nils-herrmann/clean_scirepeval huggingface dataset:
Imputation-boosted collaborative filtering using machine learning classifiers

As data sparsity remains a significant challenge for collaborative filtering (CF, we conjecture that predicted ratings based on imputed data may be more accurate than those based on the originally very sparse rating data. In this paper, we propose a framework of imputation-boosted collaborative filtering (IBCF), which first uses an imputation technique, or perhaps machine learned classifier, to fill-in the sparse user-item rating matrix, then runs a traditional Pearson correlation-based CF algorithm on this matrix to predict a novel rating. Empirical results show that IBCF using machine learning classifiers can improve predictive accuracy of CF tasks. In particular, IBCF using a classifier capable of dealing well with missing data, such as naïve Bayes, can outperform the content-boosted CF (a representative hybrid CF algorithm) and IBCF using PMM (predictive mean matching, a state-of-the-art imputation technique), without using external content information.
Magnesium homeostasis and aging.

Aging is very often associated with magnesium (Mg) deficit. Total plasma magnesium concentrations are remarkably constant in healthy subjects throughout life, while total body Mg and Mg in the intracellular compartment tend to decrease with age. Dietary Mg deficiencies are common in the elderly population. Other frequent causes of Mg deficits in the elderly include reduced Mg intestinal absorption, reduced Mg bone stores, and excess urinary loss. Secondary Mg deficit in aging may result from different conditions and diseases often observed in the elderly (i.e. insulin resistance and/or type 2 diabetes mellitus) and drugs (i.e. use of hypermagnesuric diuretics). Chronic Mg deficits have been linked to an increased risk of numerous preclinical and clinical outcomes, mostly observed in the elderly population, including hypertension, stroke, atherosclerosis, ischemic heart disease, cardiac arrhythmias, glucose intolerance, insulin resistance, type 2 diabetes mellitus, endothelial dysfunction, vascular remodeling, alterations in lipid metabolism, platelet aggregation/thrombosis, inflammation, oxidative stress, cardiovascular mortality, asthma, chronic fatigue, as well as depression and other neuropsychiatric disorders. Both aging and Mg deficiency have been associated to excessive production of oxygen-derived free radicals and low-grade inflammation. Chronic inflammation and oxidative stress are also present in several age-related diseases, such as many vascular and metabolic conditions, as well as frailty, muscle loss and sarcopenia, and altered immune responses, among others. Mg deficit associated to aging may be at least one of the pathophysiological links that may help to explain the interactions between inflammation and oxidative stress with the aging process and many age-related diseases.
Investigations of the urban street tree forest of Mendoza, Argentina

The investigation of the urban street trees was undertaken in the oasis city Mendoza, Argentina. The analysis included 1,680 urban street trees in their structure (species, age, and spatial structure), vitality and irrigation conditions. A questioning of 120 residents analysed the perception and acceptance of the urban trees by urban dwellers. Different urban structures, residential, commercial and residential mixed structures and the city centre were investigated. The results show clearly the insufficient maintenance of the trees (composition, aging, irrigation, pruning etc.) and damaged irrigation system with leads to a loss of vitality. The questioning showed that easily recognisable problems (insufficient pruning etc.) are more reflected by people than long term problems (irrigation). There is a different reflection of urban street tree problems in the different urban areas, related to status, information level and management. The maintenance and relation to urban street trees was best in the residential area. Not only better maintenance of the trees but also a clearer recognition of street tree problems by residents and by the responsible municipal institutions is urgently needed. A public-private partnership and more environmental education could help to overcome the problems.
A -Optimality for Active Learning of Logistic Regression Classifiers

Over the last decade there has been growing interest in pool-based active learning techniques, where instead of receiving an i.i.d. sample from a pool of unlabeled data, a learner may take an active role in selecting examples from the pool. Queries to an oracle (a human annotator in most applications) provide label information for the selected observations, but at a cost. The challenge is to end up with a model that provides the best possible generalization error at the least cost. Popular methods such as uncertainty sampling often work well, but sometimes fail badly. We take the A-optimality criterion used in optimal experimental design, and extend it so that it can be used for pool-based active learning of logistic regression classifiers. A-optimality has attractive theoretical properties, and empirical evaluation confirms that it offers a more robust approach to active learning for logistic regression than alternatives.
An Improved K-Means Clustering Algorithm

Lot of research work has been done on cluster based mining on relational databases. K-means is a basic algorithm, which is used in many of them. The main drawback of k-means is that it does not give a high precision rate and results are affected by random initialization of cluster centroids. It may produce empty clusters depending on the initial centroids, which reduce the performance of the system. In this paper, we have proposed an Improved K-means algorithm, which improve data clustering by removing empty clusters. Further, it improves the computational time of the algorithm by reusing stored information of previous iterations. The results obtained from our experiments show improvement in accuracy, precision rate and efficiency of the algorithm. The complexity of the algorithm is also reduced from O(nlk) to O(nk).
Target detection using GPS signals of opportunity

We investigate the capability of GPS signals of opportunity to detect and localize targets on the sea surface. The proposed approach to target detection is new, and stems from the advantages offered by GPS-Reflectometry (GPS-R) in terms of spatial and temporal sampling, and low cost/low power technology, extending the range of applications of GPS-R beyond remote sensing. Here the exploitation of GPS signals backscattered from a target is proposed, to enhance the target return with respect to the sea clutter. A link budget is presented, showing that the target return is stronger than the background sea clutter when certain conditions are verified. The findings agree with the only empirical measurement found in literature, where a strong return from a target was fortuitously registered during an airborne campaign. This study provides a first proof-of-concept of GPS-based target detection, highlighting all the potentials of this innovative approach.
Implementation of Back Propagation Algorithm ( of neural networks ) in VHDL

authentic record of my own work carried under the supervision of Mr. SANJAY SHARMA and Mrs. MANU BANSAL at TIET, PATIALA. The matter presented in this thesis has not been submitted in any other University or Institute for the award of Master of Engineering. Signature of the student This is certified that the above statement made by the candidate is correct to the best of my knowledge. ACKNOWLEGEMENT Words are often too less to reveal one's deep regards. An understanding of the work like this is never the outcome of the efforts of a single person. I take this opportunity to express my profound sense of gratitude and respect to all those who helped me through the duration of this thesis. Lecturer, TIET, PATIALA. Their enthusiasm and optimism made this experience both rewarding and enjoyable. Most of the novel ideas and solutions found in this thesis are the result of our numerous stimulating discussions. His feedback and editorial comments were also invaluable for the writing of this thesis. facilities for the completion of thesis. I take pride of my self being daughter of ideal great parents whose everlasting desire, sacrifice, affectionate blessing and help without which it would have not been possible for me to complete my studies. At last, I would like to thank all the members and employees of Electronics and Communication Department,  Patiala whose love and affection made this possible.
High capacity associative memories and connection constraints

High capacity associative neural networks can be built from networks of perceptrons, trained using simple perceptron training. Such networks perform much better than those trained using the standard Hopfield one shot Hebbian learning. An experimental investigation into how such networks perform when the connection weights are not free to take any value is reported. The three restrictions investigated are: a symmetry constraint, a sign constraint and a dilution constraint. The selection of these constraints is motivated by both engineering and biological considerations.
Treatment of Dissecting Cellulitis of the Scalp and the Use of Marsupialization : A Review

Dissecting cellulitis of the scalp (DCS), or perifolliculitis capitis abscedens et suffodiens, is a rare, chronic, relapsing and remitting inflammatory disease of the scalp. We present a case of a patient with severe DCS refractory to medical management on chronic high-dose prednisone. We describe the procedure of marsupialization under general anesthesia which may be an effective treatment option for severe cases of DCS.
Electroencephalogram signatures of loss and recovery of consciousness from propofol.

Unconsciousness is a fundamental component of general anesthesia (GA), but anesthesiologists have no reliable ways to be certain that a patient is unconscious. To develop EEG signatures that track loss and recovery of consciousness under GA, we recorded high-density EEGs in humans during gradual induction of and emergence from unconsciousness with propofol. The subjects executed an auditory task at 4-s intervals consisting of interleaved verbal and click stimuli to identify loss and recovery of consciousness. During induction, subjects lost responsiveness to the less salient clicks before losing responsiveness to the more salient verbal stimuli; during emergence they recovered responsiveness to the verbal stimuli before recovering responsiveness to the clicks. The median frequency and bandwidth of the frontal EEG power tracked the probability of response to the verbal stimuli during the transitions in consciousness. Loss of consciousness was marked simultaneously by an increase in low-frequency EEG power (<1 Hz), the loss of spatially coherent occipital alpha oscillations (8-12 Hz), and the appearance of spatially coherent frontal alpha oscillations. These dynamics reversed with recovery of consciousness. The low-frequency phase modulated alpha amplitude in two distinct patterns. During profound unconsciousness, alpha amplitudes were maximal at low-frequency peaks, whereas during the transition into and out of unconsciousness, alpha amplitudes were maximal at low-frequency nadirs. This latter phase-amplitude relationship predicted recovery of consciousness. Our results provide insights into the mechanisms of propofol-induced unconsciousness, establish EEG signatures of this brain state that track transitions in consciousness precisely, and suggest strategies for monitoring the brain activity of patients receiving GA.
New Insights Into Single Event Transient Propagation in Chains of Inverters—Evidence for Propagation-Induced Pulse Broadening

The generation and propagation of single event transients (SET) is measured and modeled in SOI inverter chains with different designs. SET propagation in inverter chains induces significant modifications of the transient width. In some cases, a "propagation-induced pulse broadening" (PIPB) effect is observed. Initially narrow transients, less than 200 ps at the struck node, are progressively broadened up to the nanosecond range, with the degree of broadening dependent on the transistor design and the length of propagation. The chain design (transistor size and load) is shown to have a major impact on the transient width modification.
Spatiotemporal Residual Networks for Video Action Recognition

Two-stream Convolutional Networks (ConvNets) have shown strong performance for human action recognition in videos. Recently, Residual Networks (ResNets) have arisen as a new technique to train extremely deep architectures. In this paper, we introduce spatiotemporal ResNets as a combination of these two approaches. Our novel architecture generalizes ResNets for the spatiotemporal domain by introducing residual connections in two ways. First, we inject residual connections between the appearance and motion pathways of a two-stream architecture to allow spatiotemporal interaction between the two streams. Second, we transform pretrained image ConvNets into spatiotemporal networks by equipping them with learnable convolutional filters that are initialized as temporal residual connections and operate on adjacent feature maps in time. This approach slowly increases the spatiotemporal receptive field as the depth of the model increases and naturally integrates image ConvNet design principles. The whole model is trained end-to-end to allow hierarchical learning of complex spatiotemporal features. We evaluate our novel spatiotemporal ResNet using two widely used action recognition benchmarks where it exceeds the previous state-of-the-art.
XRel: a path-based approach to storage and retrieval of XML documents using relational databases

This article describes XRel, a novel approach for storage and retrieval of XML documents using relational databases. In this approach, an XML document is decomposed into nodes on the basis of its tree structure and stored in relational tables according to the node type, with path information from the root to each node. XRel enables us to store XML documents using a fixed relational schema without any information about DTDs and also to utilize indices such as the B+-tree and the R-tree supported by database management systems. Thus, XRel does not need any extension of relational databases for storing XML documents. For processing XML queries, we present an algorithm for translating a core subset of XPath expressions into SQL queries. Finally, we demonstrate the effectiveness of this approach through several experiments using actual XML documents.
RMediation: an R package for mediation analysis confidence intervals.

This article describes the RMediation package,which offers various methods for building confidence intervals (CIs) for mediated effects. The mediated effect is the product of two regression coefficients. The distribution-of-the-product method has the best statistical performance of existing methods for building CIs for the mediated effect. RMediation produces CIs using methods based on the distribution of product, Monte Carlo simulations, and an asymptotic normal distribution. Furthermore, RMediation generates percentiles, quantiles, and the plot of the distribution and CI for the mediated effect. An existing program, called PRODCLIN,  has been widely cited and used by researchers to build accurate CIs. PRODCLIN has several limitations: The program is somewhat cumbersome to access and yields no result for several cases. RMediation described herein is based on the widely available R software, includes several capabilities not available in PRODCLIN, and provides accurate results that PRODCLIN could not.
The role of primary care in preventing ambulatory care sensitive conditions.

To examine the postulated relationship between Ambulatory Care Sensitive Conditions (ACSC) and Primary Health Care (PHC) in the US context for the European context, in order to develop an ACSC list as markers of PHC effectiveness and to specify which PHC activities are primarily responsible for reducing hospitalization rates.



To apply the criteria proposed by Solberg and Weissman to obtain a list of codes of ACSC and to consider the PHC intervention according to a panel of experts. Five selection criteria: i) existence of prior studies; ii) hospitalization rate at least 1/10,000 or 'risky health problem'; iii) clarity in definition and coding; iv) potentially avoidable hospitalization through PHC; v) hospitalization necessary when health problem occurs. Fulfilment of all criteria was required for developing the final ACSC list. A sample of 248,050 discharges corresponding to 2,248,976 inhabitants of Catalonia in 1996 provided hospitalization rate data. A Delphi survey was performed with a group of 44 experts reviewing 113 ICD diagnostic codes (International Classification of Diseases, 9th Revision, Clinical Modification), previously considered to be ACSC.



The five criteria selected 61 ICD as a core list of ACSC codes and 90 ICD for an expanded list.



A core list of ACSC as markers of PHC effectiveness identifies health conditions amenable to specific aspects of PHC and minimizes the limitations attributable to variations in hospital admission policies. An expanded list should be useful to evaluate global PHC performance and to analyse market responsibility for ACSC by PHC and Specialist Care.
Towards Information-Seeking Agents

We develop a general problem setting for training and testing the ability of agents to gather information efficiently. Specifically, we present a collection of tasks in which success requires searching through a partially-observed environment, for fragments of information which can be pieced together to accomplish various goals. We combine deep architectures with techniques from reinforcement learning to develop agents that solve our tasks. We shape the behavior of these agents by combining extrinsic and intrinsic rewards. We empirically demonstrate that these agents learn to search actively and intelligently for new information to reduce their uncertainty, and to exploit information they have already acquired.
Recurrent Convolutional Networks for Pulmonary Nodule Detection in CT Imaging

Computed tomography (CT) generates a stack of cross-sectional images covering a region of the body. The visual assessment of these images for the identification of potential abnormalities is a challenging and time consuming task due to the large amount of information that needs to be processed. In this article we propose a deep artificial neural network architecture, ReCTnet, for the fully-automated detection of pulmonary nodules in CT scans. The architecture learns to distinguish nodules and normal structures at the pixel level and generates three-dimensional probability maps highlighting areas that are likely to harbour the objects of interest. Convolutional and recurrent layers are combined to learn expressive image representations exploiting the spatial dependencies across axial slices. We demonstrate that leveraging intra-slice dependencies substantially increases the sensitivity to detect pulmonary nodules without inflating the false positive rate. On the publicly available LIDC/IDRI dataset consisting of 1,018 annotated CT scans, ReCTnet reaches a detection sensitivity of 90.5% with an average of 4.5 false positives per scan. Comparisons with a competing multi-channel convolutional neural network for multislice segmentation and other published methodologies using the same dataset provide evidence that ReCTnet offers significant performance gains. 1 ar X iv :1 60 9. 09 14 3v 1 [ st at .M L ] 2 8 Se p 20 16
Prospective evaluation of the use of Mitchell shoes and dynamic abduction brace for idiopathic clubfeet.

Ponseti treatment for clubfoot has been successful, but recurrence continues to be an issue. After correction, patients are typically braced full time with a static abduction bar and shoes. Patient compliance with bracing is a modifiable risk factor for recurrence. We hypothesized that the use of Mitchell shoes and a dynamic abduction brace would increase compliance and thereby reduce the rate of recurrence. A prospective, randomized trial was carried out with consecutive patients treated for idiopathic clubfeet from 2008 to 2012. After casting and tenotomy, patients were randomized into either the dynamic or static abduction bar group. Both groups used Mitchell shoes. Patient demographics, satisfaction, and compliance were measured with self-reported questionnaires throughout follow-up. Thirty patients were followed up, with 15 in each group. Average follow-up was 18.7 months (range 3-40.7 months). Eight recurrences (26.7%) were found, with four in each group. Recurrences had a statistically significant higher number of casts and a longer follow-up time. Mean income, education level, patient-reported satisfaction and compliance, and age of caregiver tended to be lower in the recurrence group but were not statistically significant. No differences were found between the two brace types. Our study showed excellent patient satisfaction and reported compliance with Mitchell shoes and either the dynamic or static abduction bar. Close attention and careful education should be directed towards patients with known risk factors or difficult casting courses to maximize brace compliance, a modifiable risk factor for recurrence.
High-resolution magnetic resonance-guided posterior femoral cutaneous nerve blocks

To assess the feasibility, technical success, and effectiveness of high-resolution magnetic resonance (MR)-guided posterior femoral cutaneous nerve (PFCN) blocks. A retrospective analysis of 12 posterior femoral cutaneous nerve blocks in 8 patients [6 (75 %) female, 2 (25 %) male; mean age, 47 years; range, 42–84 years] with chronic perineal pain suggesting PFCN neuropathy was performed. Procedures were performed with a clinical wide-bore 1.5-T MR imaging system. High-resolution MR imaging was utilized for visualization and targeting of the PFCN. Commercially available, MR-compatible 20-G needles were used for drug delivery. Variables assessed were technical success (defined as injectant surrounding the targeted PFCN on post-intervention MR images) effectiveness, (defined as post-interventional regional anesthesia of the target area innervation downstream from the posterior femoral cutaneous nerve block), rate of complications, and length of procedure time. MR-guided PFCN injections were technically successful in 12/12 cases (100 %) with uniform perineural distribution of the injectant. All blocks were effective and resulted in post-interventional regional anesthesia of the expected areas (12/12, 100 %). No complications occurred during the procedure or during follow-up. The average total procedure time was 45 min (30–70) min. Our initial results demonstrate that this technique of selective MR-guided PFCN blocks is feasible and suggest high technical success and effectiveness. Larger studies are needed to confirm our initial results.
Pancreas transplant alone.

The present article aims to review the current state of diabetes, including its treatment options, and highlight current issues in pancreas transplantation.



Compared with other areas of transplantation, pancreas transplant/transplantation alone in the absence of kidney disease remains a relatively small field. As a consequence, reported new research articles are few in number, and often data regarding pancreas transplant/transplantation alone are mixed in with simultaneous kidney-pancreas and pancreas after kidney transplantation, which are covered separately. The prevalence of diabetes is reaching epidemic levels and continues to increase. New developments in diabetes care include the bionic pancreas and immunotherapy. Persistent efforts at gene and stem cell therapies are ongoing. Pancreas transplantation outcomes are improving, yet numbers are declining, even though pancreas transplantation still offers the most optimal glycemic control.



Pancreas transplantation has improving outcomes but stands in competition with other diabetes management options.