cssi/SciSciGPT-SciSciNet huggingface dataset:
New constructions of RIP matrices with fast multiplication and fewer rows

In this paper, we present novel constructions of matrices with the restricted isometry property (RIP) that support fast matrix-vector multiplication. Our guarantees are the best known, and can also be used to obtain the best known guarantees for fast Johnson Lindenstrauss transforms.In compressed sensing, the restricted isometry property is a sufficient condition for the efficient reconstruction of a nearly k-sparse vector x e Cd from m linear measurements Φx. It is desirable for m to be small, and further it is desirable for Φ to support fast matrix-vector multiplication. Among other applications, fast multiplication improves the runtime of iterative recovery algorithms which repeatedly multiply by Φ or Φ*.The main contribution of this work is a novel randomized construction of RIP matrices Φ e Cmxd, preserving the e2 norms of all k-sparse vectors with distortion 1 + e, where the matrix-vector multiply Φx can be computed in nearly linear time. The number of rows m is on the order of e-2k log dlog2(kl oge d), an improvement on previous analyses by a logarithmic factor. Our construction, together with a connection between RIP matrices and the Johnson-Lindenstrauss lemma in [Krahmer-Ward, SIAM. J. Math. Anal. 2011], also implies fast Johnson-Lindenstrauss embeddings with asymptotically fewer rows than previously known.Our construction is actually a recipe for improving any existing family of RIP matrices. Briefly, we apply an appropriate sparse hash matrix with sign flips to any suitable family of RIP matrices. We show that the embedding properties of the original family are maintained, while at the same time improving the number of rows. The main tool in our analysis is a recent bound for the supremum of certain types of Rademacher chaos processes in [Krahmer-Mendelson-Rauhut, Comm. Pure Appl. Math. to appear].
Schur multipliers of unitriangular groups

Abstract An explicit formula for the Schur multiplier of the group of unitriangular matrices over products of cyclic rings Z / m Z and Z is derived. We use it to provide presentations of the corresponding covering groups and touch upon the question of finding their nonisomorphic representatives.
Thermal Modeling of Zero Boil Off Tank Experiment

Understanding fluid behavior in microgravity environments is essential to further development of cryogenic storage in space environments. The Zero Boil Off Tank (ZBOT) experiment was designed to investigate two-phase pressurization and depressurization of a tank in a microgravity environment. The test fluid was the refrigerant Perfluoro-normal-Pentane (PNP). Thermal modeling for the ZBOT model was conducted using Thermal Desktop and SINDA/FLUINT. The temperature distribution within the fluid of the tank is of particular interest. This particular work is centered on ascertaining the thermal behavior of the refrigerant in order to build more complete models of fluid in microgravity. Separate cases were run modeling experiments that were conducted both on the ground and on the International Space Station (ISS) to compare 1g and microgravity environments. The microgravity modeling cases consisted of a fluid lump representing the vapor ullage suspended in a solid to represent the liquid. Mass flow between the liquid and vapor was modeled using the Schrage equation for mass flow. Initial results indicate that the pressure rise and temperature increase within the fluid closely align with the experimental data by matching initial conditions of the experiment. This work is ongoing and will yield further insights into the thermal behavior of fluid mixing in microgravity.
Observability Analysis of Aided INS With Heterogeneous Features of Points, Lines, and Planes

In this article, we perform a thorough observability analysis for linearized inertial navigation systems (INS) aided by exteroceptive range and/or bearing sensors (such as cameras, LiDAR, and sonars) with different geometric features (points, lines, planes, or their combinations). In particular, by reviewing common representations of geometric features, we introduce two sets of unified feature representations, i.e., the quaternion and closest point (CP) parameterizations. While the observability of vision-aided INS (VINS) with point features has been extensively studied in the literature, we analytically show that the general aided INS with point features preserves the same observability property, i.e., four unobservable directions, corresponding to the global yaw and the global translation of the sensor platform. We further prove that there are at least five (or seven) unobservable directions for the linearized aided INS with a single line (plane) feature, and, for the first time, analytically derive the unobservable subspace for the case of multiple lines or planes. Building upon this analysis for homogeneous features, we examine the observability of the same system but with combinations of heterogeneous features, and show that, in general, the system preserves at least four unobservable directions, while if global measurements are available, as expected, the unobservable subspace will have lower dimensions. We validate our analysis in Monte–Carlo simulations using both EKF-based visual-inertial SLAM and visual-inertial odometry (VIO) with different geometric features.
Effect of volume fraction on droplet break-up in an emulsion flowing through a microfluidic constriction

This paper reports the effect of the droplet volume fraction on the breakup of droplets within an emulsion flowing as a two-dimensional monolayer through a tapered microchannel into a constriction. To obtain emulsions with different volume fractions, a concentrated emulsion with droplet volume fraction φ = 0.85 is injected into the channel and diluted on-chip by introducing an additional continuous phase at different flow rates. At a fixed flow rate, the breakup fraction decreases significantly when the droplet volume fraction φ decreases below 0.50. This result is consistent with our previous report showing that droplet breakup in the emulsion arises primarily from droplet-droplet interactions, which are expected to decrease significantly in dilute emulsions. Furthermore, an optimal location for the introduction of the additional continuous phase is identified to be approximately one to two droplet diameters upstream of the constriction. Away from this optimal location, the dilution of the emulsion is ineffective. Finally, we find that while a higher emulsion volume fraction packs more drops per unit volume, the propensity of the drops to undergo breakup limits droplet throughput if droplet integrity and assay accuracy are to be maintained. At a droplet breakup fraction of 0.10, diluting the emulsion 2.1 times from φ = 0.85 to φ = 0.40 increases the droplet throughput by ∼1.5 times.
Flexible connectivity under physiological constraints

Abstract Physiological constraints define allowed configurations of synaptic weights for neural circuits. How this affects circuit function remains little understood. We examine the hypothesis that neural circuits may be structured to make constraints flexible: to allow many configurations of synaptic weights. The size of these allowed weight spaces depends on the number of inputs to a neuron: its connectivity degree. We predict degree distributions that optimize simple constraints on a neuron’s total synaptic weight. We also find the degrees of connectivity that maximize the number of allowed synaptic weight configurations. To test these predictions, we examine reconstructions of the mushroom bodies from the first instar larva and the adult Drosophila melanogaster. Overall, flexibility under a homeostatically fixed total synaptic weight describes Kenyon cell connectivity better than other models, suggesting a principle shaping the apparently random structure of Kenyon cell wiring.
Compliant mechanism design with non-linear materials using topology optimization

In this paper, compliant mechanism design with non-linear materials using topology optimization is presented. A general displacement functional with non-linear material model is used in the topology optimization formulation. Sensitivity analysis of this displacement functional is derived from the adjoint method. Optimal compliant mechanism examples for maximizing the mechanical advantage are presented and the effect of non-linear material on the optimal design are considered.
Design and Implementation of Ecological Adaptive Cruise Control for Autonomous Driving with Communication to Traffic Lights

This paper presents the design and implementation results of an ecological adaptive cruise controller (ECO-ACC) which exploits driving automation and connectivity. The controller avoids front collisions and traffic light violations, and is designed to reduce the energy consumption of connected automated vehicles by utilizing historical and real-time signal phase and timing data of traffic lights that adapt to the current traffic conditions. We propose an optimization-based generation of a reference velocity, and a velocity-tracking model predictive controller that avoids front collisions and violations. We present an experimental setup encompassing the real vehicle and controller in the loop, and an environment simulator in which the traffic flow and the traffic light patterns are calibrated on real-world data. We present simulation and experimental results, finding a significant potential for energy consumption reduction, even in the presence of traffic.
Intrinsic linking and knotting in tournaments

A directed graph G is intrinsically linked if every embedding of that graph contains a nonsplit link L, where each component of L is a consistently oriented cycle in G. A tournament is a directed g...
Gradient Coding Based on Block Designs for Mitigating Adversarial Stragglers

Distributed implementations of gradient-based methods, wherein a server distributes gradient computations across worker machines, suffer from slow running machines, called stragglers. Gradient coding is a coding-theoretic framework to mitigate stragglers by enabling the server to recover the gradient sum in the presence of stragglers. Approximate gradient codes are variants of gradient codes that reduce computation and storage overhead per worker by allowing the server to approximately reconstruct the gradient sum.In this work, our goal is to construct approximate gradient codes that are resilient to stragglers selected by a computationally unbounded adversary. Our motivation for constructing codes to mitigate adversarial stragglers stems from the challenge of tackling stragglers in massive-scale elastic and serverless systems, wherein it is difficult to statistically model stragglers. Towards this end, we propose a class of approximate gradient codes based on balanced incomplete block designs (BIBDs). We show that the approximation error for these codes depends only on the number of stragglers, and thus, adversarial straggler selection has no advantage over random selection. In addition, the proposed codes admit computationally efficient decoding at the server. Next, to characterize fundamental limits of adversarial straggling, we consider the notion of adversarial threshold – the smallest number of workers that an adversary must straggle to inflict certain approximation error. We compute a lower bound on the adversarial threshold, and show that codes based on symmetric BIBDs maximize this lower bound among a wide class of codes, making them excellent candidates for mitigating adversarial stragglers.
ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging

Grasping and manipulating objects is an important human skill. Since hand-object contact is fundamental to grasping, capturing it can lead to important insights. However, observing contact through external sensors is challenging because of occlusion and the complexity of the human hand. We present ContactDB, a novel dataset of contact maps for household objects that captures the rich hand-object contact that occurs during grasping, enabled by use of a thermal camera. Participants in our study grasped 3D printed objects with a post-grasp functional intent. ContactDB includes 3750 3D meshes of 50 household objects textured with contact maps and 375K frames of synchronized RGB-D+thermal images. To the best of our knowledge, this is the first large-scale dataset that records detailed contact maps for human grasps. Analysis of this data shows the influence of functional intent and object size on grasping, the tendency to touch/avoid ‘active areas’, and the high frequency of palm and proximal finger contact. Finally, we train state-of-the art image translation and 3D convolution algorithms to predict diverse contact patterns from object shape. Data, code and models are available at https://contactdb.cc.gatech.edu.
An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs

The high rate of false alarms in intensive care units (ICUs) is one of the top challenges of using medical technology in hospitals. These false alarms are often caused by patients’ movements, detachment of monitoring sensors, or different sources of noise and interference that impact the collected signals from different monitoring devices. In this paper, we propose a novel set of high-level features based on unsupervised feature learning technique in order to effectively capture the characteristics of different arrhythmia in electrocardiogram (ECG) signal and differentiate them from irregularity in signals due to different sources of signal disturbances. This unsupervised feature learning technique, first extracts a set of low-level features from all existing heart cycles of a patient, and then clusters these segments for each individual patient to provide a set of prominent high-level features. The objective of the clustering phase is to enable the classification method to differentiate between the high-level features extracted from normal and abnormal cycles (i.e., either due to arrhythmia or different sources of distortions in signal) in order to put more attention to the features extracted from abnormal portion of the signal that contribute to the alarm.
"Cancer Life ReiMagined:" The CaLM model of whole-person cancer care.

74Background: Despite advances in cancer treatment, the orientation of our health system does not address the whole cancer patient or support the wellbeing of the heart, soul, and mind. We launched...
Spin transport in an insulating ferrimagnetic-antiferromagnetic-ferrimagnetic trilayer as a function of temperature

We present a study of the transport properties of thermally generated spin currents in an insulating ferrimagnetic-antiferromagnetic-ferrimagnetic trilayer over a wide range of temperature. Spin currents generated by the spin Seebeck effect (SSE) in a yttrium iron garnet (YIG) YIG/NiO/YIG trilayer on a gadolinium gallium garnet (GGG) substrate were detected using the inverse spin Hall effect (ISHE) in Pt. By studying samples with different NiO thicknesses, the spin diffusion length of NiO was determined to be ∼3.8 nm at room temperature. Surprisingly, a large increase of the SSE signal was observed below 30 K, and the field dependence of the signal closely follows a Brillouin function for an S=7/2 spin. The increase of the SSE signal at low temperatures could thus be associated with the paramagnetic SSE from the GGG substrate. Besides, a broad peak in the SSE response was observed around 100 K. These observations are important in understanding the generation and transport properties of spin currents through magnetic insulators and the role of a paramagnetic substrate in spin current generation.
AGING INDUCES NLRP3 INFLAMMASOME DEPENDENT ADIPOSE B CELL EXPANSION TO IMPAIR METABOLIC HOMEOSTASIS

Summary Visceral adiposity in elderly is associated with alterations in adipose tissue immune cells leading to inflammation and metabolic dysfunction. The Nlrp3 inflammasome is a critical regulator of macrophage activation, inflammation, and immunometabolism in visceral adipose tissue during aging; however, the potential contribution of adipose tissue B cells is unexplored. Here, we show that aging expands adipose-resident B cells and fat-associated lymphoid clusters (FALCs) in visceral white adipose tissue. Adipose tissue B cells exhibit a memory-like B cell profile similar to the phenotype of aged B cells that are increased in spleen of old mice. Mechanistically, the age-induced FALC formation and adipose B cell expansion, but not B cell transcriptional program, is dependent on the Nlrp3 inflammasome. Furthermore, B cell depletion in aged mice restores lipolysis and defense against loss of core body temperature during cold stress. These data reveal that inhibiting Nlrp3-dependent B cell accumulation can be targeted to reverse metabolic impairment in aging adipose tissue. Highlights - Adipose-resident aged B cells are increased in fat-associated lymphoid clusters (FALC) - FALC formation and adipose-resident B cell expansion during aging are regulated by the Nlrp3 inflammasome - Nlrp3 and B cell depletion in aging restores lipolysis and improves cold tolerancea
Multivariate Alexander quandles, III. Sublinks

If L is a classical link then the multivariate Alexander quandle, QA(L), is a substructure of the multivariate Alexander module, MA(L). In the first paper of this series, we showed that if two link...
GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment

Effective biomedical literature retrieval (BLR) plays a central role inprecision medicine informatics. In this paper, we propose GRAPHENE,which is a deep learning based framework for precise BLR. GRAPHENEconsists of three main different modules 1) graph-augmented doc-ument representation learning; 2) query expansion and represen-tation learning and 3) learning to rank biomedical articles. Thegraph-augmented document representation learning module con-structs a document-concept graph containing biomedical conceptnodes and document nodes so that global biomedical related con-cept from external knowledge source can be captured, which isfurther connected to a BiLSTM so both local and global topics canbe explored. Query expansion and representation learning moduleexpands the query with abbreviations and different names, and thenbuilds a CNN-based model to convolve the expanded query andobtain a vector representation for each query. Learning to rank min-imizes a ranking loss between biomedical articles with the queryto learn the retrieval function. Experimental results on applyingour system to TREC Precision Medicine track data are provided todemonstrate its effectiveness.
Outsourcing Criminal Deportees

Source-labor countries—states that “export” migrants—are virtually invisible in immigration law scholarship. Notwithstanding the growing recognition that ever more people live transnational lives, the dominant conceptualization of immigration law and policy in the United States remains uni-nationally oriented. Under this approach, most immigration questions hinge on reforming and enacting federal laws. “Crimmigration” scholarship—located at the intersection of criminal and immigration law—embodies this uni-national paradigm. Recent contributions by Professors Mariano-Florentino Cuellar and David Sklansky epitomize this focus on the domestic political economies of immigration law enforcement. According to Professors Cuellar and Sklansky, prosecutorial choices have been warped by transaction costs in policy making that implicate coordination between entities with overlapping and sometimes conflicting mandates. Absurdly, this results in immigration disproportionately consuming federal law enforcement resources. Indeed, immigration cases—most involving relatively minor infractions—now constitute the majority of federal criminal prosecutions. Compounding these absurd enforcement choices, the United States spends significant resources on incarcerating deportees-in-waiting, aliens with final convictions who are already subject to deportation orders. While Professors Cuellar and Sklansky solely consider domestic decision makers, Professor Peter Schuck believes that this domestic focus exacerbates resource constraints. His contribution is groundbreaking: unlike traditional scholarship, Professor Schuck considers how the United States allocates its enforcement resources more broadly in the context of international decision makers. Given the voluminous enforcement obligations imposed by multiple immigration statutes, US law enforcement cannot merely identify and remove the myriad people subject to removal, even if they narrow their focus to aliens who commit (presumably more serious) nonimmigration crimes. Simply put, the system is overwhelmed. Where better to look for help than to countries from which these aliens came? As Professor Schuck notes, “Simply stated, the federal government should deport some immigrant criminals before they enter prison, not after.” Professor Schuck appears impatient with US officials whom he considers too “soft” with countries—like Mexico—that repeatedly resist entreaties to expeditiously receive their deported nationals. Professor Schuck advocates a tougher approach wherein the United States strategically utilizes its regional heft to pressure the reluctant Latin American and Caribbean countries— from which most migrant criminals originate.
Geometry-Induced Rigidity in Nonspherical Pressurized Elastic Shells

We present results from an experimental investigation of the indentation of nonspherical pressurized elastic shells with a positive Gauss curvature. A predictive framework is proposed that rationalizes the dependence of the local rigidity of an indented shell on the curvature in the neighborhood of the locus of indentation, the in-out pressure differential, and the material properties. In our approach, we combine classic theory for spherical shells with recent analytical developments for the pressurized case, and proceed, for the most part, by analogy, guided by our own experiments. By way of example, our results elucidate why an eggshell is significantly stiffer when compressed along its major axis, as compared to doing so along its minor axis. The prominence of geometry in this class of problems points to the relevance and applicability of our findings over a wide range of length scales.
Frustration and Fluctuations in Systems with Quenched Disorder

As Phil Anderson noted long ago, frustration can be generally defined by measuring the fluctuations in the coupling energy across a plane boundary between two large blocks of material. Since that time, a number of groups have studied the free energy fluctuations between (putative) distinct spin glass thermodynamic states. While upper bounds on such fluctuations have been obtained, useful lower bounds have been more difficult to derive. I present a history of these efforts, and briefly discuss recent work showing that free energy fluctuations between certain classes of distinct thermodynamic states (if they exist) scale as the square root of the volume. The perspective offered here is that the power and generality of the Anderson conception of frustration suggests a potential approach toward resolving some longstanding and central issues in spin glass physics.
Counting rectangles and an improved restriction estimate for the paraboloid in $F_p^3$

Given $A \subset F_{p}^2$ a sufficiently small set in the plane over a prime residue field, we prove that there are at most $O_\epsilon (|A|^{\frac{99}{41}+\epsilon})$ rectangles with corners in $A$. The exponent $\frac{99}{41} = 2.413\ldots$ improves slightly on the exponent of $\frac{17}{7} = 2.428\ldots$ due to Rudnev and Shkredov. Using this estimate we prove that the extension operator for the three dimensional paraboloid in prime order fields maps $L^2 \rightarrow L^{r}$ for $r >\frac{188}{53}=3.547\ldots$ improving the previous range of $r\geq \frac{32}{9}= 3.\overline{555}$.
Predicting the CPU availability of time-shared Unix systems on the computational grid

In this paper we focus on the problem of making short and medium term forecasts of CPU availability on time-shared Unix systems. We evaluate the accuracy with which availability can be measured using Unix load average, the Unix utility vmstat, and the Network Weather Service CPU sensor that uses both. We also examine the autocorrelation between successive CPU measurements to determine their degree of self-similarity. While our observations show a long-range autocorrelation dependence, we demonstrate how this dependence manifests itself in the short and medium term predictability of the CPU resources in our study.
Online unit covering in Euclidean space

Abstract We revisit the online Unit Covering problem in higher dimensions: Given a set of n points in R d , that arrive one by one, cover the points by balls of unit radius, so as to minimize the number of balls used. In this paper, we work in R d using the Euclidean distance. (I) We give an online deterministic algorithm with competitive ratio O ( 1.321 d ) , thereby improving on the previous record, O ( 2 d d log ⁡ d ) , due to Charikar et al. (2004), by an exponential factor. In particular, the competitive ratios are 5 in the plane and 12 in 3-space (the previous ratios were 7 and 21, respectively). For d = 3 , the ratio of our online algorithm matches the ratio of the current best offline algorithm for the same problem due to Biniaz et al. (2017), which is remarkable (and rather unusual). (II) We show that the competitive ratio of every deterministic online algorithm for Unit Covering in R d under the L 2 norm is at least d + 1 for every d ≥ 1 . This greatly improves upon the previous best lower bound, Ω ( log ⁡ d / log ⁡ log ⁡ log ⁡ d ) , due to Charikar et al. (2004). (III) We generalize the above result to Unit Covering in R d under the L C norm, where C is a centrally symmetric convex body, via the illumination number. (IV) We obtain lower bounds of 4 and 5 for the competitive ratio of any deterministic algorithm for online Unit Covering in R 2 and R 3 , respectively; the previous best lower bounds were 3 for both cases. (V) When the input points are from the square or hexagonal lattice in R 2 , we give deterministic online algorithms for Unit Covering with an optimal competitive ratio of 3. For the cubic lattice in R 3 , we give a deterministic online algorithm with a competitive ratio of 5.
Did the Affordable Care Act Reach Penetrating Trauma Patients

Abstract Background The benefits of the Affordable Care Act (ACA) for trauma patients have been well established. However, the ACA's impact on penetrating trauma patients (PTPs), a population that is historically young and uninsured, has not been defined. We hypothesized that PTPs in the post-ACA era would have better outcomes. Material and methods The National Trauma Data Bank (NTDB) was queried for all PTPs from 2009 (pre-ACA) and 2011-2014 (post-ACA). Subset analysis was performed in patients aged 19-25 y, as this group was eligible for the ACA's dependent care provision (DCP). Results There were 9,714,471 patients in the study, with 2,053,501 (21.1%) pre-ACA and 7,660,970 (78.9%) post-ACA. When compared to pre-ACA, patients in the post-ACA cohort were more likely to have commercial/private insurance, less likely to have Medicaid, and more likely to be uninsured. On logistic regression, the pre-ACA era was associated with mortality (HR: 1.02, 95% CI: 1.01-1.04, P = 0.004). Being uninsured was associated with mortality (HR: 1.89, 95% CI: 1.87-1.92, P Conclusions Although the ACA provided a survival benefit to PTPs overall, it did not increase insurance coverage for this population. In addition, the DCP of the ACA did not improve insurance access for PTP in the eligible age group. Further efforts are needed to extend insurance access to this population.
Centers of disks in Riemannian manifolds

We prove the existence of a center, or continuous selection of a point, in the relative interior of $C^1$ embedded $k$-disks in Riemannian $n$-manifolds. If $k\le 3$ the center can be made equivariant with respect to the isometries of the manifold, and under mild assumptions the same holds for $k=4=n$. By contrast, for every $n\ge k\ge 6$ there are examples where an equivariant center does not exist. The center can be chosen to agree with any of the classical centers defined on the set of convex compacta in the Euclidean space.
CTRL - a label-free artificial intelligence method for dynamic measurement of single-cell volume.

Measuring the physical size of the cell is valuable in understanding cell growth control. Current single-cell volume measurement methods for mammalian cells are labor-intensive, inflexible, and can cause cell damage. We introduce CTRL: Cell Topography Reconstruction Learner, a label-free technique incorporating the Deep Learning algorithm and the Fluorescence Exclusion method for reconstructing cell topography and estimating mammalian cell volume from DIC microscopy images alone. The method achieves quantitative accuracy, requires minimal sample preparation, and applies to a wide range of biological and experimental conditions. The method can be used to track single-cell volume dynamics over arbitrarily long time periods. Using this method, we observe that bigger newborn cells grow larger (sizer) for HT1080 fibrosarcoma cells and there is a noticeable reduction in cell size fluctuations at 25% completion of the cell cycle in HT1080 fibrosarcoma cells.
Characterizing cospectral vertices via isospectral reduction

Abstract Two emerging topics in graph theory are the study of cospectral vertices of a graph, and the study of isospectral reductions of graphs. In this paper, we prove a fundamental relationship between these two areas, which is that two vertices of a graph are cospectral if and only if the isospectral reduction over these vertices has a nontrivial automorphism. It is well known that if two vertices of a graph are symmetric, i.e. if there exists a graph automorphism permuting these two vertices, then they are cospectral. This paper extends this result showing that any two cospectral vertices are symmetric in some reduced version of the graph. We also prove that two vertices are strongly cospectral if and only if they are cospectral and the isospectral reduction over these two vertices has simple eigenvalues. We further describe how these results can be used to construct new families of graphs with cospectral vertices.
Trials and tribulations: Lost energy access gains in rural India

Abstract Rural electrification has advanced rapidly in many developing countries. Under conditions of poverty and weak infrastructure, however, households face a risk of backsliding. We use two rounds from the ACCESS survey of rural households in six northern Indian states to explore factors that drive losses in household electricity access. About 7% of households with electricity in 2015 lost it by 2018. We identify household wealth and off-grid access as major drivers of lost energy access. A standard deviation's increase in a household's wealth index reduces the likelihood of disconnection by 1.5 percentage points. Off-grid households are 8 percentage points more likely to lose access than grid-connected households. These findings underscore the importance of defending realized gains in countries where household electrification is driven by policy while rural poverty remains prevalent.
Sample-Based Learning Model Predictive Control for Linear Uncertain Systems

We present a sample-based Learning Model Predictive Controller (LMPC) for constrained uncertain linear systems subject to bounded additive disturbances. The proposed controller builds on earlier work on LMPC for deterministic systems. First, we introduce the design of the safe set and value function used to guarantee safety and performance improvement. Afterwards, we show how these quantities can be approximated using noisy historical data. The effectiveness of the proposed approach is demonstrated through a numerical example. We show that the LMPC is able to safely explore the state space and to iteratively improve the worst-case closed-loop performance, while robustly satisfying state and input constraints.
Monitor-Based Runtime Assurance for Temporal Logic Specifications

This paper introduces the safety controller architecture as a runtime assurance mechanism for system specifications expressed as safety properties in Linear Temporal Logic. The safety controller uses a monitor, constructed as a finite state machine, to analyze a desired control input policy online and form a sequence of control inputs that is guaranteed to keep the system safe for all time. A case study is presented which details the construction and implementation of a safety controller on a cyber-physical system with a nondeterministic dynamical model.
Simulating metagenomic stable isotope probing datasets with MetaSIPSim

DNA-stable isotope probing (DNA-SIP) links microorganisms to their in-situ function in diverse environmental samples. Combining DNA-SIP and metagenomics (metagenomic-SIP) allows us to link genomes from complex communities to their specific functions and improves the assembly and binning of these targeted genomes. However, empirical development of metagenomic-SIP methods is hindered by the complexity and cost of these studies. We developed a toolkit, ‘MetaSIPSim,’ to simulate sequencing read libraries for metagenomic-SIP experiments. MetaSIPSim is intended to generate datasets for method development and testing. To this end, we used MetaSIPSim generated data to demonstrate the advantages of metagenomic-SIP over a conventional shotgun metagenomic sequencing experiment. Through simulation we show that metagenomic-SIP improves the assembly and binning of isotopically labeled genomes relative to a conventional metagenomic approach. Improvements were dependent on experimental parameters and on sequencing depth. Community level G + C content impacted the assembly of labeled genomes and subsequent binning, where high community G + C generally reduced the benefits of metagenomic-SIP. Furthermore, when a high proportion of the community is isotopically labeled, the benefits of metagenomic-SIP decline. Finally, the choice of gradient fractions to sequence greatly influences method performance. Metagenomic-SIP is a valuable method for recovering isotopically labeled genomes from complex communities. We show that metagenomic-SIP performance depends on optimization of experimental parameters. MetaSIPSim allows for simulation of metagenomic-SIP datasets which facilitates the optimization and development of metagenomic-SIP experiments and analytical approaches for dealing with these data.
Distributed Small-Signal Stability Conditions for Inverter-Based Unbalanced Microgrids

The proliferation of inverter-based generation and advanced sensing, controls, and communication infrastructure have facilitated the accelerated deployment of microgrids. A coordinated network of microgrids can maintain reliable power delivery to critical facilities during extreme events. Low-inertia offered by the power-electronics interfaced energy resources, however, can present significant challenges to ensuring stable operation of the microgrids. In this work, distributed small-signal stability conditions for inverter-based microgrids are developed that involve the droop-controller parameters and the network parameters (e.g. line impedances, loads). The distributed closed-form parametric stability conditions derived in this paper can be verified in a computationally efficient manner, facilitating the reliable design and operations of networks of microgrids. Dynamic phasor models have been used to capture the effects of electromagnetic transients. Numerical results are presented, along with PSCAD simulations, to validate the analytical stability conditions. Effects of design choices, such as the conductor types, and inverter sizes, on the small-signal stability of inverter-based microgrids are investigated to identify interpretable stable/unstable region estimates.
Assessing the Benefits of Air-Quality Improvements in General Equilibrium: A Review

The vast majority of existing attempts to measure the benefits and costs of air-quality regulations assume no interaction between the behavioral responses that determine the market-based costs of these policies and the targeted environmental benefits themselves. Nevertheless, general equilibrium theory suggests a number of channels through which important interdependencies might arise, including health impacts on labor supply and the demand for medical care, complementarities between air quality and demand for leisure activities, and interactions between multiple services derived from a common, impacted ecosystem. We develop a unified theoretical framework to assess the nascent literature focused on incorporating air-quality impacts into general equilibrium models. Our primary focus is on quantitative studies employing computable general equilibrium (CGE) models. We conclude by identifying priorities for future research in this field.
Versatile simulations of admixture and accurate local ancestry inference with mixnmatch and ancestryinfer

It has become clear that hybridization between species is much more common than previously recognized. As a result, we now know that the genomes of many modern species, including our own, are a patchwork of regions derived from past hybridization events. Increasingly researchers are interested in disentangling which regions of the genome originated from each parental species using local ancestry inference methods. Due to the diverse effects of admixture, this interest is shared across disparate fields, from human genetics to research in ecology and evolutionary biology. However, local ancestry inference methods are sensitive to a range of biological and technical parameters which can impact accuracy. Here we present paired simulation and ancestry inference pipelines, mixnmatch and ancestryinfer, to help researchers plan and execute local ancestry inference studies. mixnmatch can simulate arbitrarily complex demographic histories in the parental and hybrid populations, selection on hybrids, and technical variables such as coverage and contamination. ancestryinfer takes as input sequencing reads from simulated or real individuals, and implements an efficient local ancestry inference pipeline. We perform a series of simulations with mixnmatch to pinpoint factors that influence accuracy in local ancestry inference and highlight useful features of the two pipelines. mixnmatch is a powerful tool for simulations of hybridization while ancestryinfer facilitates local ancestry inference on real or simulated data.
Higher-Order Label Homogeneity and Spreading in Graphs

Do higher-order network structures aid graph semi-supervised learning? Given a graph and a few labeled vertices, labeling the remaining vertices is a high-impact problem with applications in several tasks, such as recommender systems, fraud detection and protein identification. However, traditional methods rely on edges for spreading labels, which is limited as all edges are not equal. Vertices with stronger connections participate in higher-order structures in graphs, which calls for methods that can leverage these structures in the semi-supervised learning tasks. To this end, we propose Higher-Order Label Spreading (HOLS) to spread labels using higher-order structures. HOLS has strong theoretical guarantees and reduces to standard label spreading in the base case. Via extensive experiments, we show that higher-order label spreading using triangles in addition to edges is up to 4.7% better than label spreading using edges alone. Compared to prior traditional and state-of-the-art methods, the proposed method leads to statistically significant accuracy gains in all-but-one cases, while remaining fast and scalable to large graphs.
Case study: verifying the safety of an autonomous racing car with a neural network controller

This paper describes a verification case study on an autonomous racing car with a neural network (NN) controller. Although several verification approaches have been recently proposed, they have only been evaluated on low-dimensional systems or systems with constrained environments. To explore the limits of existing approaches, we present a challenging benchmark in which the NN takes raw LiDAR measurements as input and outputs steering for the car. We train a dozen NNs using reinforcement learning (RL) and show that the state of the art in verification can handle systems with around 40 LiDAR rays. Furthermore, we perform real experiments to investigate the benefits and limitations of verification with respect to the sim2real gap, i.e., the difference between a system's modeled and real performance. We identify cases, similar to the modeled environment, in which verification is strongly correlated with safe behavior. Finally, we illustrate LiDAR fault patterns that can be used to develop robust and safe RL algorithms.
3 – Exponents and Polynomials

Publisher Summary This chapter reviews the expressions and equations in mathematics that involve exponents and polynomials. The product of two expressions with the same base is equivalent to the base raised to the sum of the exponents from the original two expressions. The chapter also presents scientific notation. Scientific notation is a way to write very large or very small numbers in a more manageable form. A number is written in scientific notation if it is written as the product of a number between 1 and 10 with an integer power of 10. A number written in scientific notation has the form n × 10r where 1 ≤ n < 10 and r = an integer.
OPEC'S KINKED DEMAND CURVE

I estimate world demand for crude oil, non-OPEC supply, and the effects of changes in price on world GDP using quarterly data covering 1973 to 2010. World GDP falls more with increases in price than it rises with decreases in price. Therefore, instability in price negatively impacts GDP. World demand and net demand to OPEC are kinked due to the asymmetric effects. The kink implies a vertical discontinuity in OPEC’s marginal revenue curve. Shifts in cost and horizontal shifts in demand are less destabilizing with the kink. However, vertical shifts cause larger changes in price, and this accentuates destabilizing feedback between changes in price and GDP. The kink also implies a range of equilibrium prices and quantities of production for the cartel. If OPEC’s long run marginal cost, inclusive of marginal user cost, is $35/bl in 2014, I estimate that any price between $99/bl and $106/bl would provide OPEC no incentive to change under its long run demand schedule. Using a demand curve applicable to a twelve month period, an increase (shock) to prices above $142/bl would be profitable for OPEC. Net demand to OPEC is quite inelastic in the short run, and estimated contemporaneous effects of price on GDP are negative. Therefore, unstable prices generate countercyclic profits for OPEC, which have hedging value in financial markets, further incenting OPEC to set unstable prices.
Are the Effects of Monetary Policy Shocks Big or Small

This paper studies the small estimated effects of monetary policy shocks from standard VAR's versus the large effects from the Romer and Romer (2004) approach. The differences are driven by three factors: the different contractionary impetus, the period of reserves targeting and lag length selection. Accounting for these factors, the real effects of policy shocks are consistent across approaches and most likely medium. Alternative monetary policy shock measures from estimated Taylor rules also yield medium-sized real effects and indicate that the historical contribution of monetary policy shocks to real fluctuations has been significant, particularly during the 1970s and early 1980s.
Exogenous Resource Shocks and Economic Freedom

An extensive literature has identified the tendency of natural resource rents to inhibit the development of quality institutions, though few studies have investigated the effects on economic institutions. We use a data set of plausibly exogenous “giant” oil field discoveries as a means of testing whether the presence of large resource rents impacts a country’s economic institutions. We find evidence of short run effects of these discoveries on the size of government spending, but find no evidence of an effect on economic institutions in general. At least for this set of resource discoveries, there is no resource curse for economic institutions.
Multi-Institution Team Teaching (MITT): A Novel Approach to Highly Specialized Graduate Education

As engineering becomes more and more specialized, both the faculty resources and number of interested students become limited. Consequently, very frequently highly specialized graduate courses are not offered, especially in disciplines like Materials with small faculty and enrollment. NSF’s International Materials Institute for New Functionality in Glass (IMI-NFG) has successfully addressed this problem by successfully introducing the concept of multi-institution team teaching (MITT). It brings together via internet both the expert professors and students from many universities. By pooling the talent of various instructors, the courses become technically stronger and students learn advanced topics that would be available otherwise. As an example, a recent MITT course included instructors from 10 US institutions, and students from many more US and international universities. Software such as ‘Adobe Connect’ is used for the live delivery of lectures, wherein students can see the instructor and Power Point slides as in a normal classroom. The students may ask questions any time during the lecture, and the instructor would respond immediately. They register and pay tuition at their home institution, so that no exchange of funds is involved between universities. Survey results support that a majority of the enrolled students liked the format and delivery of the course, and more than 75% students felt that multiple instructors, who “taught information of their expertise”, made the course stronger. In conclusion, the concept of MITT has been successfully demonstrated for teaching highly specialized graduate courses.
Bias from Loss to Follow-up †

Loss to follow-up bias results when subjects lost from a cohort have different health response distributions from subjects who remain in follow-up. Keywords: bias; loss to follow-up; missing data in epidemiologic studies; bias from nonresponse
On Human Papillomaviruses

Even Lewis Thomas, who concluded his essay "On Warts" by proposing a National Institute of Warts,1 did not envisage the recent explosion in papillomavirus research or anticipate the compelling association of these viruses with certain human cancers. The viral nature of human warts was first suggested in 1907 by Ciuffo, who demonstrated their transmission by cell-free filtrates.2 Shope first described the papillomavirus as the etiologic agent of infectious papillomatosis of rabbits in 1933.3 Subsequent studies by Rous established that benign rabbit papillomas could advance to become malignant squamous-cell carcinomas and that specific nonviral cofactors (i.e., coal tar or methylcholanthrene) could . . .
Tailoring critical loss to the competitive process

Abstract In 1989, Barry Harris & Joseph Simons developed a quantitative method to implement the Horizontal Merger Guidelines’ hypothetical monopolist test with a market-level “critical loss” analysis. The appeal of Harris & Simons’ framework is that it created a simple, intuitive approach to delineating markets—with relatively parsimonious data requirements. After over a decade of use, however, economists began to propose alternative approaches to the classic critical loss analysis—using theory to impose structure on firm-level demand. This allowed researchers to reformulate the critical loss test in terms of diversion ratios. The purpose of this paper is to discuss when the classic, market-level approach to critical loss is more appropriate and when firm-level critical loss offers an important refinement. We illustrate, with a detailed example, that under certain plausible demand scenarios, a diversion-based firm-level analysis could easily reach the wrong answer on market definition. Thus, the analyst needs to carefully study the competitive environment before deciding on the appropriate analysis. As a bottom line, the choice between market-level and firm-level analysis depends on the specific factual situation.