nils-herrmann/clean_scirepeval huggingface dataset:
When random search is not enough: Sample Efficient and Noise Robust Blackbox Optimization of RL Policies
Venue: ArXiv
Year: 2019

Interest in derivative free optimization (DFO) and "evolutionary strategies" (ES) has recently surged in the Reinforcement Learning (RL) community, with growing evidence that they match state of the art methods for policy optimization tasks. However, blackbox DFO methods suffer from high sampling complexity since they require a substantial number of policy rollouts for reliable updates. They can also be very sensitive to noise in the rewards, actuators or the dynamics of the environment. In this paper we propose to replace the standard ES derivative free paradigm for RL based on simple reward weighted averaged random perturbations for policy updates, that has recently become a subject of voluminous research     by an algorithm where gradients of blackbox RL functions are estimated via regularized regression methods. In particular, we propose to use L1/L2 regularized regression based gradient estimation to exploit sparsity and smoothness, as well as LP decoding techniques for handling adversarial stochastic and deterministic noise. Our methods can be naturally aligned with sliding trust region techniques for efficient samples reuse to further reduce sampling complexity. This is not the case for standard ES methods requiring independent sampling in each epoch. We show that our algorithms can be applied in locomotion tasks, where training is conducted in the presence of substantial noise, e.g. for learning in sim transferable stable walking behaviors for quadruped robots or training quadrupeds how to follow a path. We further demonstrate our methods on several OpenAI Gym Mujoco RL tasks. We manage to train effective policies even if up to 25% of all measurements are arbitrarily corrupted, where standard ES methods produce sub optimal policies or do not manage to learn at all. Our empirical results are backed by theoretical guarantees.
Online travel mode detection method using automated machine learning and feature engineering
Venue: Future Gener. Comput. Syst.
Year: 2019

Online travel mode detection provides context information useful for location based services, in order to deliver a customized user experience. In the last years, many smartphone based travel mode detection techniques have been proposed, but few explored the usage of dimensionality reduction in conjunction with hyperparameter optimization to improve accuracy with a reduced cost. In this paper, we propose a method to improve the accuracy and computational cost trade off of travel mode detection, in which use state of the art Feature Engineering and Automated Machine Learning techniques. In addition, we apply the proposed method in a real mobility dataset using different features and parameters. Our experiments showed that the combination of these techniques can greatly improve online detection performance.
A review on lignin structure, pretreatments, fermentation reactions and biorefinery potential.
Venue: Bioresource technology
Year: 2019

In recent years, lignin valorization is commercially an important and advanced sustainable process for lignocellulosic biomass based industries, primarily through the depolymerization path. The conversion of the lignin moieties into biofuels and other high value added products are still challenging to the researchers due to the heterogeneity and complex structure of lignin containing biomass. Besides, the involvement of different microorganisms that carries varying metabolic and enzymatic complex systems towards degradation and conversion of the lignin moieties also discussed. These microorganisms are frequently short of the traits which are obligatory for the industrial application to achieve maximum yields and productivity. This review mainly focuses on the current progress and developments in the pretreatment routes for enhancing lignin degradation and also assesses the liquid and gaseous biofuel production by fermentation, gasification and hybrid technologies along with the biorefinery schemes which involves the synthesis of high value added chemicals, biochar and other valuable products.
Integrated market selection and production planning: complexity and solution approaches
Venue: Math. Program.
Year: 2012

Emphasis on effective demand management is becoming increasingly recognized as an important factor in operations performance. Operations models that account for supply costs and constraints as well as a supplier's ability to influence demand characteristics can lead to an improved match between supply and demand. This paper presents a class of optimization models that allow a supplier to select, from a set of potential markets, those markets that provide maximum profit when production/procurement economies of scale exist in the supply process. The resulting optimization problem we study possesses an interesting structure and we show that although the general problem is \mathcal{NP} complete, a number of relevant and practical special cases can be solved in polynomial time. We also provide a computationally very efficient and intuitively attractive heuristic solution procedure that performs extremely well on a large number of test instances.
A survey of deep learning frameworks
Venue: 2017 International Conference on Inventive Systems and Control (ICISC)
Year: 2017

Deep learning is a model of machine learning loosely based on our brain. Artificial neural network has been around since the 1950s, but recent advances in hardware like graphical processing units (GPU) software like cuDNN, TensorFlow, Torch, Caffe, Theano, Deeplearning4j, etc. and new training methods have made training artificial neural networks fast and easy. In this paper, we are comparing some of the deep learning frameworks on the basis of parameters like modeling capability, interfaces available, platforms supported, parallelizing techniques supported, availability of pre trained models, community support and documentation quality.
Association between nucleos(t)ide analog and tumor recurrence in hepatitis B virus related hepatocellular carcinoma after radiofrequency ablation
Venue: Hepatology
Year: 2016

Radiofrequency ablation (RFA) is the best choice for curative treatment of hepatocellular carcinoma (HCC) cases not suitable for surgical intervention, but efforts should be made to reduce the risk of tumor recurrence. We aimed to investigate the association between nucleos(t)ide analog (NA) therapy for hepatitis B virus (HBV) and the risk of HCC recurrence post RFA. Using the Taiwan National Health Insurance Research Database between July 1, 2004 and December 31, 2012, we screened 48,807 patients with newly diagnosed HBV related HCC. We identified 850 patients (200 patients who used NAs for more than 90 days and 650 who never used NA post RFA) who received RFA as a potentially curative treatment for HCC. Patients in the NA treated cohort were randomly matched 1:2 with patients in the untreated cohort by age, sex, cirrhosis, and the time period between RFA and initiation of NA therapy. Finally, 133 patients were recruited in the NA treated group and 266 in the untreated group for analysis. Cumulative incidences of and hazard ratios (HRs) for HCC recurrence were analyzed after adjusting for competing mortality. The HCC recurrence rate of the NA treated group was significantly lower than that of the untreated group (2 year recurrence rate: 41.8% 95% confidence interval [CI] 32.9 50.6 vs. 54.3% 95% CI: 48.0 60.6; modified log rank test: P 0.05) In modified Cox's regression analysis, NA therapy was independently associated with a decreased risk of HCC recurrence (HR, 0.69; 95% CI: 0.50 0.95; P 0.05) Multivariate stratified analyses verified the association of NA therapy and decreased HCC recurrence in almost all patient subgroups.  NA therapy was associated with a decreased risk of HCC recurrence among patients with HBV related HCC post RFA.
Anonymous Chat sebagai Dampak Maraknya Cyberbully
Venue: Jurnal Spektrum Komunikasi
Year: 2019

Pengguna internet di Indonesia mencapai 75,57 juta orang dan telah mencapai 82 juta orang dimana hampir 50% penggunanya adalah remaja berusia 15 22 tahun menurut Markplus Insight tahun 2013 dan Kemenkominfo tahun 2014. Dengan pengguna internet yang tinggi, Akamai Technologies, Inc. melaporkan pada 17 Oktober 2013 cyberbully di Indonesia merupakan yang tertinggi dengan angka 38% Fakta ini meninggalkan dua pilihan: tetap menggunakan wadah online sebaik baiknya dengan kerja keras tentunya, atau meninggalkan aktivitas online sama sekali. Padahal, menurut Horney (1945) setiap orang itu pada dasarnya memiliki tiga kebutuhan dasar. Kebutuhan pertama adalah kebutuhan mendekati orang lain atau publik guna mendapatkan cinta atau pun pengakuan. Ini bisa berarti berbagi curhat (curahan hati) Kebutuhan kedua adalah menjauhi publik guna mendapat kebebasan dan kemandirian. Terakhir adalah kebutuhan menentang publik guna mendapatkan kekuasaan atau kekuatan. Hanya saja, mengingat internet merupakan hal yang tak mungkin ditinggalkan jika ingin tetap menjadi bagian dari dunia yang berkembang pesat ini, berkomunikasi melalui online dan jejaring sosial merupakan keharusan. Maka, tercipta lah wadah komunikasi bagi orang orang yang rentan terhadap cyberbully: anonymous chat. Aplikasi yang tidak menyediakan fitur berbagi foto, video atau pun identitas personal. Dominasi pengguna anonymous chat yang rentan terhadap cyberbully, merupakan orang orang berkepribadian introvert yang tak senang membagi identitas personal di depan publik melalui media sosial seperti Instagram atau Facebook. Karena pengguna merasa aman dari penghakiman massa dan memiliki kemerdekaan menyuarakan isi kepala. Data ini didapatkan setelah wawancara penulis dengan 20 pengguna Whisper (salah satu aplikasi anonymous chat) Kata kunci: Cyberbully, Anonymous chat, Whisper, kebebasan bersuara
Association between testosterone, semen parameters, and live birth in men with unexplained infertility in an intrauterine insemination population.
Venue: Fertility and sterility
Year: 2019

To determine whether men with unexplained infertility and low total T (TT) have abnormal spermatogenesis and lower fecundity.  Secondary analysis of the prospective, randomized, multicenter clinical trial, Assessment of Multiple Intrauterine Gestations from Ovarian Stimulation (AMIGOS) SETTING Infertility clinics.  Nine hundred couples with unexplained infertility enrolled in AMIGOS. Semen analysis with an ejaculate of at least 5 million total motile sperm was required for enrollment. For inclusion in this secondary analysis, a fasting TT was required.  Logistic regression, adjusted for age and body mass index, assessed the association between low TT (defined as <264 ng/dL) semen parameters, and pregnancy outcome. RESULT(S) Seven hundred eighty one men (mean age, 34.2 5.7 years) with a median (interquartile range) TT of 411 (318 520) ng/dL were included. Men with TT <264 ng/dL were less likely to have normal =4% strict Kruger) morphology (unadjusted odds ratio [OR] 0.56; 95% confidence interval [CI] 0.34, 0.92; adjusted OR, 0.59; 95% CI, 0.35, 0.99) There was no association between low TT and semen volume 1.5 mL, sperm concentration 15 x 106/mL, or motility 40% Among couples whose male partner had low TT, 21 (18.8% had a live birth, compared with 184 (27.5% live births in couples with a male partner having TT 264 ng/dL. The odds of live birth decreased by 40% in couples whose male partner had low TT (unadjusted OR, 0.60; 95% CI, 0.36, 1.00; adjusted OR, 0.65; 95% CI, 0.38, 1.12) CONCLUSION(S) In couples with unexplained infertility, low TT in the male partner was associated with abnormal sperm morphology and lower live birth rates.
Culturally competent health care for sex workers: an examination of myths that stigmatize sex work and hinder access to care
Venue: Sexual and relationship therapy journal of the British Association for Sexual and Relationship Therapy
Year: 2019

Sex workers are individuals who offer sexual services in exchange for compensation (i.e. money, goods, or other services) Within the United States, the full service sex work (FSSW) industry generates 14 billion dollars annually there are estimated to be 1 2 million FSSWers, though experts believe this number to be an underestimate. Many FSSWers face the possibility of violence, legal involvement, and social stigmatization. As a result, this population experiences increased risk for mental health disorders. Given these risks and vulnerabilities, FSSWers stand to benefit from receiving mental health care however a constellation of individual, organizational, and systemic barriers limit care utilization. Destigmatization of FSSW and offering of culturally competent mental health care can help empower this traditionally marginalized population. The objective of the current review is to (1) educate clinicians on sex work and describe the unique struggles faced by FSSW and vulnerability factors clinicians must consider, (2) address five common myths about FSSW that perpetuate stigma, and (3) advance a research and culturally competent clinical training agenda that can optimize mental health care engagement and utilization within the sex work community.
Improved watershed histogram thresholding with probabilistic neural networks for lung cancer diagnosis for CBMIR systems
Venue: Multimedia Tools and Applications
Year: 2019

The past few decades have witnessed a steep increase in image data analysis for lung cancer, leading to huge repositories in the area of research in the medical sector. Content Based medical Image Retrieval (CBMIR) methods for lung cancer have been tried with the objective of facilitating access to image data. Many research works have been developed in content based medical image retrieval. But the techniques have the drawback of low efficiency and high computation cost. Image segmentation, extraction and classification methods of various kinds was taken upusing traditional methodswhich involves extraction of a specific region of interest and given to medical experts for diagnosis. The extracted region of interest region provides information useful for the for diagnosis of the disease. But the segmentation methods have some limitations such as flat valleys, noise sensitive and computational expensive which lead to reduction in the entire system performance. This is addressed by animproved watershed histogram thresholding using the probabilistic neural networks (IWHT PNN) approach. The algorithm introduced outperforms the existing techniques improving the segmentation ratio and recognition accuracy of lung cancer which can be validated using experimental analysis.
Diagnostic yield of Calcofluor White in the identification of candida albicans in oral squamous cell carcinoma
Year: 2021

Although oral cancer is multifactorial in origin only few had evaluated the diagnostic parameters for candidal infection in oral squamous cell carcinoma (OSCC) We aims to compare and assess the accuracy of Calcofluor Whilte (CFW) to that of conventional staining methods to identify the presence of candidal infection in OSCC. Archival collection of 43 OSCC were retrieved for this investigation. Standard staining protocol was followed for the index test (CFW) and reference standard (Periodic Acid Schiff) Two examiners were blinded for either one of the staining techniques. Diagnostic test evaluation and Kappa statistic was calculated using MedCalc software. The study comprised of 37 males, six females, and their mean age at the time of presentation was 51 (range 23 to 75 years) The sensitivity and specificity of CFW was 75.8% (CI 57.7 to 88.9% and 10.0% (CI 0.2 to 44.5% While the positive predictive and negative predictive values were 63.1% (CI 50.6% 74.0% and 67.0% (CI 49.2% 81.0% Our results show minimal agreement with PAS with a Kappa score of 0.148 (95% CI 0.39 to 0.09) Overall detection rate was similar for both the index test and reference standard although there was considerable number of intermediate test results. Sensitivity of CFW was comparable to that of earlier studies but specificity was low and the degree of concordance of 60.4% Although CFW staining can come with high false positive they are an useful test for ruling out candida infection when the test result is negative.
Benefits of Simulation and Role Playing to Teach Performance of Functional Assessments
Venue: Nursing education perspectives
Year: 2017

The use of simulation is an innovative teaching strategy that has proven to be valuable in nursing education. This article describes the benefits of a simulation lab involving faculty role play to teach baccalaureate nursing students how to properly assess the functional status of older adults. Details about the simulation lab, which involved functional assessments of two elderly community dwelling residents, are presented, along with student and faculty evaluations of this teaching modality.
Health Practitioners' Perceptions of Falls and Fall Prevention in Older People: A Metasynthesis
Venue: Qualitative health research
Year: 2019

Fall prevention has received a great deal of consideration and funding, however fall rates have not reduced accordingly. Health practitioners are key stakeholders in the process of implementing fall prevention evidence into their clinical assessment and management of older people at risk of falling. Investigating health practitioners' clinical experiences and perceptions has been identified as a means to enhance the translation of knowledge. Four databases were searched for studies exploring health practitioners' perceptions of falls and fall prevention. A metasynthesis of eight qualitative studies was conducted. The findings suggest that health practitioners face substantial barriers in the implementation of fall prevention practices. These include personal, interpersonal, and clinical barriers in addition to limitations of the research evidence. This knowledge hopes to enhance targeted dissemination of knowledge, reducing the research practice gap and improving clinical outcomes for older people at risk of falls.
Shortening patient reported outcome measures through optimal test assembly: application to the Social Appearance Anxiety Scale in the Scleroderma Patient centered Intervention Network Cohort
Venue: BMJ Open
Year: 2019

The Social Appearance Anxiety Scale (SAAS) is a 16 item measure that assesses social anxiety in situations where appearance is evaluated. The objective was to use optimal test assembly (OTA) methods to develop and validate a short form SAAS based on objective and reproducible criteria.  This study was a cross sectional analysis of baseline data from adults enrolled in the Scleroderma Patient centered Intervention Network (SPIN) Cohort.  Adults in the SPIN Cohort in the present study were enrolled at 28 centres in Canada, the USA and the UK.  The SAAS was administered to 926 adults with scleroderma. Primary and secondary measures The SAAS, Brief Fear of Negative Evaluation II (BFNE II) Brief Satisfaction with Appearance Scale (Brief SWAP) Patient Health Questionnaire 8 (PHQ8) and Social Interaction Anxiety Scale 6 (SIAS 6) were collected, as well as demographic characteristics.  OTA methods identified a maximally informative shortened version for each possible form length between 1 and 15 items. The final shortened version was selected based on prespecified criteria for reliability, concurrent validity and statistically equivalent convergent validity with the BFNE II scale. A five item short version was selected (SAAS 5) The SAAS 5 had a Cronbach's a of 0.95 and had high concurrent validity with the full length form (r=0.97) The correlation of the SAAS 5 with the BFNE II was 0.66, which was statistically equivalent to that of the full length form. Furthermore, the correlation of the SAAS 5 with the two subscales of the Brief SWAP, and the SIAS 6, were statistically equivalent to that of the full length form.  OTA was an efficient method for shortening the full length SAAS to create the SAAS 5.
Failure rates and associated risk factors of orthodontic miniscrew implants: a meta analysis.
Venue: American journal of orthodontics and dentofacial orthopedics official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
Year: 2012

Risk factors concerning orthodontic miniscrew implants have not been adequately assessed. In this systematic review, we summarize the knowledge from published clinical trials regarding the failure rates of miniscrew implants used for orthodontic anchorage purposes and identify the factors that possibly affect them.  Nineteen electronic databases and reference lists of included studies were searched up to February 2011, with no restrictions. Only randomized controlled trials, prospective controlled trials, and prospective cohort studies were included. Study selection and data extraction were performed twice. Failure event rates, relative risks, and the corresponding 95% confidence intervals were calculated. The random effects model was used to assess each factor's impact. Subgroup and meta regression analyses were also implemented.  Fifty two studies were included for the overall miniscrew implant failure rate and 30 studies for the investigation of risk factors. From the 4987 miniscrew implants used in 2281 patients, the overall failure rate was 13.5% (95% confidence interval, 11.5 15.8) Failures of miniscrew implants were not associated with patient sex or age and miniscrew implant insertion side, whereas they were significantly associated with jaw of insertion. Certain trends were identified through exploratory analysis; however, because of the small number of original studies, no definite conclusions could be drawn.  Orthodontic miniscrew implants have a modest small mean failure rate, indicating their usefulness in clinical practice. Although many factors seem to affect their failure rates, the majority of them still need additional evidence to support any possible associations.
Document level Multi aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings
Venue: COLING
Year: 2018

Document level multi aspect sentiment classification aims to predict user's sentiment polarities for different aspects of a product in a review. Existing approaches mainly focus on text information. However, the authors (i.e. users) and overall ratings of reviews are ignored, both of which are proved to be significant on interpreting the sentiments of different aspects in this paper. Therefore, we propose a model called Hierarchical User Aspect Rating Network (HUARN) to consider user preference and overall ratings jointly. Specifically, HUARN adopts a hierarchical architecture to encode word, sentence, and document level information. Then, user attention and aspect attention are introduced into building sentence and document level representation. The document representation is combined with user and overall rating information to predict aspect ratings of a review. Diverse aspects are treated differently and a multi task framework is adopted. Empirical results on two real world datasets show that HUARN achieves state of the art performances.