cssi/SciSciGPT-SciSciNet huggingface dataset:
A pharmacokinetic model of lead absorption and calcium competitive dynamics.

Lead is a naturally-occurring element. It has been known to man for a long time, and it is one of the longest established poisons. The current consensus is that no level of lead exposure should be deemed “safe”. New evidence regarding the blood levels at which morbidities occur has prompted the CDC to reduce the screening guideline of 10 μg/dl to 2 μg/dl. Measurable cognitive decline (reduced IQ, academic deficits) have been found to occur at levels below 10 μg/dl, especially in children. Knowledge of lead pharmacology allows us to better understand its absorption and metabolization, mechanisms that produce its medical consequences. Based upon an original and very simplified compartmental model of Rabinowitz (1973) with only three major compartments (blood, bone and soft tissue), extensive biophysical models sprouted over the following two decades. However, none of these models have been specifically designed to use new knowledge of lead molecular dynamics to understand its deleterious effects on the brain. We build and analyze a compartmental model of lead pharmacokinetics, focused specifically on addressing neurotoxicity. We use traditional phase space methods, parameter sensitivity analysis and bifurcation theory to study the transitions in the system’s behavior in response to various physiological parameters. We conclude that modeling the complex interaction of lead and calcium along their dynamic trajectory may successfully explain counter-intuitive effects on systemic function and neural behavior which could not be addressed by existing linear models. Our results encourage further efforts towards using nonlinear phenomenology in conjunction with empirically driven system parameters, to obtain a biophysical model able to provide clinical assessments and predictions.
The role of vasodilating β-blockers in patients with complicated hypertension: focus on nebivolol

Despite their proven mortality and morbidity outcomes benefits, β-blockers remain substantially underused in patients with cardiac conditions. Reluctance to prescribe β-blockers may be owing to concerns about tolerability with the traditional drugs in this class. β-Blockers with vasodilatory properties, such as carvedilol and nebivolol, may overcome the tolerability and metabolic issues associated with traditional β-blockers. Because endothelial dysfunction, the pathophysiologic hallmark of hypertension, may be heightened in populations with difficult-to-treat hypertension (e.g., elderly patients, African American patients), a vasodilating β-blocker may be a particularly appropriate choice for these patient groups.
Ethical Issues in Using Statistics, Statistical Methods, and Statistical Sources in Work Related to Homeland Security†

The article emphasizes that there are distinctive ethical, as well as scientific and legal, issues involved in deploying statistical methods and data for homeland security, even as we note the overlaps between science and ethics on the one hand and law and ethics on the other hand. The article discusses (a) ethical challenges that arise from using inappropriate methods, whether they are “quick-fix” shortcuts and approximations or elaborate models; (b) the ethical imperative to avoid causing harm to innocent people embodied in the need to respect the principle of statistical confidentiality and the need to take into account the intermediary and long-term substantive objectives of any risk assessment; and (c) the common ethical failure involved in using only the technical indicator, “risk of disclosure”, rather than its ethically informed counterpart “the harm arising, given the disclosure”, in the assessment of the efficacy alternative approaches. The special ethical challenges that arise when data systems are used to target individuals or population subgroups based on racial, ethnic, religious, or similar attributes are also discussed. The article concludes by stressing the importance of establishing a mechanism for appropriate ethical review and oversight lest, in our untempered zeal for the value of the data and methods we employ or the important objectives of work on homeland security, we become complicit in harming innocent individuals or vulnerable population subgroups. Keywords: census; counterterrorism; data mining; Department of Homeland Security (DHS); federal statistical system; microdata; mesodata; population registration; statistical confidentiality; targeting
Whither the Regulatory "War on Coal"? Scapegoats, Saviors, and Stock Market Reactions

Complaints about excessive economic burdens associated with regulation abound in contemporary political and legal rhetoric. In recent years, perhaps nowhere have these complaints been heard as loudly as in the context of U.S. regulations targeting the use of coal to supply power to the nation’s electricity system, as production levels in the coal industry dropped by nearly half between 2008 and 2016. The coal industry and its political supporters, including the president of the United States, have argued that a suite of air pollution regulations imposed by the U.S. Environmental Protection Agency during the Obama administration seriously undermined coal companies’ bottom lines, presenting an existential threat to the industry. Under the Trump administration, industry players have lobbied hard for (and sometimes received) financial subsidies and regulatory changes, with the president seemingly all too happy to play the role of the industry’s savior. Stepping back, we consider the extent to which regulations have really led to the decline in demand for coal and how much the coal industry can actually expect to gain from the deregulatory policies of the current administration. To illuminate these questions, we statistically analyze stock market reactions to important events in what critics called the regulatory “war on coal” during the Obama administration. Using an event-study framework that measures abnormal market activity in the immediate wake of these events, we are able to isolate any potential impact of regulatory developments above and beyond market factors, such as secular trends in natural gas prices and market performance as a whole. Surprisingly, we find no systemic evidence consistent with a “war on coal” based on investor assessments of the industry’s financial prospects in the wake of new regulatory developments, even though our methods do find evidence of stock market reactions to other events, such as bankruptcies of other companies. Coal firms’ investors—the very actors with financial stakes in understanding the impact of regulation on the industry—appear to have behaved as if they never actually bought into the regulatory “war on coal” narrative. Our findings are consistent both with broader evidence about the effects of regulation and with an underlying political economy of regulatory scapegoating, according to which actors in a declining industry prefer to blame regulation rather than competitive factors for their businesses’ decline. By calling attention to the pervasive incentives for scapegoating and cheap talk by politicians seeking to be saviors, we offer an account that can explain the mismatch between our findings and the rhetoric of the “war on coal.” Along the way, our account reinforces how important it is for courts, elected officials, and the public to demand that government agencies base their regulatory decisions on evidence instead of relying on political rhetoric.
Quantum many-body scars from virtual entangled pairs

Quantum many-body scarring is a recently discovered weak ergodicity-breaking phenomenon in quantum many-body systems. It was first seen in experiments with Rydberg atom array simulators, and characterized by nonthermalizing periodic revivals of certain initial-state configurations. Here, the authors extend our understanding of the physical origins of this phenomenon by showing through an explicit construction that scars can arise through the presence of entangled virtual degrees of freedom undergoing periodic dynamics. This demonstrates a novel analytic scenario, whereby intrinsically entangled states can exhibit periodic revivals in an otherwise chaotic many-body system, enriching the phenomenology of quantum many-body scarring.
Lifelong learning in costly feature spaces

Abstract An important long-term goal in machine learning systems is to build learning agents that, like humans, can learn many tasks over their lifetime, and moreover use information from these tasks to improve their ability to do so efficiently. In this work, our goal is to provide new theoretical insights into the potential of this paradigm. In particular, we propose a lifelong learning framework that adheres to a novel notion of resource efficiency that is critical in many real-world domains where feature evaluations are costly. That is, our learner aims to reuse information from previously learned related tasks to learn future tasks in a feature-efficient manner. Furthermore, we consider novel combinatorial ways in which learning tasks can relate. Specifically, we design lifelong learning algorithms for two structurally different and widely used families of target functions: decision trees/lists and monomials/polynomials. We also provide strong feature-efficiency guarantees for these algorithms; in fact, we show that in order to learn future targets, we need only slightly more feature evaluations per training example than what is needed to predict on an arbitrary example using those targets. We also provide algorithms with guarantees in an agnostic model where not all the targets are related to each other. Finally, we also provide lower bounds on the performance of a lifelong learner in these models, which are in fact tight under some conditions.
Field-of-values analysis of preconditioned linearized Rayleigh–Bénard convection problems

Abstract In this paper we use the notion of field-of-values (FOV) equivalence of matrices to study a class of block-triangular preconditioners for the fixed-point linearization of the Rayleigh–Benard convection problem discretized with inf–sup stable finite element spaces. First, sufficient conditions on the nondimensional parameters of the problem are determined in order to establish the FOV-equivalence between the system matrix and the preconditioners. Four upper triangular block preconditioners belonging to the general proposed class are then considered. Numerical experiments show that the Generalized Minimal Residual (GMRES) convergence is robust with respect to the mesh size for these preconditioned systems. We also compare the performance of the different preconditioners in terms of computational time.
Functional and Comparative Genomic Analysis of Integrated Prophage-Like Sequences in " Candidatus Liberibacter asiaticus"

Huanglongbing disease (HLB; yellow shoot disease) is a severe worldwide infectious disease for citrus family plants. The pathogen "Candidatus Liberibacter asiaticus" is an alphaproteobacterium of the Rhizobiaceae family that has been identified as the causative agent of HLB. The virulence of "Ca. Liberibacter asiaticus" has been attributed, in part, to prophage-carried genes. Prophage and prophage-like elements have been identified in 12 of the 15 available "Ca. Liberibacter asiaticus" genomes and are classified into three prophage types. Here, we reexamined all 15 "Ca. Liberibacter asiaticus" genomes using a de novo prediction approach and expanded the number of prophage-like elements from 16 to 33. Further, we found that all of the "Ca. Liberibacter asiaticus" genomes contained at least one prophage-like sequence. Comparative analysis revealed a prevalent, albeit previously unknown, prophage-like sequence type that is a remnant of an integrated prophage. Notably, this remnant prophage is found in the Ishi-1 "Ca. Liberibacter asiaticus" strain that had previously been reported as lacking prophages. Our findings provide both a resource for data and new insights into the evolutionary relationship between phage and "Ca. Liberibacter asiaticus" pathogenicity.IMPORTANCE Huanglongbing (HLB) disease is threatening citrus production worldwide. The causative agent is "Candidatus Liberibacter asiaticus." Prior work using mapping-based approaches identified prophage-like sequences in some "Ca. Liberibacter asiaticus" genomes but not all. Here, we utilized a de novo approach that expands the number of prophage-like elements found in "Ca. Liberibacter asiaticus" from 16 to 33 and identified at least one prophage-like sequence in all "Ca. Liberibacter asiaticus" strains. Furthermore, we identified a prophage-like sequence type that is a remnant of an integrated prophage-expanding the number of prophage types in "Ca. Liberibacter asiaticus" from 3 to 4. Overall, the findings will help researchers investigate the role of prophage in the ecology, evolution, and pathogenicity of "Ca. Liberibacter asiaticus."
Magnesium oxide at extreme temperatures and pressures studied with first-principles simulations.

We combine two first-principles computer simulation techniques, path integral Monte Carlo and density functional theory molecular dynamics, to determine the equation of state of magnesium oxide in the regime of warm dense matter, with densities ranging from 0.35 to 71 g cm−3 and temperatures ranging from 10 000 K to 5 × 108 K. These conditions are relevant for the interiors of giant planets and stars as well as for shock wave compression measurements and inertial confinement fusion experiments. We study the electronic structure of MgO and the ionization mechanisms as a function of density and temperature. We show that the L-shell orbitals of magnesium and oxygen hybridize at high density. This results in a gradual ionization of the L-shell with increasing density and temperature. In this regard, MgO behaves differently from pure oxygen, which is reflected in the shape of the MgO principal shock Hugoniot curve. The curve of oxygen shows two compression maxima, while that of MgO shows only one. We predict a maximum compression ratio of 4.66 to occur for a temperature of 6.73 × 107 K. Finally, we study how multiple shocks and ramp waves can be used to cover a large range of densities and temperatures.
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle

Q-learning with function approximation is one of the most popular methods in reinforcement learning. Though the idea of using function approximation was proposed at least 60 years ago, even in the simplest setup, i.e, approximating Q-functions with linear functions, it is still an open problem how to design a provably efficient algorithm that learns a near-optimal policy. The key challenges are how to efficiently explore the state space and how to decide when to stop exploring in conjunction with the function approximation scheme. The current paper presents a provably efficient algorithm for Q-learning with linear function approximation. Under certain regularity assumptions, our algorithm, Difference Maximization Q-learning, combined with linear function approximation, returns a near-optimal policy using polynomial number of trajectories. Our algorithm introduces a new notion, the Distribution Shift Error Checking (DSEC) oracle. This oracle tests whether there exists a function in the function class that predicts well on a distribution $\mathcal{D}_1$, but predicts poorly on another distribution $\mathcal{D}_2$, where $\mathcal{D}_1$ and $\mathcal{D}_2$ are distributions over states induced by two different exploration policies. For the linear function class, this oracle is equivalent to solving a top eigenvalue problem. We believe our algorithmic insights, especially the DSEC oracle, are also useful in designing and analyzing reinforcement learning algorithms with general function approximation.
Optimizing the Spin Reversal Transform on the D-Wave 2000Q

Commercial quantum annealers from D-Wave Systems make it possible to obtain approximate solutions of high quality for certain NP-hard problems in nearly constant time. Before solving a problem on D-Wave, several pre-processing methods can be applied, one of them being the so-called spin reversal or gauge transform. The spin reversal transform flips the sign of selected variables and coefficients of the Ising or QUBO (quadratic unconstrained binary optimization) representation of the problem that D-Wave minimizes. The spin reversal transform leaves the ground state of the Ising model invariant, but can average out the biases induced through analog and systematic errors on the device, thus improving the quality of the solution that D-Wave returns. This work investigates the effectiveness of the spin reversal transform for D-Wave 2000Q. We consider two important NP-hard problems, the Maximum Clique and the Minimum Vertex Cover problems, and show on a variety of input problem graphs that using the spin reversal transform can yield substantial improvements in solution quality. In contrast to the native spin reversal built into D-Wave, we consider more general ways to reverse individual spins and we investigate the dependence on the problem type, on the spin reversal probability, and possible advantages of carrying out reversals on the qubit instead of the chain level. Most importantly, for a given individual problem, we use our findings to optimize the spin reversal transform using a genetic optimization algorithm.
Asymptotics of polynomials orthogonal over circular multiply connected domains

Abstract Let D be a domain obtained by removing, out of the unit disk { z : | z | 1 } , finitely many mutually disjoint closed disks, and for each integer n ≥ 0 , let P n ( z ) = z n + ⋯ be the monic n th-degree polynomial satisfying the planar orthogonality condition ∫ D P n ( z ) z m ¯ d x d y = 0 , 0 ≤ m n . Under a certain assumption on the domain D , we establish asymptotic expansions and formulae that describe the behavior of P n ( z ) as n → ∞ at every point z of the complex plane. We also give an asymptotic expansion for the squared norm ∫ D | P n | 2 d x d y .
TTBK2 and primary cilia are essential for the connectivity and survival of cerebellar Purkinje neurons

Primary cilia are vital signaling organelles that extend from most types of cells, including neurons and glia. These structures are essential for development of many tissues and organs; however, their function in adult tissues, particularly neurons in the brain, remains largely unknown. Tau tubulin kinase 2 (TTBK2) is a critical regulator of ciliogenesis, and is also mutated in a hereditary neurodegenerative disorder, spinocerebellar ataxia type 11 (SCA11). Here, we show that conditional knockout of Ttbk2 in adult mice results in degenerative cerebellar phenotypes that recapitulate aspects of SCA11 including motor coordination deficits and defects to Purkinje cell (PC) integrity. We also find that the Ttbk2 conditional mutant mice quickly lose cilia throughout the brain. We show that conditional knockout of the key ciliary trafficking gene Ift88 in adult mice results in nearly identical cerebellar phenotypes to those of the Ttbk2 knockout, indicating that disruption of ciliary signaling is a key driver of these phenotypes. Our data suggest that primary cilia play an integral role in maintaining the function of PCs in the adult cerebellum and reveal novel insights into mechanisms involved in neurodegeneration.
An asymptotically optimal transform of Pearson’s correlation statistic

It is shown that for any correlation-parametrized model of dependence and any given significance level α ∈ (0, 1), there is an asymptotically optimal transform of Pearson’s correlation statistic R, for which the generally leading error term for the normal approximation vanishes for all values ρ ∈ (−1, 1) of the correlation coefficient. This general result is then applied to the bivariate normal (BVN) model of dependence and to what is referred to in this paper as the SquareV model. In the BVN model, Pearson’s R turns out to be asymptotically optimal for a rather unusual significance level α ≈ 0.240, whereas Fisher’s transform RF of R is asymptotically optimal for the limit significance level α = 0. In the SquareV model, Pearson’s R is asymptotically optimal for a still rather high significance level α ≈ 0.159, whereas Fisher’s transform RF of R is not asymptotically optimal for any α ∈ [0, 1]. Moreover, it is shown that in both the BVN model and the SquareV model, the transform optimal for a given value of α is in fact asymptotically better than R and RF in wide ranges of values of the significance level, including α itself. Extensive computer simulations for the BVN and SquareV models of dependence suggest that, for sample sizes n ≥ 100 and significance levels α ∈ {0.01, 0.05}, the mentioned asymptotically optimal transform of R generally outperforms both Pearson’s R and Fisher’s transform RF of R, the latter appearing generally much inferior to both R and the asymptotically optimal transform of R in the SquareV model.
Depurination of Colibactin-Derived Interstrand Cross-Links

Colibactin is a genotoxic gut microbiome metabolite long suspected of playing an etiological role in colorectal cancer. Evidence suggests that colibactin forms DNA interstrand cross-links (ICLs) in...
Tactile-Based Insertion for Dense Box-Packing

We study the problem of using high-resolution tactile sensors to control the insertion of objects in a boxpacking scenario. In this paper, we propose an insertion strategy that leverages tactile sensing to: 1) safely probe the box with the grasped object while monitoring incipient slip to maintain a stable grasp on the object. 2) estimate and correct for residual position uncertainties to insert the object into a designated gap without disturbing the environment.Our proposed methodology is based on two neural networks that estimate the error direction and error magnitude, from a stream of tactile imprints, acquired by two GelSlim fingers, during the insertion process. The system is trained on four objects with basic geometric shapes, which we show generalizes to four other common objects. Based on the estimated positional errors, a heuristic controller iteratively adjusts the position of the object and eventually inserts it successfully without requiring prior knowledge of the geometry of the object. The key insight is that dense tactile feedback contains useful information with respect to the contact interaction between the grasped object and its environment. We achieve high success rate and show that unknown objects can be inserted with an average of 6 attempts of the probe-correct loop. The method’s ability to generalize to novel objects makes it a good fit for box packing in warehouse automation.
Endothelial sphingosine 1-phosphate receptors promote vascular normalization and antitumor therapy

Sphingosine 1-phosphate receptor-1 (S1PR1) is essential for embryonic vascular development and maturation. In the adult, it is a key regulator of vascular barrier function and inflammatory processes. Its roles in tumor angiogenesis, tumor growth, and metastasis are not well understood. In this paper, we show that S1PR1 is expressed and active in tumor vessels. Murine tumor vessels that lack S1PR1 in the vascular endothelium (S1pr1 ECKO) show excessive vascular sprouting and branching, decreased barrier function, and poor perfusion accompanied by loose attachment of pericytes. Compound knockout of S1pr1, 2, and 3 genes further exacerbated these phenotypes, suggesting compensatory function of endothelial S1PR2 and 3 in the absence of S1PR1. On the other hand, tumor vessels with high expression of S1PR1 (S1pr1 ECTG) show less branching, tortuosity, and enhanced pericyte coverage. Larger tumors and enhanced lung metastasis were seen in S1pr1 ECKO, whereas S1pr1 ECTG showed smaller tumors and reduced metastasis. Furthermore, antitumor activity of a chemotherapeutic agent (doxorubicin) and immune checkpoint inhibitor blocker (anti-PD-1 antibody) were more effective in S1pr1 ECTG than in the wild-type counterparts. These data suggest that tumor endothelial S1PR1 induces vascular normalization and influences tumor growth and metastasis, thus enhancing antitumor therapies in mouse models. Strategies to enhance S1PR1 signaling in tumor vessels may be an important adjunct to standard cancer therapy of solid tumors.
Coding for Crowdsourced Classification with XOR Queries

This paper models the crowdsourced labeling/classification problem as a sparsely encoded source coding problem, where each query answer, regarded as a code bit, is the XOR of a small number of labels, as source information bits. In this paper we leverage the connections between this problem and well-studied codes with sparse representations for the channel coding problem to provide querying schemes with almost optimal number of queries, each of which involving only a constant number of labels. We also extend this scenario to the case where some workers can be unresponsive. For this case, we propose querying schemes where each query involves only $\log n$ items, where n is the total number of items to be labeled. Furthermore, we consider classification of two correlated labeling systems and provide two-stage querying schemes with almost optimal number of queries each involving a constant number of labels.
Surveillance for collision avoidance with integrity using raw measurements in the automatic dependent surveillance-broadcast

This paper discusses an alternative ADS-B implementation that uses available provisions (Mode-S, UAT and GPS receivers) and existing GPS algorithms and techniques. This alternative has many advantages over the current ADS-B implementation, especially with respect to integrity of the solution. The paper will describe the methodology, its advantages, simulation results and implementation issues.
Representation Homology of Topological Spaces

In this paper, we introduce and study representation homology of topological spaces, which is a natural homological extension of representation varieties of fundamental groups. We give an elementary construction of representation homology parallel to the Loday-Pirashvili construction of higher Hochschild homology; in fact, we establish a direct geometric relation between the two theories by proving that the representation homology of the suspension of a (pointed connected) space is isomorphic to its higher Hochschild homology. We also construct some natural maps and spectral sequences relating representation homology to other homology theories associated with spaces (such as Pontryagin algebras, $S^1$-equivariant homology of the free loop space and stable homology of automorphism groups of f.g. free groups). We compute representation homology explicitly (in terms of known invariants) in a number of interesting cases, including spheres, suspensions, complex projective spaces, Riemann surfaces and some 3-dimensional manifolds, such as link complements in $\R^3$ and the lens spaces $ L(p,q) $. In the case of link complements, we identify the representation homology in terms of ordinary Hochschild homology, which gives a new algebraic invariant of links in $\R^3$.
On Dirichlet-to-Neumann Maps and Some Applications to Modified Fredholm Determinants

We consider Dirichlet-to-Neumann maps associated with (not necessarily self-adjoint) Schrodinger operators in L 2 (Ω; d n x), where Ω ⊂ ℝ n , n=2, 3, are open sets with a compact, nonempty boundary ∂Ω satisfying certain regularity conditions. As an application we describe a reduction of a certain ratio of modified Fredholm perturbation determinants associated with operators in L 2(Ω; d n x) to modified Fredholm perturbation determinants associated with operators in L 2(∂Ω; d n−1 σ), n=2, 3. This leads to a two- and three-dimensional extension of a variant of a celebrated formula due to Jost and Pais, which reduces the Fredholm perturbation determinant associated with a Schrodinger operator on the half-line (0, ∞) to a simple Wronski determinant of appropriate distributional solutions of the underlying Schrodinger equation.
Large X-Linked Palindromes Undergo Arm-to-Arm Gene Conversion across Mus Lineages.

Large (>10 kb), nearly identical (>99% nucleotide identity), palindromic sequences are enriched on mammalian sex chromosomes. Primate Y-palindromes undergo high rates of arm-to-arm gene conversion, a proposed mechanism for maintaining their sequence integrity in the absence of X-Y recombination. It is unclear whether X-palindromes, which can freely recombine in females, undergo arm-to-arm gene conversion and, if so, at what rate. We generated high-quality sequence assemblies of Mus molossinus and M. spretus X-palindromic regions and compared them with orthologous M. musculus X-palindromes. Our evolutionary sequence comparisons find evidence of X-palindrome arm-to-arm gene conversion at rates comparable to autosomal allelic gene conversion rates in mice. Mus X-palindromes also carry more derived than ancestral variants between species, suggesting that their sequence is rapidly diverging. We speculate that in addition to maintaining genes' sequence integrity via sequence homogenization, palindrome arm-to-arm gene conversion may also facilitate rapid sequence divergence.
Saving Lives Now from Ebola in Africa

In his letter, Peace is a better focus than Ebola in the Democratic Republic of the Congo (DRC), David M. Brett-Major provides a vital reminder of the tragic undercurrent of violence and political instability dominating African regions currently impacted by the second worst Ebola outbreak in modern history. He characterizes health-centric activities as a "common mistake" to remedy the "vicious cycle" of endemic violence and disease outbreaks in DRC and surrounding areas. What is truly needed is a "concerted peace and development process, with health as a voice in a chorus - not alone."
A software toolkit for TMS electric-field modeling with boundary element fast multipole method: an efficient MATLAB implementation

Objective To present and disseminate our transcranial magnetic stimulation (TMS) modeling software toolkit, including several new algorithmic developments, and to apply this software to realistic TMS modeling scenarios given a high-resolution model of the human head including cortical geometry and an accurate coil model. Approach The recently developed charge-based boundary element fast multipole method (BEM-FMM) is employed as an alternative to the 1st order finite element method (FEM) most commonly used today. The BEM-FMM approach provides high accuracy and unconstrained numerical field resolution close to and across cortical interfaces. Here, the previously proposed BEM-FMM algorithm has been improved in several novel ways. Main results The improvements resulted in a threefold increase in computational speed while maintaining the same solution accuracy. The computational code based on the MATLAB® platform is made available to all interested researchers, along with a coil model repository and examples to create custom coils, head model repository, and supporting documentation. The presented software toolkit may be useful for post-hoc analyses of navigated TMS data using high-resolution subject-specific head models as well as accurate and fast modeling for the purposes of TMS coil/hardware development. Significance TMS is currently the only non-invasive neurostimulation modality that enables painless and safe supra-threshold stimulation by employing electromagnetic induction to efficiently penetrate the skull. Accurate, fast, and high resolution modeling of the electric fields may significantly improve individualized targeting and dosing of TMS and therefore enhance the efficiency of existing clinical protocols as well as help establish new application domains.
LI-BIST: A Low-Cost Self-Test Scheme for SoC Logic Cores and Interconnects

For system-on-chips (SoC) using deep submicron (DSM) technologies, interconnects are becoming critical determinants for performance, reliability and power. Buses and long interconnects being susceptible to crosstalk noise, may lead to functional and timing failures. Existing at-speed interconnect crosstalk test methods propose inserting dedicated interconnect self-test structures in the SoC to generate vectors which have high crosstalk defect coverage. However, these methods may have a prohibitively high area overhead. To reduce this overhead, existing logic BIST structures like LFSRs could be reused to deliver interconnect tests. But, as shown by our experiments, use of LFSR tests achieve poor crosstalk defect coverage. Additionally, it has been shown that the power consumed during testing can potentially become a significant concern. In this paper, we present Logic-Interconnect BIST (LI-BIST), a comprehensive self-test solution for both the logic of the cores and the SoC interconnects. LI-BIST reuses existing logic BIST structures but generates high-quality tests for interconnect crosstalk defects, while minimizing the area overhead and interconnect power consumption. The application of the LI-BIST methodology on example SoCs indicates that LI-BIST is a viable, low-cost, yet comprehensive solution for testing SoCs.
Assessment of Specimen Pooling to Conserve SARS CoV-2 Testing Resources.

OBJECTIVES: To establish the optimal parameters for group testing of pooled specimens for the detection of SARS-CoV-2. METHODS: The most efficient pool size was determined to be five specimens using a web-based application. From this analysis, 25 experimental pools were created using 50 µL from one SARS-CoV-2 positive nasopharyngeal specimen mixed with 4 negative patient specimens (50 µL each) for a total volume of 250 µL. Viral RNA was subsequently extracted from each pool and tested using the CDC SARS-CoV-2 RT-PCR assay. Positive pools were consequently split into individual specimens and tested by extraction and PCR. This method was also tested on an unselected group of 60 nasopharyngeal specimens grouped into 12 pools. RESULTS: All 25 pools were positive with cycle threshold (Ct) values within 0 and 5.03 Ct of the original individual specimens. The analysis of 60 specimens determined that 2 pools were positive followed by identification of 2 individual specimens among the 60 tested. This testing was accomplished while using 22 extractions/PCR tests, a savings of 38 reactions. CONCLUSIONS: When the incidence rate of SARS-CoV-2 infection is 10% or less, group testing will result in the saving of reagents and personnel time with an overall increase in testing capability of at least 69%.
Communication-Constrained Expansion Planning for Resilient Distribution Systems

Distributed generation and remotely controlled switches have emerged as important technologies to improve the resiliency of distribution grids against extreme weather-related disturbances. Therefor...
Warmback analysis to determine fracture geometry of a single-stage hydraulic fracturing stimulation

Abstract Significant variation in hydraulic fracturing efficiency over the length of horizontal wells has been observed in the field. Monitoring and diagnosis of the fracture treatment is a critical step to evaluate the efficiency of the fracturing stimulation and the fracture geometry. Different diagnostic tools have been used to determine fracture placement and fracture effectiveness such as pressure decline analysis, microseismic, and tiltmeter fracture mapping technology. Recently, temperature monitoring has been evolved as a new technique can be analyzed to infer fracturing zone and fracture characteristics. Temperature behavior during fracture propagation and the subsequent warmback period can reveal important information about the dimensions and flow characteristics of developed fracture. In this work, we present forward and inverse modelling approaches to determine the fracture geometry using the temperature measured at the horizontal lateral. An analytical model is first developed to obtain the transient temperature in the fracture-matrix system during injection/propagation and the subsequent warmback period. The analytical solutions are developed by solving mass conservation equation along with energy balance equation in the fracture and the surrounding matrix assuming linear fluid flow and negligible leak-off effect which can be feasible for ultra-tight formations with low filtration coefficient. The developed solutions are validated against a thermally coupled numerical reservoir simulator as well as published field data. The solutions for warmback period are obtained and presented in the form of graphical type curves. The temperature measurements obtained during warmback period along with the developed solutions are used to estimate fracture width, fracture half-length, and geothermal reservoir temperature. The analytical results provide good agreement with the numerical results during forward and inverse modelling.
Non-Maxwellian velocity distribution functions associated with steep temperature gradients in the solar transition region. Paper 2: The effect of non-Maxwellian electron distribution functions on ionization equilibrium calculations for carbon, nitrogen and oxygen

Non-Maxwellian electron velocity distribution functions, previously computed for Dupree's model of the solar transition region are used to calculate ionization rates for ions of carbon, nitrogen, and oxygen. Ionization equilibrium populations for these ions are then computed and compared with similar calculations assuming Maxwellian distribution functions for the electrons. The results show that the ion populations change (compared to the values computed with a Maxwellian) in some cases by several orders of magnitude depending on the ion and its temperature of formation.
Predicting cancer origins with a DNA methylation-based deep neural network model

Cancer origin determination combined with site-specific treatment of metastatic cancer patients is critical to improve patient outcomes. Existing pathology and gene expression-based techniques often have limited performance. In this study, we developed a deep neural network (DNN)-based classifier for cancer origin prediction using DNA methylation data of 7,339 patients of 18 different cancer origins from The Cancer Genome Atlas (TCGA). This DNN model was evaluated using four strategies: (1) when evaluated by 10-fold cross-validation, it achieved an overall specificity of 99.72% (95% CI 99.69%-99.75%) and sensitivity of 92.59% (95% CI 91.87%-93.30%); (2) when tested on hold-out testing data of 1,468 patients, the model had an overall specificity of 99.83% and sensitivity of 95.95%; (3) when tested on 143 metastasized cancer patients (12 cancer origins), the model achieved an overall specificity of 99.47% and sensitivity of 95.95%; and (4) when tested on an independent dataset of 581 samples (10 cancer origins), the model achieved overall specificity of 99.91% and sensitivity of 93.43%. Compared to existing pathology and gene expression-based techniques, the DNA methylation-based DNN classifier showed higher performance and had the unique advantage of easy implementation in clinical settings. In summary, our study shows that DNA methylation-based DNN models has potential in both diagnosis of cancer of unknown primary and identification of cancer cell types of circulating tumor cells.
The role of multinational production in a risky environment

This paper explores the aggregate consequences of Foreign Direct Investment (FDI) on the opportunities for risk diversification available to consumers. The crucial difference between FDI and other international financial flows is that the former involves technology flows across countries. We present a model where firm-embedded productivity can be transferred costly across countries through the activity of multinational firms. We find that risk patterns affect multinationals' location decisions and, in turn, these decisions change the scope for international risk diversification even in a world with complete financial markets.
Knowledge Distillation for End-to-End Person Search.

We introduce knowledge distillation for end-to-end person search. End-to-End methods are the current state-of-the-art for person search that solve both detection and re-identification jointly. These approaches for joint optimization show their largest drop in performance due to a sub-optimal detector. We propose two distinct approaches for extra supervision of end-to-end person search methods in a teacher-student setting. The first is adopted from state-of-the-art knowledge distillation in object detection. We employ this to supervise the detector of our person search model at various levels using a specialized detector. The second approach is new, simple and yet considerably more effective. This distills knowledge from a teacher re-identification technique via a pre-computed look-up table of ID features. It relaxes the learning of identification features and allows the student to focus on the detection task. This procedure not only helps fixing the sub-optimal detector training in the joint optimization and simultaneously improving the person search, but also closes the performance gap between the teacher and the student for model compression in this case. Overall, we demonstrate significant improvements for two recent state-of-the-art methods using our proposed knowledge distillation approach on two benchmark datasets. Moreover, on the model compression task our approach brings the performance of smaller models on par with the larger models.
A State-Level Analysis of the Great Moderation

A number of studies have documented a reduction in aggregate macroeconomic volatility beginning in the early 1980s. Using an empirical model of business cycles, we extend this line of research to state-level employment data, …nd signi…cant heterogeneity in the timing and magnitude of the state-level volatility reductions. In fact, some states experience no statistically-signi…cant reduction in volatility. We then exploit this cross-sectional heterogeneity to evaluate three hypotheses about the origin of the aggregate volatility reduction. We show that states with relatively higher manufacturing concentration experience later breaks, a result that tends to contradict improved inventory management and a decline in the volatility of productivity shocks as possible explanations. Our results, then, are more consistent with monetary policy as the origin of the aggregate volatility reduction. [JEL: C22, E24, E32] � The authors thank Michelle T. Armesto and Kristie M. Engemann for research assistance. This paper bene…ted from conversations with Jim Hamilton and Valerie Ramey. Views expressed here are the authors’ alone and do not re‡ect the opinions of the Federal Reserve Bank of St. Louis or the Federal Reserve System. The usual disclaimers apply. y Corresponding author: wall@stls.frb.org
Strong mixed-integer programming formulations for trained neural networks

We present an ideal mixed-integer programming (MIP) formulation for a rectified linear unit (ReLU) appearing in a trained neural network. Our formulation requires a single binary variable and no additional continuous variables beyond the input and output variables of the ReLU. We contrast it with an ideal “extended” formulation with a linear number of additional continuous variables, derived through standard techniques. An apparent drawback of our formulation is that it requires an exponential number of inequality constraints, but we provide a routine to separate the inequalities in linear time. We also prove that these exponentially-many constraints are facet-defining under mild conditions. Finally, we study network verification problems and observe that dynamically separating from the exponential inequalities (1) is much more computationally efficient and scalable than the extended formulation, (2) decreases the solve time of a state-of-the-art MIP solver by a factor of 7 on smaller instances, and (3) nearly matches the dual bounds of a state-of-the-art MIP solver on harder instances, after just a few rounds of separation and in orders of magnitude less time.
What Is Hydraulic Engineering

This paper, written to mark ASCE’s 150th anniversary, traces the role of hydraulic engineering from early or mid-twentieth-century to the beginning of the twenty-first century. A half-century ago hydraulic engineering was central in building the economies of the United States and many other countries by designing small and large water works. That process entailed a concentrated effort in research that ranged from the minute details of fluid flow to a general study of economics and ecology. Gradually over the last half-century, hydraulic engineering has evolved from a focus on large construction projects to now include the role of conservation and preservation. Although the hydraulic engineer has traditionally had to interface with other disciplines, that aspect of the profession has taken on a new urgency and, fortunately, is supported by exciting new technological developments. He/she must acquire new skills, in addition to retaining and improving the traditional skills, and form close partnerships with ...
Increasing returns and the evolution of violent crime: the case of Colombia

Abstract This paper presents an explanation of the recent escalation of violent crime in Colombia. The paper considers three implicit models that isolate different types of externalities among criminals. In the first model, criminals make crime more appealing to nearby residents by congesting the law enforcement system and, hence, lowering the probability of punishment. In the second model, the interaction of career criminals and local crooks speeds up the diffusion of criminal know-how and criminal technology. In the third model, the daily contact of youth with criminal adults and criminal peers results in the erosion of morals and hence in a greater predisposition toward crime. The paper shows that a myriad empirical evidence — both statistical and anecdotal — lends support to the previous models in general and to the congestion-in-law-enforcement model in particular.
How individuals respond to a liquidity shock: Evidence from the 2013 government shutdown

Abstract Using comprehensive account records, this paper examines how individuals adjusted spending and saving in response to a temporary drop in liquidity due to the 2013 U.S. government shutdown. The shutdown cut paychecks by 40% for affected employees, which was recovered within 2 weeks. Because the shutdown affected only the timing of payments, it provides a distinctive experiment allowing estimates of the response to a liquidity shock holding income constant. Spending dropped sharply, implying a naive estimate of 58 cents less spending for every dollar of lost liquidity. This estimate overstates the consumption response. While many individuals had low liquid assets, they used multiple sources of short-term liquidity to smooth consumption. Sources of short-term liquidity include delaying recurring payments such as for mortgages and credit card balances.
A Framework for Deep Constrained Clustering - Algorithms and Advances

The area of constrained clustering has been extensively explored by researchers and used by practitioners. Constrained clustering formulations exist for popular algorithms such as k-means, mixture models, and spectral clustering but have several limitations. A fundamental strength of deep learning is its flexibility, and here we explore a deep learning framework for constrained clustering and in particular explore how it can extend the field of constrained clustering. We show that our framework can not only handle standard together/apart constraints (without the well documented negative effects reported earlier) generated from labeled side information but more complex constraints generated from new types of side information such as continuous values and high-level domain knowledge. (Source code available at: http://github.com/blueocean92/deep_constrained_clustering)