cssi/SciSciGPT-SciSciNet huggingface dataset:
DNA Identification Databases: Legality, Legitimacy, and the Case for Population-Wide Coverage

Over the past decade, law enforcement authorities have amassed huge collections of DNA samples and the identifying profiles derived from them. Large DNA databanks routinely help to identify the guilty and to exonerate the innocent, but as the databanks grow, so do fears about civil liberties. Perhaps the most controversial policy issue in the creation of these databases is the question of coverage: Whose DNA profiles should be stored in them? The possibilities extend from convicted violent sex offenders to all convicted felons, to everyone arrested, to the entire population. This Article questions the rationales for drawing the line at all convicted offenders or at all arrestees. It suggests that such coverage results in sampling DNA disproportionately from racial minorities, which exacerbates racial tensions and undermines the preventative and investigative value of the databases. It argues that a population-wide database with strict privacy protections may supply the better answer to the coverage question, and to the privacy concerns raised by any government program to take and analyze individuals' DNA.Part I discusses two theories that might dictate which offenders should be included in these databases. The first theory holds that by virtue of a conviction, offenders forfeit the Fourth Amendment right to be free from unreasonable searches and seizures. This forfeiture-of-rights theory would confine the databases to convicted offenders, but it cannot be squared with settled constitutional principles. The second theory holds that because convicted offenders are more likely to re-offend than are other groups, they pose a special risk that justifies the incursion on their Fourth Amendment rights. Although this recidivism theory may seem more protective of individual liberties, it fails to constrict coverage to convicted offenders or even to arrestees.Part II considers extensions of coverage to various groups of people who have not been convicted of crimes. It suggests that the Constitution may permit DNA to be collected from persons who have only been arrested, and even from certain groups of persons who have neither been convicted nor arrested.Part III questions the desirability of limiting database coverage to convicted offenders, or to convicted offenders plus arrestees (or "suspects"). It notes that by restricting coverage to these groups, we are fast producing a racially distorted system in which, however lawfully the DNA samples are taken, they are taken disproportionately from members of racial minorities. We conclude that a population-wide database would be more effective and more fair than any system in which conviction or arrest is the threshold for database inclusion. We also indicate how such a system can be structured to protect personal privacy.
Behavior in Contests

Standard theoretical prediction is that rational economic agents participating in rent-seeking contests should engage in socially inefficient behavior by exerting costly efforts. Experimental studies find that the actual efforts of participants are significantly higher than predicted and that over-dissipation of rents (or overbidding or over-expenditure of resources) can occur. Although the standard theory cannot explain over-dissipation, this phenomenon can be explained by incorporating behavioral dimensions into the rent-seeking contest, such as (1) the utility of winning, (2) relative payoff maximization, (3) bounded rationality, and (4) judgmental biases. These explanations are not exhaustive, but they provide a coherent picture of important behavioral dimensions that should be considered when studying rent-seeking behavior in theory and in practice.
Doing More or Doing Less for the Environment: Shedding Light on EPA's "Stealth" Method of Environmental Enforcement

Since the 1970s, environmental protection goals have gone from general statements of political desire to highly articulated systems of environmental regulation implemented by federal, state, and local governments. Environmental statutes have been enacted giving administrative agencies such as the U.S. Environmental Protection Agency (EPA) the responsibility for translating broad policy goals into specific regulatory requirements. Through its enforcement program, EPA seeks to assure that these general goals are achieved by individual actors. This Article examines a recent trend in EPA’s practices, increased reliance on internal agency methods of enforcement. The study analyzes EPA’s administrative enforcement system with particular emphasis on the imposition of civil penalties. Its central conclusion is that EPA’s administrative enforcement dominates the Agency’s enforcement practices, dwarfing judicially supervised enforcement. In addition, this mechanism yields outcomes emphasizing settlement, at process at variance with EPA rules that renders outcomes in a context largely invisible from public scrutiny.
Pure technology gaps and production predictability

Abstract An average machine lags in terms of productivity and technological advancement behind a cutting-edge machine. This lag was first defined by Cummins and Violante (2002) as the technology gap. Using the vector error correction model, I show that the technology gap is cointegrated with human capital factors, and then decompose it into a long-run trend and a transitory mean-reverting component, which I term as the pure technology gap. I show that the pure technology gap has a predictive power for the aggregate production. Intuitively, a high pure technology gap acts as an economic shock that increases production in the long term due to a higher future productivity level.
Examining the Air We Breathe: EPA Should Evaluate Cumulative Impacts When It Promulgates National Ambient Air Quality Standards

Inhaling air pollutants can lead to a variety of adverse respiratory and cardiovascular health effects. This potential risk for health impacts is likely greater when the mixture of pollutants that exists in ambient air, rather than isolated pollutants, are inhaled. Despite the evidence of potential cumulative impacts, EPA has continued to focus its analysis of health impacts on isolated pollutants instead of the actual mixture we breathe. This article proposes that EPA should evaluate and consider cumulative health impacts when it sets national ambient air quality standards under the Clean Air Act. EPA is considering two pollutants together to determine their impact on the environment; it should do the same type of evaluation for human health impacts. Consideration of cumulative health impacts is consistent with the Act’s requirement to set standards at a level requisite to protect public health, could translate into a more accurate way to estimate risks, and could provide a tool for prioritization of emission reductions in the most heavily impacted communities. * The author thanks Helen Kang, Caroline Koch, and the clients, staff, and students at the Environmental Law and Justice Clinic for their invaluable insights, assistance, and support which contributed greatly to this article. The author also thanks Golden Gate University School of Law for its support of this article.
The Ottawa Convention banning landmines, the role of international non-governmental organizations and the idea of international civil society

Establishment of the Ottawa Convention Banning Landmines was regarded by many international law scholars, international activists, diplomats and international organization personnel as a defining, 'democratizing' change in the way international law is made. By bringing international NGOs — what is often called 'international civil society' — into the diplomatic and international law-making process, many believe that the Ottawa Convention represented both a democratization of, and a new source of legitimacy for, international law, in part because it was presumably made 'from below.' This article sharply questions whether the Ottawa Convention and the process leading up to it represents any real 'democratization' of international law, challenges the idea that there is even such a thing as 'international civil society,' at least in the sense that it is democratic and comes 'from below,' and disputes that there can be such a thing as 'democratic' processes at the global level. It suggests, by way of alternative, that the Ottawa Convention and the process leading up to it should be seen as a step in the development of global transnational elites at the expense of genuinely democratic, but hence local, processes.
Recurrent issues in the review of medical research on human subjects.

Medical researchers must enlist the participation of human subjects to conduct their investigations. Some subjects are healthy; others are ill. All must understand, however, that the studies in which they participate are intended, wholly or in part, to benefit others rather than themselves. Individual institutions’ IRBs (Institutional Review Boards) bear the responsibility to review, evaluate, and approve or disapprove researchers’ plans to include human subjects in their investigations. This article suggests several approaches that IRB members may take in reviewing proposals for research projects involving human subjects. Each approach is intended to strike a reasonable balance among the competing considerations while allowing IRB members to fulfill their responsibilities to human subjects.
Understanding Social Preferences with Simple Tests

Departures from self-interest in economic experiments have recently inspired models of “social preferences”. We design a range of simple experimental games that test these theories more directly than existing experiments. Our experiments show that subjects are more concerned with increasing social welfare—sacrificing to increase the payoffs for all recipients, especially low-payoff recipients—than with reducing differences in payoffs (as supposed in recent models). Subjects are also motivated by reciprocity: They withdraw willingness to sacrifice to achieve a fair outcome when others are themselves unwilling to sacrifice, and sometimes punish unfair behavior.
Fine-Grained Activity Recognition for Assembly Videos

In this letter we address the task of recognizing assembly actions as a structure ( e.g. a piece of furniture or a toy block tower) is built up from a set of primitive objects. Recognizing the full range of assembly actions requires perception at a level of spatial detail that has not been attempted in the action recognition literature to date. We extend the fine-grained activity recognition setting to address the task of assembly action recognition in its full generality by unifying assembly actions and kinematic structures within a single framework. We use this framework to develop a general method for recognizing assembly actions from observation sequences, along with observation features that take advantage of a spatial assembly's special structure. Finally, we evaluate our method empirically on two application-driven data sources: 1) An IKEA furniture-assembly dataset, and 2) A block-building dataset. On the first, our system recognizes assembly actions with an average framewise accuracy of 70% and an average normalized edit distance of 10%. On the second, which requires fine-grained geometric reasoning to distinguish between assemblies, our system attains an average normalized edit distance of 23%—a relative improvement of 69% over prior work.
On Light Spanners, Low-treewidth Embeddings and Efficient Traversing in Minor-free Graphs

Understanding the structure of minor-free metrics, namely shortest path metrics obtained over a weighted graph excluding a fixed minor, has been an important research direction since the fundamental work of Robertson and Seymour. A fundamental idea that helps both to understand the structural properties of these metrics and lead to strong algorithmic results is to construct a “small-complexity” graph that approximately preserves distances between pairs of points of the metric. We show the two following structural results for minor-free metrics: 1)Construction of a light subset spanner. Given a subset of vertices called terminals, and $\epsilon$ , in polynomial time we construct a sub graph that preserves all pairwise distances between terminals up to a multiplicative $1+\epsilon$ factor, of total weight at most $O_{\epsilon}(1)$ times the weight of the minimal Steiner tree spanning the terminals. 2)Construction of a stochastic metric embedding into low treewidth graphs with expected additive distortion $\epsilon D$ . Namely, given a minor-free graph $G= (V, E, w)$ of diameter $D$ , and parameter $\epsilon$ , we construct a distribution $\mathcal{D}$ over dominating metric embeddings into treewidth- $O_{\epsilon}(\log n)$ graphs such that $\forall u, v\in V,\ \mathbb{E}_{f\sim \mathcal{D}}[d_{H}(f(u), f(v))]\leq d_{G}(u, v)+\epsilon D$ . Our results have the following algorithmic consequences: (1) the first efficient approximation scheme for subset TSP in minor-free metrics; (2) the first approximation scheme for bounded-capacity vehicle routing in minor-free metrics; (3) the first efficient approximation scheme for bounded-capacity vehicle routing on bounded genus metrics. En route to the latter result, we design the first FPT approximation scheme for bounded-capacity vehicle routing on bounded-treewidth graphs (parameterized by the treewidth).
Advisory Committee on Immunization Practices recommended immunization schedule for adults aged 19 years or older — United States, 2019

In October 2018, the Advisory Committee on Immunization Practices (ACIP)* voted to recommend approval of the Recommended Immunization Schedule for Adults, Aged 19 Years or Older, United States, 2019. The 2019 adult immunization schedule, available at https://www.cdc.gov/vaccines/schedules,† summarizes ACIP recommendations in two tables and accompanying notes. The 2019 adult immunization schedule has been approved by the CDC Director, the American College of Physicians, the American Academy of Family Physicians, the American College of Obstetricians and Gynecologists, and the American College of Nurse-Midwives.
Length-Preserving Directions and Some Diophantine Equations

We study directions along which the norms of vectors are preserved under a linear map. In particular, we find families of matrices for which these directions are determined by integer vectors. We c...
Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach

This paper introduces an energy-efficient, software-defined vehicular edge network for the growing intelligent connected transportation system. A joint user-centric virtual cell formation and resource allocation problem is investigated to bring eco-solutions at the edge. This joint problem aims to combat against the power-hungry edge nodes while maintaining assured reliability and data rate. More specifically, by prioritizing the downlink communication of dynamic eco-routing, highly mobile autonomous vehicles are served with multiple low-powered access points (APs) simultaneously for ubiquitous connectivity and guaranteed reliability of the network. The formulated optimization is exceptionally troublesome to solve within a polynomial time, due to its complicated combinatorial structure. Hence, a distributed multi-agent reinforcement learning (D-MARL) algorithm is proposed for eco-vehicular edges, where multiple agents cooperatively learn to receive the best reward. First, the algorithm segments the centralized action space into multiple smaller groups. Based on the model-free distributed Q learner, each edge agent takes its actions from the respective group. Also, in each learning state, a software-defined controller chooses the global best action from individual bests of the distributed agents. Numerical results validate that our learning solution achieves near-optimal performances within a small number of training episodes as compared with existing baselines.
Moderate adolescent chronic intermittent ethanol exposure sex-dependently disrupts synaptic transmission and kappa opioid receptor function in the basolateral amygdala of adult rats.

Abstract Adolescent alcohol exposure is associated with many consequences in adulthood, including altered affective and reward-related behaviors. However, the long-term neurological disruptions underlying these behaviors are not fully understood. Shifts in the excitatory/inhibitory balance in the basolateral amygdala (BLA) relate to the expression of these behaviors and changes to BLA physiology are seen during withdrawal immediately following adolescent ethanol exposure, but no studies have examined whether these changes persist long-term. The kappa opioid receptor (KOR) neuromodulatory system mediates negative affective behaviors, and alterations of this system are implicated in behavioral changes following adult and adolescent chronic ethanol exposure. In the BLA, the KOR system undergoes functional changes across development, but whether BLA KOR function is disrupted by adolescent ethanol exposure is unknown. In this study, male and female Sprague-Dawley rats were exposed to a vapor model of moderate adolescent chronic intermittent ethanol (aCIE) and assessed for long-term effects on GABAergic and glutamatergic neurotransmission within the adult BLA and KOR modulation of these systems. aCIE exposure increased presynaptic glutamate transmission in females but had no effect in males or on GABA transmission in either sex. Additionally, aCIE exposure disrupted male KOR modulation of GABA release, with no effects in females or on glutamate transmission. These data suggest that aCIE produces sex-dependent and long-term changes to BLA physiology and KOR function. This is the first study to examine these persistent adaptations following adolescent alcohol exposure and opens a broad avenue for future investigation into other adolescent ethanol-induced disruptions of these systems.
Sampling Arborescences in Parallel

We study the problem of sampling a uniformly random directed rooted spanning tree, also known as an arborescence, from a possibly weighted directed graph. Classically, this problem has long been known to be polynomial-time solvable; the exact number of arborescences can be computed by a determinant [Tutte, 1948], and sampling can be reduced to counting [Jerrum et al., 1986; Jerrum and Sinclair, 1996]. However, the classic reduction from sampling to counting seems to be inherently sequential. This raises the question of designing efficient parallel algorithms for sampling. We show that sampling arborescences can be done in RNC. For several well-studied combinatorial structures, counting can be reduced to the computation of a determinant, which is known to be in NC [Csanky, 1975]. These include arborescences, planar graph perfect matchings, Eulerian tours in digraphs, and determinantal point processes. However, not much is known about efficient parallel sampling of these structures. Our work is a step towards resolving this mystery.
Virtual reality mobile application to improve videoscopic airway training: A randomised trial.

Introduction Flexible bronchoscopic intubation (FBI) is an important technique in managing an anticipated difficult airway, yet it is rarely performed and has a steep learning curve. We aim to evaluate if the integration of virtual reality gaming application into routine FBI training for emergency department doctors would be more effective than traditional teaching methods. Methods We conducted a randomised controlled trial to compare self-directed learning using the mobile application, Airway Ex* in the intervention group versus the control group without use of the mobile application. All participants underwent conventional didactic teaching and low-fidelity simulation with trainer's demonstration and hands-on practice on a manikin for FBI. Participants randomised to the intervention arm received an additional 30 minutes of self-directed learning using Airway Ex, preloaded on electronic devices while the control arm did not. The primary outcome was time taken to successful intubation. Results Forty-five physicians (20 junior and 25 senior physicians) were enrolled, with male predominance (57.8%, 26/45). There was no difference in time taken to successful intubation (median 48 seconds [interquartile range, IQR 41-69] versus 44 seconds [IQR 37-60], P=0.23) between the control and intervention groups, respectively. However, the intervention group received better ratings (median 4 [IQR 4-5]) for the quality of scope manipulation skills compared to control (median 4 [IQR 3-4], adjusted P=0.03). This difference remains significant among junior physicians in stratified analysis. Conclusion Incorporating virtual reality with traditional teaching methods allows learners to be trained on FBI safely without compromising patient care. Junior physicians appear to benefit more compared to senior physicians.
Saccharide analysis of onion outer epidermal walls

Epidermal cell walls have special structural and biological roles in the life of the plant. Typically they are multi-ply structures encrusted with waxes and cutin which protect the plant from dehydration and pathogen attack. These characteristics may also reduce chemical and enzymatic deconstruction of the wall for sugar analysis and conversion to biofuels. We have assessed the saccharide composition of the outer epidermal wall of onion scales with different analytical methods. This wall is a particularly useful model for cell wall imaging and mechanics. Epidermal walls were depolymerized by acidic methanolysis combined with 2M trifluoracetic acid hydrolysis and the resultant sugars were analyzed by high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD). Total sugar yields based on wall dry weight were low (53%). Removal of waxes with chloroform increased the sugar yields to 73% and enzymatic digestion did not improve these yields. Analysis by gas chromatography/mass spectrometry (GC/MS) of per-O-trimethylsilyl (TMS) derivatives of the sugar methyl glycosides produced by acidic methanolysis gave a high yield for galacturonic acid (GalA) but glucose (Glc) was severely reduced. In a complementary fashion, GC/MS analysis of methyl alditols produced by permethylation gave substantial yields for glucose and other neutral sugars, but GalA was severely reduced. Analysis of the walls by 13C solid-state NMR confirmed and extended these results and revealed 15% lipid content after chloroform extraction (potentially cutin and unextractable waxes). Although exact values vary with the analytical method, our best estimate is that polysaccharide in the outer epidermal wall of onion scales is comprised of homogalacturonan (~ 50%), cellulose (~ 20%), galactan (~ 10%), xyloglucan (~ 10%) and smaller amounts of other polysaccharides. Low yields of specific monosaccharides by some methods may be exaggerated in epidermal walls impregnated with waxes and cutin and call for cautious interpretation of the results.
An IoT Based Patient Health Monitoring System

There are a number of scopes for IoT in order to make a difference in lives of patients. The devices can capture as well as monitor related data regarding patient and allows the providers to obtain the insights without bringing the patients visiting. The procedure can assist the patient results as well as preventing the possible communications for the process that involves risk. However, lack of electronic health record (EHR) system integration is one of the major issues faced while using IoT in healthcare. Some of the EHR systems allow the patients importing data into the record. However, it remains limited to a few dominant where the EHR players as well as leaves providers unspecific of the processing data that can be helpful for the organization to use the process. The challenges for interoperability in order to keep data in distinctive medical devices depend on the purpose and ordering physician.
The Value of Personalized Pricing

Increased availability of high-quality customer information has fueled interest in personalized pricing strategies, that is, strategies that predict an individual customer’s valuation for a product...
Teaching and Learning Hand in Hand: Adaptive Teaching and Self-Regulated Learning:

This article presents case studies of two novice teachers and their mentors who, without formal knowledge of self-regulation theory, established a classroom environment that promoted self-regulated...
Parallel symbolic state-space exploration is difficult, but what is the alternative? ∗

State-space exploration is an essential first step in many modeling and analys is problems. Its goal is to find and store all the states reachable from the initial state(s) of a discre te-state high-level model described, for example, using pseudocode or Petri nets. The state space can then be used to answer important questions, such as “Is there a dead state?” and “Can variable n ever become negative?”, or as the starting point for sophisticated investigations expressed in temporal logic . Unfortunately, the state space is often so large that ordinary explicit data s tructures and sequential algorithms simply cannot cope, prompting the exploration of parallel or symbolic approaches. The former uses multiple processors, from simple networks of workstations to expensive shared-memory supercomputers or, more recently, powerful multicore workstations, to satisfy the large memory and runtime requirements. The latter uses decision diagrams to compactly encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space gen eration is challenging, but close to linear speedup can be achieved, thus its scalability can be quite g ood; however, the analysis is ultimately and obviously limited by the amount of memory and number of processors available overall. Symbolic methods rely on the heuristic properties of decision diagrams, which can encode many, but by no means all, functions over a structured and exponentially large domain in polynomial space; here the pitfalls are subtler, as the performance of symbolic approaches can vary widely depending on the particular class of decision diagram chosen, on the order in which the variables describing the state are considered, and on many obscure algorithmic parameters. In this paper, we survey both approaches. Observing that symbolic approaches are often enormously more efficient than explicit ones for many practical models (although it is rar ely obvious a priori whether this will be the case on a particular application), we argue for the need to par allelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. Unfortunately, this is a very challenging endeavor, as the most efficient symbolic algorithm, Satura tion, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal.
On the one dimensional Dirac equation with potential

Abstract We investigate L 1 → L ∞ dispersive estimates for the one dimensional Dirac equation with a potential. In particular, we show that the Dirac evolution satisfies the natural t − 1 2 decay rate, which may be improved to t − 3 2 at the cost of spatial weights when the thresholds are regular. We classify the structure of threshold obstructions, showing that there is at most a one dimensional space at each threshold. We show that, in the presence of a threshold resonance, the Dirac evolution satisfies the natural decay rate, and satisfies the faster weighted bound except for a piece of rank at most two, one per threshold. Further, we prove high energy dispersive bounds that are near optimal with respect to the required smoothness of the initial data. To do so we use a variant of a high energy argument that was originally developed to study Kato smoothing estimates for magnetic Schrodinger operators. This method has never been used before to obtain L 1 → L ∞ estimates. As a consequence of our analysis we prove a uniform limiting absorption principle, Strichartz estimates, and prove the existence of an eigenvalue free region for the one dimensional Dirac operator with a non-self-adjoint potential.
Understanding The Dynamics Of Information Flow During Disaster Response Using Absorbing Markov Chains

This paper aims to derive a quantitative model to evaluate the impact of information flow on the effectiveness of disaster response. At the core of the model is a specialized absorbing Markov chain that models the process of delivering federal assistance to the community while considering stakeholder interactions and information flow uncertainty. Using the proposed model, the probability of community satisfaction is computed to reflect the effectiveness of disaster response. A hypothetical example is provided to demonstrate the applicability and interpretability of the derived quantitative model. Practically, the research provides governmental stakeholders interpretable insights for evaluating the impact of information flow on their disaster response effectiveness so that proactive actions can be targeted for enhanced disaster response.
Comparing People with Bibliometrics

Bibliometric indicators, citation counts and/or download counts are increasingly being used to inform personnel decisions such as hiring or promotions. These statistics are very often misused. Here we provide a guide to the factors which should be considered when using these so-called quantitative measures to evaluate people. Rules of thumb are given for when begin to use bibliometric measures when comparing otherwise similar candidates.
Vector boson tagged jets and jet substructure

In these proceedings, we report on recent results related to vector boson-tagged jet production in heavy ion collisions and the related modification of jet substructure, such as jet shapes and jet momentum sharing distributions. Z 0 -tagging and γ -tagging of jets provides new opportunities to study parton shower formation and propagation in the quark-gluon plasma and has been argued to provide tight constrains on the energy loss of reconstructed jets. We present theoretical predictions for isolated photon-tagged and electroweak boson-tagged jet production in Pb+Pb collisions at √sNN = 5.02 TeV at the LHC, addressing the modification of their transverse momentum and transverse momentum imbalance distributions. Comparison to recent ATLAS and CMS experimental measurements is performed that can shed light on the medium-induced radiative corrections and energy dissipation due to collisional processes of predominantly quark-initiated jets. The modification of parton splitting functions in the QGP further implies that the substructure of jets in heavy ion collisions may differ significantly from the corresponding substructure in proton-proton collisions. Two such observables and the implication of tagging on their evaluation is also discussed.
Cassini Attitude Control Configuration for Huygens Probe Release

The Cassini spacecraft, including the Orbiter and Huygens Probe, is the largest and most complex interplanetary spacecraft ever built to explore the Saturnian system and undertake a series of ambitious and unique scientific observations of planet Saturn, its largest moon, Titan, and other Cronian (= Saturnian, just like Jovian) satellites. The Huygens Probe, manufactured by the European Space Agency, successfully separated from Cassini on December 24, 2004 during the third flyby of Titan. Three weeks later, the probe landed successfully on a predetermined landing site on the surface of Titan. This important event by itself marked the landing of a man made probe on the farthest celestial body from our planet to date, not to mention the vast, invaluable scientific data from the early Earth-like environment of Titan relayed to Earth by the probe through Cassini. All of this was only possible because of the accurate separation of the Huygens Probe from Cassini; in fact, this successful separation event and the postseparation health and safety of both vehicles were crucial to the entire Cassini-Huygens mission. This paper describes the dynamics and attitude control challenges associated with the design of the Probe release sequence, and how these challenges were overcome in order to not only meet but exceed all the functional requirements. The simulation and test methodologies to ensure a robust Probe release event are also discussed. Finally, the engineering flight data of the orbiter before and after Probe separation are reviewed leading to the reconstruction of attitude control functionalities of the orbiter, along with the post-separation kinematics and dynamics of both the orbiter and probe.
Challenges for cosmic-ray experiments

This paper is a commentary on presentations at ISVHECRI 2016 related to cosmic-rays, gamma-rays and neutrinos. Its goal is to highlight the unanswered questions raised during the conference about the sources of these cosmic particles and the relations among them.
Efficient sampling from the Bingham distribution

We give a algorithm for exact sampling from the Bingham distribution $p(x)\propto \exp(x^\top A x)$ on the sphere $\mathcal S^{d-1}$ with expected runtime of $\operatorname{poly}(d, \lambda_{\max}(A)-\lambda_{\min}(A))$. The algorithm is based on rejection sampling, where the proposal distribution is a polynomial approximation of the pdf, and can be sampled from by explicitly evaluating integrals of polynomials over the sphere. Our algorithm gives exact samples, assuming exact computation of an inverse function of a polynomial. This is in contrast with Markov Chain Monte Carlo algorithms, which are not known to enjoy rapid mixing on this problem, and only give approximate samples. As a direct application, we use this to sample from the posterior distribution of a rank-1 matrix inference problem in polynomial time.
Spiked separable covariance matrices and principal components

We study a class of separable sample covariance matrices of the form Q˜1:=A˜1/2XB˜X∗A˜1/2. Here, A˜ and B˜ are positive definite matrices whose spectrums consist of bulk spectrums plus several spikes, that is, larger eigenvalues that are separated from the bulks. Conceptually, we call Q˜1 a spiked separable covariance matrix model. On the one hand, this model includes the spiked covariance matrix as a special case with B˜=I. On the other hand, it allows for more general correlations of datasets. In particular, for spatio-temporal dataset, A˜ and B˜ represent the spatial and temporal correlations, respectively. In this paper, we study the outlier eigenvalues and eigenvectors, that is, the principal components, of the spiked separable covariance model Q˜1. We prove the convergence of the outlier eigenvalues λ˜i and the generalized components (i.e., ⟨v,ξ˜i⟩ for any deterministic vector v) of the outlier eigenvectors ξ˜i with optimal convergence rates. Moreover, we also prove the delocalization of the nonoutlier eigenvectors. We state our results in full generality, in the sense that they also hold near the so-called BBP transition and for degenerate outliers. Our results highlight both the similarity and difference between the spiked separable covariance matrix model and the spiked covariance matrix model in (Probab. Theory Related Fields 164 (2016) 459–552). In particular, we show that the spikes of both A˜ and B˜ will cause outliers of the eigenvalue spectrum, and the eigenvectors can help to select the outliers that correspond to the spikes of A˜ (or B˜).
Orchestrated trios: compiling for efficient communication in Quantum programs with 3-Qubit gates

Current quantum computers are especially error prone and require high levels of optimization to reduce operation counts and maximize the probability the compiled program will succeed. These computers only support operations decomposed into one- and two-qubit gates and only two-qubit gates between physically connected pairs of qubits. Typical compilers first decompose operations, then route data to connected qubits. We propose a new compiler structure, Orchestrated Trios, that first decomposes to the three-qubit Toffoli, routes the inputs of the higher-level Toffoli operations to groups of nearby qubits, then finishes decomposition to hardware-supported gates. This significantly reduces communication overhead by giving the routing pass access to the higher-level structure of the circuit instead of discarding it. A second benefit is the ability to now select an architecture-tuned Toffoli decomposition such as the 8-CNOT Toffoli for the specific hardware qubits now known after the routing pass. We perform real experiments on IBM Johannesburg showing an average 35% decrease in two-qubit gate count and 23% increase in success rate of a single Toffoli over Qiskit. We additionally compile many near-term benchmark algorithms showing an average 344% increase in (or 4.44x) simulated success rate on the Johannesburg architecture and compare with other architecture types.
DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning

Abstract Searches for low-surface-brightness galaxies (LSBGs) in galaxy surveys are plagued by the presence of a large number of artifacts (e.g., objects blended in the diffuse light from stars and galaxies, Galactic cirrus, star-forming regions in the arms of spiral galaxies, etc.) that have to be rejected through time consuming visual inspection. In future surveys, which are expected to collect hundreds of petabytes of data and detect billions of objects, such an approach will not be feasible. We investigate the use of convolutional neural networks (CNNs) for the problem of separating LSBGs from artifacts in survey images. We take advantage of the fact that we have available a large number of labeled LSBGs and artifacts from the Dark Energy Survey, that we use to train, validate, and test a CNN model. That model, which we call DeepShadows, achieves a test accuracy of 92.0%, a significant improvement relative to feature-based machine learning models. We also study the ability to use transfer learning to adapt this model to classify objects from the deeper Hyper-Suprime-Cam survey, and we show that after the model is retrained on a very small sample from the new survey, it can reach an accuracy of 87.6%. These results demonstrate that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.
MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity.

Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci with evidence of allelic heterogeneity, that is, containing multiple causal variants. MRLocus makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of the extent of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against other state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five candidate causal genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus’s estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.
SparsePipe: Parallel Deep Learning for 3D Point Clouds

We propose SparsePipe, an efficient and asynchronous parallelism approach for handling 3D point clouds with multi-GPU training. SparsePipe is built to support 3D sparse data such as point clouds. It achieves this by adopting generalized convolutions with sparse tensor representation to build expressive high-dimensional convolutional neural networks. Compared to dense solutions, the new models can efficiently process irregular point clouds without densely sliding over the entire space, significantly reducing the memory requirements and allowing higher resolutions of the underlying 3D volumes for better performance. SparsePipe exploits intra-batch parallelism that partitions input data into multiple processors and further improves the training throughput with inter-batch pipelining to overlap communication and computing. Besides, it suitably partitions the model when the GPUs are heterogeneous such that the computing is load-balanced with reduced communication overhead. Using experimental results on an eight-GPU platform, we show that SparsePipe can parallelize effectively and obtain better performance on current point cloud benchmarks for both training and inference, compared to its dense solutions.
Learning lyapunov functions for hybrid systems

We propose a sampling-based approach to learn Lyapunov functions for a class of discrete-time autonomous hybrid systems that admit a mixed-integer representation. Such systems include autonomous piecewise affine systems, closed-loop dynamics of linear systems with model predictive controllers, piecewise affine/linear complementarity/mixed-logical dynamical systems in feedback with a ReLU neural network controller, etc. The proposed method comprises an alternation between a learner and a verifier to search for a Lyapunov function from a family of parameterized Lyapunov function candidates. In each iteration, the learner uses a collection of state samples to select a Lyapunov function candidate through a convex program in the parameter space. The verifier then solves a nonconvex mixed-integer quadratic program in the state space to either validate the proposed Lyapunov function candidate or reject it with a counterexample, i.e., a state where the Lyapunov condition fails. This counterexample is then added to the sample set of the learner to refine the set of Lyapunov function candidates in the next iteration. By designing the learner and the verifier according to the analytic center cutting-plane method from convex optimization, we show that when the set of Lyapunov functions is full-dimensional in the parameter space, our method finds a Lyapunov function in a finite number of steps. We demonstrate our stability analysis method on closed-loop MPC dynamical systems and a ReLU neural network controlled PWA system.
Low-rank factorization for rank minimization with nonconvex regularizers

Rank minimization is of interest in machine learning applications such as recommender systems and robust principal component analysis. Minimizing the convex relaxation to the rank minimization problem, the nuclear norm, is an effective technique to solve the problem with strong performance guarantees. However, nonconvex relaxations have less estimation bias than the nuclear norm and can more accurately reduce the effect of noise on the measurements. We develop efficient algorithms based on iteratively reweighted nuclear norm schemes, while also utilizing the low rank factorization for semidefinite programs put forth by Burer and Monteiro. We prove convergence and computationally show the advantages over convex relaxations and alternating minimization methods. Additionally, the computational complexity of each iteration of our algorithm is on par with other state of the art algorithms, allowing us to quickly find solutions to the rank minimization problem for large matrices.
Alpaca: Intermittent Execution without Checkpoints

The emergence of energy harvesting devices creates the potential for batteryless sensing and computing devices. Such devices operate only intermittently, as energy is available, presenting a number of challenges for software developers. Programmers face a complex design space requiring reasoning about energy, memory consistency, and forward progress. This paper introduces Alpaca, a low-overhead programming model for intermittent computing on energy-harvesting devices. Alpaca programs are composed of a sequence of user-defined tasks. The Alpaca runtime preserves execution progress at the granularity of a task. The key insight in Alpaca is the privatization of data shared between tasks. Shared values written in a task are detected using idempotence analysis and copied into a buffer private to the task. At the end of the task, modified values from the private buffer are atomically committed to main memory, ensuring that data remain consistent despite power failures. Alpaca provides a familiar programming interface, a highly efficient runtime model, and places fewer restrictions on a target device's hardware architecture. We implemented a prototype of Alpaca as an extension to C with an LLVM compiler pass. We evaluated Alpaca, and directly compared to two systems from prior work. Alpaca eliminates checkpoints, which improves performance up to 15x, and avoids static multi-versioning, which improves memory consumption by up to 5.5x.
Locally Private k-Means in One Round

We provide an approximation algorithm for k-means clustering in the one-round (aka non-interactive) local model of differential privacy (DP). This algorithm achieves an approximation ratio arbitrarily close to the best non private approximation algorithm, improving upon previously known algorithms that only guarantee large (constant) approximation ratios. Furthermore, this is the first constant-factor approximation algorithm for k-means that requires only one round of communication in the local DP model, positively resolving an open question of Stemmer (SODA 2020). Our algorithmic framework is quite flexible; we demonstrate this by showing that it also yields a similar near-optimal approximation algorithm in the (one-round) shuffle DP model.
Analyzing the flared landing task with pitch-rate flight control systems

A closed-loop handling qualities methodology is applied to an analysis of the flared landing task with pitch-rate flight control systems. A model of pilot behavior throughout approach and flare is developed which postulates the manner in which the pilot may move from pitch attitude to flight path angle control. Twenty-five configurations flight tested on the NC-131H Total In-flight Simulator aircraft are analyzed using a structural pilot model ad a handling qualities methodology previously reported in the literature. Closed-loop simulation of the simplified landing task is undertaken using the structural model. The pilot ratings from flight test extended the data base supporting the utility of a model-based handling qualities metric. A handling qualities sensitivity function is introduced which may have potential as a design tool.