nils-herrmann/clean_scirepeval huggingface dataset:
Social support and protection from depression: systematic review of current findings in Western countries.

Numerous studies report an association between social support and protection from depression, but no systematic review or meta-analysis exists on this topic.



To review systematically the characteristics of social support (types and source) associated with protection from depression across life periods (childhood and adolescence; adulthood; older age) and by study design (cross-sectional v cohort studies).



A systematic literature search conducted in February 2015 yielded 100 eligible studies. Study quality was assessed using a critical appraisal checklist, followed by meta-analyses.



Sources of support varied across life periods, with parental support being most important among children and adolescents, whereas adults and older adults relied more on spouses, followed by family and then friends. Significant heterogeneity in social support measurement was noted. Effects were weaker in both magnitude and significance in cohort studies.



Knowledge gaps remain due to social support measurement heterogeneity and to evidence of reverse causality bias.
PACT: Parameterized Clipping Activation for Quantized Neural Networks

Deep learning algorithms achieve high classification accuracy at the expense of significant computation cost. To address this cost, a number of quantization schemes have been proposed but most of these techniques focused on quantizing weights, which are relatively smaller in size compared to activations. This paper proposes a novel quantization scheme for activations during training that enables neural networks to work well with ultra low precision weights and activations without any significant accuracy degradation. This technique, PArameterized Clipping acTivation (PACT), uses an activation clipping parameter α that is optimized during training to find the right quantization scale. PACT allows quantizing activations to arbitrary bit precisions, while achieving much better accuracy relative to published state-of-the-art quantization schemes. We show, for the first time, that both weights and activations can be quantized to 4-bits of precision while still achieving accuracy comparable to full precision networks across a range of popular models and datasets. We also show that exploiting these reduced-precision computational units in hardware can enable a super-linear improvement in inferencing performance due to a significant reduction in the area of accelerator compute engines coupled with the ability to retain the quantized model and activation data in on-chip memories.
Leveraging uncertainty information from deep neural networks for disease detection

Deep learning (DL) has revolutionized the field of computer vision and image processing. In medical imaging, algorithmic solutions based on DL have been shown to achieve high performance on tasks that previously required medical experts. However, DL-based solutions for disease detection have been proposed without methods to quantify and control their uncertainty in a decision. In contrast, a physician knows whether she is uncertain about a case and will consult more experienced colleagues if needed. Here we evaluate drop-out based Bayesian uncertainty measures for DL in diagnosing diabetic retinopathy (DR) from fundus images and show that it captures uncertainty better than straightforward alternatives. Furthermore, we show that uncertainty informed decision referral can improve diagnostic performance. Experiments across different networks, tasks and datasets show robust generalization. Depending on network capacity and task/dataset difficulty, we surpass 85% sensitivity and 80% specificity as recommended by the NHS when referring 0−20% of the most uncertain decisions for further inspection. We analyse causes of uncertainty by relating intuitions from 2D visualizations to the high-dimensional image space. While uncertainty is sensitive to clinically relevant cases, sensitivity to unfamiliar data samples is task dependent, but can be rendered more robust.
SAR ADC Algorithm with Redundancy and Digital Error Correction

This paper describes an algorithm for Successive Approximation Register (SAR) ADCs with overlapping steps that allow comparison decision errors (due to, such as DAC incomplete settling) to be digitally corrected. We generalize this non-binary search algorithm, and clarify which decision errors it can digitally correct. This algorithm requires more SAR ADC conversion steps than a binary search algorithm, but we show that the sampling speed of an SAR ADC using this algorithm can be faster than that of a conventional binary-search SAR ADC — because the latter must wait for the settling time of the DAC inside the SAR ADC. key words: SAR ADC, digital error correction, non-binary, redundancy
Local trajectory planning and control for autonomous vehicles using the adaptive potential field

In this paper, a new potential field approach is proposed for trajectory planning and control in autonomous vehicles. The potential field of the surrounding environment is generated including vehicles, road boundaries and lane centers. Based on the predicted positions of the vehicles, the location of the ego vehicle and the surrounding potentials are synchronized. In addition, the potential fields of the surrounding vehicles are adaptively modified in shape depending on the relative velocity of the surrounding vehicles. The longitudinal distance required for the lateral avoidance is mathematically calculated and reflected in the potential field. Based on the proposed potential field, the trajectory of the autonomous vehicle is selected as the suboptimal path and the MPC (Model Predictive Control) method is applied for tracking control and the lateral stability of the vehicle. The performance of the proposed algorithm is verified in simulations under various conditions.
FACSCaps: Pose-Independent Facial Action Coding with Capsules

Most automated facial expression analysis methods treat the face as a 2D object, flat like a sheet of paper. That works well provided images are frontal or nearly so. In real-world conditions, moderate to large head rotation is common and system performance to recognize expression degrades. Multi-view Convolutional Neural Networks (CNNs) have been proposed to increase robustness to pose, but they require greater model sizes and may generalize poorly across views that are not included in the training set. We propose FACSCaps architecture to handle multi-view and multi-label facial action unit (AU) detection within a single model that can generalize to novel views. Additionally, FACSCaps's ability to synthesize faces enables insights into what is leaned by the model. FACSCaps models video frames using matrix capsules, where hierarchical pose relationships between face parts are built into internal representations. The model is trained by jointly optimizing a multi-label loss and the reconstruction accuracy. FACSCaps was evaluated using the FERA 2017 facial expression dataset that includes spontaneous facial expressions in a wide range of head orientations. FACSCaps outperformed both state-of-the-art CNNs and their temporal extensions.
How exactly does word 2 vec work ?

Perhaps the most amazing property of these word embeddings is that somehow these vector encodings effectively capture the semantic meanings of the words. The question one might ask is how or why? The answer is that because the vectors adhere surprisingly well to our intuition. For instance, words that we know to be synonyms tend to have similar vectors in terms of cosine similarity and antonyms tend to have dissimilar vectors. Even more surprisingly, word vectors tend to obey the laws of analogy. For example, consider the analogy ”Woman is to queen as man is to king”. It turns out that
Efficient Correlated Topic Modeling with Topic Embedding

Correlated topic modeling has been limited to small model and problem sizes due to their high computational cost and poor scaling. In this paper, we propose a new model which learns compact topic embeddings and captures topic correlations through the closeness between the topic vectors. Our method enables efficient inference in the low-dimensional embedding space, reducing previous cubic or quadratic time complexity to linear w.r.t the topic size. We further speedup variational inference with a fast sampler to exploit sparsity of topic occurrence. Extensive experiments show that our approach is capable of handling model and data scales which are several orders of magnitude larger than existing correlation results, without sacrificing modeling quality by providing competitive or superior performance in document classification and retrieval.
Wearability Assessment of a Mobile Augmented Reality System

Wearable computers place loads on the human body. Previous studies on load carriage have found that wearing backpacks and head mounted loads can result in physiological, biomechanical and comfort effects. This paper establishes a methodology for assessing these effects in a series of tests for determining the wearability of a prototype mobile augmented reality system. The outcome of the results suggest that further improvements to the prototype system need to be taken into consideration in further design, reinforcing the need for an iterative design lifecycle, while establishing an important framework for assessing effects that relate to issues of health and safety of wearable devices.
Anomaly Detection and Root Cause Analysis for LTE Radio Base Stations

This project aims to detect possible anomalies in the resource consumption of radio base stations within the 4G LTE Radio architecture. This has been done by analyzing the statistical data that each node generates every 15 minutes, in the form of "performance maintenance counters". In this thesis, we introduce methods that allow resources to be automatically monitored after software updates, in order to detect any anomalies in the consumption patterns of the different resources compared to the reference period before the update. Additionally, we also attempt to narrow down the origin of anomalies by pointing out parameters potentially linked to the issue.
Struck: Structured Output Tracking with Kernels

Adaptive tracking-by-detection methods are widely used in computer vision for tracking arbitrary objects. Current approaches treat the tracking problem as a classification task and use online learning techniques to update the object model. However, for these updates to happen one needs to convert the estimated object position into a set of labelled training examples, and it is not clear how best to perform this intermediate step. Furthermore, the objective for the classifier (label prediction) is not explicitly coupled to the objective for the tracker (estimation of object position). In this paper, we present a framework for adaptive visual object tracking based on structured output prediction. By explicitly allowing the output space to express the needs of the tracker, we avoid the need for an intermediate classification step. Our method uses a kernelised structured output support vector machine (SVM), which is learned online to provide adaptive tracking. To allow our tracker to run at high frame rates, we (a) introduce a budgeting mechanism that prevents the unbounded growth in the number of support vectors that would otherwise occur during tracking, and (b) show how to implement tracking on the GPU. Experimentally, we show that our algorithm is able to outperform state-of-the-art trackers on various benchmark videos. Additionally, we show that we can easily incorporate additional features and kernels into our framework, which results in increased tracking performance.
Putting the 'theory' back into grounded theory: guidelines for grounded theory studies in information systems

Over the past decade, there has been increasing interest in the use of grounded theory in information systems research. Grounded theory is a qualitative research method that seeks to develop theory that is grounded in data systematically gathered and analysed. The purpose of this paper is to suggest guidelines for grounded theory studies in information systems. Our guidelines are based on a framework for theorizing in grounded theory studies that focuses on conceptualization and theory scope. Our hope is that the guidelines will help to raise the quality and aspirations of grounded theory studies in information systems.
Stunting Is Associated with Food Diversity while Wasting with Food Insecurity among Underfive Children in East and West Gojjam Zones of Amhara Region, Ethiopia

Food insecurity has detrimental effects in protecting child undernutrition.This study sought to determine the level of child undernutrition and its association with food insecurity.



A community based comparative cross-sectional study design involving multistage sampling technique was implemented from 24th of May to 20th of July 2013. Using two population proportion formula, a total of 4110 randomly selected households were included in the study. Availability of the productive safety net programme was used for grouping the study areas. A multiple linear regression model was used to assess the association between food insecurity and child malnutrition. Clustering effects of localities were controlled during analysis.



Stunting (37.5%), underweight (22.0%) and wasting (17.1%) were observed in East Gojjam zone, while 38.3% stunting, 22.5% underweight, and 18.6% wasting for the West Gojjam zone. Food insecurity was significantly associated with wasting (β = - 0.108, P < 0.05).Food diversity and number of meals the child ate per day significantly associated with stunting (β = 0.039, P < 0.01) and underweight (β = 0.035, P < 0.05) respectively. Residential area was the significant predictor of all indices.



The magnitude of child undernutrition was found to be very high in the study areas. Food insecurity was the significant determinant of wasting. Food diversity and number of meals the child ate per day were the significant determinants of stunting and underweight respectively. Child nutrition intervention strategies should take into account food security, dietary diversity, and carefully specified with regard to residential locations. Addressing food insecurity is of paramount importance.
Effects of training in minimalist shoes on the intrinsic and extrinsic foot muscle volume.

Minimalist shoes have gained popularity recently because it is speculated to strengthen the foot muscles and foot arches, which may help to resist injuries. However, previous studies provided limited evidence supporting the link between changes in muscle size and footwear transition. Therefore, this study sought to examine the effects of minimalist shoes on the intrinsic and extrinsic foot muscle volume in habitual shod runners. The relationship between participants' compliance with the minimalist shoes and changes in muscle õvolume was also evaluated.



Twenty habitual shod runners underwent a 6-month self-monitoring training program designed for minimalist shoe transition. Another 18 characteristics-matched shod runners were also introduced with the same program but they maintained running practice with standard shoes. Runners were monitored using an online surveillance platform during the program. We measured overall intrinsic and extrinsic foot muscle volume before and after the program using MRI scans.



Runners in the experimental group exhibited significantly larger leg (P=0.01, Cohen's d=0.62) and foot (P<0.01, Cohen's d=0.54) muscle after transition. Foot muscle growth was mainly contributed by the forefoot (P<0.01, Cohen's d=0.64) but not the rearfoot muscle (P=0.10, Cohen's d=0.30). Leg and foot muscle volume of runners in the control group remained similar after the program (P=0.33-0.95). A significant positive correlation was found between participants' compliance with the minimalist shoes and changes in leg muscle volume (r=0.51; P=0.02).



Habitual shod runners who transitioned to minimalist shoes demonstrated significant increase in leg and foot muscle volume. Additionally, the increase in leg muscle volume was significantly correlated associated with the compliance of minimalist shoe use.
Developing Trust in Virtual Teams

A research project with distributed electronic teams was conducted to examine how virtual temporary teams quickly develop and maintain trust relationships with people that they hardly know, and may never meet again, with the goal of producing interdependent work. We collected data from 14 teams of students from three different universities with the aim of providing a theoretical and empirical explanation of what temporary, distributed teams do to produce trust as a foundation for cooperative work. Our results suggest that high levels of trust were maintained in teams that engaged in continuous and frequent interaction, were more efficient in moving through the phases of the project, focused on the work content of their projects, and achieved sufficient amounts of social penetration during the first part of the project to increase their work effectiveness throughout its conclusion. The implications of trust in virtual teams are discussed.
Leveraging Multiscale Hessian-Based Enhancement With a Novel Exudate Inpainting Technique for Retinal Vessel Segmentation

Accurate vessel detection in retinal images is an important and difficult task. Detection is made more challenging in pathological images with the presence of exudates and other abnormalities. In this paper, we present a new unsupervised vessel segmentation approach to address this problem. A novel inpainting filter, called neighborhood estimator before filling, is proposed to inpaint exudates in a way that nearby false positives are significantly reduced during vessel enhancement. Retinal vascular enhancement is achieved with a multiple-scale Hessian approach. Experimental results show that the proposed vessel segmentation method outperforms state-of-the-art algorithms reported in the recent literature, both visually and in terms of quantitative measurements, with overall mean accuracy of 95.62% on the STARE dataset and 95.81% on the HRF dataset.
Improved Deep Embedded Clustering with Local Structure Preservation

Deep clustering learns deep feature representations that favor clustering task using neural networks. Some pioneering work proposes to simultaneously learn embedded features and perform clustering by explicitly defining a clustering oriented loss. Though promising performance has been demonstrated in various applications, we observe that a vital ingredient has been overlooked by these work that the defined clustering loss may corrupt feature space, which leads to non-representative meaningless features and this in turn hurts clustering performance. To address this issue, in this paper, we propose the Improved Deep Embedded Clustering (IDEC) algorithm to take care of data structure preservation. Specifically, we manipulate feature space to scatter data points using a clustering loss as guidance. To constrain the manipulation and maintain the local structure of data generating distribution, an under-complete autoencoder is applied. By integrating the clustering loss and autoencoder’s reconstruction loss, IDEC can jointly optimize cluster labels assignment and learn features that are suitable for clustering with local structure preservation. The resultant optimization problem can be effectively solved by mini-batch stochastic gradient descent and backpropagation. Experiments on image and text datasets empirically validate the importance of local structure preservation and the effectiveness of our algorithm.
Impact of Fall Prevention on Nurses and Care of Fall Risk Patients.

Falls are common events for hospitalized older adults, resulting in negative outcomes both for patients and hospitals. The Center for Medicare and Medicaid (CMS) has placed pressure on hospital administrators by identifying falls as a "never event", resulting in a zero falls goal for many hospitals. Staff nurses are responsible for providing direct care to patients and for meeting the hospital no falls goal. Little is known about the impact of "zero falls" on nurses, patients and the organization.



A qualitative study, using Grounded Dimensional Analysis (GDA) was conducted to explore nurses' experiences with fall prevention in hospital settings and the impact of those experiences on how nurses provide care to fall risk patients. Twenty-seven registered nurses and certified nursing assistants participated in in-depth interviews. Open, axial and selective coding was used to analyze data. A conceptual model which illustrates the impact of intense messaging from nursing administration to prevent patient falls on nurses, actions nurses take to address the message and the consequences to nurses, older adult patients and to the organization was developed.



Intense messaging from hospital administration to achieve zero falls resulted in nurses developing a fear of falls, protecting self and unit, and restricting fall risk patients as a way to stop messages and meet the hospital goal.



Results of this study identify unintended consequences of fall prevention message on nurses and older adult patients. Further research is needed understand how nurse care for fall risk patients.
Named-Entity Linking Using Deep Learning For Legal Documents: A Transfer Learning Approach

In the legal domain it is important to differentiate between words in general, and afterwards to link the occurrences of the same entities. The topic to solve these challenges is called Named-Entity Linking (NEL). Current supervised neural networks designed for NEL use publicly available datasets for training and testing. However, this paper focuses especially on the aspect of applying transfer learning approach using networks trained for NEL to legal documents. Experiments show consistent improvement in the legal datasets that were created from the European Union law in the scope of this research. Using transfer learning approach, we reached F1-score of 98.90% and 98.01% on the legal small and large test dataset.
Anomaly-Based Web Attack Detection: A Deep Learning Approach

As the era of cloud technology arises, more and more people are beginning to migrate their applications and personal data to the cloud. This makes web-based applications an attractive target for cyber-attacks. As a result, web-based applications now need more protections than ever. However, current anomaly-based web attack detection approaches face the difficulties like unsatisfying accuracy and lack of generalization. And the rule-based web attack detection can hardly fight unknown attacks and is relatively easy to bypass. Therefore, we propose a novel deep learning approach to detect anomalous requests. Our approach is to first train two Recurrent Neural Networks (RNNs) with the complicated recurrent unit (LSTM unit or GRU unit) to learn the normal request patterns using only normal requests unsupervisedly and then supervisedly train a neural network classifier which takes the output of RNNs as the input to discriminate between anomalous and normal requests. We tested our model on two datasets and the results showed that our model was competitive with the state-of-the-art. Our approach frees us from feature selection. Also to the best of our knowledge, this is the first time that the RNN is applied on anomaly-based web attack detection systems.