cssi/SciSciGPT-SciSciNet huggingface dataset:
Accelerating Channel Estimation and Demodulation of Uplink OFDM symbols for Large Scale Antenna Systems using GPU

Increase in the number of antennas in the front-end increases the volume of data to be processed at the back-end. This establishes a need for acceleration in back-end processing. To solve the issue of high volume data processing at back-end, a GPU is utilized. Acceleration for Least Squares channel estimation and demodulation of uplink OFDM symbols is provided by using a combination of CPU and GPU at the back-end. Single user uplink scenario is implemented in near real-time manner using the USRP platform present in the Large scale antenna systems in ORBIT Testbed. The number of antennas and FFT length are varied to provide different scenarios for comparison. The performance of both CPU and GPU is compared for each process.
Network Construction with Ordered Constraints

In this paper, we study the problem of constructing a network by observing ordered connectivity constraints, which we define herein. These ordered constraints are made to capture realistic properties of real-world problems that are not reflected in previous, more general models. We give hardness of approximation results and nearly-matching upper bounds for the offline problem, and we study the online problem in both general graphs and restricted sub-classes. In the online problem, for general graphs, we give exponentially better upper bounds than exist for algorithms for general connectivity problems. For the restricted classes of stars and paths we are able to find algorithms with optimal competitive ratios, the latter of which involve analysis using a potential function defined over pq-trees.
Formal Verification of Medina's Sequence of Polynomials for Approximating Arctangent

The verification of many algorithms for calculating transcendental functions is based on polynomial approximations to these functions, often Taylor series approximations. However, computing and verifying approximations to the arctangent function are very challenging problems, in large part because the Taylor series converges very slowly to arctangent-a 57th-degree polynomial is needed to get three decimal places for arctan(0.95). Medina proposed a series of polynomials that approximate arctangent with far faster convergence-a 7th-degree polynomial is all that is needed to get three decimal places for arctan(0.95). We present in this paper a proof in ACL2(r) of the correctness and convergence rate of this sequence of polynomials. The proof is particularly beautiful, in that it uses many results from real analysis. Some of these necessary results were proven in prior work, but some were proven as part of this effort.
An approach to studying quasiconformal mappings on generalized Grushin planes

We demonstrate that the complex plane and a class of generalized Grushin planes Gr, where r is a function satisfying specific requirements, are quasisymmetrically equivalent. Then using conjugation we are able to develop an analytic definition of quasisymmetry for homeomorphisms on Gr spaces. In the last section we show our analytic definition of quasisymmetry is consistent with earlier notions of conformal mappings on the Grushin plane. This leads to several characterizations of conformal mappings on the generalized Grushin planes.
Lotka's inverse square law of scientific productivity: Its methods and statistics

This brief communication analyzes the statistics and methods Lotka used to derive his inverse square law of scientific productivity from the standpoint of modern theory. It finds that he violated the norms of this theory by extremely truncating his data on the right. It also proves that Lotka himself played an important role in establishing the commonly used method of identifying power-law behavior by the R2 fit to a regression line on a log-log plot that modern theory considers unreliable by basing the derivation of his law on this very method.
A Dimension-Independent Discriminant Between Distributions

Henze-Penrose divergence is a non-parametric divergence measure that can be used to estimate a bound on the Bayes error in a binary classification problem. In this paper, we show that a cross-match statistic based on optimal weighted matching can be used to directly estimate Henze-Penrose divergence. Unlike an earlier approach based on the Friedman-Rafsky minimal spanning tree statistic, the proposed method is dimension-independent. The new approach is evaluated using simulation and applied to real datasets to obtain Bayes error estimates.
Polar coding for interference networks

A polar coding scheme for interference networks is introduced. The scheme combines Arikan's monotone chain rules for multiple-access channels and a method by Hassani and Urbanke to 'align' two incompatible polarization processes. It achieves the Han--Kobayashi inner bound for two-user interference channels and generalizes to interference networks.
Amenability and Uniqueness for Groupoids Associated with Inverse Semigroups

We investigate recent uniqueness theorems for reduced \(C^*\)-algebras of Hausdorff etale groupoids in the context of inverse semigroups. In many cases the distinguished subalgebra is closely related to the structure of the inverse semigroup. In order to apply our results to full \(C^*\)-algebras, we also investigate amenability. More specifically, we obtain conditions that guarantee amenability of the universal groupoid for certain classes of inverse semigroups. These conditions also imply the existence of a conditional expectation onto a canonical subalgebra.
Propensity Score-Based Estimators with Multiple Error-Prone Covariates

Propensity score methods are an important tool to help reduce confounding in nonexperimental studies. Most propensity score methods assume that covariates are measured without error. However, covariates are often measured with error, which leads to biased causal effect estimates if the true underlying covariates are the actual confounders. Although some groups have investigated the impact of a single mismeasured covariate on estimating a causal effect and proposed methods for handling the measurement error, fewer have investigated the case where multiple covariates are mismeasured, and we found none that discussed correlated measurement errors. In this study, we examined the consequences of multiple error-prone covariates when estimating causal effects using propensity score-based estimators via extensive simulation studies and real data analyses. We found that causal effect estimates are less biased when the propensity score model includes mismeasured covariates whose true underlying values are strongly correlated with each other. However, when the measurement errors are correlated with each other, additional bias is introduced. In addition, it is beneficial to include correctly measured auxiliary variables that are correlated with confounders whose true underlying values are mismeasured in the propensity score model.
A remark on a converse theorem of Cogdell and Piatetski-Shapiro

In this paper, we reprove a global converse theorem of Cogdell and Piatetski-Shapiro using purely global methods.
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks.

This paper investigates recently proposed approaches for defending against adversarial examples and evaluating adversarial robustness. The existence of adversarial examples in trained neural networks reflects the fact that expected risk alone does not capture the model's performance against worst-case inputs. We motivate the use of adversarial risk as an objective, although it cannot easily be computed exactly. We then frame commonly used attacks and evaluation metrics as defining a tractable surrogate objective to the true adversarial risk. This suggests that models may be obscured to adversaries, by optimizing this surrogate rather than the true adversarial risk. We demonstrate that this is a significant problem in practice by repurposing gradient-free optimization techniques into adversarial attacks, which we use to decrease the accuracy of several recently proposed defenses to near zero. Our hope is that our formulations and results will help researchers to develop more powerful defenses.
Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model

Transcribing voice communications in NASA’s launch control center is important for information utilization. However, automatic speech recognition in this environment is particularly challenging due to the lack of training data, unfamiliar words in acronyms, multiple different speakers and accents, and conversational characteristics of speaking. We used bidirectional deep recurrent neural networks to train and test speech recognition performance. We showed that data augmentation and custom language models can improve speech recognition accuracy. Transcribing communications from the launch control center will help the machine analyze information and accelerate knowledge generation.
Raising nilpotent orbits in wave-front sets

We study wave-front sets of representations of reductive groups over global or non-archimedean local fields.
Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning

Recent advances in combining deep learning and Reinforcement Learning have shown a promising path for designing new control agents that can learn optimal policies for challenging control tasks. These new methods address the main limitations of conventional Reinforcement Learning methods such as customized feature engineering and small action/state space dimension requirements. In this paper, we leverage one of the state-of-the-art Reinforcement Learning methods, known as Trust Region Policy Optimization, to tackle intersection management for autonomous vehicles. We show that using this method, we can perform fine-grained acceleration control of autonomous vehicles in a grid street plan to achieve a global design objective.
On the nature of filamentary superconductivity in metal-doped hydrocarbon organic materials

High temperature superconductivity in K-doped 1,2:8,9-dibenzenopentacene (C30H18) has been recently reported [1] with Tc = 33 K, the highest among organic superconductors at ambient pressure. Here we report on our search for superconductivity in K, Ba, and Ca-doped hydrocarbon organic materials. We find that Ba-anthracene (C14H10) and K-Picene (C22H14) show features characteristics of superconducting state, although very weak. The data suggests that Baanthracene might be a new organic superconductor with Tc ~ 35 K.
Fusion: a General Framework for Hierarchical Tilings

One well studied way to construct quasicrystalline tilings is via in ate-and-subdivide (a.k.a. substitution) rules. These produce self-similar tilings the Penrose, octagonal, and pinwheel tilings are famous examples. We present a di erent model for generating hierarchical tilings we call fusion rules . In ate-and-subdivide rules are a special case of fusion rules, but general fusion rules are more exible and allow for defects, changes in geometry, and even constrained randomness. A condition that produces homogeneous structures and a method for computing frequency for fusion tiling spaces are discussed.
Language Modeling with Power Low Rank Ensembles

We present power low rank ensembles (PLRE), a flexible framework for n-gram language modeling where ensembles of low rank matrices and tensors are used to obtain smoothed probability estimates of words in context. Our method can be understood as a generalization of ngram modeling to non-integer n, and includes standard techniques such as absolute discounting and Kneser-Ney smoothing as special cases. PLRE training is efficient and our approach outperforms stateof-the-art modified Kneser Ney baselines in terms of perplexity on large corpora as well as on BLEU score in a downstream machine translation task.
A Simple and Efficient Strategy for the Coin Weighing Problem with a Spring Scale

This paper considers a generalized version of the coin weighing problem with a spring scale that lies at the intersection of group testing and compressed sensing problems. Given a collection of $n\geq 2$ coins of total weight $d$ (for a known integer d), where the weight of each coin is an unknown integer in the range of {0, 1, …, k} (for a known integer $k\geq 1$ ), the goal is to determine the weight of each coin by weighing subsets of coins in a spring scale. The problem is to devise a weighing strategy that minimizes the average number of weighings over all possible weight configurations. For $d$ = $k$ = 1, an adaptive bisecting weighing strategy is known to be optimal. However, even the simplest non-trivial case of the problem, i.e., $d$ = $k$ == 2, is still open. For this case, we propose and analyze a simple and effective adaptive weighing strategy. Our analysis shows that the proposed strategy requires about $1.365\log_{2}n-0.5$ weighings on average. As $n$ grows unbounded, the proposed strategy, when compared to an optimal strategy within the commonly-used class of nested strategies, requires about 31.75% less number of weighings on average; and in comparison with the information-theoretic lower bound, it requires at most about 8.16% extra number of weighings on average.
Weighted KS statistics for inference on conditional moment inequalities

Abstract This paper proposes set estimators and conservative confidence regions for the identified set in conditional moment inequality models using Kolmogorov–Smirnov statistics with a truncated inverse variance weighting with increasing truncation points. The new weighting differs from those proposed in the literature in two important ways. First, this paper shows that estimators based on KS statistics with the proposed weighting function converge to the identified set at a faster rate than existing procedures based on bounded weight functions in a broad class of models. This provides a theoretical justification for inverse variance weighting in this context, and contrasts with analogous results for conditional moment equalities in which optimal weighting only affects the asymptotic variance. The results on rates of convergence of set estimators are the first such results even for the existing procedures, and involve developing the first general framework for determining consistency and rates of convergence for set estimators and confidence regions in this context. Second, the new weighting changes the asymptotic behavior, including the rate of convergence, of the KS statistic itself, requiring a new asymptotic theory in choosing the critical value. A series of examples illustrate the broad applicability of the results.
The Lifespan of Small Data Solutions in Two Dimensional Capillary Water Waves

This article is concerned with the incompressible, irrotational infinite depth water wave equation in two space dimensions, without gravity but with surface tension. We consider this problem expressed in position–velocity potential holomorphic coordinates, and prove that small data solutions have at least cubic lifespan while small localized data leads to global solutions.
Robust Partially-Compressed Least-Squares

Randomized matrix compression techniques, such as the Johnson-Lindenstrauss transform, have emerged as an effective and practical way for solving large-scale problems efficiently. With a focus on computational efficiency, however, forsaking solutions quality and accuracy becomes the trade-off. In this paper, we investigate compressed least-squares problems and propose new models and algorithms that address the issue of error and noise introduced by compression. While maintaining computational efficiency, our models provide robust solutions that are more accurate--relative to solutions of uncompressed least-squares--than those of classical compressed variants. We introduce tools from robust optimization together with a form of partial compression to improve the error-time trade-offs of compressed least-squares solvers. We develop an efficient solution algorithm for our Robust Partially-Compressed (RPC) model based on a reduction to a one-dimensional search. We also derive the first approximation error bounds for Partially-Compressed least-squares solutions. Empirical results comparing numerous alternatives suggest that robust and partially compressed solutions are effectively insulated against aggressive randomized transforms.
Square Hellinger Subadditivity for Bayesian Networks and its Applications to Identity Testing

We show that the square Hellinger distance between two Bayesian networks on the same directed graph, $G$, is subadditive with respect to the neighborhoods of $G$. Namely, if $P$ and $Q$ are the probability distributions defined by two Bayesian networks on the same DAG, our inequality states that the square Hellinger distance, $H^2(P,Q)$, between $P$ and $Q$ is upper bounded by the sum, $\sum_v H^2(P_{\{v\} \cup \Pi_v}, Q_{\{v\} \cup \Pi_v})$, of the square Hellinger distances between the marginals of $P$ and $Q$ on every node $v$ and its parents $\Pi_v$ in the DAG. Importantly, our bound does not involve the conditionals but the marginals of $P$ and $Q$. We derive a similar inequality for more general Markov Random Fields. As an application of our inequality, we show that distinguishing whether two Bayesian networks $P$ and $Q$ on the same (but potentially unknown) DAG satisfy $P=Q$ vs $d_{\rm TV}(P,Q)>\epsilon$ can be performed from $\tilde{O}(|\Sigma|^{3/4(d+1)} \cdot n/\epsilon^2)$ samples, where $d$ is the maximum in-degree of the DAG and $\Sigma$ the domain of each variable of the Bayesian networks. If $P$ and $Q$ are defined on potentially different and potentially unknown trees, the sample complexity becomes $\tilde{O}(|\Sigma|^{4.5} n/\epsilon^2)$, whose dependence on $n, \epsilon$ is optimal up to logarithmic factors. Lastly, if $P$ and $Q$ are product distributions over $\{0,1\}^n$ and $Q$ is known, the sample complexity becomes $O(\sqrt{n}/\epsilon^2)$, which is optimal up to constant factors.
Diving into the shallows: a computational perspective on large-scale shallow learning

Remarkable recent success of deep neural networks has not been easy to analyze theoretically. It has been particularly hard to disentangle relative significance of architecture and optimization in achieving accurate classification on large datasets. On the flip side, shallow methods (such as kernel methods) have encountered obstacles in scaling to large data, despite excellent performance on smaller datasets, and extensive theoretical analysis. Practical methods, such as variants of gradient descent used so successfully in deep learning, seem to perform below par when applied to kernel methods. This difficulty has sometimes been attributed to the limitations of shallow architecture. In this paper we identify a basic limitation in gradient descent-based optimization methods when used in conjunctions with smooth kernels. Our analysis demonstrates that only a vanishingly small fraction of the function space is reachable after a polynomial number of gradient descent iterations. That drastically limits the approximating power of gradient descent leading to over-regularization. The issue is purely algorithmic, persisting even in the limit of infinite data. To address this shortcoming in practice, we introduce EigenPro iteration, a simple and direct preconditioning scheme using a small number of approximately computed eigenvectors. It can also be viewed as learning a kernel optimized for gradient descent. Injecting this small, computationally inexpensive and SGD-compatible, amount of approximate second-order information leads to major improvements in convergence. For large data, this leads to a significant performance boost over the state-of-the-art kernel methods. In particular, we are able to match or improve the results reported in the literature at a small fraction of their computational budget. For complete version of this paper see https://arxiv.org/abs/1703.10622.
A Remark on Two Notions of Order of Contact

We recall two measurements of the order of contact of an ideal in the ring of germs of holomorphic functions at a point, and we provide a class of examples in which they differ.
Saddle point least squares preconditioning of mixed methods

Abstract We present a simple way to discretize and precondition mixed variational formulations. Our theory connects with, and takes advantage of, the classical theory of symmetric saddle point problems and the theory of preconditioning symmetric positive definite operators. Efficient iterative processes for solving the discrete mixed formulations are proposed and choices for discrete spaces that are always compatible are provided. For the proposed discrete spaces and solvers, a basis is needed only for the test spaces and assembly of a global saddle point system is avoided. We prove sharp approximation properties for the discretization and iteration errors and also provide a sharp estimate for the convergence rate of the proposed algorithm in terms of the condition number of the elliptic preconditioner and the discrete inf − sup and sup − sup constants of the pair of discrete spaces.
Duality, Bridgeland wall-crossing and flips of secant varieties

Let vd(ℙ2) ⊂|&#x1d4aa; ℙ2(d)| denote the d-uple Veronese surface. After studying some general aspects of the wall-crossing phenomena for stability conditions on surfaces, we are able to describe a sequence...
TWISTED ALEXANDER INVARIANTS OF TWISTED LINKS

Let L = l1 ∪⋯∪ld+1 be an oriented link in &#x1d54a;3, and let L(q) be the d-component link l1 ∪⋯∪ld regarded in the homology 3-sphere that results from performing 1/q-surgery on ld+1. Results about the Alexander polynomial and twisted Alexander polynomials of L(q) corresponding to finite-image representations are obtained. The behavior of the invariants as q increases without bound is described.
The topology of probability distributions on manifolds

Let $$\mathcal {P}$$ be a set of $$n$$ random points in $$\mathbb {R}^d$$ , generated from a probability measure on a $$m$$ -dimensional manifold $$\mathcal {M}\subset \mathbb {R}^d$$ . In this paper we study the homology of $$\mathcal {U}(\mathcal {P},r)$$ —the union of $$d$$ -dimensional balls of radius $$r$$ around $$\mathcal {P}$$ , as $$n\rightarrow \infty $$ , and $$r\rightarrow 0$$ . In addition we study the critical points of $$d_{\mathcal {P}}$$ —the distance function from the set $$\mathcal {P}$$ . These two objects are known to be related via Morse theory. We present limit theorems for the Betti numbers of $$\mathcal {U}(\mathcal {P},r)$$ , as well as for number of critical points of index $$k$$ for $$d_{\mathcal {P}}$$ . Depending on how fast $$r$$ decays to zero as $$n$$ grows, these two objects exhibit different types of limiting behavior. In one particular case ( $$n r^m \ge C \log n$$ ), we show that the Betti numbers of $$\mathcal {U}(\mathcal {P},r)$$ perfectly recover the Betti numbers of the original manifold $$\mathcal {M}$$ , a result which is of significant interest in topological manifold learning.
Rank-metric codes with local recoverability

We construct rank-metric codes with locality constraints under the rank-metric. Our motivation stems from designing codes for efficient data recovery from correlated and/or mixed (i.e., complete and partial) failures in distributed storage systems. Specifically, the proposed local rank-metric codes can recover locally from crisscross failures, which affect a limited number of rows and/or columns of the storage system. First, we prove a Singleton-like upper bound on the minimum rank-distance of linear codes with rank-locality constraints. Second, we construct a family of locally recoverable rank-metric codes that achieve this bound for a broad range of parameters. The proposed construction builds upon Tamo and Barg's method for constructing locally repairable codes with optimal minimum Hamming distance.
Compositional performance certification of interconnected systems using ADMM

A compositional performance certification method is presented for interconnected systems using subsystem dissipativity properties and the interconnection structure. A large-scale optimization problem is formulated to search for the most relevant dissipativity properties. The alternating direction method of multipliers (ADMM) is employed to decompose and solve this problem, and is demonstrated on several examples.
Electrochemical stability and light-harvesting ability of silicon photoelectrodes in aqueous environments

We study the factors that affect the photoactivity of silicon electrodes for the water-splitting reaction using a self-consistent continuum solvation model of the solid-liquid interface. This model allows us to calculate the charge-voltage response, Schottky barriers, and surface stability of different terminations while accounting for the interactions between the charge-pinning centers at the surface and the depletion region of the semiconductor. We predict that the most stable oxidized surface does not have a favorable Schottky barrier, which further explains the low solar-to-hydrogen performance of passivated silicon electrodes.
The Problem of Bigness: From Standard Oil to Google

This article sets recent expressions of alarm about the monopoly power of technology giants such as Google and Amazon in the long history of Americans' response to big business. I argue that we cannot understand that history unless we realize that Americans have always been concerned about the political and economic dangers of bigness, not just the threat of high prices. The problem policymakers faced after the rise of Standard Oil was how to protect society against those dangers without punishing firms that grew large because they were innovative. The antitrust regime put in place in the early twentieth century managed this balancing act by focusing on large firms' conduct toward competitors and banning practices that were anticompetitive or exclusionary. Maintaining this balance was difficult, however, and it gave way over time—first to a preoccupation with market power during the post–World War II period, and then to a fixation on consumer welfare in the late twentieth century. Refocusing policy on large firms' conduct would do much to address current fears about bigness without penalizing firms whose market power comes from innovation.
A Phase Variable Approach for Improved Rhythmic and Non-Rhythmic Control of a Powered Knee-Ankle Prosthesis

Although there has been recent progress in control of multi-joint prosthetic legs for rhythmic tasks such as walking, control of these systems for non-rhythmic motions and general real-world maneuvers is still an open problem. In this article, we develop a new controller that is capable of both rhythmic (constant-speed) walking, transitions between speeds and/or tasks, and some common volitional leg motions. We introduce a new piecewise holonomic phase variable, which, through a finite state machine, forms the basis of our controller. The phase variable is constructed by measuring the thigh angle, and the transitions in the finite state machine are formulated through sensing foot contact along with attributes of a nominal reference gait trajectory. The controller was implemented on a powered knee-ankle prosthesis and tested with a transfemoral amputee subject, who successfully performed a wide range of rhythmic and non-rhythmic tasks, including slow and fast walking, quick start and stop, backward walking, walking over obstacles, and kicking a soccer ball. Use of the powered leg resulted in clinically significant reductions in amputee compensations for rhythmic tasks (including vaulting and hip circumduction) when compared to use of the take-home passive leg. In addition, considerable improvements were also observed in the performance for non-rhythmic tasks. The proposed approach is expected to provide a better understanding of rhythmic and non-rhythmic motions in a unified framework, which in turn can lead to more reliable control of multi-joint prostheses for a wider range of real-world tasks.
A Permutation Test on Complex Sample Data

Permutation tests are a distribution free way of performing hypothesis tests. These tests rely on the condition that the observed data are exchangeable among the groups being tested under the null hypothesis. This assumption is easily satisfied for data obtained from a simple random sample or a controlled study after simple adjustments to the data, but there is no general method for adjusting survey data collected using a complex sample design to allow for permutation tests. In this article, we propose a general method for performing a pseudo- permutation test that accounts for the complex sample design. The proposed method is not a true permutation test in that the new values do not come from the set of observed values in general, but of an expanded set of values satisfying a random-effects model on the clustered residuals. Tests using a simulated population comparing the performance of the proposed method to permutation tests that ignore the sample design demonstrate that it is necessary to account for certain design features in order to obtain reasonable p-value estimates.
Divalent cations can control a switch-like behavior in heterotypic and homotypic RNA coacervates

Liquid-liquid phase separation (LLPS) of RNA-protein complexes plays a major role in the cellular function of membraneless organelles (MLOs). MLOs are sensitive to changes in cellular conditions, such as fluctuations in cytoplasmic ion concentrations. To investigate the effect of these changes on MLOs, we studied the influence of divalent cations on the physical and chemical properties of RNA coacervates. Using a model system comprised of an arginine-rich peptide and RNA, we predicted and observed that variations in signaling cations exert interaction-dependent effects on RNA LLPS. Changing the ionic environment has opposing effects on the propensity for heterotypic peptide-RNA and homotypic RNA LLPS, which results in a switch between coacervate types. Furthermore, divalent ion variations continuously tune the microenvironments and fluid properties of heterotypic and homotypic droplets. Our results may provide a general mechanism for modulating the biochemical environment of RNA coacervates in a cellular context.
Detection-by- Tracking Boosted Online 3D Multi-Object Tracking

In the Multi-Object Tracking (MOT) scenario, on top of existing image-based approaches, 3D information can also be essential to improving tracking performance. This paper introduces a way to leverage 3D information, and establishes a Bayesian model that can easily be integrated with 2D and 3D features. Furthermore, to remedy fragmented trajectories due to detection failures in the tracking-by-detection framework, we propose a novel detection-by-tracking method that prevents trajectory interruption. We evaluate our online tracking approach on the KITTI tracking benchmark, and show that by using our proposed 2D and 3D features along with detection-by-tracking, we are able to achieve state-of-the-art results on both cars and pedestrians with a runtime as fast as 16–50 FPS.
Deep Generalized Method of Moments for Instrumental Variable Analysis

Instrumental variable analysis is a powerful tool for estimating causal effects when randomization or full control of confounders is not possible. The application of standard methods such as 2SLS, GMM, and more recent variants are significantly impeded when the causal effects are complex, the instruments are high-dimensional, and/or the treatment is high-dimensional. In this paper, we propose the DeepGMM algorithm to overcome this. Our algorithm is based on a new variational reformulation of GMM with optimal inverse-covariance weighting that allows us to efficiently control very many moment conditions. We further develop practical techniques for optimization and model selection that make it particularly successful in practice. Our algorithm is also computationally tractable and can handle large-scale datasets. Numerical results show our algorithm matches the performance of the best tuned methods in standard settings and continues to work in high-dimensional settings where even recent methods break.
Longitudinal spin Seebeck effect in pyrochlore iridates with bulk and interfacial Dzyaloshinskii-Moriya interaction

The longitudinal spin Seebeck effect (SSE) in magnetic insulator/nonmagnetic metal heterostructures has been theoretically studied, primarily with the assumption of an isotropic interfacial exchange coupling. Here, we present a general theory of the SSE in the case of an antisymmetric Dzyaloshinskii-Moriya interaction (DMI) at the interface in addition to the usual Heisenberg form. We numerically evaluate the dependence of the spin current on the temperature and bulk DMI using a pyrochlore iridate as a model insulator with an all-in-all-out ground-state configuration. We also compare the results of different crystalline surfaces arising from different crystalline orientations and conclude that the relative angles between the interfacial moments and Dzyaloshinskii-Moriya vectors play a significant role in the spin transfer. Our work extends the theory of the SSE by including the anisotropic nature of the interfacial Dzyaloshinskii-Moriya exchange interaction in magnetic insulator/nonmagnetic metal heterostructures and can suggest possible materials to optimize the interfacial spin transfer in spintronic devices.
Compositional Set Invariance in Network Systems with Assume-Guarantee Contracts

This paper presents an assume-guarantee reasoning approach to the computation of robust invariant sets for network systems. Parameterized signal temporal logic (pSTL) is used to formally describe the behaviors of the subsystems, which we use as the template for the contract. We show that set invariance can be proved with a valid assume-guarantee contract by reasoning about individual subsystems. If a valid assume-guarantee contract with monotonic pSTL template is known, it can be further refined by value iteration. When such a contract is not known, an epigraph method is proposed to solve for a contract that is valid, -an approach that has linear complexity for a sparse network. A microgrid example is used to demonstrate the proposed method. The simulation result shows that together with control barrier functions, the states of all the subsystems can be bounded inside the individual robust invariant sets.
Rate of convergence for the weighted Hermite variations of the fractional Brownian motion

In this paper, we obtain a rate of convergence in the central limit theorem for high order weighted Hermite variations of the fractional Brownian motion. The proof is based on the techniques of Malliavin calculus and the quantitative stable limit theorems proved by Nourdin et al. (Ann Probab 44:1–41, 2016).
Collaborative Intelligent Cross-Camera Video Analytics at Edge: Opportunities and Challenges

Nowadays, video cameras are deployed in large scale for spatial monitoring of physical places (e.g., surveillance systems in the context of smart cities). The massive camera deployment, however, presents new challenges for analyzing the enormous data, as the cost of high computational overhead of sophisticated deep learning techniques imposes a prohibitive overhead, in terms of energy consumption and processing throughput, on such resource-constrained edge devices. To address these limitations, this paper envisions a collaborative intelligent cross-camera video analytics paradigm at the network edge in which camera nodes adjust their pipelines (e.g., inference) to incorporate correlated observations and shared knowledge from other nodes' contents. By harassing redundant spatio-temporal to reduce the size of the inference search space in one hand, and intelligent collaboration between video nodes on the other, we discuss how such collaborative paradigm can considerably improve accuracy, reduce latency and decrease communication bandwidth compared to non-collaborative baselines. This paper also describes major opportunities and challenges in realizing such a paradigm.
Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes.

PURPOSEThe medical literature relevant to germline genetics is growing exponentially. Clinicians need tools that help to monitor and prioritize the literature to understand the clinical implication...
Potential Liability for Physicians Using Artificial Intelligence

Artificial intelligence (AI) is quickly making inroads into medical practice, especially in forms that rely on machine learning, with a mix of hope and hype. Multiple AI-based products have now been approved or cleared by the US Food and Drug Administration (FDA), and health systems and hospitals are increasingly deploying AI-based systems. For example, medical AI can support clinical decisions, such as recommending drugs or dosages or interpreting radiological images. One key difference from most traditional clinical decision support software is that some medical AI may communicate results or recommendations to the care team without being able to communicate the underlying reasons for those results.
Vae/Wgan-Based Image Representation Learning For Pose-Preserving Seamless Identity Replacement In Facial Images

We present a novel variational generative adversarial network (VGAN) based on Wasserstein loss to learn a latent representation from a face image that is invariant to identity but preserves head-pose information. This facilitates synthesis of a realistic face image with the same head pose as a given input image, but with a different identity. One application of this network is in privacy-sensitive scenarios; after identity replacement in an image, utility, such as head pose, can still be recovered. Extensive experimental validation on synthetic and real human-face image datasets performed under 3 threat scenarios confirms the ability of the proposed network to preserve head pose of the input image, mask the input identity, and synthesize a good-quality realistic face image of a desired identity. We also show that our network can be used to perform pose-preserving identity morphing and identity-preserving pose morphing. The proposed method improves over a recent state-of-the-art method in terms of quantitative metrics as well as synthesized image quality.
Grazing disturbance promotes exotic annual grasses by degrading soil biocrust communities

Exotic invasive plants threaten ecosystem integrity, and their success depends on a combination of abiotic factors, disturbances, and interactions with existing communities. In dryland ecosystems, soil biocrusts (communities of lichens, bryophytes, and microorganisms) can limit favorable microsites needed for invasive species establishment, but the relative importance of biocrusts for landscape-scale invasion patterns remains poorly understood. We examine effects of livestock grazing in habitats at high risk for invasion to test the hypothesis that disturbance indirectly favors exotic annual grasses by reducing biocrust cover. We present some of the first evidence that biocrusts increase site resistance to invasion at a landscape scale and mediate the effects of disturbance. Biocrust species richness, which is reduced by livestock grazing, also appears to promote native perennial grasses. Short mosses, as a functional group, appear to be particularly valuable for preventing invasion by exotic annual grasses. Our study suggests that maintaining biocrust communities with high cover, species richness, and cover of short mosses can increase resistance to invasion. These results highlight the potential of soil surface communities to mediate invasion dynamics and suggest promising avenues for restoration in dryland ecosystems.
Digital Democracy: Episode IV -- A New Hope, How a Corporation for Public Software Could Transform Digital Engagement for Government and Civil Society

Though successive generations of digital technology have become increasingly powerful in the past twenty years, digital democracy has yet to realize its potential for deliberative transformation. The undemocratic exploitation of massive social media systems continued this trend, but it only worsened an existing problem of modern democracies, which were already struggling to develop deliberative infrastructure independent of digital technologies. There have been many creative conceptions of civic tech, but implementation has lagged behind innovation. This essay argues for implementing one such vision of digital democracy through the establishment of a public corporation. Modeled on the Corporation for Public Broadcasting in the U.S., this entity would foster the creation of new digital technology by providing a stable source of funding to nonprofit technologists, interest groups, civic organizations, government, researchers, private companies, and the public. Funded entities would produce and maintain software infrastructure for public benefit. The concluding sections identify what circumstances might create and sustain such an entity.
Deductive Semiparametric Estimation in Double-Sampling Designs with Application to PEPFAR

Non-ignorable dropout is common in studies with long follow-up time, and it can bias study results unless handled carefully in the study design and the statistical analysis. A double-sampling design allocates additional resources to pursue a subsample of the dropouts and find out their outcomes, which can address potential biases due to non-ignorable dropout. It is desirable to construct semiparametric estimators for the double-sampling design because of their robustness properties. However, obtaining such semiparametric estimators remains a challenge due to the requirement of the analytic form of the efficient influence function (EIF), the derivation of which can be ad hoc and difficult for the double-sampling design. Recent work has shown how the derivation of EIF can be made deductive and computerizable using the functional derivative representation of the EIF in nonparametric models. This approach, however, requires deriving the mixture of a continuous distribution and a point mass, which can itself be challenging for complicated problems such as the double-sampling design. We propose semiparametric estimators for the survival probability in double-sampling designs by generalizing the deductive and computerizable estimation approach. In particular, we propose to build the semiparametric estimators based on a discretized support structure, which approximates the possibly continuous observed data distribution and circumvents the derivation of the mixture distribution. Our approach is deductive in the sense that it is expected to produce semiparametric locally efficient estimators within finite steps without knowledge of the EIF. We apply the proposed estimators to estimating the mortality rate in a double-sampling design component of the President’s Emergency Plan for AIDS Relief (PEPFAR) program. We evaluate the impact of double-sampling selection criteria on the mortality rate estimates. Simulation studies are conducted to evaluate the robustness of the proposed estimators.
Answer Them All! Toward Universal Visual Question Answering Models

Visual Question Answering (VQA) research is split into two camps: the first focuses on VQA datasets that require natural image understanding and the second focuses on synthetic datasets that test reasoning. A good VQA algorithm should be capable of both, but only a few VQA algorithms are tested in this manner. We compare five state-of-the-art VQA algorithms across eight VQA datasets covering both domains. To make the comparison fair, all of the models are standardized as much as possible, e.g., they use the same visual features, answer vocabularies, etc. We find that methods do not generalize across the two domains. To address this problem, we propose a new VQA algorithm that rivals or exceeds the state-of-the-art for both domains.
Deformation and Swelling of Kerogen Matrix in Light Hydrocarbons and Carbon Dioxide

Shale gas has become an attractive alternative to conventional fossil fuels due to its clean-burning characteristics. Fluid molecules reside in micropores and mesopores in shale formations. Molecul...
A Practical Introduction to Regression Discontinuity Designs: Foundations

In this Element and its accompanying second Element, A Practical Introduction to Regression Discontinuity Designs: Extensions, Matias Cattaneo, Nicolas Idrobo, and Rociio Titiunik provide an accessible and practical guide for the analysis and interpretation of regression discontinuity (RD) designs that encourages the use of a common set of practices and facilitates the accumulation of RD-based empirical evidence. In this Element, the authors discuss the foundations of the canonical Sharp RD design, which has the following features: (i) the score is continuously distributed and has only one dimension, (ii) there is only one cutoff, and (iii) compliance with the treatment assignment is perfect. In the second Element, the authors discuss practical and conceptual extensions to this basic RD setup.
Artificial intelligence and the future of psychiatry: Insights from a global physician survey.

Abstract Background Futurists have predicted that new autonomous technologies, embedded with artificial intelligence (AI) and machine learning (ML), will lead to substantial job losses in many sectors disrupting many aspects of healthcare. Mental health appears ripe for such disruption given the global illness burden, stigma, and shortage of care providers. Objective To characterize the global psychiatrist community’s opinion regarding the potential of future autonomous technology (referred to here as AI/ML) to replace key tasks carried out in mental health practice. Design Cross sectional, random stratified sample of psychiatrists registered with Sermo, a global networking platform open to verified and licensed physicians. Main outcome measures We measured opinions about the likelihood that AI/ML tools would be able to fully replace – not just assist – the average psychiatrist in performing 10 key psychiatric tasks. Among those who considered replacement likely, we measured opinions about how many years from now such a capacity might emerge. We also measured psychiatrist’s perceptions about whether benefits of AI/ML would outweigh the risks. Results Survey respondents were 791 psychiatrists from 22 countries representing North America, South America, Europe and Asia-Pacific. Only 3.8 % of respondents felt it was likely that future technology would make their jobs obsolete and only 17 % felt that future AI/ML was likely to replace a human clinician for providing empathetic care. Documenting and updating medical records (75 %) and synthesizing information (54 %) were the two tasks where a majority predicted that AI/ML could fully replace human psychiatrists. Female- and US-based doctors were more uncertain that the benefits of AI would outweigh risks than male- and non-US doctors, respectively. Around one in 2 psychiatrists did however predict that their jobs would be substantially changed by AI/ML. Conclusions Our findings provide compelling insights into how physicians think about AI/ML which in turn may help us better integrate technology and reskill doctors to enhance mental health care.