nils-herrmann/clean_scirepeval huggingface dataset:
Incentives for subcontractors to adopt CO2 emission reporting and reduction techniques
Year: 2011

We investigate the incentives for subcontractors (couriers) of a transport and logistics company to report about their CO2 emissions and to implement CO2 reducing technologies. Furthermore, we try to find out whether these incentives differ between British and Dutch couriers. We find that several incentives play a significant role. Subcontractors in the Netherlands predominantly are extrinsically motivated to engage in CO2 reporting and reduction techniques. This is because they are mainly driven by regulatory compliance, energy costs and implementation costs. In contrast, British subcontractors are much more intrinsically motivated to comply. They are predominantly driven by energy costs, environmental awareness, relationship building and reputation building. The contractor will have to account for these differences in making its policies work.
Performance of the Firms in a Free Trade Zone: The Role of Institutional Factors and Resources
Year: 2019

Many countries have tried to establish free zones in order to strengthen the competitive advantage of their local companies in global competition. Judging the role of free zones in the performance of companies without analyzing the relevant institutional factors can be confusing, especially in countries like Iran where the government extensively intervenes in the economy. In such economies, institutional factors (institutional support, political relationship, and legitimacy) coupled with resources (financial, human and physical resources) can be more effective at addressing companies' performance. Using the resource based view (RBV) and institutional theory, this paper surveyed 151 companies located in the Anzali Free Trade and Industrial Zone (AFTIZ) The results show that the resources have a positive impact on the firms' competitive advantage and performance. While institutional factors strengthen the resources, they do not moderate the relationship among resources, competitive advantage, and performance.
Reliability and Validity of Data Obtained From Alcohol, Cannabis, and Gambling Populations on Amazon's Mechanical Turk
Venue: Psychology of addictive behaviors journal of the Society of Psychologists in Addictive Behaviors
Year: 2017

Researchers recently have begun using Mechanical Turk (MTurk) an online crowdsourcing platform, to recruit addiction populations. However, whether the data obtained from substance users and gamblers on MTurk are reliable and valid is unknown. Herein, we assessed the internal and retest reliability of and concurrent and convergent validity of data obtained from addiction populations on MTurk. Current drinkers (N 208) cannabis users (N 200) and gamblers (N 200) residing in the United States completed measures of alcohol, cannabis, and gambling severity, psychological constructs (e.g. impulsivity) related to addictions, overt and subtle measures of valid responding, and motivations for completing MTurk studies. Of the original sample, 88 92% of participants who provided informed consent for recontact completed a reassessment 1 week later. The internal consistency of the addiction severity measures ranged from &agr; .75 to .93. The stability over 1 week ranged from &kgr; .57 to .70 for categorical classification, and intraclass correlation coefficient (ICC) .71 to .86 for continuous measures. The addiction measures were significantly correlated with each other and with other constructs related to addictive behaviors. Overall, 80 85% of participants provided valid responses. They reported attending and answering questions honestly, with financial motives being the most frequently endorsed motivation. After invalid responses were excluded, results remained the same for alcohol and gambling, but significant differences emerged for the cannabis sample. The results suggest that the self report data obtained from alcohol and gambling populations are of high quality, however, caution is warranted with cannabis populations. MTurk shows promise as a recruitment tool for some addictive behaviors.
"Plutchik" artificial intelligence chatbot for searching NCBI databases
Venue: Journal of the Medical Library Association JMLA
Year: 2018

As genetic testing gains ground in medicine, the ability to search across the suite of biomedical and clinical care databases offered through the National Library of Medicine/National Center for Biotechnology Information (NCBI) such as PubMed, GENE, Structure, the Genetic Testing Registry, and others holds the potential to enhance quality of clinical care best practices. "Plutchik" is a voice enabled, embodied artificial intelligence (AI) chatbot that can perform highly technical medical searches in and across the NCBI suite of databases.
TextCaps: a Dataset for Image Captioning with Reading Comprehension
Venue: ECCV
Year: 2020

Image descriptions can help visually impaired people to quickly understand the image content. While we made significant progress in automatically describing images and optical character recognition, current approaches are unable to include written text in their descriptions, although text is omnipresent in human environments and frequently critical to understand our surroundings. To study how to comprehend text in the context of an image we collect a novel dataset, TextCaps, with 145k captions for 28k images. Our dataset challenges a model to recognize text, relate it to its visual context, and decide what part of the text to copy or paraphrase, requiring spatial, semantic, and visual reasoning between multiple text tokens and visual entities, such as objects. We study baselines and adapt existing approaches to this new task, which we refer to as image captioning with reading comprehension. Our analysis with automatic and human studies shows that our new TextCaps dataset provides many new technical challenges over previous datasets.
video 2 net Extracting dynamic interaction networks from multi person discussion videos
Year: 2018

Networks in multi person discussions, such as meetings and games, elicit interpersonal and group dynamics that reveal liking, trust, and deception relations. Extracting interaction networks from videos of group discussions is challenging because discussions are highly dynamic and all participants' behaviors are inter dependent. Here we present an algorithm, called video2net, a multi layered collective classification model to extract dynamic interaction networks from multi person discussion videos. Every participant is modeled using a separate classifier that predicts the participant's focus of attention every 1/3rd second. video2net incorporates two crucial dependencies in the model: temporal dependency and interparticipant dependency. First, video2net ensures temporal smoothness of a participant's prediction by using the participant's output from the previous time step as an input in the current time step. Second, video2net incorporates inter participant dependencies by creating a multi layer collective classification model such that the predictions of all participants' classifiers are used as inputs to every participant's classifier. We evaluate the performance of video2net on a novel dataset of 5 videos (total of 35 participants, 109 minutes, and 7604 total ground truth labels) on the popular Resistance game in supervised prediction. We compare video2net with three state of the art algorithms. We show that video2net outperforms the best algorithm by 11.5% accuracy in predicting a participant's focus of attention in the next 1/3rd second. More interestingly, we show that video2net consistently performs the best in longer term predictions up to 3 seconds into the future. Finally, we conduct an ablation study to demonstrate that the collective classification component of video2net contributes the most the performance. Altogether, this work presents a computational approach to extract dynamic interaction networks from discussion videos and paves the way towards network analysis of discussions.  Chongyang Bai, Srijan Kumar, Jure Leskovec, Miriam Metzger, Jay Nunamaker, and V.S. Subrahmanian. 2018.  Extracting dynamic interaction networks from multi person discussion videos.  Conference'17, July 2017, Washington, DC, USA 2018. ACM ISBN 978 x xxxx xxxx x/YY/MM. $15.00 https:/doi.org/10.1145/nnnnnnn.nnnnnnn
The effect of an acute sleep hygiene strategy following a late night soccer match on recovery of players
Venue: Chronobiology international
Year: 2016

Elite soccer players are at risk of reduced recovery following periods of sleep disruption, particularly following late night matches. It remains unknown whether improving sleep quality or quantity in such scenarios can improve post match recovery. Therefore, the aim of this study was to investigate the effect of an acute sleep hygiene strategy (SHS) on physical and perceptual recovery of players following a late night soccer match. In a randomised cross over design, two highly trained amateur teams (20 players) played two late night (20:45) friendly matches against each other seven days apart. Players completed an SHS after the match or proceeded with their normal post game routine (NSHS) Over the ensuing 48 h, objective sleep parameters (sleep duration, onset latency, efficiency, wake episodes) countermovement jump (CMJ; height, force production) YoYo Intermittent Recovery test (YYIR2; distance, maximum heart rate, lactate) venous blood (creatine kinase, urea and c reactive protein) and perceived recovery and stress markers were collected. Sleep duration was significantly greater in SHS compared to NSHS on match night (P 0.002, d 1.50) with NSHS significantly less than baseline (P 0.001, d 1.95) Significant greater wake episodes occurred on match night for SHS (P 0.04, d 1.01) without significant differences between or within conditions for sleep onset latency (P 0.12) efficiency (P 0.39) or wake episode duration (P 0.07) No significant differences were observed between conditions for any physical performance or venous blood marker (all P 0.05) although maximum heart rate during the YYIR2 was significantly higher in NSHS than SHS at 36 h post match (P 0.01; d 0.81) There were no significant differences between conditions for perceptual "overall recovery" (P 0.47) or "overall stress" (P 0.17) Overall, an acute SHS improved sleep quantity following a late night soccer match; albeit without any improvement in physical performance, perceptual recovery or blood borne markers of muscle damage and inflammation.
Reconstructing 3D Lung Shape from a Single 2D Image during the Deaeration Deformation Process using Model based Data Augmentation
Venue: 2019 IEEE EMBS International Conference on Biomedical Health Informatics (BHI)
Year: 2019

Three dimensional (3D) shape reconstruction is particularly important for computer assisted medical systems, especially in the case of lung surgeries, where large deaeration deformation occurs. Recently, 3D reconstruction methods based on machine learning techniques have achieved considerable success in computer vision. However, it is difficult to apply these approaches to the medical field, because the collection of a massive amount of clinic data for training is impractical. To solve this problem, this paper proposes a novel 3D shape reconstruction method that adopts both data augmentation techniques and convolutional neural networks. In the proposed method, a deformable statistical model of the 3D lungs is designed to augment various training data. As the experimental results demonstrate, even with a small database, the proposed method can realize 3D shape reconstruction for lungs during a deaeration deformation process from only one captured 2D image. Moreover, the proposed data augmentation technique can also be used in other fields where the training data are insufficient.
Challenging \beta$ VAE with \beta 1$ for Disentanglement Via Dynamic Learning
Year: 2020

This paper challenges the common assumption that the weight of \beta$ VAE should be larger than $1$ in order to effectively disentangle latent factors. We demonstrate that \beta$ VAE with \beta \leq 1$ can not only obtain good disentanglement but significantly improve the reconstruction accuracy via dynamic control. The goal of this paper is to deal with the trade off problem between reconstruction accuracy and disentanglement with unsupervised learning. The existing methods, such as \beta$ VAE and FactorVAE, assign a large weight in the objective, leading to high reconstruction errors in order to obtain better disentanglement. To overcome this problem, ControlVAE is recently developed to dynamically tune the weight to achieve the trade off between disentangling and reconstruction using control theory. However, ControlVAE cannot fully decouple disentanglement learning and reconstruction, because it suffers from overshoot problem of the designed controller and does not timely respond to the target KL divergence at the beginning of model training. In this paper, we propose a novel DynamicVAE that leverages an incremental PI controller, a variant of proportional integral derivative controller (PID) controller, and moving average as well as hybrid annealing method to effectively decouple the reconstruction and disentanglement learning. We then theoretically prove the stability of the proposed approach. Evaluation results on benchmark datasets demonstrate that DynamicVAE significantly improves the reconstruction accuracy for the comparable disentanglement compared to the existing methods. More importantly, we discover that our method is able to separate disentanglement learning and reconstruction without introducing any conflict between them.
Prognostic Role of Serum Albumin, Total Lymphocyte Count, and Mini Nutritional Assessment on Outcomes After Geriatric Hip Fracture Surgery: A Meta Analysis and Systematic Review.
Venue: The Journal of arthroplasty
Year: 2019

Hip fracture is a significant health risk for older adults and malnutrition indicates hip fracture risk.  We evaluated whether nutrition status could predict clinical outcomes and mortality after hip fracture surgery in older adults. MEDLINE, Cochrane, EMBASE, and Google Scholar databases were searched for studies published until July 1, 2018, in patients with serum albumin or total lymphocyte count (TLC) at admission, nutritional status by Mini Nutritional Assessment (MNA) and in hospital follow up. Data extracted were analyzed using random effects or fixed effects models.  Nineteen studies with 34,363 adults aged 74 85 years receiving hip fracture surgery were eligible for inclusion. Among these studies, 13 were screened for low albumin, 4 were evaluated for TLC, and 4 for nutritional status by MNA. Hypoalbuminemia was significantly associated with higher total mortality and higher risk of in hospital death (both P .001) Low TLC and MNA results "at risk of malnutrition" (hazard ratio, 1.67; 95% confidence interval 1.28 2.18) and "malnourished" nutritional status (hazard ratio, 2.65; 95% CI 1.81 3.88) also were significantly associated with higher total mortality (all P .001) CONCLUSION Low serum albumin level is a sole indicator for increased risk of in hospital death, postoperative complications, and total mortality after hip fracture surgery in older adults. Low TLC and malnutrition classified by MNA predict increased mortality. These indicators provide valuable prognostic information and routine use may be prudent.
Intermittent high dose treatment with erlotinib enhances therapeutic efficacy in EGFR mutant lung cancer
Venue: Oncotarget
Year: 2015

Treatment with EGFR kinase inhibitors improves progression free survival of patients with EGFR mutant lung cancer. However, all patients with initial response will eventually acquire resistance and die from tumor recurrence. We found that intermittent high dose treatment with erlotinib induced apoptosis more potently and improved tumor shrinkage significantly than the established low doses. In mice carrying EGFR mutant xenografts intermittent high dose treatment (200 mg/kg every other day) was tolerable and prolonged progression free survival and reduced the frequency of acquired resistance. Intermittent EGFR targeted high dose schedules induce more profound as well as sustained target inhibition and may afford enhanced therapeutic efficacy.
Fatigue behavior of corroded prestressed concrete beams
Year: 2016

This paper presents the results of an experimental study on the fatigue behavior of pretensioned partially prestressed concrete beams with corroded prestressing wires or rebars. A three dimensional laser scanning technique was employed to obtain the geometric models of corroded prestressing wires or rebars. It was observed that fatigue cracks initiated and propagated rapidly around corrosion pits under cyclic loading. The cyclic loading led to fatigue fracturing of the prestressing wires (rebars) at the minimum cross section where the corrosion pits had formed. With the increased corrosion of the prestressing wires, the sooner fracture of the first wire and the shorter interval between the failures of the two wires in the test beam were found, resulting in a shorter fatigue life of the beam. A higher partially prestressed ratio or load range brought a larger stress level, stress range, and thus a shorter fatigue life of corroded beams. Compared with the uncorroded beam, it was also found that the corroded beams exhibited a faster stiffness degradation and development of deflections and crack widths under cyclic loading.
Model based filtered backprojection algorithm: A tutorial
Venue: Biomedical engineering letters
Year: 2014

PurposePeople have been wandering for a long time whether a filtered backprojection (FBP) algorithm is able to incorporate measurement noise in image reconstruction. The purpose of this tutorial is to develop such an FBP algorithm that is able to minimize an objective function with an embedded noise model.MethodsAn objective function is first set up to model measurement noise and to enforce some constraints so that the resultant image has some pre specified properties. An iterative algorithm is used to minimize the objective function, and then the result of the iterative algorithm is converted into the Fourier domain, which in turn leads to an FBP algorithm. The model based FBP algorithm is almost the same as the conventional FBP algorithm, except for the filtering step.ResultsThe model based FBP algorithm has been applied to low dose x ray CT, nuclear medicine, and real time MRI applications. Compared with the conventional FBP algorithm, the model based FBP algorithm is more effective in reducing noise. Even though an iterative algorithm can achieve the same noise reducing performance, the model based FBP algorithm is much more computationally efficient.ConclusionsThe model based FBP algorithm is an efficient and effective image reconstruction tool. In many applications, it can replace the state of the art iterative algorithms, which usually have a heavy computational cost. The model based FBP algorithm is linear and it has advantages over a nonlinear iterative algorithm in parametric image reconstruction and noise analysis.
Evolution of "Boshi":From Perspective of Historical Pragmatics
Year: 2010

The Chinese word boshi has undergone incessant evolution since its origin in the Warring States period more than two thousand years ago.In the modern times,the degree system was adopted from the West;hence the word is used to refer to doctor,the highest university degree.Nevertheless,some new meanings are incessantly derived due to the influence of pragmatic inferenc and language users' cognitive processing.The evolution of boshi and the motives for and the mechanism of its pragmatic development are investigated.It is argued that this process is motivated by iconicity,social cultural influences,language acquisition and strategic interaction between communicators,and that analogy,abduction,borrowing as well as the fossilization and vanishing of some contextual meanings are either separately or integratively involved as its mechanisms.
Sex differences in severity and mortality from COVID 19: are males more vulnerable?
Venue: Biology of sex differences
Year: 2020

Coronavirus disease 2019 (COVID 19) has shown high infection and mortality rates all over the world, and despite the global efforts, there is so far no specific therapy available for COVID 19. Interestingly, while the severity and mortality of COVID 19 are higher in males than in females, the underlying molecular mechanisms are unclear. In this review, we explore sex related differences that may be contributing factors to the observed male biased mortality from COVID 19. Males are considered the weaker sex in aspects related to endurance and infection control. Studies show that viral RNA clearance is delayed in males with COVID 19. A recent study has indicated that the testis can harbor coronavirus, and consequently, males show delayed viral clearance. However, the role of testis involvement in COVID 19 severity and mortality needs further research. Males and females show a distinct difference in immune system responses with females eliciting stronger immune responses to pathogens. This difference in immune system responses may be a major contributing factor to viral load, disease severity, and mortality. In addition, differences in sex hormone milieus could also be a determinant of viral infections as estrogen has immunoenhancing effects while testosterone has immunosuppressive effects. The sex specific severity of COVID 19 infections indicates that further research on understanding the sex differences is needed. Inclusion of both males and females in basic research and clinical trials is required to provide critical information on sex related differences that may help to better understand disease outcome and therapy.
Weakly Supervised Hierarchical Text Classification
Venue: AAAI
Year: 2019

Hierarchical text classification, which aims to classify text documents into a given hierarchy, is an important task in many real world applications. Recently, deep neural models are gaining increasing popularity for text classification due to their expressive power and minimum requirement for feature engineering. However, applying deep neural networks for hierarchical text classification remains challenging, because they heavily rely on a large amount of training data and meanwhile cannot easily determine appropriate levels of documents in the hierarchical setting. In this paper, we propose a weakly supervised neural method for hierarchical text classification. Our method does not require a large amount of training data but requires only easy to provide weak supervision signals such as a few class related documents or keywords. Our method effectively leverages such weak supervision signals to generate pseudo documents for model pre training, and then performs self training on real unlabeled data to iteratively refine the model. During the training process, our model features a hierarchical neural structure, which mimics the given hierarchy and is capable of determining the proper levels for documents with a blocking mechanism. Experiments on three datasets from different domains demonstrate the efficacy of our method compared with a comprehensive set of baselines.