cssi/SciSciGPT-SciSciNet huggingface dataset:
Lloyd-Topor Completion and General Stable Models

We investigate the relationship between the generalization of program completion defined in 1984 by Lloyd and Topor and the generalization of the stable model semantics introduced recently by Ferraris et al. The main theorem can be used to characterize, in some cases, the general stable models of a logic program by a first-order formula. The proof uses Truszczynski's stable model semantics of infinitary propositional formulas.
Fixed-PSNR Lossy Compression for Scientific Data

Error-controlled lossy compression has been studied for years because of extremely large volumes of data being produced by today's scientific simulations. None of existing lossy compressors, however, allow users to fix the peak signal-to-noise ratio (PSNR) during compression, although PSNR has been considered as one of the most significant indicators to assess compression quality. In this paper, we propose a novel technique providing a fixed-PSNR lossy compression for scientific data sets. We implement our proposed method based on the SZ lossy compression framework and release the code as an open-source toolkit. We evaluate our fixed-PSNR compressor on three realworld high-performance computing data sets. Experiments show that our solution has a high accuracy in controlling PSNR, with an average deviation of 0.1 ~ 5.0 dB on the tested data sets.
Distributed k-means and k-median Clustering on General Topologies

This paper provides new algorithms for distributed clustering for two popular center-based objectives, k-median and k-means. These algorithms have provable guarantees and improve communication complexity over existing approaches. Following a classic approach in clustering by [13], we reduce the problem of finding a clustering with low cost to the problem of finding a coreset of small size. We provide a distributed method for constructing a global coreset which improves over the previous methods by reducing the communication complexity, and which works over general communication topologies. Experimental results on large scale data sets show that this approach outperforms other coreset-based distributed clustering algorithms.
PAMELA satellite data as a signal of non-thermal wino LSP dark matter

Abstract We argue there is a very well motivated theoretical framework (which existed before data) consistent with the interpretation that dark matter annihilation is being observed by the PAMELA satellite detector. The dark matter is (mainly) the neutral W boson superpartner, the wino. Using the program GALPROP extensively we study the annihilation products and the backgrounds together. A wino mass approximately in the 180–200 GeV range gives a good description of the PAMELA data, with antimatter and gammas from annihilating winos contributing or dominating the data below this energy range but not contributing above it. We explain why PAMELA data does not imply no antiproton signal was observed by PAMELA or earlier experiments, and explain why the antiproton analysis was misunderstood by earlier papers. Normalizing the wino density to the local relic density, no “boost factors” are needed. At higher energies we expect astrophysical mechanisms to contribute, and we simply parameterize them, and check that the combination can describe all the data. We emphasize several predictions for satellite data to test the wino interpretation, particularly the flattening or turndown of the positron and antiproton spectra above 100 GeV, and predictions for the Fermi detector. We emphasize that interpretations also depend very strongly on assumptions about the cosmological history of the universe, and that it is crucial to have independent criteria to define the background for any given process.
Bayesian model selection of stochastic block models

A central problem in analyzing networks is partitioning them into modules or communities. One of the best tools for this is the stochastic block model, which clusters vertices into blocks with statistically homogeneous pattern of links. Despite its flexibility and popularity, there has been a lack of principled statistical model selection criteria for the stochastic block model. Here we propose a Bayesian framework for choosing the number of blocks as well as comparing it to the more elaborate degree-corrected block models, ultimately leading to a universal model selection framework capable of comparing multiple modeling combinations. We will also investigate its theoretic connection to the minimum description length principle.
He Gives C-Sieves on the CSIDH

Recently, Castryck, Lange, Martindale, Panny, and Renes proposed CSIDH (pronounced “sea-side”) as a candidate post-quantum “commutative group action.” It has attracted much attention and interest, in part because it enables noninteractive Diffie–Hellman-like key exchange with quite small communication. Subsequently, CSIDH has also been used as a foundation for digital signatures.
Skew-symmetric entropy stable modal discontinuous Galerkin formulations

High order entropy stable discontinuous Galerkin (DG) methods for nonlinear conservation laws satisfy an inherent discrete entropy inequality. The construction of such schemes has relied on the use of carefully chosen nodal points (Gassner in SIAM J Sci Comput 35(3):A1233–A1253, 2013; Fisher and Carpenter in J Comput Phys 252:518–557, 2013; Carpenter et al. in SIAM J Sci Comput 36(5):B835–B867, 2014; Crean et al. in J Comput Phys 356:410–438, 2018; Chan et al. in Efficient entropy stable Gauss collocation methods, 2018. arXiv:1809.01178 ) or volume and surface quadrature rules (Chan in J Comput Phys 362:346–374, 2018; Chan and Wilcox in J Comput Phys 378:366–393, 2019) to produce operators which satisfy a summation-by-parts (SBP) property. In this work, we show how to construct “modal” DG formulations which are entropy stable for volume and surface quadratures under which the SBP property in Chan (2018) does not hold. These formulations rely on an alternative skew-symmetric construction of operators which automatically satisfy the SBP property. Entropy stability then follows for choices of volume and surface quadrature which satisfy sufficient accuracy conditions. The accuracy of these new SBP operators depends on a separate set of conditions on quadrature accuracy, with design order accuracy recovered under the usual assumptions of degree $$2N-1$$ volume quadratures and degree 2N surface quadratures. We conclude with numerical experiments verifying the accuracy and stability of the proposed formulations, and discuss an application of these formulations for entropy stable DG schemes on mixed quadrilateral-triangle meshes.
Nanopore sequencing for fast determination of plasmids, phages, virulence markers, and antimicrobial resistance genes in Shiga toxin-producing Escherichia coli

Whole genome sequencing can provide essential public health information. However, it is now known that widely used short-read methods have the potential to miss some randomly-distributed segments of genomes. This can prevent phages, plasmids, and virulence factors from being detected or properly identified. Here, we compared assemblies of three complete Shiga toxin-producing Escherichia coli (STEC) O26:H11/H- genomes from two different sequence types (ST21 and 29), each acquired using the Nextera XT MiSeq, MinION nanopore-based sequencing, and Pacific Biosciences (PacBio) sequencing. Each closed genome consisted of a single chromosome, approximately 5.7 Mb for CFSAN027343, 5.6 Mb for CFSAN027346, and 5.4 MB for CFSAN027350. However, short-read whole genome sequencing (WGS) using Nextera XT MiSeq failed to identify some virulence genes in plasmids and on the chromosome, both of which were detected using the long-read platforms. Results from long-read MinION and PacBio allowed us to identify differences in plasmid content: a single 88 kb plasmid in CFSAN027343; a 157kb plasmid in CFSAN027350; and two plasmids in CFSAN027346 (one 95 Kb, one 72 Kb). These data enabled rapid characterization of the virulome, detection of antimicrobial genes, and composition/location of Stx phages. Taken together, positive correlations between the two long-read methods for determining plasmids, virulome, antimicrobial resistance genes, and phage composition support MinION sequencing as one accurate and economical option for closing STEC genomes and identifying specific virulence markers.
A Learning Framework for High Precision Industrial Assembly

Automatic assembly has broad applications in industries. Traditional assembly tasks utilize predefined trajectories or tuned force control parameters, which make the automatic assembly time-consuming, difficult to generalize, and not robust to uncertainties. In this paper, we propose a learning framework for high precision industrial assembly. The framework combines both the supervised learning and the reinforcement learning. The supervised learning utilizes trajectory optimization to provide the initial guidance to the policy, while the reinforcement learning utilizes actor-critic algorithm to establish the evaluation system even the supervisor is not accurate. The proposed learning framework is more efficient compared with the reinforcement learning and achieves better stability performance than the supervised learning. The effectiveness of the method is verified by both the simulation and experiment. Experimental videos are available at [1].
Algebraic description of limit models in classes of abelian groups

Abstract We study limit models in the class of abelian groups with the subgroup relation and in the class of torsion-free abelian groups with the pure subgroup relation. We show: Theorem 0.1 (1) If G is a limit model of cardinality λ in the class of abelian groups with the subgroup relation, then G ≅ ( ⊕ λ Q ) ⊕ ⊕ p prime ( ⊕ λ Z ( p ∞ ) ) . (2) If G is a limit model of cardinality λ in the class of torsion-free abelian groups with the pure subgroup relation, then: • If the length of the chain has uncountable cofinality, then G ≅ ( ⊕ λ Q ) ⊕ Π p prime ( ⊕ λ Z ( p ) ) ‾ . • If the length of the chain has countable cofinality, then G is not algebraically compact. We also study the class of finitely Butler groups with the pure subgroup relation, we show that it is an AEC, Galois-stable and ( ℵ 0 ) -tame and short.
Functional liver-image guided hepatic therapy (FLIGHT): A technique to maximize hepatic functional reserve.

ABSTRACT Introduction: Radiation planning approaches for liver radiation often do not consider the regional variation that can exist in liver function. This study dosimetrically compares functional liver image-guided hepatic therapy (FLIGHT) to standard stereotactic body radiation therapy (SBRT) plans. In the FLIGHT plans, functional data from hepatobiliary iminodiacetic acid (HIDA) single photon emission computed tomography (SPECT) scans serve as a road map to guide beam arrangement. While meeting the same target volume coverage, plans are optimized to reduce dose to high-functioning liver. Materials and Methods: The study included 10 patients with hepatocellular carcinoma (HCC) with baseline HIDA SPECT imaging. Standard SBRT plans which did not systematically incorporate these scans had previously been completed on all 10 plans. Retrospectively, FLIGHT plans were created based on the use of contours of relative liver function from the HIDA SPECT as avoidance structures. Resulting dose to each relative functional liver structure was examined and compared qualitatively and using Wilcoxin rank-sum tests. Target coverage, doses to organs at risk (OARs), conformity index (CI), and gradient index (GI) were also evaluated. Results: While maintaining the same target coverage, FLIGHT plans reduced the mean dose to the high functioning liver by a median of 3.0 Gy (range 0.7 to 4.6 Gy), which represented a 31.4% mean reduction compared to standard planning. FLIGHT plans reduced the volume of high functioning liver receiving 15 Gy by a mean of 59.3 cc (range 7 to 170 cc), for a mean reduction of 41.9%. The mean dose to areas of liver function defined by 25% to 100% and 50% to 100% maximum was reduced with FLIGHT from 10.5 Gy to 8.5 Gy and from 10.5 Gy to 7.5 Gy, respectively (p
The Commitment Conundrum of Inventory Sharing

In recent years, inventory sharing, in addition to the traditional within-firm model, also appears in the sharing-economy model where different firms can share their inventory via an internet platform. Two prominent issues, shaping the behaviors of inventory sharing systems, are the commitment of the transfer price and the commitment to share. In this paper we investigate, through a behavioral lens, the decisions of transfer price, order quantity and the resulting supply chain efficiency in different transshipment commitment scenarios. In particular, we consider two transfer price commitment settings (ex-ante or ex-post, depending on whether or not the inventory transfer price is committed before demand realization) and two sharing commitment rules (automatic or voluntary, depending on whether or not inventory sharing are pre-committed). Our experimental results suggest that individuals set transfer prices much lower, and order much less, than what Nash equilibrium predicts. We also find substantial treatment effects where rational model can not explain. The magnitude of disparities relative to the Nash equilibrium prediction appears to be most substantial in the situation where transfer price is set ex ante and inventory sharing is voluntary. Motivated by these observations, we develop a behavioral model that incorporates quantal response equilibrium and fairness concerns. Empirical analysis indicates that our model provides a compelling explanation of the behavior observed in the data. This study provides implication on the design of the commitment rules in decentralized inventory sharing platforms.
Long Time Behavior of Solutions to the 2D Boussinesq Equations with Zero Diffusivity

We address long time behavior of solutions to the 2D Boussinesq equations with zero diffusivity in the cases of the torus, $${{\mathbb {R}}}^2$$ , and on a bounded domain with Lions or Dirichlet boundary conditions. In all the cases, we obtain bounds on the long time behavior for the norms of the velocity and the vorticity. In particular, we obtain that the norm $$\Vert (u,\rho )\Vert _{H^2\times H^{1}}$$ is bounded by a single exponential, improving earlier bounds.
Placement and Allocation of Virtual Network Functions: Multi-dimensional Case

Network function virtualization (NFV) is an emerging design paradigm that replaces physical middlebox devices with software modules running on general purpose commodity servers. While gradually transitioning to NFV, Internet service providers face the problem of where to introduce NFV in order to make the most benefit of that; here, we measure the benefit by the amount of traffic that can be serviced through the NFV. This problem is non-trivial as it is composed of two challenging subproblems: 1) placement of nodes to support virtual network functions (referred to as VNF-nodes); and 2) allocation of the VNF-nodes resources to network flows; the two subproblems need to be considered jointly to satisfy the objective of serving the maximum amount of traffic. This problem has been studied recently but for the one-dimensional setting, where all network flows require one network function, which requires a unit of resource to process a unit of flow. In this work, we extend to the multi-dimensional setting, where flows can require multiple network functions, which can also require a different amount of each resource to process a unit of flow. The multi-dimensional setting introduces new challenges in addition to those of the onedimensional setting (e.g., NP-hardness and non-submodularity) and also makes the resource allocation a multi-dimensional generalization of the generalized assignment problem with assignment restrictions. To address these difficulties, we propose a novel two-level relaxation method and utilize the primal-dual technique to design two approximation algorithms that achieve an approximation ratio of$\displaystyle \frac {(Z-1)(\mathrm {e}-1)}{2\mathrm {e}^{2}Z(kR)^{1/(Z-1)}}$ (and $\displaystyle \frac {(\mathrm {e}-1)(Z-1)}{2\mathrm {e}(Z-1+\mathrm {e}ZR^{1/(Z-1)})}$, where k (resp. R) is the number of VNF-nodes (resp. resources), and Z is a measure of the available resource compared to flow demand. Finally, we perform extensive trace-driven simulations to show the effectiveness of the proposed algorithms.
Finite groups representation theory with Coq

Representation theory is a branch of algebra that allows the study of groups through linear applications, i.e. matrices. Thus problems in abstract groups can be reduced to problems on matrices. Representation theory is the basis of character theory. In this paper we present a formalization of finite groups representation theory in the Coq system that includes a formalization of Maschke's theorem on reducible finite group algebra. This work is a part of the Mathematical Components project which aims to develop a formal proof of the Feit-Thompson theorem.
Multiscale Multiobjective Systems Analysis (MiMoSA): an advanced metabolic modeling framework for complex systems

In natural environments, cells live in complex communities and experience a high degree of heterogeneity internally and in the environment. Even in ‘ideal’ laboratory environments, cells can experience a high degree of heterogeneity in their environments. Unfortunately, most of the metabolic modeling approaches that are currently used assume ideal conditions and that each cell is identical, limiting their application to pure cultures in well-mixed vessels. Here we describe our development of Multiscale Multiobjective Systems Analysis (MiMoSA), a metabolic modeling approach that can track individual cells in both space and time, track the diffusion of nutrients and light and the interaction of cells with each other and the environment. As a proof-of concept study, we used MiMoSA to model the growth of Trichodesmium erythraeum, a filamentous diazotrophic cyanobacterium which has cells with two distinct metabolic modes. The use of MiMoSA significantly improves our ability to predictively model metabolic changes and phenotype in more complex cell cultures.
Association between the 4p16 genomic locus and different types of congenital heart disease: results from adult survivors in the UK Biobank

Congenital heart disease is the most common birth defect in newborns and the leading cause of death in infancy, affecting nearly 1% of live births. A locus in chromosome 4p16, adjacent to MSX1 and STX18, has been associated with atrial septal defects (ASD) in multiple European and Chinese cohorts. Here, genotyping data from the UK Biobank was used to test for associations between this locus and congenital heart disease in adult survivors of left ventricular outflow tract obstruction (n = 164) and ASD (n = 223), with a control sample of 332,788 individuals, and a meta-analysis of the new and existing ASD data was performed. The results show an association between the previously reported markers at 4p16 and risk for either ASD or left ventricular outflow tract obstruction, with effect sizes similar to the published data (OR between 1.27–1.45; all p < 0.05). Differences in allele frequencies remained constant through the studied age range (40–70 years), indicating that the variants themselves do not drive lethal genetic defects. Meta-analysis shows an OR of 1.35 (95% CI: 1.25–1.46; p < 10−4) for the association with ASD. The findings show that the genetic associations with ASD can be generalized to adult survivors of both ASD and left ventricular lesions. Although the 4p16 associations are statistically compelling, the mentioned alleles confer only a small risk for disease and their frequencies in this adult sample are the same as in children, likely limiting their clinical significance. Further epidemiological and functional studies may elicit factors triggering disease in interaction with the risk alleles.
What Motivates Innovative Entrepreneurs? Evidence from a Global Field Experiment

Entrepreneurial motivation is important to the process of economic growth. However, evidence on the motivations of innovative entrepreneurs, and how those motivations differ across fundamental char...
Institutional Transformation Reflected: Engagement in Sensemaking and Organizational Learning in Florida’s Developmental Education Reform

Following a major statewide developmental education reform in Florida, we explored institutional transformation among Florida College System institutions. We used statewide survey data to examine lead administrators’ perceptions of challenges encountered during the planning process, ways in which colleges engaged in sensemaking (i.e., social processes for developing shared understanding) and organizational learning, and perceptions of the institutional transformation processes and outcomes following the reform. We found that institutions engaged in numerous types of sensemaking and organizational learning practices to promote change. Yet, despite different approaches taken to institutional transformation, almost all respondents reported that the change process was highly collaborative and involved a broad range of stakeholders.
Power analysis of knockoff filters for correlated designs

The knockoff filter introduced by Barber and Cand\`es 2016 is an elegant framework for controlling the false discovery rate in variable selection. While empirical results indicate that this methodology is not too conservative, there is no conclusive theoretical result on its power. When the predictors are i.i.d.\ Gaussian, it is known that as the signal to noise ratio tend to infinity, the knockoff filter is consistent in the sense that one can make FDR go to 0 and power go to 1 simultaneously. In this work we study the case where the predictors have a general covariance matrix $\bsigma$. We introduce a simple functional called \emph{effective signal deficiency (ESD)} of the covariance matrix of the predictors that predicts consistency of various variable selection methods. In particular, ESD reveals that the structure of the precision matrix plays a central role in consistency and therefore, so does the conditional independence structure of the predictors. To leverage this connection, we introduce \emph{Conditional Independence knockoff}, a simple procedure that is able to compete with the more sophisticated knockoff filters and that is defined when the predictors obey a Gaussian tree graphical models (or when the graph is sufficiently sparse). Our theoretical results are supported by numerical evidence on synthetic data.
Exogenous Alginate Protects Staphylococcus aureus from Killing by Pseudomonas aeruginosa.

ABSTRACT Cystic fibrosis (CF) patients chronically infected with both Pseudomonas aeruginosa and Staphylococcus aureus have worse health outcomes than patients who are monoinfected with either P. aeruginosa or S. aureus. We showed previously that mucoid strains of P. aeruginosa can coexist with S. aureusin vitro due to the transcriptional downregulation of several toxic exoproducts typically produced by P. aeruginosa, including siderophores, rhamnolipids, and HQNO (2-heptyl-4-hydroxyquinoline N-oxide). Here, we demonstrate that exogenous alginate protects S. aureus from P. aeruginosa in both planktonic and biofilm coculture models under a variety of nutritional conditions. S. aureus protection in the presence of exogenous alginate is due to the transcriptional downregulation of pvdA, a gene required for the production of the iron-scavenging siderophore pyoverdine as well as the downregulation of the PQS (Pseudomonas quinolone signal) (2-heptyl-3,4-dihydroxyquinoline) quorum sensing system. The impact of exogenous alginate is independent of endogenous alginate production. We further demonstrate that coculture of mucoid P. aeruginosa with nonmucoid P. aeruginosa strains can mitigate the killing of S. aureus by the nonmucoid strain of P. aeruginosa, indicating that the mechanism that we describe here may function in vivo in the context of mixed infections. Finally, we investigated a panel of mucoid clinical isolates that retain the ability to kill S. aureus at late time points and show that each strain has a unique expression profile, indicating that mucoid isolates can overcome the S. aureus-protective effects of mucoidy in a strain-specific manner. IMPORTANCE CF patients are chronically infected by polymicrobial communities. The two dominant bacterial pathogens that infect the lungs of CF patients are P. aeruginosa and S. aureus, with ∼30% of patients coinfected by both species. Such coinfected individuals have worse outcomes than monoinfected patients, and both species persist within the same physical space. A variety of host and environmental factors have been demonstrated to promote P. aeruginosa-S. aureus coexistence, despite evidence that P. aeruginosa kills S. aureus when these organisms are cocultured in vitro. Thus, a better understanding of P. aeruginosa-S. aureus interactions, particularly mechanisms by which these microorganisms are able to coexist in proximal physical space, will lead to better-informed treatments for chronic polymicrobial infections.
Detonation initiation by compressible turbulence thermodynamic fluctuations

Abstract Theory and computations have established that thermodynamic gradients created by hot spots in reactive gas mixtures can lead to spontaneous detonation initiation. However, the current laminar theory of the temperature-gradient mechanism for detonation initiation is restricted to idealized physical configurations. Thus, it only predicts conditions for the onset of detonations in quiescent gases, where an isolated hot spot is formed on a timescale shorter than the chemical and acoustic timescales of the gas. In this work, we extend the laminar temperature-gradient mechanism into a statistical model for predicting the detonability of an autoignitive gas experiencing compressible isotropic turbulence fluctuations. Compressible turbulence forms non-monotonic temperature fields with tightly-spaced local minima and maxima that evolve over a range of timescales, including those much larger than chemical and acoustic timescales. We examine the utility of the adapted statistical model through direct numerical simulations of compressible isotropic turbulence in premixed hydrogen-air reactants for a range of conditions. We find strong, but not conclusive, evidence that the model can predict the degree of detonability in an autoignitive gas due to turbulence-induced thermodynamic gradients.
Dynamic transcriptome, accessible genome, and PGR cistrome profiles in the human myometrium.

The myometrium undergoes structural and functional remodeling during pregnancy. We hypothesize that myometrial genomic elements alter correspondingly in preparation for parturition. Human myometrial tissues from nonpregnant (NP) and term pregnant (TP) human subjects were examined by RNAseq, ATACseq, and PGR ChIPseq assays to profile transcriptome, assessible genome, and PGR occupancy. NP and TP specimens exhibit 2890 differentially expressed genes, reflecting an increase of metabolic, inflammatory, and PDGF signaling, among others, in adaptation to pregnancy. At the epigenome level, patterns of accessible genome change between NP and TP myometrium, leading to the altered enrichment of binding motifs for hormone and muscle regulators such as the progesterone receptor (PGR), Kruppel-like factors, and MEF2A transcription factors. PGR genome occupancy exhibits a significant difference between the two stages of the myometrium, concomitant with distinct transcriptomic profiles including genes such as ENO1, LHDA, and PLCL1 in the glycolytic and calcium signaling pathways. Over-representation of SRF, MYOD, and STAT binding motifs in PGR occupying sites further suggests interactions between PGR and major muscle regulators for myometrial gene expression. In conclusion, changes in accessible genome and PGR occupancy are part of the myometrial remodeling process and may serve as mechanisms to formulate the state-specific transcriptome profiles.
High-dimensional Statistical Arbitrage with Factor Models and Stochastic Control

ABSTRACTThe present paper provides a study of high-dimensional statistical arbitrage that combines factor models with the tools from stochastic control, obtaining closed-form optimal strategies whi...
Keepers of the double stars

Astronomers have long tracked double stars in efforts to find those that are gravitationally-bound binaries and then to determine their orbits. Early catalogues by the Herschels, Struves, and others began with their own discoveries. In 1906 court reporter and amateur astronomer Sherburne Wesley Burnham published a massive double star catalogue containing data from many observers on more than 13,000 systems. Lick Observatory astronomer Robert Grant Aitken produced a much larger catalogue in 1932 and coordinated with Robert Innes of Johannesburg, who catalogued the southern systems. Aitken maintained and expanded Burnham ‘s records of observations on handwritten file cards, and eventually turned them over to the Lick Observatory, where astrometrist Hamilton Jeffers further expanded the collection and put all the observations on punched cards. With the aid of Frances M. ―Rete‖ Greeby he made two catalogues: an Index Catalogue with basic data about each star, and a complete catalogue of observations, with one observation per punched card. He enlisted Willem van den Bos of Johannesburg to add southern stars, and together they published the Index Catalogue of Visual Double Stars, 1961.0. As Jeffers approached retirement he became greatly concerned about the disposition of the catalogues. He wanted to be replaced by another ―double star man,‖ but Lick Director Albert E. Whitford had the new 120-inch reflector, the world‘s second largest telescope, and he wanted to pursue modern astrophysics instead. Jeffers was vociferously opposed to turning over the card files to another institution, and especially against their coming under the control of Kaj Strand of the United States Naval Observatory. In the end the USNO got the files and has maintained the records ever since, first under Charles Worley, and, since 1997, under Brian Mason. Now called the Washington Double Star Catalog (WDS), it is completely online and currently contains more than 1,200,000 measures of more than 125,000 star systems.
Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees

We propose a meta path planning algorithm named \emph{Neural Exploration-Exploitation Trees~(NEXT)} for learning from prior experience for solving new path planning problems in high dimensional continuous state and action spaces. Compared to more classical sampling-based methods like RRT, our approach achieves much better sample efficiency in high-dimensions and can benefit from prior experience of planning in similar environments. More specifically, NEXT exploits a novel neural architecture which can learn promising search directions from problem structures. The learned prior is then integrated into a UCB-type algorithm to achieve an online balance between \emph{exploration} and \emph{exploitation} when solving a new problem. We conduct thorough experiments to show that NEXT accomplishes new planning problems with more compact search trees and significantly outperforms state-of-the-art methods on several benchmarks.
Hidden-Trigger Backdoor Attacks

With the success of deep learning algorithms in various domains, studying adversarial attacks to secure deep models in real world applications has become an important research topic. Backdoor attacks are a form of adversarial attacks on deep networks where the attacker provides poisoned data to the victim to train the model with, and then activates the attack by showing a specific small trigger pattern at the test time. Most state-of-the-art backdoor attacks either provide mislabeled poisoning data that is possible to identify by visual inspection, reveal the trigger in the poisoned data, or use noise to hide the trigger. We propose a novel form of backdoor attack where poisoned data look natural with correct labels and also more importantly, the attacker hides the trigger in the poisoned data and keeps the trigger secret until the test time. We perform an extensive study on various image classification settings and show that our attack can fool the model by pasting the trigger at random locations on unseen images although the model performs well on clean data. We also show that our proposed attack cannot be easily defended using a state-of-the-art defense algorithm for backdoor attacks.
Adaptive Greedy versus Non-Adaptive Greedy for Influence Maximization

We consider the adaptive influence maximization problem: given a network and a budget k, iteratively select k seeds in the network to maximize the expected number of adopters. In the full-adoption feedback model, after selecting each seed, the seed-picker observes all the resulting adoptions. In the myopic feedback model, the seed-picker only observes whether each neighbor of the chosen seed adopts. Motivated by the extreme success of greedy-based algorithms/heuristics for influence maximization, we propose the concept of greedy adaptivity gap, which compares the performance of the adaptive greedy algorithm to its non-adaptive counterpart. Our first result shows that, for submodular influence maximization, the adaptive greedy algorithm can perform up to a (1-1/e)-fraction worse than the non-adaptive greedy algorithm, and that this ratio is tight. More specifically, on one side we provide examples where the performance of the adaptive greedy algorithm is only a (1-1/e) fraction of the performance of the non-adaptive greedy algorithm in four settings: for both feedback models and both the independent cascade model and the linear threshold model. On the other side, we prove that in any submodular cascade, the adaptive greedy algorithm always outputs a (1-1/e)-approximation to the expected number of adoptions in the optimal non-adaptive seed choice. Our second result shows that, for the general submodular cascade model with full-adoption feedback, the adaptive greedy algorithm can outperform the non-adaptive greedy algorithm by an unbounded factor. Finally, we propose a risk-free variant of the adaptive greedy algorithm that always performs no worse than the non-adaptive greedy algorithm.
The trade-off between pension costs and salary expenditures in the public sector

We examine pension-cost crowd out of salary expenditures in the public sector using a 15-year data panel of state teacher pension plans spanning the Great Recession. While there is no evidence of salary crowd out prior to the Great Recession, there is a shift in the post-recession years such that a 1% (of salaries) increase in the annual required pension contribution corresponds to a decrease in total teacher salary expenditures of 0.24%. The effect operates through changes to the size of the teaching workforce, not changes to teacher wages. An explanation for the effect heterogeneity pre- and post-recession is that public employers are less able to shield the workforce from pension costs during times of fiscal stress. This problem is exacerbated because unlike other benefit costs, such as for health care, pension costs are countercyclical.
Impact of dogs with deltamethrin-impregnated collars on prevalence of visceral leishmaniasis.

Abstract Leishmaniasis is a vector borne zoonosis which is classified as a neglected tropical disease. Among the three most common forms of the disease, Visceral Leishmaniasis (VL) is the most threatening to human health, causing 20,000 to 30,000 deaths worldwide each year. Areas where VL is mostly endemic have unprotected dogs in community and houses. The "presence of dogs usually increases VL risk for humans since dogs are the principal reservoir host for the parasite of the disease. Based on this fact, most earlier studies consider culling dogs as a control measure for the spread of VL. A more recent control measure has been the use of deltamethrin-impregnated dog collars ( D I D C s) to protect both humans and dogs by putting D I D C s on dogs neck. The presence of dogs helps to grow the sandfly population faster by offering a more suitable blood-meal source. On the other hand, the presence of D I D C s on dogs helps to reduce sandfly population by the lethality of deltamethrin insecticide. This study brings an ecological perspective to this public health concern, aiming to understand the impact of an additional host (here, protected dogs) on disease risk to a primary host (here, humans). To answer this question, we compare two different settings: a community without dogs, and a community with dogs protected with D I D C . Our analysis shows the presence of protected dogs can reduce VL infection risk in humans. However, this disease risk reduction depends on dogs’ tolerance for sandfly bites.
Financial stability with sovereign debt

Abstract Are government guarantees or financial regulation a more effective way to prevent banking crises? I study this question in the presence of a negative feedback loop between the fiscal position of the government and the health of the banking sector. I construct a model of financial intermediation in which the government issues, and may default on, debt. Banks hold some of this debt, which ties their health to that of the government. The government's tax revenue, in turn, depends on the quantity of investment that banks are able to finance. I compare the effectiveness of government guarantees, liquidity regulation, and a combination of these policies in preventing self-fulfilling bank runs. In some cases, a combination of the two policies is needed to prevent a run. In other cases, liquidity regulation alone is effective and adding guarantees would make the financial system fragile.
Thrombopoietin: from theory to reality.

Much is now known about the physiology of platelets and their role in primary hemostasis; however, until recently far less was understood about their immediate precursors, marrow megakaryocytes (MKs). With the cloning and characterization of thrombopoietin (TPO), the principle regulator of the growth and development of MKs, research has been rapid and broad-based. In several laboratories TPO was cloned based on it’s binding to the product of the proto-oncogenec-mpl, at the time an orphan cytokine receptor, and was subsequently found to affect nearly all aspects of thrombopoiesis. Many of the molecular pathways that mediate TPO action have been explored. Like all other members of the hematopoietic cytokine receptor family upon hormone binding members of the JAK family of kinases are activated, which, in turn, phosphorylate the TPO receptor, generating docking sites for second messengers that affect multiple signaling pathways. Ultimately, cellular proliferative and anti-apoptotic mechanisms are initiated, increasing MK numbers, and a process termed endomitosis (EnM) begins, generating large, highly polyploid cells. The net result is the expansion of cells that give rise to mature platelets, a process that can be accentuated by the therapeutic administration of the hormone to thrombocytopenic patients.
Variations and expression features of CYP2D6 contribute to schizophrenia risk.

Genome-wide association studies (GWAS) have successfully identified 145 loci implicated in schizophrenia (SCZ). However, the underlying mechanisms remain largely unknown. Here, we analyze 1497 RNA-seq data in combination with their genotype data and identify SNPs that are associated with expression throughout the genome by dissecting expression features to genes (eGene) and exon–exon junctions (eJunction). Then, we colocalize eGene and eJunction with SCZ GWAS using SMR and fine mapping. Multiple ChIP-seq data and DNA methylation data generated from brain were used for identifying the causal variants. Finally, we used a hypothesis-free (no SCZ risk loci considered) enrichment analysis to determine implicated pathways. We identified 171 genes and eight splicing junctions located within four genes (SNX19, ARL6IP4, APOPT1, and CYP2D6) that potentially contribute to SCZ susceptibility. Among the genes, CYP2D6 is significantly associated with SCZ SNPs in eGene and eJunction. In-depth examination of the CYP2D6 region revealed that a nonsynonymous single nucleotide variant rs16947 is strongly associated with a higher abundance of CYP2D6 exon 3 skipping junctions. While we found rs133377 and other functional SNPs in high linkage disequilibrium with rs16947 (r2 = 0.9539), histone acetylation analysis showed they are located within active transcription start sites. Furthermore, our data-driven enrichment analysis showed that CYP2D6 is significantly involved in drug metabolism of codeine, tamoxifen, and citalopram. Our study facilitates an understanding of the genetic architecture of SCZ and provides new drug targets.
Neutron transport criticality calculations using a parallel monolithic multilevel Schwarz preconditioner together with a nonlinear diffusion acceleration method

Abstract The multigroup neutron transport criticality calculations using modern supercomputers have been widely employed in a nuclear reactor analysis for studying whether or not a system is self-sustaining. However, the design and development of efficient parallel algorithms for the transport criticality calculations is challenging especially when the number of processor cores is large and an unstructured mesh is adopted. In particular, both the compute time and the memory usage have to be carefully taken into consideration due to the dimensionality of the neutron transport equations. In this paper, we study a monolithic multilevel Schwarz preconditioner for the transport criticality calculations based on a nonlinear diffusion acceleration (NDA) method. In NDA, the multigroup nonlinear diffusion equations are computed using an inexact Jacobian-free Newton method with an initial guess generated from a few inverse power iterations. The computed scalar fluxes and eigenvalue are used to evaluate the fission and scattering terms of the transport equations, and then the nonlinear system of transport equations is simplified to a linear system of equations. The linear systems of equations arising from the discretizations of the nonlinear diffusion equations and the transport equations need to be efficiently solved. We propose a monolithic multilevel Schwarz method that is capable of efficiently handling the systems of linear equations for both the transport system and the diffusion system. However, in the multilevel method, algebraically constructing coarse spaces is expensive and often unscalable. We study a subspace-based coarsening algorithm to address such a challenge by exploring the matrix structures of the transport equations and the nonlinear diffusion equations. We numerically demonstrate that the monolithic multilevel preconditioner with the subspace-based coarsening algorithm is twice as fast as that equipped with an unmodified coarsening approach on thousands of processor cores for an unstructured mesh neutron transport problem with billions of unknowns.
Chance-constrained Unit Commitment via the Scenario Approach

Keeping the balance between supply and demand is a fundamental task in power system operational planning practices. This task becomes particularly challenging due to the deepening penetration of renewable energy resources, which induces a significant amount of uncertainties. In this paper, we propose a chance-constrained Unit Commitment (c-UC) framework to tackle challenges from uncertainties of renewables. The proposed c-UC framework seeks cost-efficient scheduling of generators while ensuring operation constraints with guaranteed probability. We show that the scenario approach can be used to solve c-UC despite of the non-convexity from binary decision variables. We reveal the salient structural properties of c-UC, which could significantly reduce the sample complexity required by the scenario approach and speed up computation. Case studies are performed on a modified 118-bus system.
Entropy-Regularized Stochastic Games

In zero-sum stochastic games, where two competing players make decisions under uncertainty, a pair of optimal strategies is traditionally described by Nash equilibrium and computed under the assumption that the players have perfect information about the stochastic transition model of the environment. However, implementing such strategies may make the players vulnerable to unforeseen changes in the environment. In this paper, we introduce entropy-regularized stochastic games where each player aims to maximize the causal entropy of its strategy in addition to its expected payoff. The regularization term balances each player’s rationality with its belief about the level of misinformation about the transition model. We consider both entropy-regularized N-stage and entropy-regularized discounted stochastic games, and establish the existence of a value in both games. Moreover, we prove the sufficiency of Markovian and stationary mixed strategies to attain the value, respectively, in N-stage and discounted games. Finally, we present algorithms, which are based on convex optimization problems, to compute the optimal strategies. In a numerical example, we demonstrate the proposed method on a motion planning scenario and illustrate the effect of the regularization term on the expected payoff.
The Role of the Parker Instability in Structuring the Interstellar Medium

The Parker instability, a Rayleigh-Taylor like instability of thermal gas supported against gravity by magnetic fields and cosmic rays, is thought to be dynamically important for galaxy evolution, possibly promoting molecular cloud formation and the galactic dynamo. In previous work, we examined the effect of three different cosmic ray transport models on the Parker instability: decoupled ($\gamma_c = 0$), locked to the thermal gas ($\gamma_c = 4/3$) and coupled to the gas with streaming by self-confinement. We expand upon that work here by considering radiative cooling, a smooth gravitational potential, and simulations into the nonlinear regime. We determine that cosmic ray transport away from compression points, whether by diffusion or streaming, is the largest driver of the instability. Heating due to cosmic ray streaming is also destabilizing and especially affects the nonlinear regime. While cooling de-pressurizes the dense gas, streaming cosmic rays heat and inflate the diffuse extraplanar gas, greatly modifying the phase structure of the medium. In 3D, we find that the fastest growth favors short wavelength modes in the horizontal direction perpendicular to the background magnetic field; this is imprinted on Faraday rotation measure maps that may be used to detect the Parker instability. The modifications to the Parker instability that we observe in this work have large implications for the structure and evolution of galaxies, and they highlight the major role that cosmic rays play in shaping their environments.
Workflow Design Analysis for High Resolution Satellite Image Analysis

Ecological sciences are using imagery from a variety of sources to monitor and survey populations and ecosystems. Very High Resolution (VHR) satellite imagery provide an effective dataset for large scale surveys. Convolutional Neural Networks have successfully been employed to analyze such imagery and detect large animals. As the datasets increase in volume, O(TB), and number of images, O(1k), utilizing High Performance Computing (HPC) resources becomes necessary. In this paper, we investigate a task-parallel data-driven workflows design to support imagery analysis pipelines with heterogeneous tasks on HPC. We analyze the capabilities of each design when processing a dataset of 3,000 VHR satellite images for a total of 4~TB. We experimentally model the execution time of the tasks of the image processing pipeline. We perform experiments to characterize the resource utilization, total time to completion, and overheads of each design. Based on the model, overhead and utilization analysis, we show which design approach to is best suited in scientific pipelines with similar characteristics.