nils-herrmann/clean_scirepeval huggingface dataset:
Nomogram of nasal bone length at 11-14 week of gestation in Indian women and their follow up till delivery

The measurement of nasal bone length (NBL) according to gestational age and setting nomogram helps to ascertain NB hypoplasia. This can be used for early detection of trisomy 21 and other chromosomal abnormalities in low risk population. The objective of the study was to obtain nomogram of fetal nasal bone length at 11-14 weeks gestation with known prognosis pregnancies.  A single centre, prospective crosses sectional study. Nasal bone length of 323 choromosomal and structural normal fetuses at 11-14 weeks gestation were measured by transabdominal ultrasonography, prospectively. The correlation between nasal bone and other biometric parameters was assessed by regression analysis and average±SD of nasal bone lengths were measured.  Fetal NB length (NBL) was visualized and measured in 281 patients (94.29%). Mean NB length was 3.07 mm, ranged between 1.60 mm to 4.6 mm and SD was 0.534. A linear growth pattern was observed between nasal bone and fetal biometric parameters. The significant correlation were found between the nasal bone and biparietal diameter (BPD) [NBL= 2.184+0.046 x BPD (r= 0.286, p value 0.000)], femoral length (FL) [NBL= 2.234+0.095xFL (r= 0.369, p value 0.000)] and crown-rump length (CRL) [NBL= 2.205+0.014 x CRL (r= 0.291, p value 0.000)]. Prenasal thickness (PT) increased linearly with CRL and POG. [PT= 1.081+0.006 x CRL (r= 0.16, p value= 0.02) and PT= 0.430+0.08 x POG (r= 0.15, p value= 0.028)].  Measurement of the nasal bone length at 11-14 weeks of gestation shows a linear growth pattern according to other biometric parameters of fetuses. Nomogram of nasal bone length was performed for early detection of trisomy 21 and other chromosomal abnormalities at low risk population.
Outcomes of rigid night splinting and activity modification in the treatment of cubital tunnel syndrome.

To prospectively analyze, using validated outcome measures, symptom improvement in patients with mild to moderate cubital tunnel syndrome treated with rigid night splinting and activity modifications.



Nineteen patients (25 extremities) were enrolled prospectively between August 2009 and January 2011 following a diagnosis of idiopathic cubital tunnel syndrome. Patients were treated with activity modifications as well as a 3-month course of rigid night splinting maintaining 45° of elbow flexion. Treatment failure was defined as progression to operative management. Outcome measures included patient-reported splinting compliance as well as the Quick Disabilities of the Arm, Shoulder, and Hand questionnaire and the Short Form-12. Follow-up included a standardized physical examination. Subgroup analysis included an examination of the association between splinting success and ulnar nerve hypermobility.



Twenty-four of 25 extremities were available at mean follow-up of 2 years (range, 15-32 mo). Twenty-one of 24 (88%) extremities were successfully treated without surgery. We observed a high compliance rate with the splinting protocol during the 3-month treatment period. Quick Disabilities of the Arm, Shoulder, and Hand scores improved significantly from 29 to 11, Short Form-12 physical component summary score improved significantly from 45 to 54, and Short Form-12 mental component summary score improved significantly from 54 to 62. Average grip strength increased significantly from 32 kg to 35 kg, and ulnar nerve provocative testing resolved in 82% of patients available for follow-up examination.



Rigid night splinting when combined with activity modification appears to be a successful, well-tolerated, and durable treatment modality in the management of cubital tunnel syndrome. We recommend that patients presenting with mild to moderate symptoms consider initial treatment with activity modification and rigid night splinting for 3 months based on a high likelihood of avoiding surgical intervention.



Therapeutic II.
An effective similarity metric for application traffic classification

Application level traffic classification is one of the major issues in network monitoring and traffic engineering. In our previous study, we proposed a new traffic classification method that utilizes a flow similarity function based on Cosine Similarity. This paper compares the classification accuracy of three similarity metrics, Jaccard Similarity, Cosine Similarity, and Gaussian Radius Based Function, to select appropriate similarity metrics for application traffic classification. This paper also defines a new two-stage traffic classification algorithm that can guarantee high classification accuracy even under an asymmetric routing environment, with reasonable complexity.
M-CLOCK: migration-optimized page replacement algorithm for hybrid DRAM and PCM memory architecture

Phase Change Memory (PCM) has drawn great attention as a main memory due to its attractive characteristics such as non-volatility, byte-addressability, and in-place update. However, since the capacity of PCM is not fully mature yet, hybrid memory architecture that consists of DRAM and PCM has been suggested. In addition, page replacement algorithm based on hybrid memory architecture is actively being studied because existing page replacement algorithms cannot be used on hybrid memory architecture in that they do not consider the two weaknesses of PCM: high write latency and low endurance. In this paper, to mitigate the above hardware limitations of PCM, we revisit the page cache layer for the hybrid memory architecture. We also propose a novel page replacement algorithm, called M-CLOCK, to improve the performance of hybrid memory architecture and the lifespan of PCM. In particular, M-CLOCK aims to reduce the number of PCM writes that negatively affect the performance of hybrid memory architecture. Experimental results clearly show that M-CLOCK outperforms the state-of-the-art page replacement algorithms in terms of the number of PCM writes and effective memory access time by up to 98% and 34%, respectively.
When Waiting Is Not an Option: Learning Options With a Deliberation Cost

Recent work has shown that temporally extended actions (options) can be learned fully end-to-end as opposed to being specified in advance. While the problem of how to learn options is increasingly well understood, the question of what good options should be has remained elusive. We formulate our answer to what good options should be in the bounded rationality framework (Simon, 1957) through the notion of deliberation cost. We then derive practical gradient-based learning algorithms to implement this objective. Our results in the Arcade Learning Environment (ALE) show increased performance and interpretability.
Deep image mining for diabetic retinopathy screening

Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient pathology detectors or classifiers can be trained with virtually no expert knowledge about the target pathologies. However, deep learning algorithms, including the popular ConvNets, are black boxes: little is known about the local patterns analyzed by ConvNets to make a decision at the image level. A solution is proposed in this paper to create heatmaps showing which pixels in images play a role in the image-level predictions. In other words, a ConvNet trained for image-level classification can be used to detect lesions as well. A generalization of the backpropagation method is proposed in order to train ConvNets that produce high-quality heatmaps. The proposed solution is applied to diabetic retinopathy (DR) screening in a dataset of almost 90,000 fundus photographs from the 2015 Kaggle Diabetic Retinopathy competition and a private dataset of almost 110,000 photographs (e-ophtha). For the task of detecting referable DR, very good detection performance was achieved: Az=0.954 in Kaggle's dataset and Az=0.949 in e-ophtha. Performance was also evaluated at the image level and at the lesion level in the DiaretDB1 dataset, where four types of lesions are manually segmented: microaneurysms, hemorrhages, exudates and cotton-wool spots. For the task of detecting images containing these four lesion types, the proposed detector, which was trained to detect referable DR, outperforms recent algorithms trained to detect those lesions specifically, with pixel-level supervision. At the lesion level, the proposed detector outperforms heatmap generation algorithms for ConvNets. This detector is part of the Messidor® system for mobile eye pathology screening. Because it does not rely on expert knowledge or manual segmentation for detecting relevant patterns, the proposed solution is a promising image mining tool, which has the potential to discover new biomarkers in images.
Blindfold Baselines for Embodied QA

We explore blindfold (question-only) baselines for Embodied Question Answering. The EmbodiedQA task requires an agent to answer a question by intelligently navigating in a simulated environment, gathering necessary visual information only through first-person vision before finally answering. Consequently, a blindfold baseline which ignores the environment and visual information is a degenerate solution, yet we show through our experiments on the EQAv1 dataset that a simple question-only baseline achieves state-of-the-art results on the EmbodiedQA task in all cases except when the agent is spawned extremely close to the object.
Interactivity and user participation in the television lifecycle: creating, sharing, and controlling content

Interactive TV research encompasses a rather diverse body of work (e.g. multimedia, HCI, CSCW, UIST, user modeling, media studies) that has accumulated over the past 20 years. In this article, we highlight the state-of-the-art and consider two basic issues: What is interactive TV research? Can it help us reinvent the practices of creating, sharing and watching TV? We survey the literature and identify three concepts that have been inherent in interactive TV research: 1) interactive TV as content creation, 2) interactive TV as a content and experience sharing process, and 3) interactive TV as control of audiovisual content. We propose this simple taxonomy (create-share-control) as an evolutionary step over the traditional hierarchical produce-distribute-consume paradigm. Moreover, we highlight the importance of sociability in all phases of the create-share-control model.
FatCBST: Concurrent Binary Search Tree with Fatnodes

Effective design of concurrent tree implementation plays a major role in improving the scalability of applications in a multicore environment. We introduce a concurrent binary search tree with fatnodes (FatCBST) and present algorithms to perform basic operations on it. Unlike a simple node with single value, a fatnode consists of a set of values. FatCBST concept allows a thread to perform update operations on an existing fatnode without changing the tree structure, making it less disruptive to other threads' operations. Fatnodes help to take advantage of the spatial locality in the cache hierarchy and also reduce the height of the concurrent binary search tree. Our FatCBST implementation allows multiple threads to perform update operations on the same existing fatnode at the same time. Experimental results show that the FatCBST implementations that have small fatnode sizes provide better throughput for high and medium contention workloads; and large fatnode sizes provide better throughput for low contention workloads, as compared to the current state-of-the-art implementations.
The Role of Review Arousal in Online Reviews: Insights from EEG Data

This paper examines the effects of review arousal on perceived helpfulness of online reviews, and on consumers’ emotional responses elicited by the reviews. Drawing on emotion theories in psychology and neuroscience, we focus on four emotions – anger, anxiety, excitement, and enjoyment that are common in the context of online reviews. The effects of the four emotions embedded in online reviews were examined using a controlled experiment. Our preliminary results show that reviews embedded with the four emotions (arousing reviews) are perceived to be more helpful than reviews without the emotions embedded (non-arousing reviews). However, reviews embedded with anxiety and enjoyment (low-arousal reviews) are perceived to be more helpfulness that reviews embedded with anger and excitement (high-arousal reviews). Furthermore, compared to reviews embedded with anger, reviews embedded with anxiety are associated with a higher EEG activity that is generally linked to negative emotions. The results suggest a non-linear relationship between review arousal and perceived helpfulness, which can be explained by the consumers’ emotional responses elicited by the reviews.
Motion boundary based sampling and 3D co-occurrence descriptors for action recognition

Recent studies witness the success of Bag-of-Features (BoF) frameworks for video based human action recognition. The detection and description of local interest regions are two fundamental problems in BoF framework. In this paper, we propose a motion boundary based sampling strategy and spatialtemporal (3D) co-occurrence descriptors for action video representation and recognition. Our sampling strategy is partly inspired by the recent success of dense trajectory (DT) based features  for action recognition. Compared with DT, we densely sample spatial-temporal cuboids along motion boundary which can greatly reduce the number of valid trajectories while preserve the discriminative power. Moreover, we develop a set of 3D co-occurrence descriptors which take account of the spatial-temporal context within local cuboids and deliver rich information for recognition. Furthermore, we decompose each 3D co-occurrence descriptor at pixel level and bin level and integrate the decomposed components with a multi-channel framework, which can improve the performance significantly. To evaluate the proposed methods, we conduct extensive experiments on three benchmarks including KTH, YouTube and HMDB51. The results show that our sampling strategy significantly reduces the computational cost of point tracking without degrading performance. Meanwhile, we achieve superior performance than the state-ofthe-art methods. We report 95.6% on KTH, 87.6% on YouTube and 51.8% on HMDB51.
Rearch on incentive-constraint mechanism of Chinese private enterprises manager

Due to inconsistent objectives and asymmetric information, serious principal-agent problems often occur between private business owners and their professional managers. The first problem of the principal-agent problem in private businesses is monitoring, i.e., the problem of asymmetric information, while the second probelem is the incentive-constraint problem based on the monitoring. Only effective monitoring can well evaluate the contribution of professional managers, which can more effectively motivate professional managers.
Gas Sensors Based on One Dimensional Nanostructured Metal-Oxides: A Review

Recently one dimensional (1-D) nanostructured metal-oxides have attracted much attention because of their potential applications in gas sensors. 1-D nanostructured metal-oxides provide high surface to volume ratio, while maintaining good chemical and thermal stabilities with minimal power consumption and low weight. In recent years, various processing routes have been developed for the synthesis of 1-D nanostructured metal-oxides such as hydrothermal, ultrasonic irradiation, electrospinning, anodization, sol-gel, molten-salt, carbothermal reduction, solid-state chemical reaction, thermal evaporation, vapor-phase transport, aerosol, RF sputtering, molecular beam epitaxy, chemical vapor deposition, gas-phase assisted nanocarving, UV lithography and dry plasma etching. A variety of sensor fabrication processing routes have also been developed. Depending on the materials, morphology and fabrication process the performance of the sensor towards a specific gas shows a varying degree of success. This article reviews and evaluates the performance of 1-D nanostructured metal-oxide gas sensors based on ZnO, SnO(2), TiO(2), In(2)O(3), WO(x), AgVO(3), CdO, MoO(3), CuO, TeO(2) and Fe(2)O(3). Advantages and disadvantages of each sensor are summarized, along with the associated sensing mechanism. Finally, the article concludes with some future directions of research.
Why are "natively unfolded" proteins unstructured under physiologic conditions?

"Natively unfolded" proteins occupy a unique niche within the protein kingdom in that they lack ordered structure under conditions of neutral pH in vitro. Analysis of amino acid sequences, based on the normalized net charge and mean hydrophobicity, has been applied to two sets of proteins: small globular folded proteins and "natively unfolded" ones. The results show that "natively unfolded" proteins are specifically localized within a unique region of charge-hydrophobicity phase space and indicate that a combination of low overall hydrophobicity and large net charge represent a unique structural feature of "natively unfolded" proteins.
Security threats in the application layer in IOT applications

The Internet of things aspires to connect anyone with anything at any point of time at any place. Internet of Thing is generally made up of three-layer architecture. Namely Perception, Network and Application layers. A lot of security principles should be enabled at each layer for proper and efficient working of these applications. This paper represents the overview of Security principles, Security Threats and Security challenges at the application layer and its countermeasures to overcome those challenges. The Application layer plays an important role in all of the Internet of Thing applications. The most widely used application layer protocol is MQTT. The security threats for Application Layer Protocol MQTT is particularly selected and evaluated. Comparison is done between different Application layer protocols and security measures for those protocols. Due to the lack of common standards for IoT protocols, a lot of issues are considered while choosing the particular protocol.
Electrically switchable Fresnel lens using a polymer-separated composite film.

A Fresnel lens with electrically-tunable diffraction efficiency while possessing high image quality is demonstrated using a phase-separated composite film (PSCOF). The light scattering-free PSCOF is obtained by anisotropic phase separation between liquid crystal and polymer. Such a lens can be operated below 12 volts and its switching time is reasonably fast (~10 ms). The maximum diffraction efficiency reaches ~35% for a linearly polarized light, which is close to the theoretical limit of 41%.
Autoerotic asphyxiation in a female.

Autoerotic asphyxiation is an unusual but increasingly more frequently occurring phenomenon, with >1000 fatalities in the United States per year. Understanding of this manner of death is likewise increasing, as noted by the growing number of cases reported in the literature. However, this form of accidental death is much less frequently seen in females (male:female ratio >50:1), and there is correspondingly less literature on female victims of autoerotic asphyxiation. The authors present the case of a 31-year-old woman who died of an autoerotic ligature strangulation and review the current literature on the subject. The forensic examiner must be able to discern this syndrome from similar forms of accidental and suicidal death, and from homicidal hanging/strangulation.
Local Learning to Improve Bag of Visual Words Model for Facial Expression Recognition

In this paper we propose a novel computer vision method for classifying human facial expression from low resolution images. Our method uses the bag of words representation. It extracts dense SIFT descriptors either from the whole image or from a spatial pyramid that divides the image into increasingly fine sub-regions. Then, it represents images as normalized (spatial) presence vectors of visual words from a codebook obtained through clustering image descriptors. Linear kernels are built for several choices of spatial presence vectors, and combined into weighted sums for multiple kernel learning (MKL). For machine learning, the method makes use of multi-class one-versus-all SVM on the MKL kernel computed using this representation, but with an important twist, the learning is local, as opposed to global – in the sense that, for each face with an unknown label, a set of neighbors is selected to build a local classification model, which is eventually used to classify only that particular face. Empirical results indicate that the use of presence vectors, local learning and spatial information improve recognition performance together by more than 5%. Finally, the proposed model ranked fourth in the Facial Expression Recognition Challenge, with an accuracy of 67.484% on the final test set. ICML 2013 Workshop on Representation Learning, Atlanta, Georgia, USA, 2013.
Deep learning approach for Fourier ptychography microscopy.

Convolutional neural networks (CNNs) have gained tremendous success in solving complex inverse problems. The aim of this work is to develop a novel CNN framework to reconstruct video sequences of dynamic live cells captured using a computational microscopy technique, Fourier ptychographic microscopy (FPM). The unique feature of the FPM is its capability to reconstruct images with both wide field-of-view (FOV) and high resolution, i.e. a large space-bandwidth-product (SBP), by taking a series of low resolution intensity images. For live cell imaging, a single FPM frame contains thousands of cell samples with different morphological features. Our idea is to fully exploit the statistical information provided by these large spatial ensembles so as to make predictions in a sequential measurement, without using any additional temporal dataset. Specifically, we show that it is possible to reconstruct high-SBP dynamic cell videos by a CNN trained only on the first FPM dataset captured at the beginning of a time-series experiment. Our CNN approach reconstructs a 12800×10800 pixel phase image using only ∼25 seconds, a 50× speedup compared to the model-based FPM algorithm. In addition, the CNN further reduces the required number of images in each time frame by ∼ 6×. Overall, this significantly improves the imaging throughput by reducing both the acquisition and computational times. The proposed CNN is based on the conditional generative adversarial network (cGAN) framework. We further propose a mixed loss function that combines the standard image domain loss and a weighted Fourier domain loss, which leads to improved reconstruction of the high frequency information. Additionally, we also exploit transfer learning so that our pre-trained CNN can be further optimized to image other cell types. Our technique demonstrates a promising deep learning approach to continuously monitor large live-cell populations over an extended time and gather useful spatial and temporal information with sub-cellular resolution.
DISCO: A System Leveraging Semantic Search in Document Review

This paper presents Disco, a prototype for supporting knowledge workers in exploring, reviewing and sorting collections of textual data. The goal is to facilitate, accelerate and improve the discovery of information. To this end, it combines Semantic Relatedness techniques with a review workflow developed in a tangible environment. Disco uses a semantic model that is leveraged on-line in the course of search sessions, and accessed through natural hand-gesture, in a simple and intuitive way.