Miracle-dz/UniFAR-FTUs huggingface dataset:
fMRI study of effect on brain activity according to stimulation method at LI11, ST36: painful pressure and acupuncture stimulation of same acupoints.

OBJECTIVES The objective of this study was to assess differences in brain responses between pressure and acupuncture stimulation at the same acupoint using functional magnetic resonance imaging (fMRI). SUBJECTS A total of 10 healthy right-handed volunteers were studied. DESIGN fMRI was performed with two different paradigms; namely, pressure and acupuncture stimulation at acupuncture points LI11 and ST36 on the left. fMRI data were analyzed using SPM2. RESULTS In comparison with the left LI11 pressure stimulation, both sides of the parahippocampal gyrus, cerebellum, left side of thalamus, and right side of posterior cingulate regions were more activated by the left LI11 acupuncture stimulation. In comparison with the left ST36 pressure stimulation, the secondary motor cortex, limbic system (cingulate gyrus, posterior cingulate), primary visual cortex, pons, and medulla regions were more activated by left ST36 acupuncture stimulation. In comparison with the left ST36 pressure stimulation, both side of BA 4 and BA 6 were more activated by the LI11 pressure stimulation. In comparison with the left LI11 acupuncture stimulation, left BA 6, BA 8, and anterior cingulate cortex (ACC) were more activated by the left ST36 acupuncture stimulation. CONCLUSIONS In conclusion, brain signal activation patterns according to the stimulation methods and acupoints were observed to differ. Acupuncture stimulation activated more regions than pressure at the same acupoint. In particular, acupuncture stimulation activated the limbic system, such as the parahippocampal gyrus and ACC.
Spatio-Temporal RANSAC for Robust Estimation of Ground Plane in Video Range Images for Automotive Applications

This paper considers the problem of ground plane estimation in range image data obtained from Time-of-Flight camera. We extend the 3D spatial RANSAC for ground plane estimation to 4D spatio-temporal RANSAC by incorporating a time axis. Ground plane models are derived from spatio-temporal random data points, thereby robustifying the algorithm against short term temporal effects such as passing cars, pedestrians, etc. The computationally fast and robust estimation of ground plane leads to reliable identification of obstacles and pedestrians using statistically derived spatial thresholds. Experimental results with real video data from range sensor mounted on a vehicle moving in a car park are presented.
Study on the Internet Behavior's Activity Oriented to Network Survivability

This paper gives an impact analysis of Internet behavior¿s activity on the network survivability. The power-law exists in the distribution of network behavior¿s activity according to our empirical study. Besides, it is obvious that there is community effect in network communications. Our results overthrow some usual hypothesises as preconditions of trustworthy network study. It¿s helpful to assess network vulnerability and to enhance network survivability.
A Survey of Shilling Attacks in Collaborative Filtering Recommender Systems

Recommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. However, collaborative recommender systems are known to be highly vulnerable to attacks. Attackers can inject biased profile data to have a significant impact on the recommendations produced. This paper provides a comprehensive review of shilling attack in recommender systems. We present a survey of existing research on the shilling model, algorithm dependence, attack detection, and attack evaluation metrics. Keywordscollaborative filtering; shilling; attack model; attack detection
Image Set Classification Using Holistic Multiple Order Statistics Features and Localized Multi-kernel Metric Learning

This paper presents a new approach for image set classification, where each training and testing example contains a set of image instances of an object captured from varying viewpoints or under varying illuminations. While a number of image set classification methods have been proposed in recent years, most of them model each image set as a single linear subspace or mixture of linear subspaces, which may lose some discriminative information for classification. To address this, we propose exploring multiple order statistics as features of image sets, and develop a localized multi-kernel metric learning (LMKML) algorithm to effectively combine different order statistics information for classification. Our method achieves the state-of-the-art performance on four widely used databases including the Honda/UCSD, CMU Mobo, and Youtube face datasets, and the ETH-80 object dataset.
Nested Named Entity Recognition in Historical Archive Text

This paper describes work on Named Entity Recognition (NER), in preparation for Relation Extraction (RE), on data from a historical archive organisation. As is often the case in the cultural heritage domain, the source text includes a high percentage of specialist terminology, and is of very variable quality in terms of grammaticality and completeness. The NER and RE tasks were carried out using a specially annotated corpus, and are themselves preliminary steps in a larger project whose aim is to transform discovered relations into a graph structure that can be queried using standard tools. Experimental results from the NER task are described, with emphasis on dealing with nested entities using a multi-word token method. The overall objective is to improve access by non-specialist users to a valuable cultural resource.
A Backward-Simulation Based Rao-Blackwellized Particle Smoother for Conditionally Linear Gaussian Models

Abstract In this article, we develop a new Rao-Blackwellized Monte Carlo smoothing algorithm for conditionally linear Gaussian models. The algorithm is based on the forward-filtering backward-simulation Monte Carlo smoother concept and performs the backward simulation directly in the marginal space of the non-Gaussian state component while treating the linear part analytically. Unlike the previously proposed backward-simulation based Rao-Blackwellized smoothing approaches, it does not require sampling of the Gaussian state component and is also able to overcome certain normalization problems of two-filter smoother based approaches. The performance of the algorithm is illustrated in a simulated application.
The classification of a plant family based on morpho-fractal dimension

The present paper is dedicated to systemic botany and proposes a more objective morphological classification of Gentianaceae plant family into species and genera using the fractal theory. A collection of 19 Romanian species are collected from the herbarium of Botanical Garden, Cluj-Napoca. The research is conducted on the study of the three genera: Gentiana, Gentianella and Gentianopsis, which were previously tagged into a single genre. Also, two types of corrections were done for quantifying the fractal dimension modification between the herbarium and vivid plant and the modification from the seedlings to the mature phase.
Overcoming Parallax and Sampling Density Issues in Image Mosaicing of Non-Planar Scenes

Image mosaicing constructs a wide field-of-view result from multiple source frames. In order to ensure a perceptually correct result, mosaicing typically requires either a planar or near-planar scene, parallax-free camera motion between source frames, or a dense sampling of the scene. When these conditions are not satisfied, various artifacts may result. A novel mosaicing approach is introduced that overcomes these limitations, building on the techniques of image-based rendering and manifold mosaicing, while permitting the synthesis of an effective mosaic of a non-planar scene from a sparse set of translated cameras. Our method first generates a series of intermediate virtual frames to reduce the disparities between neighboring images. Next, a series of vertical slices are chosen from the array of both real and virtual frames and connected according to a cost function that maximizes the similarity between adjacent slices. Experimental results indicate significant improvements over competing methods.
One-year follow-up of patients with first-episode schizophrenia (comparison between remitters and non-remitters)

Patients admitted to hospital after being diagnosed with first-episode schizophrenia were comprehensively assessed prior to acute treatment (on admission), at the end of the acute treatment (at discharge), and at follow-up after 1 year. The psychopathology was evaluated using the Positive and Negative Syndrome Scale (PANSS). 93 patients were reassessed after 1 year. 73/93 (78%) of the patients fulfilled the criteria for remission. No statistically significant differences in the total PANSS or subscales scores were found between remitters and non-remitters before or after the first episode treatment. However, non-remitters had a significantly higher total PANSS score after 1 year than remitters. There was no significant difference in mean psychopathology on admission or at discharge, with the exception of items conceptual disorganization, difficulty in abstract thinking, and lack of judgment and insight between remitters and non-remitters. However, significantly higher mean values were found for all items after 1 year in non-remitters than remitters. On admission the occurrence of positive, negative and general symptoms was balanced; at discharge and after 1 year negative and general symptoms were the most frequently observed. At the 1-year follow-up the impairment of insight and judgment is one of the most frequent symptoms in both remitters (10%) and non-remitters (70%).
Economic perspectives in test automation: balancing automated and manual testing with opportunity cost

Testing is a major cost factor in software development. Test automation has been proposed as one solution to reduce these costs. Test automation tools promise to increase the number of tests they run and the frequency at which they run them. So why not automate every test? In this paper we discuss the question "When should a test be automated? " and the trade-off between automated and manual testing. We reveal problems in the overly simplistic cost models commonly used to make decisions about automating testing. We introduce an alternative model based on opportunity cost and present influencing factors on the decision of whether or not to invest in test automation. Our aim is to stimulate discussion about these factors as well as their influence on the benefits and costs of automated testing in order to support researchers and practitioners reflecting on proposed automation approaches.
For Personal Use. Only Reproduce with Permission from the Lancet

Introduction WHO has recommended classifications of bodyweight that include degrees of underweight and gradations of excess weight or overweight that are associated with increased risk of some non-communicable diseases. 1,2 These classifications are based on body-mass index (BMI), calculated as weight in kilograms divided by height in metres squared (kg/m 2). As a measure of relative weight, BMI is easy to obtain. It is an acceptable proxy for thinness and fatness, and has been directly related to health risks and death rates in many populations. In 1993, a WHO expert committee meeting 1 proposed BMI cutoff points of 25·0–29·9 kg/m 2 for overweight grade 1, 30·0–39·9 kg/m 2 for overweight grade 2, and у40·0 kg/m 2 for overweight grade 3. In 1997, a WHO expert consultation 2 proposed an additional subdivision at a BMI of 35·0–39·9 kg/m 2 , recognising that management options for dealing with obesity differ above a BMI of 35 kg/m 2. The 1993 expert committee emphasised that weight gain in adult life is associated with increased morbidity and mortality at increasing BMIs, and that cutoff points for the amount of overweight should not be interpreted in isolation but in combination with other risk factors of morbidity and mortality. Type 2 diabetes, cardiovascular disease and increased mortality are the most important sequelae of obesity and abdominal fatness, but other associations are seen in musculoskeletal disorders, limitations of respiratory function, and reduced physical functioning and quality of life. 3 The WHO BMI classifications of overweight and obesity are intended for international use. They reflect risk for type 2 diabetes and cardiovascular diseases, which are rapidly becoming major causes of death in adults in all populationseven in those who still have substantial malnutrition. However, the absolute prevalence and incidence of type 2 diabetes varies greatly among ethnic groups, such as the very high prevalence in Pima Indians, and including some who have simliar BMIs, such as higher rates in Taiwanese and Japanese Americans than in European populations. 3 Three specific factors led WHO to convene another expert consultation on BMI classifications. First, there was increasing evidence of the emerging high prevalence of type 2 diabetes and increased cardiovascular risk factors in parts of Asia where the average BMI is below the cutoff point of 25 kg/m 2 that defines overweight in the current WHO classification. Second, there was increasing evidence that the associations between BMI, percentage of body fat, …
Underprovisioning the Grid Power Infrastructure for Green Datacenters

While there have been prior studies on underprovisioning the power distribution infrastructure for a grid-based datacenter, how to save grid capital investment by means of leveraging renewable energy to underprovision the grid power infrastructure in green datacenters remains largely an unexplored, open issue. Aggressively underprovisioning grid infrastructure can trigger power emergency in which simultaneous peak power draws across the datacenter exceed the tightly budgeted grid power capacity, leading to possible serious consequences including power shutdown. The resulting power emergency mandates a graceful reaction mechanism to sustain the power requirement to avoid power overdraw. While leveraging renewable energy in a green datacenter provides a possibility to prevent power overdraw during such a power emergency, the intermittent nature of renewable energy makes it a very challenging task because of the potentially unpredictable performance impact to individual applications. This paper addresses this issue by designing a novel renewable energy delivery infrastructure and considering performance consequences to individual applications of underprovisioning the grid power infrastructure in the presence of varied renewable power and limited battery's energy capacity in a datacenter. We build an experimental prototype to demonstrate such grid power underprovisioning on a cluster of 10 servers, with a simulated solar power generator. Using representative datacenter benchmarks to evaluate the effectiveness of the renewable solution in handling power emergencies, we show that renewable energy by itself can sustain different duration lengths of power emergency when its supply is sufficient. Batteries play an important role for performance boost when the supply of the renewable energy is insufficient. Our theoretical solution, in conjunction with workload migration, provides a seamless bridge across the whole spectrum of duration lengths of power emergency.
LA-GPS : A location-aware geographical pervasive system

The advancement of mobile information systems is going in parallel with Geographical Information Systems where they both meet in providing location-aware services. New systems are providing a new pervasive GIS dimension where access to geographical attributes is being possible "anytime, anyplace and anyhow". In this article, we propose a system that provides mobile users with a service that corresponds not only to their requests but also to their preferences, location and querying time. Finally, we project the query results to construct a GIS map layer that is added to original GIS area map in order to provide more indications to the path that the user should follow in order to reach the demanded service.
The Level of DING Proteins Is Increased in HIV-Infected Patients: In Vitro and In Vivo Studies

DING proteins constitute an interesting family, owing to their intriguing and important activities. However, after a decade of research, little is known about these proteins. In humans, at least five different DING proteins have been identified, which were implicated in important biological processes and diseases, including HIV. Indeed, recent data from different research groups have highlighted the anti-HIV activity of some DING representatives. These proteins share the ability to inhibit the transcriptional step of HIV-1, a key step of the viral cycle that is not yet targeted by the current therapies. Since such proteins have been isolated from humans, we undertook a comprehensive study that focuses on the relationship between these proteins and HIV-infection in an infectious context. Hence, we developed a home-made ELISA for the quantification of the concentration of DING proteins in human serum. Using this method, we were able to determine the concentration of DING proteins in healthy and HIV-infected patients. Interestingly, we observed a significant increase of the concentration of DING proteins in non treated and treated HIV-infected patients compared to controls. In addition, cell cultures infected with HIV also show an increased expression of DING proteins, ruling out the possible role of antiretroviral treatment in the increase of the expression of DING proteins. In conclusion, results from this study show that the organism reacts to HIV-infection by an overexpression of DING proteins.
Dopaminergic Therapy Modulates Cortical Perfusion in Parkinson Disease With and Without Dementia According to Arterial Spin Labeled Perfusion Magnetic Resonance Imaging

Abstract Arterial spin labeling (ASL) magnetic resonance imaging analyses allow for the quantification of altered cerebral blood flow, and provide a novel means of examining the impact of dopaminergic treatments. The authors examined the cerebral perfusion differences among 17 Parkinson disease (PD) patients, 17 PD with dementia (PDD) patients, and 17 healthy controls and used ASL-MRI to assess the effects of dopaminergic therapies on perfusion in the patients. The authors demonstrated progressive widespread cortical hypoperfusion in PD and PDD and robust effects for the dopaminergic therapies. Specifically, dopaminergic medications further decreased frontal lobe and cerebellum perfusion in the PD and PDD groups, respectively. These patterns of hypoperfusion could be related to cognitive dysfunctions and disease severity. Furthermore, desensitization to dopaminergic therapies in terms of cortical perfusion was found as the disease progressed, supporting the concept that long-term therapies are associated with the therapeutic window narrowing. The highly sensitive pharmaceutical response of ASL allows clinicians and researchers to easily and effectively quantify the absolute perfusion status, which might prove helpful for therapeutic planning.
Tissue-Specific Actions of Glucocorticoids on Apoptosis: A Double-Edged Sword

First described for their metabolic and immunosuppressive effects, glucocorticoids are widely prescribed in clinical settings of inflammation. However, glucocorticoids are also potent inducers of apoptosis in many cell types and tissues. This review will focus on the established mechanisms of glucocorticoid-induced apoptosis and outline what is known about the apoptotic response in cells and tissues of the body after exposure to glucocorticoids. Glucocorticoid-induced apoptosis affects the skeletal system, muscular system, circulatory system, nervous system, endocrine system, reproductive system, and the immune system. Interestingly, several cell types have an anti-apoptotic response to glucocorticoids that is cytoprotective. Lastly, we will discuss the pro- and anti-apoptotic effects of glucocorticoids in cancers and their clinical implications.
The Effects of Age, Adiposity, and Physical Activity on the Risk of Seven Site‐Specific Fractures in Postmenopausal Women

Risk factors for fracture of the neck of the femur are relatively well established, but those for fracture at other sites are little studied. In this large population study we explore the role of age, body mass index (BMI), and physical activity on the risk of fracture at seven sites in postmenopausal women. As part of the Million Women Study, 1,154,821 postmenopausal UK women with a mean age of 56.0 (SD 4.8) years provided health and lifestyle data at recruitment in 1996 to 2001. All participants were linked to National Health Service (NHS) hospital records for day‐case or overnight admissions with a mean follow‐up of 11 years per woman. Adjusted absolute and relative risks for seven site‐specific incident fractures were calculated using Cox regression models. During follow‐up, 4931 women had a fracture of the humerus; 2926 of the forearm; 15,883 of the wrist; 9887 of the neck of the femur; 1166 of the femur (not neck); 3199 a lower leg fracture; and 10,092 an ankle fracture. Age‐specific incidence rates increased gradually with age for fractures of forearm, lower leg, ankle, and femur (not neck), and steeply with age for fractures of neck of femur, wrist, and humerus. When compared to women with desirable BMI (20.0 to 24.9 kg/m2), higher BMI was associated with a reduced risk of fracture of the neck of femur, forearm, and wrist, but an increased risk of humerus, femur (not neck), lower leg, and ankle fractures (p < 0.001 for all). Strenuous activity was significantly associated with a decreased risk of fracture of the humerus and femur (both neck and remainder of femur) (p < 0.001), but was not significantly associated with lower leg, ankle, wrist, and forearm fractures. Postmenopausal women are at a high lifetime risk of fracture. BMI and physical activity are modifiable risk factors for fracture, but their associations with fracture risk differ substantially across fracture sites. © 2016 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research (ASBMR)
Generic Entity Resolution in the SERF Project

The SERF project at Stanford deals with the Entity Resolution (ER) problem, in which records determined to represent the same real-life ``entities'' (such as people or products) are successively located and combined. The approach we pursue is ``generic'', in the sense that the specific functions used to match and merge records are viewed as black boxes, which permits efficient, expressive and extensible ER solutions. This paper motivates and introduces the principles of generic ER, and gives an overview of the research directions we have been exploring in the SERF project over the past two years.
Overclocking datapath for latency-error tradeoff

Relaxing constraints of 100% accuracy in datapath can provide the freedom to create designs with better performance or energy efficiency. This paper develops probabilistic models, which enable us to explore these trade-offs for key arithmetic primitives. We show that because specific input patterns are required to cause timing violations and that these patterns arise rarely, a lower expected error can be attained by allowing some timing variations to occur, instead of reducing the precision of a circuit to meet a target latency. Experiments show that a mean reduction of 5.6× ~ 36.7× in error expectation and an improvement of 7.2dB ~ 19.7dB in signal-to-noise ratio can be obtained for practical applications.