nils-herrmann/clean_scirepeval huggingface dataset:
Where is an appropriate injection point for an ultrasound-guided transversus thoracic muscle plane block?

We previously reported an ultrasound-guided transversus thoracic abdominis plane (TTP) block, able to target many anterior branches of the intercostal nerve (Th2-6), releasing the pain in the internal mammary area [1–3]. The injection point for this TTP block was located between the transversus thoracic muscle and the internal intercostal muscle, amid the third and fourth left ribs next to the sternum. However, analgesia efficacy in the region of an anterior branch of the sixth intercostal nerve was unstable. We subsequently investigated a more appropriate injection point for an ultrasound-guided TTP block. We selected 10 healthy volunteers for this study. All volunteers received bilateral TTP blocks. Right lateral TTP blocks of all cases involved the injection of 20 mL of 0.375% levobupivacaine into the fascial plane between the transversus thoracic muscle and the internal intercostal muscle at between the third and fourth ribs connecting at the sternum. On the other hand, all left lateral TTP blocks were administered by injection of 20 mL of 0.375% levobupivacaine into the fascial plane between the transversus thoracic muscle and the internal intercostal muscle between the fourth and fifth connecting at the sternum. In 20 minutes after the injections, we investigated the spread of local anesthetic on the TTP by an ultrasound machine  and the analgesic effect by a sense testing. The sense testing is blindly the cold testing. The spread of local anesthetic is detailed in Table 1. As for the analgesic effect of sense testing, both sides gain sensory extinction in the region of multiple anterior branches of inter-
Trends in the Development of Machinery and Associated Technology " TMT 2008 , Istanbul , Turkey , 26-30 August , 2008 INVESTIGATION OF THE FATIGUE STRENGTH OF THE WELDED JOINTS TREATED BY TIG DRESSING

The fatigue fracture of structural details subjected to cyclic loads mostly occurs at a critical cross section with stress concentration. The welded joint is particularly dangerous location because of sinergetic harmful effects of stress concentration, tensile residual stresses, deffects, microstructural heterogeneity. Because of these reasons many methods for improving the fatigue resistance of welded joints are developed. Significant increase in fatigue strength and fatigue life was proved and could be attributed to improving weld toe profile, the material microstructure, removing deffects at the weld toe and modifying the original residual stress field. One of the most useful methods to improve fatigue behaviour of welded joints is TIG dressing. The magnitude of the improvement in fatigue performance depends on base material strength, type of welded joint and type of loading. Improvements of the fatigue behaviour of the welded joints in low-carbon structural steel treated by TIG dressing is considered in this paper.
Sparse Stereo Disparity Map Densification Using Hierarchical Image Segmentation

We describe a novel method for propagating disparity values using hierarchical segmentation by waterfall and robust regression models. High confidence disparity values obtained by state of the art stereo matching algorithms are interpolated using a coarse to fine approach. We start from a coarse segmentation of the image and try to fit each region’s disparities using robust regression models. If the fit is not satisfying, the process is repeated on a finer region’s segmentation. Erroneous values in the initial sparse disparity maps are generally excluded, as we use robust regressions algorithms and left-right consistency checks. Final disparity maps are therefore not only denser but can also be more accurate. The proposed method is general and independent from the sparse disparity map generation: it can therefore be used as a post-processing step for any stereo-matching algorithm.
Personalized Hotel Recommendation Using Text Mining and Mobile Browsing Tracking

With the prevalence of mobile devices such as smartphones and tablets, the ways people access to the Internet have changed enormously. In addition to the information that can be recorded by traditional Web-based e-commerce like frequent online shopping stores and browsing histories, mobile devices are capable of tracking sophisticated browsing behavior. The aim of this study is to utilize users' browsing behavior of reading hotel reviews on mobile devices and subsequently apply text-mining techniques to construct user interest profiles to make personalized hotel recommendations. Specifically, we design and implement an app where the user can search hotels and browse hotel reviews, and every gesture the user has performed on the touch screen when reading the hotel reviews is recorded. We then identify the paragraphs of hotel reviews that a user has shown interests based on the gestures the user has performed. Text mining techniques are applied to construct the interest profile of the user according to the review content the user has seriously read. We collect more than 5,000 reviews of hotels in Taipei, the largest metropolitan area of Taiwan, and recruit 18 users to participate in the experiment. Experimental results demonstrate that the recommendations made by our system better match the user's hotel selections than previous approaches.
Materialized Views in Oracle

Oracle Materialized Views (MVs) are designed for data warehousing and replication. For data warehousing, MVs based on inner/outer equijoins with optional aggregation, can be refreshed on transaction boundaries, on demand, or periodically. Refreshes are optimized for bulk loads and can use a multi-MV scheduler. MVs based on subqueries on remote tables support bidirectional replication. Optimization with MVs includes transparent query rewrite based on costbased selection method. The ability to rewrite a large class of queries based on a small set of MVs is supported by using Dimensions (new Oracle object), losslessness of joins, functional dependency, column equivalence, join derivability, joinback and aggregate rollup.
A new deep learning architecture for detection of long linear infrastructure

The use of drones in infrastructure monitoring aims at decreasing the human effort and in achieving consistency. Accurate aerial image analysis is the key block to achieve the same. Reliable detection and integrity checking of power line conductors in a diverse background are the most challenging in drone based automatic infrastructure monitoring. Most techniques in literature use first principle approach that tries to represent the image as features of interest. This paper proposes a machine learning approach for power line detection. A new deep learning architecture is proposed with very good results and is compared with GoogleNet pre-trained model. The proposed architecture uses Histogram of Gradient features as the input instead of the image itself to ensure capture of accurate line features. The system is tested on aerial image collected using drone. A healthy F-score of 84.6% is obtained using the proposed architecture as against 81% using GoogleNet model.
Constraint-Based Refactoring with Foresight

Constraint-based refactoring tools as currently implemented generate their required constraint sets from the programs to be refactored, before any changes are performed. Constraint generation is thus unable to see — and regard — the changed structure of the refactored program, although this new structure may give rise to new constraints that need to be satisfied for the program to maintain its original behaviour. To address this problem, we present a framework allowing the constraint-generation process to foresee all changes a refactoring might perform, generating — at the outset of the refactoring — all constraints necessary to constrain these changes. As we are able to demonstrate, the computational overhead imposed by our framework, although threatening viability in theory, can be reduced to tractable sizes.
Exploring Serverless Computing for Neural Network Training

Serverless or functions as a service runtimes have shown significant benefits to efficiency and cost for event-driven cloud applications. Although serverless runtimes are limited to applications requiring lightweight computation and memory, such as machine learning prediction and inference, they have shown improvements on these applications beyond other cloud runtimes. Training deep learning can be both compute and memory intensive. We investigate the use of serverless runtimes while leveraging data parallelism for large models, show the challenges and limitations due to the tightly coupled nature of such models, and propose modifications to the underlying runtime implementations that would mitigate them. For hyperparameter optimization of smaller deep learning models, we show that serverless runtimes can provide significant benefit.
Thoracic outlet syndrome: a review.

: Arterial and venous thoracic outlet syndrome (TOS) were recognized in the late 1800s and neurogenic TOS in the early 1900s. Diagnosis and treatment of the 2 vascular forms of TOS are generally accepted in all medical circles. On the other hand, neurogenic TOS is more difficult to diagnose because there is no standard objective test to confirm clinical impressions.



: The clinical features of arterial, venous, and neurogenic TOS are described. Because neurogenic TOS is by far the most common type, the pathology, pathophysiology, diagnostic tests, differential and associate diagnoses, and treatment are detailed and discussed. The controversial area of objective and subjective diagnostic criteria is addressed.



: Arterial and venous TOS are usually not difficult to recognize and the diagnosis can be confirmed by angiography. The diagnosis of neurogenic TOS is more challenging because its symptoms of nerve compression are not unique. The clinical diagnosis relies on documenting several positive findings on physical examination. To date there is still no reliable objective test to confirm the diagnosis, but measurements of the medial antebrachial cutaneous nerve appear promising.
A simulated "acquired" imperforate hymen following the genital trauma of sexual abuse.

Imperforate hymen, a condition in which the hymen has no aperture, usually occurs congenitally, secondary to failure of development of a lumen. A case of a documented simulated "acquired" imperforate hymen is presented in this article. The patient, a 5-year-old girl, was the victim of sexual abuse. Initial examination showed tears, scars, and distortion of the hymen, laceration of the perineal body, and loss of normal anal tone. Follow-up evaluations over the next year showed progressive healing. By 7 months after the injury, the hymen was replaced by a thick, opaque scar with no orifice. Patients with an apparent imperforate hymen require a sensitive interview and careful visual inspection of the genital and anal areas to delineate signs of injury. The finding of an apparent imperforate hymen on physical examination does not eliminate the possibility of antecedent vaginal penetration and sexual abuse.
MindfulWatch: A Smartwatch-Based System For Real-Time Respiration Monitoring During Meditation

With a wealth of scientifically proven health benefits, meditation was enjoyed by about 18 million people in the U.S. alone, as of 2012. Yet, there remains a stunning lack of convenient tools for promoting long-term and effective meditation practice. In this paper, we present MindfulWatch, a practical smartwatch-based sensing system that monitors respiration in real-time during meditation -- offering essential biosignals that can potentially be used to empower various future applications such as tracking changes in breathing pattern, offering real-time guidance, and providing an accurate bio-marker for meditation research. To this end, MindfulWatch is designed to be convenient for everyday use with no training required. Operating solely on a smartwatch, MindfulWatch can immediately reach the growing population of smartwatch users, making it ideal for longitudinal data collection for meditation studies. Specifically, it utilizes motion sensors to sense the subtle “micro” wrist rotation (0.01 rad/s) induced by respiration. To accurately capture breathing, we developed a novel self-adaptive model that tracks changes in both breathing pattern and meditation posture over time. MindfulWatch was evaluated based on data from 36 real-world meditation sessions (8.7 hours, 11 subjects). The results suggest that MindfulWatch offers reliable real-time respiratory timing measurement (70% errors under 0.5 seconds).
Classification Techniques in Gene Expression Microarray Data

Cancer nowadays is a common and heterogeneous disease affecting all people of all ages. Gene expression data can serve to understand cancer or other types of disease well. Building classification system using gene expression dataset that can properly classify new samples is a challenging task due to the nature of gene expression data that is usually composed of dozens of samples characterized by thousands of genes. This paper put a light on different classification methods used in classifying gene expression data including SVM, NB, C4.5 and some of the state-of-the-art techniques.  Gene expression microarray; data mining techniques; cancer classification; classification; microarray data analysis
Self-transcendence and emotional well-being in women with advanced breast cancer.

Self-transcendence has been associated, in previous studies, with stressful life events and emotional well-being. This study examined the relationships among self-transcendence, emotional well-being, and illness-related distress in women with advanced breast cancer. The study employed a cross-sectional correlational design in a convenience sample (n = 107) of women with Stage IIIb and Stage IV breast cancer. Subjects completed a questionnaire that included Reed's Self-Transcendence Scale; Bradburn's Affect Balance Scale (ABS); a Cognitive Well-Being (CWB) Scale based on work by Campbell, Converse, and Rogers; McCorkle and Young's Symptom Distress Scale (SDS); and the Karnofsky Performance Scale (KPS). Data were analyzed using factor analytic structural equations modeling. Self-transcendence decreased illness distress (assessed by the SDS and the KPS) through the mediating effect of emotional well-being (assessed by the ABS and the CWB Scale). Self-transcendence directly affected emotional well-being (beta = 0.69), and emotional well-being had a strong negative effect on illness distress (beta = -0.84). A direct path from self-transcendence to illness distress (beta = -0.31) became nonsignificant (beta = -0.08) when controlling for emotional well-being. Further research using longitudinal data will seek to validate these relationships and to explain how nurses can promote self-transcendence in women with advanced breast cancer, as well as in others with life-threatening illnesses.
High performance SOI RF switches for wireless applications

This paper describes 0.18um CMOS silicon-on-insulator (SOI) technology and design techniques for SOI RF switch designs for wireless applications. The measured results of SP4T (single pole four throw) and SP8T (single pole eight throw) switch reference designs are presented. It has been demonstrated that SOI RF switch performance, in terms of power handling, linearity, insertion loss and isolation, is very competitive with those utilizing GaAs pHEMT and silicon-on-sapphire (SOS) technologies, while maintaining a cost and manufacturing advantage.
Aggression Detection on Social Media Text Using Deep Neural Networks

In the past few years, bully and aggressive posts on social media have grown significantly, causing serious consequences for victims/users of all demographics. Majority of the work in this field has been done for English only. In this paper, we introduce a deep learning based classification system for Facebook posts and comments of Hindi-English Code-Mixed text to detect the aggressive behaviour of/towards users. Our work focuses on text from users majorly in the Indian Subcontinent. The dataset that we used for our models is provided by TRAC-11 in their shared task. Our classification model assigns each Facebook post/comment to one of the three predefined categories: “Overtly Aggressive”, “Covertly Aggressive” and “Non-Aggressive”. We experimented with 6 classification models and our CNN model on a 10 K-fold crossvalidation gave the best result with the prediction accuracy of 73.2%.
Loneliness and Internet use

The Internet is becoming increasingly influential, but some observers have noted that heavy Internet users seem alienated from normal social contacts and may even cut these off as the Internet becomes the predominate social factor in their lives. Kraut, Patterson, Lundmark, Kiesler, Mukopadhyay, and Scherlis [American Psychologist 53 (1998) 65] carried out a longitudinal study from which they concluded that Internet use leads to loneliness among its users. However, their study did not take into account that the population of Internet users is not uniform and comprises many different personality types. People use the Internet in a variety of ways in keeping with their own personal preference. Therefore, the results of this interaction between personality and Internet use are likely to vary among different individuals and similarly the impact on user well-being will not be uniform. One of the personality characteristics that has been found to influence Internet use is that of extroversion and neuroticism [Hamburger & Ben-Artzi, Computers in Human Behavior 16 (2000) 441]. For this study, 89 participants completed questionnaires pertaining to their own Internet use and feelings of loneliness and extroversion and neuroticism. The results were compared to two models (a) the Kraut et al. (1998) model which argues that Internet use leads to loneliness (b) an alternative model which argues that it is those people who are already lonely who spend time on the Internet. A satisfactory goodness of fit was found for the alternative model. Building on these results, several different directions are suggested for continuing research in this field.
Salient Object Detection With Spatiotemporal Background Priors for Video

Saliency detection for images has been studied for many years, for which a lot of methods have been designed. In saliency detection, background priors, which are often regarded as pseudo-background, are effective clues to find salient objects in images. Although image boundary is commonly used as background priors, it does not work well for images of complex scenes and videos. In this paper, we explore how to identify the background priors for a video and propose a saliency-based method to detect the visual objects by using the background priors. For a video, we integrate multiple pairs of scale-invariant feature transform flows from long-range frames, and a bidirectional consistency propagation is conducted to obtain the accurate and sufficient temporal background priors, which are combined with spatial background priors to generate spatiotemporal background priors. Next, a novel dual-graph-based structure using spatiotemporal background priors is put forward in the computation of saliency maps, fully taking advantage of appearance and motion information in videos. Experimental results on different challenging data sets show that the proposed method robustly and accurately detects the video objects in both simple and complex scenes and achieves better performance compared with other the state-of-the-art video saliency models.
A theory of personal budgeting

Prominent research argues that consumers often use personal budgets to manage self-control problems. This paper analyzes the link between budgeting and selfcontrol problems in consumption-saving decisions. It shows that the use of goodspecific budgets depends on the combination of a demand for commitment and the demand for flexibility resulting from uncertainty about intratemporal trade-offs between goods. It explains the subtle mechanism which renders budgets useful commitments, their interaction with minimum-savings rules (another widely-studied form of commitment), and how budgeting depends on the intensity of self-control problems. This theory matches several empirical findings on personal budgeting.
Object-Location-Aware Hashing for Multi-Label Image Retrieval via Automatic Mask Learning

Learning-based hashing is a leading approach of approximate nearest neighbor search for large-scale image retrieval. In this paper, we develop a deep supervised hashing method for multi-label image retrieval, in which we propose to learn a binary “mask” map that can identify the approximate locations of objects in an image, so that we use this binary “mask” map to obtain length-limited hash codes which mainly focus on an image’s objects but ignore the background. The proposed deep architecture consists of four parts: 1) a convolutional sub-network to generate effective image features; 2) a binary “mask” sub-network to identify image objects’ approximate locations; 3) a weighted average pooling operation based on the binary “mask” to obtain feature representations and hash codes that pay most attention to foreground objects but ignore the background; and 4) the combination of a triplet ranking loss designed to preserve relative similarities among images and a cross entropy loss defined on image labels. We conduct comprehensive evaluations on four multi-label image data sets. The results indicate that the proposed hashing method achieves superior performance gains over the state-of-the-art supervised or unsupervised hashing baselines.
53BP1: A guardian for centrosomal integrity.

53BP1 is known as a mediator in DNA damage response and a regulator of DNA double-stranded breaks (DSBs) repair. 53BP1 was recently reported to be a centrosomal protein and a binding partner of mitotic polo-like kinase 1 (Plk1). The stability of 53BP1, in response to DSBs, is regulated by its phosphorylation, deubiquitination, and ubiquitination. During mitosis, 53BP1 is stabilized by phosphorylation at S380, a putative binding region with polo-box domain of Plk1, and deubiquitination by ubiquitin-specific protease 7 (USP7). In the absence of DSBs, 53BP1 is abundant in the nucleoplasm; DSB formation results in its rapid localization to the damaged chromatin. Mitotic 53BP1 is also localized at the centrosome and spindle pole. 53BP1 depletion induces mitotic defects such as disorientation of spindle poles attributed to extra centrosomes or mispositioning of centrosomes, leading to phenotypes similar to those in USP7-deficient cells. Here, we discuss how 53BP1 controls the centrosomal integrity through its interaction with USP7 and centromere protein F by regulation of its stability and its physiology in response to DNA damage.
Pornography Detection Using Support Vector Machine

As Internet grows quickly, pornography, which is often printed into a small quantity of publication in the past, becomes one of the highly distributed information over Internet. However, pornography may be harmful to children, and may affect the efficiency of workers. In this paper, we design an easy scheme for detecting pornography. We exploit primitive information from pornography and use this knowledge for determining whether a given photo belongs to pornography or not. In the beginning, we extract skin region from photos, and find out the correlation in skin region and non-skin region. Then, we use these correlations as the input of support vector machine (SVM), an excellent tool for classification with learning abilities. After a period of training SVM model, we achieved about 75% of accuracy, 35% of false alarm rate, and only 14% of mis-detection rate. Moreover, we also provide a simple tool based on our scheme.