cssi/SciSciGPT-SciSciNet huggingface dataset:
Vector models and generalized SYK models

We consider the relation between SYK-like models and vector models by studying a toy model where a tensor field is coupled with a vector field. By integrating out the tensor field, the toy model reduces to the Gross-Neveu model in 1 dimension. On the other hand, a certain perturbation can be turned on and the toy model flows to an SYK-like model at low energy. A chaotic-nonchaotic phase transition occurs as the sign of the perturbation is altered. We further study similar models that possess chaos and enhanced reparameterization symmetries.
Counting problems for geodesics on arithmetic hyperbolic surfaces

It is a longstanding problem to determine the precise relationship between the geodesic length spectrum of a hyperbolic manifold and its commensurability class. A well known result of Reid, for instance, shows that the geodesic length spectrum of an arithmetic hyperbolic surface determines the surface's commensurability class. It is known, however, that non-commensurable arithmetic hyperbolic surfaces may share arbitrarily large portions of their length spectra. In this paper we investigate this phenomenon and prove a number of quantitative results about the maximum cardinality of a family of pairwise non-commensurable arithmetic hyperbolic surfaces whose length spectra all contain a fixed (finite) set of nonnegative real numbers.
The income elasticity of import demand: Micro evidence and an application

Abstract We construct a synthetic panel of household expenditures from the Consumer Expenditure Survey (CEX) and use the Quadratic Almost Ideal Demand System to estimate expenditure shares and income elasticities of demand that vary by good-time-income. We show that the size and distribution of income shocks drives expenditure change in a manner that varies profoundly across traded goods. Our estimates of expenditure shares and income elasticities could be useful in many applications that seek to explain changes in trade behavior from the demand side, and indicate the strong sensitivity of trade to changes in the tails of the income distribution. We explore an application involving the Great Trade Collapse. Income-induced expenditure changes are positively correlated with the cross-good pattern of import changes, generating a predicted change as much as 40% as large as the raw variation in import declines.
Algorithmic Persuasion with No Externalities

We study the algorithmics of information structure design --- a.k.a. persuasion or signaling --- in a fundamental special case introduced by Arieli and Babichenko: multiple agents, binary actions, and no inter-agent externalities. Unlike prior work on this model, we allow many states of nature. We assume that the principal's objective is a monotone set function, and study the problem both in the public signal and private signal models, drawing a sharp contrast between the two in terms of both efficacy and computational complexity. When private signals are allowed, our results are largely positive and quite general. First, we use linear programming duality and the equivalence of separation and optimization to show polynomial-time equivalence between (exactly) optimal signaling and the problem of maximizing the objective function plus an additive function. This yields an efficient implementation of the optimal scheme when the objective is supermodular or anonymous. Second, we exhibit a (1-1/e)-approximation of the optimal private signaling scheme, modulo an additive loss of e, when the objective function is submodular. These two results simplify, unify, and generalize results of [Arieli and Babichenko, 2016] and [Babichenko and Barman, 2016], extending them from a binary state of nature to many states (modulo the additive loss in the latter result). Third, we consider the binary-state case with a submodular objective, and simplify and slightly strengthen the result of [Babichenko and Barman, 2016] to obtain a (1-1/e)-approximation via a scheme which (i) signals independently to each receiver and (ii) is "oblivious" in that it does not depend on the objective function so long as it is monotone submodular. When only a public signal is allowed, our results are negative. First, we show that it is NP-hard to approximate the optimal public scheme, within any constant factor, even when the objective is additive. Second, we show that the optimal private scheme can outperform the optimal public scheme, in terms of maximizing the sender's objective, by a polynomial factor.
Characterizing the Structure-Function Relationship of a Naturally Occurring RNA Thermometer

A large number of bacteria have been found to govern virulence and heat shock responses using temperature-sensing RNAs known as RNA thermometers. A prime example is the agsA thermometer known to regulate the production of the AgsA heat shock protein in Salmonella enterica using a “fourU” structural motif. Using the SHAPE-Seq RNA structure-probing method in vivo and in vitro, we found that the regulator functions by a subtle shift in equilibrium RNA structure populations that leads to a partial melting of the helix containing the ribosome binding site. We also demonstrate that binding of the ribosome to the agsA mRNA causes changes to the thermometer structure that appear to facilitate thermometer helix unwinding. These results demonstrate how subtle RNA structural changes can govern gene expression and illuminate the function of an important bacterial regulatory motif.
The effect of e-cigarette indoor vaping restrictions on adult prenatal smoking and birth outcomes

We estimate the effect of county-level e-cigarette indoor vaping restrictions on adult prenatal smoking and birth outcomes using United States birth record data for 7 million pregnant women living in places already comprehensively banning the indoor use of traditional cigarettes. We use both cross-sectional and panel data to estimate our difference-in-difference models. Our panel model results suggest that adoption of a comprehensive indoor vaping restriction increased prenatal smoking by 2.0 percentage points, which is double the estimate obtained from a cross-sectional model. We also document heterogeneity in effect sizes along lines of age, education, and type of insurance.
Institutional variants for lymph node counts after pancreatic resections

Abstract Background Lymph node (LN) counts from pancreatectomy are postulated as quality metric for surgical therapy of pancreatic malignancy. Methods Prospectively collected data from a single surgeon's pancreatectomy experience were analyzed for predictors of LN counts. Results Of 315 consecutive patients (54% female, median age: 65, range 18–88), 239 had a proven cancer diagnosis (76%). Operations included pancreatoduodenectomy (69%), distal pancreatectomy (26%), total pancreatectomy (1%) and others (4%). Patients were treated in 4 different tertiary cancer center settings (Institution A: 11%; B: 46%; C: 27%; D: 16%) with consistent regional dissection standards. Mean total LN counts differed between institutions for malignancies (A: 18, B: 13, C: 26, D: 26, p Conclusions Pathologic processing should be standardized if LN numbers are to be adopted as quality metric for pancreatic cancer resections.
Optimal auctions through deep learning

Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981. Even after 30--40 years of intense research, the problem remains unsolved for settings with two or more items. We overview recent research results that show how tools from deep learning are shaping up to become a powerful tool for the automated design of near-optimal auctions auctions. In this approach, an auction is modeled as a multilayer neural network, with optimal auction design framed as a constrained learning problem that can be addressed with standard machine learning pipelines. Through this approach, it is possible to recover to a high degree of accuracy essentially all known analytically derived solutions for multi-item settings and obtain novel mechanisms for settings in which the optimal mechanism is unknown.
Financial Clusters, Industry Groups, and Stock Return Correlations

Industry classifications are used by investors, economists, and policy makers for a great variety of purposes. The traditional economic‐activity‐based systems (Global Industry Classification Standard, North American Industry Classification System, Standard Industrial Classification, and Fama–French) have been supplemented in recent years by alternative classification systems. Our purpose is to provide another alternative system that forms classification groups based on the structure of firm financial statements. Using cluster analysis, a multivariate tool that forms groups where their characteristics are similar within groups and distinct across groups, we form clusters of large U.S. firms based on their common‐size financial statements (percentage breakdowns of balance sheets and income statements). We characterize the financial clusters based on their industry classifications and other economic information and assess the ability of financial clusters and industry groups, separately and jointly, to explain stock return correlations of all pairs of firms. Our results demonstrate that using financial clusters and industry groups together proves advantageous relative to using either alone.
Low-complexity optimization for two-dimensional direction-of-arrival estimation via decoupled atomic norm minimization

This paper presents an efficient optimization technique for super-resolution two-dimensional (2D) direction of arrival (DOA) estimation by introducing a new formulation of atomic norm minimization (ANM). ANM allows gridless angle estimation for correlated sources even when the number of snapshots is far less than the antenna size, yet it incurs huge computational cost in 2D processing. This paper introduces a novel formulation of ANM via semi-definite programming, which expresses the original high-dimensional problem by two decoupled Toeplitz matrices in one dimension, followed by 1D angle estimation with automatic angle pairing. Compared with the state-of-the-art 2D ANM, the proposed technique reduces the computational complexity by several orders of magnitude with respect to the antenna size, while retaining the benefits of ANMin terms of super-resolution performance with use of a small number of measurements, and robustness to source correlation and noise. The complexity benefits are particularly attractive for large-scale antenna systems such as massive MIMO and radio astronomy.
Become one with the force: optimising mechanotherapy through an understanding of mechanobiology

The musculoskeletal system generates, absorbs and transmits force, enabling functional movement. Given this mechanical role, it follows that musculoskeletal tissues adapt to mechanical demands. Mechanical forces direct musculoskeletal cellular activities, altering tissue mass, structure and/or quality (figure 1).1 The net result is altered tissue-level stresses and strains to applied loads and, subsequently, altered injury risk. Figure 1 Mechanical forces direct cellular activities to induce tissue adaptation. Extrinsically and intrinsically generated mechanical forces load musculoskeletal tissues, with the characteristics of the resultant tissue forces being dependent on the ability of the tissue to resist those forces. Tissue forces are transmitted to the micromechanical environment of resident cells, with cellular mechanical properties influencing the characteristics of the cellular forces. Cells can modify their micromechanical environment via cytoskeletal rearrangement, which feedbacks to alter cellular sensitivity to incoming forces. When cellular forces are sufficient, the cell initiates a molecular response, which ultimately alters synthesis and/or degradation of the extracellular matrix. The latter alters tissue mechanical properties, which feeds back to influence tissue forces. (Reprinted from Thompson et al ,1 by permission of Oxford University Press and the American Physical Therapy Association.) Therapists have long identified the therapeutic potential of mechanical forces. Nearly every rehabilitation intervention introduces mechanical forces, irrespective of whether they are generated extrinsically (eg, via external modalities) or intrinsically (eg, via physical activity). These ‘mechanotherapies’ harness musculoskeletal mechanosensitivity to induce adaptation; however, how the adaptation occurs remains underappreciated. Khan and Scott2 previously introduced readers of BJSM to the concept of mechanotransduction, whereby biophysical forces are converted into cellular and molecular responses. For …
iSTART-ALL: Confronting adult low literacy with intelligent tutoring for reading comprehension

There is little empirical research available on the substantial problem of adult low literacy rates, and limited educational technologies are available to address distinct instructional needs of this population. This paper reports on development and testing of a version of Interactive Strategy Training for Active Reading and Thinking (iSTART) for Adult Literacy Learners (iSTART-ALL) We describe modifications of iSTART to accommodate adult literacy learners, including new practice modules (i.e., summarization, question asking), a new library of texts, and an interactive narrative for adult literacy learners to engage in extended practice of reading comprehension strategies. We report results of a study examining reactions to iSTART-ALL and performance data while engaging with the interactive narrative. The attitudinal study, conducted with 38 adult literacy learners, demonstrated generally positive reactions to the narrative. Results also revealed that task performance was strongly related to individual difference scores on reading comprehension assessments, and more so with higher-level comprehension skills than basic word-level skills, providing concurrent validity for the interactive narrative tasks.
Benefits and Risks of Circumcision

Circumcisions are performed either prophylactically in the neonatal period or therapeutically at a later age. About 10% of males not circumcised at birth will eventually require circumcision. The present neonatal circumcision rate is about 80% in the United States and 40% in Canada. The single most important determinant of whether a newborn male will be circumcised is the attitude of the attending physician. The literature was reviewed to determine the proven benefits of circumcision and to compare these with the known risks. Circumcising the newborn facilitates penile hygiene, prevents cancer of the penis and decreases the incidence of genital herpes in later life. Whether it decreases the incidence of cancer of the cervix is still uncertain. More important, neonatal circumcision is associated with much lower morbidity and mortality and with lower costs than therapeutic circumcision. Thus, prophylactic circumcision is recommended for the male population as a whole.
The Kraske Approach to the Repair of Recurrent Rectourethral Fistula

Rectourethral fistula is an uncommon surgical complication that may follow correction of anorectal anomalies. Previously described operations to repair the fistula have a high incidence of failure. Operative success is measured by achieving permanent separation of rectum from urethra, preventing urethral injury, and preserving urinary and fecal continence. Two patients with recurrent rectourethral fistula were repaired successfully. The operation described is performed through a posterior transsphincteric Kraske approach with direct repair of the fistula and resection of the fistula containing anorectal tube.
Correction of Hypocitraturia and Prevention of Stone Formation by Combined Thiazide and Potassium Citrate Therapy in Thiazide-Unresponsive Hypercalciuric Nephrolithiasis

Thirteen patients with hypercalciuric calcium nephrolithiasis continued to form calcium stones when treated with thiazide (4.69 +/- 6.62 [mean +/- SD] stones per patient-year to 5.12 +/- 10.87 stones per patient-year), despite adequate hypocalciuric response (a reduction in urinary calcium levels from 303 +/- 119 mg per day to 193 +/- 88 mg per day, p less than 0.01). Because they had hypocitraturia (250 +/- 86 mg per day versus 643 +/- 236 mg per day in normal subjects, p less than 0.001), potassium citrate (10 to 20 meq three times per day) was added to the ongoing treatment program. During combined treatment with thiazide and potassium citrate, urinary pH significantly rose, and normal levels of urinary citrate were restored. Ten patients stopped forming new stones and all 13 had reduced stone formation rate. Thus, potassium citrate supplementation should be considered in patients requiring thiazide therapy for the control of hypercalciuric nephrolithiasis, especially if they have concurrent hypocitraturia or if it develops during thiazide therapy.
Data-Driven Robust Control for Type 1 Diabetes Under Meal and Exercise Uncertainties

We present a fully closed-loop design for an artificial pancreas (AP) which regulates the delivery of insulin for the control of Type I diabetes. Our AP controller operates in a fully automated fashion, without requiring any manual interaction (e.g. in the form of meal announcements) with the patient. A major obstacle to achieving closed-loop insulin control is the uncertainty in those aspects of a patient’s daily behavior that significantly affect blood glucose, especially in relation to meals and physical activity. To handle such uncertainties, we develop a data-driven robust model-predictive control framework, where we capture a wide range of individual meal and exercise patterns using uncertainty sets learned from historical data. These sets are then used in the controller and state estimator to achieve automated, precise, and personalized insulin therapy. We provide an extensive in silico evaluation of our robust AP design, demonstrating the potential of this approach, without explicit meal announcements, to support high carbohydrate disturbances and to regulate glucose levels in large clusters of virtual patients learned from population-wide survey data.
Altitude Transitions in Energy Climbs

Abstract The aircraft energy-climb trajectory for configurations with a sharp transonic drag rise is well known to possess two branches in the altitude/Mach-number plane. Transition in altitude between the two branches occurs instantaneously, a “corner” in the minimum-time solution obtained with the energy-state model. If the initial and final values of altitude do not lie on the energy-climb trajectory, then additional jumps (crude approximations to dives and zooms) are required at the initial and terminal points. With a singular-perturbation approach, a “boundary-layer” - correction is obtained for each altitude jump, the transonic jump being a so-called “internal” boundary layer, different in character from the initial and terminal layers. The determination of this internal boundary layer is examined and some computational results for an example presented.
DIFFERENCES IN STRENGTH AND GAIT MECHANICS BETWEEN A PERSONALISED TOTAL KNEE REPLACEMENT AND A CUSTOMISED PARTIAL KNEE REPLACEMENT

Background More and more patients with end-stage knee OA are treated with total knee replacements (TKR). A modern TKR (Persona PS system, Zimmer Inc.) was designed with the hope to improve fit by providing additional sizing options on the femur and tibia. To date, there is very little information regarding the knee strength and knee mechanics during gait after the TKR. Furthermore, as a great percentage of knee OA patients have OA limited in one knee compartment and in the patellofemoral joint, a bi-compartmental knee replacement (BKR) (iDUO system, ConforMIS Inc.) was designed to treat OA at these affected areas. The BKR re-creates the individual9s knee shape while correcting for any deformity. In addition, the BKR procedure results in less bone loss and retains the cruciate ligaments. To date, the influence of the BKR on knee strength and knee mechanics remains unknown. The purpose of the study was to evaluate knee strength and mechanics during level walking after the TKR and BKR surgeries. Methods Twelve healthy control participants (age=57±6 yr.; mass=82±11 kg; height=175±11 cm), eight patients (age=63±10 yr.; mass=87±20 kg; height=166±8 cm) with ten BKR systems (post-op time = 17±9 mo.), and nine patients (age=65±9 yr.; mass=90±35 kg; Height=169±12 cm) with twelve TKR systems (post-op time = 14±5 mo.) participated in the study. In a laboratory setting, maximal isometric knee strength was evaluated. Motion capture and 3D kinematic and kinetic analyses were conducted for level walking. One way ANOVA was used to determine differences among the BKR, TKR, and the healthy control knees. Findings The TKR knee extensor strength was 34% and 20% less than that of the control limb ( p p =0.07), respectively. The TKR limb had less knee extensor moment during walking than both the control limb (40% less) and the BKR limb (24% less) ( p p p p 0.05). Interpretations BKR patients saw their knee extensor strength returned to a normal level and were able to produce the same level of knee extensor moment of the healthy control limbs during walking. The TKR patients still experienced knee strength deficit after one year post-surgery. Both the TKR and BKR groups exhibited similar frontal plane mechanics when compared to the control limbs during walking. However, BKR patients were able to walk significantly faster than their TKR counterparts, at speeds similar to the control subjects. Patients with OA limited in the medial/lateral compartment and the patellofemoral joint may consider the BKR procedure for better knee strength recovery and functional outcomes. Acknowledgement Funding source: ConforMIS Inc.
Idiosyncratic Drug Induced Liver Injury in African–Americans Is Associated With Greater Morbidity and Mortality Compared to Caucasians

Idiosyncratic Drug Induced Liver Injury in African–Americans Is Associated With Greater Morbidity and Mortality Compared to Caucasians
Computing platform for automotive electronics of automated driving generation

This paper addresses requirements from future automotive electronics system concept/design to computing technology or platform filling processing characteristics which will be used in applications of automated driving generation. We cannot completely predict future trends of functionality or application, so it is important to have flexible and scalable computing platform for that generation. The platform should also have a reasonable coverage of processing characteristics especially for parallelism point of view, because it will strongly affect to automotive electronics system efficiency and quality. Here, we'll mainly point to automotive unique processing characteristics which should be improved in near future.
Minority households’ willingness to pay for public and private wildfire risk reduction in Florida

The purpose of this work is to estimate willingness to pay (WTP) for minority (African-American and Hispanic) homeowners in Florida for private and public wildfire risk-reduction programs and also to test for differences in response between the two groups. A random parameter logit and latent class model allowed us to determine if there is a difference in wildfire mitigation program preferences, whether WTP is higher for public or private actions for wildfire risk reduction, and whether households with personal experience and who perceive that they live in higher-risk areas have significantly higher WTP. We also compare Florida minority homeowners’ WTP values with Florida original homeowners’ estimates. Results suggest that Florida minority homeowners are willing to invest in public programs, with African-Americans WTP values at a higher rate than Hispanics. In addition, the highest priority for cost-sharing funds would go to low-income homeowners, especially to cost-share private actions on their own land. These results may help fire managers optimise allocation of scarce cost-sharing funds for public v. private actions.
The market for private student loans: an analysis of credit union exposure, risk, and returns

Beginning in 2011, credit unions in the United States have been required to report in their quarterly call reports their holdings of private student loans. Since this time, private student loans have been the fastest growing loan product among credit unions. The empirical results here indicate credit unions respond to external market forces and internal exposure to interest rate risk in their decision to hold private student loans. The effect of which, to date, has led to lower returns on their assets and no effect on overall risk. Credit unions looking to diversify their loan portfolio should do so with caution. Private student loans being in deferral reduce both delinquency and charge-off rates, which will rise over time with their seasoning and as interest rates rise.
Quantifying Early Time Quantum Decoherence Dynamics through Fluctuations

We introduce a general but simple relation between the timescale for quantum coherence loss and the initial fluctuations of operators that couple a quantum system with a surrounding bath. The relation allows the prediction and measurement of early time decoherence dynamics for any open quantum system, through purity, without reconstructing the system’s many-body density matrix. It is applied to predict the decoherence time for basic models—the Holstein chain, spin-boson and Caldeira-Legget models—commonly employed to capture electronic, vibrational, and vibronic dynamics in molecules. Such development also offers a practical platform to test the ability of approximate quantum dynamics methods to capture decoherence. In particular, a class of mixed quantum-classical schemes for molecular dynamics where the bath is treated classically, such as Ehrenfest dynamics, are shown to correctly capture short-time decoherence when the initial conditions are sampled from the Wigner distribution. These advances provide...
Process convolution approaches for modeling interacting trajectories

Gaussian processes are a fundamental statistical tool used in a wide range of applications. In the spatiotemporal setting, several families of covariance functions exist to accommodate a wide variety of dependence structures arising in different applications. These parametric families can be restrictive and are insufficient in some situations. In contrast, process convolutions represent a flexible, interpretable approach to defining the covariance of a Gaussian process and have modest requirements to ensure validity. We introduce a generalization of the process convolution approach that employs multiple convolutions sequentially to form a “process convolution chain”. In our proposed multistage framework, complex dependencies that arise from a combination of different interacting mechanisms are decomposed into a series of interpretable kernel smoothers. We demonstrate an application of process convolution chains to model killer whale movement, in which the paths taken by multiple individuals are not independent but reflect dynamic social interactions within the population. Our proposed model for dependent movement provides inference for the latent dynamic social structure in the study population. Additionally, by leveraging the positive dependence among individual paths, we achieve a reduction in uncertainty for the estimated locations of the killer whales, compared with a model that treats paths as independent.
Estimated effects of implementing an open access policy for grantees at a private foundation

BACKGROUND The Gordon and Betty Moore Foundation (GBMF) was interested in understanding the potential effects of requiring that grantees publish their peer-reviewed research in open access journals. METHODS We collected data on more than 2,000 publications in over 500 journals that were generated by GBMF grantees since 2001. We then examined the journal policies to establish how two possible open access policies might have affected grantee publishing habits. RESULTS We found that 99.3% of the articles published by grantees would have complied with a policy that requires open access within 12 months of publication. We also estimated the maximum annual costs to GBMF for covering fees associated with "gold open access" to be between $400,000 and $2,600,000 annually. DISCUSSION Based in part on this study, GBMF has implemented a new open access policy that requires grantees make peer-reviewed publications fully available within 12 months.
Yolk Sac Erythromyeloid Progenitors Expressing Gain Of Function PTPN11 Have Functional Features Of JMML But Are Not Sufficient To Cause Disease In Mice

Background: Accumulating evidence suggests the origin of Juvenile Myelomonocytic Leukemia (JMML) is closely associated with fetal development. Nevertheless, the contribution of embryonic progenitors to JMML pathogenesis remains unexplored. We hypothesized that expression of JMML-initiating PTPN11 mutations in HSC-independent yolk sac erythromyeloid progenitors (YS EMPs) would result in a mouse model of pediatric myeloproliferative neoplasm (MPN). Results: E9.5 YS EMPs from VavCre+;PTPN11D61Y embryos demonstrated growth hypersensitivity to GM-CSF and hyperactive RAS-ERK signaling. Mutant EMPs engrafted the spleens of neonatal recipients, but did not cause disease. To assess MPN development during unperturbed hematopoiesis we generated CSF1R-MCM+;PTPN11E76K;ROSAYFP mice in which oncogene expression was restricted to EMPs. YFP+ progeny of mutant EMPs persisted in tissues one year after birth and demonstrated hyperactive RAS-ERK signaling. Nevertheless, these mice had normal survival and did not demonstrate features of MPN. Conclusions: YS EMPs expressing mutant PTPN11 demonstrate functional and molecular features of JMML but do not cause disease following transplantation nor following unperturbed development. This article is protected by copyright. All rights reserved.
Colonialism, Landlords, and Public Goods Provision in India: A Controlled Comparative Analysis

What is the impact of colonialism on public goods provision? This article examines India, once the world’s largest colony. While recent studies of the Indian case are divided, they overwhelmingly r...
What's in a Name? Initial Evidence of U.S. Audit Partner Identification Using Difference-in-Differences Analyses

ABSTRACT We investigate changes in the quality and cost of audit services surrounding PCAOB Rule 3211, which requires disclosure of audit partner names in Form AP. To isolate changes due to Rule 32...
Constraints on gene patent protection fuel secrecy concerns: A qualitative study

Concern is mounting that innovators are responding to recent changes in patent eligibility by increasingly choosing to protect their discoveries as trade secrets. Due to the clandestine nature of trade secrets, it is impossible to quantify the extent to which innovators actually elect to protect their inventions as trade secrets rather than patents. Nevertheless, interest in each strategy may be gauged through qualitative means. We conducted semi-structured interviews of legal and scientific experts (n = 30) to understand the effect of recent patent eligibility changes on interest in patenting and trade secrecy of genetic innovations. Interview data indicate that secrecy may have increased in strategic appeal relative to patent protection in some areas of genetic innovation, although the actual election of secrecy strategies is often limited as a practical matter. The data also suggest that the burden of navigating the new intellectual property landscape may be falling disproportionately on those who translate gene-based discoveries into clinical applications. Some interviewees expressed concern about the normative implications of secrecy on advancements in and access to genetic medicine. Our findings are potentially relevant to policy proposals intended to restore some of the legal protection that was lost as a result of recent changes to patent eligibility, including amending the federal patent statute and expanding regulatory exclusivities for some genetic technologies.
Using Organic Synthesis and Chemical Analysis to Understand the Photochemistry of Spore Photoproduct and Other Pyrimidine Dimers

Pyrimidine dimerization is the dominant DNA photoreaction occurring in vitro and in vivo. Three types of dimers, cyclobutane pyrimidine dimers (CPDs), pyrimidine (6-4) pyrimidone photoproducts (6-4PPs), and the spore photoproduct (SP), are formed from the direct dimerization process; it is of significance to understand the photochemistry and photobiology of these dimers. Traditionally, pyrimidine dimerization was studied by using the natural pyrimidine residues thymine and cytosine, which share similar chemical structures and similar reactivity, making it sometimes less straightforward for one to identify the key pyrimidine residue that needs to be excited to trigger the photoreaction. We thus adopted synthetic chemistry to selectively modify the pyrimidine residues or to introduce pyrimidine analogs to the selected positions before UV irradiation is applied. By monitoring the subsequent outcomes from the photoreaction, we were able to gain unique mechanistic insights into the photochemistry of SP as well as of CPDs and 6-4PPs. Moreover, our approaches have resulted in several useful “tools” that can facilitate the understanding of lesion photobiology. Our results summarized in this account illustrate what organic synthesis/chemical analysis may allow us to achieve in future DNA lesion biology studies. 1 Introduction 2 Using the Deuterium Labeling Strategy to Understand SP Formation 3 Using Microcrystals to Reveal the Reaction Intermediates in SP Formation 4 Using a Phosphate Isostere to Understand the SP Structure 5 Synthesis of SP Phosphoramidite and SP Structural Studies 6 Using a Thymine Isostere to Understand CPD Formation 7 Using a Thymine Isostere to Understand 6-4PP Photoreaction 8 Understanding the Chemical Stability of SP 9 Understanding the Chemical Stability of 6-4PP 10 Summary and Perspectives for Future Research
Matrix Completion and Related Problems via Strong Duality

This work studies the strong duality of non-convex matrix factorization problems: we show that under certain dual conditions, these problems and its dual have the same optimum. This has been well understood for convex optimization, but little was known for non-convex problems. We propose a novel analytical framework and show that under certain dual conditions, the optimal solution of the matrix factorization program is the same as its bi-dual and thus the global optimality of the non-convex program can be achieved by solving its bi-dual which is convex. These dual conditions are satisfied by a wide class of matrix factorization problems, although matrix factorization problems are hard to solve in full generality. This analytical framework may be of independent interest to non-convex optimization more broadly. We apply our framework to two prototypical matrix factorization problems: matrix completion and robust Principal Component Analysis (PCA). These are examples of efficiently recovering a hidden matrix given limited reliable observations of it. Our framework shows that exact recoverability and strong duality hold with nearly-optimal sample complexity guarantees for matrix completion and robust PCA.
A Lagrangian model for laser-induced fluorescence and its application to measurements of plasma ion temperature and electrostatic waves

Extensive information can be obtained on wave-particle interactions and wave fields by a direct measurement of perturbed ion distribution functions using laser-induced fluorescence (LIF). For practical purposes, LIF is frequently performed on metastable states that are produced from neutral gas particles and ions in other electronic states. If the laser intensity is increased to obtain a better LIF signal, then optical pumping can produce systematic effects depending on the collision rates which control metastable population and lifetime. We numerically simulate the ion velocity distribution measurement and wave-detection process using a Lagrangian model for the LIF signal for the case where metastables are produced directly from neutrals. This case requires more strict precautions and is important for discharges with energetic primary electrons and a high density of neutrals. Some of the results also apply to metastables produced from pre-existing ions. The simulations show that optical pumping broadenin...
Characterizing the impact of production adaptive cruise control on traffic flow: an investigation

This research conducted a comprehensive study to better estimate the impact of production Adaptive Cruise Control (ACC) on uninterrupted flow facilities using the highly cited ACC car-following mod...
Farey–Lorenz Permutations for Interval Maps

Lorenz-like maps arise in models of neuron activity, among other places. Motivated by questions about the pattern of neuron firing in such a model, we study periodic orbits and their itineraries fo...
Big-video mining of road appearances in full spectrums of weather and illuminations

Autonomous and safety driving require the control of vehicles within roads. Compared to lane mark tracking, road edge detection is more difficult because of the large variation in road and off-road materials and the influence from weather and illuminations. This work investigates visual appearances of roads under a spectrum of weather conditions. We use big-data mining on large scale naturalistic driving videos taken over a year through four seasons. Large video volumes are condensed to compact road profile images for analysis. Clusters are extracted from all samples with unsupervised learning. Typical views of a spectrum of weather/illuminations are generated from the clusters. Further, by changing the number of clusters we find a stable number for clustering. The learned data are used to classify driving videos into typical illumination types briefly. The surveyed data can also be used in the development of road edge detection algorithm and system as well as their testing.
Cloud-Based or On-Device: An Empirical Study of Mobile Deep Inference

Modern mobile applications are benefiting significantly from the advancement in deep learning, e.g., implementing real-time image recognition and conversational system. Given a trained deep learning model, applications usually need to perform a series of matrix operations based on the input data, in order to infer possible output values. Because of computational complexity and size constraints, these trained models are often hosted in the cloud. To utilize these cloud-based models, mobile apps will have to send input data over the network. While cloud-based deep learning can provide reasonable response time for mobile apps, it restricts the use case scenarios, e.g. mobile apps need to have network access. With mobile specific deep learning optimizations, it is now possible to employ on-device inference. However, because mobile hardware, such as GPU and memory size, can be very limited when compared to its desktop counterpart, it is important to understand the feasibility of this new on-device deep learning inference architecture. In this paper, we empirically evaluate the inference performance of three Convolutional Neural Networks (CNNs) using a benchmark Android application we developed. Our measurement and analysis suggest that on-device inference can cost up to two orders of magnitude greater response time and energy when compared to cloud-based inference, and that loading model and computing probability are two performance bottlenecks for on-device deep inferences.
The informational basis of emerging social assistance in low and middle-income countries

Abstract The paper examines the informational basis of emerging social assistance institutions in low- and middle-income countries. The recent expansion of social assistance presents a challenge to researchers concerned with the development of welfare institutions there. Diversity in practice, the absence of data, and incipient conceptual frameworks are significant obstacles to comparative analysis. This paper proposes an informational basis for social assistance in low- and middle-income countries capable of guiding the construction of a comparative database.
Involuntary migration, context of reception, and social mobility: the case of Vietnamese refugee resettlement in the United States

In this study, we examine the Vietnamese population of the United States as a case study in the integration of a refugee group. We first offer a brief review of Vietnamese refugee resettlement in t...
Speaker Naming in Movies

We propose a new model for speaker naming in movies that leverages visual, textual, and acoustic modalities in an unified optimization framework. To evaluate the performance of our model, we introduce a new dataset consisting of six episodes of the Big Bang Theory TV show and eighteen full movies covering different genres. Our experiments show that our multimodal model significantly outperforms several competitive baselines on the average weighted F-score metric. To demonstrate the effectiveness of our framework, we design an end-to-end memory network model that leverages our speaker naming model and achieves state-of-the-art results on the subtitles task of the MovieQA 2017 Challenge.
How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD

Stochastic gradient descent (SGD) gives an optimal convergence rate when minimizing convex stochastic objectives $f(x)$. However, in terms of making the gradients small, the original SGD does not give an optimal rate, even when $f(x)$ is convex. If $f(x)$ is convex, to find a point with gradient norm $\varepsilon$, we design an algorithm SGD3 with a near-optimal rate $\tilde{O}(\varepsilon^{-2})$, improving the best known rate $O(\varepsilon^{-8/3})$. If $f(x)$ is nonconvex, to find its $\varepsilon$-approximate local minimum, we design an algorithm SGD5 with rate $\tilde{O}(\varepsilon^{-3.5})$, where previously SGD variants only achieve $\tilde{O}(\varepsilon^{-4})$. This is no slower than the best known stochastic version of Newton's method in all parameter regimes.
Topical Dermatologic Therapy

Although topical therapy can be one of the most satisfying treatment modalities available to the physician and patient, it also creates special problems. The author analyzes factors that may interfere with results expected from topical therapy and provides a guide to the practical application of topical medications and the appropriate use of liquid, cream, ointment, paste, and powder preparations.
A variance minimization criterion to active learning on graphs

We consider the problem of active learning over the vertices in a graph, without feature representation. Our study is based on the common graph smoothness assumption, which is formulated in a Gaussian random field model. We analyze the probability distribution over the unlabeled vertices conditioned on the label information, which is a multivariate normal with the mean being the harmonic solution over the field. Then we select the nodes to label such that the total variance of the distribution on the unlabeled data, as well as the expected prediction error, is minimized. In this way, the classifier we obtain is theoretically more robust. Compared with existing methods, our algorithm has the advantage of selecting data in a batch offline mode with solid theoretical support. We show improved performance over existing label selection criteria on several real world data sets.
An Optimal Policy for Patient Laboratory Tests in Intensive Care Units

Laboratory testing is an integral tool in the management of patient care in hospitals, particularly in intensive care units (ICUs). There exists an inherent trade-off in the selection and timing of lab tests between considerations of the expected utility in clinical decision-making of a given test at a specific time, and the associated cost or risk it poses to the patient. In this work, we introduce a framework that learns policies for ordering lab tests which optimizes for this trade-off. Our approach uses batch off-policy reinforcement learning with a composite reward function based on clinical imperatives, applied to data that include examples of clinicians ordering labs for patients. To this end, we develop and extend principles of Pareto optimality to improve the selection of actions based on multiple reward function components while respecting typical procedural considerations and prioritization of clinical goals in the ICU. Our experiments show that we can estimate a policy that reduces the frequency of lab tests and optimizes timing to minimize information redundancy. We also find that the estimated policies typically suggest ordering lab tests well ahead of critical onsets-such as mechanical ventilation or dialysis-that depend on the lab results. We evaluate our approach by quantifying how these policies may initiate earlier onset of treatment.
Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images Using Bayesian Deep Learning

Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases. In this paper, we propose a method for retinal layer segmentation and quantification of uncertainty based on Bayesian deep learning. Our method not only performs end-to-end segmentation of retinal layers, but also gives the pixel wise uncertainty measure of the segmentation output. The generated uncertainty map can be used to identify erroneously segmented image regions which is useful in downstream analysis. We have validated our method on a dataset of 1487 images obtained from 15 subjects (OCT volumes) and compared it against the state-of-the-art segmentation algorithms that does not take uncertainty into account. The proposed uncertainty based segmentation method results in comparable or improved performance, and most importantly is more robust against noise.
Impacts of Being Downwind of a Coal-Fired Power Plant on Infant Health at Birth: Evidence from the Precedent-Setting Portland Rule

We conduct the first study on the impacts of prenatal exposure to a uniquely identified large polluter, a coal-fired power plant located near the border of two states, on the birth outcomes of the downwind state. For mothers who live as far as 20 to 40 miles away but downwind of the power plant, being exposed to power plant emissions, in particular sulfur dioxide, during the first month of pregnancy could increase the likelihood of having full-term babies but with low birth weight, an indicator of slow fetal growth, by as much as 42 percent. This adverse impact could be driven by reactive sulfur species-induced intrauterine oxidative stress, arising from maternal exposure to emissions of sulfur dioxide, whose travelling from the emission source to the downwind region has been confirmed in the Portland Rule. In light of EPA’s continual efforts in regulating power plant emissions, our study is aimed at broadening the scope of cross-border pollution analysis by taking into account adverse infant heath impacts from upwind polluters, which can burden the downwind states disproportionately.