snu-aidas/HVMap-dataset huggingface dataset:
Suicide prevention strategies: a systematic review.

CONTEXT
In 2002, an estimated 877,000 lives were lost worldwide through suicide. Some developed nations have implemented national suicide prevention plans. Although these plans generally propose multiple interventions, their effectiveness is rarely evaluated.


OBJECTIVES
To examine evidence for the effectiveness of specific suicide-preventive interventions and to make recommendations for future prevention programs and research.


DATA SOURCES AND STUDY SELECTION
Relevant publications were identified via electronic searches of MEDLINE, the Cochrane Library, and PsychINFO databases using multiple search terms related to suicide prevention. Studies, published between 1966 and June 2005, included those that evaluated preventative interventions in major domains; education and awareness for the general public and for professionals; screening tools for at-risk individuals; treatment of psychiatric disorders; restricting access to lethal means; and responsible media reporting of suicide.


DATA EXTRACTION
Data were extracted on primary outcomes of interest: suicidal behavior (completion, attempt, ideation), intermediary or secondary outcomes (treatment seeking, identification of at-risk individuals, antidepressant prescription/use rates, referrals), or both. Experts from 15 countries reviewed all studies. Included articles were those that reported on completed and attempted suicide and suicidal ideation; or, where applicable, intermediate outcomes, including help-seeking behavior, identification of at-risk individuals, entry into treatment, and antidepressant prescription rates. We included 3 major types of studies for which the research question was clearly defined: systematic reviews and meta-analyses (n = 10); quantitative studies, either randomized controlled trials (n = 18) or cohort studies (n = 24); and ecological, or population- based studies (n = 41). Heterogeneity of study populations and methodology did not permit formal meta-analysis; thus, a narrative synthesis is presented.


DATA SYNTHESIS
Education of physicians and restricting access to lethal means were found to prevent suicide. Other methods including public education, screening programs, and media education need more testing.


CONCLUSIONS
Physician education in depression recognition and treatment and restricting access to lethal methods reduce suicide rates. Other interventions need more evidence of efficacy. Ascertaining which components of suicide prevention programs are effective in reducing rates of suicide and suicide attempt is essential in order to optimize use of limited resources.
11.5 A time-correlated single-photon-counting sensor with 14GS/S histogramming time-to-digital converter

Time-correlated single photon counting (TCSPC) is a photon-efficient technique to record ultra-fast optical waveforms found in numerous applications such as time-of-flight (ToF) range measurement (LIDAR) [1], ToF 3D imaging [2], scanning optical microscopy [3], diffuse optical tomography (DOT) and Raman sensing [4]. Typical instrumentation consists of a pulsed laser source, a discrete detector such as an avalanche photodiode (APD) or photomultiplier tube (PMT), time-to-digital converter (TDC) card and a FPGA or PC to assemble and compute histograms of photon time stamps. Cost and size restrict the number of channels of TCSPC hardware. Having few detection and conversion channels, the technique is limited to processing optical waveforms with low intensity, with less than one returned photon per laser pulse, to avoid pile-up distortion [4]. However, many ultra-fast optical waveforms exhibit high dynamic range in the number of photons emitted per laser pulse. Examples are signals observed at close range in ToF with multiple reflections, diffuse reflected photons in DOT or local variations in fluorescent dye concentration in microscopy. This paper provides a single integrated chip that reduces conventional TCSPC pile-up mechanisms by an order of magnitude through ultra-parallel realizations of both photon detection and time-resolving hardware. A TDC architecture is presented which combines the two step iterated TCSPC process of time-code generation, followed by memory lookup, increment and write, into one parallel direct-to-histogram conversion. The sensor achieves 71.4ps resolution, over 18.85ns dynamic range, with 14GS/s throughput. The sensor can process 1.7Gphoton/s and generate 21k histograms/s (with 4.6μs readout time), each capturing a total of 1.7kphotons in a 1μs exposure.
23.8 A 34V charge pump in 65nm bulk CMOS technology

Recent advances in MEMS-based oscillators have resulted in their proliferation in timing applications that were once exclusive to quartz-based devices [1]. For applications requiring low phase noise - e.g., cellular, GPS and high-speed serial links - one possible approach is to bias the MEMS resonator at a higher DC voltage to reduce its motional impedance and increase signal energy [2]. Realizing high-voltage charge pumps in bulk CMOS technology is limited by the breakdown voltage of the well/substrate diodes shown in Fig. 23.8.1(a) and Fig. 23.8.1(b). This breakdown limit is even lower with technology scaling and is <;10V in a 22nm CMOS node. Systems with high-voltage requirements often resort to older, high-voltage-tolerant nodes or exotic technologies that limit MEMS integration into SoCs. This work demonstrates a charge pump design in 65nm technology with a three-fold increase in the output voltage range. Highvoltage tolerance is enabled by the proposed well-biasing arrangement and oxide isolation. The pump achieves 34V output by using three different charge pump cells that tradeoff achievable voltage range and power efficiency to achieve a peak efficiency of 38%. Additionally, finger capacitors are optimized to ensure reliability while maintaining efficiency.
WreckWatch: Automatic Traffic Accident Detection and Notification with Smartphones

Traffic accidents are one of the leading causes of fatalities in the US. An important indicator of survival rates after an accident is the time between the accident and when emergency medical personnel are dispatched to the scene. Eliminating the time between when an accident occurs and when first responders are dispatched to the scene decreases mortality rates by 6%. One approach to eliminating the delay between accident occurrence and first responder dispatch is to use in-vehicle automatic accident detection and notification systems, which sense when traffic accidents occur and immediately notify emergency personnel. These in-vehicle systems, however, are not available in all cars and are expensive to retrofit for older vehicles. This paper describes how smartphones, such as the iPhone and Google Android platforms, can automatically detect traffic accidents using accelerometers and accoustic data, immediately notify a central emergency dispatch server after an accident, and provide situational awareness through photographs, GPS coordinates, VOIP communication channels, and accident data recording. This paper provides the following contributions to the study of detecting traffic accidents via smartphones: (1) we present a formal model for accident detection that combines sensors and context data, (2) we show how smartphone sensors, network connections, and web services can be used to provide situational awarenss to first responders, and (3) we provide empirical results demonstrating the efficacy of different approaches employed by smartphone accident detection systems to prevent false positives. J. White and H. Turner Dept. of Electrical and Computer Engineering Virginia Tech E-mail: {julesw,hturner0}@vt.edu C. Thompson, B. Dougherty, and D.C. Schmidt Dept. of Electrical Engineering and Computer Science Vanderbilt University E-mail: {cthompson,briand,schmidt}@dre.vanderbilt.edu
A multiperiod set covering location model for dynamic redeployment of ambulances

Emergency medical service (EMS) providers continually seek ways to improve system performance particularly the response time to incidents. The demand for ambulances fluctuate throughout the week, depending on the day of week, and even the time of day, therefore EMS operators can improve system performance by dynamic relocation/redeployment of ambulances in response to fluctuating demand patters. The objective of the model is to determine the minimum number of ambulances and their locations for each time cluster in which significant changes in demand pattern occur while meeting coverage requirement with a predetermined reliability. The model is further enhanced by calculating ambulance specific busy probabilities and validated by a comprehensive simulation model. Computational results on experimental data sets and data from an EMS agency are provided. 2006 Elsevier Ltd. All rights reserved.
Contribution of obesity and abdominal fat mass to risk of stroke and transient ischemic attacks.

BACKGROUND AND PURPOSE
Waist circumference has been shown to be a better predictor of cardiovascular risk than body mass index (BMI). Our case-control study aimed to evaluate the contribution of obesity and abdominal fat mass to the risk of stroke and transient ischemic attacks (TIA).


METHODS
We recruited 1137 participants: 379 cases with stroke/TIA and 758 regional controls matched for age and sex. Associations between different markers of obesity (BMI, waist-to-hip ratio, waist circumference and waist-to-stature ratio) and risk of stroke/TIA were assessed by using conditional logistic regression adjusted for other risk factors.


RESULTS
BMI showed a positive association with cerebrovascular risk which became nonsignificant after adjustment for physical inactivity, smoking, hypertension, and diabetes (odds ratio 1.18; 95% CI, 0.77 to 1.79, top tertile versus bottom tertile). Markers of abdominal adiposity were strongly associated with the risk of stroke/TIA. For the waist-to-hip ratio, adjusted odds ratios for every successive tertile were greater than that of the previous one (2nd tertile: 2.78, 1.57 to 4.91; 3rd tertile: 7.69, 4.53 to 13.03). Significant associations with the risk of stroke/TIA were also found for waist circumference and waist-to-stature ratio (odds ratio 4.25, 2.65 to 6.84 and odds ratio 4.67, 2.82 to 7.73, top versus bottom tertile after risk adjustment, respectively).


CONCLUSIONS
Markers of abdominal adiposity showed a graded and significant association with risk of stroke/TIA, independent of other vascular risk factors. Waist circumference and related ratios can better predict cerebrovascular events than BMI.
BGP-RCN: improving BGP convergence through root cause notification

This paper presents a new mechanism, called BGP with root cause notification (BGP-RCN), that provides an upper bound of O(d) on routing convergence delay for BGP, where d is the network diameter as measured by the number of AS hops. BGP-RCN lets each routing update message carry the information about the specific cause which triggered the update message. Once a node v receives the first update message triggered by a link failure, v can avoid using any paths that have been obsoleted by the same failure. The basic approach in BGP-RCN is applicable to path vector routing protocols in general. Our analysis and simulation show that BGP-RCN can achieve substantial reduction in both BGP convergence time and the total number of intermediate route changes. 2004 Elsevier B.V. All rights reserved.
Using hybrid data mining and machine learning clustering analysis to predict the turnover rate for technology professionals

This study applies clustering analysis for data mining and machine learning to predict trends in technology professional turnover rates, including the hybrid artificial neural network and clustering analysis known as the self-organizing map (SOM). This hybrid clustering method was used to study the individual characteristics of turnover trend clusters. Using a transaction questionnaire, we studied the period of peak turnover, which occurs after the Chinese New Year, for individuals divided into various age groups. The turnover trend of technology professionals was examined in well-known Taiwanese companies. The results indicate that the high outstanding turnover trend circle was primarily caused by a lack of inner fidelity identification, leadership and management. Based on cross-verification, the clustering accuracy rate was 92.7%. This study addressed problems related to the rapid loss of key human resources and should help organizations learn how to enhance competitiveness and efficiency. 2012 Elsevier Ltd. All rights reserved.
Fast and Accurate Detection and Classification of Plant Diseases

We propose and experimentally evaluate a software solution for automatic detection and classification of plant leaf diseases. The proposed solution is an improvement to the solution proposed in [1] as it provides faster and more accurate solution. The developed processing scheme consists of four main phases as in [1]. The following two steps are added successively after the segmentation phase. In the first step we identify the mostlygreen colored pixels. Next, these pixels are masked based on specific threshold values that are computed using Otsu's method, then those mostly green pixels are masked. The other additional step is that the pixels with zeros red, green and blue values and the pixels on the boundaries of the infected cluster (object) were completely removed. The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases. The developed algorithm‟s efficiency can successfully detect and classify the examined diseases with a precision between 83% and 94%, and can achieve 20% speedup over the approach proposed in [1].
An experimental study on the applicability of evolutionary algorithms to craniofacial superimposition in forensic identification

0020-0255/$ see front matter 2009 Elsevier Inc doi:10.1016/j.ins.2008.12.029 * Corresponding author. Tel.: +34 985 456545; fa E-mail address: oscar.ibanez@softcomputing.es ( Photographic supra-projection is a forensic process that aims to identify a missing person from a photograph and a skull found. One of the crucial tasks throughout all this process is the craniofacial superimposition which tries to find a good fit between a 3D model of the skull and the 2D photo of the face. This photographic supra-projection stage is usually carried out manually by forensic anthropologists. It is thus very time consuming and presents several difficulties. In this paper, we aim to demonstrate that real-coded evolutionary algorithms are suitable approaches to tackle craniofacial superimposition. To do so, we first formulate this complex task in forensic identification as a numerical optimization problem. Then, we adapt three different evolutionary algorithms to solve it: two variants of a real-coded genetic algorithm and the state of the art evolution strategy CMA-ES. We also consider an existing binary-coded genetic algorithm as a baseline. Results on several superimposition problems of real-world identification cases solved by the Physical Anthropology lab at the University of Granada (Spain) are considered to test our proposals. 2009 Elsevier Inc. All rights reserved.
AtlanticWave-SDX : An International SDX to Support Science Data Applications

New scientific instruments that are being designed and deployed in the coming years will dramatically increase the need for large, real-time data transfers among scientists throughout the world. One such instrument is the Large Synoptic Survey Telescope being built in Chile that will produce 6.4 GB images every 17 seconds. This paper describes an ongoing effort to meet the demands of these large data scientific instruments through the development of an international software defined exchange point (SDX) that will meet the provisioning needs for the scientific users. The specific planned and ongoing work in SDX architecture is described with specific consideration for policy specification and security. Keywords—Software Defined Exchange; Science Data
Pustulobullous variant of SDRIFE (symmetrical drug-related intertriginous and flexural exanthema).

We report on a 64-year-old patient from Sri Lanka who presented to the emergency room with a 24-hour history of pruritic, erythematous patches – with occasional pustules and blisters – in the groins, axillae, and antecubital fossae. The patient reported no pain or general symptoms. Six days earlier, he had started taking cefuroxime (500 mg BID) for the first time. The antibiotic had been prescribed following temporary ureteral stent placement due to nephrolithiasis. He denied any other medical or dermatological conditions, and no other drugs had been taken. At initial presentation, he exhibited sharply demarcated, symmetrically disseminated, dark-red patches in the groins, axillae, and antecubital fossae. Some lesions showed pustules and blisters (Figure 1a–c). The physical exam was otherwise unremarkable; in particular, there was no fever. Histology revealed a superficial perivascular, spongiform dermatitis with lymphocytes, eosinophils, and neutrophils. Focally, necrotic keratinocytes and neutrophils were noted in the epidermis. Overall, the findings were consistent with a drug reaction (Figure 2). Apart from slightly elevated CRP levels (5.7 mg/dL, normal range: < 0.5) and mild thrombocytopenia (111,000, normal range: 166,000–308,000), laboratory tests were within normal limits. The detection of Escherichia coli in a swab taken from a pustule in the inguinal region was thought to represent contamination; the pathological lab results were considered to be consistent with an inflammatory response to nephrolithiasis. Taking the history, clinical symptoms, and histological findings into account, the patient was diagnosed with a pustulobullous variant of SDRIFE (symmetrical drug-related intertriginous and flexural exanthema). Since 2004, the acronym SDRIFE has been used for the condition previously known as baboon syndrome. First reported in 1984, the term baboon syndrome referred to the acute development of erythematous lesions in the gluteal area following contact with mercury from broken fever thermometers [1, 2]. In particular, the new term SDRIFE takes into account that systemic drugs, too, may induce flexural skin lesions without prior sensitization [3–6]. In the present case, the development of skin lesions six days after the intake of cefuroxime suggests first exposure to the drug. The diagnosis of SDRIFE includes five clinical criteria that are summarized in Table 1. Atypical disease courses with pustules, papules, and blisters have also been described [4]. The onset of SDRIFE is independent of patient age, and occurs a few hours or even up to eight days after the administration/application of the triggering factor. Apart from systemic antibiotics – in particular β-lactam antibiotics – corticosteroids, psychopharmaceuticals, biologics, and many other drug classes are
Face Detection: A Survey

In this paper we present a comprehensive and critical survey of face detection algorithms. Face detection is a necessary first-step in face recognition systems, with the purpose of localizing and extracting the face region from the background. It also has several applications in areas such as content-based image retrieval, video coding, video conferencing, crowd surveillance, and intelligent human–computer interfaces. However, it was not until recently that the face detection problem received considerable attention among researchers. The human face is a dynamic object and has a high degree of variability in its apperance, which makes face detection a difficult problem in computer vision. A wide variety of techniques have been proposed, ranging from simple edge-based algorithms to composite high-level approaches utilizing advanced pattern recognition methods. The algorithms presented in this paper are classified as either feature-based or image-based and are discussed in terms of their technical approach and performance. Due to the lack of standardized tests, we do not provide a comprehensive comparative evaluation, but in cases where results are reported on common datasets, comparisons are presented. We also give a presentation of some proposed applications and possible application areas. c © 2001 Academic Press
Survey on Cloud Computing Security

Cloud is a modern age computing paradigm which has potential to bring great benefits to information and Communication Technology (ICT) and ICT enabled business. The term Cloud comes from the graphic that was often used to show the heterogeneous networks and complex infrastructure. This graphic was adopted to describe the many aspects of Cloud Computing. In this paper, we aim to identify the security issues in cloud computing. Here we present an analysis of security issues in a cloud environment. Solutions exist to a certain extent for various issues. Keywords—Cloud computing, security challenges, threats
Facial Expression Analysis

The face is one of the most powerful channels of nonverbal communication. Facial expression provides cues about emotion, intention, alertness, pain, personality, regulates interpersonal behavior, and communicates psychiatric and biomedical status among other functions. Within the past 15 years, there has been increasing interest in automated facial expression analysis within the computer vision and machine learning communities. This chapter reviews fundamental approaches to facial measurement by behavioral scientists and current efforts in automated facial expression recognition. We consider challenges, review databases available to the research community, approaches to feature detection, tracking, and representation, and both supervised and unsupervised learning. keywords : Facial expression analysis, Action unit recognition, Active Appearance Models, temporal clustering.
Mux-Kmeans: multiplex kmeans for clustering large-scale data set

Kmeans clustering algorithm is widely used in a number of scientific applications due to its simple iterative nature and ease of implementation. The quality of clustering result highly depends on the selection of initial centroids. Different selections of initial centroids result in different clustering results. In practice, people run a series of Kmeans processes with multiple initial centroid groups serially and return the best clustering result among them. However, in the era of big data, a Kmeans process is implemented on MapReduce to scale to large data sets. Even a single Kmeans process on MapReduce requires considerable long runtime. This paper proposes Mux-Kmeans. Rather than executing multiple Kmeans processes serially, Mux-Kmeans launches these Kmeans processes concurrently with multiple centroid groups. In each iteration, Mux-Kmeans (i) evaluates these Kmeans processes, (ii) prunes the low-quality Kmeans processes, and (iii) incubates new Kmeans processes. After a certain number of iterations, it finally obtains the best among these local optimal results. We implement Mux-Kmeans on MapReduce and evaluate it on Amazon EC2. The experimental results show that starting from the same initial centroid groups, the clustering result of Mux-Kmeans is always non-worse than the best of a series of Kmeans processes. Mux-Kmeans also saves elapsed time than serial multiple Kmeans processes.