cssi/SciSciGPT-SciSciNet huggingface dataset:
Improving performance and retention in introductory biology with a utility-value intervention.

One way to encourage performance and persistence in STEM fields is to have students write about the utility value (UV) or personal relevance of course topics to their life. This intervention has been shown to increase engagement and performance in introductory courses. However, questions remain about the longevity of the effects and how best to implement the intervention in terms of dosage and timing. We tested a UV intervention in the first semester of a two-semester introductory biology sequence. For each of three units across the semester, students (N = 577) were randomly assigned to receive either a UV writing assignment, in which they explained why course material was useful to them personally, or a control assignment, in which they summarized course material. This fully crossed design tested the effect of UV dosage level (0, 1, 2, or 3 UV assignments) as well as the effect of timing (e.g., UV first, control first). We found that students exposed to any dosage of UV earned higher grades in the course, were more likely to enroll in the second course of the biology sequence, and were less likely to abandon their STEM major than students who did not receive any UV assignments. In terms of timing, students with a history of poor performance benefitted from writing a UV essay in the beginning of the semester, whereas higher-performing students benefitted from a UV essay at the end of the semester. Recommendations for practice are discussed.
Interactome INSIDER: a structural interactome browser for genomic studies

We present Interactome INSIDER, a tool to link genomic variant information with structural protein-protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces in human and seven model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit functional properties similar to those of known interfaces, including enrichment for disease mutations and recurrent cancer mutations. Through 2,164 de novo mutagenesis experiments, we show that mutations of predicted and known interface residues disrupt interactions at a similar rate and much more frequently than mutations outside of predicted interfaces. To spur functional genomic studies, Interactome INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether variants or disease mutations are enriched in known and predicted interaction interfaces at various resolutions. Users may explore known population variants, disease mutations, and somatic cancer mutations, or they may upload their own set of mutations for this purpose.
Aggregation, Capital Heterogeneity, and the Investment CAPM

This paper provides a careful treatment of aggregation, and to a lesser extent, capital heterogeneity in the investment CAPM. Firm-level investment returns are constructed from firm-level variables, and then aggregated to the portfolio level to match with portfolio-level stock returns. Current assets form a separate production input besides physical capital. The model fits well the value, momentum, investment, and profitability premiums simultaneously, and partially explains the positive stock-investment return correlations, the procyclical and short-term dynamics of the momentum and profitability premiums, as well as the countercyclical and long-term dynamics of the value and investment premiums. However, the model fails to explain momentum crashes.
The Effects of Computers and Acquired Skills on Earnings, Employment and College Enrollment: Evidence from a Field Experiment and California UI Earnings Records

Author(s): Fairlie, Robert W; Bahr, Peter Riley | Abstract: This paper provides the first evidence on the earnings, employment and college enrollment effects of computers and acquired skills from a randomized controlled trial providing computers to entering college students. We matched confidential administrative data from California Employment Development Department (EDD)/Unemployment Insurance (UI) system earnings records, the California Community College system, and the National Student Clearinghouse to all study participants for seven years after the random provision of computers. The experiment does not provide evidence that computer skills have short- or medium-run effects on earnings. These null effects are found along both the extensive and intensive margins of earnings (although the estimates are not precise). We also do not find evidence of positive or negative effects on college enrollment. A non-experimental analysis of CPS data reveals large, positive and statistically significant relationships between home computers, and earnings, employment and college enrollment, raising concerns about selection bias in non-experimental studies.
U.S. federal laboratories and their research partners: a quantitative case study

The Stevenson–Wydler Technology Innovation Act of 1980 made explicit the technology transfer responsibilities of U.S. Federal laboratories. The Federal Technology Transfer Act of 1986 and the National Competitiveness Technology Transfer Act of 1989 further enhanced the technology transfer activities of laboratories by permitting Cooperative Research and Development Agreements (CRADAs). However, very little is known about the characteristics of CRADA activity in Federal laboratories. Using a new, robust dataset of CRADA activity at the National Institute of Standards and Technology (NIST), we describe research partnerships over the years 1978 through 2014, and we explore several research questions. When did the Federal Technology Transfer Act have an impact on CRADA activity at NIST? Is CRADA activity at NIST a cyclical phenomenon? At what frequency do private sector establishments engage in CRADA activity with NIST? We find suggestive evidence that the Federal Technology Transfer Act began to influence NIST’s CRADA activity within 2–3 years after its passage, and we find that CRADA activity moves with the business cycle. We also find that most establishments that were engaged in CRADA activity were engaged only once over this time period; it was only the larger establishments that continued to engage in CRADAs with NIST. We speculate about the implications of these findings, and we suggest a broader research agenda into CRADA activity in Federal laboratories.
Geochemistry of fluids from Earth's deepest ridge-crest hot-springs: Piccard hydrothermal field, Mid-Cayman Rise

Abstract Hosted in basaltic substrate on the ultra-slow spreading Mid-Cayman Rise, the Piccard hydrothermal field is the deepest currently known seafloor hot-spring (4957–4987 m). Due to its great depth, the Piccard site is an excellent natural system for investigating the influence of extreme pressure on the formation of submarine vent fluids. To investigate the role of rock composition and deep circulation conditions on fluid chemistry, the abundance and isotopic composition of organic, inorganic, and dissolved volatile species in high temperature vent fluids at Piccard were examined in samples collected in 2012 and 2013. Fluids from the Beebe Vents and Beebe Woods black smokers vent at a maximum temperature of 398 °C at the seafloor, however several lines of evidence derived from inorganic chemistry (Cl, SiO2, Ca, Br, Fe, Cu, Mn) support fluid formation at much higher temperatures in the subsurface. These high temperatures, potentially in excess of 500 °C, are attainable due to the great depth of the system. Our data indicate that a single deep-rooted source fluid feeds high temperature vents across the entire Piccard field. High temperature Piccard fluid H2 abundances (19.9 mM) are even higher than those observed in many ultramafic-influenced systems, such as the Rainbow (16 mM) and the Von Damm hydrothermal fields (18.2 mM). In the case of Piccard, however, these extremely high H2 abundances can be generated from fluid-basalt reaction occurring at very high temperatures. Magmatic and thermogenic sources of carbon in the high temperature black smoker vents are described. Dissolved ΣCO2 is likely of magmatic origin, CH4 may originate from a combination of thermogenic sources and leaching of abiotic CH4 from mineral-hosted fluid inclusions, and CO abundances are at equilibrium with the water–gas shift reaction. Longer-chained n-alkanes (C2H6, C3H8, n-C4H10, i-C4H10) may derive from thermal alteration of dissolved and particulate organic carbon sourced from the original seawater source, entrainment of microbial ecosystems peripheral to high temperature venting, and/or abiotic mantle sources. Dissolved ΣHCOOH in the Beebe Woods fluid is consistent with thermodynamic equilibrium for abiotic production via ΣCO2 reduction with H2 at 354 °C measured temperature. A lack of ΣHCOOH in the relatively higher temperature 398 °C Beebe Vent fluids demonstrates the temperature sensitivity of this equilibrium. Abundant basaltic seafloor outcrops and the axial location of the vent field, along with multiple lines of geochemical evidence, support extremely high temperature fluid-rock reaction with mafic substrate as the dominant control on Piccard fluid chemistry. These results expand the known diversity of vent fluid composition, with implications for supporting microbiological life in both the modern and ancient ocean.
The fewest clues problem

Abstract When analyzing the computational complexity of well-known puzzles, most papers consider the algorithmic challenge of solving a given instance of (a generalized form of) the puzzle. We take a different approach by analyzing the computational complexity of designing a “good” puzzle. We assume a puzzle maker designs part of an instance, but before publishing it, wants to ensure that the puzzle has a unique solution. Given a puzzle, we introduce the FCP (fewest clues problem) version of the problem: Given an instance to a puzzle, what is the minimum number of clues we must add in order to make the instance uniquely solvable? We analyze this question for the Nikoli puzzles Sudoku, Shakashaka, and Akari. Solving these puzzles is NP -complete, and we show their FCP versions are Σ 2 P -complete. Along the way, we show that the FCP versions of Triangle Partition , Planar 1- in -3 SAT , and Latin Square are all Σ 2 P -complete. We show that even problems in P have difficult FCP versions, sometimes even Σ 2 P -complete, though “closed under cluing” problems are in the (presumably) smaller class NP ; for example, FCP 2 SAT is NP -complete.
A Coding Theorem for f-Separable Distortion Measures

In this work we relax the usual separability assumption made in rate-distortion literature and propose f -separable distortion measures, which are well suited to model non-linear penalties. The main insight behind f -separable distortion measures is to define an n-letter distortion measure to be an f -mean of single-letter distortions. We prove a rate-distortion coding theorem for stationary ergodic sources with f -separable distortion measures, and provide some illustrative examples of the resulting rate-distortion functions. Finally, we discuss connections between f -separable distortion measures, and the subadditive distortion measure previously proposed in literature.
Single-exposure visual memory judgments are reflected in inferotemporal cortex.

As we go about our daily lives, we store visual memories of the objects and scenes that we encounter. This type of memory, known as visual recognition memory, can be remarkably powerful. Imagine viewing thousands of images for only a few seconds each, for example. Several days later, you will still be able to distinguish most of those images from previously unseen ones. How does the brain do this? Visual information travels from the eyes to an area of the brain called visual cortex. Neurons in a region of visual cortex called inferotemporal cortex fire in a particular pattern to reflect what is being seen. These neurons also reflect memories of whether those things have been seen before, by firing more when things are new and less when they are viewed again. This decrease in firing, known as repetition suppression, may be the signal in the brain responsible for the sense of remembering. Meyer and Rust have now tested this idea by training macaque monkeys to report whether images on a screen were new or familiar. The monkeys were very good at remembering the images they had seen more recently, although they tended to forget some of the images with time. Then, the rate at which the monkeys forgot the images was compared with the rate at which repetition suppression disappeared in inferotemporal cortex. The results showed that the total number of firing events in this region was not a great predictor of how long the monkeys remembered images. However, a decrease in the number of firing events for a particular subset of the neurons did predict the remembering and forgetting. Repetition suppression in certain inferotemporal cortex neurons can thus account for visual recognition memory. Brain disorders and aging can both give rise to memory deficits. Identifying the mechanisms underlying memory may lead to new treatments for memory-related disorders. Visual recognition memory may be a good place to start because of our existing knowledge of how the brain processes visual information. Understanding visual recognition memory could help us understand the mechanisms of memory more broadly.
Differential Role of Prefrontal and Parietal Cortices in Controlling Level of Consciousness

Consciousness is determined both by level (e.g., being awake versus being anesthetized) and content (i.e., the qualitative aspects of experience). Subcortical areas are known to play a causal role in regulating the level of consciousness [1-9], but the role of the cortex is less well understood. Clinical and correlative data have been used both to support and refute a role for prefrontal and posterior cortices in the level of consciousness [10-22]. The prefrontal cortex has extensive reciprocal connections to wake-promoting centers in the brainstem and diencephalon [23, 24], and hence is in a unique position to modulate level of consciousness. Furthermore, a recent study suggested that the prefrontal cortex might be important in regulating level of consciousness [25] but causal evidence, and a comparison with more posterior cortical sites, is lacking. Therefore, to test the hypothesis that prefrontal cortex plays a role in regulating level of consciousness, we attempted to reverse sevoflurane anesthesia by cholinergic or noradrenergic stimulation of the prefrontal prelimbic cortex and two areas of parietal cortex in rat. General anesthesia was defined by loss of the righting reflex, a widely used surrogate measure in rodents. We demonstrate that cholinergic stimulation of prefrontal cortex, but not parietal cortex, restored wake-like behavior, despite continuous exposure to clinically relevant concentrations of sevoflurane anesthesia. Noradrenergic stimulation of the prefrontal and parietal areas resulted in electroencephalographic activation but failed to produce any signs of wake-like behavior. We conclude that cholinergic mechanisms in prefrontal cortex can regulate the level of consciousness.
NF1 deficiency correlates with estrogen receptor signaling and diminished survival in breast cancer.

The key negative regulatory gene of the RAS pathway, NF1, is mutated or deleted in numerous cancer types and is associated with increased cancer risk and drug resistance. Even though women with neurofibromatosis (germline NF1 mutations) have a substantially increased breast cancer risk at a young age and NF1 is commonly mutated in sporadic breast cancers, we have a limited understanding of the role of NF1 in breast cancer. We utilized CRISPR–Cas9 gene editing to create Nf1 rat models to evaluate the effect of Nf1 deficiency on tumorigenesis. The resulting Nf1 indels induced highly penetrant, aggressive mammary adenocarcinomas that express estrogen receptor (ER) and progesterone receptor (PR). We identified distinct Nf1 mRNA and protein isoforms that were altered during tumorigenesis. To evaluate NF1 in human breast cancer, we analyzed genomic changes in a data set of 2000 clinically annotated breast cancers. We found NF1 shallow deletions in 25% of sporadic breast cancers, which correlated with poor clinical outcome. To identify biological networks impacted by NF1 deficiency, we constructed gene co-expression networks using weighted gene correlation network analysis (WGCNA) and identified a network connected to ESR1 (estrogen receptor). Moreover, NF1-deficient cancers correlated with established RAS activation signatures. Estrogen-dependence was verified by estrogen-ablation in Nf1 rats where rapid tumor regression was observed. Additionally, Nf1 deficiency correlated with increased estrogen receptor phosphorylation in mammary adenocarcinomas. These results demonstrate a significant role for NF1 in both NF1-related breast cancer and sporadic breast cancer, and highlight a potential functional link between neurofibromin and the estrogen receptor.
Price-Linked Subsidies and Imperfect Competition in Health Insurance

Policymakers subsidizing health insurance often face uncertainty about future market prices. We study the implications of one policy response: linking subsidies to prices, to target a given post-subsidy premium. We show that these price-linked subsidies weaken competition, raising prices for the government and/or consumers. However, price-linking also ties subsidies to health care cost shocks, which may be desirable. Evaluating this tradeoffs empirically using a model estimated with Massachusetts insurance exchange data, we find that price-linking increases prices 1-6%, and much more in less competitive markets. For cost uncertainty reasonable in a mature market, these losses outweigh the benefits of price-linking.
Rationale to reduce calcium intake in adult patients with chronic kidney disease.

Purpose of reviewCalcium is an essential ion for the maintenance of normal bone health and physiologic functions. The extracellular and intracellular levels of calcium are maintained through hormonal regulation called homeostasis. Balance, the net intake minus excretion of calcium, is maintained by
Coz: finding code that counts with causal profiling

Improving performance is a central concern for software developers. To locate optimization opportunities, developers rely on software profilers. However, these profilers only report where programs spend their time: optimizing that code may have no impact on performance. Past profilers thus both waste developer time and make it difficult for them to uncover significant optimization opportunities. This paper introduces causal profiling. Unlike past profiling approaches, causal profiling indicates exactly where programmers should focus their optimization efforts, and quantifies their potential impact. Causal profiling works by running performance experiments during program execution. Each experiment calculates the impact of any potential optimization by virtually speeding up code: inserting pauses that slow down all other code running concurrently. The key insight is that this slowdown has the same relative effect as running that line faster, thus "virtually" speeding it up. We present Coz, a causal profiler, which we evaluate on a range of highly-tuned applications such as Memcached, SQLite, and the PARSEC benchmark suite. Coz identifies previously unknown optimization opportunities that are both significant and targeted. Guided by Coz, we improve the performance of Memcached by 9%, SQLite by 25%, and accelerate six PARSEC applications by as much as 68%; in most cases, these optimizations involve modifying under 10 lines of code.
Robust Structured Multi-Task Multi-View Sparse Tracking

Sparse representation is a viable solution to visual tracking. In this paper, we propose a structured multi-task multi-view tracking (SMTMVT) method, which exploits the sparse appearance model in the particle filter framework to track targets under different challenges. Specifically, we extract features of the target candidates from different views and sparsely represent them by a linear combination of templates of different views. Unlike the conventional sparse trackers, SMTMVT not only jointly considers the relationship between different tasks and different views but also retains the structures among different views in a robust multi-task multi-view formulation. We introduce a numerical algorithm based on the proximal gradient method to quickly and effectively find the sparsity by dividing the optimization problem into two subproblems with the closed-form solutions. Both qualitative and quantitative evaluations on the benchmark of challenging image sequences demonstrate the superior performance of the proposed tracker against various state-of-the-art trackers.
Designing Graceful Degradation into Complex Systems: The Interaction Between Causes of Degradation and the Association with Degradation Prevention and Recovery

System resilience is critical to safety in air traffic control. An important element of maintaining resilience is the ability of systems to degrade gracefully. Of the available graceful degradation research, a majority of studies have focused primarily on technological causes of degradation only, limiting an ecologically valid understanding of the causes of degradation in air traffic control, and the preventative and mitigative strategies that enable graceful degradation. The current study aimed to address this research gap by investigating causes of degradation in air traffic control across the broad categories of technology, the environment, and the human operator, and the potential interactions between these causes. 12 retired controllers participated in semi-structured interviews focused on previous experience of causes of degradation and mitigation strategies. Findings provide an understanding of causation of degradation in air traffic control, and the prevention and mitigation strategies that moderate the relationship between cause and system effect. Findings confirmed that causes appear to interact to create compound, multiple effects on overall system performance. Findings also revealed prevention and mitigation strategies utilized to moderate the effect of the cause on the system. In order to gain an ecologically valid understanding of the causes of degradation, and effective prevention or mitigation strategies, causes from multiple categories, and the interactions between them, must be identified. Findings have implications for designers of future air traffic control systems to ensure the ability of the system to gracefully degrade, as well as risk assessment and system validation processes.
Disentangling reporting and disease transmission.

Second-order statistics such as the variance and autocorrelation can be useful indicators of the stability of randomly perturbed systems, in some cases providing early warning of an impending, dramatic change in the system’s dynamics. One specific application area of interest is the surveillance of infectious diseases. In the context of disease (re-)emergence, a goal could be to have an indicator that is informative of whether the system is approaching the epidemic threshold, a point beyond which a major outbreak becomes possible. Prior work in this area has provided some proof of this principle but has not analytically treated the effect of imperfect observation on the behavior of indicators. This work provides expected values for several moments of the number of reported cases, where reported cases follow a binomial or negative binomial distribution with a mean based on the number of deaths in a birth-death-immigration process over some reporting interval. The normalized second factorial moment and the decay time of the number of reported cases are two indicators that are insensitive to the reporting probability. Simulation is used to show how this insensitivity could be used to distinguish a trend of increased reporting from a trend of increased transmission. The simulation study also illustrates both the high variance of estimates and the possibility of reducing the variance by averaging over an ensemble of estimates from multiple time series.
Extending edge‐colorings of complete hypergraphs into regular colorings

Let $\binom{X}{h}$ be the collection of all $h$-subsets of an $n$-set $X\supseteq Y$. Given a coloring (partition) of a set $S\subseteq \binom{X}{h}$, we are interested in finding conditions under which this coloring is extendible to a coloring of $\binom{X}{h}$ so that the number of times each element of $X$ appears in each color class (all sets of the same color) is the same number $r$. The case $S=\varnothing, r=1$ was studied by Sylvester in the 18th century, and remained open until the 1970s. The case $h=2,r=1$ is extensively studied in the literature and is closely related to completing partial symmetric Latin squares. For $S=\binom{Y}{h}$, we settle the cases $h=4, |X|\geq 4.847323|Y|$, and $h=5, |X|\geq 6.285214|Y|$ completely. Moreover, we make partial progress toward solving the case where $S=\binom{X}{h}\backslash \binom{Y}{h}$. These results can be seen as extensions of the famous Baranyai's theorem, and make progress toward settling a 40-year-old problem posed by Cameron.
Deleterious variation shapes the genomic landscape of introgression

While it is appreciated that population size changes can impact patterns of deleterious variation in natural populations, less attention has been paid to how gene flow affects and is affected by the dynamics of deleterious variation. Here we use population genetic simulations to examine how gene flow impacts deleterious variation under a variety of demographic scenarios, mating systems, dominance coefficients, and recombination rates. Our results show that admixture between populations can temporarily reduce the genetic load of smaller populations and cause increases in the frequency of introgressed ancestry, especially if deleterious mutations are recessive. Additionally, when fitness effects of new mutations are recessive, between-population differences in the sites at which deleterious variants exist creates heterosis in hybrid individuals. Together, these factors lead to an increase in introgressed ancestry, particularly when recombination rates are low. Under certain scenarios, introgressed ancestry can increase from an initial frequency of 5% to 30-75% and fix at many loci, even in the absence of beneficial mutations. Further, deleterious variation and admixture can generate correlations between the frequency of introgressed ancestry and recombination rate or exon density, even in the absence of other types of selection. The direction of these correlations is determined by the specific demography and whether mutations are additive or recessive. Therefore, it is essential that null models of admixture include both demography and deleterious variation before invoking other mechanisms to explain unusual patterns of genetic variation.
New Approaches to Juvenile Age Estimation in Forensics: Application of Transition Analysis via the Shackelford et al. Method to a Diverse Modern Subadult Sample

Dental development is one of the most widely utilized and accurate methods available for estimating age in subadult skeletal remains. The timing of tooth growth and development is regulated by genetics and less affected by external factors, allowing reliable estimates of chronological age. Traditional methodology focuses on comparing tooth developmental scores to corresponding age charts. Using the Moorrees, Fanning, and Hunt (MFH) developmental scores, Shackelford and colleagues embed the dental development method in a statistical framework based on transition analysis. They generated numerical parameters underlining each "stage" and age-at-death distribution and applied them to fossil hominins and Neanderthals with limited application to modern humans. We use this same method on a subadult test sample (n = 201), representing modern individuals that may become part of the forensic record. We assess the probability coverage of the Shackelford et al. method derived from MFH standards as it applies to all available dentition. Results indicate promise: the age range at 90% and 95% confidence levels includes the chronological age of almost every individual tested. The maximum likelihood age estimates underestimate age by 0.5-2.5 years for individuals 0-15 years of age and by >2.5 years for individuals 16-18 years of age, as previously shown. In an attempt to refine the method, we adjusted the numerical parameters underlying the stages for developing teeth based on a combined modern reference sample (n = 1,964) and tested these revised parameters using the same test sample. The estimated ages from the modified method differ from the original Shackelford et al. methodology by underestimating age to a lesser degree. The modified method does include mean age-at-attainment values for earlier stages of several teeth, allowing for the calculation of narrower confidence intervals. While this study highlights areas of future research in refining dental developmental aging by transition analysis, it also demonstrates that the Shackelford et al. method is applicable and accurate when aging modern subadults in forensic work.
GRXCR2 Regulates Taperin Localization Critical for Stereocilia Morphology and Hearing.

Summary Mutations in human GRXCR2, which encodes a protein of undetermined function, cause hearing loss by unknown mechanisms. We found that mouse GRXCR2 localizes to the base of the stereocilia, which are actin-based mechanosensing organelles in cochlear hair cells that convert sound-induced vibrations into electrical signals. The stereocilia base also contains taperin, another protein of unknown function required for human hearing. We show that taperin and GRXCR2 form a complex and that taperin is diffused throughout the stereocilia length in Grxcr2-deficient hair cells. Stereocilia lacking GRXCR2 are longer than normal and disorganized due to the mislocalization of taperin, which could modulate the actin cytoskeleton in stereocilia. Remarkably, reducing taperin expression levels could rescue the morphological defects of stereocilia and restore the hearing of Grxcr2-deficient mice. Thus, our findings suggest that GRXCR2 is critical for the morphogenesis of stereocilia and auditory perception by restricting taperin to the stereocilia base.
Colorism Against Legal Immigrants to the United States

Data from the 2003 wave of the New Immigrant Survey established that immigrants to the United States with darker skin color experienced a substantial pay penalty that is not explained by extensive ...
Low-Complexity Adaptive Beam and Channel Tracking for Mobile mmWave Communications

In this paper, we study low-complexity algorithms for beam and channel tracking for millimeter-wave (mmWave) communications. In particular, the least mean squares (LMS) and bidirectional LMS (BiLMS) algorithms are derived for a mobile mmWave transmission scenario, where channel measurement is a nonlinear function of the unknown angle-of-arrival (AoA) and angle-of-departure (AoD). Numerical results confirm that LMS is superior to widely used Extended Kalman Filter (EKF) algorithm in tracking the mmWave beam, when the initialization of AoA/AoD and channel gains is imperfect (i.e., performed using noisy channel estimates). Moreover, BiLMS exhibits a very good mean square error (MSE) performance as compared to both LMS and EKF, which makes it a promising channel tracking algorithm for a mobile mmWave transmission scenario. We also show that LMS and BiLMS algorithms are more robust against the impairments due to the non-optimal antenna array size as compared to EKF, and show relatively faster convergence characteristic along with increasing signal-to-noise ratio (SNR).
Using ACL2 in the Design of Efficient, Verifiable Data Structures for High-Assurance Systems.

Verification of algorithms and data structures utilized in modern autonomous and semi-autonomous vehicles for land, sea, air, and space presents a significant challenge. Autonomy algorithms, e.g., route planning, pattern matching, and inference, are based on complex data structures such as directed graphs and algebraic data types. Proof techniques for these data structures exist, but are oriented to unbounded, functional realizations, which are not typically efficient in either space or time. Autonomous systems designers, on the other hand, generally limit the space and time allocations for any given function, and require that algorithms deliver results within a finite time, or suffer a watchdog timeout. Furthermore, high-assurance design rules frown on dynamic memory allocation, preferring simple array-based data structure implementations. In order to provide efficient implementations of high-level data structures used in autonomous systems with the high assurance needed for accreditation, we have developed a verifying compilation technique that supports the "natural" functional proof style, but yet applies to more efficient data structure implementations. Our toolchain features code generation to mainstream programming languages, as well as GPU-based and hardware-based realizations. We base the Intermediate Verification Language for our toolchain upon higher-order logic; however, we have used ACL2 to develop our efficient yet verifiable data structure design. ACL2 is particularly well-suited for this work, with its sophisticated libraries for reasoning about aggregate data structures of arbitrary size, efficient execution of formal specifications, as well as its support for "single-threaded objects" -- functional datatypes with imperative "under the hood" implementations. In this paper, we detail our high-assurance data structure design approach, including examples in ACL2 of common algebraic data types implemented using this design approach, proofs of correctness for those data types carried out in ACL2, as well as sample ACL2 implementations of relevant algorithms utilizing these efficient, high-assurance data structures.
Regional Variation in Rivalry: Canadians Really are Friendlier

Spectator sports embody social group conflict, where consumers periodically interact with opposing fans, thereby providing outlets for negative brand affect in the form of acrimony toward rivals. T...
The Federal Reserve as global lender of last resort, 2007-2010

Passage of the Dodd-Frank financial reform bill, in conjunction with a Supreme Court ruling supporting a Freedom of Information Act request, required the Federal Reserve (Fed) to disclose bank-specific information about its emergency lending during the financial crisis. The disclosures revealed the extent to which the Fed had served as a global lender of last resort, providing dollar liquidity to foreign banks and foreign central banks. I exploit these unanticipated disclosures on two levels. First, I use the disclosed information to evaluate the motivations behind the Fed's global lending during the crisis. My findings indicate that the Fed supported foreign banks in countries in which U.S. money center banks had high loan exposures, which suggests that the Fed served the interests of major U.S. banks. Second, I explore the congressional response to the revelation of the Fed's massive global lending. I analyze an "Audit the Fed" vote in the House of Representatives that would end the Fed's confidentiality about the banks and countries it supports and reduce its monetary policy independence. I find the influence of U.S. money center banks also extends to Congress by way of campaign contributions: contributions from these banks significantly reduce the likelihood that a representative will vote in favor of the bill. In addition, I find that right-wing representatives were substantially more likely than their left-wing peers to support the bill, which suggests that new congressional coalitions are forming on the role of the Fed in the (global) economy.
Hormone resistance, invasiveness, and metastatic potential in breast cancer

Critical phenotypic changes that occur during the progression of breast cancer include the loss of hormone-dependence, acquired resistance to systemic therapies, and increased metastatic potential. We have isolated a series of MCF-7 human breast cancer variants which exhibit hormone-independent growth, antiestrogen resistance, and increased metastatic potential. Analysis of the phenotypes of these variants strongly suggests that changes in the expression of specific genes may be critical to the generation of phenotypic diversity in the process of malignant progression in breast cancer. Epigenetic changes may contribute significantly to the generation of these phenotypic changes observed during breast cancer progression. Many of the characteristics of the progressed phenotypes appear to have arisen in response to appropriate selective pressures (growth in ovariectomized nude mice; growth in the presence of antiestrogens). These observations are consistent with the concept of clonal selection and expansion in the process of malignant progression.
Reinforcement Learning based Curriculum Optimization for Neural Machine Translation

We consider the problem of making efficient use of heterogeneous training data in neural machine translation (NMT). Specifically, given a training dataset with a sentence-level feature such as noise, we seek an optimal curriculum, or order for presenting examples to the system during training. Our curriculum framework allows examples to appear an arbitrary number of times, and thus generalizes data weighting, filtering, and fine-tuning schemes. Rather than relying on prior knowledge to design a curriculum, we use reinforcement learning to learn one automatically, jointly with the NMT system, in the course of a single training run. We show that this approach can beat uniform baselines on Paracrawl and WMT English-to-French datasets by +3.4 and +1.3 BLEU respectively. Additionally, we match the performance of strong filtering baselines and hand-designed, state-of-the-art curricula.
Precision Matrix Estimation with Noisy and Missing Data

Estimating conditional dependence graphs and precision matrices are some of the most common problems in modern statistics and machine learning. When data are fully observed, penalized maximum likelihood-type estimators have become standard tools for estimating graphical models under sparsity conditions. Extensions of these methods to more complex settings where data are contaminated with additive or multiplicative noise have been developed in recent years. In these settings, however, the relative performance of different methods is not well understood and algorithmic gaps still exist. In particular, in high-dimensional settings these methods require using non-positive semidefinite matrices as inputs, presenting novel optimization challenges. We develop an alternating direction method of multipliers (ADMM) algorithm for these problems, providing a feasible algorithm to estimate precision matrices with indefinite input and potentially nonconvex penalties. We compare this method with existing alternative solutions and empirically characterize the tradeoffs between them. Finally, we use this method to explore the networks among US senators estimated from voting records data.
Testing for Shifts in a Time Trend Panel Data Model with Serially Correlated Error Component Disturbances

This paper studies testing of shifts in a time trend panel data model with serially correlated error component disturbances, without any prior knowledge of whether the error term is stationary or n...
Effects of cerebellar transcranial direct current stimulation on the cognitive stage of sequence learning.

The cerebellum contributes to motor and cognitive processes. Investigating the cognitive contributions of the cerebellum in explicit sequence learning stands to provide new insights into this learn...
Role of Mechanical Bowel Preparation and Perioperative Antibiotics in Pediatric Pull-Through Procedures.

BACKGROUND There are no clear guidelines for the use of mechanical bowel preparation and postoperative antibiotics in children undergoing elective colorectal pull-through surgery. The objective of this study was to determine whether preoperative bowel preparation administration or duration of postoperative antibiotics impacted the rate of complications after elective pediatric pull-through surgery. MATERIALS AND METHODS Patients aged <18 y who underwent a pull-through procedure between 2011 and 2017 were retrospectively identified. Patient data included diagnosis, procedure, administration of mechanical bowel preparation, and duration of perioperative intravenous (IV) antibiotics. Outcomes of interest included surgical site infections and anastomotic complications. RESULTS A total of 180 patients met inclusion criteria, of which 47.2% received mechanical bowel preparation. The combined rate of infectious and anastomotic complications was 12.2%. There was no significant difference in combined complication rate among those receiving bowel preparation compared with those who did not (14.1% versus 10.5%, P = 0.46). Administration of bowel preparation in the perineal anoplasty subgroup was associated with higher rates of wound infection (33.3% versus 3.3%, P = 0.05). One hundred five patients (58.3%) received perioperative IV antibiotics for ≤24 h. This group had similar rates of complications (13.3%) compared with those receiving IV antibiotics for longer than 24 h (11.6%, P = 0.74). CONCLUSIONS Although mechanical bowel preparation did not affect the overall complication rate for pull-through procedures, it was associated with more wound infections in those undergoing perineal anoplasty. Duration of postoperative IV antibiotics was not significantly associated with the rate of wound and anastomotic complications.
Graph Matching Networks for Learning the Similarity of Graph Structured Objects

This paper addresses the challenging problem of retrieval and matching of graph structured objects, and makes two key contributions. First, we demonstrate how Graph Neural Networks (GNN), which have emerged as an effective model for various supervised prediction problems defined on structured data, can be trained to produce embedding of graphs in vector spaces that enables efficient similarity reasoning. Second, we propose a novel Graph Matching Network model that, given a pair of graphs as input, computes a similarity score between them by jointly reasoning on the pair through a new cross-graph attention-based matching mechanism. We demonstrate the effectiveness of our models on different domains including the challenging problem of control-flow-graph based function similarity search that plays an important role in the detection of vulnerabilities in software systems. The experimental analysis demonstrates that our models are not only able to exploit structure in the context of similarity learning but they can also outperform domain-specific baseline systems that have been carefully hand-engineered for these problems.
Salesforce Contracting under Uncertain Demand and Supply: Double Moral Hazard and Optimality of Smooth Contracts

Uncertainty in both demand and supply as well as unobservable firm actions related to supply are important for determining the right sales force compensation contract.
Threats on Logic Locking: A Decade Later

To reduce the cost of ICs and to meet the market's demand, a considerable portion of manufacturing supply chain, including silicon fabrication, packaging and testing may be pushed offshore. Utilizing a global IC manufacturing supply chain, and inclusion of non-trusted parties in the supply chain has raised concerns over security and trust related challenges including those of overproduction, counterfeiting, IP piracy, and Hardware Trojans to name a few. To reduce the risk of IC manufacturing in an untrusted and globally distributed supply chain, the researchers have proposed various locking and obfuscation mechanisms for hiding the functionality of the ICs during the manufacturing, that requires the activation of the IP after fabrication using the key value(s) that is only known to the IP/IC owner. At the same time, many such proposed obfuscation and locking mechanisms are broken with attacks that exploit the inherent vulnerabilities in such solutions. The past decade of research in this area, has resulted in many such defense and attack solutions. In this paper, we review a decade of research on hardware obfuscation from an attacker perspective, elaborate on attack and defense lessons learned, and discuss future directions that could be exploited for building stronger defenses.
A Froggatt–Nielsen flavor model for neutrino physics

Superheavy neutrinos can, via the seesaw model, provide a mechanism for lepton number violation. If they are combined with flavor violation as characterized by the Froggatt–Nielsen mechanism, then ...
New roles for icosahedral clusters in intermetallic phases: micelle-like segregation of Ca-Cd and Cu-Cd interactions in Ca10Cd27Cu2.

Despite significant progress in the structural characterization of the quasicrystalline state, the chemical origins of long- and short-range icosahedral order remain mysterious and a subject of debate. In this Article, we present the crystal structure of a new complex intermetallic phase, Ca10Cd27Cu2 (mC234.24), whose geometrical features offer clues to the driving forces underlying the icosahedral clusters that occur in Bergman-type quasicrystals. Ca10Cd27Cu2 adopts a C-centered monoclinic superstructure of the 1/1 Bergman approximant structure, in which [110] layers of Bergman clusters in the 1/1 structure are separated through the insertion of additional atoms (accompanied by substantial positional disorder). An examination of the coordination environments of Ca and Cu (in the ordered regions) reveals that the structure can be viewed as a combination of coordination polyhedra present in the nearest binary phases in the Ca–Cd–Cu compositional space. A notable feature is the separation of Ca–Cd and Cu–Cd...
Exploiting selection at linked sites to infer the rate and strength of adaptation

Genomic data encode past evolutionary events and have the potential to reveal the strength, rate and biological drivers of adaptation. However, joint estimation of adaptation rate (α) and adaptation strength remains challenging because evolutionary processes such as demography, linkage and non-neutral polymorphism can confound inference. Here, we exploit the influence of background selection to reduce the fixation rate of weakly beneficial alleles to jointly infer the strength and rate of adaptation. We develop a McDonald–Kreitman-based method to infer adaptation rate and strength, and estimate α = 0.135 in human protein-coding sequences, 72% of which is contributed by weakly adaptive variants. We show that, in this adaptation regime, α is reduced ~25% by linkage genome-wide. Moreover, we show that virus-interacting proteins undergo adaptation that is both stronger and nearly twice as frequent as the genome average (α = 0.224, 56% due to strongly beneficial alleles). Our results suggest that, while most adaptation in human proteins is weakly beneficial, adaptation to viruses is often strongly beneficial. Our method provides a robust framework for estimation of adaptation rate and strength across species. A method to infer the strength and rate of adaptation based on the influence of background selection to reduce the fixation rate of weakly beneficial alleles shows that weakly beneficial alleles contribute substantially to adaptation in humans.
Rare genetic variation and balanced polymorphisms are important for survival in global change conditions

Standing genetic variation is important for population persistence in extreme environmental conditions. While some species may have the capacity to adapt to predicted average future global change c...
Synthesis of 3-vinylpiperidine

A novel synthesis of 3-vinylpiperidine from commercially available ethyl 3-pyridylacetate is described.