nils-herrmann/clean_scirepeval huggingface dataset:
Ampicillin plus ceftriaxone is as effective as ampicillin plus gentamicin for treating enterococcus faecalis infective endocarditis.

The aim of this study was to compare the effectiveness of the ampicillin plus ceftriaxone (AC) and ampicillin plus gentamicin (AG) combinations for treating Enterococcus faecalis infective endocarditis (EFIE).



An observational, nonrandomized, comparative multicenter cohort study was conducted at 17 Spanish and 1 Italian hospitals. Consecutive adult patients diagnosed of EFIE were included. Outcome measurements were death during treatment and at 3 months of follow-up, adverse events requiring treatment withdrawal, treatment failure requiring a change of antimicrobials, and relapse.



A larger percentage of AC-treated patients (n = 159) had previous chronic renal failure than AG-treated patients (n = 87) (33% vs 16%, P = .004), and AC patients had a higher incidence of cancer (18% vs 7%, P = .015), transplantation (6% vs 0%, P = .040), and healthcare-acquired infection (59% vs 40%, P = .006). Between AC and AG-treated EFIE patients, there were no differences in mortality while on antimicrobial treatment (22% vs 21%, P = .81) or at 3-month follow-up (8% vs 7%, P = .72), in treatment failure requiring a change in antimicrobials (1% vs 2%, P = .54), or in relapses (3% vs 4%, P = .67). However, interruption of antibiotic treatment due to adverse events was much more frequent in AG-treated patients than in those receiving AC (25% vs 1%, P < .001), mainly due to new renal failure (≥25% increase in baseline creatinine concentration; 23% vs 0%, P < .001).



AC appears as effective as AG for treating EFIE patients and can be used with virtually no risk of renal failure and regardless of the high-level aminoglycoside resistance status of E. faecalis.
A novel layerwise pruning method for model reduction of fully connected deep neural networks

Deep neural networks (DNN) are powerful models for many pattern recognition tasks, yet they tend to have many layers and many neurons resulting in a high computational complexity. This limits their application to high-performance computing platforms. In order to evaluate a trained DNN on a lower-performance computing platform like a mobile or embedded device, model reduction techniques which shrink the network size and reduce the number of parameters without considerable performance degradation performance are highly desirable. In this paper, we start with a trained fully connected DNN and show how to reduce the network complexity by a novel layerwise pruning method. We show that if some neurons are pruned and the remaining parameters (weights and biases) are adapted correspondingly to correct the errors introduced by pruning, the model reduction can be done almost without performance loss. The main contribution of our pruning method is a closed-form solution that only makes use of the first and second order moments of the layer outputs and, therefore, only needs unlabeled data. Using three benchmark datasets, we compare our pruning method with the low-rank approximation approach.
DR 2-Net : D eep R esidual R econstruction N etwork for Image Compressive Sensing

Most traditional algorithms for compressive sensing image reconstruction suffer from the intensive computation. Recently, deep learning-based reconstruction algorithms have been reported, which dramatically reduce the time complexity than iterative reconstruction algorithms. In this paper, we propose a novel Deep Residual Reconstruction Network (DRNet) to reconstruct the image from its Compressively Sensed (CS) measurement. The DR-Net is proposed based on two observations: 1) linear mapping could reconstruct a high-quality preliminary image, and 2) residual learning could further improve the reconstruction quality. Accordingly, DR-Net consists of two components, i.e., linear mapping network and residual network, respectively. Specifically, the fully-connected layer in neural network implements the linear mapping network. We then expand the linear mapping network to DR-Net by adding several residual learning blocks to enhance the preliminary image. Extensive experiments demonstrate that the DR-Net outperforms traditional iterative methods and recent deep learning-based methods by large margins at measurement rates 0.01, 0.04, 0.1, and 0.25, respectively. The code of DR-Net has been released on: https://github.com/coldrainyht/caffe dr2
A Survey on Automatic Twitter Event Summarization

Twitter is one of the most popular social platforms for online users to share trendy information and views on any event. Twitter reports an event faster than any other medium and contains enormous information and views regarding an event. Consequently, Twitter topic summarization is one of the most convenient ways to get instant gist of any event. However, the information shared on Twitter is often full of nonstandard abbreviations, acronyms, out of vocabulary (OOV) words and with grammatical mistakes which create challenges to find reliable and useful information related to any event. Undoubtedly, Twitter event summarization is a challenging task where traditional text summarization methods do not work well. In last decade, various research works introduced different approaches for automatic Twitter topic summarization. The main aim of this survey work is to make a broad overview of promising summarization approaches on a Twitter topic. We also focus on automatic evaluation of summarization techniques by surveying recent evaluation methodologies. At the end of the survey, we emphasize on both current and future research challenges in this domain through a level of depth analysis of the most recent summarization approaches.
Evaluating the impact of a computerized surveillance algorithm and decision support system on sepsis mortality

We created a system using a triad of change management, electronic surveillance, and algorithms to detect sepsis and deliver highly sensitive and specific decision support to the point of care using a mobile application. The investigators hypothesized that this system would result in a reduction in sepsis mortality.



A before-and-after model was used to study the impact of the interventions on sepsis-related mortality. All patients admitted to the study units were screened per the Institute for Healthcare Improvement Surviving Sepsis Guidelines using real-time electronic surveillance. Sepsis surveillance algorithms that adjusted clinical parameters based on comorbid medical conditions were deployed for improved sensitivity and specificity. Nurses received mobile alerts for all positive sepsis screenings as well as severe sepsis and shock alerts over a period of 10 months. Advice was given for early goal-directed therapy. Sepsis mortality during a control period from January 1, 2011 to September 30, 2013 was used as baseline for comparison.



The primary outcome, sepsis mortality, decreased by 53% (P = 0.03; 95% CI, 1.06-5.25). The 30-day readmission rate reduced from 19.08% during the control period to 13.21% during the study period (P = 0.05; 95% CI, 0.97-2.52). No significant change in length of hospital stay was noted. The system had observed sensitivity of 95% and specificity of 82% for detecting sepsis compared to gold-standard physician chart review.



A program consisting of change management and electronic surveillance with highly sensitive and specific decision support delivered to the point of care resulted in significant reduction in deaths from sepsis.
Deep Captioning with Attention-Based Visual Concept Transfer Mechanism for Enriching Description

In this paper, we propose a novel deep captioning framework called Attention-based multimodal recurrent neural network with Visual Concept Transfer Mechanism (A-VCTM). There are three advantages of the proposed A-VCTM. (1) A multimodal layer is used to integrate the visual representation and context representation together, building a bridge that connects context information with visual information directly. (2) An attention mechanism is introduced to lead the model to focus on the regions corresponding to the next word to be generated (3) We propose a visual concept transfer mechanism to generate novel visual concepts and enrich the description sentences. Qualitative and quantitative results on two standard benchmarks, MSCOCO and Flickr30K show the effectiveness and practicability of the proposed A-VCTM framework.
Virtual Chromoendoscopy With FICE for the Classification of Polypoid and Nonpolypoid Raised Lesions in Ulcerative Colitis.

GOALS
The aim of this study was to analyze the performance of Fuji Intelligent Color Enhancement (FICE) using the classification of Kudo in the differentiation of neoplastic and non-neoplastic raised lesions in ulcerative colitis (UC).



The Kudo classification of mucosal pit patterns is an aid for the differential diagnosis of colorectal polyps in the general population, but no systematic studies are available for all forms of raised lesions in UC.



All raised, polypoid and nonpolypoid, lesions found during consecutive surveillance colonoscopies with FICE for long-standing UC were included. In the primary prospective analysis, the Kudo classification was used to predict the histology by FICE. In a post hoc analysis, further endoscopic markers were also explored.



Two hundred and five lesions (mean size, 8 mm; range, 2 to 30 mm) from 59 patients (mean age, 56 y; range, 21 to 79 y) were analyzed. Twenty-three neoplastic (11%), 18 hyperplastic (9%), and 164 inflammatory (80%) lesions were found. Thirty-one lesions (15%), none of which were neoplastic, were unclassifiable according to Kudo. After logistic regression, a strong negative association resulted between endoscopic activity and neoplasia, whereas the presence of a fibrin cap was significantly associated with endoscopic activity. Using FICE, the sensitivity, specificity, and positive and negative likelihood ratios of the Kudo classification were 91%, 76%, 3.8, and 0.12, respectively. The corresponding values by adding the fibrin cap as a marker of inflammation were 91%, 93%, 13, and 0.10, respectively.



FICE can help to predict the histology of raised lesions in UC. A new classification of pit patterns, based on inflammatory markers, should be developed in the setting of UC to improve the diagnostic performance.
Attosecond physics at the nanoscale.

Recently two emerging areas of research, attosecond and nanoscale physics, have started to come together. Attosecond physics deals with phenomena occurring when ultrashort laser pulses, with duration on the femto- and sub-femtosecond time scales, interact with atoms, molecules or solids. The laser-induced electron dynamics occurs natively on a timescale down to a few hundred or even tens of attoseconds (1 attosecond  =  1 as  =  10-18 s), which is comparable with the optical field. For comparison, the revolution of an electron on a 1s orbital of a hydrogen atom is  ∼152 as. On the other hand, the second branch involves the manipulation and engineering of mesoscopic systems, such as solids, metals and dielectrics, with nanometric precision. Although nano-engineering is a vast and well-established research field on its own, the merger with intense laser physics is relatively recent. In this report on progress we present a comprehensive experimental and theoretical overview of physics that takes place when short and intense laser pulses interact with nanosystems, such as metallic and dielectric nanostructures. In particular we elucidate how the spatially inhomogeneous laser induced fields at a nanometer scale modify the laser-driven electron dynamics. Consequently, this has important impact on pivotal processes such as above-threshold ionization and high-order harmonic generation. The deep understanding of the coupled dynamics between these spatially inhomogeneous fields and matter configures a promising way to new avenues of research and applications. Thanks to the maturity that attosecond physics has reached, together with the tremendous advance in material engineering and manipulation techniques, the age of atto-nanophysics has begun, but it is in the initial stage. We present thus some of the open questions, challenges and prospects for experimental confirmation of theoretical predictions, as well as experiments aimed at characterizing the induced fields and the unique electron dynamics initiated by them with high temporal and spatial resolution.
An Efficient Feature Selection System to Integrating SVM with Genetic Algorithm for Large Medical Datasets

This work focuses on the combining a feature selection technique based on genetic algorithm and support vector machines (SVM) of medical disease classification. SVMs have been used for various applications as a powerful tool for pattern classification. We use evolutionary computation which is a subfield of artificial intelligence or computational intelligence that involves combinatorial optimization problems. Evolutionary computation uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search using parallel processing to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution. The proposed genetic-SVM classifier is used to the best subset of features that can optimize the SVM classifier. It was concluded that the proposed genetic-SVM with a existing approach performs generally betterl and shown that our proposed method been very powerful for different data sets. The obtained results using the genetic algorithms approach show that the proposed method is able to find an appropriate feature subset and SVM classifier achieves better results than other methods.
iMASKO: A Genetic Algorithm Based Optimization Framework for Wireless Sensor Networks

In this paper we present the design and implementation of a generic GA-based optimization framework iMASKO (iNL@MATLAB Genetic Algorithm-based Sensor NetworK Optimizer) to optimize the performance metrics of wireless sensor networks. Due to the global search property of genetic algorithms, the framework is able to automatically and quickly fine tune hundreds of possible solutions for the given task to find the best suitable tradeoff. We test and evaluate the framework by using it to explore a SystemC-based simulation process to tune the configuration of the unslotted CSMA/CA algorithm of IEEE 802.15.4, aiming to discover the most available tradeoff solutions for the required performance metrics. In particular, in the test cases different sensor node platforms are under investigation. A weighted sum based cost function is used to measure the optimization effectiveness and capability of the framework. In the meantime, another experiment is performed to test the framework’s optimization characteristic in multi-scenario and multi-objectives conditions.
Statistical binary patterns and post-competitive representation for pattern recognition

During the last decade, sparse representations have been successfully applied to design highperforming classification algorithms such as the classical sparse representation based classification (SRC) algorithm. More recently, collaborative representation based classification (CRC) has emerged as a very powerful approach, especially for face recognition. CRC takes advantage of sparse representation based classification through the notion of collaborative representation, relying on the observation that the collaborative property is more crucial for classification than the l1-norm sparsity constraint on coding coefficients used in SRC. This paper follows the same general philosophy of CRC and its main novelty is the application of a virtual collaborative projection (VCP) routine designed to train images of every class against the other classes to improve fidelity before the projection of the query image. We combine this routine with a method of local feature extraction based on high-order statistical moments to further improve the representation. We demonstrate using extensive experiments of face recognition and classification that our approach performs very competitively with respect to state-of-the-art classification methods. For instance, using the AR face dataset, our method reaches 100% of accuracy for dimensionality 300.
Aesthetic labia minora reduction with inferior wedge resection and superior pedicle flap reconstruction.

Aesthetic surgery of female genitalia is an uncommon procedure, and of the techniques available, labia minora reduction can achieve excellent results. Recently, more conservative labia minora reduction techniques have been developed, because the simple isolated strategy of straight amputation does not ensure a favorable outcome. This study was designed to review a series of labia minora reductions using inferior wedge resection and superior pedicle flap reconstruction.



Twenty-one patients underwent inferior wedge resection and superior pedicle flap reconstruction. The mean follow-up was 46 months. Aesthetic results and postoperative outcomes were collected retrospectively and evaluated.



Twenty patients (95.2 percent) underwent bilateral procedures, and 90.4 percent of patients had a congenital labia minora hypertrophy. Five complications occurred in 21 patients (23.8 percent). Wound-healing problems were observed more frequently. The cosmetic result was considered to be good or very good in 85.7 percent of patients, and 95.2 percent were very satisfied with the procedure. All complications except one were observed immediately after the procedure.



The results of this study demonstrate that inferior wedge resection and superior pedicle flap reconstruction is a simple and consistent technique and deserves a place among the main procedures available. The complications observed were not unexpected and did not extend hospital stay or interfere with the normal postoperative period. The success of the procedure depends on patient selection, careful preoperative planning, and adequate intraoperative management.
Iris code matching using adaptive Hamming distance

The most popular metric distance used in iris code matching is Hamming distance. In this paper, we improve the performance of iris code matching stage by applying adaptive Hamming distance. Proposed method works with Hamming subsets with adaptive length. Based on density of masked bits in the Hamming subset, each subset is able to expand and adjoin to the right or left neighbouring bits. The adaptive behaviour of Hamming subsets increases the accuracy of Hamming distance computation and improves the performance of iris code matching. Results of applying proposed method on Chinese Academy of Science Institute of Automation, CASIA V3.3 shows performance of 99.96% and false rejection rate 0.06.
Pair of Replacement Algorithms MFMR and AF-LRU on L1 and L2 Cache for Proxy Server

Access to the internet and WWW is growing extensively, which results in heavy network traffic. To reduce the network traffic proxy server is used. Proxy server reduces the load of server. If the cache replacement algorithm of proxy server’s cache is efficient then proxy server will be helpful to reduce the network traffic in more efficient manner. In this paper we are considering proxy server cache to be Level 1 (L1) cache and storage cache of proxy server to be Level 2 (L2) cache. For collecting the real trace proxy server is used in an organization. Log of proxy server gives the information of various URLs accessed by various clients with time. For performing experiments various URLs were given a numeric identity. This paper analyzes the behavior of various replacement algorithms. The replacement algorithms taken into consideration are Least Recently Used (LRU), Least Frequently Used (LFU), First In First Out (FIFO). This paper proposes a new Replacement Algorithm named MFMR (Most Frequent with Maximum Reusability) for L1 which works about 16% better than existing algorithms considered in this paper. This paper also proposes a new replacement policy for L2 named AF_LRU (Average Frequency, Least Recently Used). Simulation results shows that pair of MFMR and AF-LRU is approximately 28% better than other existing pairs of replacement algorithms considered. ; web access pattern; Replacement Algorithm; Proxy server; client.
Understanding Topic Models in Context: A Mixed-Methods Approach to the Meaningful Analysis of Large Document Collections

In recent years, we have witnessed an unprecedented proliferation of large document collections. This development has spawned the need for appropriate analytical means. In particular, to seize the thematic composition of large document collections, researchers increasingly draw on quantitative topic models. Among their most prominent representatives is the Latent Dirichlet Allocation (LDA). Yet, these models have significant drawbacks, e.g. the generated topics lack context and thus meaningfulness. Prior research has rarely addressed this limitation through the lens of mixed-methods research. We position our paper towards this gap by proposing a structured mixedmethods approach to the meaningful analysis of large document collections. Particularly, we draw on qualitative coding and quantitative hierarchical clustering to validate and enhance topic models through re-contextualization. To illustrate the proposed approach, we conduct a case study of the thematic composition of the AIS Senior Scholars' Basket of Journals.
A Branch-and-Cut Algorithm for the Resolution of Large-Scale Symmetric Traveling Salesman Problems

An algorithm is described for solving large-scale instances of the Symmetric Traveling Salesman Problem (STSP) to optimality. The core of the algorithm is a "polyhedral" cutting-plane procedure that exploits a subset of the system of linear inequalities defining the convex hull of the incidence vectors of the hamiltonian cycles of a complete graph. The cuts are generated by several identification procedures that have been described in a companion paper. Whenever the cutting-plane procedure does not terminate with an optimal solution the algorithm uses a tree-search strategy that, as opposed to branch-and-bound, keeps on producing cuts after branching. The algorithm has been implemented in FORTRAN. Two different linear programming (LP) packages have been used as the LP solver. The implementation of the algorithm and the interface with one of the LP solvers is described in sufficient detail to permit the replication of our experiments. Computational results are reported with up to 42 STSPs with sizes ranging from 48 to 2,392 nodes. Most of the medium-sized test problems are taken from the literature; all others are large-scale real-world problems. All of the instances considered in this study were solved to optimality by the algorithm in "reasonable" computation times.
Comparison of different feature sets for identification of variants in progressive aphasia

We use computational techniques to extract a large number of different features from the narrative speech of individuals with primary progressive aphasia (PPA). We examine several different types of features, including part-of-speech, complexity, context-free grammar, fluency, psycholinguistic, vocabulary richness, and acoustic, and discuss the circumstances under which they can be extracted. We consider the task of training a machine learning classifier to determine whether a participant is a control, or has the fluent or nonfluent variant of PPA. We first evaluate the individual feature sets on their classification accuracy, then perform an ablation study to determine the optimal combination of feature sets. Finally, we rank the features in four practical scenarios: given audio data only, given unsegmented transcripts only, given segmented transcripts only, and given both audio and segmented transcripts. We find that psycholinguistic features are highly discriminative in most cases, and that acoustic, context-free grammar, and part-of-speech features can also be important in some circumstances.