Miracle-dz/UniFAR-FTUs huggingface dataset:
Mental Health Care Access Using Geographic Information Systems: An Integrative Review

Abstract A decade after the Mental Health Parity and Addiction Equity Act was implemented to ensure access to mental health and substance abuse services for U.S. citizens, accessing mental health care still is problematic for many needing services. More than 123 million Americans reside in federally-designated Mental Health Professional Shortage Areas (MHPSA) compared to 84 million living in similarly-designated primary care shortage areas and 62 million in dental health shortage areas. Health professional shortage areas are geographic areas that have a shortage of primary medical, dental, or mental health providers. Geographic Information Systems (GIS) with spatial analysis approaches provide tools to understand the ever-changing distribution of health care, outcomes, and delivery to improve care. The aim of this integrative review is to describe and synthesize the literature on GIS approaches to improve access to mental health care services. GIS Bibliography, PubMed, CINAHL, PsycINFO, Academic Search Premier, Social Sciences Citation Index, and Oalster were searched for articles between January 1, 1998 and January 1, 2018 that met established inclusion/exclusion criteria. Among the 138 articles reviewed, 18 met criteria and were included in the review. GIS approaches to improve access to mental health care can be categorized as (1) type of care (integrated, community), (2) contributions to access (distance, time, cost, perception of traveling, inequalities), and (3) the utilization of services. Results from the literature suggest closer examination of measures used to assess geographic variations in accessibility is needed for ultimately improving quality of care for people in MHPSA.
Landslide susceptibility mapping along existing and proposed pipeline corridors in Canada

The Canadian Energy Pipeline Association (CEPA) measures approximately 580,000 km of pipeline in Canada with the longest crude oil pipeline in the world. In 2007, approximately 2.65 million barrels per day of crude oil and roughly 17.1 billion cubic feet of natural gas per day traveled through Canada’s pipeline networks. There are several proposed new pipeline routes including the Yukon Alaska Highway Pipeline (YAHP) Project and the Mackenzie Valley natural gas Pipeline project (MVP) located in Canada’s sub-Arctic region. This paper compares two examples of regional landslide susceptibility mapping studies along an existing pipeline corridor in southern British Columbia (BC) and a proposed pipeline (MVP) in Canada. Both methods are semi-quantitative and rely on expert judgment. Landslide inventory information overlain on the resulting landslide susceptibility maps indicates good correlation with potential for improvement. The paper also discusses the federal government’s role in providing geoscience information for environmental assessments of the MVP and YAHP projects.
OCR-D: An end-to-end open source OCR framework for historical printed documents

Various research projects were concerned with the development and adaptation of methods for OCR specifically for historical printed documents (cf. METAe [20], IMPACT [1], eMOP [9]). However, these initiatives have ended before the wide adoption of deep neural networks and, despite the various project's achievements, there remains a lack of OCR software that is a) comprehensive with regard to the challenges presented by the wide variety of historical documents and b) available as ready-to-use Free Software. The OCR-D project aims to rectify that. In this paper we introduce the background of OCR-D, the main challenges and shortcomings in the availability of open tools and resources for OCR of historical printed documents and discuss the various software modules and related components (repositories, workflows) that are being made available through OCR-D. Finally we provide an outlook to a number of remaining challenges that are not addressed by OCR-D and point out several examples for the positive community aspects arisen through the creation and sharing of open resources for historical German OCR.
Quantifying the Coach–Athlete–Parent (C–A–P) Relationship in Youth Sport: Initial Development of the Positive and Negative Processes in the C–A–P Questionnaire (PNPCAP)

Youth sport participation is valued worldwide. Coaches, parents, and athlete youth routinely interact. These interactions impact youth sport participation. To date, only a 48-item measure exits assessing the overall perception of the coach–athlete–parent relationship with the same question set for coaches, parents, and athletes. However, this 48-item measure has not undergone quantitative development. Hence, we sought to assess these 48 items and to further develop a valid and reliable instrument measuring the coach–athlete–parent relationship. To do so, two studies were conducted. In Study 1, 308 participants completed the existing 48-item measure, resulting in 15 items that were fit into two dimensions, positive and negative group processes. In Study 2, 678 participants completed the 15-item measure. After examining the analyses, 11 items remained to form the Positive and Negative Processes in the Coach–Athlete–Parent Questionnaire (PNPCAP). In summary, the PNPCAP is a valid brief measure for assessing interpersonal relationships among coach–athlete–parents in both team and individual sport contexts. Future research is needed to continue to develop the scale for construct validity as well as translate the scale into multiple languages to determine validity in across countries.
Vessel navigation for single beam ultrasound Doppler using transparent transducer and optics

Pulsed-wave ultrasound Doppler has a potential for being developed as wearable forms with a single element transducer. However, an optimal Doppler gating method is required to overcome the absence of image guidance from array imaging system. In this study, we developed a real-time Doppler imaging system, vessel navigator device, and a three-dimensional vessel navigation algorithm for pulsed-wave Doppler using single beam. An optically transparent 5 MHz single element transducer was merged with a reflective pulse oximetry sensor as the vessel navigator. The vessel navigator was implemented in a custom-made real-time Doppler imaging system. Furthermore, acquired acoustic and optic signals were employed for navigating axial and lateral position of the vessel, respectively. Flow phantom study was employed to evaluate the proposed device and algorithm. The Doppler signal was successfully acquired from the algorithm-guided vessel position.
Xerostomia: Few dry facts about dry mouth

Xerostomia is the subjective feeling of oral dryness, which is often associated with hypofunction of the salivary gland. Xerostomia can also have a major impact on a patient′s oral health and quality of life. Patients with xerostomia complaints of problems with mastication, phonetics, deglutition and wearing dentures. Treatment of this condition always depends on the cause. Oral physicians should be able to diagnose this condition and able to give proper treatment to provide comfort to their patients. This article reviews the causes of xerostomia and its management.
Use of Longitudinal EEG Measures in Estimating Language Development in Infants With and Without Familial Risk for Autism Spectrum Disorder

Language development in children with autism spectrum disorder (ASD) varies greatly among affected individuals and is a strong predictor of later outcomes. Younger siblings of children with ASD have increased risk of ASD, but also language delay. Identifying neural markers of language outcomes in infant siblings could facilitate earlier intervention and improved outcomes. This study aimed to determine whether electroencephalography (EEG) measures from the first 2 years of life can explain heterogeneity in language development in children at low and high risk for ASD, and whether associations between EEG measures and language development are different depending on ASD risk status or later ASD diagnosis. In this prospective longitudinal study, EEG measures collected between 3 and 24 months were used in a multivariate linear regression model to estimate participants’ 24-month language development. Individual baseline longitudinal EEG measures included (1) the slope of EEG power across 3 to 12 months or 3 to 24 months of life for six canonical frequency bands, (2) the estimated EEG power at 6 months of age for the same frequency bands, and (3) terms representing the interaction between ASD risk status and EEG power measures. Modeled 24-month language scores using EEG data from either the first 2 years (Pearson p = 0.70, 95% CI [0.595, 0.783], p = 1 × 10−18) or the first year of life (Pearson p = 0.66, 95% CI [0.540, 0.761], p = 2.5 × 10−14) were highly correlated with observed scores. All models included significant interaction effects of risk on EEG measures, suggesting that EEG-language associations are different depending on risk status, and that different brain mechanisms affect language development in low- versus high-risk infants.
Eigenvalues and triangles in graphs

Abstract Bollobás and Nikiforov (J. Combin. Theory Ser. B. 97 (2007) 859–865) conjectured the following. If G is a Kr+1-free graph on at least r+1 vertices and m edges, then ${\rm{\lambda }}_1^2(G) + {\rm{\lambda }}_2^2(G) \le (r - 1)/r \cdot 2m$, where λ1 (G)and λ2 (G) are the largest and the second largest eigenvalues of the adjacency matrix A(G), respectively. In this paper we confirm the conjecture in the case r=2, by using tools from doubly stochastic matrix theory, and also characterize all families of extremal graphs. Motivated by classic theorems due to Erdös and Nosal respectively, we prove that every non-bipartite graph of order and size contains a triangle if one of the following is true: (i) ${{\rm{\lambda }}_1}(G) \ge \sqrt {m - 1} $ and $G \ne {C_5} \cup (n - 5){K_1}$, and (ii) ${{\rm{\lambda }}_1}(G) \ge {{\rm{\lambda }}_1}(S({K_{[(n - 1)/2],[(n - 1)/2]}}))$ and $G \ne S({K_{[(n - 1)/2],[(n - 1)/2]}})$, where $S({K_{[(n - 1)/2],[(n - 1)/2]}})$ is obtained from ${K_{[(n - 1)/2],[(n - 1)/2]}}$ by subdividing an edge. Both conditions are best possible. We conclude this paper with some open problems.
ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation

The uptake of deep learning in natural language generation (NLG) led to the release of both small and relatively large parallel corpora for training neural models. The existing data-to-text datasets are, however, aimed at task-oriented dialogue systems, and often thus limited in diversity and versatility. They are typically crowdsourced, with much of the noise left in them. Moreover, current neural NLG models do not take full advantage of large training data, and due to their strong generalizing properties produce sentences that look template-like regardless. We therefore present a new corpus of 7K samples, which (1) is clean despite being crowdsourced, (2) has utterances of 9 generalizable and conversational dialogue act types, making it more suitable for open-domain dialogue systems, and (3) explores the domain of video games, which is new to dialogue systems despite having excellent potential for supporting rich conversations.
A Bin Encoding Training of a Spiking Neural Network Based Voice Activity Detection

Advances of deep learning for Artificial Neural Networks (ANNs) have led to significant improvements in the performance of digital signal processing systems implemented on digital chips. Although recent progress in low-power chips is remarkable, neuromorphic chips that run Spiking Neural Networks (SNNs) based applications offer an even lower power consumption, as a consequence of the ensuing sparse spikebased coding scheme. In this work, we develop a SNN-based Voice Activity Detection (VAD) system that belongs to the building blocks of any audio and speech processing system. We propose to use the bin encoding, a novel method to convert log mel filterbank bins of single-time frames into spike patterns. We integrate the proposed scheme in a bilayer spiking architecture which was evaluated on the QUT-NOISE-TIMIT corpus. Our approach shows that SNNs enable an ultra low-0power implementation of a VAD classifier that consumes only 3.8 µW, while achieving state-of-the-art performance. The code is freely available on Code Ocean [1].
The Quantum Illumination Story

Superposition and entanglement, the quintessential characteristics of quantum physics, have been shown to provide communication, computation, and sensing capabilities that go beyond what classical physics will permit. It is natural, therefore, to explore their application to radar, despite the fact that decoherence---caused by the loss and noise encountered in radar sensing---destroys these fragile quantum properties. This paper tells the story of "quantum illumination", an entanglement-based approach to quantum radar, from its inception to its current understanding. Remarkably, despite loss and noise that destroy its initial entanglement, quantum illumination does offer a target-detection performance improvement over a classical radar of the same transmitted energy. A realistic assessment of that improvement's utility, however, shows that its value is severely limited. Nevertheless, the fact that entanglement can be of value on an entanglement-breaking channel---the meta-lesson of the quantum illumination story---should spur continued research on quantum radar.
Intrusion Detection using Sequential Hybrid Model

A large amount of work has been done on the KDD 99 dataset, most of which includes the use of a hybrid anomaly and misuse detection model done in parallel with each other. In order to further classify the intrusions, our approach to network intrusion detection includes use of two different anomaly detection models followed by misuse detection applied on the combined output obtained from the previous step. The end goal of this is to verify the anomalies detected by the anomaly detection algorithm and clarify whether they are actually intrusions or random outliers from the trained normal (and thus to try and reduce the number of false positives). We aim to detect a pattern in this novel intrusion technique itself, and not the handling of such intrusions. The intrusions were detected to a very high degree of accuracy.
Next Generation Education for Prevention: Defining Educational Needs, Attitudes, Concerns, Life Plans of 18-24 Year Old Daughters of BRCA1/2 Mutation Carriers

Abstract : The goals of this study were to 1) describe in-depth the knowledge, attitudes, health behaviors, and life plans of a cohort of 40 daughters, ages 18-24 years, of mothers who are BRCA1/2 mutation carriers, and 2) define specific health educational, psychological, insurance and medical needs of this population. We found significant genetics knowledge gaps, high cancer-related distress, worries about the impact of BRCA1/2 risk and risk reduction options on marriage and childbearing and high levels of interest in a targeted, web-based resource about BRCA1/2 for young, high-risk women. The major data source for this project were the 40 in-depth, qualitative (semi-structured) telephone interviews with 18-24 year old daughters of BRCA1/2 mutation carriers. Additional quantitative data included demographic and family history questionnaires, the Brief Symptom Inventory-18, Impact of Event Scale and the Breast Cancer Genetic Testing Knowledge Scale. Subjects were selected from among the age-eligible daughters of mutation-positive women tested at the DFCI, Mass General Hospital (MGH), and Beth Israel Deaconess Medical Center (BIDMC) between 2000-2009. Having this data has enabled us to proceed towards development of a psycho-educational health and support intervention targeted to the identified needs of 18-24-year-old daughters of BRCA1/2 mutation carriers which could ultimately reduce mortality and morbidity. We believe that an educational intervention to help inform young women as they come to an age at which they can make independent decisions about genetic testing and breast cancer screening would be valuable and potentially life-saving in its impact. This intervention will, we hope, encourage high-risk, young women to consider earlier the choices they face with regard to hereditary cancer risk and will empower them through provision of more accurate genetic information.
Task-Oriented Language Grounding for Language Input with Multiple Sub-Goals of Non-Linear Order

In this work, we analyze the performance of general deep reinforcement learning algorithms for a task-oriented language grounding problem, where language input contains multiple sub-goals and their order of execution is non-linear. We generate a simple instructional language for the GridWorld environment, that is built around three language elements (order connectors) defining the order of execution: one linear - "comma" and two non-linear - "but first", "but before". We apply one of the deep reinforcement learning baselines - Double DQN with frame stacking and ablate several extensions such as Prioritized Experience Replay and Gated-Attention architecture. Our results show that the introduction of non-linear order connectors improves the success rate on instructions with a higher number of sub-goals in 2-3 times, but it still does not exceed 20%. Also, we observe that the usage of Gated-Attention provides no competitive advantage against concatenation in this setting. Source code and experiments' results are available at this https URL
Efficient QP-ADMM Decoder for Binary LDPC Codes and Its Performance Analysis

This paper presents an efficient quadratic programming (QP) decoder via the alternating direction method of multipliers (ADMM) technique, called QP-ADMM, for binary low-density parity-check (LDPC) codes. Its main contents are as follows: first, we relax the maximum likelihood (ML) decoding problem to a non-convex quadratic program. Then, we develop an ADMM solving algorithm for the formulated non-convex QP decoding model. In the proposed QP-ADMM decoder, complex Euclidean projections onto the check polytope are eliminated and variables in each updated step can be solved analytically in parallel. Moreover, it is proved that the proposed ADMM algorithm converges to a stationary point of the non-convex QP problem under the assumption of sequence convergence. We also verify that the proposed decoder satisfies the favorable property of the all-zeros assumption. Furthermore, by exploiting the inside structures of the QP model, the complexity of the proposed algorithm in each iteration is shown to be linear in terms of LDPC code length. Simulation results demonstrate the effectiveness of the proposed QP-ADMM decoder.
Blood Vessel Detection Using Modified Multiscale MF-FDOG Filters for Diabetic Retinopathy

Blindness in diabetic patients caused by retinopathy (characterized by an increase in the diameter and new branches of the blood vessels inside the retina) is a grave concern. Many efforts have been made for the early detection of the disease using various image processing techniques on retinal images. However, most of the methods are plagued with the false detection of the blood vessel pixels. Given that, here, we propose a modified matched filter with the first derivative of Gaussian. The method uses the top-hat transform and contrast limited histogram equalization. Further, we segment the modified multiscale matched filter response by using a binary threshold obtained from the first derivative of Gaussian. The method was assessed on a publicly available database (DRIVE database). As anticipated, the proposed method provides a higher accuracy compared to the literature. Moreover, a lesser false detection from the existing matched filters and its variants have been observed.
A Robotic Framework to Facilitate Sensory Experiences for Children with Autism Spectrum Disorder

The diagnosis of Autism Spectrum Disorder (ASD) in children is commonly accompanied by a diagnosis of sensory processing disorders. Abnormalities are usually reported in multiple sensory processing domains, showing a higher prevalence of unusual responses, particularly to tactile, auditory, and visual stimuli. This article discusses a novel robot-based framework designed to target sensory difficulties faced by children with ASD in a controlled setting. The setup consists of a number of sensory stations, together with two different robotic agents that navigate the stations and interact with the stimuli. These stimuli are designed to resemble real-world scenarios that form a common part of one's everyday experiences. Given the strong interest of children with ASD in technology in general and robots in particular, we attempt to utilize our robotic platform to demonstrate socially acceptable responses to the stimuli in an interactive, pedagogical setting that encourages the child's social, motor, and vocal skills, while providing a diverse sensory experience. A preliminary user study was conducted to evaluate the efficacy of the proposed framework, with a total of 18 participants (5 with ASD and 13 typically developing) between the ages of 4 and 12 years. We derive a measure of social engagement, based on which we evaluate the effectiveness of the robots and sensory stations to identify key design features that can improve social engagement in children.
A generalized reserving model: bridging the gap between pricing and individual reserving

Insurers record detailed information related to claims (e.g. the cause of the claim) and policies (e.g. the value of the insured risk) for pricing insurance contracts. However, this information is largely neglected when estimating the reserve for future liabilities originating from past exposures. We present a flexible, yet highly interpretable framework for including these claim and policy-specific covariates in a reserving model. Our framework focuses on three building blocks in the development process of a claim: the time to settlement, the number of payments and the size of each payment. We carefully choose a generalized linear model (GLM) to model each of these stochastic building blocks in discrete time. Since GLMs are applied in the pricing of insurance contracts, our project bridges the gap between pricing and reserving methodology. We propose model selection techniques for GLMs adapted for censored data to select the relevant covariates in these models and demonstrate how the selected covariates determine the granularity of our reserving model. At one extreme, including many covariates captures the heterogeneity in the development process of individual claims, while at the other extreme, including no covariates corresponds to specifying a model for data aggregated in two-dimensional contingency tables, similar to the run-off triangles traditionally used by reserving actuaries. The set of selected covariates then naturally determines the position the actuary should take in between those two extremes. We illustrate our method with case studies on real life insurance data sets. These case studies provide new insights in the covariates driving the development of claims and demonstrate the accuracy and robustness of the reserving methodology over time.
Testing equivalence of clustering

In this paper, we test whether two datasets share a common clustering structure. As a leading example, we focus on comparing clustering structures in two independent random samples from two mixtures of multivariate normal distributions. Mean parameters of these normal distributions are treated as potentially unknown nuisance parameters and are allowed to differ. Assuming knowledge of mean parameters, we first determine the phase diagram of the testing problem over the entire range of signal-to-noise ratios by providing both lower bounds and tests that achieve them. When nuisance parameters are unknown, we propose tests that achieve the detection boundary adaptively as long as ambient dimensions of the datasets grow at a sub-linear rate with the sample size.