Miracle-dz/UniFAR-FTUs huggingface dataset:
The use and effectiveness of interactive progressive drawing in anatomy education

Anatomical relationships are challenging concepts for first‐year medical students. The use of progressive drawing, where an image is created from a blank template, has long been utilized for outlining anatomical relationships and continuity from one region to another, and has shown positive outcomes for student learning. More recently, computerized progressive drawing has been introduced; however, challenges, including issues with visual clarity, have been described. In this study, 17 computerized screencasts of drawings covering neurovasculature of the limbs, abdomen, pelvis, head, and neck were created and provided to first‐year medical students at Morehouse School of Medicine. An animated method for drawing was utilized to increase visual clarity. Surveys were provided to 181 first‐year medical students to collect feedback about these screencasts. Sixty percent (n = 108) of students completed at least one survey. Respondents rated all 17 screencasts with a minimum of 4.7/5 for helpfulness in learning the material for course examinations. A majority of students (77.8%) reported viewing the screencasts more than once on at least one survey, and students reported varying methods for utilizing the screencasts. A majority of students provided positive feedback relating to technical quality. Some significant differences in course performance were seen based on screencast usage. The positive responses from students indicate that this is a useful method in medical education. Anat Sci Educ 11: 445–460. © 2018 American Association of Anatomists.
Content-adaptive pentary steganography using the multivariate generalized Gaussian cover model

The vast majority of steganographic schemes for digital images stored in the raster format limit the amplitude of embedding changes to the smallest possible value. In this paper, we investigate the possibility to further improve the empirical security by allowing the embedding changes in highly textured areas to have a larger amplitude and thus embedding there a larger payload. Our approach is entirely model driven in the sense that the probabilities with which the cover pixels should be changed by a certain amount are derived from the cover model to minimize the power of an optimal statistical test. The embedding consists of two steps. First, the sender estimates the cover model parameters, the pixel variances, when modeling the pixels as a sequence of independent but not identically distributed generalized Gaussian random variables. Then, the embedding change probabilities for changing each pixel by 1 or 2, which can be transformed to costs for practical embedding using syndrome-trellis codes, are computed by solving a pair of non-linear algebraic equations. Using rich models and selection-channel-aware features, we compare the security of our scheme based on the generalized Gaussian model with pentary versions of two popular embedding algorithms: HILL and S-UNIWARD.
AMYCNE: Confident copy number assessment using whole genome sequencing data

Copy number variations (CNVs) within the human genome have been linked to a diversity of inherited diseases and phenotypic traits. The currently used methodology to measure copy numbers has limited resolution and/or precision, especially for regions with more than 4 copies. Whole genome sequencing (WGS) offers an alternative data source to allow for the detection and characterization of the copy number across different genomic regions in a single experiment. A plethora of tools have been developed to utilize WGS data for CNV detection. None of these tools are designed specifically to accurately estimate copy numbers of complex regions in a small cohort or clinical setting. Herein, we present AMYCNE (automatic modeling functionality for copy number estimation), a CNV analysis tool using WGS data. AMYCNE is multifunctional and performs copy number estimation of complex regions, annotation of VCF files, and CNV detection on individual samples. The performance of AMYCNE was evaluated using AMY1A ddPCR measurements from 86 unrelated individuals. In addition, we validated the accuracy of AMYCNE copy number predictions on two additional genes (FCGR3A and FCGR3B) using datasets available through the 1000 genomes consortium. Finally, we simulated levels of mosaic loss and gain of chromosome X and used this dataset for benchmarking AMYCNE. The results show a high concordance between AMYCNE and ddPCR, validating the use of AMYCNE to measure tandem AMY1 repeats with high accuracy. This opens up new possibilities for the use of WGS for accurate copy number determination of other complex regions in the genome in small cohorts or single individuals.
Phenological Differences Between Understory and Overstory: A Case Study Using the Long-Term Harvard Forest Records

The timing of phenological events varies both among species, and also among individuals of the same species. Here we use a 12-year record of spring and autumn phenology for 33 woody species at the Harvard Forest to investigate these differences. Specifically, we focus on patterns of leaf budburst, expansion, coloration and fall, in the context of differences between canopy and understory species, and between canopy and understory individuals of the same species. Many understory species appear to adopt a strategy of "phenological escape" in spring but not autumn, taking advantage of the high-light period in spring before canopy development. For all but a few of these species, the spring escape period is very brief. Relationships between canopy and understory conspecifics (i.e. individuals of the same species) varied among species, with leaf budburst and leaf fall occurring earlier in understory individuals of some species, but later in other species. We fit standard models of varying complexity to the budburst time series for each species to investigate whether biological responses to environmental cues differed among species. While there was no clear consensus model, Akaike's Information Criterion (AIC) indicated that a simple two-parameter "Spring warming" model was best supported by the data for more than a third of all species, and well supported for two-thirds of all species. More highly-parameterized models involving various chilling requirements (e.g., Alternating, Parallel or Sequential chilling) were less well supported by the data. Species-specific model parameterization suggested that responses to both chilling and forcing temperatures vary among species. While there were no obvious differences in this regard between canopy and understory species, or between early- and late-budburst species, these results imply that species can be expected to differ in their responses to future climate change.
DESCRIPTION OF A NEW GENUS OF PFIESTERIA‐LIKE DINOFLAGELLATE, LUCIELLA GEN. NOV. (DINOPHYCEAE), INCLUDING TWO NEW SPECIES: LUCIELLA MASANENSIS SP. NOV. AND LUCIELLA ATLANTIS SP. NOV. 1

A new genus of Pfiesteria‐like heterotrophic dinoflagellate, Luciella gen. nov., and two new species, Luciella masanensis sp. nov. and Luciella atlantis sp. nov., are described. These species commonly occur with other small (<20 μm) heterotrophic and mixotrophic dinoflagellates in estuaries from Florida to Maryland and the southern coast of Korea, suggesting a possible global distribution. An SEM analysis indicates that members of the genus Luciella have the enhanced Kofoidian plate formula of Po, cp, X, 4′, 2a, 6″, 6c, PC, 5+s, 5‴, 0p, and 2″″. The two four‐sided anterior intercalary plates are diamond shaped. The genus Luciella differs from the other genera in the Pfiesteriaceae by a least one plate in the plate tabulation and in the configuration of the two anterior intercalary plates. An SSU rDNA phylogenetic analysis confirmed the genus as monophyletic and distinct from the other genera in the Pfiesteriaceae. The morphology of Luciella masanensis closely resembles Pfiesteria piscicida Steid. et J. M. Burkh. and other Pfiesteria‐like dinoflagellates in size and shape, making it easily misidentified using LM. Luciella atlantis, in contrast, has a more distinctive morphology. It can be distinguished from L. masanensis and other Pfiesteria‐like organisms by a larger cell size, a more conical‐shaped epitheca and hypotheca, larger rhombic‐shaped intercalary plates, and an asymmetrical hypotheca. The genus Luciella is assigned to the order Peridiniales and the family Pfiesteriaceae based on plate tabulation, plate pattern, general morphology, and phylogenetic analysis.
Efficient Detection of Unusual Words

Words that are, by some measure, over- or underrepresented in the context of larger sequences have been variously implicated in biological functions and mechanisms. In most approaches to such anomaly detections, the words (up to a certain length) are enumerated more or less exhaustively and are individually checked in terms of observed and expected frequencies, variances, and scores of discrepancy and significance thereof. Here we take the global approach of annotating the suffix tree of a sequence with some such values and scores, having in mind to use it as a collective detector of all unexpected behaviors, or perhaps just as a preliminary filter for words suspicious enough to undergo a more accurate scrutiny. We consider in depth the simple probabilistic model in which sequences are produced by a random source emitting symbols from a known alphabet independently and according to a given distribution. Our main result consists of showing that, within this model, full tree annotations can be carried out in a time-and-space optimal fashion for the mean, variance and some of the adopted measures of significance. This result is achieved by an ad hoc embedding in statistical expressions of the combinatorial structure of the periods of a string. Specifically, we show that the expected value and variance of all substrings in a given sequence of n symbols can be computed and stored in (optimal) O(n2) overall worst-case, O (n log n) expected time and space. The O (n2) time bound constitutes an improvement by a linear factor over direct methods. Moreover, we show that under several accepted measures of deviation from expected frequency, the candidates over- or underrepresented words are restricted to the O(n) words that end at internal nodes of a compact suffix tree, as opposed to the theta(n2) possible substrings. This surprising fact is a consequence of properties in the form that if a word that ends in the middle of an arc is, say, overrepresented, then its extension to the nearest node of the tree is even more so. Based on this, we design global detectors of favored and unfavored words for our probabilistic framework in overall linear time and space, discuss related software implementations and display the results of preliminary experiments.
Robust Holt-Winter Based Control Chart for Monitoring Autocorrelated Simple Linear Profiles With Contaminated Data

Profile monitoring is a useful technique in statistical process control used when the product or process quality is represented by a function over a time period. This function represents the relationship between a response variable and one or more explanatory variables. Most existing control charts for monitoring profiles are based on the assumption that the observations within each profile are independent of each other which is often violated in practice. Sometimes there are one or more outliers in each profile, which leads to poor statistical performance of the control chart. This paper focuses on Phase II monitoring of a simple linear profile with autocorrelation within profile data in the presence of outliers. In this paper, we propose a new combined control chart based on the robust Holt-Winter model to decrease the effect of outliers. We first evaluate the effect of outliers on the performance of the proposed combined control chart. Then, we apply robust Holt-Winter and design a robust combined control chart to overcome the effect of outliers. The performance of the proposed robust Holt-Winter control chart is evaluated through extensive simulation studies. The results show that the proposed robust control chart performs well.
A model of back-channel acknowledgements in spoken dialogue

Spoken dialogue systems would be more acceptable if they were able to produce backchannel continuers such as mm-hmm in naturalistic locations during the user's utterances. Using the HCRC Map Task Corpus as our data source, we describe models for predicting these locations using only limited processing and features of the user's speech that are commonly available, and which therefore could be used as a low-cost improvement for current systems. The baseline model inserts continuers after a predetermined number of words. One further model correlates back-channel continuers with pause duration, while a second predicts their occurrence using trigram POS frequencies. Combining these two models gives the best results.
High-intensity interval training in patients with lifestyle-induced cardiometabolic disease: a systematic review and meta-analysis

Background/Aim Cardiorespiratory fitness (CRF) is a strong determinant of morbidity and mortality. In athletes and the general population, it is established that high-intensity interval training (HIIT) is superior to moderate-intensity continuous training (MICT) in improving CRF. This is a systematic review and meta-analysis to quantify the efficacy and safety of HIIT compared to MICT in individuals with chronic cardiometabolic lifestyle diseases. Methods The included studies were required to have a population sample of chronic disease, where poor lifestyle is considered as a main contributor to the disease. The procedural quality of the studies was assessed by use of a modified Physiotherapy Evidence Base Database (PEDro) scale. A meta-analysis compared the mean difference (MD) of preintervention versus postintervention CRF (VO2peak) between HIIT and MICT. Results 10 studies with 273 patients were included in the meta-analysis. Participants had coronary artery disease, heart failure, hypertension, metabolic syndrome and obesity. There was a significantly higher increase in the VO2peak after HIIT compared to MICT (MD 3.03 mL/kg/min, 95% CI 2.00 to 4.07), equivalent to 9.1%. Conclusions HIIT significantly increases CRF by almost double that of MICT in patients with lifestyle-induced chronic diseases.
A Product Search Engine Supporting “ Best Product ” Queries

In this study, a novel product search engine system which supports “find the best products for a given category” type queries is proposed. The product search engine system consists of a focused crawler, a record linkage system, a sentiment analyzer, and a query engine system. The focused crawler is used to crawl product information from various e-commerce sites; the record linkage system determines the identical products that are crawled from different e-commerce sites; the sentiment analyzer classifies users’ reviews about the products as positive or negative so that our product search engine can decide which product is the best for a given category; and the query engine takes the user queries and displays the result. All implementations are done by using C# programming language in . NET 4.5 framework, and MS-SQL Server 2014 database management system is employed for data storage. The core of our system is the record linkage part which is based on a modified incremental Hierarchical Agglomerative Clustering algorithm. To improve the success of record linkage process we also develop a product code matching system such that if the two products from different e-commerce sites have the same product code they are considered as the same. In our experimental analysis we observe 96.25% F-measure in record linkage of E-commerce products and 100% precision in most related products search. Our system can successfully offer best products for a given category. The proposed system achieves to provide better user experience than the existing systems.
University of Huddersfield Repository Generating Fast Domain-specific Planners by Automatically Configuring a Generic Parameterised Planner Original Citation Generating Fast Domain-specific Planners by Automatically Configuring a Generic Parameterised Planner

Users may access full items free of charge; copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational or not-for-profit purposes without prior permission or charge, provided: • The authors, title and full bibliographic details is credited in any copy; • A hyperlink and/or URL is included for the original metadata page; and • The content is not changed in any way. Abstract When designing state-of-the-art, domain-independent planning systems, many decisions have to be made with respect to the domain analysis or compilation performed during prepro-cessing, the heuristic functions used during search, and other features of the search algorithm. These design decisions can have a large impact on the performance of the resulting planner. By providing many alternatives for these choices and exposing them as parameters, planning systems can in principle be configured to work well on different domains. However, usually planners are used in default configurations that have been chosen because of their good average performance over a set of benchmark domains, with limited experimentation of the potentially huge range of possible configurations. In this work, we propose a general framework for automatically configuring a parameterised planner, showing that substantial performance gains can be achieved. We apply the framework to the well-known LPG planner, which has 62 parameters and over 6.5 × 10 17 possible configurations. We demonstrate that by using this highly parameterised planning system in combination with the off-the-shelf, state-of-the-art automatic algorithm configuration procedure ParamILS, substantial performance improvements on specific planning domains can be obtained.
The Role of Social Supports, Spirituality, Religiousness, Life Meaning and Affiliation with 12-Step Fellowships in Quality of Life Satisfaction Among Individuals in Recovery from Alcohol and Drug Problems

SUMMARY Many recovering substance users report quitting drugs because they wanted a better life. The road of recovery is the path to a better life but a challenging and stressful path for most. There has been little research among recovering persons in spite of the numbers involved, and most research has focused on substance use outcomes. This study examines stress and quality of life as a function of time in recovery, and uses structural equation modeling to test the hypothesis that social supports, spirituality, religiousness, life meaning, and 12-step affiliation buffer stress toward enhanced life satisfaction. Recovering persons (N = 353) recruited in New York City were mostly inner-city ethnic minority members whose primary substance had been crack or heroin. Longer recovery time was significantly associated with lower stress and with higher quality of life. Findings supported the study hypothesis; the ‘buffer’ constructs accounted for 22% of the variance in life satisfaction. Implications for research and clinical practice are discussed.
Correlation-aware sparsity-enforcing sensor placement for spatio-temporal field estimation

In this work, we propose a generalized framework for designing optimal sensor constellations for spatio-temporally correlated field estimation using wireless sensor networks. The accuracy of the field intensity estimate in every point of a given service area strongly depends upon the number and the constellation of the sensors along with the spatio-temporal statistics of the field. We formulate and solve a sparsity-enforcing optimization problem to select the best sensor locations that achieve some desired estimation performance. The sparsity-enforcing iterative selection algorithm is aware of the non-separable space-time covariance structure of the field.
The universal homogeneous binary tree

A partial order is called semilinear iff the upper bounds of each element are linearly ordered and any two elements have a common upper bound. There exists, up to isomorphism, a unique countable existentially closed semilinear order, which we denote by S2. We study the reducts of S2, that is, the relational structures with the same domain as S2 all of whose relations are first-order definable in S2. Our main result is a classification of the model-complete cores of the reducts of S2. From this, we also obtain a classification of reducts up to first-order interdefinability, which is equivalent to a classification of all closed permutation groups that contain the automorphism group of S2.
Learning Motion Categories using both Semantic and Structural Information

Current approaches to motion category recognition typically focus on either full spatiotemporal volume analysis (holistic approach) or analysis of the content of spatiotemporal interest points (part-based approach). Holistic approaches tend to be more sensitive to noise e.g. geometric variations, while part-based approaches usually ignore structural dependencies between parts. This paper presents a novel generative model, which extends probabilistic latent semantic analysis (pLSA), to capture both semantic (content of parts) and structural (connection between parts) information for motion category recognition. The structural information learnt can also be used to infer the location of motion for the purpose of motion detection. We test our algorithm on challenging datasets involving human actions, facial expressions and hand gestures and show its performance is better than existing unsupervised methods in both tasks of motion localisation and recognition.
Successor Features for Transfer in Reinforcement Learning

Transfer in reinforcement learning refers to the notion that generalization should occur not only within a task but also across tasks. We propose a transfer framework for the scenario where the reward function changes between tasks but the environment's dynamics remain the same. Our approach rests on two key ideas: "successor features", a value function representation that decouples the dynamics of the environment from the rewards, and "generalized policy improvement", a generalization of dynamic programming's policy improvement operation that considers a set of policies rather than a single one. Put together, the two ideas lead to an approach that integrates seamlessly within the reinforcement learning framework and allows the free exchange of information across tasks. The proposed method also provides performance guarantees for the transferred policy even before any learning has taken place. We derive two theorems that set our approach in firm theoretical ground and present experiments that show that it successfully promotes transfer in practice, significantly outperforming alternative methods in a sequence of navigation tasks and in the control of a simulated robotic arm.