cssi/SciSciGPT-SciSciNet huggingface dataset:
Adaptive Normalization in Streaming Data

In today's digital era, data are everywhere from Internet of Things to health care or financial applications. This leads to potentially unbounded ever-growing Big data streams and it needs to be utilized effectively. Data normalization is an important preprocessing technique for data analytics. It helps prevent mismodeling and reduce the complexity inherent in the data especially for data integrated from multiple sources and contexts. Normalization of Big Data stream is challenging because of evolving inconsistencies, time and memory constraints, and non-availability of whole data beforehand. This paper proposes a distributed approach to adaptive normalization for Big data stream. Using sliding windows of fixed size, it provides a simple mechanism to adapt the statistics for normalizing changing data in each window. Implemented on Apache Storm, a distributed real-time stream data framework, our approach exploits distributed data processing for efficient normalization. Unlike other existing adaptive approaches that normalize data for a specific use (e.g., classification), ours does not. Moreover, our adaptive mechanism allows flexible controls, via user-specified thresholds, for normalization tradeoffs between time and precision. The paper illustrates our proposed approach along with a few other techniques and experiments on both synthesized and real-world data. The normalized data obtained from our proposed approach, on 160,000 instances of data stream, improves over the baseline by 89% with 0.0041 root-mean-square error compared with the actual data.
Neonatal early onset sepsis in Middle Eastern countries: a systematic review

Background Early onset neonatal sepsis (EOS) accounts for a significant portion of neonatal mortality, which accounts for 46% of global under five child mortality. Objective This systematic review studies the bacterial aetiology of EOS in the Middle East, susceptibility patterns to recommended empirical antibiotic therapy and whether this differs between high-income and middle-income countries in the region. Methods Articles were collected from Medline, Web of Science, the Cochrane Library and Index Medicus for the Eastern Mediterranean Region. The articles included in our systematic review met the following criteria: published after January 2000, data relevant to the Middle East, data specific for early onset sepsis, no language restriction. Data on aetiology and susceptibility were extracted from prospective and retrospective studies. Risk of bias was assessed using the Newcastle-Ottawa Scale. This study focused on EOS but does include data regarding neonatal late-onset sepsis antibiotic susceptibility. The data regarding coagulase-negative Staphylococcus species were excluded from final analysis, as possible contaminants. The protocol for this systematic review was registered on PROSPERO: CRD42017060662. Results 33 articles from 10 countries were included in the analysis. There were 2215 cases of culture-positive EOS, excluding coagulase-negative Staphylococcus. In middle-income countries, Klebsiella species (26%), Staphylococcus aureus (17%) and Escherichia coli (16%) were the most common pathogens, in contrast to group B Streptococcus (26%), E. coli (24%) and Klebsiella (9%) in high-income countries. Overall susceptibility to ampicillin/gentamicin and third-generation cephalosporin were 40% and 37%, respectively, in middle-income countries versus 93% and 91%, respectively, in high-income countries. Conclusions EOS in middle-income countries was more likely to be due to Gram-negative pathogens and less likely to be susceptible to empirical antibiotic therapy. This has important public health implications regarding neonatal mortality in the Middle East region.
HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories

GitHub has become an important platform for code sharing and scientific exchange. With the massive number of repositories available, there is a pressing need for topic-based search. Even though the topic label functionality has been introduced, the majority of GitHub repositories do not have any labels, impeding the utility of search and topic-based analysis. This work targets the automatic repository classification problem as keyword-driven hierarchical classification. Specifically, users only need to provide a label hierarchy with keywords to supply as supervision. This setting is flexible, adaptive to the users' needs, accounts for the different granularity of topic labels and requires minimal human effort. We identify three key challenges of this problem, namely (1) the presence of multi-modal signals; (2) supervision scarcity and bias; (3) supervision format mismatch. In recognition of these challenges, we propose the HiGitClass framework, comprising of three modules: heterogeneous information network embedding; keyword enrichment; topic modeling and pseudo document generation. Experimental results on two GitHub repository collections confirm that HiGitClass is superior to existing weakly-supervised and dataless hierarchical classification methods, especially in its ability to integrate both structured and unstructured data for repository classification. Code and datasets related to this paper are available at https://github.com/yuzhimanhua/HiGitClass.
A fiscal theory of monetary policy with partially-repaid long-term debt

Abstract I construct a simple model with sticky prices and interest rate targets, closed by fiscal theory of the price level with long-term debt and fiscal and monetary policy rules. Fiscal surpluses rise following deficits, to repay accumulated debt, but surpluses do not respond to all values of unexpected inflation and deflation. This specification avoids common puzzles and produces reasonable responses to fiscal and monetary policy shocks. It allows an easy translation of any new-Keynesian model, and it allows one to study a whole sample with active fiscal policy.
Separation between second price auctions with personalized reserves and the revenue optimal auction

Abstract When buyer valuations are drawn IID from a known regular distribution, a second price auction with a symmetric reserve price is the revenue-optimal single-item auction. When this distribution is irregular, we provide the first separation result showing that a second price auction with reserves earns at most 0.778 times the revenue of Myerson’s optimal auction, even when the reserves can be asymmetric. Since the lower bound is 0.745 for i.i.d. buyers, our result is nearly tight.
Interactive music for multisensory e‐commerce: The moderating role of online consumer involvement in experiential value, cognitive value, and purchase intention

Background music adds a multi-sensory element to marketing and e-commerce. Applying interactive sensory-enabling technologies (SETs) to online shopping websites is an area of interest of sensory marketing. This research examines interactive background music in ecommerce and investigates how online consumer involvement moderates the effects of interactive music. Single-factor experiments with three conditions (interactive music, static background music, and control) were conducted to investigate its impact on experiential value, cognitive value, and purchase intention of high- and low-involvement consumers among both students (Study 1, N = 251) and non-student samples (Study 2, N = 218). Different music genres were applied to stimuli of the two studies to demonstrate generalizability of the findings. Results find that interactive music enhances the experiential value of e-commerce for low-involvement consumers. By contrast, high-involvement consumers show greater purchase intention under the interactive music condition due to a heightened level of perceived cognitive value. Involvement is an effective predictor of elaboration and purchase intention under the interactive music condition, but not under the other two conditions. The contribution is twofold: 1) it shows the impact of music as an interactive SET and, 2) demonstrates the moderating role of consumer involvement in the context of multi-sensory integration in e-commerce. Theoretical and practical implications are discussed along with limitations and directions for future research.
Inclusion of quebracho tannin extract in a high-roughage cattle diet alters digestibility, nitrogen balance, and energy partitioning.

Condensed tannins (CT) might improve animal and system-level efficiency due to enhanced protein efficiency and reduced CH4. This study evaluated the impact of quebracho tannin (QT) extract fed at 0%, 1.5%, 3%, and 4.5% of dry matter (DM), within a roughage-based diet on apparent digestibility of DM, organic matter (OM), fibrous fractions, and N retention and energy partitioning of growing steers (236 ± 16 kg BW). A Latin rectangle design with eight animals and four periods was used to determine the whole-animal exchange of CO2, O2, and CH4 as well as the collection of total feces and urine over a 48-h period, using two open-circuit, indirect calorimetry respiration chambers. Following the removal of steers from respiration chambers, rumen inoculum was collected to determine ruminal parameter, including volatile fatty acids (VFA) and ammonia. Animals were fed a 56.5% roughage diet at 1.7% BW (dry matter basis). Dry matter and gross energy intakes were influenced by the level of QT inclusion (P ≤ 0.036). Digestibility of DM, OM, and N was reduced with QT inclusion (P 0.123). QTs altered the N excretion route, average fecal N-to-total N ratio excreted increased 14%, and fecal N-to-urinary N ratio increased 38% (P < 0.001) without altering the retained N. Increased fecal energy with QT provision resulted in reduced dietary digestible energy (DE) concentration (Mcal/kg DM; P = 0.024). There were no differences in urinary energy (P = 0.491), but CH4 energy decreased drastically (P = 0.007) as QT inclusion increased. Total ruminal VFA concentration did not differ across treatments, but VFA concentration increased linearly with QT inclusion (P = 0.049). Metabolizable energy (ME) was not affected by the QT rate, and the conversion efficiency of DE-to-ME did not differ. Heat energy decreased (P = 0.013) with increased QT provision likely due to changes in the DE intake, but there was no difference in retained energy. There were no differences for retained energy or N per CO2 equivalent emission produced (P = 0.774 and 0.962, respectively), but improved efficiency for energy retention occurred for 3% QT. We concluded that QT provided up to 4.5% of dry matter intake (about 3.51% of CT, dry matter basis) does not affect N and energy retention within the current setting. Feeding QT reduced energy losses in the form of CH4 and heat, but the route of energy loss appears to be influenced by the rate of QT inclusion.
Hard X-ray Microflares down to 3 keV

The excellent sensitivity, spectral and spatial resolution, and energy coverage down to 3 keV provided by the Reuven Ramaty High-Energy Solar Spectroscopic Imager mission (RHESSI) allows for the first time the detailed study of the locations and the spectra of solar microflares down to 3 keV. During a one-hour quiet interval (GOES soft X-ray level around B6) on 2 May, 1:40–2:40 UT, at least 7 microflares occurred with the largest peaking at A6 GOES level. The microflares are found to come from 4 different active regions including one behind the west limb. At 7′′ resolution, some events show elongated sources, while others are unresolved point sources. In the impulsive phase of the microflares, the spectra can generally be fitted better with a thermal model plus power law above ∼ 6–7 keV than with a thermal only. The decay phase sometimes can be fitted with a thermal only, but in some events, power-law emission is detected late in the event indicating particle acceleration after the thermal peak of the event. The behind-the-limb microflare shows thermal emissions only, suggesting that the non-thermal power law emission originates lower, in footpoints that are occulted. The power-law fits extend to below 7 keV with exponents between −5 and −8, and imply a total non-thermal electron energy content between 1026–1027 erg. Except for the fact that the power-law indices are steeper than what is generally found in regular flares, the investigated microflares show characteristics similar to large flares. Since the total energy in non-thermal electrons is very sensitive to the value of the power law and the energy cutoff, these observations will give us better estimates of the total energy input into the corona. (Note that color versions of figures are on the accompanying CD-ROM.)
Urinary Volatile Terpenes Analyzed by Gas Chromatography-Mass Spectrometry to Monitor Breast Cancer Treatment Efficacy in Mice.

Urinary volatile terpene (VT) levels are significantly altered with induced models of breast cancer in mice. The question arises whether VTs can detect efficacy of anti-tumor treatments. BALB/c mic...
Secondary Motor Cortex Transforms Spatial Information into Planned Action during Navigation

Fluid navigation requires constant updating of planned movements to adapt to evolving obstacles and goals. For that reason, a neural substrate for navigation demands spatial and environmental information and the ability to effect actions through efferents. The secondary motor cortex (M2) is a prime candidate for this role given its interconnectivity with association cortices that encode spatial relationships and its projection to the primary motor cortex. Here, we report that M2 neurons robustly encode both planned and current left/right turning actions across multiple turn locations in a multi-route navigational task. Comparisons within a common statistical framework reveal that M2 neurons differentiate contextual factors, including environmental position, route, action sequence, orientation, and choice availability. Despite significant modulation by environmental factors, action planning, and execution are the dominant output signals of M2 neurons. These results identify the M2 as a structure integrating spatial information toward the updating of planned movements.
Fix–Neyman Process†

Introduced in 1951 in their study of survival of cancer patients, Fix–Neyman process is a continuous time stochastic process with two transient states S1 and S2 and two absorbing states R1 and R2, where S1 is the wellness state, S2 the illness state, R1 the death state from cancer and R2 the death state from other causes. Let pij(0, t) be the transition probability from state si at t = 0 to state sj at time t. Two simultaneous differential equations are established and solutions derived for pij(0, t), for i, j = 1, 2. Formulas for the death transition probabilities from si to Rd, Qid(0, t), are also given. Another useful concept is the expected duration of stay in each of the four states during (0, t). The model is useful in areas of research where states S1 and S2 can be defined. Keywords: stochastic processes; transition probabilities; expected duration of stay
Openness, Relative Prices and Macro Policies

Abstract This paper analyzes the role of relative prices in the conduct of wage indexation, monetary policy, and exchange rate policy in a small economy producing traded and nontraded goods under a managed float exchange rate regime. It is shown that the beneficial effect of using relative prices in addition to aggregate prices as indicators for the conduct of the above policies increases with openness. The response of policies to relative prices rises with openness, and has dampening effects on the volatility of deviations from purchasing power parity.
Structure of a Language for a Numerical Analysis Problem Solving System

The Numerical Analysis Problem Solving System (NAPSS) project has been undertaken at Purdue University to design and construct an interactive system for solving numerical problems [1]. The system is designed to accept input in a language which is very close to natural mathematical notation, and also to provide for the solution of problems without requiring specially trained programmers and numerical analysts.
Lifetime Models and Risk Assessment

In this article we review several methods used in analyzing lifetime data. Lifetime data have found many applications in biomedical research, engineering, as well as in social sciences. Lifetime data can be defined as the times to the occurrence of a certain event. Statistical methods for analyzing lifetime data include the definition of survival time, censored data, prediction of probability of survival, prediction of instantaneous hazards, and evaluation of cure rates. We review different types of lifetime data, methods for estimating hazard and survival functions, methods for risk assessment, as well as multivariate methods for survival data. Lifetime models based on stochastic processes hitting a boundary, such as threshold regression for risk assessment, are also discussed. Keywords: hazard function; survival function; censored data; failure-time models; first-hitting-time models; threshold regression
Wilkinson's tests and econometric software

The Wilkinson Tests, entry-level tests for assessing the numerical accuracy of statistical computations, have been applied to statistical software packages. Some software developers, having failed these tests, have corrected deficiencies in subsequent versions. Thus these tests have had a meliorative impact on the state of statistical software. These same tests are applied to several econometrics packages. Many deficiencies are noted.
Chapter 7 – Recrystallization

The earlier chapters have described creep as a process where dislocation hardening is accompanied by dynamic recovery. It should be discussed at this point that dynamic recovery is not the only (dynamic) restoration mechanism that may occur with dislocation hardening. Recrystallization can also occur and this process can also “restore” the metal and reduce the flow stress. Often, recrystallization during deformation (dynamic recrystallization (DRX)) is observed at relatively high strain rates that are outside the common creep realm. However, any complete discussion of elevated temperature creep, and particularly, a discussion of high temperature plasticity must include this restoration mechanism. An understanding of the hot working (high strain rates and high temperatures) requires an appreciation of both dynamic recovery and DRX processes. Some definitions are probably useful, and we will use those definitions adopted by Doherty et al.
Uncertainty and Labor Contract Durations

This paper provides an empirical investigation into the relationship between ex ante U.S. labor contract durations and uncertainty over the period 1970 to 1995. We construct measures of inflation uncertainty as well as aggregate nominal and real uncertainty. The results not only corroborate previous findings of an inverse relationship between contract durations and inflation uncertainty, but also document that this relationship extends to both measures of aggregate uncertainty. We also explore the robustness of this relationship to various measures of inflation uncertainty that have appeared in the literature.
3 – Elliptic Equations

Publisher Summary This chapter discusses the elliptic equations. Equilibrium problems in two-dimensional and higher, continua give rise to elliptic partial differential equations. A prototype is the famous equation of Laplace is uxx + uyy + uzz = 0. This equation holds for the steady temperature in an isotropic medium, characterizes gravitational or electrostatic potentials at points of empty space, and describes the velocity potential of an irrotational, incompressible fluid flow. The two-dimensional counterpart of equation lies at the foundation of theory of analytic functions of a complex variable. An alternative argument employs the so-called maximum modulus theorem states that the solution of equation has no maxima or minima at interior points of the domain of integration. A closed boundary is desirable for a well-posed elliptic problem. The requirement of analyticity on the elements of A and h does not, on the face of it, seem to be too serious a restriction as any continuous function can be approximated arbitrarily closely and uniformly in any given interval by polynomials, which are clearly analytic.
Finite type invariants of 3-manifolds

This is a survey article on finite type invariants of 3-manifolds written for the Encyclopedia of Mathematical Physics to be published by Elsevier.
In utero estrogenic endocrine disruption alters the stroma to increase extracellular matrix density and mammary gland stiffness

In utero endocrine disruption is linked to increased risk of breast cancer later in life. Despite numerous studies establishing this linkage, the long-term molecular changes that predispose mammary cells to carcinogenic transformation are unknown. Herein, we investigated how endocrine disrupting compounds (EDCs) drive changes within the stroma that can contribute to breast cancer susceptibility. We utilized bisphenol A (BPA) as a model of estrogenic endocrine disruption to analyze the long-term consequences in the stroma. Deregulated genes were identified by RNA-seq transcriptional profiling of adult primary fibroblasts, isolated from female mice exposed to in utero BPA. Collagen staining, collagen imaging techniques, and permeability assays were used to characterize changes to the extracellular matrix. Finally, gland stiffness tests were performed on exposed and control mammary glands. We identified significant transcriptional deregulation of adult fibroblasts exposed to in utero BPA. Deregulated genes were associated with cancer pathways and specifically extracellular matrix composition. Multiple collagen genes were more highly expressed in the BPA-exposed fibroblasts resulting in increased collagen deposition in the adult mammary gland. This transcriptional reprogramming of BPA-exposed fibroblasts generates a less permeable extracellular matrix and a stiffer mammary gland. These phenotypes were only observed in adult 12-week-old, but not 4-week-old, mice. Additionally, diethylstilbestrol, known to increase breast cancer risk in humans, also increases gland stiffness similar to BPA, while bisphenol S does not. As breast stiffness, extracellular matrix density, and collagen deposition have been directly linked to breast cancer risk, these data mechanistically connect EDC exposures to molecular alterations associated with increased disease susceptibility. These alterations develop over time and thus contribute to cancer risk in adulthood.
The geographic accessibility of child care subsidies and evidence on the impact of subsidy receipt on childhood obesity

Abstract This paper examines the impact of the spatial accessibility of public human services agencies on the likelihood of receiving a child care subsidy among disadvantaged mothers with young children. In particular, we collect data on the location of virtually every human services agency in the US and use this information to calculate the approximate distance that families must travel from home in order to reach the nearest office that administers the subsidy application process. Using data from the Kindergarten cohort of the Early Childhood Longitudinal Study (ECLS-K), our results indicate that an increase in the distance to a public human services agency reduces the likelihood that a family receives a child care subsidy. Specifically, we estimate an elasticity of subsidy receipt with respect to distance of −0.13. The final section of the paper provides an empirical application in which we use variation in families’ travel distance to identify the causal effect of child care subsidies on children’s weight outcomes. Our instrumental variables estimates suggest that subsidized child care leads to sizeable increases in the prevalence of overweight and obesity among low-income children.
Cryptanalysis of a Message Recognition Protocol by Mashatan and Stinson.

At CANS 2008, Mashatan and Stinson suggested a message recognition protocol for ad hoc pervasive networks. The protocol provides a procedure to resynchronize in case of a (possibly adversarial) disruption of communication. We show that this resynchronization process does not provide the functionality intended and in fact enables an adversary to create selective forgeries. The computational effort for the attack is negligible and allows the insertion of arbitrary messages.
Seizure onset location shapes dynamics of initiation.

Abstract Objective Ictal electrographic patterns are widely thought to reflect underlying neural mechanisms of seizures. Here we studied the degree to which seizure patterns are consistent in a given patient, relate to particular brain regions and if two candidate biomarkers (high-frequency oscillations, HFOs; infraslow activity, ISA) and network activity, as assessed with cross-frequency interactions, can discriminate between seizure types. Methods We analyzed temporal changes in low and high frequency oscillations recorded during seizures, as well as phase-amplitude coupling (PAC) to monitor the interactions between delta/theta and ripple/fast ripple frequency bands at seizure onset. Results Seizures of multiple electrographic patterns were observed in a given patient and brain region. While there was an increase in HFO rate across different electrographic patterns, there are specific relationships between types of HFO activity and onset region. Similarly, changes in PAC dynamics were more closely related to seizure onset region than they were to electrographic patterns while ISA was a poor indicator for seizure onset. Conclusions Our findings suggest that the onset region sculpts neurodynamics at seizure initiation and that unique features of the cytoarchitecture and/or connectivity of that region play a significant role in determining seizure mechanism. Significance To learn how seizures are initiated, researchers would do well to consider other aspects of their manifestation, in addition to their electrographic patterns. Examination of onset pattern in conjunction with the interactions between different oscillatory frequencies in the context of different brain regions might be more informative and lead to more reliable clinical inference as well as novel therapeutic approaches.
Discussion of the Paper “Prediction, Estimation, and Attribution” by B. Efron

We enjoyed reading Professor Efron’s (Brad) paper just as much as we enjoyed listening to his June 2019 lecture in Leiden. One of the core values underlying statistical research is in how it enable...
Taste of time: A porous-medium model for human tongue surface with implications for early taste perception

Most sensory systems are remarkable in their temporal precision, reflected in such phrases as "a flash of light" or "a twig snap". Yet taste is complicated by the transport processes of stimuli through the papilla matrix to reach taste receptors, processes that are poorly understood. We computationally modeled the surface of the human tongue as a microfiber porous medium and found that time-concentration profiles within the papilla zone rise with significant delay that well match experimental ratings of perceived taste intensity to a range of sweet and salty stimuli for both rapid pulses and longer sip-and-hold exposures. Diffusivity of these taste stimuli, determined mostly by molecular size, correlates greatly with time and slope to reach peak intensity: smaller molecular size may lead to quicker taste perception. Our study demonstrates the novelty of modeling the human tongue as a porous material to drastically simplify computational approaches and that peripheral transport processes may significantly affect the temporal profile of taste perception, at least to sweet and salty compounds.
Fluent Response Generation for Conversational Question Answering

Question answering (QA) is an important aspect of open-domain conversational agents, garnering specific research focus in the conversational QA (ConvQA) subtask. One notable limitation of recent ConvQA efforts is the response being answer span extraction from the target corpus, thus ignoring the natural language generation (NLG) aspect of high-quality conversational agents. In this work, we propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness. From a technical perspective, we use data augmentation to generate training data for an end-to-end system. Specifically, we develop Syntactic Transformations (STs) to produce question-specific candidate answer responses and rank them using a BERT-based classifier (Devlin et al., 2019). Human evaluation on SQuAD 2.0 data (Rajpurkar et al., 2018) demonstrate that the proposed model outperforms baseline CoQA and QuAC models in generating conversational responses. We further show our model’s scalability by conducting tests on the CoQA dataset. The code and data are available at https://github.com/abaheti95/QADialogSystem.
Is Your Classifier Actually Biased? Measuring Fairness under Uncertainty with Bernstein Bounds

Most NLP datasets are not annotated with protected attributes such as gender, making it difficult to measure classification bias using standard measures of fairness (e.g., equal opportunity). However, manually annotating a large dataset with a protected attribute is slow and expensive. Instead of annotating all the examples, can we annotate a subset of them and use that sample to estimate the bias? While it is possible to do so, the smaller this annotated sample is, the less certain we are that the estimate is close to the true bias. In this work, we propose using Bernstein bounds to represent this uncertainty about the bias estimate as a confidence interval. We provide empirical evidence that a 95% confidence interval derived this way consistently bounds the true bias. In quantifying this uncertainty, our method, which we call Bernstein-bounded unfairness, helps prevent classifiers from being deemed biased or unbiased when there is insufficient evidence to make either claim. Our findings suggest that the datasets currently used to measure specific biases are too small to conclusively identify bias except in the most egregious cases. For example, consider a co-reference resolution system that is 5% more accurate on gender-stereotypical sentences -- to claim it is biased with 95% confidence, we need a bias-specific dataset that is 3.8 times larger than WinoBias, the largest available.
The returns on government and corporate securities with prices propped up by central bank purchases using unlimited quantities of currencies with less intrinsic value than toilet paper

This article evaluates several investment scenarios that could result from the rather dire economic situation existing in early 2020 caused by the coronavirus pandemic that is restricting economic production of goods and services at the same time that cost-push factors and the unlimited creation of money by central banks worldwide create upward inflationary pressures. Just as during the similar situation in Germany in 1923, the nominal prices of both equities and fixed-rate instruments might be propped up, but all security investments would generate highly negative real returns short-term. Only stock prices might eventually match the inflation rate. TOPIC:Financial crises and financial market history Key Findings • This article provides a timely evaluation of the investment outlook in the context of various scenarios that may unfold as a result of the various measures implemented to prevent the spread of the coronavirus that are inhibiting the production of goods and services at the same time that unlimited monetary and fiscal stimulus is being applied. • With the economic outlook in 2020 being very similar to that in Germany in 1923, the returns on stocks and bonds in that hyperinflation over a century ago are explained to provide perspective on the potential returns on different asset types if the worldwide monetization of the large fiscal deficits continues unabated.
Divergent Synthesis of Monosubstituted and Unsymmetrical 3,6-Disubstituted Tetrazines from Carboxylic Ester Precursors

Since tetrazines are important tools to the field of bioorthogonal chemistry, there is a need for new approaches to synthesize unsymmetrical and 3-monosubstituted tetrazines. Described here is a general, one-pot method for converting (3-methyloxetan-3-yl)methyl carboxylic esters into 3-thiomethyltetrazines. These versatile intermediates were applied to the synthesis of unsymmetrical tetrazines through Pd-catalyzed cross-coupling and in the first catalytic thioether reduction to access monosubstituted tetrazines. This method enables the development of new tetrazine compounds possessing a favorable combination of kinetics, small size, and hydrophilicity. It was applied to a broad range of aliphatic and aromatic ester precursors and to the synthesis of heterocycles including BODIPY fluorophores and biotin. In addition, a series of tetrazine probes for monoacylglycerol lipase (MAGL) were synthesized and the most reactive one was applied to the labeling of endogenous MAGL in live cells.
A Framework for Sample Efficient Interval Estimation with Control Variates

We consider the problem of estimating confidence intervals for the mean of a random variable, where the goal is to produce the smallest possible interval for a given number of samples. While minimax optimal algorithms are known for this problem in the general case, improved performance is possible under additional assumptions. In particular, we design an estimation algorithm to take advantage of side information in the form of a control variate, leveraging order statistics. Under certain conditions on the quality of the control variates, we show improved asymptotic efficiency compared to existing estimation algorithms. Empirically, we demonstrate superior performance on several real world surveying and estimation tasks where we use the output of regression models as the control variates.
A cellular wireless local area network with QoS guarantees for heterogeneous traffic

A wireless local area network (WLAN) or a cell with quality-of-service (QoS) guarantees for various types of traffic is considered. A centralized (i.e., star) network is adopted as the topology of a cell which consists of a base station and a number of mobile clients. Dynamic Time Division Duplexed (TDD) transmission is used, and hence, the same frequency channel is time-shared for downlink and uplink transmissions under the dynamic control of the base station. We divide traffic into two classes: class I (real-time) and II (non-real-time). Whenever there is no eligible class-I traffic for transmission, class-II traffic which requires no delay-bound guarantees is transmitted, while uplink transmissions are controlled with a reservation scheme. Class-I traffic which requires a bounded delay and guaranteed throughput is handled with the framing strategy (Golestani, IEEE J. Selected Areas Commun. 9(7), 1991) which consists of a smoothness traffic model and the stop-and-go queueing scheme. We also establish the admission test for adding new class-I connections. We present a modified framing strategy for class-I voice uplink transmissions which utilizes the wireless link efficiently at the cost of some packet losses. Finally, we present the performance (average delay and throughput) evaluation of the reservation scheme for class-II traffic using both analytical calculations and simulations.
Are All Languages Created Equal in Multilingual BERT

Multilingual BERT (mBERT) trained on 104 languages has shown surprisingly good cross-lingual performance on several NLP tasks, even without explicit cross-lingual signals. However, these evaluations have focused on cross-lingual transfer with high-resource languages, covering only a third of the languages covered by mBERT. We explore how mBERT performs on a much wider set of languages, focusing on the quality of representation for low-resource languages, measured by within-language performance. We consider three tasks: Named Entity Recognition (99 languages), Part-of-speech Tagging and Dependency Parsing (54 languages each). mBERT does better than or comparable to baselines on high resource languages but does much worse for low resource languages. Furthermore, monolingual BERT models for these languages do even worse. Paired with similar languages, the performance gap between monolingual BERT and mBERT can be narrowed. We find that better models for low resource languages require more efficient pretraining techniques or more data.
The Steinberg quotient of a tilting character

Let G be a simple algebraic group over an algebraically closed field of prime characteristic. If M is a finite dimensional G-module that is projective over the Frobenius kernel of G, then its character is divisible by the character of the Steinberg module. In this paper we study such quotients, showing that if M is an indecomposable tilting module, then the multiplicities of the orbit sums appearing in its “Steinberg quotient” are well behaved.
Investigation of microbial community interactions between Lake Washington methanotrophs using -------genome-scale metabolic modeling.

Background The role of methane in global warming has become paramount to the environment and the human society, especially in the past few decades. Methane cycling microbial communities play an important role in the global methane cycle, which is why the characterization of these communities is critical to understand and manipulate their behavior. Methanotrophs are a major player in these communities and are able to oxidize methane as their primary carbon source. Results Lake Washington is a freshwater lake characterized by a methane-oxygen countergradient that contains a methane cycling microbial community. Methanotrophs are a major part of this community involved in assimilating methane from lake water. Two significant methanotrophic species in this community are Methylobacter and Methylomonas. In this work, these methanotrophs are computationally studied via developing highly curated genome-scale metabolic models. Each model was then integrated to form a community model with a multi-level optimization framework. The competitive and mutualistic metabolic interactions among Methylobacter and Methylomonas were also characterized. The community model was next tested under carbon, oxygen, and nitrogen limited conditions in addition to a nutrient-rich condition to observe the systematic shifts in the internal metabolic pathways and extracellular metabolite exchanges. Each condition showed variations in the methane oxidation pathway, pyruvate metabolism, and the TCA cycle as well as the excretion of formaldehyde and carbon di-oxide in the community. Finally, the community model was simulated under fixed ratios of these two members to reflect the opposing behavior in the two-member synthetic community and in sediment-incubated communities. The community simulations predicted a noticeable switch in intracellular carbon metabolism and formaldehyde transfer between community members in sediment-incubated vs. synthetic condition. Conclusion In this work, we attempted to predict the response of a simplified methane cycling microbial community from Lake Washington to varying environments and also provide an insight into the difference of dynamics in sediment-incubated microcosm community and synthetic co-cultures. Overall, this study lays the ground for in silico systems-level studies of freshwater lake ecosystems, which can drive future efforts of understanding, engineering, and modifying these communities for dealing with global warming issues.
PMU-Based Distributed Non-Iterative Algorithm for Real-Time Voltage Stability Monitoring

The Phasor measurement unit (PMU) measurements are mandatory to monitor the power system’s voltage stability margin in an online manner. Monitoring is key to the secure operation of the grid. Traditionally, online monitoring of voltage stability using synchrophasors required a centralized communication architecture, which leads to the high investment cost and cyber-security concerns. The increasing importance of cyber-security and low investment costs have recently led to the development of distributed algorithms for online monitoring of the grid that are inherently less prone to malicious attacks. In this work, we proposed a novel distributed non-iterative voltage stability index (VSI) by recasting the power flow equations as circles. The processors embedded at each bus in the smart grid with the help of PMUs and communication of voltage phasors between neighboring buses perform simultaneous online computations of VSI. The distributed nature of the index enables the real-time identification of the critical bus of the system with minimal communication infrastructure. The effectiveness of the proposed distributed index is demonstrated on IEEE test systems and contrasted with existing methods to show the benefits of the proposed method in speed, interpretability, identification of outage location, and low sensitivity to noisy measurements.
Guilt by Association: Finding Cosmic Ray Sources Using Hierarchical Bayesian Clustering

The Earth is continuously showered by charged cosmic ray particles, naturally produced atomic nuclei moving with velocity close to the speed of light. Among these are ultra high energy cosmic ray particles with energy exceeding 5 ×1019 eV, which is ten million times more energetic than the most energetic particles produced at the Large Hadron Collider. Astrophysical questions include: what phenomenon accelerates particles to such high energies, and what sort of nuclei are energized? Also, the magnetic deflection of the trajectories of the cosmic rays makes them potential probes of galactic and intergalactic magnetic fields. We develop a Bayesian hierarchical model that can be used to compare different association models between the cosmic rays and source population, using Bayes factors. A measurement model with directional uncertainties and accounting for non-uniform sky exposure is incorporated into the model. The methodology allows us to learn about astrophysical parameters, such as those governing the source luminosity function and the cosmic magnetic field.
Voice spoofing detection corpus for single and multi-order audio replays

Abstract The evolution of modern voice-controlled devices (VCDs) has revolutionized the Internet of Things (IoT) and resulted in the increased realization of smart homes, personalization, and home automation through voice commands. These VCDs can be exploited in IoT driven environments to generate various spoofing attacks, including the chaining of replay attacks (i.e. multi-order replay attacks). Existing datasets like ASVspoof 2017, ASVspoof 2019, and ReMASC contain only first-order replay recordings (i.e. replayed once); therefore, they cannot offer evaluation of anti-spoofing algorithms capable of detecting multi-order replay attacks. Additionally, large-scale datasets like ASVspoof 2017 and ASVspoof 2019 do not capture the characteristics of microphone arrays, which are an essential characteristic of modern VCDs. Therefore, there exists a need for a diverse replay spoofing detection corpus that consists of multi-order replay recordings against bona fide voice samples. This paper presents a novel voice spoofing detection corpus (VSDC) to evaluate the performance of multi-order replay anti-spoofing methods. The proposed VSDC consists of first-order (i.e. replayed once) and second-order replay (i.e. replayed twice) samples against the bona fide audio recordings. We ensured to create a diverse replay spoofing detection corpus in terms of environments, recording and playback devices, speakers, configurations, replay scenarios, etc. More specifically, we used 35 microphones, 25 different recording configurations, and 60 different playback configurations for first- and second-order replays to generate a total of 14,050 samples belonging to 19 speakers. Additionally, the proposed VSDC can also be used to evaluate the performance of speaker verification systems in terms of independent speaker verification. To the best of our knowledge, this is the first publicly available replay spoofing detection corpus comprised of first and second-order replay samples. Experimental results signify the effectiveness of the proposed VSDC in terms of evaluating the performance of anti-spoofing methods under multi-order replay attacks and diverse conditions.
Neural Network-Optimized Channel Estimator and Training Signal Design for MIMO Systems With Few-Bit ADCs

This paper is concerned with channel estimation in MIMO systems with few-bit ADCs. In these systems, a linear minimum mean-squared error (MMSE) channel estimator obtained in closed-form is not an optimal solution. We first consider a deep neural network (DNN) and train it as a nonlinear MMSE channel estimator for few-bit MIMO systems. We then present a first attempt to use DNN in optimizing the training signal and the MMSE channel estimator concurrently. Specifically, we propose an autoencoder with a specialized first layer, whose weights embed the training signal matrix. Consequently, the trained autoencoder prompts a new training signal designed specifically for the MIMO channel model under consideration.
Reducing veterans’ risk for suicidal behaviors: a qualitative study to inform development of the RECLAIM health promotion program

In an effort to reduce the high rate of suicide among post-9/11 veterans, a collaborative team within the Department of Veterans Affairs (VA) has developed a holistic community-based health promotion program designed to facilitate social and self-connectedness. The purpose of this study was to elicit veteran and stakeholder feedback to prepare the program for piloting and implementation. Focus groups and interviews were conducted with post-9/11 veterans and veteran stakeholders (e.g., VA clinicians) to elicit feedback regarding the health promotion program. Focus groups and interviews were audio-recorded and transcribed. Qualitative thematic analysis identified key themes emerging from the focus groups and interviews. Seven focus groups (3 Veteran groups, 4 stakeholder groups) and 3 interviews (2 Veterans, 1 stakeholder) were conducted with 41 participants (14 veterans, 27 stakeholders). Overall, participants had a positive perception of the program. Thematic analysis revealed shared perspectives that provided insight into 1) enhancing program recruitment and retention, 2) the perceived ability of a health promotion program to provide more holistic, veteran-centered care, and 3) using health promotion programs to help veterans establish structure in their daily lives. Findings indicated an overall acceptance of the program, and participants’ perspectives on how to reduce barriers and enhance facilitators can inform the development of a larger-scale health promotion program that can be tested through future research. While discussion questions were specifically focused on the program in this study, findings can be considered more broadly for the design and implementation of related programs to effectively improve the health and wellness of post-9/11 veterans.
Adaptive spline fitting with particle swarm optimization

In fitting data with a spline, finding the optimal placement of knots can significantly improve the quality of the fit. However, the challenging high-dimensional and non-convex optimization problem associated with completely free knot placement has been a major roadblock in using this approach. We present a method that uses particle swarm optimization (PSO) combined with model selection to address this challenge. The problem of overfitting due to knot clustering that accompanies free knot placement is mitigated in this method by explicit regularization, resulting in a significantly improved performance on highly noisy data. The principal design choices available in the method are delineated and a statistically rigorous study of their effect on performance is carried out using simulated data and a wide variety of benchmark functions. Our results demonstrate that PSO-based free knot placement leads to a viable and flexible adaptive spline fitting approach that allows the fitting of both smooth and non-smooth functions.