Miracle-dz/UniFAR-FTUs huggingface dataset:
The Aggregate Impact of Online Retail

To study the impact of online retail on aggregate welfare, I use a spatial model to calculate a new measure of store level retail productivity and each store's equilibrium response to increased competitive pressure from online retailers. The model is estimated on confidential store-level data spanning the universe of US retail stores, detailed local-level demographic data and shortest-route data between locations. From counterfactual exercises mimicking improvements in shipping and increased internet access, I estimate that improvements in online retail increased aggregate welfare from retail activities by 13.4 percent. Roughly two-thirds of the increase can be attributed to welfare improvements holding fixed market shares, with the remainder due to reallocation. Surprisingly, 8.2 percent of firms actually benefit as they absorb market share from closed stores. Finally, I estimate that the proposed Marketplace Fairness Act would claw back roughly one-third of sales that would otherwise have gone to online retailers between 2007-12.
Situation Awareness and Motion Sickness in Automated Vehicle Driving Experience: A Preliminary Study of Peripheral Visual Information

Future automated car users are expected to perform non-driving activities when the car is operated in full automated mode. Hence, situation awareness (SA) is expected to decrease whereas chances of motion sickness (MS) to occur have increased as a result of the mismatch between what is felt and seen. In this study, we try to assess the level of SA and MS when users are watching a video and subjected to accelerations produced by automated defensive driving style. A light-based prototype was placed within the users' peripheral field of view to indicate the future direction of the automated car. The experiment was done on a real road condition using a modified instrumented vehicle under the urban driving context. A within-subject design, with and without a prototype, was conducted. The preliminary result (N=10) indicates that higher SA and lower MS with the peripheral visual prototype compare to the controlled condition.
Text Sentiment Analysis based on Fusion of Structural Information and Serialization Information

Tree-structured Long Short-Term Memory (Tree-LSTM) has been proved to be an effective method in the sentiment analysis task. It extracts structural information on text, and uses Long Short-Term Memory (LSTM) cell to prevent gradient vanish. However, though combining the LSTM cell, it is still a kind of model that extracts the structural information and almost not extracts serialization information. In this paper, we propose three new models in order to combine those two kinds of information: the structural information generated by the Constituency Tree-LSTM and the serialization information generated by Long-Short Term Memory neural network. Our experiments show that combining those two kinds of information can give contributes to the performance of the sentiment analysis task compared with the single Constituency Tree-LSTM model and the LSTM model.
Common occurrence of TERT promoter mutations in thyroid cancer

The 2013 discovery of Telomerase reverse transcriptase (TERT) promoter mutations chr5, 1,295,228 COT (C228T) and 1,295,250 COT (C250T) in thyroid cancer represents an important event in the thyroid cancer field and much progress has occurred since then. This article provides a comprehensive review of this exciting new thyroid cancer field. The oncogenic role of TERT promoter mutations involves their creation of consensus binding sites for E-twenty-six transcriptional factors. TERT C228T is far more common than TERT C250Tand their collective prevalence is, on average, 0, 11.3, 17.1, 43.2 and 40.1% in benign thyroid tumors, papillary thyroid cancer (PTC), follicular thyroid cancer, poorly differentiated thyroid cancer and anaplastic thyroid cancer, respectively, displaying an association with aggressive types of thyroid cancer. TERT promoter mutations are associated with aggressive thyroid tumor characteristics, tumor recurrence and patient mortality as well as BRAF V600E mutation. Coexisting BRAF V600E and TERT promoter mutations have a robust synergistic impact on the aggressiveness of PTC, including a sharply increased tumor recurrence and patient mortality, while either mutation alone has a modest impact. Thus, TERT with promoter mutations represents a prominent new oncogene in thyroid cancer and the mutations are promising new diagnostic and prognostic genetic markers for thyroid cancer, which, in combination with BRAF V600E mutation or other genetic markers (e.g. RAS mutations), are proving to be clinically useful for the management of thyroid cancer. Future studies will specifically define such clinical utilities, elucidate the biological mechanisms and explore the potential as therapeutic targets of TERT promoter mutations in thyroid cancer.
On the use of eigenvectors for signal detection and classification in multiple antenna cognitive radios

This paper presents a comparative analysis of eigenvector-based signal detection algorithms and a new eigenvector-based modulation classifier for multiple antenna cognitive radio applications. The signal detection algorithms considered in this work are: Feature Template Matching (FTM), Multiple Feature Matching (MFM) and Principal Components (PC)-based detection. A software-defined radio (SDR) platform is used to experimentally evaluate the detection performance of these algorithms. It is observed that MFM, which uses four signal features extracted across space, time and phase dimensions, provides the best performance among the considered algorithms. This paper also presents a new idea on the use of eigenvectors for signal classification in multiple antenna systems. Using computer simulations, it is shown that eigenvectors corresponding to different modulation schemes allow for reliable signal identification even at very low SNR values (on the order of -20 dB), therefore they can be used effectively for signal classification in cognitive radio applications.
Statin therapy does not improve lung function in asthma: A meta-analysis of randomized controlled trials

Objective To assess the efficacy of statins in the treatment of asthma. Methods Electronic databases were searched to identify randomized controlled trials that measured the efficacy of statins in the treatment of asthma, and a meta-analysis of the pooled data was performed. Results Five trials were identified; four met the inclusion criteria (total number of patients 200). Compared with controls, patients in the statin groups had higher forced expiratory volume in 1 s (FEV1) values before inhaled corticosteroids (0.09 l, 95% confidence interval [CI] −0.06, 0.23), higher FEV1 values after inhaled corticosteroids (0.06 l, 95% CI −0.09, 0.22), and higher morning peak expiratory flow rates (9.87 l, 95% CI −15.66, 35.40). These results were not statistically significant and, furthermore, publication bias was detected. Conclusion In conclusion, there is currently insufficient evidence to show that statins improve lung function in patients with asthma.
Aligned-Layer Text Search in Clinical Notes

Search techniques in clinical text need to make fine-grained semantic distinctions, since medical terms may be negated, about someone other than the patient, or at some time other than the present. While natural language processing (NLP) approaches address these fine-grained distinctions, a task like patient cohort identification from electronic health records (EHRs) simultaneously requires a much more coarse-grained combination of evidence from the text and structured data of each patient’s health records. We thus introduce aligned-layer language models, a novel approach to information retrieval (IR) that incorporates the output of other NLP systems. We show that this framework is able to represent standard IR queries, formulate previously impossible multi-layered queries, and customize the desired degree of linguistic granularity.
Optoelectronic application of multi-layer epitaxial graphene on a Si substrate

In this work, the epitaxial graphene channel formed on 3C-SiC grown on a Si substrate backgate transistor was designed, fabricated and characterized for electronic and optoelectronic applications. Even though a significant amount of the gate leakage current is observed, the experimental results show the device works as an n-type transistor as well as an infrared photovoltaic transistor with the backgate modulation. The observation of the ambipolar behavior verifies the unique property of the graphene layers. The epitaxial graphene is believed to be unintentionally p-type with the Fermi level offset around +0.11∼+0.12 V at the Dirac point. The drain saturated current of the graphene channel transistors is on the order of mA/mm. The photo-generated current can be achieved up to almost 20nA, corresponding to 0.06 mA/W in photo-responsivity at 0.5-V drain-source bias voltage and 0.5-V gate voltage. The backgate voltage tuning spectral characteristic is also demonstrated. The graphene based transistors have a potential application in infrared detection.
Prezi meeting: collaboration in a zoomable canvas based environment

We are introducing a zoomable, canvas based editor called Prezi, in which multiple users can collaborate synchronously, and share a common workspace for various purposes. They can develop a presentation together, create a mindmap, a storyline or do brainstorming. In this paper we would like to describe our key ideas, when designing the user experience of the collaboration function. We are arguing that the use of avatars in zoomable user interfaces are providing a uniquely efficient environment for collaboration in productivity applications. This kind of representation in a ZUI collaboration environment is raising group awareness, articulation work, and also gamifies presentaion editing, facilitating casual interaction between the participants.
A Preliminary Study of the Effects of Electroconvulsive Therapy on Regional Brain Glucose Metabolism in Patients with Major Depression

Animal studies have shown that a course of electroconvulsive shock (ECS) leads to a significant reduction in glucose metabolism in rat brains 1 day after the last ECS. In humans, of the two positron emission tomography (PET) studies that assessed the effects of a course of electroconvulsive therapy (ECT) on brain glucose metabolism in depressed patients, one reported no change while the other found a trend for reduction in glucose metabolism in frontal cortical region 24 hours after last ECT. The changes in glucose metabolism detected 24 hours after the last ECS/ECT treatment might simply be due to subacute effects of a seizure. We hypothesized that the changes in brain metabolism that persist 1 week after a course of ECT are more likely to underlie the therapeutic effects of ECT. We, therefore, investigated the effects of a course of ECT on brain glucose metabolism 1 week after last ECT by using PET and [18F]fluorodeoxyglucose (FDG). Six patients who met DSM-IV criteria for a diagnosis of major depressive disorder (unipolar), and were referred for ECT as the clinically indicated treatment were recruited. They underwent two PET scans, one prior to first ECT and the second a week after last ECT. The number of ECT treatments subjects received ranged from 8 to 12 with a mean of 11. Five out of six patients responded to the ECT treatment. Cerebral metabolic rates for glucose were slightly lower in most regions post treatment compared with pretreatment but the differences were not statistically significant. Similarly, there was no significant correlation between changes in regional cerebral metabolic rates for glucose (rCMRglc) and changes in Hamilton Depression Rating Scale (HAM-D 21-item) scores. Our results might suggest that rCMRglc rates are not altered 1 week after a therapeutic course of ECT in depressed patients. Further studies using new generation PET scanners, which have a higher resolution, in larger numbers of depressed patients, are clearly needed before firm conclusions can be drawn.
Cardiovascular Manifestation of a Family with Marfan’s Syndrome

Marfan’s syndrome (MFS) is a genetic disorder associated with autosomal dominant inheritance. In MFS, the most prevalent cause of death is cardiovascular involvement. Here we introduce a family with a severe penetration of MFS. Eleven members of this family have MFS (father, two daughters, three sons, and five grandchildren). The most common cardiac involvement in our patients was mitral valve prolapse as manifested by mitral regurgitation. At ten years’ follow-up, two patients had aortic root dilatation running the risk of aortic dissection; they were, therefore, scheduled for cardiac surgery, during which the Bental procedure was successfully performed. Fortunately, all of the members of this family are currently alive.
The RESID Database of protein structure modifications and the NRL-3D Sequence-Structure Database

The RESID Database is a comprehensive collection of annotations and structures for protein post-translational modifications including N-terminal, C-terminal and peptide chain cross-link modifications. The RESID Database includes systematic and frequently observed alternate names, Chemical Abstracts Service registry numbers, atomic formulas and weights, enzyme activities, taxonomic range, keywords, literature citations with database cross-references, structural diagrams and molecular models. The NRL-3D Sequence-Structure Database is derived from the three-dimensional structure of proteins deposited with the Research Collaboratory for Structural Bioinformatics Protein Data Bank. The NRL-3D Database includes standardized and frequently observed alternate names, sources, keywords, literature citations, experimental conditions and searchable sequences from model coordinates. These databases are freely accessible through the National Cancer Institute-Frederick Advanced Biomedical Computing Center at these web sites: http://www. ncifcrf.gov/RESID, http://www.ncifcrf.gov/NRL-3D; or at these National Biomedical Research Foundation Protein Information Resource web sites: http://pir.georgetown. edu/pirwww/dbinfo/resid . html, http://pir.georgetown. edu/pirwww/dbinfo/nrl3d . html
Using Augmented Virtuality for Remote Collaboration

This paper describes the concept, prototypical implementation, and usability evaluation of the Augmented Virtuality (AV)-based videoconferencing (VC) system cAR/PE! We present a solution that allows three participants at different locations to communicate over a network in an environment simulating a traditional face-to-face meeting. Integrated into the AV environment are live video streams of the participants spatially arranged around a virtual table, a large virtual presentation screen for 2D display and application sharing, and 3D geometry (models) within the room and on top of the table. We describe the general concept and application scenario as well as the actual hardware setup, the implementation, and the use of the system in its current state. Results of two usability studies with 87 subjects are presented that show the general usability of our approach as well as good overall satisfaction. Parts of the work described here were presented as a poster at the second International Symposium on Mixed and Augmented Reality (Regenbrecht, Ott, Wagner, Lum, Kohler, et al., 2003. An Augmented Virtuality Approach to 3D Videoconferencing. Poster at 2nd Int. Symp. on Mixed and Aug. Reality, Tokyo.).
Coupled Transformations of Graph Structures applied to Model Migration

Model-Driven Engineering (MDE) is a relatively new paradigm in software engineering that pursues the goal to master the increased complexity of modern software products. While software applications have been developed for a specific platform in the past, today they are targeting various platforms and devices from classical desktop PCs to smart phones. In addition, they interact with other applications. To easier cope with these new requirements, software applications are specified in MDE at a high abstraction level in so called models prior to their implementation. Afterward, model transformations are used to automate recurring development tasks as well as to generate software artifacts for different runtime environments. Thereby, software artifacts are not necessarily files containing program code, they can also cover configuration files as well as machine readable input for model checking tools. However, MDE does not only address software engineering problems, it also raises new challenges. One of these new challenges is connected to the specification of modeling languages, which are used to create models. The creation of a modeling language is a creative process that requires several iterations similar to the creation of models. New requirements as well as a better understanding of the application domain result in an evolution of modeling languages over time. Models developed in an earlier version of a modeling language often needs to be co-adopted (migrated) to language changes. This migration should be automated, as migrating models manually is time consuming and error-prone. While application modelers use ad-hoc solutions to migrate their models, there is still a lack of theory to ensure well-defined migration results. This work contributes to a formalization of modeling language evolution with corresponding model migration on the basis of algebraic graph transformations that have successfully been used earlier as theoretical foundations of model transformation. The goal of this research is to develop a theory that considers the problem of modeling language evolution with corresponding model migration on a conceptual level, independent of a specific modeling framework.
LP radiation from a metamaterial-based loop antenna

A loop antenna whose electrical peripheral length is equal to two (Cperi = 2) at two different frequencies is investigated. The antenna arm of this loop is composed of numerous cells, each having one inductive and two capacitive elements, so that the current on the loop has a negative propagation phase constant in addition to its inherent positive propagation phase constant. The radiation pattern shows a dual-peak pattern. The VSWR is small at the two frequencies where Cperi = 2, as desired.
Rethinking the Measurement of Adversity

Research on adverse childhood experiences (ACEs) has unified the study of interrelated risks and generated insights into the origins of disorder and disease. Ten indicators of child maltreatment and household dysfunction are widely accepted as ACEs, but further progress requires a more systematic approach to conceptualizing and measuring ACEs. Using data from a diverse, low-income sample of women who received home visiting services in Wisconsin (N = 1,241), this study assessed the prevalence of and interrelations among 10 conventional ACEs and 7 potential ACEs: family financial problems, food insecurity, homelessness, parental absence, parent/sibling death, bullying, and violent crime. Associations between ACEs and two outcomes, perceived stress and smoking, were examined. The factor structure and test–retest reliability of ACEs was also explored. As expected, prevalence rates were high compared to studies of more representative samples. Except for parent/sibling death, all ACEs were intercorrelated and associated at the bivariate level with perceived stress and smoking. Exploratory factor analysis confirmed that conventional ACEs loaded on two factors, child maltreatment and household dysfunction, though a more complex four-factor solution emerged once new ACEs were introduced. All ACEs demonstrated acceptable test–retest reliability. Implications and future directions toward a second generation of ACE research are discussed.
Managed Futures: A Composite CTA Performance Review

The data and time dependency of empirical financial research is a common concern to both academics and practitioners. Changes in regulatory, trading and investor environments may result in dramatic changes in the underlying viability of any investment vehicle and/or trading process. This is especially true for managed futures programs for which a single commonly used database does not exist and which often are dynamic in nature and are impacted by changes in trading instruments and underlying markets. As a result, empirical analysis of the potential benefits of Managed Futures (e.g., Commodity Trading Advisors (CTAs)) may be impacted by the period of analysis and the strategy composition of the database or index used to represent the managed futures investment. In this analysis, we conduct a series of empirical tests on CTA indices which are designed to represent the overall return to the reporting universe of CTAs (e.g., composite CTA indices). These tests are similar to those previously conducted on a series of ‘composite’ hedge fund indices (Schneeweis et. al., 2012). Using major composite CTA indices as a surrogate for CTA portfolios, these tests include cross-sectional and time series analysis. Results reflect the common wisdom that performance results may be dominated by the period of analysis as well as the index and multi-factor regression model used.
WISC-IV and WISC-III Profiles in Children With ADHD

Wechsler Intelligence Scale for Children, 3rd and 4th editions (WISC-III n = 586 and WISC-IV n = 118), profiles were compared for children with ADHD and normal intelligence. Mean Verbal Comprehension Index (VCI) and Perceptual Organization/Perceptual Reasoning Index (POI/PRI) scores were significantly higher than Freedom From Distractibility/Working Memory Index (FDI/WMI) and Processing Speed Index (PSI), and Symbol Search was higher than Coding. FDI/WMI and PSI scores were similar on both tests, but VCI and POI/PRI were higher on the WISC-IV than on the WISC-III. Therefore, index discrepancies were greater for the WISC-IV, suggesting that the WISC-IV might be better than the WISC-III in delineating the strengths and weaknesses of children with ADHD. All children in the WISC-IV sample scored lowest on WMI or PSI, whereas only 88% of the WISC-III children scored lowest on FDI or PSI. Thus, the WISC-IV may be more helpful in diagnosing ADHD than the WISC-III.