nils-herrmann/clean_scirepeval huggingface dataset:
A comprehensive method for multilingual video text detection, localization, and extraction

Text in video is a very compact and accurate clue for video indexing and summarization. Most video text detection and extraction methods hold assumptions on text color, background contrast, and font style. Moreover, few methods can handle multilingual text well since different languages may have quite different appearances. This paper performs a detailed analysis of multilingual text characteristics, including English and Chinese. Based on the analysis, we propose a comprehensive, efficient video text detection, localization, and extraction method, which emphasizes the multilingual capability over the whole processing. The proposed method is also robust to various background complexities and text appearances. The text detection is carried out by edge detection, local thresholding, and hysteresis edge recovery. The coarse-to-fine localization scheme is then performed to identify text regions accurately. The text extraction consists of adaptive thresholding, dam point labeling, and inward filling. Experimental results on a large number of video images and comparisons with other methods are reported in detail.
ImWalkMF: Joint matrix factorization and implicit walk integrative learning for recommendation

Data sparsity and cold-start problems are prevalent in recommender systems. To address such problems, both the observable explicit social information (e.g., user-user trust connections) and the inferable implicit correlations (e.g., implicit neighbors computed by similarity measurement) have been introduced to complement user-item ratings data for improving the performances of traditional model-based recommendation algorithms such as matrix factorization. Although effective, (1) the utilization of the explicit user-user social relationships suffers from the weakness of unavailability in real systems such as Netflix or the issue of sparse observable content like 0.03% trust density in Epinions, thus there is no or little explicit social information that can be employed to improve baseline model in real applications; (2) the current similarity measurement approaches focus on inferring implicit correlations between a user (item) and their direct neighbors or top-k similar neighbors based on user-item ratings bipartite network, so that they fail to comprehensively unfold the indirect potential relationships among users and items. To solve these issues regarding both explicit/implicit social recommendation algorithms, we design a joint model of matrix factorization and implicit walk integrative learning, i.e., ImWalkMF, which only uses explicit ratings information yet models both direct rating feedbacks and multiple direct/indirect implicit correlations among users and items from a random walk perspective. We further propose a combined strategy for training two independent components in the proposed model based on sampling. The experimental results on two real-world sparse datasets demonstrate that ImWalkMF outperforms the traditional regularized/probabilistic matrix factorization models as well as other competitive baselines that utilize explicit/implicit social information.
Wideband Inline Coaxial to Ridge Waveguide Transition With Tuning Capability for Ridge Gap Waveguide

An ultra-wideband inline coaxial-to-single ridge waveguide transition is designed to feed the ridge gap waveguide (RGW). The transition covers the frequency band of 11.5 to 33 GHz. This bandwidth cannot be covered by a single RGW. Therefore, two RGWs are manufactured covering two different bands to be used as devices under test. Simulations and measurements of back-to-back transitions have achieved better than 15 dB return loss. In addition, a tuning capability in the transition is simulated using two different commercial simulators showing that the transition may cover up to 40 GHz with over 90% bandwidth to cover the entire <italic>Ku</italic>, <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula>, and <italic>Ka</italic>-bands.
Real-time rendering of sky scene considering scattering and refraction

Realistic rendering of sky scene is important in game development and virtual reality. Traditional methods did not consider both the affect of atmospheric scattering and refraction, thus failing to realistically simulate the change of shape, color, and light ring of sky scene. In this paper, a new sky light model considering atmospheric scattering and refraction is proposed. We first calculate the refractional track of light through the atmosphere according to the refraction index. Then we adopt the scattered volume model to simplify the calculation of scattering light intensity. By adapting a path tracing algorithm considering refraction, the intensity distribution of sky light is calculated. Finally, various sky scenes in sunny day, foggy day, and that with rainbow and mirage under different conditions are realistically rendered in real time.
Comprehensive surgical aesthetic enhancement and rejuvenation of the perioral region.

There has been minimal attention paid in the literature to the aesthetics of the perioral area, either in youth or in senescence. Aging around the lips traditionally was thought to result from a combination of thinning skin surrounding the area, ptosis, and loss of volume in the lips. The atrophy of senescence was treated by adding volume to the lips and filling the deep nasolabial creases. There is now a growing appreciation for the role of volume enhancement in the perioral region and the sunken midface, as well as for dentition, in the resting and dynamic appearance of the perioral area (particularly in youth). In this article, the authors describe the senior author's (BG) preferred methods for aesthetic enhancement of the perioral region and his rejuvenative techniques developed over the past 28 years. The article describes the etiologies behind the dysmorphologies in this area and presents a problem-oriented algorithm for treating them.
Semantic annotation tools survey

The Web has come a long way since its invention by Berners-Lee, when it focused essentially on visualization and presentation of content for human consumption (Syntactic Web), to a Web providing meaningful content, facilitating the integration between people and machines (Semantic Web). This paper presents a survey of different tools that provide the enrichment of the Web with understandable annotation, in order to make its content available and interoperable between systems. We can group Semantic Annotation tools into the diverse dimensions: dynamicity, storage, information extraction process, scalability and customization. The analysis of the different annotation tools shows that (semi-)automatic and automatic systems aren't as efficient as needed without human intervention and will continue to evolve to solve the challenge. Microdata, RDFa and the new HTML5 standard will certainly bring new contributions to this issue.
Kinematics and workspace analysis of a novel 3-DOF spatial parallel robot

In this study, kinematics design and workspace analysis of a novel spatial parallel mechanism is elaborated. The proposed mechanism is to be exploited in a hybrid serial-parallel mobile robot to fulfill stable motion of the robotic system when handling heavy objects by a serial manipulator mounted on the parallel mechanism. In fact, this parallel mechanism has been designed to obtain an appropriate maneuverability and fulfill the tipover stability of a hybrid serial-parallel mobile robotic system after grasping heavy objects. The proposed mechanism is made of three legs while each leg has one active degree of freedom (DOF) so that the mechanism contains 3-DOF. In order to investigate this novel parallel mechanism, inverse and forward kinematics model is developed and verified using two maneuvers. Next, the workspace of the manipulator is demonstrated symbolically, besides a numerical analysis will be studied. Finally, the advantages of exploiting such a parallel manipulator are discussed in order to be used in a hybrid serial-parallel mobile robotic system.
Decision tree rule-based feature selection for large-scale imbalanced data

A class imbalance problem often appears in many real world applications, e.g. fault diagnosis, text categorization, fraud detection. When dealing with a large-scale imbalanced dataset, feature selection becomes a great challenge. To confront it, this work proposes a feature selection approach based on a decision tree rule. The effectiveness of the proposed approach is verified by classifying a large-scale dataset from Santander Bank. The results show that our approach can achieve higher Area Under the Curve (AUC) and less computational time. We also compare it with filter-based feature selection approaches, i.e., Chi-Square and F-statistic. The results show that it outperforms them but needs slightly more computational efforts.
Rehospitalizations among patients in the Medicare fee-for-service program.

Reducing rates of rehospitalization has attracted attention from policymakers as a way to improve quality of care and reduce costs. However, we have limited information on the frequency and patterns of rehospitalization in the United States to aid in planning the necessary changes.



We analyzed Medicare claims data from 2003-2004 to describe the patterns of rehospitalization and the relation of rehospitalization to demographic characteristics of the patients and to characteristics of the hospitals.



Almost one fifth (19.6%) of the 11,855,702 Medicare beneficiaries who had been discharged from a hospital were rehospitalized within 30 days, and 34.0% were rehospitalized within 90 days; 67.1% [corrected] of patients who had been discharged with medical conditions and 51.5% of those who had been discharged after surgical procedures were rehospitalized or died within the first year after discharge. In the case of 50.2% [corrected] of the patients who were rehospitalized within 30 days after a medical discharge to the community, there was no bill for a visit to a physician's office between the time of discharge and rehospitalization. Among patients who were rehospitalized within 30 days after a surgical discharge, 70.5% were rehospitalized for a medical condition. We estimate that about 10% of rehospitalizations were likely to have been planned. The average stay of rehospitalized patients was 0.6 day longer than that of patients in the same diagnosis-related group whose most recent hospitalization had been at least 6 months previously. We estimate that the cost to Medicare of unplanned rehospitalizations in 2004 was $17.4 billion.



Rehospitalizations among Medicare beneficiaries are prevalent and costly.
Professional knowledge and the epistemology of reflective practice.

Reflective practice is one of the most popular theories of professional knowledge in the last 20 years and has been widely adopted by nursing, health, and social care professions. The term was coined by Donald Schön in his influential books The Reflective Practitioner, and Educating the Reflective Practitioner, and has garnered the unprecedented attention of theorists and practitioners of professional education and practice. Reflective practice has been integrated into professional preparatory programmes, continuing education programmes, and by the regulatory bodies of a wide range of health and social care professions. Yet, despite its popularity and widespread adoption, a problem frequently raised in the literature concerns the lack of conceptual clarity surrounding the term reflective practice. This paper seeks to respond to this problem by offering an analysis of the epistemology of reflective practice as revealed through a critical examination of philosophical influences within the theory. The aim is to discern philosophical underpinnings of reflective practice in order to advance increasingly coherent interpretations, and to consider the implications for conceptions of professional knowledge in professional life. The paper briefly examines major philosophical underpinnings in reflective practice to explicate central themes that inform the epistemological assumptions of the theory. The study draws on the work of Donald Schön, and on texts from four philosophers: John Dewey, Nelson Goodman, Michael Polanyi, and Gilbert Ryle. Five central epistemological themes in reflective practice are illuminated: (1) a broad critique of technical rationality; (2) professional practice knowledge as artistry; (3) constructivist assumptions in the theory; (4) the significance of tacit knowledge for professional practice knowledge; and (5) overcoming mind body dualism to recognize the knowledge revealed in intelligent action. The paper reveals that the theory of reflective practice is concerned with deep epistemological questions of significance to conceptions of knowledge in health and social care professions.
Look, Listen and Learn - A Multimodal LSTM for Speaker Identification

Speaker identification refers to the task of localizing the face of a person who has the same identity as the ongoing voice in a video. This task not only requires collective perception over both visual and auditory signals, the robustness to handle severe quality degradations and unconstrained content variations are also indispensable. In this paper, we describe a novel multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies both visual and auditory modalities from the beginning of each sequence input. The key idea is to extend the conventional LSTM by not only sharing weights across time steps, but also sharing weights across modalities. We show that modeling the temporal dependency across face and voice can significantly improve the robustness to content quality degradations and variations. We also found that our multimodal LSTM is robustness to distractors, namely the non-speaking identities. We applied our multimodal LSTM to The Big Bang Theory dataset and showed that our system outperforms the state-of-the-art systems in speaker identification with lower false alarm rate and higher recogni-
Using the WordNet Ontology for Interpreting Medical Records

As hospitals throughout Europe are striving exploit advantages of IT and network technologies, electronic medical records systems are starting to replace paper based archives. This paper suggests and describes an add-on service to electronic medical record systems that will help regular patients in getting insight to their diagnoses and medical record. The add-on service is based annotating polysemous and foreign terms with WordNet synsets. By exploiting the way that relationships between synsets are structured and described in WordNet, it is shown how patients can get interactive opportunities to generalize and understand their personal records.
Acute urinary retention in children.

Acute urinary retention in children is a relatively rare entity. There are a variety of causes that are poorly defined in the literature. We review our cases of acute urinary retention in children at three major pediatrics centers in Iran.



Between 1996 and 2003, children (up to 14 years old) who had been referred due to acute urinary retention were examined. Urinary retention was defined as inability to empty the bladder volitionally for more than 12 hours with a urine volume greater than expected for age or a palpably distended bladder. All data from the patients' past medical history, physical examination, and laboratory and radiographic assessments were collected. Also, cystourethroscopy and urodynamic procedures had been carried out according to patient's conditions. Patients with secondary urinary retention, including those with surgical history, immobility or chronic neurological disorders, mental retardation, and drugs or narcotics consumption were excluded from study.



There were 86 patients meeting the inclusion criteria, consisting of 58 males with a median age of 4 years (range 1 month to 14 years) and 58 females with a median age of 4 years (range 4 month to 14 years). Etiologies were lower urinary tract stone in 27.9%, neurological disorders in 10.4%, trauma in 10.4%, local inflammatory causes in 9.1%, urinary tract infection in 7.4%, ureterocele in 7.4%, benign obstructing lesions in 5.8%, iatrogenic in 5.8%, constipation in 4.6%, imperforated hymen in 3.5%, and large prostate utricle, urethral foreign body, and rhabdomyosarcoma each in 1 case (1.1%).



The most common cause of acute urinary retention was lower urinary tract stone in our pediatric cases. Ureterocele and stone were the main findings in girls and boys, respectively, and urinary retention in boys was twice as prevalent as that in girls.
First Classified Annotated Bibliography of NLP Tasks in the Burmese Language of Myanmar

Natural Language Processing (NLP) has emerged with a wide scope of research in the area. The Burmese language, also called the Myanmar Language is a resource scarce, tonal, analytical, syllable-timed and principally monosyllabic language with Subject-Object-Verb (SOV) ordering. NLP of Burmese language is also challenged by the fact that it has no white spaces and word boundaries. Keeping these facts in view, the current paper is a first formal attempt to present a bibliography of research works pertinent to NLP tasks in Burmese language. Instead of presenting mere catalogue, the current work is also specifically elaborated by annotations as well as classifications of NLP task research works in NLP related categories. The paper presents the state-of-the-art of Burmese NLP tasks. Both annotations and classifications of NLP tasks of Burmese language are useful to the scientific community as it shows where the field of research in Burmese NLP is going. In fact, to the best of author’s knowledge, this is first work of its kind worldwide for any language. For a period spanning more than 25 years, the paper discusses Burmese language Word Identification, Segmentation, Disambiguation, Collation, Semantic Parsing and Tokenization followed by Part-Of-Speech (POS) Tagging, Machine Translation Systems (MTS), Text Keying/Input, Recognition and Text Display Methods. Burmese language WordNet, Search Engine and influence of other languages on Burmese language are also discussed.
Ensemble Manifold Segmentation for Model Distillation and Semi-supervised Learning

Manifold theory has been the central concept of many learning methods. However, learning modern CNNs with manifold structures has not raised due attention, mainly because of the inconvenience of imposing manifold structures onto the architecture of the CNNs. In this paper we present ManifoldNet, a novel method to encourage learning of manifold-aware representations. Our approach segments the input manifold into a set of fragments. By assigning the corresponding segmentation id as a pseudo label to every sample, we convert the problem of preserving the local manifold structure into a point-wise classification task. Due to its unsupervised nature, the segmentation tends to be noisy. We mitigate this by introducing ensemble manifold segmentation (EMS). EMS accounts for the manifold structure by dividing the training data into an ensemble of classification training sets that contain samples of local proximity. CNNs are trained on these ensembles under a multi-task learning framework to conform to the manifold. ManifoldNet can be trained with only the pseudo labels or together with task-specific labels. We evaluate ManifoldNet on two different tasks: network imitation (distillation) and semi-supervised learning. Our experiments show that the manifold structures are effectively utilized for both unsupervised and semi-supervised learning.
Dynamical model development and parameter identification for an anaerobic wastewater treatment process.

This paper deals with the development and the parameter identification of an anaerobic digestion process model. A two-step (acidogenesis-methanization) mass-balance model has been considered. The model incorporates electrochemical equilibria in order to include the alkalinity, which has to play a central role in the related monitoring and control strategy of a treatment plant. The identification is based on a set of dynamical experiments designed to cover a wide spectrum of operating conditions that are likely to take place in the practical operation of the plant. A step by step identification procedure to estimate the model parameters is presented. The results of 70 days of experiments in a 1-m(3) fermenter are then used to validate the model.
SimCEC: A Collaborative VR-Based Simulator for Surgical Teamwork Education

Traditionally, the evaluation of surgical procedures in virtual reality (VR) simulators has been restricted to their individual technical aspects disregarding the procedures carried out by teams. However, some decision models have been proposed to support the collaborative training evaluation process of surgical teams in collaborative virtual environments. The main objective of this article is to present a collaborative simulator based on VR, named SimCEC, as a potential solution for education, training, and evaluation in basic surgical routines for teams of undergraduate students. The simulator considers both tasks performed individually and those carried in a collaborative manner. The main contribution of this work is to improve the discussion about VR simulators requirements (design and implementation) to provide team training in relevant topics, such as users’ feedback in real time, collaborative training in networks, interdisciplinary integration of curricula, and continuous evaluation.
Dictionary Learning for Robotic Grasp Recognition and Detection

The ability to grasp ordinary and potentially never-seen objects is an important feature in both domestic and industrial robotics. For a system to accomplish this, it must autonomously identify grasping locations by using information from various sensors, such as Microsoft Kinect 3D camera. Despite numerous progress, significant work still remains to be done in this field. To this effect, we propose a dictionary learning and sparse representation (DLSR) framework for representing RGBD images from 3D sensors in the context of determining such good grasping locations. In contrast to previously proposed approaches that relied on sophisticated regularization or very large datasets, the derived perception system has a fast training phase and can work with small datasets. It is also theoretically founded for dealing with masked-out entries, which are common with 3D sensors. We contribute by presenting a comparative study of several DLSR approach combinations for recognizing and detecting grasp candidates on the standard Cornell dataset. Importantly, experimental results show a performance improvement of 1.69% in detection and 3.16% in recognition over current state-of-the-art convolutional neural network (CNN). Even though nowadays most popular vision-based approach is CNN, this suggests that DLSR is also a viable alternative with interesting advantages that CNN has not.
Series Resistance Reduction in Stacked Nanowire FETs for 7-nm CMOS Technology

Vertically stacked nanowire field effect transistors currently dominate the race to become mainstream devices for 7-nm CMOS technology node. However, these devices are likely to suffer from the issue of nanowire stack position dependent drain current. In this paper, we show that the nanowire located at the bottom of the stack is farthest away from the source/drain silicide contacts and suffers from higher series resistance as compared to the nanowires that are higher up in the stack. It is found that upscaling the diameter of lower nanowires with respect to the upper nanowires improved uniformity of the current in each nanowire, but with the drawback of threshold voltage reduction. We propose to increase source/drain trench silicide depth as a more promising solution to this problem over the nanowire diameter scaling, without compromising on power or performance of these devices.