cssi/SciSciGPT-SciSciNet huggingface dataset:
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data

The ability to segment unknown objects in depth images has potential to enhance robot skills in grasping and object tracking. Recent computer vision research has demonstrated that Mask R-CNN can be trained to segment specific categories of objects in RGB images when massive hand-labeled datasets are available. As generating these datasets is time-consuming, we instead train with synthetic depth images. Many robots now use depth sensors, and recent results suggest training on synthetic depth data can transfer successfully to the real world. We present a method for automated dataset generation and rapidly generate a synthetic training dataset of 50,000 depth images and 320,000 object masks using simulated heaps of 3D CAD models. We train a variant of Mask R-CNN with domain randomization on the generated dataset to perform category-agnostic instance segmentation without any hand-labeled data and we evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask R-CNN, on a set of real, high-resolution depth images of challenging, densely-cluttered bins containing objects with highly-varied geometry. SD Mask R-CNN outperforms point cloud clustering baselines by an absolute 15% in Average Precision and 20% in Average Recall on COCO benchmarks, and achieves performance levels similar to a Mask R-CNN trained on a massive, hand-labeled RGB dataset and fine-tuned on real images from the experimental setup. We deploy the model in an instance-specific grasping pipeline to demonstrate its usefulness in a robotics application. Code, the synthetic training dataset, and supplementary material are available at https://bit.ly/2letCuE.
The status of women in health science computing

The computing profession is now just over 25 years old. During this 25-year period, women increased in the labor force from 34 percent of all women ages 16 and over in 1950 to 46 percent in 1975. 1 Since computing developed during the period when females became a larger proportion of the work force, the question arises as to whether women are equitably represented in this profession. The present decade has brought a consciousness-raising with respect to woman’s role in the professions in general, with an interest in the role of women in the computing profession following naturally. The health care industries have become an ever more important and visible component of the U. S. Gross National Product, resulting in increasing scrutiny of the conduct within the health care professions. With this background, the question of the status of women in Health Science Computing appears to be a logical one to consider.
A stochastic composite gradient method with incremental variance reduction

We consider the problem of minimizing the composition of a smooth (nonconvex) function and a smooth vector mapping, where the inner mapping is in the form of an expectation over some random variable or a finite sum. We propose a stochastic composite gradient method that employs incremental variance-reduced estimators for both the inner vector mapping and its Jacobian. We show that this method achieves the same orders of complexity as the best known first-order methods for minimizing expected-value and finite-sum nonconvex functions, despite the additional outer composition which renders the composite gradient estimator biased. This finding enables a much broader range of applications in machine learning to benefit from the low complexity of incremental variance-reduction methods.
Analysis of Deterministic Longest-Chain Protocols

Most classical consensus protocols rely on a leader to coordinate nodes' voting efforts. One novel idea that stems from blockchain-style consensus is to rely, instead, on a "longestchain" idea for such coordination. Such a longest-chain idea was initially considered in randomized protocols, where in each round, a node has some probability of being elected a leader who can propose the next block. Recently, well-known systems have started implementing the deterministic counterpart of such longest-chain protocols — the deterministic counterpart is especially attractive since it is even simpler to implement than their randomized cousins. A notable instantiation is the Aura protocol which is widely shipped with Parity's open-source Ethereum implementation. Interestingly, mathematical analyses of deterministic, longest-chain protocols are lacking even though there exist several analyses of randomized versions. In this paper, we provide the first formal analysis of deterministic, longest-chain-style consensus. We show that a variant of the Aura protocol can defend against a Byzantine adversary that controls fewer than 1 fraction of the nodes, and this resilience parameter is tight. 3 Based on insights gained through our mathematical treatment, we point out that Aura's concrete instantiation actually fails to achieve the resilience level they claim and thus clarify existing misconceptions. Finally, while our tight proof for the longest-chain protocol is rather involved and non-trivial; we show that a variant of the "longest-chain" idea which we call "largest-set" enables a textbook construction that admits a simple proof (albeit with slower confirmation).
Adaptation of Mycobacterium tuberculosis to Biofilm Growth Is Genetically Linked to Drug Tolerance

Mycobacterium tuberculosis spontaneously grows at the air-medium interface, forming pellicle biofilms, which harbor more drug-tolerant persisters than planktonic cultures. The underlying basis for increased persisters in M. tuberculosis biofilms is unknown. Using a transposon sequencing (Tn-seq) approach, we show here that multiple genes that are necessary for fitness of M. tuberculosis cells within biofilms, but not in planktonic cultures, are also implicated in tolerance of bacilli to a diverse set of stressors and antibiotics. Thus, development of M. tuberculosis biofilms appears to be associated with an enrichment of population, in which challenging growth conditions within biofilm architecture select for cells that maintain intrinsic tolerance to exogenous stresses, including antibiotic exposure. We further observed that the intrinsic drug tolerance of constituent cells of a biofilm determines the frequency of persisters. These findings together allow us to propose that the selection of elite cells during biofilm development promotes the frequency of persisters. Furthermore, probing the possibility that the population enrichment is an outcome of unique environment within biofilms, we demonstrate biofilm-specific induction in the synthesis of isonitrile lipopeptide (INLP). Mutation analysis indicates that INLP is necessary for the architecture development of M. tuberculosis biofilms. In summary, this study offers an insight into persistence of M. tuberculosis biofilms under antibiotic exposure, while identifying INLP as a potential biomarker for further investigation of this phenomenon.
Adaptive learning in large populations.

We consider the adaptive learning rule of Harley (J Theor Biol 89:611–633, 1981) for behavior selection in symmetric conflict games in large populations. This rule uses organisms’ past, accumulated rewards as the predictor for future behavior, and can be traced in many life forms from bacteria to humans. We derive a partial differential equation for the distribution of agents in the space of stimuli to select a particular strategy which describes the evolution of learning in heterogeneous populations. We analyze the solutions of the PDE model for symmetric $$2 \times 2$$ games. It is found that in games with small residual stimuli, adaptive learning rules with larger memory factor converge faster to the optimal outcome.
Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process

The objective is to study an on-line Hidden Markov model (HMM) estimation-based Q-learning algorithm for partially observable Markov decision process (POMDP) on finite state and action sets. When the full state observation is available, Q-learning finds the optimal action-value function given the current action (Q-function). However, Q-learning can perform poorly when the full state observation is not available. In this paper, we formulate the POMDP estimation into a HMM estimation problem and propose a recursive algorithm to estimate both the POMDP parameter and Q-function concurrently. Also, we show that the POMDP estimation converges to a set of stationary points for the maximum likelihood estimate, and the Q-function estimation converges to a fixed point that satisfies the Bellman optimality equation weighted on the invariant distribution of the state belief determined by the HMM estimation process.
Enhancing Power System Cyber-Security With Systematic Two-Stage Detection Strategy

State estimation estimates the system condition in real-time and provides a base case for other energy management system (EMS) applications including real-time contingency analysis and security-constrained economic dispatch. Recent work in the literature shows malicious cyber-attack can inject false measurements that bypass traditional bad data detection in state estimation and cause actual overloads. Thus, it is very important to detect such cyber-attack. In this paper, multiple metrics are proposed to monitor abnormal load deviations and suspicious branch flow changes. A systematic two-stage approach is proposed to detect false data injection (FDI) cyber-attack. The first stage determines whether the system is under attack while the second stage identifies the target branch. Numerical simulations verify that FDI can cause severe system violations and demonstrate the effectiveness of the proposed two-stage FDI detection (FDID) method. It is concluded that the proposed FDID approach can efficiently detect FDI cyber-attack and identify the target branch, which will substantially improve operators' situation awareness in real-time.
Trace Reconstruction: Generalized and Parameterized

In the beautifully simple-to-state problem of trace reconstruction, the goal is to reconstruct an unknown binary string $x$ given random “traces” of $x$ where each trace is generated by deleting each coordinate of $x$ independently with probability $p . The problem is well studied both when the unknown string is arbitrary and when it is chosen uniformly at random. For both settings, there is still an exponential gap between upper and lower sample complexity bounds and our understanding of the problem is still surprisingly limited. In this paper, we consider natural parameterizations and generalizations of this problem in an effort to attain a deeper and more comprehensive understanding. Perhaps our most surprising results are: 1) We prove that $\exp (O(n^{1/4} \sqrt {\log n}))$ traces suffice for reconstructing arbitrary matrices. In the matrix version of the problem, each row and column of an unknown $\sqrt {n}\times \sqrt {n}$ matrix is deleted independently with probability $p$ . Our results contrasts with the best known results for sequence reconstruction where the best known upper bound is $\exp (O(n^{1/3}))$ . 2) An optimal result for random matrix reconstruction: we show that $\Theta (\log n)$ traces are necessary and sufficient. This is in contrast to the problem for random sequences where there is a super-logarithmic lower bound and the best known upper bound is $\exp ({O}(\log ^{1/3} n))$ . 3) We show that $\exp (O(k^{1/3}\log ^{2/3} n))$ traces suffice to reconstruct $k$ -sparse strings, providing an improvement over the best known sequence reconstruction results when $k = o(n/\log ^{2}\,\,n)$ . 4) We show that $\textrm {poly}(n)$ traces suffice if $x$ is $k$ -sparse and we additionally have a “separation” promise, specifically that the indices of 1 ’s in $x$ all differ by $\Omega (k \log n)$ .
Computational Analysis of Energy Landscapes Reveals Dynamic Features That Contribute to Binding of Inhibitors to CFTR-Associated Ligand

The CFTR-associated ligand PDZ domain (CALP) binds to the cystic fibrosis transmembrane conductance regulator (CFTR) and mediates lysosomal degradation of mature CFTR. Inhibition of this interaction has been explored as a therapeutic avenue for cystic fibrosis. Previously, we reported the ensemble-based computational design of a novel peptide inhibitor of CALP, which resulted in the most binding-efficient inhibitor to date. This inhibitor, kCAL01, was designed using osprey and evinced significant biological activity in in vitro cell-based assays. Here, we report a crystal structure of kCAL01 bound to CALP and compare structural features against iCAL36, a previously developed inhibitor of CALP. We compute side-chain energy landscapes for each structure to not only enable approximation of binding thermodynamics but also reveal ensemble features that contribute to the comparatively efficient binding of kCAL01. Finally, we compare the previously reported design ensemble for kCAL01 vs the new crystal structure and show that, despite small differences between the design model and crystal structure, significant biophysical features that enhance inhibitor binding are captured in the design ensemble. This suggests not only that ensemble-based design captured thermodynamically significant features observed in vitro, but also that a design eschewing ensembles would miss the kCAL01 sequence entirely.
ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data.

Mental disorders such as Autism Spectrum Disorders (ASD) are heterogeneous disorders that are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioural observation of symptomology (DSM-5/ICD-10) and may be prone to over-prescribing of drugs due to misdiagnosis. In order to move the field towards more quantitative fashion, we need advanced and scalable machine learning infrastructure that will allow us to identify reliable biomarkers of mental health disorders. In this paper, we propose a framework called ASD-DiagNet for classifying subjects with ASD from healthy subjects by using only fMRI data. We designed and implemented a joint learning procedure using an autoencoder and a single layer perceptron which results in improved quality of extracted features and optimized parameters for the model. Further, we designed and implemented a data augmentation strategy, based on linear interpolation on available feature vectors, that allows us to produce synthetic datasets needed for training of machine learning models. The proposed approach is evaluated on a public dataset provided by Autism Brain Imaging Data Exchange including 1035 subjects coming from 17 different brain imaging centers. Our machine learning model outperforms other existing methods from 10 imaging centers with increase in classification accuracy up to 28% with maximum accuracy of 82%. The machine learning technique presented in this paper, in addition to yielding better quality, gives enormous advantages in terms of execution time (40 minutes vs. 7 hours on other methods). The implemented code is available as GPL license on GitHub portal of our lab (https://github.com/pcdslab/ASD-DiagNet).
Comparative Genomics Guides Elucidation of Vitamin B12 Biosynthesis in Novel Human-Associated Akkermansia Strains

Akkermansia muciniphila is a mucin-degrading bacterium found in the gut of most humans and is considered a "next-generation probiotic." However, knowledge of the genomic and physiological diversity of human-associated Akkermansia sp. strains is limited. Here, we reconstructed 35 metagenome-assembled genomes and combined them with 40 publicly available genomes for comparative genomic analysis. We identified at least four species-level phylogroups (AmI to AmIV), with distinct functional potentials. Most notably, we identified genes for cobalamin (vitamin B12) biosynthesis within the AmII and AmIII phylogroups. To verify these predictions, 10 Akkermansia strains were isolated from adults and screened for vitamin B12 biosynthesis genes via PCR. Two AmII strains were positive for the presence of cobalamin biosynthesis genes, while all 9 AmI strains tested were negative. To demonstrate vitamin B12 biosynthesis, we measured the production of acetate, succinate, and propionate in the presence and absence of vitamin supplementation in representative strains of the AmI and AmII phylogroups, since cobalamin is an essential cofactor in propionate metabolism. Results showed that the AmII strain produced acetate and propionate in the absence of supplementation, which is indicative of vitamin B12 biosynthesis. In contrast, acetate and succinate were the main fermentation products for the AmI strains when vitamin B12 was not supplied in the culture medium. Lastly, two bioassays were used to confirm vitamin B12 production by the AmII phylogroup. This novel physiological trait of human-associated Akkermansia strains may affect how these bacteria interact with the human host and other members of the human gut microbiome.IMPORTANCE There is significant interest in the therapeutic and probiotic potential of the common gut bacterium Akkermansia muciniphila However, knowledge of both the genomic and physiological diversity of this bacterial lineage is limited. Using a combination of genomic, molecular biological, and traditional microbiological approaches, we identified at least four species-level phylogroups with differing functional potentials that affect how these bacteria interact with both their human host and other members of the human gut microbiome. Specifically, we identified and isolated Akkermansia strains that were able to synthesize vitamin B12 The ability to synthesize this important cofactor broadens the physiological capabilities of human-associated Akkermansia strains, fundamentally altering our understanding of how this important bacterial lineage may affect human health.
OMGS: Optical Map-Based Genome Scaffolding

Due to the current limitations of sequencing technologies, de novo genome assembly is typically carried out in two stages, namely contig (sequence) assembly and scaffolding. While scaffolding is computationally easier than sequence assembly, the scaffolding problem can be challenging due to the high repetitive content of eukaryotic genomes, possible mis-joins in assembled contigs and inaccuracies in the linkage information. Genome scaffolding tools either use paired-end/mate-pair/linked/Hi-C reads or genome-wide maps (optical, physical or genetic) as linkage information. Optical maps (in particular Bionano Genomics maps) have been extensively used in many recent large-scale genome assembly projects (e.g., goat, apple, barley, maize, quinoa, sea bass, among others). However, the most commonly used scaffolding tools have a serious limitation: they can only deal with one optical map at a time, forcing users to alternate or iterate over multiple maps. In this paper, we introduce a novel scaffolding algorithm called OMGS that for the first time can take advantages of multiple optical maps. OMGS solves several optimization problems to generate scaffolds with optimal contiguity and correctness. Extensive experimental results demonstrate that our tool outperforms existing methods when multiple optical maps are available, and produces comparable scaffolds using a single optical map. OMGS can be obtained from https://github.com/ucrbioinfo/OMGS.
Minimal Fractal Manifold as Foundation of Quantum Information Theory

Derived from the mathematics of the Renormalization Group, the minimal fractal manifold (MFM) represents a spacetime continuum endowed with arbitrarily small deviations from four dimensions ( ϵ = 4 - D ≪ 1). The geometrical structure of the MFM can be conveniently formulated using the concept of dimensional quaternion , a vector-like entity built from component deviations along the four spacetime coordinates. Our analysis shows that dimensional quaternions form a natural basis for qubit systems and Quantum Information Theory.
Anthropomorphic left ventricular mesh phantom: a framework to investigate the accuracy of SQUEEZ using Coherent Point Drift for the detection of regional wall motion abnormalities.

We present an anthropomorphically accurate left ventricular (LV) phantom derived from human computed tomography (CT) data to serve as the ground truth for the optimization and the spatial resolution quantification of a CT-derived regional strain metric (SQUEEZ) for the detection of regional wall motion abnormalities. Displacements were applied to the mesh points of a clinically derived end-diastolic LV mesh to create analytical end-systolic poses with physiologically accurate endocardial strains. Normal function and regional dysfunction of four sizes [1, 2/3, 1/2, and 1/3 American Heart Association (AHA) segments as core diameter], each exhibiting hypokinesia (70% reduction in strain) and subtle hypokinesia (40% reduction in strain), were simulated. Regional shortening (RSCT) estimates were obtained by registering the end-diastolic mesh to each simulated end-systolic mesh condition using a nonrigid registration algorithm. Ground-truth models of normal function and of hypokinesia were used to identify the optimal parameters in the registration algorithm and to measure the accuracy of detecting regional dysfunction of varying sizes and severities. For normal LV function, RSCT values in all 16 AHA segments were accurate to within ±5 % . For cases with regional dysfunction, the errors in RSCT around the dysfunctional region increased with decreasing size of dysfunctional tissue.
The exact power law for Buffon’s needle landing near some random Cantor sets

In this paper, we study the Favard length of some random Cantor sets of Hausdorff dimension 1. We start with a unit disk in the plane and replace the unit disk by 4 disjoint subdisks (with equal distance to each other) of radius 1/4 inside and tangent to the unit disk. By repeating this operation in a self-similar manner and adding a random rotation in each step, we can generate a random Cantor set D(ω). Let Dn be the n-th generation in the construction, which is comparable to the 4−n-neighborhood of D. We are interested in the decay rate of the Favard length of these sets Dn as n→∞, which is the likelihood (up to a constant) that "Buffon's needle'' dropped randomly will fall into the 4−n-neighborhood of D. It is well known that the lower bound of the Favard length of Dn(ω) is a constant multiple of n−1. We show that the upper bound of the Favard length of Dn(ω) is Cn−1 for some C>0 in the average sense. We also prove the a similar linear decay for the Favard length of Ddn(ω), which is the d−n-neighborhood of a self-similar random Cantor set with degree d greater than 4. Notice that in the non-random case where the self-similar set has degree greater than 4, the best known result for the decay rate of the Favard length is e−clogn√.
Transcranial alternating current stimulation attenuates BOLD adaptation and increases functional connectivity.

Concurrent transcranial alternating current stimulation (tACS) and functional MRI show that tACS affects the human brain by attenuating adaptation and increasing functional connectivity in a dose-d...
Multiyear Hybrid Prediction of Atlantic Tropical Cyclone Activity and the Predictability Sources

AbstractA hybrid statistical–dynamical model is developed to predict multiyear variability of Atlantic tropical cyclone (TC) activity. A Poisson model takes sea surface temperature (SST) averaged o...
The Weil–Petersson Isometry Group

We prove that, for 3g−3+n>1 and (g,n)≠(1,2), the group of Weil–Petersson isometries of the Teichmuller space Tg,n coincides with the extended mapping class group.
Near-optimal discrete optimization for experimental design: a regret minimization approach

The experimental design problem concerns the selection of k points from a potentially large design pool of p-dimensional vectors, so as to maximize the statistical efficiency regressed on the selected k design points. Statistical efficiency is measured by optimality criteria, including A(verage), D(eterminant), T(race), E(igen), V(ariance) and G-optimality. Except for the T-optimality, exact optimization is challenging, and for certain instances of D/E-optimality exact or even approximate optimization is proven to be NP-hard. We propose a polynomial-time regret minimization framework to achieve a $$(1+\varepsilon )$$ approximation with only $$O(p/\varepsilon ^2)$$ design points, for all the optimality criteria above. In contrast, to the best of our knowledge, before our work, no polynomial-time algorithm achieves $$(1+\varepsilon )$$ approximations for D/E/G-optimality, and the best poly-time algorithm achieving $$(1+\varepsilon )$$ -approximation for A/V-optimality requires $$k=\varOmega (p^2/\varepsilon )$$ design points.
Effects of life expectancy on economic growth: new results using the flexible Box–Cox power transformation model

ABSTRACTThis article revisits the effects of life expectancy (LE) on economic growth reported in previous research. Maximum likelihhod estimates are obtained based on the monotonic nonlinear flexib...
Efficiently Constructing Tangent Circles

In this short note we present, by what we surmise is the most efficient method, a straight-edge and compass construction of four mutually tangent circles in a plane.
pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage

Transparent research in musculoskeletal imaging is fundamental to reliably investigate diseases such as knee osteoarthritis (OA), a chronic disease impairing femoral knee cartilage. To study cartilage degeneration, researchers have developed algorithms to segment femoral knee cartilage from magnetic resonance (MR) images and to measure cartilage morphology and relaxometry. The majority of these algorithms are not publicly available or require advanced programming skills to be compiled and run. However, to accelerate discoveries and findings, it is crucial to have open and reproducible workflows. We present pyKNEEr, a framework for open and reproducible research on femoral knee cartilage from MR images. pyKNEEr is written in python, uses Jupyter notebook as a user interface, and is available on GitHub with a GNU GPLv3 license. It is composed of three modules: 1) image preprocessing to standardize spatial and intensity characteristics; 2) femoral knee cartilage segmentation for intersubject, multimodal, and longitudinal acquisitions; and 3) analysis of cartilage morphology and relaxometry. Each module contains one or more Jupyter notebooks with narrative, code, visualizations, and dependencies to reproduce computational environments. pyKNEEr facilitates transparent image-based research of femoral knee cartilage because of its ease of installation and use, and its versatility for publication and sharing among researchers. Finally, due to its modular structure, pyKNEEr favors code extension and algorithm comparison. We tested our reproducible workflows with experiments that also constitute an example of transparent research with pyKNEEr, and we compared pyKNEEr performances to existing algorithms in literature review visualizations. We provide links to executed notebooks and executable environments for immediate reproducibility of our findings.
Outlier-Robust Spatial Perception: Hardness, General-Purpose Algorithms, and Guarantees

Spatial perception is the backbone of many robotics applications, and spans a broad range of research problems, including localization and mapping, point cloud alignment, and relative pose estimation from camera images. Robust spatial perception is jeopardized by the presence of incorrect data association, and in general, outliers. Although techniques to handle outliers do exist, they can fail in unpredictable manners (e.g., RANSAC, robust estimators), or can have exponential runtime (e.g., branch-and-bound). In this paper, we advance the state of the art in outlier rejection by making three contributions. First, we show that even a simple linear instance of outlier rejection is inapproximable: in the worst-case one cannot design a quasi-polynomial time algorithm that computes an approximate solution efficiently. Our second contribution is to provide the first per-instance sub-optimality bounds to assess the approximation quality of a given outlier rejection outcome. Our third contribution is to propose a simple general-purpose algorithm, named adaptive trimming, to remove outliers. Our algorithm leverages recently-proposed global solvers that are able to solve outlier-free problems, and iteratively removes measurements with large errors. We demonstrate the proposed algorithm on three spatial perception problems: 3D registration, two-view geometry, and SLAM. The results show that our algorithm outperforms several state-of-the-art methods across applications while being a general-purpose method.
Why does D=4, rather than more (or less)? An Orwellian explanation

Two simple, if draconian, assumptions about classical gravity actions fix spacetimes dimension at D4.
Geostatistical visualization of ecological interactions in tumors

Recent advances in our understanding of cancer progression have highlighted the roles played by molecular heterogeneity and by the tumor microenvironment in driving drug resistance and metastasis. The coupling of single-cell measurement technologies with algorithms, such as $t$ -sne and SPADE, have enabled deep investigation of tumor heterogeneity. However, such techniques only capture molecular heterogeneity and do not enable the quantification nor visualization of intercellular interactions. They additionally do not allow the visualization of ecological niches that are critical to understanding tumor behavior. Novel computational tools to quantify and visualize spatial patterns in the tumor microenvironment are critically needed. Here, we take a tumor ecology perspective to examine how predation, mutualism, commensalism, and parasitism may impact tumor development and spatial patterning. We additionally quantify local spatial heterogeneity and the emergent global spatial behavior of the models using geostatistics. By visualizing emergent spatial patterns we demonstrate the potential utility of a geostatistical analysis in differentiating amongst cell-cell interactions in the tumor microenvironment. These studies introduce both an ecological framework for characterizing intercellular interactions in cancer and a novel way of quantifying and visualizing spatial patterns in cancer.
Relative Age of Information: A New Metric for Status Update Systems

In this paper, we introduce a new data freshness metric, relative Age of Information (rAoI), and examine it in a single server system with various packet management schemes. The (classical) AoI metric was introduced to measure the end to end freshness of status updates at the receiver with respect to their generation at the source. In order to understand the efficiency of the update delivery system in isolation, we introduce rAoI metric to measure how fresh the data is at the receiver with respect to the data at the transmitter. This metric introduces an explicit dependence on the arrival process in the evaluation of age. We investigate several queuing disciplines and provide closed form expressions for rAoI and numerical comparisons.
The problem with descriptive correctness

In the 1980s and early 1990s, the normativity of meaning was thought to be more-or-less 'incontestable.' But in the last 25 years, many philosophers of mind and language have contested it in several seemingly different ways. This, however, is somewhat illusory. There is an unappreciated commonality among most anti-normativist arguments, and this commonality, I argue, poses a problem for anti-normativism. The result, however, is not a wholesale rejection of anti-normativism. Rather, an insight from the anti-normativist position can be harnessed to reveal an unappreciated position in the normativity of meaning debate.
Model-Predictive Control With Inverse Statics Optimization for Tensegrity Spine Robots

Robots with flexible spines based on tensegrity structures have potential advantages over traditional designs with rigid torsos. However, these robots can be difficult to control due to their high-dimensional nonlinear dynamics and actuator constraints. This article presents two controllers for tensegrity spine robots, using model-predictive control (MPC) and inverse statics (IS) optimization. The controllers introduce two different approaches to making the control problem computationally tractable. The first utilizes smoothing terms in the MPC problem. The second uses a new IS optimization algorithm, which gives the first feasible solutions to the problem for certain tensegrity robots, to generate reference input trajectories in combination with MPC. Tracking the IS reference input trajectory significantly reduces the number of tuning parameters. The controllers are validated against simulations of 2-D and 3-D tensegrity spines. Both approaches show noise insensitivity and low tracking error and can be used for different control goals. The results here demonstrate the first closed-loop control of such structures.
Learning Fast Adaptation With Meta Strategy Optimization

The ability to walk in new scenarios is a key milestone on the path toward real-world applications of legged robots. In this work, we introduce Meta Strategy Optimization, a meta-learning algorithm for training policies with latent variable inputs that can quickly adapt to new scenarios with a handful of trials in the target environment. The key idea behind MSO is to expose the same adaptation process, Strategy Optimization (SO), to both the training and testing phases. This allows MSO to effectively learn locomotion skills as well as a latent space that is suitable for fast adaptation. We evaluate our method on a real quadruped robot and demonstrate successful adaptation in various scenarios, including sim-to-real transfer, walking with a weakened motor, or climbing up a slope. Furthermore, we quantitatively analyze the generalization capability of the trained policy in simulated environments. Both real and simulated experiments show that our method outperforms previous methods in adaptation to novel tasks.
Designing for Emergent Security in Heterogeneous Human-Machine teams

This work seeks to design decisionmaking rules for autonomous agents to jointly influence and optimize the behavior of teamed human decisionmakers in the presence of an adversary. We study a situation in which computational jobs are scheduled on servers by a collection of autonomous machines in concert with self-interested human decisionmakers, and the human and machine schedulers must react to an adversary’s attack on one of the servers. We show a simple machine scheduling policy such that if all schedulers have permission to schedule jobs on all servers, increasing the penetration of machine schedulers always increases the level of security in the system, even when the machine schedulers have no explicit coordination or communication amongst themselves. However, we show a companion result in which simple constraints on server availability can nullify the machine schedulers’ ability to effectively influence human schedulers; here, even if machine schedulers control an overwhelming majority of jobs, are socially-aware, and fully coordinated amongst themselves, they are incapable of influencing human decisionmakers to mitigate the harm of an attack.
Constructing sparse Davenport-Schinzel sequences

Abstract For any sequence u , the extremal function Ex ( u , j , n ) is the maximum possible length of a j -sparse sequence with n distinct letters that avoids u . We prove that if u is an alternating sequence a b a b … of length s , then Ex ( u , j , n ) = Θ ( s n 2 ) for all j ≥ 2 and s ≥ n , answering a question of Wellman and Pettie (2018) and extending the result of Roselle and Stanton that Ex ( u , 2 , n ) = Θ ( s n 2 ) for any alternation u of length s ≥ n (Roselle and Stanton, 1971). Wellman and Pettie also asked how large must s ( n ) be for there to exist n -block D S ( n , s ( n ) ) sequences of length Ω ( n 2 − o ( 1 ) ) . We answer this question by showing that the maximum possible length of an n -block D S ( n , s ( n ) ) sequence is Ω ( n 2 − o ( 1 ) ) if and only if s ( n ) = Ω ( n 1 − o ( 1 ) ) . We also show related results for extremal functions of forbidden 0–1 matrices with any constant number of rows and extremal functions of forbidden sequences with any constant number of distinct letters.
The Potential Impact of COVID-19 on GDP and Trade : A Preliminary Assessment

The virus that triggered a localized shock in China is now delivering a significant global shock. This study simulates the potential impact of COVID-19 on gross domestic product and trade, using a standard global computable general equilibrium model. It models the shock as underutilization of labor and capital, an increase in international trade costs, a drop in travel services, and a redirection of demand away from activities that require proximity between people. A baseline global pandemic scenario sees gross domestic product fall by 2 percent below the benchmark for the world, 2.5 percent for developing countries, and 1.8 percent for industrial countries. The declines are nearly 4 percent below the benchmark for the world, in an amplified pandemic scenario in which containment is assumed to take longer and which now seems more likely. The biggest negative shock is recorded in the output of domestic services affected by the pandemic, as well as in traded tourist services. Since the model does not capture fully the social isolation induced independent contraction in demand and the decline in investor confidence, the eventual economic impact may be different. This exercise is illustrative, because it is still too early to make an informed assessment of the full impact of the pandemic. But it does convey the likely extent of impending global economic pain, especially for developing countries and their potential need for assistance.
On the Nonnegativity of Stringy Hodge Numbers

We study the nonnegativity of stringy Hodge numbers of a projective variety with Gorenstein canonical singularities, which was conjectured by Batyrev. We prove that the $(p,1)$-stringy Hodge numbers are nonnegative, and for threefolds we obtain new results about the stringy Hodge diamond, which hold even when the stringy $E$-function is not a polynomial. We also use the Decomposition Theorem and mixed Hodge theory to prove Batyrev's conjecture for a class of fourfolds.
Chapter 11 – Hydrogen Gas

Publisher Summary The highly flammable hydrogen gas is colorless and odorless. It is produced by catalytic process or by electrolytic decomposition of water; separating it into hydrogen and oxygen. It is essential to keep the hydrogen and oxygen or air separate because a small amount of these two gases produces an explosive mixture. When not mixed with air, hydrogen gas is not explosive although its leakage is potentially dangerous, the odorless gas may escape entirely unnoticed, because of its diffusion rate. Hydrogen gas is used in many ways— such as, for cooling large generators because it is an excellent heat conductor, and for chemical process plants. For cooling purposes, hydrogen gas cylinders should be located in well ventilated locations, separated from the generator. Maintain a minimum safe clearance of 15- 25 feet between any general purpose electrical equipment and storage containers. The hydrogen gas containers should not be considered as the actual source of hazard. Devices— such as, pressure reducers, pressure relief valves, manifolds, and valves connected to the piping system with low integrity seal connectors are normally the actual sources of hazard.
Security Fungibility and the Cost of Capital: Evidence from Global Bonds

This paper examines the potential benefits of security fungibility by conducting the first comprehensive analysis of global bonds. Unlike other debt securities, global bonds' fungibility allows them to be placed simultaneously in bond markets around the world; they trade, clear, and settle efficiently within as well as across markets. We test the impact of issuing these securities on firms' cost of capital, issuing costs, liquidity, and shareholder wealth. Using a sample of 230 global bond issues by 94 companies from the U.S. and abroad over the period 1996–2003, we find that firms lower their cost of (debt) capital by issuing these fungible securities. We also document that the stock price reaction to the announcement of global bond issuance is positive and significant, while comparable domestic and eurobond issues over the same time period are associated with insignificant changes in shareholder wealth.
STSDAS: The Space Telescope Science Data Analysis System

STSDAS is a system of some 750,000 lines of code and documentation designed for the calibration and analysis of data from the Hubble Space Telescope. At its inception in 1981, when it was known simply as ‘SDAS’, the system was dependent on the VAX VMS operating system and architecture and was limited in scope to HST data analysis, excluding calibration. As it was realized that astronomers would want to be able to do their own calibrations of HST data, and that it was not cost-effective, either for end-users or for the software development effort, to have a system dependent on a proprietary operating system, the scope and objectives of the system were modified. The most fundamental change was the decision to fully layer the STSDAS software on the Image Reduction and Analysis Facility -IRAF – from the National Optical Astronomy Observatories. For a review of the historical aspects of the development of STSDAS, please see Hanisch (1989).
Agricultural labor productivity in the Lower South, 1720–1800

Abstract The primary factor determining the rate of economic growth in 18th-century British America was the pace of improvement in agricultural productivity. This paper examines agricultural productivity advance in the Lower South between 1720 and 1800. We extend and refine previous measures based on export performance, and offer new productivity measures based on the real value of slave labor, and total agricultural production in the region. Despite differences in their short-term behavior, all of the indices show that long-run productivity improvements were modest at best, and may have been negative. Surprisingly, taking account of production for domestic consumption yields the most favorable long-term performance.
The rise of the states: U.S. fiscal decentralization in the postwar period

article i nfo Article history: One of the most dramatic changes in the fiscal federalism landscape during the postwar period has been the rapid growth in state budgets, which almost tripled as a share of GDP and doubled as a share of government spending between 1952 and 2006. We argue that the greater role of states cannot be easily explained by changes in Tiebout forces of fiscal competition, such as mobility and voting patterns, and are not accounted for by demographic or income trends. Rather, we demonstrate that much of the growth in state budgets has been driven by changes in intergovernmental interactions. Restricted federal grants to states have increased, and federal policy and legal constraints have also mandated or heavily incentivized state own-source spending, particularly in the areas of education, health and public welfare. These outside pressures moderate the forces of fiscal competition and must be taken into account when assessing the implications of observed revenue and spending patterns.
Consultations in Child Abuse Pediatrics

This study describes a hospital-based child abuse pediatrics consultation service. Medical records for all child abuse pediatrics consultations during 2006 to 2009 were reviewed. Descriptive statistics were used for data analysis. Of 2495 consultations, 13 were excluded due to insufficient information, 1682 were examinations for suspected sexual abuse, and 800 were examinations for nonsexual abuse concerns. Among the latter group of 800 patients, the most common reasons for consultation were fracture (33.5%), nonburn skin injury (16.8%), burn (15.4%), and intracranial injury (13.2%). Median patient age was 11 months (range = 3 days to 16 years). Case fatality rate was 3.9%. Final diagnosis was classified as definite/likely abuse in 40.0%, questionable/unknown in 24.5%, definite/likely accident in 23.6%, no injury in 4.6%, neglect in 4.0%, and a medical condition in 3.2%. Therefore, among consultations requested for suspected child maltreatment, a child abuse pediatrician concluded that abuse was definite or likely in less than half of patients.
GASTROESOPHAGEAL REFLUX DISEASE

Gastroesophageal reflux disease (GERD) is a spectrum of disease that can best be defined as the symptoms and/or signs of esophageal or adjacent organ injury secondary to the reflux of gastric contents into the esophagus or, beyond, into the oral cavity or airways. Injury is defined based on symptoms or organ damage resulting in esophagitis, laryngeal inflammation, or acute and/or chronic pulmonary injury. Though specific studies are lacking, GERD seems to occur more frequently in older people. In general, because it has been present for a longer period of time, older patients will often present with more complicated GERD, including severe erosive esophagitis, peptic stricture, and Barrett's esophagus, a premalignant condition, making treatment of older patients with GERD potentially more difficult and complex. Though it is not usually life-threatening, GERD can have a major effect on patients' well-being and quality of life. This article reviews the presentation, pathophysiology, diagnosis, and treatment of GERD as it relates to the older patient.
Deligne–Lusztig constructions for division algebras and the local Langlands correspondence, II

In 1979, Lusztig proposed a cohomological construction of supercuspidal representations of reductive p-adic groups, analogous to Deligne–Lusztig theory for finite reductive groups. In this paper we establish a new instance of Lusztig’s program. Precisely, let X be the Deligne–Lusztig (ind-pro-)scheme associated to a division algebra D over a non-Archimedean local field K of positive characteristic. We study the $$D^\times $$ -representations $$H_\bullet (X)$$ by establishing a Deligne–Lusztig theory for families of finite unipotent groups that arise as subquotients of $$D^\times $$ . There is a natural correspondence between quasi-characters of the (multiplicative group of the) unramified degree-n extension of K and representations of $$D^{\times }$$ given by $$\theta \mapsto H_\bullet (X)[\theta ]$$ . For a broad class of characters $$\theta ,$$ we show that the representation $$H_\bullet (X)[\theta ]$$ is irreducible and concentrated in a single degree. After explicitly constructing a Weil representation from $$\theta $$ using $$\chi $$ -data, we show that the resulting correspondence matches the bijection given by local Langlands and therefore gives a geometric realization of the Jacquet–Langlands transfer between representations of division algebras.
Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality

We study the distribution and uncertainty of nonconvex optimization for noisy tensor completion -- the problem of estimating a low-rank tensor given incomplete and corrupted observations of its entries. Focusing on a two-stage estimation algorithm proposed by Cai et al. (2019), we characterize the distribution of this nonconvex estimator down to fine scales. This distributional theory in turn allows one to construct valid and short confidence intervals for both the unseen tensor entries and the unknown tensor factors. The proposed inferential procedure enjoys several important features: (1) it is fully adaptive to noise heteroscedasticity, and (2) it is data-driven and automatically adapts to unknown noise distributions. Furthermore, our findings unveil the statistical optimality of nonconvex tensor completion: it attains un-improvable $\ell_{2}$ accuracy -- including both the rates and the pre-constants -- when estimating both the unknown tensor and the underlying tensor factors.