nils-herrmann/clean_scirepeval huggingface dataset:
A bibliometric analysis of acupuncture research trends in Acupuncture in Medicine
Venue: Acupuncture in medicine journal of the British Medical Acupuncture Society
Year: 2019

Over the past 20 years, acupuncture research has steadily increased, and its growth rate is twofold greater than that of biomedical research overall.1 The two primary questions associated with acupuncture treatment are: (1) whether it works; and (2) how it works. A large number of clinical studies have investigated the efficacy of acupuncture treatment for various diseases2,3 and many have evaluated its underlying mechanisms in humans and other animals.4,5 However, it is necessary to understand the current status of acupuncture research as a whole. Bibliometric analysis has been used to estimate research trends in many scientific fields and is a powerful quantitative analysis tool that provides a macroscopic overview of a significant amount of academic literature, providing supporting evidence for policy making, clinical guidelines, and research  Thus, the present study analysed and sought patterns in acupuncture research to comprehend the current state of research in this area. To accomplish this, bibliometric analyses were applied to evaluate research trends in the journal Acupuncture in Medicine.
A vehicle assisted offloading scheme for hotspot base stations on metropolitan streets
Venue: 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)
Year: 2017

With field measurement data, it indicates that the data traffic of cellular networks is highly nonuniform along the city streets. The nonuniformity of the data traffic leads to overload in hot zones and inefficient use of wireless resources in light loaded zones. However, the intuitive solution is costly for such scenario of streets. To solve this problem, we propose a vehicle assisted offloading (VAO) scheme, which utilizes the vehicle queue stopping at the red light to offload data from the busy intersection cell to its adjacent cells that are largely idle. The performance of VAO is theoretically analyzed by considering both the road traffic conditions and the vehicle pedestrian communications. An architecture is also proposed to enable the offloading scheme. Simulations with practical parameters have confirmed our analysis and showed that the VAO is capable of achieving significant performance gain.
A literatura dramatica de Maria Clara Machado no teatro brasileiro moderno
Year: 2020

Esse texto surgiu a partir de uma pesquisa em nivel de mestrado e apresenta os desdobramentos das experiencias com o uso da literatura dramatica de Maria Clara Machado no ambiente escolar. O referencial teorico esta apoiado em textos cientificos de Beatriz Cabral, Sabato Magaldi, Bruno Betelheim entre outros. A proposta desse artigo e apontar o quao potente e a literatura dramatica de Maria Clara Machado, podendo a sua obra ser mais utilizada como conteudo pedagogico na educacao.
Detecting spammers on social networks
Venue: Neurocomputing
Year: 2015

Social network has become a very popular way for internet users to communicate and interact online. Users spend plenty of time on famous social networks (e.g. Facebook, Twitter, Sina Weibo, etc. reading news, discussing events and posting messages. Unfortunately, this popularity also attracts a significant amount of spammers who continuously expose malicious behavior (e.g. post messages containing commercial URLs, following a larger amount of users, etc. leading to great misunderstanding and inconvenience on users? social activities. In this paper, a supervised machine learning based solution is proposed for an effective spammer detection. The main procedure of the work is: first, collect a dataset from Sina Weibo including 30,116 users and more than 16 million messages. Then, construct a labeled dataset of users and manually classify users into spammers and non spammers. Afterwards, extract a set of feature from message content and users? social behavior, and apply into SVM (Support Vector Machines) based spammer detection algorithm. The experiment shows that the proposed solution is capable to provide excellent performance with true positive rate of spammers and non spammers reaching 99.1% and 99.9% respectively.
Modeling of Wind Turbine Simulator for analysis of the wind energy conversion system using MATLAB/Simulink
Venue: 2015 IEEE Conference on Energy Conversion (CENCON)
Year: 2015

This paper presents mathematical modeling, design and development of Wind Turbine Simulator (WTS) based on DC motor, suitable for analysis of the wind energy conversion system. The WTS is designed with the help of built in blocks available in MATLAB/Simulink. Instead of the wind turbine rotor, a DC motor has been used as a source of torque generating which is driven by controlled DC voltage source. Specially designed PI controller is used which guarantees that the DC motor is following the theoretical (reference) rotational speed of the turbine rotor. The designed system offers wide range flexibility and provides a friendly environment in which wind speed and turbine parameters can be changed easily. This designed simulator will help researchers, engineers and designers to analyze the operation and understand the behavior of the wind energy conversion system. The system design, the model used, and obtained results are presented in this paper.
Impacts of recycled crumb rubber powder and natural rubber latex on the modified asphalt rheological behaviour, bonding, and resistance to shear
Year: 2020

The objective of this study is to investigate the rheological properties, bonding and resistancetoshear of the rubberized modified binders under different conditions. A base asphalt binder with penetration grade 60/70 was utilised in this study. The crumb rubber and natural rubber latex were added at different percentages for the modified bitumen production, and computed based on the mass of bitumen. Silane additives at the rate of 0.1% by mass of asphalt binder were used as a surfactant. A series of rheological properties and recovery test were conducted on all binders. While bond test and Layer Parallel Direct Shear (LPDS) test were carried out in order to simulate the crack movement of pavement. The results show that the addition of crumb rubber and latex positively impacts the performance of the asphalt binder. The crumb rubber and natural rubber latex modified asphalt binder has comparatively identical elastic recovery outcomes, while it also helps in enhancing the stiffness in terms of softening and penetration value. However, natural rubber latex modified asphalt binder performed much better than crumb rubber modified asphalt binder in terms of torsional recovery. Whereas, rotational viscosity test has been useful in adopting the suitable temperature which is 160 degC in order to satisfy the needs of better workability and to ensure it is pumpable. Storage stability test proved that the modified asphalt binders are homogenous since the temperature differences are less than 5 degC. The dynamic shear rheometer (DSR) test proved that the modified asphalt binder has better resistance to rutting. Through DSR findings and activation energy, the modified asphalt binder were catogorize as less susceptible to temperature changes. Finally, the crumb rubber modified asphalt binder did well in terms of bonding strength, while the natural rubber latex modified asphalt binder performed well in terms of resistance to shear. Overall, both rubberized modified binders performed better than the control sample.
Nanotube mode locked, wavelength and pulsewidth tunable thulium fiber laser.
Venue: Optics express
Year: 2019

Mode locked oscillators with highly tunable output characteristics are desirable for a range of applications. Here, with a custom made tunable filter, we demonstrate a carbon nanotube (CNT) mode locked thulium fiber laser with widely tunable wavelength, spectral bandwidth, and pulse duration. The demonstrated laser's wavelength tuning range reached 300 nm (from 1733 nm to 2033 nm) which is the widest ever that was reported for rare earth ion doped fiber oscillators in the near infrared. At each wavelength, the pulse duration can be regulated by changing the filter's bandwidth. For example, at ~1902 nm, the pulse duration can be adjusted from 0.9 ps to 6.4 ps (the corresponding output spectral bandwidth from 4.3 nm to 0.6 nm) Furthermore, we experimentally and numerically study the spectral evolution of the mode locked laser in presence of a tunable filter, a topic that has not been thoroughly investigated for thulium doped fiber lasers. The detailed dynamical change of the mode locked spectra is presented and we observed gradual suppression of the Kelly sidebands as the filter's bandwidth is reduced. Further, using the polarization maintaiing (PM) cavity ensures that the laser is stable and the output laser's polarization extinction ratio is measured to exceed 20 dB.
Skeleton embedded motion body partition for human action recognition using depth sequences
Venue: Signal Process.
Year: 2018

The low cost depth cameras have facilitated the research of human action recognition in the last decades. Despite various approaches have been presented to improve the recognition accuracy, they are rarely extended to online recognition task in clutter scenes. In this paper, we propose an effective approach, which is insensitive to various temporal duration and adequate for complex background, for human action recognition using depth sequences. By embedding the skeleton information into depth maps, the human body is partitioned to a set of motion parts, which could take account of the geometrical structure of human body and contribute to the recognition task in complex background. A local spatio temporal scaled pyramid is applied to obtain compact local feature representation. The simplified Fisher vector encoding method is introduced to aggregate local coarse features into a discriminative representation with unified form. The proposed approach is validated on three public benchmark datasets, i.e. MSR Daily Activity 3D, MSR Action Pairs, and MSR Action 3D. The experimental results demonstrate the effectiveness and feasibility of proposed approach for real time applications.
Affective Reactivity to Daily Stressors and Long Term Risk of Reporting a Chronic Physical Health Condition
Venue: Annals of behavioral medicine a publication of the Society of Behavioral Medicine
Year: 2013

BackgroundDaily stressors, such as an argument with a spouse or an impending deadline, are associated with short term changes in physical health symptoms. Whether these minor hassles have long term physical health ramifications, however, is largely unknown.PurposeThe current study examined whether exposure and reactivity to daily stressors is associated with long term risk of reporting a chronic physical health condition.MethodsParticipants (N 435) from the National Study of Daily Experiences completed a series of daily diary interviews between 1995 and 1996 and again 10 years later.ResultsGreater affective (i.e. emotional) reactivity to daily stressors at time 1 was associated with an increased risk of reporting a chronic physical health condition at time 2.ConclusionResults indicate that how people respond to the daily stressors in their lives is predictive of future chronic health conditions.
Managing the semiotics of skin tone: Race and aesthetic labor in the fashion modeling industry
Year: 2012

This article explores the aesthetic labor of embodying race. The author's research on fashion models in New York City uncovered a demand for aesthetic labor that differs along racial lines, namely, black models must fit themselves to a narrower set of standards, and experience their race as both an asset and a liability. This difference is evident in the context of the market for black models, where the "white gaze," and the "corporate gaze" intersect. Yet both employers' desire for workers with a particular "look," and workers' willingness to call on personal resources to style that "look" for the job foster a structural bias toward racist practices that are masked by appeals to "aesthetics." Managing one's racial appearance reveals a unique quality of aesthetic labor that emerges only when race is taken into account, arguing for its inclusion among the characteristics workers manipulate when their work is studied as aesthetic labor.
Jean Baptiste Charcot in Rio de Janeiro: glamorous trip and celebrity in 1908.
Venue: Arquivos de neuro psiquiatria
Year: 2015

The authors review the visit of Commander Charcot and the crew of his ship, the "Pourquoi Pas?" to Rio de Janeiro, Brazil, in 1908, where he stayed for eight days, while en route as part of the second French expedition to the Antarctic. It was a glamorous stay as Commander Charcot was treated as a true star and international celebrity, befitting his position.
Pulse proteins: secondary structure, functionality and applications
Venue: Journal of Food Science and Technology
Year: 2019

AbstractPulses are the second most important source of food for humans after cereals. They hold an important position in human nutrition. They are rich source of proteins, complex carbohydrates, essential vitamins, minerals and phytochemicals and are low in lipids. Pulses are also considered the most suitable for preparing protein ingredients (concentrates and isolates) because of their high protein content, wide acceptability and low cost. In addition, pulse proteins exhibit functional properties (foaming and emulsification, water and fat absorption and gelation) as well as nutraceutical/health benefiting properties which makes them healthier and low cost alternative to conventional protein sources like soy, wheat and animals. Proteins from different pulses (beans, peas, lentils, cowpeas, chickpeas, pigeon peas, etc. differ in their composition and structure hence for finished product suitability. Therefore, this article aimed to review composition, structure function relationship and current applications of different pulse proteins in the food industry.
Recall Traces: Backtracking Models for Efficient Reinforcement Learning
Venue: ICLR
Year: 2019

In many environments only a tiny subset of all states yield high reward. In these cases, few of the interactions with the environment provide a relevant learning signal. Hence, we may want to preferentially train on those high reward states and the probable trajectories leading to them. To this end, we advocate for the use of a backtracking model that predicts the preceding states that terminate at a given high reward state. We can train a model which, starting from a high value state (or one that is estimated to have high value) predicts and sample for which the (state, action) tuples may have led to that high value state. These traces of (state, action) pairs, which we refer to as Recall Traces, sampled from this backtracking model starting from a high value state, are informative as they terminate in good states, and hence we can use these traces to improve a policy. We provide a variational interpretation for this idea and a practical algorithm in which the backtracking model samples from an approximate posterior distribution over trajectories which lead to large rewards. Our method improves the sample efficiency of both on and off policy RL algorithms across several environments and tasks.
Improving the performance of EEG decoding using anchored STFT in conjunction with gradient norm adversarial augmentation
Venue: ArXiv
Year: 2020

Brain computer interfaces (BCIs) enable direct communication between humans and machines by translating brain activity into control commands. EEG is one of the most common sources of neural signals because of its inexpensive and non invasive nature. However, interpretation of EEG signals is non trivial because EEG signals have a low spatial resolution and are often distorted with noise and artifacts. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. Nowadays, state of the art deep learning algorithms have proven to be quite efficient in learning hidden, meaningful patterns. However, the performance of the deep learning algorithms depends upon the quality and the amount of the provided training data. Hence, a better input formation (feature extraction) technique and a generative model to produce high quality data can enable the deep learning algorithms to adapt high generalization quality. In this study, we proposed a novel input formation (feature extraction) method in conjunction with a novel deep learning based generative model to harness new training examples. The feature vectors are extracted using a modified Short Time Fourier Transform (STFT) called anchored STFT. Anchored STFT, inspired by wavelet transform, tries to minimize the tradeoff between time and frequency resolution. As a result, it extracts the inputs (feature vectors) with better time and frequency resolution compared to the standard STFT. Secondly, we introduced a novel generative adversarial data augmentation technique called gradient norm adversarial augmentation (GNAA) for generating more training data. Thirdly, we investigated the existence and significance of adversarial inputs in EEG data. Our approach obtained the kappa value of 0.814 for BCI competition II dataset III and 0.755 for BCI competition IV dataset 2b for session to session transfer on test data.
NLRP3 Inflammasome Mediated Interleukin 1b Production in Cancer Associated Fibroblast Contributes to ALA PDT for Cutaneous Squamous Cell Carcinoma
Venue: Cancer management and research
Year: 2019

Long term tumor control following PDT is a result of its direct effect on tumor and vasculature in combination with induction of inflammatory reactions upregulating the immune system. When PDT induces necrosis of tumors and vascular system, an immune cascade can be initiated to release all kinds of cytokines including IL1b. This further leads to the activation of inflammatory cells and hence death of tumor cells.  Ultraviolet irradiation was used to induce cSCC mice model, gene chip was used to screen inflammatory cytokines, qPCR, ELISA and implanted tumor mice model were used to verify the changes and important role of interleukin 1b, and WB preliminarily explored the production mechanism of interleukin 1b.  Inflammatory cytokines and receptors transcript screening identify IL1r1 as the top4. After ALA PDT, IL1r1 and IL1b increased in patients' biopsies, principally in mesenchymal cells. In vivo, the inhibition of ALA PDT on tumor growth of cutaneous squamous cell carcinoma (cSCC) mice in the group with intralesional injection of anti IL1b mAb or caspase1 inhibitor was significantly weaker than the control groups. Furthermore, NLRP3 inflammasome and p p65/p65 were elevated after ALA PDT mediated IL1b production in cancer associated fibroblasts.  By means of activating NLRP3 inflammasome with IL1b production in CAFs, PDT stimulates local acute inflammatory response, which further promotes PDT effect for cSCC.
A Survey and Taxonomy of FPGA based Deep Learning Accelerators
Venue: J. Syst. Archit.
Year: 2019

Deep learning, the fastest growing segment of Artificial Neural Network (ANN) has led to the emergence of many machine learning applications and their implementation across multiple platforms such as CPUs, GPUs and reconfigurable hardware (Field Programmable Gate Arrays or FPGAs) However, inspired by the structure and function of ANNs, large scale deep learning topologies require a considerable amount of parallel processing, memory resources, high throughput and significant processing power. Consequently, in the context of real time hardware systems, it is crucial to find the right trade off between performance, energy efficiency, fast development, and cost. Although limited in size and resources, several approaches have showed that FPGAs provide a good starting point for the development of future deep learning implementation architectures. Through this paper, we briefly review recent work related to the implementation of deep learning algorithms in FPGAs. We will analyze and compare the design requirements and features of existing topologies to finally propose development strategies and implementation architectures for better use of FPGA based deep learning topologies. In this context, we will examine the frameworks used in these studies, which will allow testing a lot of topologies to finally arrive at the best implementation alternatives in terms of performance and energy efficiency.
Fruits and vegetables intake and gastric cancer risk: A pooled analysis within the Stomach cancer Pooling Project
Venue: International journal of cancer
Year: 2020

A low intake of fruits and vegetables is a risk factor for gastric cancer, although there is uncertainty regarding the magnitude of the associations. In our study, the relationship between fruits and vegetables intake and gastric cancer was assessed, complementing a previous work on the association betweenconsumption of citrus fruits and gastric cancer. Data from 25 studies (8456 cases and 21 133 controls) with information on fruits and/or vegetables intake were used. A two stage approach based on random effects models was used to pool study specific adjusted (sex, age and the main known risk factors for gastric cancer) odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) Exposure response relations, including linear and nonlinear associations, were modeled using one and two order fractional polynomials. Gastric cancer risk was lower for a higher intake of fruits (OR: 0.76, 95% CI: 0.64 0.90) noncitrus fruits (OR: 0.86, 95% CI: 0.73 1.02) vegetables (OR: 0.68, 95% CI: 0.56 0.84) and fruits and vegetables (OR: 0.61, 95% CI: 0.49 0.75) results were consistent across sociodemographic and lifestyles categories, as well as study characteristics. Exposure response analyses showed an increasingly protective effect of portions/day of fruits (OR: 0.64, 95% CI: 0.57 0.73 for six portions) noncitrus fruits (OR: 0.71, 95% CI: 0.61 0.83 for six portions) and vegetables (OR: 0.51, 95% CI: 0.43 0.60 for 10 portions) A protective effect of all fruits, noncitrus fruits and vegetables was confirmed, supporting further dietary recommendations to decrease the burden of gastric cancer.
Italian suggestions for pulmonary rehabilitation in COVID 19 patients recovering from acute respiratory failure: results of a Delphi process.
Venue: Monaldi archives for chest disease Archivio Monaldi per le malattie del torace
Year: 2020

There is a need of consensus about the pulmonary rehabilitation (PR) in patients with COVID 19 after discharge from acute care. To facilitate the knowledge of the evidence and its translation into practice, we developed suggestions based on experts' opinion. A steering committee identified areas and questions sent to experts. Other international experts participated to a RAND Delphi method in reaching consensus and proposing further suggestions. Strong agreement in suggestions was defined when the mean agreement was >7 (1 no agreement and 9 maximal agreement) Panelists response rate was >95% Twenty three questions from 4 areas: Personnel protection equipment, phenotypes, assessments, interventions, were identified and experts answered with 121 suggestions, 119 of which received high level of concordance. The evidence based suggestions provide the clinicians with current evidence and clinical experts opinion. This framework can be used to facilitate clinical decision making within the context of the individual patient. Further studies will evaluate the clinical usefulness of these suggestions.