, Winner, Sujono, and Sheldon showed how to reduce marginalization in a class of integer latent variable models to evaluating a probability generating function which contains many levels of nested high-order derivatives. We contribute faster and more stable AD algorithms for this challenging problem and a novel algorithm to compute exact gradients for learning. These contributions lead to significantly faster and more accurate learning algorithms, and are the first AD algorithms whose running time is polynomial in the number of levels of nesting.
Estimation of High-Dimensional Graphical Models Using Regularized Score Matching.

Graphical models are widely used to model stochastic dependences among large collections of variables. We introduce a new method of estimating undirected conditional independence graphs based on the score matching loss, introduced by Hyvarinen (2005), and subsequently extended in Hyvarinen (2007). The regularized score matching method we propose applies to settings with continuous observations and allows for computationally efficient treatment of possibly non-Gaussian exponential family models. In the well-explored Gaussian setting, regularized score matching avoids issues of asymmetry that arise when applying the technique of neighborhood selection, and compared to existing methods that directly yield symmetric estimates, the score matching approach has the advantage that the considered loss is quadratic and gives piecewise linear solution paths under l1 regularization. Under suitable irrepresentability conditions, we show that l1-regularized score matching is consistent for graph estimation in sparse high-dimensional settings. Through numerical experiments and an application to RNAseq data, we confirm that regularized score matching achieves state-of-the-art performance in the Gaussian case and provides a valuable tool for computationally efficient estimation in non-Gaussian graphical models.
Constant bearing pursuit on branching graphs

Cyclic pursuit frameworks provide an efficient way to create useful global behaviors out of pairwise interactions in a collective of autonomous robots. Earlier work studied cyclic pursuit with a constant bearing (CB) pursuit law, and has demonstrated the existence of a variety of interesting behaviors for the corresponding dynamics. In this work, by attaching multiple branches to a single cycle, we introduce a modified version of this framework which allows us to consider any weakly connected pursuit graph where each node has an outdegree of 1. This provides a further generalization of the cyclic pursuit setting. Then, after showing existence of relative equilibria (rectilinear or circling motion), pure shape equilibria (spiraling motion) and periodic orbits, we also derive necessary conditions for stability of a 3-agent collective. By paving a way for individual agents to join or leave a collective without perturbing the motion of others, our approach leads to improved reliability of the overall system.
Spectral Algorithms for Computing Fair Support Vector Machines.

Classifiers and rating scores are prone to implicitly codifying biases, which may be present in the training data, against protected classes (i.e., age, gender, or race). So it is important to understand how to design classifiers and scores that prevent discrimination in predictions. This paper develops computationally tractable algorithms for designing accurate but fair support vector machines (SVM's). Our approach imposes a constraint on the covariance matrices conditioned on each protected class, which leads to a nonconvex quadratic constraint in the SVM formulation. We develop iterative algorithms to compute fair linear and kernel SVM's, which solve a sequence of relaxations constructed using a spectral decomposition of the nonconvex constraint. Its effectiveness in achieving high prediction accuracy while ensuring fairness is shown through numerical experiments on several data sets.
Exchangeable random partitions from max-infinitely-divisible distributions

Abstract The hitting partitions are random partitions that arise from the investigation of so-called hitting scenarios of max-infinitely-divisible (max-i.d.) distributions. We study a class of max-i.d. laws with exchangeable hitting partitions obtained by size-biased sampling from the jumps of a Levy subordinator. We obtain explicit formulae for the distributions of these partitions in the case of the multivariate α -logistic and another family of exchangeable max-i.d. distributions. Specifically, the hitting partitions for these two cases are shown to coincide with the well-known Poisson–Dirichlet partitions PD ( α , 0 ) , α ∈ ( 0 , 1 ) and PD ( 0 , θ ) , θ > 0 .
Low-lying zeros for $L$-functions associated to Hilbert modular forms of large level

We determine the 1-level density of families of Hilbert modular forms, and show the answer agrees only with orthogonal random matrix ensembles.
Derivatives of L p eigenfunctions of schrödinger operators

Assuming the negative part of the potential is uniformly locally L 1 , we prove a pointwise L p estimate on derivatives of eigenfunctions of one-dimensional Schrodinger operators. In particular, if an eigenfunction is in L p , then so is its derivative, for 1 ≤ p ≤ ∞.
Burstiness of Intrusion Detection Process: Empirical Evidence and a Modeling Approach

We analyze sets of intrusion detection records observed on the networks of several large, nonresidential organizations protected by a form of intrusion detection and prevention service. Our analyses reveal that the process of intrusion detection in these networks exhibits a significant degree of burstiness as well as strong memory, with burstiness and memory properties that are comparable to those of natural processes driven by threshold effects, but different from bursty human activities. We explore time-series models of these observable network security incidents based on partially observed data using a hidden Markov model with restricted hidden states, which we fit using Markov Chain Monte Carlo techniques. We examine the output of the fitted model with respect to its statistical properties and demonstrate that the model adequately accounts for intrinsic “bursting” within observed network incidents as a result of alternation between two or more stochastic processes. While our analysis does not lead directly to new detection capabilities, the practical implications of gaining better understanding of the observed burstiness are significant, and include opportunities for quantifying a network’s risks and defensive efforts.
A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems

We address the problem of optimal experimental design (OED) for Bayesian nonlinear inverse problems governed by partial differential equations (PDEs). The inverse problem seeks to infer an infinite-dimensional parameter from experimental data observed at a set of sensor locations and from the governing PDEs. The goal of the OED problem is to find an optimal placement of sensors so as to minimize the uncertainty in the inferred parameter field. Specifically, we seek an optimal subset of sensors from among a fixed set of candidate sensor locations. We formulate the OED objective function by generalizing the classical A-optimal experimental design criterion using the expected value of the trace of the posterior covariance. This expected value is computed through sample averaging over the set of likely experimental data. To cope with the infinite-dimensional character of the parameter field, we construct a Gaussian approximation to the posterior at the maximum a posteriori probability (MAP) point, and use the...
Effectiveness and selectivity of a heroin conjugate vaccine to attenuate heroin, 6-acetylmorphine, and morphine antinociception in rats: Comparison with naltrexone

Abstract Background One emerging strategy to address the opioid crisis includes opioid-targeted immunopharmacotherapies. This study compared effectiveness of a heroin-tetanus toxoid (TT) conjugate vaccine to antagonize heroin, 6-acetylmorphine (6-AM), morphine, and fentanyl antinociception in rats. Methods Adult male and female Sprague Dawley rats received three doses of active or control vaccine at weeks 0, 2, and 4. Vaccine pharmacological selectivity was assessed by comparing opioid dose-effect curves in 50 °C warm-water tail-withdrawal procedure before and after active or control heroin-TT vaccine. Route of heroin administration [subcutaneous (SC) vs. intravenous [IV)] was also examined as a determinant of vaccine effectiveness. Continuous naltrexone treatment (0.0032-0.032 mg/kg/h) effects on heroin, 6-AM, and morphine antinociceptive potency were also determined as a benchmark for minimal vaccine effectiveness. Results The heroin-TT vaccine decreased potency of SC heroin (5-fold), IV heroin (3-fold), and IV 6-AM (3-fold) for several weeks without affecting IV morphine or SC and IV fentanyl potency. The control vaccine did not alter potency of any opioid. Naltrexone dose-dependently decreased antinociceptive potency of SC heroin, and treatment with 0.01 mg/kg/h naltrexone produced similar, approximate 8-fold decreases in potencies of SC and IV heroin, IV 6-AM, and IV morphine. The combination of naltrexone and active vaccine was more effective than naltrexone alone to antagonize SC heroin but not IV heroin. Conclusions The heroin-TT vaccine formulation examined is less effective, but more selective, than chronic naltrexone to attenuate heroin antinociception in rats. Furthermore, these results provide an empirical framework for future preclinical opioid vaccine research to benchmark effectiveness against naltrexone.
Superset Technique for Approximate Recovery in One-Bit Compressed Sensing

One-bit compressed sensing (1bCS) is a method of signal acquisition under extreme measurement quantization that gives important insights on the limits of signal compression and analog-to-digital conversion. The setting is also equivalent to the problem of learning a sparse hyperplane-classifier. In this paper, we propose a generic approach for signal recovery in nonadaptive 1bCS that leads to improved sample complexity for approximate recovery for a variety of signal models, including nonnegative signals and binary signals. We construct 1bCS matrices that are universal - i.e. work for all signals under a model - and at the same time recover very general random sparse signals with high probability. In our approach, we divide the set of samples (measurements) into two parts, and use the first part to recover the superset of the support of a sparse vector. The second set of measurements is then used to approximate the signal within the superset. While support recovery in 1bCS is well-studied, recovery of superset of the support requires fewer samples, which then leads to an overall reduction in sample complexity for approximate recovery.
Activity-based ratiometric FRET probe reveals oncogene-driven changes in labile copper pools induced by altered glutathione metabolism

Copper is essential for life, and beyond its well-established ability to serve as a tightly bound, redox-active active site cofactor for enzyme function, emerging data suggest that cellular copper also exists in labile pools, defined as loosely bound to low-molecular-weight ligands, which can regulate diverse transition metal signaling processes spanning neural communication and olfaction, lipolysis, rest-activity cycles, and kinase pathways critical for oncogenic signaling. To help decipher this growing biology, we report a first-generation ratiometric fluorescence resonance energy transfer (FRET) copper probe, FCP-1, for activity-based sensing of labile Cu(I) pools in live cells. FCP-1 links fluorescein and rhodamine dyes through a Tris[(2-pyridyl)methyl]amine bridge. Bioinspired Cu(I)-induced oxidative cleavage decreases FRET between fluorescein donor and rhodamine acceptor. FCP-1 responds to Cu(I) with high metal selectivity and oxidation-state specificity and facilitates ratiometric measurements that minimize potential interferences arising from variations in sample thickness, dye concentration, and light intensity. FCP-1 enables imaging of dynamic changes in labile Cu(I) pools in live cells in response to copper supplementation/depletion, differential expression of the copper importer CTR1, and redox stress induced by manipulating intracellular glutathione levels and reduced/oxidized glutathione (GSH/GSSG) ratios. FCP-1 imaging reveals a labile Cu(I) deficiency induced by oncogene-driven cellular transformation that promotes fluctuations in glutathione metabolism, where lower GSH/GSSG ratios decrease labile Cu(I) availability without affecting total copper levels. By connecting copper dysregulation and glutathione stress in cancer, this work provides a valuable starting point to study broader cross-talk between metal and redox pathways in health and disease with activity-based probes.
Naturalness versus stringy naturalness (with implications for collider and dark matter searches

This paper studies the notion of ``Stringy naturalness'' and how it lifts the Higgs boson mass to the 125 GeV range whilst also lifting sparticle masses beyond LHC search limits. As a consequence, SUSY may be revealed at high luminosity LHC via the presence of light higgsinos of mass ~100-300 GeV while a higher energy LHC upgrade may be required to access gluinos and top squarks.
Generalized chi-squared detector for LTI systems with non-Gaussian noise

Previously, we derived exact relationships between the properties of a linear time-invariant control system and properties of an anomaly detector that quantified the impact an attacker can have on the system if that attacker aims to remain stealthy to the detector. A necessary first step in this process is to be able to precisely tune the detector to a desired level of performance (false alarm rate) under normal operation, typically through the selection of a threshold parameter. To-date efforts have only considered Gaussian noises. Here we generalize the approach to tune a chi-squared anomaly detector for noises with non-Gaussian distributions. Our method leverages a Gaussian Mixture Model to represent the arbitrary noise distributions, which preserves analytic tractability and provides an informative interpretation in terms of a collection of chi-squared detectors and multiple Gaussian disturbances.
Electron pairing by remote-phonon scattering in oxide-supported graphene

Using first-principles calculations we have previously shown that placing graphene on a (111)-oriented perovskite $\mathrm{SrTi}{\mathrm{O}}_{3}$ (STO) surface provides a possible doping mechanism [D. Shin and A. A. Demkov, Phys. Rev. B 97, 075423 (2018)]. Further theoretical analysis presented here suggests that coupling of electrons in graphene to interfacial hybrid plasmon/optical modes via remote-phonon scattering may result in an effective attractive electron-electron interaction that, in turn, could lead to electron pairing and superconductivity. Specifically, we consider top-gated graphene supported by STO. Using the full dynamic polarizability within the random phase approximation for the entire system (including the hybrid modes arising from the coupling of the graphene plasmons to the optical phonons of the STO substrate and gate insulator), we estimate the superconducting transition temperature in the strong-coupling limit.
SplitFS: reducing software overhead in file systems for persistent memory

We present SplitFS, a file system for persistent memory (PM) that reduces software overhead significantly compared to state-of-the-art PM file systems. SplitFS presents a novel split of responsibilities between a user-space library file system and an existing kernel PM file system. The user-space library file system handles data operations by intercepting POSIX calls, memory-mapping the underlying file, and serving the read and overwrites using processor loads and stores. Metadata operations are handled by the kernel PM file system (ext4 DAX). SplitFS introduces a new primitive termed relink to efficiently support file appends and atomic data operations. SplitFS provides three consistency modes, which different applications can choose from, without interfering with each other. SplitFS reduces software overhead by up-to 4x compared to the NOVA PM file system, and 17x compared to ext4 DAX. On a number of micro-benchmarks and applications such as the LevelDB key-value store running the YCSB benchmark, SplitFS increases application performance by up to 2x compared to ext4 DAX and NOVA while providing similar consistency guarantees.
Silicon photodetector for integrated lithium niobate photonics

We demonstrate the integration of an amorphous silicon photodetector with a thin film lithium niobate photonic platform operating in the visible wavelength range. We present the details of the design, fabrication, integration, and experimental characterization of this metal-semiconductor-metal photodetector that features a responsivity of 22 mA/W to 37 mA/W over the wide optical bandwidth spanning in the 635 nm–850 nm wavelength range.
Classification in a Large Network

We construct and analyze the communication cost of protocols (interactive and one-way) for classifying X = (X 1 ,X 2 , …, X n ) ∈ [0,1)n ⊂ℝn, in a network with n ≥ 2 nodes, with X i known only at node i. The classifier takes the form $\sum\nolimits_{i = 1}^n {{h_i}{X_i} \gtrless a} $, with weights h i ∈ {−1,+1}. The interactive protocol (a zero-error protocol) exchanges a variable number of messages depending on the input X and its sum rate is directly proportional to its mean stopping time. An exact analysis, as well as an approximation of the mean stopping time is presented and shows that it depends on γ = α + (1/2 − β), where α = a/n and β = m/n, with m being the number of positive weights. In particular, the mean stopping time grows logarithmically in n when γ = 0, and is bounded in n otherwise. Comparisons show that the sum rate of the interactive protocol is smaller than that of the one-way protocol when the error probability for the one-way protocol is small, with the reverse being true when the error probability is large. Comparisons of the interactive protocol with lower bounds on the sum rate show the correct scaling behavior when γ 6= 0.
Self-consistent theory of random lasing in the time-domain

Random lasers are unique systems where lasing occurs due to repetitive scattering in a disordered nanostructure. Previous descriptions of random lasing are numerous, however a full time-dependent theory that describes the introduction of gain directly from first principles is lacking in the literature. This letter will present an analytic self-consistent time-dependent theory of random lasing that contains the results from the well-known steady-state ab-initio laser theory. This theory can also describe a number of temporal phenomena that have been observed in previous experiments and facilitates the incorporation of these devices into their envisioned practical applications.
A Time–Distance Helioseismology Method for Quasi-Linear Geometries

Helioseismology is the study of the solar interior, through which we extract flow and wave-speed information from Doppler velocity observations at the surface. Local helioseismology involves the study of small regions on the solar disk and is used to create a detailed picture of the interior in that particular region. Perturbations in the flow and wave-speed results indicate, e.g. magnetic-flux or temperature variations. There are multiple methods used in local-helioseismic research, but all current local-helioseismic techniques assume a point-source perturbation. For this study, we develop a new time–distance (TD) helioseismic methodology that can exploit the quasi-linear geometry of an elongated feature, allowing us to i) improve the signal-to-noise ratio of the TD results, and ii) greatly decrease the number of calculations required and therefore the computing time of the TD analysis. Ultimately, the new method will allow us to investigate solar features with magnetic-field configurations previously unexplored. We validate our new technique using a simple $f$-mode wave simulation, comparing results of point-source and linear perturbations. Results indicate that local-helioseismic analysis is dependent on the geometry of the system and can be improved by taking the magnetic-field configuration into account.
Flexible high speed CODEC (FHSC)

This paper describes the on going NASA/Harris FHSC CODEC program. The program objectives are to design and build an encoder decoder that allows operation in either burst or continuous modes at data rates of up to 300 megabits per second. The decoder handles both hard and soft decision decoding and can switch between modes on a burst by burst basis. Bandspreading is low since the code rate is greater than or equal to 7/8. The encoder and a hard decision decoder fit on a single application specific integrated circuit (ASIC) chip. A soft decision applique is implemented using 300 K ECL logic which can be easily translated to an ECL gate array.
Learning to Optimize in Swarms

Learning to optimize has emerged as a powerful framework for various optimization and machine learning tasks. Current such "meta-optimizers" often learn in the space of continuous optimization algorithms that are point-based and uncertainty-unaware. To overcome the limitations, we propose a meta-optimizer that learns in the algorithmic space of both point-based and population-based optimization algorithms. The meta-optimizer targets at a meta-loss function consisting of both cumulative regret and entropy. Specifically, we learn and interpret the update formula through a population of LSTMs embedded with sample- and feature-level attentions. Meanwhile, we estimate the posterior directly over the global optimum and use an uncertainty measure to help guide the learning process. Empirical results over non-convex test functions and the protein-docking application demonstrate that this new meta-optimizer outperforms existing competitors. The codes are publicly available at: https://github.com/Shen-Lab/LOIS.
The MPRE a Boiling Potassium Reactor System

Abstract A study carried out in 1958 indicated that, for a given peak system temperature, a Rankine vapor cycle employing either potassium or rubidium vapor should give the minimum size and weight of the radiator and other major components for turbine-generator nuclear space powerplants. Reliability studies indicated that only by going to the simplest possible system could we hope to achieve the desired reliability. Preliminary system layouts showed that a single-fluid system with the reactor serving as the boiler minimizes the number of components. Further, by eliminating the temperature losses associated with a primary fluid circuit and an intermediate heat exchanger, the single fluid system reduces the peak metal temperature required for a given vapor temperature at the turbine inlet by 200 to 300°F, and makes possible a system weight of about 25 lb/kwe for a stainless steel system with potassium boiling in the reactor at about 1500°F. A reference design for a launch package has been prepared to serve as the basis for the design of the potassium system of the reactor experiment. These two systems are compared, and some of their problems – including meteoroid protection and two-phase flow in the condenser – are discussed.
Sequences of integers with three missing separations

Abstract Let D be a set of positive integers. We study the maximum density μ ( D ) of integral sequences in which the separation between any two terms does not fall in D . The sets D considered in this article are mainly of the form { 1 , j , k } . The closely related function κ ( D ) , the parameter involved in the “lonely runner conjecture,” is also investigated. Exact values of κ ( D ) and μ ( D ) are found for many families of D = { 1 , j , k } . In particular, we prove that the boundary conditions in some earlier results of Haralambis (1977) are sharp. Consequently, our results provide counterexamples to two conjectures of Carraher et al. (2016).
The Ambiguous Basis of Judicial Deference to Administrative Rules

Much of the commentary on the Supreme Court's decision in Chevron U.S.A, Inc. v. Natural Resources Defense Council, Inc. has focused on the nature of power that agencies exercise when they promulgate rules that merit judicial deference under Chevron. Some scholars view Chevron as reading into statutes an implied delegation from Congress to agencies of legislative power to fill statutory gaps and interpret statutory ambiguities. Other scholars understand Chevron as, in effect, a delegation of interpretive power from the courts to agencies. This Article argues that neither view of Chevron is correct.
Pulse Shaped Dynamic Nuclear Polarization under Magic-Angle Spinning.

Dynamic nuclear polarization (DNP) under magic-angle spinning (MAS) is transforming the scope of solid-state NMR by enormous signal amplification through transfer of polarization from electron spins to nuclear spins. Contemporary MAS-DNP exclusively relies on monochromatic continuous-wave (CW) irradiation of the electron spin resonance. This limits control on electron spin dynamics, which renders the DNP process inefficient, especially at higher magnetic fields and non cryogenic temperatures. Pulse-shaped microwave irradiation of the electron spins is predicted to overcome these challenges but hitherto has never been implemented under MAS. Here, we debut pulse-shaped microwave irradiation using arbitrary-waveform generation (AWG) which allows controlled recruitment of a greater number of electron spins per unit time, favorable for MAS-DNP. Experiments and quantum mechanical simulations demonstrate that pulse-shaped DNP is superior to CW-DNP for mixed radical system, especially when the electron spin resonance is heterogeneously broadened and/or when its spin-lattice relaxation is fast compared to the MAS rotor period, opening new prospects for MAS-DNP.
Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference

Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) (Tsipras et al., 2019). Such a dilemma is shown to be rooted in the inherently higher sample complexity (Schmidt et al., 2018) and/or model capacity (Nakkiran, 2019), for learning a high-accuracy and robust classifier. In view of that, give a classification task, growing the model capacity appears to help draw a win-win between accuracy and robustness, yet at the expense of model size and latency, therefore posing challenges for resource-constrained applications. Is it possible to co-design model accuracy, robustness and efficiency to achieve their triple wins? This paper studies multi-exit networks associated with input-adaptive efficient inference, showing their strong promise in achieving a “sweet point" in co-optimizing model accuracy, robustness, and efficiency. Our proposed solution, dubbed Robust Dynamic Inference Networks (RDI-Nets), allows for each input (either clean or adversarial) to adaptively choose one of the multiple output layers (early branches or the final one) to output its prediction. That multi-loss adaptivity adds new variations and flexibility to adversarial attacks and defenses, on which we present a systematical investigation. We show experimentally that by equipping existing backbones with such robust adaptive inference, the resulting RDI-Nets can achieve better accuracy and robustness, yet with over 30% computational savings, compared to the defended original models.
ABSent: Cross-lingual sentence representation mapping with bidirectional GANs

A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal. However, in many real-world settings, the size of parallel annotated training data is restricted. Additionally, prior cross-lingual mapping research has mainly focused on the word level. This raises the question of whether such techniques can also be applied to effortlessly obtain cross-lingually aligned sentence representations. To this end, we propose an Adversarial Bi-directional Sentence Embedding Mapping (ABSent) framework, which learns mappings of cross-lingual sentence representations from limited quantities of parallel data. The experiments show that our method outperforms several technically more powerful approaches, especially under challenging low-resource circumstances. The source code is available from https://github.com/zuohuif/ABSent along with relevant datasets.
Trinity Strikes Back: Monetary Independence And Inflation In The Caribbean

Monetary independence is at the core of the macroeconomic policy trilemma. This study examines the relationship between monetary autonomy and inflation in Caribbean countries over the period 1980–2017. The empirical results show that greater monetary policy independence, measured as a country's ability to conduct its own monetary policy for domestic purposes independent of external monetary influences, leads to lower consumer price inflation. This relationship—robust to alternative specifications and estimation methodologies—has clear policy implications, especially for countries that maintain pegged exchange rates relative to the US dollar with a critical bearing on monetary autonomy. © 2019 John Wiley & Sons, Ltd.
Safety and Efficacy of Topically Administered Netarsudil (Rhopressa™) in Normal and Glaucomatous Dogs With ADAMTS10-open-angle Glaucoma (ADAMTS10-OAG)

Objective To evaluate safety and efficacy of topically administered 0.02% netarsudil ophthalmic solution (Rhopressa™; Aerie Pharmaceutical) in normal and glaucomatous dogs with ADAMTS10-open-angle glaucoma (ADAMTS10-OAG). Animals studied Five normal and 5 glaucomatous Beagle dogs with ADAMTS10-OAG. Procedures In each dog, left or right eye was randomly selected for netarsudil treatment. Contralateral eyes were sham-treated with balanced salt solution (BSS). Following a 1-week baseline period, dogs were treated once daily (q24h) during week 2, and twice daily (q12h) during week 3; week 4 served as washout period. Efficacy was measured by diurnal intraocular pressure (IOP) and pupil diameter. Safety was assessed by routine ophthalmic examination, gonioscopy, and pachymetry. Differences in least square means of quantitative outcome measures were compared between netarsudil and BSS sham-treated eyes by linear Gaussian model. Results Baseline IOPs were 18.5 ± 0.5 mm Hg (mean ± SEM) in normal and 27.8 ± 1.0 mm Hg in OAG dogs. Even though mean IOPs were lower in netarsudil- vs sham-treated eyes, the overall differences were neither significant nor clinically relevant, regardless of treatment frequency (q24h-normal: sham 16.4 ± 1.1 mm Hg vs treatment 15.6 ± 1.0 mm Hg; q24hr-OAG: sham 25.8 ± 2.3 mm Hg vs. treatment 25.7 ± 2.4 mm Hg; q12hr-normal: sham 15.4 ± 0.8 mm Hg vs. treatment 14.4 ± 0.8 mm Hg; q12hr-OAG: sham 26.3 ± 1.7 mm Hg vs. treatment 25.4 ± 1.8 mm Hg). Netarsudil administration was well tolerated but resulted in significant, moderate-to-severe conjunctival hyperemia (P Conclusions Once or twice daily administration of netarsudil resulted in marginal and clinically irrelevant IOP decreases in normal and OAG-affected dogs. Except for conjunctival hyperemia, the drug was well tolerated.
Conflicts of Interest in Human Subject Research: The Insufficiency of U.S. and International Standards:

Researchers, as well as individuals and institutions that oversee their conduct, sometimes have conflicts of interest that weaken or render ineffective efforts to protect human research subjects. This article analyzes United States and international standards used to address conflicts of interest and reviews evidence regarding compliance. It finds current standards are insufficient and recommends that the federal government and international organizations adopt stronger legal standards that require resolving most significant conflicts of interest and specifying how to manage conflicts of interest not resolved.
Making Marketplaces Safe: Dominant Individual Rationality and Applications to Market Design

Often market designers cannot force agents to join a marketplace rather than using pre-existing institutions. We propose a new desideratum for marketplace design that guarantees the safety of parti...
An Approximation Algorithm for a Task Allocation, Sequencing and Scheduling Problem Involving a Human-Robot Team

This article presents an approximation algorithm for a Task Allocation, Sequencing and Scheduling Problem (TASSP) involving a team of human operators and robots. The robots have to travel to a given set of targets and collaboratively work on the tasks at the targets with the human operators. The problem aims to find a sequence of targets for each robot to visit and schedule the tasks at the targets with the human operators such that each target is visited exactly once by some robot, the scheduling constraints are satisfied and the maximum mission time of any robot is minimum. This problem is a generalization of the single Traveling Salesman Problem and is NP-Hard. Given $k$ robots and $m$ human operators, an algorithm is developed for solving the TASSP with an approximation ratio equal to $\frac{5}{2}-\frac{1}{k}$ when $m\geq k$ and equal to $\frac{7}{2}-\frac{1}{k}$ otherwise. Computational results are also presented to corroborate the performance of the proposed algorithm.
The Stellar Populations in Local Group Dwarf Galaxies

The aim of the study of the populations in a stellar system is to understand and be able to describe the stellar content of a system in terms of physical parameters such as the age, star formation history, chemical enrichment history, initial mass function (IMF), environment, and dynamical history of the system. This is done given an understanding of stellar evolution and the ability to express the outcome in “observer parameters”, particularly a color-magnitude diagram (CMD), kinematics, and metallicity. From this perspective, the simplest systems are the galactic clusters and the globular clusters, where all the component stars are coeval and of the same metallicity. The current state of knowledge for these are discussed by others in this conference. We proceed to the next level of complexity (where metallicities are not necessarily all the same, and nor are the stars all coeval), and try to decompose their stellar content, particularly in terms of star formation rate and metallicity. In this regard the two classes of objects that come to mind are the dwarf spheroidals, and the dwarf irregulars. Both these classes of objects are more massive than the open clusters and globular clusters, and show evidence of complexities in their star formation histories, without being so convolved as to make such a study intractable. As we shall see, recent studies along these lines have presented some puzzling problems. Moreover, these are the smallest independent galaxies, and the study of star formation in these is likely to shed light on the history and formation of larger and more complex galaxies.
Cryptococcus neoformans Chitin Synthase 3 Plays a Critical Role in Dampening Host Inflammatory Responses.

ABSTRACT Cryptococcus neoformans infections are significant causes of morbidity and mortality among AIDS patients and the third most common invasive fungal infection in organ transplant recipients. One of the main interfaces between the fungus and the host is the fungal cell wall. The cryptococcal cell wall is unusual among human-pathogenic fungi in that the chitin is predominantly deacetylated to chitosan. Chitosan-deficient strains of C. neoformans were found to be avirulent and rapidly cleared from the murine lung. Moreover, infection with a chitosan-deficient C. neoformans strain lacking three chitin deacetylases (cda1Δcda2Δcda3Δ) was found to confer protective immunity to a subsequent challenge with a virulent wild-type counterpart. In addition to the chitin deacetylases, it was previously shown that chitin synthase 3 (Chs3) is also essential for chitin deacetylase-mediated formation of chitosan. Mice inoculated with the chs3Δ strain at a dose previously shown to induce protection with the cda1Δcda2Δcda3Δ strain die within 36 h after installation of the organism. Mortality was not dependent on viable fungi, as mice inoculated with a heat-killed preparation of the chs3Δ strain died at the same rate as mice inoculated with a live chs3Δ strain, suggesting that the rapid onset of death was host mediated, likely caused by an overexuberant immune response. Histology, cytokine profiling, and flow cytometry indicate a massive neutrophil influx in the mice inoculated with the chs3Δ strain. Mice depleted of neutrophils survived chs3Δ inoculation, indicating that death was neutrophil mediated. Altogether, these studies lead us to conclude that Chs3, along with chitosan, plays critical roles in dampening cryptococcus-induced host inflammatory responses. IMPORTANCECryptococcus neoformans is the most common disseminated fungal pathogen in AIDS patients, resulting in ∼200,000 deaths each year. There is a pressing need for new treatments for this infection, as current antifungal therapy is hampered by toxicity and/or the inability of the host’s immune system to aid in resolution of the disease. An ideal target for new therapies is the fungal cell wall. The cryptococcal cell wall is different from the cell walls of many other pathogenic fungi in that it contains chitosan. Strains that have decreased chitosan are less pathogenic and strains that are deficient in chitosan are avirulent and can induce protective responses. In this study, we investigated the host responses to a chs3Δ strain, a chitosan-deficient strain, and found that mice inoculated with the chs3Δ strain all died within 36 h and that death was associated with an aberrant hyperinflammatory immune response driven by neutrophils, indicating that chitosan is critical in modulating the immune response to Cryptococcus.