cssi/SciSciGPT-SciSciNet huggingface dataset:
Generic Preemption: Applying Conflict Preemption after Wyeth v. Levine

Despite the much-awaited Supreme Court decision in Wyeth v. Levine, preemption for generic pharmaceuticals still exists. Levine addressed conflict preemption of state failure-to-warn claims. After reviewing the federal law governing pharmaceutical warning labels, the Court found that pharmaceutical companies could modify their branded drug warning labels to meet obligations presented by state law. Consequently, conflict preemption does not apply. In contrast, generic drugs must adopt the identical label employed by their branded counterparts. The FDA does not allow the generic drug label to deviate from the branded competitor’s at any point during the approval process or thereafter. As a result, the generic manufacturer is not at liberty to make changes to its product’s warning label. State tort failure-to-warn claims pose an impossible situation – the generic manufacturer cannot comply with both the federal requirements to mirror the branded drug’s label and a state obligation to enhance or modify the label. Further, Congress eliminated much of the clinical testing requirements for generic drugs as a measure to ensure that they remain low cost options for consumers. Exposing generic drugs to tort liability under state law might serve to push generic manufacturers to conduct or support safety and efficacy testing. Conducting this type of research would certainly increase costs and drug prices, frustrating the purpose of Congress to promote generic drugs. Conflict preemption applies when it is impossible to comply with both state and federal duties under law or when state law stands as an obstacle to accomplishing the purposes of Congress. Conflict preemption applies to generic manufacturers sued under state failure-to-warn claims, but a change in the law is needed to ensure generic warning labels are adequate and that consumers injured by generic drugs are not left without any compensation for their pain, suffering, and medical bills.
COMPARATIVE STATICS OF MONEY-GOODS SPECIFICATIONS OF THE UTILITY FUNCTION

The introduction of real-cash balances into the neoclassical model of the consumer wrecks havoc, in general, on the empirically observable refutable comparative statics properties of the model. We provide the most general solution of this problem to date by deriving a symmetric and negative semidefinite generalized Slutsky matrix that is empirically observable and which contains all other such comparative statics results as a special case. In addition, we clarify and correct two aspects of Samuelson and Sato's (1984) treatment of this problem.
Debtholder responses to shareholder activism: Evidence from hedge fund interventions

We investigate the effect of shareholder activism on debtholders by examining a sample of bank loans for firms targeted by activist hedge funds. We compare loan spreads before and after intervention and show the effects of heterogeneous shareholder actions. Spreads increase when shareholder activism relies on the market for corporate control or financial restructuring. In contrast, spreads decrease when activists address managerial entrenchment. Furthermore, the effects are more pronounced when pre-existing governance mechanisms are weak. Our findings suggest that shareholder activism does not necessarily exacerbate bondholder-shareholder conflicts of interest and highlight the role of activism in aligning investors.
What is the Role of International Human Rights in the War on Terror

What role, if any, does international human rights law (IHRL) have to play in situations of armed conflict? More specifically, does IHRL have any application to the conduct of the “war on terror”? More specifically still, does IHRL partly or wholly displace the traditional law of armed conflict (LOAC) - as embodied, for instance, in the Hague and Geneva Conventions - in regulating the armed conflict that arises in the “war on terror”?We focus in this Article on the last of these questions. To sharpen the discussion, we concentrate on the applicability of the1966 International Covenant on Civil and Political Rights (ICCPR) to the conduct of hostilities in the “war on terror.” More narrowly still, we will consider the applicability of ICCPR Article 6(1) - which guarantees that “[n]o one shall be arbitrarily deprived of his life” - to combat operations in the “war on terror” by U.S. Armed Forces outside the United States.One advantage of this specific focus is that it will enable us to examine, within the brief compass of this Article, the legality of the United States’ use of unmanned Predator drone missiles to kill suspected al Qaeda targets, such as the incident involving the killing in Yemen on November 3, 2002 of Qaed Salim Sinan al-Harethi - reputedly a senior al Qaeda operative - while he was traveling with five companions in a car. Citing the ICCPR, the UN Special Rapporteur on Extrajudicial, Summary or Arbitrary Executions took the position that the attack constituted a clear case of extrajudicial killing. The United States, in reply, maintained that the ICCPR had no application to the incident on the grounds that “[t]he conduct of a government in legitimate military operations, whether against al Qaida operatives or any other legitimate military target, would be governed by the international law of armed conflict,” rather than by IHRL. The Obama Administration has continued the practice of using missiles to target and kill suspected al Qaeda and Taliban figures. It is reported that unmanned drones have become among the U.S. military’s favorite weapons in the conflicts in Iraq and Afghanistan - including for use in targeting suspected terrorist compounds - and the Obama Administration is said to be preparing a budgetary request for the development of new drone systems.In Part II, we survey the origins and growth of the LOAC and IHRL, and we discuss the beginnings of their asserted “convergence.” In Part III.A, we restate and defend the traditional view that the LOAC and IHRL fundamentally differ in their scope, purposes, and protective concerns. Then, in Part III.B, we argue that the ICCPR, in particular, was not intended, and should not be understood, to regulate the conduct of armed conflicts that are otherwise governed by the LOAC. In Part III.C, we address certain objections that have been raised against this construction of the ICCPR. Finally, in Part IV, we apply the results we have reached to the controversy over the incident involving Qaed Salim Sinan al-Harethi.
Publicity vs. impact in nonprofit disclosures and donor preferences: a sequential game with one nonprofit organization and N donors

Charitable giving is one of the essential tasks of a properly functioning civil so- ciety. This task is greatly complicated by the lack of organizational transparency and by the information asymmetries that often exist between organizations and donors in the mar- ket for charitable donations. The disclosure of financial, performance, donor-relations, and fundraising-related data is thus an important tool for nonprofit organizations attempting to attract greater donations while boosting accountability and public trust. There are, however, varying payoffs associated with such disclosure depending on the nature of donor pref- erences and the relative openness and effectiveness of competing organizations. To help understand the interplay between nonprofit organizational disclosures and individual do- nations, we present a novel game-theoretic model of disclosure-donation interactions that incorporates the predominant forms of both donor preferences and "value-relevant" infor- mation.
Evidence that Seat Belts Are as Effective as Child Safety Seats in Preventing Death for Children Aged Two and Up

Abstract Over the past thirty years, the use of child safety seats in motor vehicles has increased dramatically. There is, however, relatively little empirical evidence regarding the efficacy of child safety seats relative to the much cheaper alternative of traditional seat belts. Using data on all fatal crashes in the United States from 1975 to 2003, I find that child safety seats, in actual practice, do not provide any discernible improvement over adult lap and shoulder belts in reducing fatalities among children aged two to six. Lap-only belts are somewhat less effective, but still far superior to riding unrestrained.
Bearing False Witness: Perjured Affidavits and the Fourth Amendment

The purpose of this Article is to articulate appropriate legal doctrine to govern the problem of false statements of fact by law enforcement officers in warrant affidavits. This Article addresses the issue in the context of actions brought pursuant to 42 U.S.C. § 1983 to redress such Fourth Amendment violations. This perspective promises to be interesting and unique for two reasons. First, the fact that the guilty are ordinarily the direct beneficiaries of the Fourth Amendment has long been a matter of grave concern. In contrast, rarely, if ever, will anyone except an innocent victim of a search based on a perjured warrant affidavit be able to maintain a successful action asserting this particular Fourth Amendment violation. Second, a police officer who files a false affidavit in support of a warrant application, unlike any other defendant in a § 1983 case, is not entitled to the protection of any of the immunity doctrines which protect police officers in most cases from legal liability.
The Availability Heuristic, Intuitive Cost-Benefit Analysis, and Climate Change

Because risks are on all sides of social situations, it is not possible to be “precautionary” in general. The availability heuristic ensures that some risks stand out as particularly salient, whatever their actual magnitude. Taken together with intuitive cost-benefit balancing, the availability heuristic helps to explain differences across groups, cultures, and even nations in the assessment of precautions to reduce the risks associated with climate change. There are complex links among availability, social processes for the spreading of information, and predispositions. If the United States is to take a stronger stand against climate change, it is likely to be a result of available incidents that seem to show that climate change produces serious and tangible harm.
Estimating Individual Treatment Effects with Time-Varying Confounders

Estimating the individual treatment effect (ITE) from observational data is meaningful and practical in healthcare. Existing work mainly relies on the strong ignorability assumption that no hidden confounders exist, which may lead to bias in estimating causal effects. Some studies considering the hidden confounders are designed for static environment and not easily adaptable to a dynamic setting. In fact, most observational data (e.g., electronic medical records) is naturally dynamic and consists of sequential information. In this paper, we propose Deep Sequential Weighting (DSW) for estimating ITE with time-varying confounders. Specifically, DSW infers the hidden confounders by incorporating the current treatment assignments and historical information using a deep recurrent weighting neural network. The learned representations of hidden confounders combined with current observed data are leveraged for potential outcome and treatment predictions. We compute the time-varying inverse probabilities of treatment for re-weighting the population. We conduct comprehensive comparison experiments on fully-synthetic, semi-synthetic and real-world datasets to evaluate the performance of our model and baselines. Results demonstrate that our model can generate unbiased and accurate treatment effect by conditioning both time-varying observed and hidden confounders, paving the way for personalized medicine.
Echo-CGC: A Communication-Efficient Byzantine-Tolerant Distributed Machine Learning Algorithm in Single-Hop Radio Network

In the past few years, many Byzantine-tolerant distributed machine learning (DML) algorithms have been proposed in the point-to-point communication model. In this paper, we focus on a popular DML framework - the parameter server computation paradigm and iterative learning algorithms that proceed in rounds, e.g., [Gupta and Vaidya, 2020; El-Mhamdi et al., 2020; Chen et al., 2017]. One limitation of prior algorithms in this domain is the high communication complexity. All the Byzantine-tolerant DML algorithms that we are aware of need to send n d-dimensional vectors from worker nodes to the parameter server in each round, where n is the number of workers and d is the number of dimensions of the feature space (which may be in the order of millions). In a wireless network, power consumption is proportional to the number of bits transmitted. Consequently, it is extremely difficult, if not impossible, to deploy these algorithms in power-limited wireless devices. Motivated by this observation, we aim to reduce the communication complexity of Byzantine-tolerant DML algorithms in the single-hop radio network [Alistarh et al., 2010; Bhandari and Vaidya, 2005; Koo, 2004]. Inspired by the CGC filter developed by Gupta and Vaidya, PODC 2020 [Gupta and Vaidya, 2020], we propose a gradient descent-based algorithm, Echo-CGC. Our main novelty is a mechanism to utilize the broadcast properties of the radio network to avoid transmitting the raw gradients (full d-dimensional vectors). In the radio network, each worker is able to overhear previous gradients that were transmitted to the parameter server. Roughly speaking, in Echo-CGC, if a worker "agrees" with a combination of prior gradients, it will broadcast the "echo message" instead of the its raw local gradient. The echo message contains a vector of coefficients (of size at most n) and the ratio of the magnitude between two gradients (a float). In comparison, the traditional approaches need to send n local gradients in each round, where each gradient is typically a vector in a ultra-high dimensional space (d ≫ n). The improvement on communication complexity of our algorithm depends on multiple factors, including number of nodes, number of faulty workers in an execution, and the cost function. We numerically analyze the improvement, and show that with a large number of nodes, Echo-CGC reduces 80% of the communication under standard assumptions.
IoT-based Contact Tracing Systems for Infectious Diseases: Architecture and Analysis

The recent COVID-19 pandemic has become a major threat to human safety and well-being. Non-pharmaceutical interventions such as contact tracing solutions are important to contain the spreads of COVID-19-like infectious diseases. However, current contact tracing solutions are fragmented with limited use of sensing technologies and centered on monitoring the interactions between individuals without an analytical framework for evaluating effectiveness. Therefore, we need to first explore generic architecture for contact tracing in the context of today's Internet of Things (IoT) technologies based on a broad range of applicable sensors. A new architecture for IoT based solutions to contact tracing is proposed and its overall effectiveness for disease containment is analyzed based on the traditional epidemiological models with the simulation results. The proposed work aims to provide a framework for assisting future designs and evaluation of IoT-based contact tracing solutions and to enable data-driven collective efforts on combating current and future infectious diseases.
Using visual encounter data to improve capture–recapture abundance estimates

Capture-recapture studies are widely used in ecology to estimate population sizes and demographic rates. In some capture-recapture studies, individuals may be visually encountered but not identified. For example, if individual identification is only possible upon capture and individuals escape capture, visual encounters can result in failed captures where individual identities are unknown. In such cases, the data consist of capture histories with known individual identities, and counts of failed captures for individuals with unknown identities. These failed captures are ignored in traditional capture-recapture analyses that require known individual identities. Here we show that if animals can be encountered at most once per sampling occasion, failed captures provide lower bounds on population size that can increase the precision of abundance estimates. Analytical results and simulations indicate that visual encounter data improve abundance estimates when capture probabilities are low, and when there are few repeat surveys. We present a hierarchical Bayesian approach for integrating failed captures and auxiliary encounter data in statistical capture-recapture models. This approach can be integrated with existing capture-recapture models, and may prove particularly useful for hard to capture species in data-limited settings.
XferNAS: Transfer Neural Architecture Search.

The term Neural Architecture Search (NAS) refers to the automatic optimization of network architectures for a new, previously unknown task. Since testing an architecture is computationally very expensive, many optimizers need days or even weeks to find suitable architectures. However, this search time can be significantly reduced if knowledge from previous searches on different tasks is reused. In this work, we propose a generally applicable framework that introduces only minor changes to existing optimizers to leverage this feature. As an example, we select an existing optimizer and demonstrate the complexity of the integration of the framework as well as its impact. In experiments on CIFAR-10 and CIFAR-100, we observe a reduction in the search time from 200 to only 6 GPU days, a speed up by a factor of 33. In addition, we observe new records of 1.99 and 14.06 for NAS optimizers on the CIFAR benchmarks, respectively. In a separate study, we analyze the impact of the amount of source and target data. Empirically, we demonstrate that the proposed framework generally gives better results and, in the worst case, is just as good as the unmodified optimizer.
Physical mapping of chromosomes: A combinatorial problem in molecular biology

This paper is concerned wth the physical mapping of DNA molecules using data about the hybridization of oligonucleotide probes to a library of clones. In mathematical terms, the DNA molecule corresponds to an interval on the real line, each clone to a subinterval, and each probe occurs at a finite set of points within the interval. A stochastic model for the occurrences of the probes and the locations of the clones is assumed. Given a matrix of incidences between probes and clones, the task is to reconstruct the most likely interleaving of the clones. Combinatorial algorithms are presented for solving approximations to this problem, and computational results are presented.
Creative AI Through Evolutionary Computation: Principles and Examples

The main power of artificial intelligence is not in modeling what we already know, but in creating solutions that are new. Such solutions exist in extremely large, high-dimensional, and complex search spaces. Population-based search techniques, i.e. variants of evolutionary computation, are well suited to finding them. These techniques make it possible to find creative solutions to practical problems in the real world, making creative AI through evolutionary computation the likely “next deep learning.”
Cannulation strategies & circulation management in type-A aortic dissection

Type A aortic dissection most often requires emergent surgery to prevent malperfusion, stroke, and/or rupture of aorta. To achieve the structural goals of the operation, the conduct of the surgery is targeted from it inception at restoring true lumen flow. In this regard, institution of cardiopulmonary bypass and circulation management is key to allow adequate systemic flow, perfusion of brain and visceral organs and comprehensive systemic cooling to achieve circulatory arrest when needed. Different strategies have been used to establish adequate true lumen perfusion with varying success rates, with the most common still being femoral cannulation. More recently axillary and central cannulation strategies have shown satisfactory results by allowing more reliable true lumen flow. Cannulation approach should, therefore, depend on individual patient characteristics, presentation, and true lumen anatomy.
Just-in-Time Analytics on Large File Systems

As file systems reach the petabytes scale, users and administrators are increasingly interested in acquiring high-level analytical information for file management and analysis. Two particularly important tasks are the processing of aggregate and top-k queries which, unfortunately, cannot be quickly answered by hierarchical file systems such as ext3 and NTFS. Existing preprocessing-based solutions, e.g., file system crawling and index building, consume a significant amount of time and space (for generating and maintaining the indexes) which in many cases cannot be justified by the infrequent usage of such solutions. In this paper, we advocate that user interests can often be sufficiently satisfied by approximate-i.e., statistically accurate-answers. We develop Glance, a just-in-time sampling-based system which, after consuming a small number of disk accesses, is capable of producing extremely accurate answers for a broad class of aggregate and top-k queries over a file system without the requirement of any prior knowledge. We use a number of real-world file systems to demonstrate the efficiency, accuracy, and scalability of Glance.
Adaptive Filtering via Cumulants and LMS Algorithm

A new adaptive identification scheme is introduced for a non-Gaussian white noise driven linear, nonminimum phase FIR system. The adaptive scheme is based on non-causal auto-regressive (AR) modeling of higher-order cumulants of the system output. In particular, the matnitude and phase response estimates at each iteration are expressed in terms of the updated parameters of the non-causal AR model. The set of updated AR parameters is obtained by employing the LMS algorithm and by using higher-order cumulants instead of time samples of the output signal. It is demonstrated by means of standard examples that the new adaptive scheme works well and as expected outperform the modified (autocorrelation-based) LMS algorithm for nonminimum phase system identification.
ddcP, pstB, and excess D-lactate impact synergism between vancomycin and chlorhexidine against Enterococcus faecium 1,231,410

Vancomycin-resistant enterococci (VRE) are important nosocomial pathogens that cause life-threatening infections. To control hospital-associated infections, skin antisepsis and bathing utilizing chlorhexidine is recommended for VRE patients in acute care hospitals. Previously, we reported that exposure to inhibitory chlorhexidine levels induced the expression of vancomycin resistance genes in VanA-type Enterococcus faecium. However, vancomycin susceptibility actually increased for VanA-type E. faecium in the presence of chlorhexidine. Hence, a synergistic effect of the two antimicrobials was observed. In this study, we used multiple approaches to investigate the mechanism of synergism between chlorhexidine and vancomycin in the VanA-type VRE strain E. faecium 1,231,410. We generated clean deletions of 7 of 11 pbp, transpeptidase, and carboxypeptidase genes in this strain (ponA, pbpF, pbpZ, pbpA, ddcP, ldtfm, and vanY). Deletion of ddcP, encoding a membrane-bound carboxypeptidase, altered the synergism phenotype. Furthermore, using in vitro evolution, we isolated a spontaneous synergy escaper mutant and utilized whole genome sequencing to determine that a mutation in pstB, encoding an ATPase of phosphate-specific transporters, also altered synergism. Finally, addition of excess D-lactate, but not D-alanine, enhanced synergism to reduce vancomycin MIC levels. Overall, our work identified factors that alter chlorhexidine and vancomycin synergism in a model VanA-type VRE strain.
Variable Branching Characteristics of Peripheral Taste Neurons Indicates Differential Convergence.

Taste neurons are functionally and molecularly diverse, but their morphologic diversity remains completely unexplored. Using sparse cell genetic labeling, we provide the first reconstructions of peripheral taste neurons. The branching characteristics across 96 taste neurons show surprising diversity in their complexities. Individual neurons had 1–17 separate arbors entering between one and seven taste buds, 18 of these neurons also innervated non-taste epithelia. Axon branching characteristics are similar in gustatory neurons from male and female mice. Cluster analysis separated the neurons into four groups according to branch complexity. The primary difference between clusters was the amount of the nerve fiber within the taste bud available to contact taste-transducing cells. Consistently, we found that the maximum number of taste-transducing cells capable of providing convergent input onto individual gustatory neurons varied with a range of 1–22 taste-transducing cells. Differences in branching characteristics across neurons indicate that some neurons likely receive input from a larger number of taste-transducing cells than other neurons (differential convergence). By dividing neurons into two groups based on the type of taste-transducing cell most contacted, we found that neurons contacting primarily sour transducing cells were more heavily branched than those contacting primarily sweet/bitter/umami transducing cells. This suggests that neuron morphologies may differ across functional taste quality. However, the considerable remaining variability within each group also suggests differential convergence within each functional taste quality. Each possibility has functional implications for the system. SIGNIFICANCE STATEMENT Taste neurons are considered relay cells, communicating information from taste-transducing cells to the brain, without variation in morphology. By reconstructing peripheral taste neuron morphologies for the first time, we found that some peripheral gustatory neurons are simply branched, and can receive input from only a few taste-transducing cells. Other taste neurons are heavily branched, contacting many more taste-transducing cells than simply branched neurons. Based on the type of taste-transducing cell contacted, branching characteristics are predicted to differ across (and within) quality types (sweet/bitter/umami vs sour). Therefore, functional differences between neurons likely depends on the number of taste-transducing cells providing input and not just the type of cell providing input.
Europa Exploration Philosophy

A Viking-class Europa Lander is a high-risk, high-cost venture. In its place, Europa should be explored by a series of low-cost scouts. These will be landers and small flyby craft. These missions will ascertain the nature of Europa's surface at a scale of meters to centimeters. Some will search for the presence of organic molecules. All of them will precede a large Europa Lander.
Data-driven Solution of Stochastic Differential Equations Using Maximum Entropy Basis Functions

Abstract In this paper we present a data-driven approach for uncertainty propagation. In particular, we consider stochastic differential equations with parametric uncertainty. Solution of the differential equation is approximated using maximum entropy (maxent) basis functions similar to polynomial chaos expansions. Maxent basis functions are derived from available data by maximization of information-theoretic entropy, therefore, there is no need to specify basis functions beforehand. We compare the proposed maxent based approach with existing methods.
An improved result for Falconer’s distance set problem in even dimensions

We show that if compact set $$E\subset \mathbb {R}^d$$ has Hausdorff dimension larger than $$\frac{d}{2}+\frac{1}{4}$$ , where $$d\ge 4$$ is an even integer, then the distance set of E has positive Lebesgue measure. This improves the previously best known result towards Falconer’s distance set conjecture in even dimensions.
Spatio-Temporal Data Mining for Aviation Delay Prediction

To accommodate the unprecedented increase of commercial airlines over the next ten years, the Next Generation Air Transportation System (NextGen) has been implemented in the USA that records large-scale Air Traffic Management (ATM) data to make air travel safer, more efficient, and more economical. A key role of collaborative decision making for air traffic scheduling and airspace resource management is the accurate prediction of flight delay. There has been a lot of attempts to apply data-driven methods such as machine learning to forecast flight delay situation using air traffic data of departures and arrivals. However, most of them omit en-route spatial information of airlines and temporal correlation between serial flights which results in inaccuracy prediction. In this paper, we present a novel aviation delay prediction system based on stacked Long Short-Term Memory (LSTM) networks for commercial flights. The system learns from historical trajectories from automatic dependent surveillance-broadcast (ADS-B) messages and uses the correlative geolocations to collect indispensable features such as climatic elements, air traffic, airspace, and human factors data along posterior routes. These features are integrated and then are fed into our proposed regression model. The latent spatio-temporal patterns of data are abstracted and learned in the LSTM architecture. Compared with previous schemes, our approach is demonstrated to be more robust and accurate for large hub airports.
Effectiveness of Influenza Vaccines in the HIVE Household Cohort Over 8 Years: Is There Evidence of Indirect Protection?

BACKGROUND: The evidence that influenza vaccination programs regularly provide protection to unvaccinated individuals (ie, indirect effects) of a community is lacking. We sought to determine the direct, indirect, and total effects of influenza vaccine in the Household Influenza Vaccine Evaluation (HIVE) cohort. METHODS: Using longitudinal data from the HIVE cohort from 2010-11 through 2017-18, we estimated direct, indirect, and total influenza vaccine effectiveness (VE) and the incidence rate ratio of influenza virus infection using adjusted mixed-effect Poisson regression models. Total effectiveness was determined through comparison of vaccinated members of full or partially vaccinated households to unvaccinated individuals in completely unvaccinated households. RESULTS: The pooled, direct VE against any influenza was 30.2% (14.0-43.4). Direct VE was higher for influenza A/H1N1 43.9% (3.9 to 63.5) and B 46.7% (17.2 to 57.5) than A/H3N2 31.7% (10.5 to 47.8) and was higher for young children 42.4% (10.1 to 63.0) than adults 18.6% (-6.3 to 37.7). Influenza incidence was highest in completely unvaccinated households (10.6 per 100 person-seasons) and lower at all other levels of household vaccination coverage. We found little evidence of indirect VE after adjusting for potential confounders. Total VE was 56.4% (30.1-72.9) in low coverage, 43.2% (19.5-59.9) in moderate coverage, and 33.0% (12.1 to 49.0) in fully vaccinated households. CONCLUSIONS: Influenza vaccines may have a benefit above and beyond the direct effect but that effect in this study was small. Although there may be exceptions, the goal of global vaccine recommendations should remain focused on provision of documented, direct protection to those vaccinated.
An Optimal Reduction of TV-Denoising to Adaptive Online Learning

We consider the problem of estimating a function from $n$ noisy samples whose discrete Total Variation (TV) is bounded by $C_n$. We reveal a deep connection to the seemingly disparate problem of Strongly Adaptive online learning (Daniely et al, 2015) and provide an $O(n \log n)$ time algorithm that attains the near minimax optimal rate of $\tilde O (n^{1/3}C_n^{2/3})$ under squared error loss. The resulting algorithm runs online and optimally adapts to the unknown smoothness parameter $C_n$. This leads to a new and more versatile alternative to wavelets-based methods for (1) adaptively estimating TV bounded functions; (2) online forecasting of TV bounded trends in time series.
The Rise of Speed Deportation and the Role of Discretion

In 2013, the majority of people deported never saw a courtroom or immigration judge. Instead, they were quickly removed by the Department of Homeland Security via one of several procedures collectively referred to as “speed deportation.” The policy goals of speed deportation are economic; these processes save government resources from being spent on procedural safeguards such as a trial attorney, immigration judge, and a fundamentally fair hearing. Higher deportation numbers may also benefit the image the government seeks to portray to policymakers who support amplified immigration enforcement. However, the human consequences of speed deportation are significant and can result in the ejection of people who would otherwise qualify for relief before an immigration judge or otherwise present strong equities like family ties, long-term residence, and steady employment in the United States. Moreover, the risk that the government may wrongly classify a person as a candidate for speed deportation is more than a remote possibility. This Article examines deportations resulting from the expedited removal, administrative removal, and reinstatement of removal orders programs and the extent to which the government has discretion to give individuals who present compelling equities, including eligibility for relief, a more complete court proceeding before an immigration judge. This Article ends with recommendations the Department of Homeland Security can take to provide a “day in court” for such individuals.
Development of a Quantitative Antigen Assay to Detect Coccidioidal Chitinase-1 (CTS1) in Human Serum

Background Coccidioidomycosis is often diagnosed with a collection of tests that rely on the patient's ability to mount an immune response to the fungus (antibody-based diagnostics), making diagnosis of this infection challenging. Here we present an antigen-based assay that detects and quantifies coccidioidal chitinase-1 (CTS1) in human serum. Methods An inhibition-based enzyme-linked immunoassay (ELISA) was developed that utilizes a monoclonal antibody specific for coccidioidal CTS1. CTS1 was quantified in commercial antigen preparations using recombinant CTS1 as a standard. Sera from 192 individuals from an endemic area were tested, which included 78 patients (40.6%) with proven or probable coccidioidomycosis. Results The quantity of CTS1 in diagnostic commercial antigen preparations from different suppliers varied. CTS1 antigenemia was detected in 87.2% of patients with proven or probable coccidioidomycosis. Specificity was determined to be 96.94% using serum from individuals who reside in the Phoenix, Arizona area who did not have coccidioidomycosis. Levels of CTS1 correlated with low- and high-titer serology from patients with a coccidioidomycosis diagnosis. Conclusions Since the CTS1 inhibition ELISA described in this report does not depend on the host immune response, it is a promising diagnostic tool to aid in diagnosis and disease monitoring of coccidioidomycosis.
Clocked population protocols

Abstract We define an extension to the standard population protocol that provides each agent with a clock signal that indicates when the agent has waited long enough for the protocol to have converged. We represent “long enough” as an infinite time interval, and treat the protocol as operating over transfinite time. Over finite time intervals, the protocol behaves as in the standard model. At nonzero limit ordinals, corresponding to clock ticks, the protocol switches to a limit of previous configurations supplemented by a clock signal appearing as an extra component in some of the agents' states. Fairness generalizes to transfinite executions straightforwardly. We show that a clocked population protocol running in less than ω k time for any fixed k ≥ 2 is equivalent in power to a nondeterministic Turing machine with space complexity logarithmic in the population size, and can be simulated using only finitely many clock ticks.
Improved protein structure prediction by deep learning irrespective of co-evolution information

Predicting the tertiary structure of a protein from its primary sequence has been greatly improved by integrating deep learning and co-evolutionary analysis, as shown in CASP13 and CASP14. We describe our latest study of this idea, analysing the efficacy of network size and co-evolution data and its performance on both natural and designed proteins. We show that a large ResNet (convolutional residual neural networks) can predict structures of correct folds for 26 out of 32 CASP13 free-modelling targets and L/5 long-range contacts with precision over 80%. When co-evolution is not used, ResNet can still predict structures of correct folds for 18 CASP13 free-modelling targets, greatly exceeding previous methods that do not use co-evolution either. Even with only the primary sequence, ResNet can predict the structures of correct folds for all tested human-designed proteins. In addition, ResNet may fare better for the designed proteins when trained without co-evolution than with co-evolution. These results suggest that ResNet does not simply de-noise co-evolution signals, but instead may learn important protein sequence–structure relationships. This has important implications for protein design and engineering, especially when co-evolutionary data are unavailable. In the last few years, computational protein structure prediction has greatly advanced by combining deep learning including convolutional residual networks (ResNet) with co-evolution data. A new study finds that using deeper and wider ResNets improves predictions in the absence of co-evolution information, suggesting that the ResNets do not not simply de-noise co-evolution signals, but instead may learn important protein sequence–structure relationships.
Admixed Populations Improve Power for Variant Discovery and Portability in Genome-Wide Association Studies

Genome-wide association studies (GWAS) are primarily conducted in single-ancestry settings. The low transferability of results has limited our understanding of human genetic architecture across a range of complex traits. In contrast to homogeneous populations, admixed populations provide an opportunity to capture genetic architecture contributed from multiple source populations and thus improve statistical power. Here, we provide a mechanistic simulation framework to investigate the statistical power and transferability of GWAS under directional polygenic selection or varying divergence. We focus on a two-way admixed population and show that GWAS in admixed populations can be enriched for power in discovery by up to 2-fold compared to the ancestral populations under similar sample size. Moreover, higher accuracy of cross-population polygenic score estimates is also observed if variants and weights are trained in the admixed group rather than in the ancestral groups. Common variant associations are also more likely to replicate if first discovered in the admixed group and then transferred to an ancestral population, than the other way around (across 50 iterations with 1,000 causal SNPs, training on 10,000 individuals, testing on 1,000 in each population, p = 3.78e-6, 6.19e-101, ∼0 for FST = 0.2, 0.5, 0.8, respectively). While some of these FST values may appear extreme, we demonstrate that they are found across the entire phenome in the GWAS catalog. This framework demonstrates that investigation of admixed populations harbors significant advantages over GWAS in single-ancestry cohorts for uncovering the genetic architecture of traits and will improve downstream applications such as personalized medicine across diverse populations.
Modern Clinical Text Mining: A Guide and Review.

Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs r...
Ray-tracing analysis for cross-polarization scattering diagnostic on MAST-upgrade spherical tokamak.

A combined Doppler backscattering/cross-polarization scattering (DBS/CPS) system is being deployed on MAST-U for simultaneous measurements of local density turbulence, turbulence flows, and magnetic turbulence. In this design, CPS shares the probing beam with the DBS and uses a separate parallel-viewing receiver system. In this study, we utilize a modified GENRAY 3D ray-tracing code to simulate the propagation of the probing and scattered beams. The contributions of different scattering locations along the entire beam trajectories are considered, and the corresponding local B wavenumbers are estimated using the wavevector matching criterion. The wavenumber ranges of the local B that are detectable to the CPS system are explored for simulated L- and H-mode plasmas.
Hydrothermal Synthesis and Solid-State Laser Refrigeration of Ytterbium-Doped Potassium-Lutetium-Fluoride (KLF) Microcrystals

Fluoride crystals, due to their low phonon energies, are attractive hosts of trivalent lanthanide ions for applications in upconverting phosphors, quantum information science, and solid-state laser refrigeration. In this article, we report the rapid, low-cost hydrothermal synthesis of potassium lutetium fluoride (KLF) microcrystals for applications in solid-state laser refrigeration. Four crystalline phases were synthesized, namely orthorhombic K2LuF5 (Pnma), trigonal KLuF4 (P3121), orthorhombic KLu2F7 (Pna21), and cubic KLu3F10 (Fm3m), with each phase exhibiting unique microcrystalline morphologies. Luminescence spectra and emission lifetimes of the four crystalline phases were characterized based on the point-group symmetry of trivalent cations. Laser refrigeration was measured by observing both the optomechanical eigenfrequencies of microcrystals on cantilevers in vacuum, and also the Brownian dynamics of optically trapped microcrystals in water. Among all four crystalline phases, the most significant cooling was observed for 10%Yb:KLuF4 with cooling of 8.6 $\pm$ 2.1 K below room temperature. Reduced heating was observed with 10%Yb:K2LuF5
Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization

Unsupervised learning of the Dawid-Skene (D&S) model from noisy, incomplete and crowdsourced annotations has been a long-standing challenge, and is a critical step towards reliably labeling massive data. A recent work takes a coupled nonnegative matrix factorization (CNMF) perspective, and shows appealing features: It ensures the identifiability of the D\&S model and enjoys low sample complexity, as only the estimates of the co-occurrences of annotator labels are involved. However, the identifiability holds only when certain somewhat restrictive conditions are met in the context of crowdsourcing. Optimizing the CNMF criterion is also costly -- and convergence assurances are elusive. This work recasts the pairwise co-occurrence based D&S model learning problem as a symmetric NMF (SymNMF) problem -- which offers enhanced identifiability relative to CNMF. In practice, the SymNMF model is often (largely) incomplete, due to the lack of co-labeled items by some annotators. Two lightweight algorithms are proposed for co-occurrence imputation. Then, a low-complexity shifted rectified linear unit (ReLU)-empowered SymNMF algorithm is proposed to identify the D&S model. Various performance characterizations (e.g., missing co-occurrence recoverability, stability, and convergence) and evaluations are also presented.
Advance selling, competition, and brand substitutability

I analyze how information structure influences the profitability of advance selling in the presence of a competitor. Two firms produce different brands serving heterogeneous consumers. In advance, consumers know the utility they will get from their preferred brand but can be uninformed about its identity, how much they will dislike their nonpreferred brand, or both. The firms set prices in the main selling season (when uncertainty is resolved) simultaneously, but one firm can set an advance selling price (while there is still uncertainty). Competition enhances the profitability of advance selling when consumers are uninformed about which brand they will prefer.
Rapid kinetic fingerprinting of single nucleic acid molecules by a FRET-based dynamic nanosensor.

Abstract Biofluid-derived cell-free nucleic acids such as microRNAs (miRNAs) and circulating tumor-derived DNAs (ctDNAs) have emerged as promising disease biomarkers. Conventional detection of these biomarkers by digital PCR and next generation sequencing, although highly sensitive, requires time-consuming extraction and amplification steps that also increase the risk of sample loss and cross-contamination. To achieve the direct, rapid, and amplification-free detection of miRNAs and ctDNAs with near-perfect specificity and single-molecule level sensitivity, we herein designed a single-molecule kinetic fingerprinting assay, termed intramolecular single-molecule recognition through equilibrium Poisson sampling (iSiMREPS). iSiMREPS exploits a dynamic DNA nanosensor comprising a surface anchor and a pair of fluorescent detection probes: one probe captures a target molecule onto the surface, while the other transiently interrogates the target to generate kinetic fingerprints by intramolecular single-molecule Forster resonance energy transfer (smFRET) that are recorded by single-molecule fluorescence microscopy and identify the target after kinetic filtering and data analysis. We optimize the sensor design, use formamide to further accelerate the fingerprinting kinetics, and maximize sensitivity by removing non-target-bound probes using toehold-mediated strand displacement to reduce background. We show that iSiMREPS can detect, in as little as 10 s, two distinct, promising cancer biomarkers—miR-141 and a common EGFR exon 19 deletion—reaching a limit of detection (LOD) of ~3 fM and a mutant allele fraction among excess wild-type as low as 1 in 1 million, or 0.0001%. We anticipate that iSiMREPS will find utility in research and clinical diagnostics based on its features of rapid detection, high specificity, sensitivity, and generalizability.
Development of a pan-neuronal genetic driver in Aedes aegypti mosquitoes.

Summary The recent development of neurogenetic tools in Aedes aegypti mosquitoes is beginning to shed light on the neural basis of behaviors that make this species a major vector of human disease. However, we still lack a pan-neuronal expression driver—a key tool that provides genetic access to all neurons. Here, we describe our efforts to fill this gap via CRISPR/Cas9-mediated knock-in of reporters to broadly expressed neural genes and report on the generation of two strains, a Syt1:GCaMP6s strain that expresses synaptically localized GCaMP and a brp-T2A-QF2w driver strain that can be used to drive and amplify expression of any effector via the Q binary system. Both manipulations broadly and uniformly label the nervous system with only modest effects on behavior. We expect these strains to facilitate neurobiological research in Ae. aegypti mosquitoes and document both successful and failed manipulations as a roadmap for similar tool development in other non-model species.
Experimental investigation of counter-rotating propfan flutter at cruise conditions

The paper presents wind tunnel experimental flutter results, at transonic relative flows, for a 0.62-m diameter composite propfan model. A blade row that fluttered was tested alone, and with a stable aft counter-rotating blade row. The major objectives of the experiment were to study the effect of the second blade row on the row in flutter, and to investigate the flutter. Results show that the second row had a stabilizing effect. Two distinct flutter modes were found. For both flutter modes: flutter boundary, frequency, nodal diameter, and blade displacement data are given. The blade displacement data, obtained with an optical method, gives an indication of the flutter mode shape at a span near the blade tip.