nils-herrmann/clean_scirepeval huggingface dataset:
Improving emergency department patient flow

Emergency departments (ED) face significant challenges in delivering high quality and timely patient care on an ever-present background of increasing patient numbers and limited hospital resources. A mismatch between patient demand and the ED's capacity to deliver care often leads to poor patient flow and departmental crowding. These are associated with reduction in the quality of the care delivered and poor patient outcomes. A literature review was performed to identify evidence-based strategies to reduce the amount of time patients spend in the ED in order to improve patient flow and reduce crowding in the ED. The use of doctor triage, rapid assessment, streaming and the co-location of a primary care clinician in the ED have all been shown to improve patient flow. In addition, when used effectively point of care testing has been shown to reduce patient time in the ED. Patient flow and departmental crowding can be improved by implementing new patterns of working and introducing new technologies such as point of care testing in the ED.
A Combined Interface and Border Trap Model for High-Mobility Substrate Metal–Oxide–Semiconductor Devices Applied to $\hbox{In}_{0.53} \hbox{Ga}_{0.47}\hbox{As}$ and InP Capacitors

By taking into account simultaneously the effects of border traps and interface states, the authors model the alternating current capacitance-voltage (C-V) behavior of high-mobility substrate metal-oxide-semiconductor (MOS) capacitors. The results are validated with the experimental In0.53Ga0.47As/ high-κ and InP/high-κ (C-V) curves. The simulated C-V and conductance-voltage (G-V) curves reproduce comprehensively the experimentally measured capacitance and conductance data as a function of bias voltage and measurement frequency, over the full bias range going from accumulation to inversion and full frequency spectra from 100 Hz to 1 MHz. The interface state densities of In0.53Ga0.47As and InP MOS devices with various high-κ dielectrics, together with the corresponding border trap density inside the high-κ oxide, were derived accordingly. The derived interface state densities are consistent to those previously obtained with other measurement methods. The border traps, distributed over the thickness of the high- κ oxide, show a large peak density above the two semiconductor conduction band minima. The total density of border traps extracted is on the order of 1019 cm-3. Interface and border trap distributions for InP and In0.53Ga0.47As interfaces with high-κ oxides show remarkable similarities on an energy scale relative to the vacuum reference.
A "Greedy" Channel Router

We present a new, "greedy", channel-router that is quick, simple, and highly effective. It always succeeds, usually using no more than one track more than required by channel density. (It may be forced in rare cases to make a few connections "off the end" of the channel, in order to succeed.) It assumes that all pins and wiring lie on a common grid, and that vertical wires are on one layer, horizontal on another. The greedy router wires up the channel in a left-to-right, column-by-column manner, wiring each column completely before starting the next. Within each column the router tries to maximize the utility of the wiring produced, using simple, "greedy" heuristics. It may place a net on more than one track for a few columns, and "collapse" the net to a single track later on, using a vertical jog. It may also use a jog to move a net to a track closer to its pin in some future column. The router may occasionally add a new track to the channel, to avoid "getting stuck".
Trends in Design of Ransomware Viruses

The ransomware nightmare is taking over the internet impacting common users, small businesses and large ones. The interest and investment which are pushed into this market each month, tells us a few things about the evolution of both technical and social engineering and what to expect in the short-coming future from them. In this paper we analyze how ransomware programs developed in the last few years and how they were released in certain market segments throughout the deep web via RaaS, exploits or SPAM, while learning from their own mistakes to bring profit to the next level. We will also try to highlight some mistakes that were made, which allowed recovering the encrypted data, along with the ransomware authors preference for specific encryption types, how they got to distribute, the silent agreement between ransomwares, coin-miners and bot-nets and some edge cases of encryption, which may prove to be exploitable in the short-coming future.
Sézary syndrome: immunopathogenesis, literature review of therapeutic options, and recommendations for therapy by the United States Cutaneous Lymphoma Consortium (USCLC).

Sézary syndrome (SS) has a poor prognosis and few guidelines for optimizing therapy. The US Cutaneous Lymphoma Consortium, to improve clinical care of patients with SS and encourage controlled clinical trials of promising treatments, undertook a review of the published literature on therapeutic options for SS. An overview of the immunopathogenesis and standardized review of potential current treatment options for SS including metabolism, mechanism of action, overall efficacy in mycosis fungoides and SS, and common or concerning adverse effects is first discussed. The specific efficacy of each treatment for SS, both as monotherapy and combination therapy, is then reported using standardized criteria for both SS and response to therapy with the type of study defined by a modification of the US Preventive Services guidelines for evidence-based medicine. Finally, guidelines for the treatment of SS and suggestions for adjuvant treatment are noted.
The 2016 revision of the World Health Organization classification of lymphoid neoplasms.

A revision of the nearly 8-year-old World Health Organization classification of the lymphoid neoplasms and the accompanying monograph is being published. It reflects a consensus among hematopathologists, geneticists, and clinicians regarding both updates to current entities as well as the addition of a limited number of new provisional entities. The revision clarifies the diagnosis and management of lesions at the very early stages of lymphomagenesis, refines the diagnostic criteria for some entities, details the expanding genetic/molecular landscape of numerous lymphoid neoplasms and their clinical correlates, and refers to investigations leading to more targeted therapeutic strategies. The major changes are reviewed with an emphasis on the most important advances in our understanding that impact our diagnostic approach, clinical expectations, and therapeutic strategies for the lymphoid neoplasms.
Using colour features of cv. ‘Gala’ apple fruits in an orchard in image processing to predict yield

New apple fruit recognition algorithms based on colour features are presented to estimate the number of fruits and develop models for early prediction of apple yield, in a multi-disciplinary approach linking computer science with agricultural engineering and horticulture as part of precision agriculture. Fifty cv. ‘Gala’ apple digital images were captured twice, i.e. after June drop and during ripening, on the preferred western side of the tree row with a variability of between 70 and 170 fruit per tree, under natural daylight conditions at Bonn, Germany. Several image processing algorithms and fruit counting algorithms were used to analyse the apple images. Finally, an apple recognition algorithm with colour difference R − B (red minus blue) and G − R (green minus red) was developed for apple images after June drop, and two different colour models were used to segment ripening period apple images. The algorithm was tested on 50 images of trees in each period. Close correlation coefficients R 2 of 0.80 and 0.85 were obtained for two developmental periods between apples detected by the fruit counting algorithm and those manually counted. Two sets of data in each period were used for modelling yield prediction of the apple fruits. In the calibration data set, the R 2 values between apples detected by the fruit counting algorithm and actual harvested yield were from 0.57 for young fruit after June drop to 0.70 in the fruit ripening period. In the validation data set, the R 2 value between the number of apples predicted by the model and actual yield at harvest ranged from 0.58 to 0.71. The proposed model showed great potential for early prediction of yield for individual trees of apple and possibly other fruit crops.
Preserving Communities in Anonymized Social Networks

Social media and social networks are embedded in our society to a point that could not have been imagined only ten years ago. Facebook, LinkedIn, and Twitter are already well known social networks that have a large audience in all age groups. The amount of data that those social sites gather from their users is continually increasing and this data is very valuable for marketing, research, and various other purposes. At the same time, this data usually contain a significant amount of sensitive information which should be protected against unauthorized disclosure. To protect the privacy of individuals, this data must be anonymized such that the risk of re-identification of specific individuals is very low. In this paper we study if anonymized social networks preserve existing communities from the original social networks. To perform this study, we introduce two approaches to measure the community preservation between the initial network and its anonymized version. In the first approach we simply count how many nodes from the original communities remained in the same community after the processes of anonymization and de-anonymization. In the second approach we consider the community preservation for each node individually. Specifically, for each node, we compare the original and final communities to which the node belongs. To anonymize social networks we use two models, namely, k-anonymity for social networks and k-degree anonymity. To determine communities in social networks we use an existing community detection algorithm based on modularity quality function. Our experiments on publically available datasets show that anonymized social networks satisfactorily preserve the community structure of their original networks. 56 Alina Campan, Yasmeen Alufaisan, Traian Marius Truta TRANSACTIONS ON DATA PRIVACY 8 (2015)
Agar: A Caching System for Erasure-Coded Data

Erasure coding is an established data protection mechanism. It provides high resiliency with low storage overhead, which makes it very attractive to storage systems developers. Unfortunately, when used in a distributed setting, erasure coding hampers a storage system's performance, because it requires clients to contact several, possibly remote sites to retrieve their data. This has hindered the adoption of erasure coding in practice, limiting its use to cold, archival data. Recent research showed that it is feasible to use erasure coding for hot data as well, thus opening new perspectives for improving erasure-coded storage systems. In this paper, we address the problem of minimizing access latency in erasure-coded storage. We propose Agar-a novel caching system tailored for erasure-coded content. Agar optimizes the contents of the cache based on live information regarding data popularity and access latency to different data storage sites. Our system adapts a dynamic programming algorithm to optimize the choice of data blocks that are cached, using an approach akin to "Knapsack" algorithms. We compare Agar to the classical Least Recently Used and Least Frequently Used cache eviction policies, while varying the amount of data cached between a data chunk and a whole replica of the object. We show that Agar can achieve 16% to 41% lower latency than systems that use classical caching policies.
Curing profile of bulk-fill resin-based composites.

To evaluate the curing profile of bulk-fill resin-based composites (RBC) using micro-Raman spectroscopy (μRaman).



Four bulk-fill RBCs were compared to a conventional RBC. RBC blocks were light-cured using a polywave LED light-curing unit. The 24-h degree of conversion (DC) was mapped along a longitudinal cross-section using μRaman. Curing profiles were constructed and 'effective' (>90% of maximum DC) curing parameters were calculated. A statistical linear mixed effects model was constructed to analyze the relative effect of the different curing parameters.



Curing efficiency differed widely with the flowable bulk-fill RBCs presenting a significantly larger 'effective' curing area than the fibre-reinforced RBC, which on its turn revealed a significantly larger 'effective' curing area than the full-depth bulk-fill and conventional (control) RBC. A decrease in 'effective' curing depth within the light beam was found in the same order. Only the flowable bulk-fill RBCs were able to cure 'effectively' at a 4-mm depth for the whole specimen width (up to 4mm outside the light beam). All curing parameters were found to statistically influence the statistical model and thus the curing profile, except for the beam inhomogeneity (regarding the position of the 410-nm versus that of 470-nm LEDs) that did not significantly affect the model for all RBCs tested.



Most of the bulk-fill RBCs could be cured up to at least a 4-mm depth, thereby validating the respective manufacturer's recommendations.



According to the curing profiles, the orientation and position of the light guide is less critical for the bulk-fill RBCs than for the conventional RBC.
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network

With more and more household objects built on planned obsolescence and consumed by a fast-growing population, hazardous waste recycling has become a critical challenge. Given the large variability of household waste, current recycling platforms mostly rely on human operators to analyze the scene, typically composed of many object instances piled up in bulk. Helping them by robotizing the unitary extraction is a key challenge to speed up this tedious process. Whereas supervised deep learning has proven very efficient for such object-level scene understanding, e.g ., generic object detection and segmentation in everyday scenes, it however requires large sets of per-pixel labeled images, that are hardly available for numerous application contexts, including industrial robotics. We thus propose a step towards a practical interactive application for generating an object-oriented robotic grasp, requiring as inputs only one depth map of the scene and one user click on the next object to extract. More precisely, we address in this paper the middle issue of object segmentation in top views of piles of bulk objects given a pixel location, namely seed, provided interactively by a human operator. We propose a two-fold framework for generating edge-driven instance segments. First, we repurpose a state-of-the-art fully convolutional object contour detector for seed-based instance segmentation by introducing the notion of edge-mask duality with a novel patch-free and contour-oriented loss function. Second, we train one model using only synthetic scenes, instead of manually labeled training data. Our experimental results show that considering edge-mask duality for training an encoder-decoder network, as we suggest, outperforms a state-of-the-art patch-based network in the present application context.
Real time streaming pattern detection for eCommerce

Pattern detection over streams of events is gaining more and more attention, especially in the field of eCommerce. Our industrial partner Cdiscount, which is one of the largest eCommerce companies in France, wants to use pattern detection for real-time customer behavior analysis. The main challenges to consider are efficiency and scalability, as the detection of customer behavior must be achieved within a few seconds, while millions of unique customers visit the website every day, each performing hundreds of actions. In this paper, we present our approach to large-scale and efficient pattern detection for eCommerce. It relies on a domain-specific language to define behavior patterns. Patterns are then compiled into deterministic finite automata, which are run on a Big Data streaming platform to carry out the detection work. Our evaluation shows that our approach is efficient and scalable, and fits the requirements of Cdiscount.
Fiberoptic bronchoscopy guidelines for the anesthesiologist

Fiberoptic bronchoscopy is a widely performed procedure that is generally considered to be safe. The first performed bronchoscopy was done by Gustav Killian in 1897; however, the development of flexible fiberoptic bronchoscopy was accomplished by Ikeda in 1964(1). Flexible fiberoptic bronchoscopy is a key diagnostic and therapeutic procedure(2). It is estimated that more than 500,000 of these procedures are performed each year by pulmonologists, otolaryngologists, anesthesiologists, and cardiothoracic and trauma surgeons(3). Despite the widespread practice of diagnostic flexible bronchoscopy, there are no firm guidelines that assure a uniform acquisition of basic skills and competency in this procedure, nor are there guidelines to ensure uniform training and competency in advanced diagnostic flexible bronchoscopic techniques(4). The purpose of this review is to provide an update on 1) tracheobronchial anatomy, 2) flexible fiberoptic bronchoscopy exam, 3) training and competence on fiberoptic bronchoscopy, and 4) application of flexible fiberoptic bronchoscopy in thoracic anesthesia.
Representation Benefits of Deep Feedforward Networks

This note provides a family of classification problems, indexed by a positive integer k, where all shallow networks with fewer than exponentially (in k) many nodes exhibit error at least 1/3, whereas a deep network with 2 nodes in each of 2k layers achieves zero error, as does a recurrent network with 3 distinct nodes iterated k times. The proof is elementary, and the networks are standard feedforward networks with ReLU (Rectified Linear Unit) nonlinearities.
Exploring the relationship between IT governance practices and business/IT alignment through extreme case analysis in Belgian mid-to-large size financial enterprises

Many thought leaders are promoting information technology (IT) governance and its supporting practices as an approach to improve business/IT alignment. This paper aims to further explore this assumed positive relationship between IT governance practices and business/IT alignment.  This paper explores the relationship between the use of IT governance practices and business/IT alignment, by creating a business/IT alignment maturity benchmark and qualitatively comparing the use of IT governance practices in the extreme cases.  The main conclusion of the research is that all extreme case organisations are leveraging a broad set of IT governance practices, and that IT governance practices need to obtain at least a maturity level 2 (on a scale of 5) to positively influence business/IT alignment. Also, a list of 11 key enabling IT governance practices is identified.  This research adheres to the process theory, implying a limited definition of prediction. An important opportunity for future research lies in the domain of complementary statistical correlation research.  This research identifies key IT governance practices that organisations can leverage to improve business/IT alignment.  This research contributes to new theory building in the IT governance and alignment domain and provides practitioners with insight on how to implement IT governance in their organisations.
Heart rate variability (HRV) in deep breathing tests and 5-min short-term recordings: agreement of ear photoplethysmography with ECG measurements, in 343 subjects

We analyzed heart rate variability (HRV) taken by ECG and photoplethysmography (PPG) to assess their agreement. We also analyzed the sensitivity and specificity of PPG to identify subjects with low HRV as an example of its potential use for clinical applications. The HRV parameters: mean heart rate (HR), amplitude, and ratio of heart rate oscillation (E–I difference, E/I ratio), RMSSD, SDNN, and Power LF, were measured during 1-min deep breathing tests (DBT) in 343 individuals, followed by a 5-min short-term HRV (s-HRV), where the HRV parameters: HR, SD1, SD2, SDNN, Stress Index, Power HF, Power LF, Power VLF, and Total Power, were determined as well. Parameters were compared through correlation analysis and agreement analysis by Bland–Altman plots. PPG derived parameters HR and SD2 in s-HRV showed better agreement than SD1, Power HF, and stress index, whereas in DBT HR, E/I ratio and SDNN were superior to Power LF and RMSSD. DBT yielded stronger agreement than s-HRV. A slight overestimation of PPG HRV over HCG HRV was found. HR, Total Power, and SD2 in the s-HRV, HR, Power LF, and SDNN in the DBT showed high sensitivity and specificity to detect individuals with poor HRV. Cutoff percentiles are given for the future development of PPG-based devices. HRV measured by PPG shows good agreement with ECG HRV when appropriate parameters are used, and PPG-based devices can be employed as an easy screening tool to detect individuals with poor HRV, especially in the 1-min DBT test.
Cost sensitive modeling of credit card fraud using neural network strategy

Due to the rapid growth in e-business and electronic payment systems, Fraud is rising in banking transactions associated with credit cards. This paper intends to develop a credit card fraud detection (CCFD) model based on Artificial Neural Networks (ANN) and Meta Cost procedure to reduce risk reputation and risk of loss. ANN strategy have been used for credit card fraud prevention and detection. Because of the unbalanced nature of the data (Fraud and Non-Fraud cases), the detection of fraudulent transactions is difficult to achieve. To deal with the problem of imbalanced data, Meta Cost procedure is added. The proposed model, which is called Cost Sensitive Neural Network (CSNN), is based on misuse detection approach. Compared to the model based on Artificial Immune System (AIS), this model showed cost saving and increased detection rate. Data of this study is taken from real transactional data provided by a big Brazilian credit card issuer.
Catheter Ablation of Ventricular Tachycardia in Patients With MitraClip Device: Preliminary Findings.

Patients with mitral regurgitation are increasingly treated by percutaneous implantation of a MitraClip device (Abbott Park, IL, USA). We investigate the feasibility and safety of the transmitral catheter route for catheter ablation of ventricular tachycardia (VT) in these patients.



The mitral valve with the MitraClip in situ was crossed under transesophageal 3-dimensional echocardiographic and fluoroscopic guidance using a steerable sheath for ablation of the left ventricle.



Five patients (all males, median age 74.0 ± 16.0 years) who had previously a MitraClip implanted were referred for catheter ablation of VT. The left ventricular ejection fraction was 29.0% ± 24.0%. One patient had both an atrial septal defect and a left atrial appendage occluder device in addition to a MitraClip. The duration between MitraClip implantation and ablation was 1019.0 ± 783.0 days. After transseptal puncture, ablation catheter was successfully steered through the mitral valve with the use of fluoroscopy. A complete high-density map of the substrate in sinus rhythm could be obtained in all patients using multipolar mapping catheters. In 1 patient, mapping was carried out using a mini-basket catheter. Procedural endpoints, noninducibility of all VTs, and abolition of all late potentials were achieved in all patients. Procedure time was 255.0 ± 52.5 minute, fluoroscopy time was 23.0 ± 7.3, and the radiation dose was 61.0 ± 37.5 Gycm2 . No mitral insufficiency or worsening of regurgitation was documented after the procedure.



This is the first report demonstrating the feasibility and safety of VT ablation in patients with a MitraClip device using the anterograde transmitral catheter route.