nils-herrmann/clean_scirepeval huggingface dataset:
Guidelines for Adult Stroke Rehabilitation and Recovery.

The aim of this guideline is to provide a synopsis of best clinical practices in the rehabilitative care of adults recovering from stroke. Writing group members were nominated by the committee chair on the basis of their previous work in relevant topic areas and were approved by the American Heart Association (AHA) Stroke Council’s Scientific Statement Oversight Committee and the AHA’s Manuscript Oversight Committee. The panel reviewed relevant articles on adults using computerized searches of the medical literature through 2014. The evidence is organized within the context of the AHA framework and is classified according to the joint AHA/American College of Cardiology and supplementary AHA methods of classifying the level of certainty and the class and level of evidence. The document underwent extensive AHA internal and external peer review, Stroke Council Leadership review, and Scientific Statements Oversight Committee review before consideration and approval by the AHA Science Advisory and Coordinating Committee. Stroke rehabilitation requires a sustained and coordinated effort from a large team, including the patient and his or her goals, family and friends, other caregivers (eg, personal care attendants), physicians, nurses, physical and occupational therapists, speech-language pathologists, recreation therapists, psychologists, nutritionists, social workers, and others. Communication and coordination among these team members are paramount in maximizing the effectiveness and efficiency of rehabilitation and underlie this entire guideline. Without communication and coordination, isolated efforts to rehabilitate the stroke survivor are unlikely to achieve their full potential. Guidelines for Adult Stroke Rehabilitation and Recovery A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association
Yki/YAP, Sd/TEAD and Hth/MEIS Control Tissue Specification in the Drosophila Eye Disc Epithelium

During animal development, accurate control of tissue specification and growth are critical to generate organisms of reproducible shape and size. The eye-antennal disc epithelium of Drosophila is a powerful model system to identify the signaling pathway and transcription factors that mediate and coordinate these processes. We show here that the Yorkie (Yki) pathway plays a major role in tissue specification within the developing fly eye disc epithelium at a time when organ primordia and regional identity domains are specified. RNAi-mediated inactivation of Yki, or its partner Scalloped (Sd), or increased activity of the upstream negative regulators of Yki cause a dramatic reorganization of the eye disc fate map leading to specification of the entire disc epithelium into retina. On the contrary, constitutive expression of Yki suppresses eye formation in a Sd-dependent fashion. We also show that knockdown of the transcription factor Homothorax (Hth), known to partner Yki in some developmental contexts, also induces an ectopic retina domain, that Yki and Scalloped regulate Hth expression, and that the gain-of-function activity of Yki is partially dependent on Hth. Our results support a critical role for Yki- and its partners Sd and Hth--in shaping the fate map of the eye epithelium independently of its universal role as a regulator of proliferation and survival.
Dreaming without REM sleep

To test whether mental activities collected from non-REM sleep are influenced by REM sleep, we suppressed REM sleep using clomipramine 50mg (an antidepressant) or placebo in the evening, in a double blind cross-over design, in 11 healthy young men. Subjects were awakened every hour and asked about their mental activity. The marked (81%, range 39-98%) REM-sleep suppression induced by clomipramine did not substantially affect any aspects of dream recall (report length, complexity, bizarreness, pleasantness and self-perception of dream or thought-like mentation). Since long, complex and bizarre dreams persist even after suppressing REM sleep either partially or totally, it suggests that the generation of mental activity during sleep is independent of sleep stage.
A simulation study on air traffic control strategies

Air Traffic Control (ATC) plays a crucial role in the modern air transportation system. As a decentralized system, every control sector in the ATC network system needs to use all sorts of available information to manage local air traffic in a safe, smooth and cost-efficient way. A key issue is: how each individual ATC sector should use global traffic information to make local ATC decisions, such that the global air traffic, not just the local, can be improved. This paper reports a simulation study on ATC strategies aiming to address the above issue. The coming-in traffic to sectors is the focus, and the ATC strategy means how to define and apply various local ATC rules, such as first-come-first-served rule, to the coming-in traffic according to the global traffic information. A simplified ATC network model is set up and a software simulation system is then developed. The simulation results reveal that, even for a same set of ATC rules, a bad strategy of applying them can cause heavy traffic congestion, while a good strategy can significantly reduce delays, improve safety, and increase efficiency of using airspace.
Simulating alteration on fingerprint images

Fingerprint alteration represents one of the newest challenges in biometric identification. The aim of fingerprint mutilation is to destroy the structure of the papillary ridges so that the identity of the offender cannot be recognized by the biometric system. The problem has received little attention and there is a lack of a real world altered fingerprints database that would allow researchers to develop new algorithms and techniques for altered fingerprints detection. The major contribution of this paper is that it provides a new public database of synthetically altered fingerprints. Starting from the cases described in the literature, three methods for generating simulated altered fingerprints are proposed.
The efficacy of rebamipide add-on therapy in arthritic patients with COX-2 selective inhibitor-related gastrointestinal events: a prospective, randomized, open-label blinded-endpoint pilot study by the GLORIA study group.

We aimed to confirm the effect of combined treatment with celecoxib and rebamipide would be more effective than celecoxib alone for prevention of upper gastrointestinal (GI) events.



Patients with rheumatoid arthritis, osteoarthritis, and low back pain were enrolled in this study. Patients were randomized to two groups: a monotherapy group (100 mg celecoxib twice daily) and a combination therapy group (add on 100 mg of rebamipide three times a day). The GI mucosal injury was evaluated by endoscopic examination before treatment and at 3 months. The primary endpoint was to evaluate the preventive effect of the combination therapy group for GI events, endoscopic upper GI ulcers and intolerable GI symptoms, compared with the monotherapy group.



Seventy-five patients were enrolled. Sixty-five patients were analyzed (16 males, 49 females; mean age: 67 ± 13 years). The prevalence of upper GI events, five of endoscopic GI ulcers and one of intolerable GI symptoms, were 6/34 (17.6%) in the monotherapy group and 0/31 in the combination therapy group, p = 0.0252.



The combination therapy group was more effective than the monotherapy group for prevention of upper GI events in this study. Rebamipide might be a candidate for an option to prevent COX-2 selective inhibitor-induced upper GI events.
Integrating Palmprint and Fingerprint for Identity Verification

In this paper, we propose a multimodal biometrics system that combines fingerprint and palmprint features to overcome several limitations of unimodal biometrics—such as the inability to tolerate noise, distorted data and etc.—and thus able to improve the performance of biometrics for personal verification. The quality of fingerprint and palmprint images are first enhanced using a series of pre-processing techniques. Following, a bank of 2D Gabor filters is used to independently extract fingerprint and palmprint features, which are then concatenated into a single feature vector. We conclude that the proposed methodology has better performance and is more reliable compared to unimodal approaches using solely fingerprint or palmprint biometrics. This is supported by our experiments which are able to achieve equal error rate (EER) as low as 0.91% using the combined biometrics features.
A COMPARISON of the DEVELOPMENTAL EYE MOVEMENT TEST ( DEM ) and a MODIFIED VERSION of the ADULT DEVELOPMENTAL EYE MOVEMENT TEST ( ADEM ) with OLDER ADULTS

The Developmental Eye Movement Test (DEM) is a standardized test for evaluating saccadic eye movements in children. An adult version, the Adult Developmental Eye Movement Test (A-DEM), was recently developed for Spanish-speaking adults ages 14 to 68. No version yet exists for adults over the age of 68 and normative studies for English-speaking adults are absent. However, it is not clear if the single-digit format of the DEM or the double-digit A-DEM format should be used for further test develop-
A bit-line leakage compensation scheme for low-voltage SRAM's

The bit-line leakage current of an SRAM, induced by transistor leakage at low V/sub DD/ and dependent on cell data associated with the bit-line, is detected in a pre-charge cycle and compensated for during a read/write cycle. By this scheme, V/sub th/ can be lowered to 0.23 V/sub DD/ in a 0.07 /spl mu/m/1.0 V CMOS, as it was before, keeping V/sub th/ and delay scalability of the high-speed SRAM.
From t-Closeness-Like Privacy to Postrandomization via Information Theory

t-Closeness is a privacy model recently defined for data anonymization. A data set is said to satisfy t-closeness if, for each group of records sharing a combination of key attributes, the distance between the distribution of a confidential attribute in the group and the distribution of the attribute in the entire data set is no more than a threshold t. Here, we define a privacy measure in terms of information theory, similar to t-closeness. Then, we use the tools of that theory to show that our privacy measure can be achieved by the postrandomization method (PRAM) for masking in the discrete case, and by a form of noise addition in the general case.
Cutaneous listeriosis.

Cutaneous infections due to Listeria monocytogenes are rare. Typically, infections manifest as nonpainful, nonpruritic, self-limited, localized, papulopustular or vesiculopustular eruptions in healthy persons. Most cases follow direct inoculation of the skin in veterinarians or farmers who have exposure to animal products of conception. Less commonly, skin lesions may arise from hematogenous dissemination in compromised hosts with invasive disease. Here, we report the first case in a gardener that occurred following exposure to soil and vegetation.
Microsoft azure SQL database telemetry

Microsoft operates the Azure SQL Database (ASD) cloud service, one of the dominant relational cloud database services in the market today. To aid the academic community in their research on designing and efficiently operating cloud database services, Microsoft is introducing the release of production-level telemetry traces from the ASD service. This telemetry data set provides, over a wide set of important hardware resources and counters, the consumption level of each customer database replica. The first release will be a multi-month time-series data set that includes the full cluster traces from two different ASD global regions.
USE OF INDIGENOUSLY DESIGNED NASAL BUBBLE CONTINUOUS POSITIVE AIRWAY PRESSURE (NB-CPAP) IN NEONATES WITH RESPIRATORY DISTRESS - EXPERIENCE FROM A MILITARY HOSPITAL -

To study the efficacy and safety of an indigenously designed low cost nasal bubble continuous positive airway pressure (NB-CPAP) in neonates admitted with respiratory distress.  A descriptive study. Place and Duration of Study: Combined Military Hospital (CMH), Peshawar from Jan 2014 to May 2014.  Fifty neonates who developed respiratory distress within 6 hours of life were placed on an indigenous NB-CPAP device (costing 220 PKR) and evaluated for gestational age, weight, indications, duration on NB-CPAP, pre-defined outcomes and complications.  A total of 50 consecutive patients with respiratory distress were placed on NB-CPAP. Male to Female ratio was 2.3:1. Mean weight was 2365.85 ± 704 grams and mean gestational age was 35.41 ± 2.9 weeks. Indications for applying NB-CPAP were transient tachypnea of the newborn (TTN, 52%) and respiratory distress syndrome (RDS, 44%). Most common complications were abdominal distension (15.6%) and pulmonary hemorrhage (6%). Out of 50 infants placed on NB-CPAP, 35 (70%) were managed on NB-CPAP alone while 15 (30%) needed mechanical ventilation following a trial of NB-CPAP.  In 70% of babies invasive mechanical ventilation was avoided using NB-CPAP.
Considering Race a Problem of Transfer Learning

As biometric applications are fielded to serve large population groups, issues of performance differences between individual sub-groups are becoming increasingly important. In this paper we examine cases where we believe race is one such factor. We look in particular at two forms of problem; facial classification and image synthesis. We take the novel approach of considering race as a boundary for transfer learning in both the task (facial classification) and the domain (synthesis over distinct datasets). We demonstrate a series of techniques to improve transfer learning of facial classification; outperforming similar models trained in the target's own domain. We conduct a study to evaluate the performance drop of Generative Adversarial Networks trained to conduct image synthesis, in this process, we produce a new annotation for the Celeb-A dataset by race. These networks are trained solely on one race and tested on another - demonstrating the subsets of the CelebA to be distinct domains for this task.
Explorers Zoo: An Application Model of Global Positioning System A Case Study in Ragunan Zoo

Visitors in Ragunan Zoo, often difficulties when trying to look for animals that want to visit. This difficulty will not happen if there is android -based mobile application that can guide visitors. Global Positioning System application such as Google Maps or GPS is used as an application that can inform our position on earth. Applications that are created not just to know "where we are" but has moved toward a more advanced system that can exploit the information for the convenience of users. GPS applications has been transformed into a pleasant traveling companion. Moreover, when visiting a city or a place that has never yet been visited, GPS can easily map a place that can drive well, so do not worry about getting lost in the city. Based on the idea of using GPS applications, create GPS application that can show the way and mapping of animal cages as well as information about the knowledge of each animal. This application is made to overcome the problems to occur and to further increase the number of visitors Ragunan Zoo.
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems.
A Local Clustering Algorithm for Massive Graphs and Its Application to Nearly Linear Time Graph Partitioning

We study the design of local algorithms for massive graphs. A local graph algorithm is one that finds a solution containing or near a given vertex without looking at the whole graph. We present a local clustering algorithm. Our algorithm finds a good cluster—a subset of vertices whose internal connections are significantly richer than its external connections—near a given vertex. The running time of our algorithm, when it finds a nonempty local cluster, is nearly linear in the size of the cluster it outputs. The running time of our algorithm also depends polylogarithmically on the size of the graph and polynomially on the conductance of the cluster it produces. Our clustering algorithm could be a useful primitive for handling massive graphs, such as social networks and webgraphs. As an application of this clustering algorithm, we present a partitioning algorithm that finds an approximate sparsest cut with nearly optimal balance. Our algorithm takes time nearly linear in the number edges of the graph. Using the partitioning algorithm of this paper, we have designed a nearly linear time algorithm for constructing spectral sparsifiers of graphs, which we in turn use in a nearly linear time algorithm for solving linear systems in symmetric, diagonally dominant matrices. The linear system solver also leads to a nearly linear time algorithm for approximating the secondsmallest eigenvalue and corresponding eigenvector of the Laplacian matrix of a graph. These other results are presented in two companion papers.
3 D Point Cloud Object Detection with MultiView Convolutional Neural Network

Efficient detection of three dimensional (3D) objects in point clouds is a challenging problem. Performing 3D descriptor matching or 3D scanning-window search with detector are both time-consuming due to the 3-dimensional complexity. One solution is to project 3D point cloud into 2D images and thus transform the 3D detection problem into 2D space, but projection at multiple viewpoints and rotations produce a large amount of 2D detection tasks, which limit the performance and complexity of the 2D detection algorithm choice. We propose to use convolutional neural network (CNN) for the 2D detection task, because it can handle all viewpoints and rotations for the same class of object together, as well as predicting multiple classes of objects with the same network, without the need for individual detector for each object class. We further improve the detection efficiency by concatenating two extra levels of early rejection networks with binary outputs before the multi-class detection network. Experiments show that our method has competitive overall performance with at least one-order of magnitude speedup comparing with latest 3D point cloud detection methods.
Coupled level set segmentation using a point-based statistical shape model relying on correspondence probabilities

In order to successfully perform automatic segmentation in medical images containing noise and intensity inhomogeneities, modern algorithms often rely on a priori knowledge about the respective anatomy. This is often introduced by statistical shape models (SSMs) which are typically based on one-to-one point correspondences. In this work, we propose a unified statistical framework for image segmentation with shape prior information. The shape prior is an explicitely represented probabilistic SSM based on point correspondence probabilities, and the segmentation contour is implicitly represented by the zero level set of a higher dimensional surface. These two aspects are unified in a Maximum a Posteriori (MAP) estimation where the level set is evolved to converge towards the boundary of the organ to be segmented based on the image information while taking into account the prior given by the SSM information. The optimization of the MAP formulation leads to an alternate update of the level set and an update of the fitting of the SSM. We demonstrate the efficiency of our new algorithm with soft tissue segmentation where adaptive weights ensure that the SSM constraint is optimally exploited. Our experimental results show the well-posedness of the approach on noisy kidney CT data impaired by breathing artefacts.
Boosting the Robustness Verification of DNN by Identifying the Achilles's Heel

Deep Neural Network (DNN) is a widely used deep learning technique. How to ensure the safety of DNN-based system is a critical problem for the research and application of DNN. Robustness is an important safety property of DNN. However, existing work of verifying DNN’s robustness is timeconsuming and hard to scale to large-scale DNNs. In this paper, we propose a boosting method for DNN robustness verification, aiming to find counter-examples earlier. Our observation is DNN’s different inputs have different possibilities of existing counter-examples around them, and the input with a small difference between the largest output value and the second largest output value tends to be the achilles’s heel of the DNN. We have implemented our method and applied it on Reluplex, a state-ofthe-art DNN verification tool, and four DNN attacking methods. The results of the extensive experiments on two benchmarks indicate the effectiveness of our boosting method.
An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN

Polyp has long been considered as one of the major etiologies to colorectal cancer which is a fatal disease around the world, thus early detection and recognition of polyps plays an crucial role in clinical routines. Accurate diagnoses of polyps through endoscopes operated by physicians becomes a chanllenging task not only due to the varying expertise of physicians, but also the inherent nature of endoscopic inspections. To facilitate this process, computer-aid techniques that emphasize on fully-conventional image processing and novel machine learning enhanced approaches have been dedicatedly designed for polyp detection in endoscopic videos or images. Among all proposed algorithms, deep learning based methods take the lead in terms of multiple metrics in evolutions for algorithmic performance. In this work, a highly effective model, namely the faster region-based convolutional neural network (Faster R-CNN) is implemented for polyp detection. In comparison with the reported results of the state-of-the-art approaches on polyps detection, extensive experiments demonstrate that the Faster R-CNN achieves very competing results, and it is an efficient approach for clinical practice.