cssi/SciSciGPT-SciSciNet huggingface dataset:
Strategic Learning for Active, Adaptive, and Autonomous Cyber Defense

The increasing instances of advanced attacks call for a new defense paradigm that is active, autonomous, and adaptive, named as the ‘3A’ defense paradigm. This chapter introduces three defense schemes that actively interact with attackers to increase the attack cost and gather threat information, i.e., defensive deception for detection and counter-deception, feedback-driven Moving Target Defense (MTD), and adaptive honeypot engagement. Due to the cyber deception, external noise, and the absent knowledge of the other players’ behaviors and goals, these schemes possess three progressive levels of information restrictions, i.e., from the parameter uncertainty, the payoff uncertainty, to the environmental uncertainty. To estimate the unknown and reduce the uncertainty, we adopt three different strategic learning schemes that fit the associated information restrictions. All three learning schemes share the same feedback structure of sensation, estimation, and actions so that the most rewarding policies get reinforced and converge to the optimal ones in autonomous and adaptive fashions. This work aims to shed lights on proactive defense strategies, lay a solid foundation for strategic learning under incomplete information, and quantify the tradeoff between the security and costs.
Phase transition in random contingency tables with non-uniform margins

For parameters $n,\delta,B,$ and $C$, let $X=(X_{k\ell})$ be the random uniform contingency table whose first $\lfloor n^{\delta} \rfloor $ rows and columns have margin $\lfloor BCn \rfloor$ and the last $n$ rows and columns have margin $\lfloor Cn \rfloor$. For every $0 B_{c}$. Our main result shows that $\mathbb{E}[X_{11}]$ is uniformly bounded for $B B_{c}$. We also establish a strong law of large numbers for the row sums in top right and top left blocks.
Unbundling Corruption: Revisiting Six Questions on Corruption

Corruption is conventionally measured in global indices as a one-dimensional problem—one score for every country—a practice that has profoundly shaped our conceptualization of corruption and its relationship with capitalism. What if we unbundle corruption into qualitatively distinct types and then measure them across countries? How will this approach change our understanding of corruption? This review article serves two purposes. First, it introduces a new framework for “unbundling corruption” into four varieties and highlights their differential economic effects. Based on this typology, I piloted a new cross-national measure of these four varieties of corruption in fifteen countries, using an expert, perceptionbased survey—the Unbundled Corruption Index (UCI). Second, I review six questions on corruption through the lens of unbundling corruption. Shifting our focus of corruption from its aggregated quantity to its quality not only changes our responses to commonly asked questions about corruption, it also prompts new questions.
Residue-Level Allostery Propagates through the Effective Coarse-Grained Hessian

The long-ranged coupling between residues that gives rise to allostery in a protein is built up from short-ranged physical interactions. Computational tools used to predict this coupling and its fu...
Graham's variety and perverse sheaves on the nilpotent cone

Abstract Graham has constructed a variety with a map to the nilpotent cone which is similar in some ways to the Springer resolution. One aspect in which Graham's map differs is that it is not in general an isomorphism over the principal orbit, but rather the universal covering map. This map gives rise to a certain semisimple perverse sheaf on the nilpotent cone, and we discuss here the problem of describing the summands of this perverse sheaf. For type A n , a key tool is a known description of an affine paving of Springer fibers.
Central norepinephrine transmission is required for stress-induced repetitive behavior in two rodent models of obsessive-compulsive disorder.

Obsessive-compulsive disorder (OCD) is characterized by repetitive behaviors exacerbated by stress. Many OCD patients do not respond to available pharmacotherapies, but neurosurgical ablation of the anterior cingulate cortex (ACC) can provide symptomatic relief. Although the ACC receives noradrenergic innervation and expresses adrenergic receptors (ARs), the involvement of norepinephrine (NE) in OCD has not been investigated. To determine the effects of genetic or pharmacological disruption of NE neurotransmission on marble burying (MB) and nestlet shredding (NS), two animal models of OCD. We assessed NE-deficient (Dbh −/−) mice and NE-competent (Dbh +/−) controls in MB and NS tasks. We also measured the effects of anti-adrenergic drugs on NS and MB in control mice and the effects of pharmacological restoration of central NE in Dbh −/− mice. Finally, we compared c-fos induction in the locus coeruleus (LC) and ACC of Dbh −/− and control mice following both tasks. Dbh −/− mice virtually lacked MB and NS behaviors seen in control mice but did not differ in the elevated zero maze (EZM) model of general anxiety-like behavior. Pharmacological restoration of central NE synthesis in Dbh −/− mice completely rescued NS behavior, while NS and MB were suppressed in control mice by anti-adrenergic drugs. Expression of c-fos in the ACC was attenuated in Dbh −/− mice after MB and NS. These findings support a role for NE transmission to the ACC in the expression of stress-induced compulsive behaviors and suggest further evaluation of anti-adrenergic drugs for OCD is warranted.
Panethnicity as a reactive identity: primary panethnic identification among Latino-Hispanics in the United States

Research finds willingness to identify on panethnic terms is increasing among people of Latin American descent in the United States, as is the assertion of Latino-Hispanic panethnicity as a primary...
Penetration of HIV-1 Tat47-57 into PC/PE Bilayers Assessed by MD Simulation and X-Ray Scattering

The interactions of the basic, cell-penetrating region (Y47GRKKRRQRRR57) of the HIV-1 Tat protein with dioleoylphosphatidylcholine (DOPC) bilayers were previously assessed by comparing experimental X-ray diffuse scattering with atomistic molecular dynamics simulations. Here, we extend this investigation by evaluating the influence of phosphatidylethanolamine (PE) lipids. Using experimental bilayer form factors derivedfrom X-ray diffuse scattering data as a guide, our simulations indicate that Tat peptides localize close to the carbonyl-glycerol group in the headgroup region of bilayers composed of either DOPC or DOPC:DOPE (1:1) lipid. Our results also suggest that Tat peptides may more frequently insert into the hydrophobic core of bilayers composed of PC:PE (1:1) lipids than into bilayers composed entirely of PC lipids. PE lipids may facilitate peptide translocation across a lipid bilayer by stabilizing intermediate states in which hydrated peptides span the bilayer.
Interactions Between Connected Half-Sarcomeres Produce Emergent Mechanical Behavior in a Mathematical Model of Muscle

Most reductionist theories of muscle attribute a fiber's mechanical properties to the scaled behavior of a single half-sarcomere. Mathematical models of this type can explain many of the known mechanical properties of muscle but have to incorporate a passive mechanical component that becomes ∼300% stiffer in activating conditions to reproduce the force response elicited by stretching a fast mammalian muscle fiber. The available experimental data suggests that titin filaments, which are the mostly likely source of the passive component, become at most ∼30% stiffer in saturating Ca2+ solutions. The work described in this manuscript used computer modeling to test an alternative systems theory that attributes the stretch response of a mammalian fiber to the composite behavior of a collection of half-sarcomeres. The principal finding was that the stretch response of a chemically permeabilized rabbit psoas fiber could be reproduced with a framework consisting of 300 half-sarcomeres arranged in 6 parallel myofibrils without requiring titin filaments to stiffen in activating solutions. Ablation of inter-myofibrillar links in the computer simulations lowered isometric force values and lowered energy absorption during a stretch. This computed behavior mimics effects previously observed in experiments using muscles from desmin-deficient mice in which the connections between Z-disks in adjacent myofibrils are presumably compromised. The current simulations suggest that muscle fibers exhibit emergent properties that reflect interactions between half-sarcomeres and are not properties of a single half-sarcomere in isolation. It is therefore likely that full quantitative understanding of a fiber's mechanical properties requires detailed analysis of a complete fiber system and cannot be achieved by focusing solely on the properties of a single half-sarcomere.
Price Discrimination in Two-Sided Markets

We examine the profitability and welfare implications of targeted price discrimination (PD) in two-sided markets. First, we show that equilibrium discriminatory prices exhibit novel features relative to discriminatory prices in one-sided models and uniform prices in two-sided models. Second, we compare the profitability of perfect PD, relative to uniform prices in a two-sided market. The conventional wisdom from one-sided horizontally differentiated markets is that PD hurts the firms and benefits consumers, prisoners' dilemma. We show that PD, in a two-sided market, may actually soften the competition. Our results suggest that the conventional advice that PD is good for competition based on one-sided markets may not carry over to two-sided markets.
Testing multifield inflation: A geometric approach

We develop an approach for linking the power spectra, bispectrum, and trispectrum to the geometric and kinematical features of multifield inflationary Lagrangians. Our geometric approach can also be useful in determining when a complicated multifield model can be well approximated by a model with one, two, or a handful of fields. To arrive at these results, we focus on the mode interactions in the kinematical basis, starting with the case of no sourcing and showing that there is a series of mode conservation laws analogous to the conservation law for the adiabatic mode in single-field inflation. We then treat the special case of a quadratic potential with canonical kinetic terms, showing that it produces a series of mode sourcing relations identical in form to that for the adiabatic mode. We build on this result to show that the mode sourcing relations for general multifield inflation are an extension of this special case but contain higher-order covariant derivatives of the potential and corrections from the field metric. In parallel, we show how the mode interactions depend on the geometry of the inflationary Lagrangian and on the kinematics of the associated field trajectory. Finally, we consider how the mode interactions and effective number of fields active during inflation are reflected in the spectra and introduce a multifield consistency relation, as well as a multifield observable ${\ensuremath{\beta}}_{2}$ that can potentially distinguish two-field scenarios from scenarios involving three or more effective fields.
Who Wins in an Energy Boom? Evidence from Wage Rates and Housing

This paper presents evidence on the distributional effects of energy extraction by examining the recent U.S. energy boom. The boom increased local wage rates in almost every major occupational category. The increase occurred regardless of whether the occupation experienced a corresponding change in employment, suggesting a more competitive labor market that benefited local workers. Local housing values and rental prices both increased, thereby benefiting landowners. For renters, the increase in prices was completely offset by a contemporaneous increase in income. The results indicate that bans on drilling have negative monetary consequences for a large share of local residents.
Fifty Shades of Manipulation

Both marketers and politicians are often accused of â€œmanipulationâ€ , but the term is far from self-defining. A statement or action can be said to be manipulative if it does not sufficiently engage or appeal to peopleâ€™s capacity for reflective and deliberative choice. One problem with manipulation, thus understood, is that it fails to respect peopleâ€™s autonomy and is an affront to their dignity. Another problem is that if they are products of manipulation, peopleâ€™s choices might fail to promote their own welfare, and might instead promote the welfare of the manipulator. To that extent, the central objection to manipulation is rooted in a version of John Stuart Millâ€™s Harm Principle: People know what is in their best interests and should have a (manipulationfree) opportunity to make that decision. On welfarist grounds, the norm against manipulation can be seen as a kind of heuristic, one that generally works well, but that can also lead to serious errors, at least when the manipulator is both informed and genuinely interested in the welfare of the chooser. For politics and law, a pervasive puzzle is why manipulation is rarely policed. The simplest answer is that manipulation has so many shades, and in a social order that values-free markets and consumer sovereignty, it is exceptionally difficult to regulate manipulation as such. Those who sell products are often engaged in at least arguable forms of manipulation. But as the manipulatorâ€™s motives become more self-interested or venal, and as efforts to bypass peopleâ€™s deliberative capacities become more successful, the ethical objections to manipulation may be very forceful, and the argument for a legal response is fortified. The analysis of manipulation bears on emerging free speech issues raised by compelled disclosure, especially in the context of graphic health warnings. It can also help orient the regulation of financial products, where manipulation of consumer choices is an evident but rarely explicit concern.
Serial Analysis of Gene Expression (SAGE)

This unit provides a protocol for performing serial analysis of gene expression (SAGE). SAGE involves the generation of short fragments of DNA, or tags, from a defined point in the sequence of all cDNAs in the sample analyzed. This short tag, because of its presence in a defined point in the sequence, is typically sufficient to uniquely identify every transcript in the sample. SAGE allows one to generate a comprehensive profile of gene expression in any sample desired from as little as 100,000 cells or 1 ug of total RNA. SAGE generates absolute, rather than relative, measurements of RNA abundance levels, and this fact allows an investigator to readily and reliably compare data to those produced by other laboratories, making the SAGE data set increasingly useful as more data is generated and shared.
Chapter 4 – Beyond the Basics

Publisher Summary This chapter discusses applying the basic model for programming with sockets in Java in various programming models, such as multitasking, nonblocking I/O, and broadcasting. Java threadsa convenient mechanismservers to handle many clients simultaneously. Using threads, a single application can work on several tasks concurrently, as if multiple copies of the Java Virtual Machine were running. (In reality, a single copy of the JVM is shared or multiplexed among the different threads.) In the echo server, one can give responsibility for each client to an independently executing thread. It describes two approaches to coding concurrent servers, namely, threadper-client, where a new thread is spawned to handle each client connection, and thread pool, where connections are assigned to a prespawned set of threads. It also describes the built-in Java facilities that simplify the use of these strategies for multithreaded servers., it explores the various blocking methods and approaches for limiting blocking behavior.
Predicting Soybean Yield with NDVI Using a Flexible Fourier Transform Model

We use models incorporating the normalized difference vegetation index (NDVI) derived from remote sensing satellites to improve soybean yield predictions in 10 major producing states in the United States. Unlike traditional methods that assume an ordinary least squares model applies to all observations, we allow for global flexibility in the relationship between NDVI and soybean yield by using the flexible Fourier transform (FFT) model. FFT results confirm that there is a nonlinear response of soybean yield to NDVI over the growing season. Out-of-sample predictions indicate that allowing for global flexibility with the FFT improves the predictions in time-series prediction and forecasting.
FORTRAN Codes to Fit Curves Interactively

In the past, data fitting has usually involved a computer program for the computations, and plotting the fit (usually by hand) to be sure of a "good" fit.
Photochemical Donor Pretreatment in Clinical Kidney Transplantation—Preliminary Report

Extended experimental experience with the efficiacy of pretreating the kidney donor and the allograft by means of photochemotherapy (photosensitizer+UVA irradiation=PUVA) was adopted in clinical kidney transplantation. In a preliminary unrandomized study similar patient populations were treated by generally uniform methods. Thirty-three PUVA-pretreated kidneys (group A) were compared with the experience regarding 26 nonpretreated kidney allografts (group B). The number of rejection episodes was significantly lower in the first 3 months in group A (p< 0.05 vs group B) and fewer grafts failed because of irreversible rejection (2 vs 5). Furthermore, in group A the rate of infectious complications was lower (18% vs 34%). The cumulative allograft survival at 3 months was improved from 65% in group B to 81% in group A and at 12 months from 65% 76%, respectively. These differences were not significant. Therefore, our preliminary clinical experience with a photochemical donor pretreatment is encouraging and further use in a randomized study seems to be necessary.
Enriching hippocampal memory function in older adults through video games

Healthy aging is accompanied by a steady cognitive decline with clear losses in memory. Animal studies have consistently demonstrated that simply modifying an animal's living environment (known as environmental enrichment) can have a positive influence on age-related cognitive decline in the hippocampus. Previously, we showed that playing immersive 3D video games can improve hippocampal-based memory in young healthy adults, suggesting that the exploration of the large open worlds of modern-day video games may act as proxy for environmental enrichment in humans. Here, we replicated our previous video game study in healthy older adults, showing that playing video games for four weeks can improve hippocampal-based memory in a population that is already experiencing age-related decline in memory. Furthermore, we showed that the improvements last for up to four weeks past the intervention, highlighting the potential of video games as intervention for age-related cognitive decline.
Estimation of common change point and isolation of changed panels after sequential detection

Quick detection of common changes is critical in sequential monitoring of multistream data where a common change is a change that only occurs in a portion of panels. After a common change is detect...
Asymptomatic Seroconversion of Immunoglobulins to SARS-CoV-2 in a Pediatric Dialysis Unit.

This case series describes subclinical development of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in some patients and health care workers in a pediatric dialysis unit after contact with a seropositive patient.
Enhancing the Antiviral Efficacy of RNA-Dependent RNA Polymerase Inhibition by Combination with Modulators of Pyrimidine Metabolism.

Summary Genome-wide analysis of the mode of action of GSK983, a potent antiviral agent, led to the identification of dihydroorotate dehydrogenase as its target along with the discovery that genetic knockdown of pyrimidine salvage sensitized cells to GSK983. Because GSK983 is an ineffective antiviral in the presence of physiological uridine concentrations, we explored combining GSK983 with pyrimidine salvage inhibitors. We synthesized and evaluated analogs of cyclopentenyl uracil (CPU), an inhibitor of uridine salvage. We found that CPU was converted into its triphosphate in cells. When combined with GSK983, CPU resulted in large drops in cellular UTP and CTP pools. Consequently, CPU-GSK983 suppressed dengue virus replication in the presence of physiological concentrations of uridine. In addition, the CPU-GSK983 combination markedly enhanced the effect of RNA-dependent RNA polymerase (RdRp) inhibition on viral infection. Our findings highlight a new host-targeting strategy for potentiating the antiviral activity of RdRp inhibitors.
On parametrized families of numerical semigroups

A numerical semigroup is an additive subsemigroup of the non-negative integers. In this article, we consider parametrized families of numerical semigroups of the form Pn=〈f1(n),…,fk(n)〉 for polynom...
Mutation timing in a spatial model of evolution

Abstract Motivated by models of cancer formation in which cells need to acquire k mutations to become cancerous, we consider a spatial population model in which the population is represented by the d -dimensional torus of side length L . Initially, no sites have mutations, but sites with i − 1 mutations acquire an i th mutation at rate μ i per unit area. Mutations spread to neighboring sites at rate α , so that t time units after a mutation, the region of individuals that have acquired the mutation will be a ball of radius α t . We calculate, for some ranges of the parameter values, the asymptotic distribution of the time required for some individual to acquire k mutations. Our results, which build on previous work of Durrett, Foo, and Leder, are essentially complete when k = 2 and when μ i = μ for all i .
Local Correlation Clustering with Asymmetric Classification Errors

In the Correlation Clustering problem, we are given a complete weighted graph $G$ with its edges labeled as "similar" and "dissimilar" by a noisy binary classifier. For a clustering $\mathcal{C}$ of graph $G$, a similar edge is in disagreement with $\mathcal{C}$, if its endpoints belong to distinct clusters; and a dissimilar edge is in disagreement with $\mathcal{C}$ if its endpoints belong to the same cluster. The disagreements vector, $\text{dis}$, is a vector indexed by the vertices of $G$ such that the $v$-th coordinate $\text{dis}_v$ equals the weight of all disagreeing edges incident on $v$. The goal is to produce a clustering that minimizes the $\ell_p$ norm of the disagreements vector for $p\geq 1$. We study the $\ell_p$ objective in Correlation Clustering under the following assumption: Every similar edge has weight in the range of $[\alpha\mathbf{w},\mathbf{w}]$ and every dissimilar edge has weight at least $\alpha\mathbf{w}$ (where $\alpha \leq 1$ and $\mathbf{w}>0$ is a scaling parameter). We give an $O\left((\frac{1}{\alpha})^{\frac{1}{2}-\frac{1}{2p}}\cdot \log\frac{1}{\alpha}\right)$ approximation algorithm for this problem. Furthermore, we show an almost matching convex programming integrality gap.
Full Law Identification In Graphical Models Of Missing Data: Completeness Results

Missing data has the potential to affect analyses conducted in all fields of scientific study including healthcare, economics, and the social sciences. Several approaches to unbiased inference in the presence of non-ignorable missingness rely on the specification of the target distribution and its missingness process as a probability distribution that factorizes with respect to a directed acyclic graph. In this paper, we address the longstanding question of the characterization of models that are identifiable within this class of missing data distributions. We provide the first completeness result in this field of study - necessary and sufficient graphical conditions under which, the full data distribution can be recovered from the observed data distribution. We then simultaneously address issues that may arise due to the presence of both missing data and unmeasured confounding, by extending these graphical conditions and proofs of completeness, to settings where some variables are not just missing, but completely unobserved.
Supervised Grapheme-to-Phoneme Conversion of Orthographic Schwas in Hindi and Punjabi

Hindi grapheme-to-phoneme (G2P) conversion is mostly trivial, with one exception: whether a schwa represented in the orthography is pronounced or unpronounced (deleted). Previous work has attempted to predict schwa deletion in a rule-based fashion using prosodic or phonetic analysis. We present the first statistical schwa deletion classifier for Hindi, which relies solely on the orthography as the input and outperforms previous approaches. We trained our model on a newly-compiled pronunciation lexicon extracted from various online dictionaries. Our best Hindi model achieves state of the art performance, and also achieves good performance on a closely related language, Punjabi, without modification.
The Rapid Transformation of Cardiac Surgery Practice in the Coronavirus Disease 2019 (COVID-19) Pandemic: Insights and Clinical Strategies From a Center at the Epicenter

Background The onset of the coronavirus disease 2019 (COVID-19) pandemic has forced our cardiac surgery program and hospital to enact drastic measures that has forced us to change how we care for cardiac surgery patients, assist with COVID-19 care, and enable support for the hospital in terms of physical resources, providers, and resident training. Methods In this review, we review the cardiovascular manifestations of COVID-19 and describe our system-wide adaptations to the pandemic, including the use of telemedicine, how a severe reduction in operative volume affected our program, the process of redeployment of staff, repurposing of residents into specific task teams, the creation of operation room intensive care units, and the challenges that we faced in this process. Results We offer a revised set of definitions of surgical priority during this pandemic and how this was applied to our system, followed by specific considerations in coronary/valve, aortic, heart failure and transplant surgery. Finally, we outline a path forward for cardiac surgery for the near future. Conclusions We recognize that individual programs around the world will eventually face COVID-19 with varying levels of infection burden and different resources, and we hope this document can assist programs to plan for the future.
3D fault architecture controls the dynamism of earthquake swarms

The vibrant evolutionary patterns made by earthquake swarms are incompatible with standard, effectively two-dimensional (2D) models for general fault architecture. We leverage advances in earthquake monitoring with a deep-learning algorithm to image a fault zone hosting a 4-year-long swarm in southern California. We infer that fluids are naturally injected into the fault zone from below and diffuse through strike-parallel channels while triggering earthquakes. A permeability barrier initially limits up-dip swarm migration but ultimately is circumvented. This enables fluid migration within a shallower section of the fault with fundamentally different mechanical properties. Our observations provide high-resolution constraints on the processes by which swarms initiate, grow, and arrest. These findings illustrate how swarm evolution is strongly controlled by 3D variations in fault architecture.
GPU-Accelerated Computation of Vietoris-Rips Persistence Barcodes.

The computation of Vietoris-Rips persistence barcodes is both execution-intensive and memory-intensive. In this paper, we study the computational structure of Vietoris-Rips persistence barcodes, and identify several unique mathematical properties and algorithmic opportunities with connections to the GPU. Mathematically and empirically, we look into the properties of apparent pairs, which are independently identifiable persistence pairs comprising up to 99% of persistence pairs. We give theoretical upper and lower bounds of the apparent pair rate and model the average case. We also design massively parallel algorithms to take advantage of the very large number of simplices that can be processed independently of each other. Having identified these opportunities, we develop a GPU-accelerated software for computing Vietoris-Rips persistence barcodes, called Ripser++. The software achieves up to 30x speedup over the total execution time of the original Ripser and also reduces CPU-memory usage by up to 2.0x. We believe our GPU-acceleration based efforts open a new chapter for the advancement of topological data analysis in the post-Moore’s Law era.
Multiple working hypotheses for technology analysis

Abstract. Technology analysis focuses on technology that is a complex system formed by different elements given by incremental and radical innovations to satisfy needs, achieve goals and/or solve problems of users to take advantage of important opportunities or to cope with consequential environmental threats. This study suggests a methods of inquiry, called multiple working hypotheses (MWHs), for technology analysis that consider the development, prior to research, of different hypotheses concerning the origin and evolution of technology, which are likely due to several causes, not just one. The MWHs presented here are categorized in traditional hypotheses, such as demand for technology hypothesis, Induced-innovation hypothesis, learning by doing hypothesis, learning via diffusion hypothesis, specialization via scale hypothesis, disadvantage of beginning hypothesis, path-dependence hypothesis, competitive substitution hypothesis, predator-prey hypothesis, and modern hypotheses such as killer technology hypothesis, parasite technologies hypothesis. Scholars of technology studies should consider all suggested hypotheses for technology analysis, also considering the possibility that none of them are correct and that some new explanations may emerge in more and more complex and turbulent environment. Keywords. Technology, Technological innovation, Technology analysis, Induced innovation, Learning by doing, Technological evolution, Nature of technology, Path dependence, Technological change, Technological progress, Technological parasitism, Technological advances, Killer technology, Evolution of technology, Multiple working hypotheses. JEL. O30, O31, O33.
Non-Intrusive Parametric Model Order Reduction with Error Correction Modeling for Changing Well Locations Using a Machine Learning Framework

The objective of this paper is to develop a global non-intrusive Parametric Model Order Reduction (PMOR) methodology for the problem of changing well locations in an oil field, that can eventually be used for well placement optimization to gain significant computational savings. In this work, we propose a proper orthogonal decomposition (POD) based PMOR strategy that is non-intrusive to the simulator source code and hence extends its applicability to any commercial simulator. The non-intrusiveness of the proposed technique stems from formulating a novel Machine Learning (ML) based framework used with POD. The features of ML model are designed such that they take into consideration the temporal evolution of the state solutions and thereby avoiding simulator access for time dependency of the solutions. We represent well location changes as a parameter by introducing geometry-based features and flow diagnostics inspired physics-based features. An error correction model based on reduced model solutions is formulated later to correct for discrepancies in the state solutions at well gridblocks. It was observed that the global PMOR could predict the overall trend in pressure and saturation solutions at the well blocks but some bias was observed that resulted in discrepancies in prediction of quantities of interest (QoI). Thus, the error correction model that considers the physics based reduced model solutions as features, proved to reduce the error in QoI significantly. This workflow is applied to a heterogeneous channelized reservoir that showed good solution accuracies and speed-ups of 50x-100x were observed for different cases considered. The method is formulated such that all the simulation time steps are independent and hence can make use of parallel resources very efficiently and also avoid stability issues that can result from error accumulation over timesteps.
The effects of information overload on online conversation dynamics

The inhibiting effects of information overload on the behavior of online social media users, can affect the population-level characteristics of information dissemination through online conversations. We introduce a mechanistic, agent-based model of information overload and investigate the effects of information overload threshold and rate of information loss on observed online phenomena. We find that conversation volume and participation are lowest under high information overload thresholds and mid-range rates of information loss. Calibrating the model to user responsiveness data on Twitter, we replicate and explain several observed phenomena: (1) Responsiveness is sensitive to information overload threshold at high rates of information loss; (2) Information overload threshold and rate of information loss are Pareto-optimal and users may experience overload at inflows exceeding 30 notifications per hour; (3) Local abundance of small cascades of modest global popularity and local scarcity of larger cascades of high global popularity explains why overloaded users receive, but do not respond to large, highly popular cascades; 4) Users typically work with 7 notifications per hour; 5) Over-exposure to information can suppress the likelihood of response by overloading users, contrary to analogies to biologically-inspired viral spread. Reconceptualizing information spread with the mechanisms of information overload creates a richer representation of online conversation dynamics, enabling a deeper understanding of how (dis)information is transmitted over social media.
Conjectures on optimal nested generalized group testing algorithm

Consider a finite population of $N$ items, where item $i$ has a probability $p_i$ to be defective. The goal is to identify all items by means of group testing. This is the generalized group testing problem (hereafter GGTP). In the case of $\displaystyle p_1=\cdots=p_{N}=p$ \cite{YH1990} proved that the pairwise testing algorithm is the optimal nested algorithm, with respect to the expected number of tests, for all $N$ if and only if $\displaystyle p \in [1-1/\sqrt{2},\,(3-\sqrt{5})/2]$ (R-range hereafter) (an optimal at the boundary values). In this note, we present a result that helps to define the generalized pairwise testing algorithm (hereafter GPTA) for the GGTP. We present two conjectures: (1) when all $p_i, i=1,\ldots,N$ belong to the R-range, GPTA is the optimal procedure among nested procedures applied to $p_i$ of nondecreasing order; (2) if all $p_i, i=1,\ldots,N$ belong to the R-range, GPTA the optimal nested procedure, i.e., minimises the expected total number of tests with respect to all possible testing orders in the class of nested procedures. Although these conjectures are logically reasonable, we were only able to empirically verify the first one up to a particular level of $N$. We also provide a short survey of GGTP.
Performance satisfaction in Harpy, a thruster-assisted bipedal robot

We will report our efforts in designing feedback for the thruster-assisted walking of a bipedal robot. We will assume for well-tuned supervisory controllers and will focus on fine-tuning the desired joint trajectories to satisfy the performance being sought. In doing this, we will devise an intermediary filter based on the emerging idea of reference governors. Since these modifications and impact events lead to deviations from the desired periodic orbits, we will guarantee hybrid invariance in a robust fashion by applying predictive schemes within a short time envelope during the double support phase of a gait cycle. To achieve the hybrid invariance, we will leverage the unique features in our robot, i.e., the thruster.
Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems.

In this paper, we propose a data-driven approach for uncertainty propagation and reachability analysis in a dynamical system. The proposed approach relies on the linear lifting of a nonlinear system using linear Perron-Frobenius (P-F) and Koopman operators. The uncertainty can be characterized in terms of the moments of a probability density function. We demonstrate how the P-F and Koopman operators are used for propagating the moments. Time-series data is used for the finite-dimensional approximation of the linear operators, thereby enabling data-driven approach for moment propagation. Simulation results are presented to demonstrate the effectiveness of the proposed method.
Co-designed Land-use Scenarios and their Implications for Storm Runoff and Streamflow in New England

Landscape and climate changes have the potential to create or exacerbate problems with stormwater management, high flows, and flooding. In New England, four plausible land-use scenarios were co-developed with stakeholders to give insight to the effects on ecosystem services of different trajectories of socio-economic connectedness and natural resource innovation. With respect to water, the service of greatest interest to New England stakeholders is the reduction of stormwater and flooding. To assess the effects of these land-use scenarios, we applied the Soil and Water Assessment Tool to two watersheds under two climates. Differences in land use had minimal effects on the water balance but did affect high flows and the contribution of storm runoff to streamflow. For most scenarios, the effect on high flows was small. For one scenario-envisioned to have global socio-economic connectedness and low levels of natural resource innovation-growth in impervious areas increased the annual maximum daily flow by 10%, similar to the 5-15% increase attributable to climate change. Under modest population growth, land-use decisions have little effect on storm runoff and high flows; however, for the two scenarios characterized by global socio-economic connectedness, differences in choices regarding land use and impervious area have a large impact on the potential for flooding. Results also indicate a potential interaction between climate and land use with a shift to more high flows resulting from heavy rains than from snowmelt. These results can help inform land use and development, especially when combined with assessments of effects on other ecosystem services.
Generic Constructions and Semidualizing Modules

We investigate some general machinery for describing semidualizing modules over generic constructions like ladder determinantal rings with coefficients in a normal domain. We also pose and investigate natural localization questions that arise in the process.
Functional Genomics and Molecular Analysis of a Subtropical Harmful Algal Bloom Species, Karenia brevis

Functional genomics is an attempt not only to identify the complement of genes that a particular organism contains in its genome, but also to understand how the expression of those genes are regulated and how the gene products interact to produce the biology associated with an organism. Genomics-based studies initially focused on biomedically important model organisms, but now they are being applied to a myriad of organisms for a multitude of reasons. The study of dinoflagellates is now benefiting from these techniques and analyses. This article describes some new findings surrounding the molecular nature of a particular dinoflagellate, Karenia brevis, which is endemic to the Gulf of Mexico and causes near-yearly harmful algal blooms to one or more coastlines within the Gulf. Four independent expressed sequence tag (EST) libraries have been constructed for this dinoflagellate creating a large genomics resource for investigating facets of this organism's biology such as why and how it forms blooms, how it produces toxins, how it regulates its growth, and how it interacts with its environment. Some preliminary work is highlighted that shows how the organism may be sensing its environment and regulating its gene expression and growth accordingly.
Robust Gaussian Joint Source-Channel Coding Under the Near-Zero Bandwidth Regime

Minimum power required to achieve a distortion-noise profile, i.e., a function indicating the maximum allowed distortion value for each noise level, is studied for the transmission of Gaussian sources over Gaussian channels under a regime of bandwidth approaching zero. A simple but instrumental lower bound to the minimum required power for a given profile is presented. For an upper bound, a dirty-paper based coding scheme is proposed and its power-distortion tradeoff is analyzed. Finally, upper and lower bounds to the minimum power is analyzed and compared for specific distortion-noise profiles, namely rational profiles with order one and two.
A random covariance model for bi‐level graphical modeling with application to resting‐state fMRI data

We consider a novel problem, bi-level graphical modeling, in which multiple individual graphical models can be considered as variants of a common group-level graphical model and inference of both the group- and individual-level graphical models is of interest. Such a problem arises from many applications, including multi-subject neuro-imaging and genomics data analysis. We propose a novel and efficient statistical method, the random covariance model, to learn the group- and individual-level graphical models simultaneously. The proposed method can be nicely interpreted as a random covariance model that mimics the random effects model for mean structures in linear regression. It accounts for similarity between individual graphical models, identifies group-level connections that are shared by individuals, and simultaneously infers multiple individual-level networks. Compared to existing multiple graphical modeling methods that only focus on individual-level graphical modeling, our model learns the group-level structure underlying the multiple individual graphical models and enjoys computational efficiency that is particularly attractive for practical use. We further define a measure of degrees-of-freedom for the complexity of the model useful for model selection. We demonstrate the asymptotic properties of our method and show its finite-sample performance through simulation studies. Finally, we apply the method to our motivating clinical data, a multi-subject resting-state functional magnetic resonance imaging dataset collected from participants diagnosed with schizophrenia, identifying both individual- and group-level graphical models of functional connectivity.
Hyperbolic boundary problems with large oscillatory coefficients: Multiple amplification

Abstract We study weakly stable hyperbolic boundary problems with highly oscillatory coefficients that are large, O ( 1 ) , compared to the small wavelength ϵ of oscillations. Such problems arise, for example, in the study of classical questions concerning the stability of Mach stems and compressible vortex sheets. For such applications one seeks to prove energy estimates that are in an appropriate sense “uniform” with respect to the small wavelength ϵ, but the large oscillatory coefficients are a formidable obstacle to obtaining such estimates. In this paper we analyze a simplified form of the linearized problems that are relevant to the above stability questions, and obtain results that are both positive and negative. On the one hand we identify favorable structural conditions under which it is possible to prove uniform estimates, and then do so by a new approach. We also construct examples showing that large oscillatory coefficients can give rise to an instantaneous multiple amplification of the amplitude of solutions relative to data; for example, oscillatory boundary data of a given amplitude O ( 1 ) can immediately give rise to a solution of amplitude O ( 1 ϵ K ) , where K ≥ 2 . 1 We use the examples of multiple amplification to confirm the optimality of our uniform estimates when the favorable structural conditions hold. When those conditions do not hold, we explain how multiple amplification of infinite order may rule out useful estimates.
Forked Grating Coupler Optical Vortex beam interface for silicon photonics

The Forked Grating Coupler (FGC) is a novel silicon photonics device that can interface with Optical Vortex (OV) light beams. The FGC combines the idea of an apodized Bragg grating coupler with a forked hologram, creating a device that can efficiently couple selected OV modes at near-normal incidence into confined dielectric waveguide modes of a photonic IC. FGCs retain many of the advantages of grating couplers, including convenient chip placement, simple fabrication, reasonable bandwidth, small size, and process compatibility.
Reducing Surface Wetness Leads to Tropical Hydrological Cycle Regime Transition

The modern "wet" tropics are dominated by the Intertropical Convergence Zone, however "dry" tropics likely occurred in Earth's history. It is unclear how the tropics change between wet and dry clim...
Identifying external debt shocks in low- and middle-income countries

Abstract Using a unique loan-level dataset from the World Bank’s Debtor Reporting System, we construct new measures of external debt shocks for 120 low- and middle-income countries during the 1975–2018 period. We identify the shock in two steps by first calculating the difference between actual and predicted net disbursement on external debt obligation for each loan and then taking aggregation at the country-year level. During expansionary times, external debt shocks lead to persistent decreases in the external debt to GDP ratio, possibly due to the availability of other sources of financing. During recessionary episodes, however, we see heavy reliance on external debt financing for most of developing countries. This reliance is more substantial for countries with higher levels of external debt stock, raising serious concerns for debt distress in these countries and in their road to building resilience.
PST900: RGB-Thermal Calibration, Dataset and Segmentation Network

In this work we propose long wave infrared (LWIR) imagery as a viable supporting modality for semantic segmentation using learning-based techniques. We first address the problem of RGB-thermal camera calibration by proposing a passive calibration target and procedure that is both portable and easy to use. Second, we present PST900, a dataset of 894 synchronized and calibrated RGB and Thermal image pairs with per pixel human annotations across four distinct classes from the DARPA Subterranean Challenge. Lastly, we propose a CNN architecture for fast semantic segmentation that combines both RGB and Thermal imagery in a way that leverages RGB imagery independently. We compare our method against the state-of-the-art and show that our method outperforms them in our dataset.