LLukas22/scidocs huggingface dataset:
A thin slice perspective on the accuracy of W rst impressions

The accuracy of Wrst impressions was examined by investigating judged construct (negative aVect, positive aVect, the Big Wve personality variables, intelligence), exposure time (5, 20, 45, 60, and 300 s), and slice location (beginning, middle, end). Three hundred and thirty four judges rated 30 targets. Accuracy was deWned as the correlation between a judge’s ratings and the target’s criterion scores on the same construct. Negative aVect, extraversion, conscientiousness, and intelligence were judged moderately well after 5-s exposures; however, positive aVect, neuroticism, openness, and agreeableness required more exposure time to achieve similar levels of accuracy. Overall, accuracy increased with exposure time, judgments based on later segments of the 5-min interactions were more accurate, and 60 s yielded the optimal ratio between accuracy and slice length. Results suggest that accuracy of Wrst impressions depends on the type of judgment made, amount of exposure, and temporal location of the slice of judged social behavior. © 2007 Elsevier Inc. All rights reserved.
Added value of an intensive multidisciplinary functional rehabilitation programme for chronic low back pain patients.

BACKGROUND
Several treatments exist for chronic low back pain (cLBP) patients although none has shown superiority. Among group treatments, muscle reconditioning programmes (MRPs) are the most commonly used. Multidisciplinary functional rehabilitation programmes (MFRPs) are an alternative.


OBJECTIVE
To compare a MFRP with a MRP as few studies compare these treatments


METHODS
This was a prospective, nonrandomised, controlled study comparing cLBP patients participating in a MRP or MFRP in an outpatient setting. The predetermined primary outcome measure was the daily life activity subscale (DLA) of the Dallas Pain Questionnaire (DPQ) within one year after the end of the treatment.


RESULTS
Twenty-one patients were included in the MRP and 24 in the MFRP. At inclusion, the groups were similar in terms of age (mean 40 years), sex (50%-60% males), sick leave duration (mean 6.0 months, standard deviation (SD) 4.4 months) and follow up (mean 9.0 months, SD 2.0 months). The DLA score decreased from 59.5% (SD 16.9) to 44.8% (SD 25.4), p <0.01, in the MFRP and from 62.3 (SD 20.3) to 58.8 (SD 20.7), p = 0.3, in the MRP. The between-groups difference at follow-up was 13.8, p = 0.05, and the difference in the mean improvement was -11.5 (5.9), p = 0.058. Seventy-eight percent of MFRP patients versus 47 % in the MRP group returned to work (p = 0.08).


DISCUSSION
In a population of highly disabled cLBP patients, MFRP seems more effective in increasing function and return to work. Interpretation is limited by the small population included and by the type of trial.


CONCLUSION
Randomised studies are needed to confirm these results and explore the cost-effectiveness of MFRP.
Prevention of depression in at-risk adolescents: a randomized controlled trial.

CONTEXT
Adolescent offspring of depressed parents are at markedly increased risk of developing depressive disorders. Although some smaller targeted prevention trials have found that depression risk can be reduced, these results have yet to be replicated and extended to large-scale, at-risk populations in different settings.


OBJECTIVE
To determine the effects of a group cognitive behavioral (CB) prevention program compared with usual care in preventing the onset of depression.


DESIGN, SETTING, AND PARTICIPANTS
A multicenter randomized controlled trial conducted in 4 US cities in which 316 adolescent (aged 13-17 years) offspring of parents with current or prior depressive disorders were recruited from August 2003 through February 2006. Adolescents had a past history of depression, current elevated but subdiagnostic depressive symptoms, or both. Assessments were conducted at baseline, after the 8-week intervention, and after the 6-month continuation phase.


INTERVENTION
Adolescents were randomly assigned to the CB prevention program consisting of 8 weekly, 90-minute group sessions followed by 6 monthly continuation sessions or assigned to receive usual care alone.


MAIN OUTCOME MEASURE
Rate and hazard ratio (HR) of a probable or definite depressive episode (ie, depressive symptom rating score of > or = 4) for at least 2 weeks as diagnosed by clinical interviewers.


RESULTS
Through the postcontinuation session follow-up, the rate and HR of incident depressive episodes were lower for those in the CB prevention program than for those in usual care (21.4% vs 32.7%; HR, 0.63; 95% confidence interval [CI], 0.40-0.98). Adolescents in the CB prevention program also showed significantly greater improvement in self-reported depressive symptoms than those in usual care (coefficient, -1.1; z = -2.2; P = .03). Current parental depression at baseline moderated intervention effects (HR, 5.98; 95% CI, 2.29-15.58; P = .001). Among adolescents whose parents were not depressed at baseline, the CB prevention program was more effective in preventing onset of depression than usual care (11.7% vs 40.5%; HR, 0.24; 95% CI, 0.11-0.50), whereas for adolescents with a currently depressed parent, the CB prevention program was not more effective than usual care in preventing incident depression (31.2% vs 24.3%; HR, 1.43; 95% CI, 0.76-2.67).


CONCLUSION
The CB prevention program had a significant prevention effect through the 9-month follow-up period based on both clinical diagnoses and self-reported depressive symptoms, but this effect was not evident for adolescents with a currently depressed parent.


TRIAL REGISTRATION
clinicaltrials.gov Identifier: NCT00073671.
A survey of network anomaly detection techniques

Information and Communication Technology (ICT) has a great impact on social wellbeing, economic growth and national security in todays world. Generally, ICT includes computers, mobile communication devices and networks. ICT is also embraced by a group of people with malicious intent, also known as network intruders, cyber criminals, etc. Confronting these detrimental cyber activities is one of the international priorities and important research area. Anomaly detection is an important data analysis task which is useful for identifying the network intrusions. This paper presents an in-depth analysis of four major categories of anomaly detection techniques which include classification, statistical, information theory and clustering. The paper also discusses research challenges with the datasets used for network intrusion detection. & 2015 Published by Elsevier Ltd.
Promoting Active Learning in Respiratory Physiology - Positive Student Perception and Improved Outcomes

Background & Objective: Active learning strategies are much better recalled, enjoyed and understood. This article reports the design and evaluates on the student’s experience of lectures which included many opportunities for active involvement through various active learning strategies. Materials and Methods: Active learning strategies were incorporated in almost every lecture of Respiratory Physiology for first year medical students. Several techniques like thinkpair-share, pause procedures, minute papers, and discussion of MCQs, concept understanding by use of models, role plays, seminars, and working on mannequins were incorporated into the lecture format. Results: To evaluate the affectivity of this pattern of learning, respiratory physiology test marks of the active learning batch were compared with the previous batch of traditional learning group. Mean respiratory physiology test marks on an identical test, showed significant increase (p<0.05) in the active learning cohort in comparison to the previous batch of traditional learning cohort. Feedback questionnaire regarding their experience during active learning was administered to the students. The data was evaluated and analyzed. The overall feedback revealed that the active learning strategies helped the students in better understanding (94.1%), lead to greater interest (89.4%), better interaction among peers (83.6%) and also helped in correlating the topic to clinical content (80.5%). Conclusion: The heightened interest, understanding and interaction engaged students in the learning process. Adopting a variety of teaching-learning methods increased both teacher and learner enthusiasm. The study provides motivation to redesign our teaching strategies to encourage greater active learning among students.
Demo Abstract: Cyber-Physical Fingerprinting for Internet of Things Authentication

Current security and privacy solution to fail to meet the IoT requirements due to computational restrictions and portable nature of IoT objects. In this demo, a novel authentication framework is proposed that exploits the device-specific information to authenticate each object in the IoT. The framework is shown to effectively track the physical environment effects on objects. The experiment shows a sample IoT environment consisting of multiple Raspberry PI units operating as IoT objects. The proposed framework monitors the changes of the physical environment surrounding objects by examining the changes in the device-specific information of each IoT object. The scenario of an emulation attacker is presented in the demo as a case study. The attacker is capable of replicating all the transmitted messages and security keys of one of the IoT objects. Our proposed framework is able to effectively detect such an attack and improve the authentication accuracy for all IoT objects. -Demo Abstract.
Necrobiosis Lipoidica Diabeticorum

Necrobiosis lipoidica is a rare disorder that usually appears in the lower extremities and it is often related to diabetes mellitus. There are few reported cases of necrobiosis lipoidica in children. We present an interesting case in that the patient developed lesions on the abdomen, which is an unusual location.
Management of municipal solid wastes in Italy

The E.U. Framework Directive of 2008 draws a clear strategy for waste management, which places prevention as a priority, followed by reuse and recovery of materials and energy. Specific objectives are fixed by European legislation [1]. Italy has set a reduction target of municipal solid wastes and special nonhazardous wastes per unit of population by 5% in 2020 compared to 2010, while has also fully implemented the European target for recovery of materials by 2020 and is preparing to adapt the target of recycling [2]. Wastes production in Italy is equal to 170 million tons in 2010 [3] and shows a slight increase compared to 2009. This includes wastes called “special” (divided into hazardous and non hazardous); the municipal solid wastes constitute about one fifth of the total. In 2010, the special wastes amounted to around 138 million tons, of which about 130 Mt are non-hazardous wastes from construction and demolition, industries and waste treatment processes.
Advances in Artificial Neural Networks - Methodological Development and Application

Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological engineering. OPEN ACCESS Algorithms 2009, 2 974
Arousal Recognition Using Audio-Visual Features and FMRI-Based Brain Response

As the indicator of emotion intensity, arousal is a significant clue for users to find their interested content. Hence, effective techniques for video arousal recognition are highly required. In this paper, we propose a novel framework for recognizing arousal levels by integrating low-level audio-visual features derived from video content and human brain's functional activity in response to videos measured by functional magnetic resonance imaging (fMRI). At first, a set of audio-visual features which have been demonstrated to be correlated with video arousal are extracted. Then, the fMRI-derived features that convey the brain activity of comprehending videos are extracted based on a number of brain regions of interests (ROIs) identified by a universal brain reference system. Finally, these two sets of features are integrated to learn a joint representation by using a multimodal deep Boltzmann machine (DBM). The learned joint representation can be utilized as the feature for training classifiers. Due to the fact that fMRI scanning is expensive and time-consuming, our DBM fusion model has the ability to predict the joint representation of the videos without fMRI scans. The experimental results on a video benchmark demonstrated the effectiveness of our framework and the superiority of integrated features.
Case series of selenium toxicity from a nutritional supplement.

INTRODUCTION
Selenium is an essential trace element, but can be toxic in excess. In May 2008, US FDA reported 201 individuals with adverse reactions to liquid nutritional supplements containing excess selenium and chromium resulting in the largest epidemic of selenosis in the history of the United States.


OBJECTIVE
To describe the clinical features, biomonitoring data of selenium levels, and the estimated total dose of selenium ingestions of nine patients with selenium toxicity who presented after use of a liquid dietary supplement with a formulation error.


METHODS
A retrospective observational case series was performed on nine patients presenting to our medical toxicology clinic between March 2008 and May 2008 with symptoms of selenosis after consuming a nutritional supplement. Institutional IRB approval was obtained for this case series.


RESULTS
Supplement testing revealed almost 200 times the reported amount of selenium. There were 5 males and 4 females and their ages ranged from 15 to 57 years (median 46 years, mean 44.2 years). The mean estimated cumulative dose of selenium ingested in our patients was 1.3 gram over a mean period of 37.5 days (10-60 days). In each case, the symptoms of selenium toxicity manifested within 1 week from the start of ingestion. Initial symptoms included alopecia, dystrophic fingernail changes, GI symptoms, and memory difficulties. The initial whole blood selenium concentrations ranged from 150 to 732 mcg/L (reference mean range 123-193 mcg/L) at an average of 27 days post cessation of the formula. The urinary selenium concentrations ranged from 41 to 220 mcg/g Creat (reference < 25 mcg/g Creatinine). None of the patients required more than supportive care for symptoms and none required hospitalization.


CONCLUSION
Selenium is an essential element, which can result in significant toxicity if ingested in large amounts.
Numerical Solution of the Navier-Stokes Equations

A finite-difference method for solving the time-dependent NavierStokes equations for an incompressible fluid is introduced. This method uses the primitive variables, i.e. the velocities and the pressure, and is equally applicable to problems in two and three space dimensions. Test problems are solved, and an application to a three-dimensional convection problem is presented. Introduction. The equations of motion of an incompressible fluid are dtUi 4UjdjUi = — — dip + vV2Ui + Ei} ( V2 = Yl d2 ) , djUj = 0 , PO \ 3 ' where Ui are the velocity components, p is the pressure, p0 is the density, Ei are the components of the external forces per unit mass, v is the coefficient of kinematic viscosity, t is the time, and the indices i, j refer to the space coordinates Xi, x¡, i, j = 1, 2, 3. d, denotes differentiation with respect to Xi, and dt differentiation with respect to the time t. The summation convention is used in writing the equations. We write , Uj , Xj , _ ( d \ Ui u ' Xi " d ' p \povur
Algorithm 628: An Algorithm for Constructing Canonical Bases of Polynomial Ideals

The notion of GrGbner bases for polynomial ideals, which is central to this paper, is given by the following: Definition. A finite set F of polynomials in K[xl, . . . , x,] is called a canonical basis or Grtibner basis (for the ideal generated by F) if and only if (GB) for arbitrary polynomials f, g, h E K[xl, . . . , x,]: if f +F g, f “F h, and g, h are irreducible modulo +F, then g = h. Here, ‘f +‘I? g” means that ‘f may be reduced to g modulo F m by applying a certain reduction process that may be considered as a “generalized division.” For the exact definition of this reduction relation and examples, we refer to [4] and [6]. (GB) is equivalent to the assertion that ‘F has the Church-Rosser property, whose fundamental importance in rewrite systems is well known (see, e.g., [22]). The problem of constructing Grijbner bases for polynomial ideals is characterized by the following:
DiABlO: Optimization based design for improving diversity in recommender system

Primary task of a recommender system is to improve user’s experience by recommending relevant and interesting items to the users. To this effect, diversity in item suggestion is as important as the accuracy of recommendations. Existing literature aimed at improving diversity primarily suggests a 2-stage mechanism – an existing CF scheme for rating prediction, followed by a modified ranking strategy. This approach requires heuristic selection of parameters and ranking strategies. Also most works focus on diversity from either the user or system’s perspective. In this work, we propose a single stage optimization based solution to achieve high diversity while maintaining requisite levels of accuracy. We propose to incorporate additional diversity enhancing constraints, in the matrix factorization model for collaborative filtering. However, unlike traditional MF scheme generating dense user and item latent factor matrices, our base MF model recovers a dense user and a sparse item latent factor matrix; based on a recent work. The idea is motivated by the fact that although a user will demonstrate some affinity towards all latent factors, an item will never possess all features; thereby yielding a sparse structure. We also propose an algorithm for our formulation. The superiority of our model over existing state of the art techniques is demonstrated by the results of experiments conducted on real world movie database. © 2016 Elsevier Inc. All rights reserved.
Ironies of automation

-This paper discusses the ways in which automation of industrial processes may expand rather than eliminate problems with the human operator. Some comments will be made on methods of alleviating these problems within the "classic' approach of leaving the operator with responsibility for abnormal conditions, and on the potential for continued use of the human operator for on-line decision-making within human-computer collaboration. Irony: combination of circumstances, the result of which is the direct opposite of what might be expected. Paradox: seemingly absurd though perhaps really well-founded
The Edinburgh Postnatal Depression Scale: translation and antepartum validation for a Hungarian sample.

OBJECTIVE
the Edinburgh Postnatal Depression Scale (EPDS) is an important screening instrument routinely used during the peripartum period for the identification of depression. The purpose of the study was to assess the validity of the 10-item EPDS in screening for antepartum depression (APD) in Hungary.


DESIGN
validation study carried out between July and December 2010.


SETTING
Department of Obstetrics and Gynecology, University of Szeged, Hungary.


PARTICIPANTS
219 women attending a routine check-up at 12 weeks antepartum.


INTERVENTIONS
participants completed the newly translated Hungarian version of the EPDS and underwent a clinical assessment with the Structured Clinical Interview for DSM-IV disorders (SCID-I).


MEASUREMENT AND FINDINGS
seven (3.2%) of the mothers were diagnosed with major antepartum depression and 15 persons (6.85%) with minor depression on the basis of the SCID. Internal consistency of the EPDS was satisfactory (Cronbach α coefficients ≥0.728). The best cut-off on the Hungarian version of the EPDS for major depression was 8/9, with a sensitivity of 71.4%, and a specificity of 91.5%. The area under the ROC curve was found significant for combined depression as well and at a cut-off of 6/7 indicated a sensitivity of 81.8% and a specificity of 83.2%.


KEY CONCLUSIONS
the EPDS showed acceptable validity despite a considerable scatter in the total scores in our sample.


IMPLICATION FOR PRACTICE
the EPDS is a reliable instrument for the screening of depressive disorders, especially major depressive disorder in early pregnancy among Hungarian women.
Analysis of length of hospital stay using electronic health records: A statistical and data mining approach

BACKGROUND
The length of stay (LOS) is an important indicator of the efficiency of hospital management. Reduction in the number of inpatient days results in decreased risk of infection and medication side effects, improvement in the quality of treatment, and increased hospital profit with more efficient bed management. The purpose of this study was to determine which factors are associated with length of hospital stay, based on electronic health records, in order to manage hospital stay more efficiently.


MATERIALS AND METHODS
Research subjects were retrieved from a database of patients admitted to a tertiary general university hospital in South Korea between January and December 2013. Patients were analyzed according to the following three categories: descriptive and exploratory analysis, process pattern analysis using process mining techniques, and statistical analysis and prediction of LOS.


RESULTS
Overall, 55% (25,228) of inpatients were discharged within 4 days. The department of rehabilitation medicine (RH) had the highest average LOS at 15.9 days. Of all the conditions diagnosed over 250 times, diagnoses of I63.8 (cerebral infarction, middle cerebral artery), I63.9 (infarction of middle cerebral artery territory) and I21.9 (myocardial infarction) were associated with the longest average hospital stay and high standard deviation. Patients with these conditions were also more likely to be transferred to the RH department for rehabilitation. A range of variables, such as transfer, discharge delay time, operation frequency, frequency of diagnosis, severity, bed grade, and insurance type was significantly correlated with the LOS.


CONCLUSIONS
Accurate understanding of the factors associating with the LOS and progressive improvements in processing and monitoring may allow more efficient management of the LOS of inpatients.