nils-herrmann/clean_scirepeval huggingface dataset:
Atropellamiento de vertebrados en tres tipos de carretera de la region montanosa central de Veracruz, Mexico
Year: 2018

This study evaluates the influence of the road type and infrastructure (culverts) on the numbers of vertebrates killed on the Xalapa Puebla highway (section: Banderilla Amozoc) in the central mountainous zone of the state of Veracruz. The study was carried out on three different road types (varying according to the number of lanes and type of structure that separates the lanes) along which sites were selected where there was a culvert considered as a potential wildlife crossing. Roadkills at each site were counted and related to the presence absence of culverts, the slope of the right of way with respect to the road, as well as the road type. A greater number of roadkills occurred in the two lanes highway followed by the highway divided by central vegetation barrier and the highway with central barrier of concrete wall. In none of the types of road studied were found an effect of the presence of culverts on the number of vertebrates killed. In addition, a negative relationship was found between the number of roadkills and the slope of the right of way. This study helps to understand the factors that limit the crossing of vertebrates on the roads and discusses the implications of road infrastructure planning construction for wildlife.
A Convolutional Neural Network Approach for Post Processing in HEVC Intra Coding
Venue: MMM
Year: 2017

Lossy image and video compression algorithms yield visually annoying artifacts including blocking, blurring, and ringing, especially at low bit rates. To reduce these artifacts, post processing techniques have been extensively studied. Recently, inspired by the great success of convolutional neural network (CNN) in computer vision, some researches were performed on adopting CNN in post processing, mostly for JPEG compressed images. In this paper, we present a CNN based post processing algorithm for High Efficiency Video Coding (HEVC) the state of the art video coding standard. We redesign a Variable filter size Residue learning CNN (VRCNN) to improve the performance and to accelerate network training. Experimental results show that using our VRCNN as post processing leads to on average 4.6% bit rate reduction compared to HEVC baseline. The VRCNN outperforms previously studied networks in achieving higher bit rate reduction, lower memory cost, and multiplied computational speedup.
Biosemiotics Achievement Award for the Year 2019
Venue: Biosemiotics
Year: 2020

Established at the annual meeting of the International Society for Biosemiotic Studies (ISBS) on July 3rd 2014, in conjunction with Springer Publishing, publishers of the Society's official journal, Biosemiotics, the Annual Biosemiotic Achievement Award seeks to recognize those papers published in the journal that present novel and potentially important contributions to the ongoing project of biosemiotic research, its scientific impact, and its future prospects. Here the winner of the Biosemiotics Achievement Award for 2019 is announced: the award goes to Y.H. Hendlin for the article 'I Am a Fake Loop: the Effects of Advertising Based Artificial Selection'
Influence of steel reinforcements on the behavior of compressed high strength R.C. circular columns
Year: 2012

In the present paper the focus is on the compressive response of short high strength reinforced concrete members having circular transverse cross sections and reinforced with longitudinal steel bars and transverse spirals or hoops. An analytical model is proposed which allows one to estimate the confinement pressures exerted by transverse steel and by longitudinal bars during the loading process, taking into account the interaction of the hoops or spirals with the inner core both in the plane of the transverse steel and in the space between two successive hoops. Yielding of steel spirals or hoops and longitudinal bars including buckling phenomena and damage to the concrete core are also considered by means of variation in the elastic modulus and in the Poisson's coefficient. The proposed mechanical model is able to reproduce the load shortening curves of compressed members; it gives good agreement with several experimental data and with analytical results obtained by using other recent models available in the literature.
Energy Saving Transformation Design of Existing Residential Buildings in Qinba Mountainous Areas
Year: 2020

Through investigation, it is found that some existing buildings in Qinba Mountain have high energy consumption and poor indoor thermal comfort. The cost of demolishing and rebuilding is relatively high. Therefore, it is an important issue for the current urban residential development to study suitable local reconstruction strategies. Taking an old community in Hanzhong City of Qinba Mountain as an example, the field test of indoor and outdoor thermal environment in winter and summer was carried out for one house. The current situation of indoor thermal environment in Qinba mountain city buildings in winter and summer was analyzed. The environmental conditions are not ideal. This article uses DesignBuilder software to carry on the comprehensive transformation design to the wall of this existing building, the roof, the outer window and so on, and carries on the simulation analysis to its indoor thermal environment, the result shows that the enclosing structure has a greater influence on the thermal environment of the local building; the transformation model was obtained: 30mm EPS insulation layer was added to the wall, 50mm EPS insulation layer was installed on the roof, and plastic window (6+13A+6) was used as the outer window. After the renovation, the indoor temperature was increased by 11% 21% This result can provide a certain theoretical reference for the energy saving reconstruction of the existing residential houses in the Qinba Mountains.
Adverse effect of heavy metals (As, Pb, Hg, and Cr) on health and their bioremediation strategies: a review
Venue: International microbiology the official journal of the Spanish Society for Microbiology
Year: 2018

Currently, heavy metal pollution becomes a severe problem whole over the world, and these toxic metals enter into the environment either by natural phenomena or due to extensive industrialization. The discharged effluents containing toxic heavy metals mixed with soil/water and change their natural composition. These heavy metals have adverse effects on living beings and cause damage to the vital body organs of animals as well as humans. The heavy metal pollution also inhibits the biodegradation of the chlorinated organic compounds (another type of environmental pollution) by interacting with metabolizing enzymes and inhibits their functioning. Earlier studies described that heavy metals cannot be fully removed from the environment, but they can be effectively neutralized or transformed into less toxic form so that their harmful effect on the environment can be reduced. The distinctive enzymatic apparatus within microbial system plays a major role in the transformation of heavy metals in the environment. A considerable advancement has been made during recent years to transform the heavy metals by utilizing the bioremediation potential of genetically engineered (GE) microorganisms. These transgenics are very much efficient in heavy metal transformations and still, we have to discover more to additionally utilize their full biotransformation potential.In the present review article, the detailed description of the adverse effects of four heavy metals (arsenic, lead, mercury, and chromium) and their adverse effect on our environment and human beings is discussed. Furthermore, the use of microorganisms/GE organisms for the bioremediation of heavy metals from the environment is also discussed along with their detailed bioremediation mechanism.
Towards adversarial learning of speaker invariant representation for speech emotion recognition
Venue: ArXiv
Year: 2019

Speech emotion recognition (SER) has attracted great attention in recent years due to the high demand for emotionally intelligent speech interfaces. Deriving speaker invariant representations for speech emotion recognition is crucial. In this paper, we propose to apply adversarial training to SER to learn speaker invariant representations. Our model consists of three parts: a representation learning sub network with time delay neural network (TDNN) and LSTM with statistical pooling, an emotion classification network and a speaker classification network. Both the emotion and speaker classification network take the output of the representation learning network as input. Two training strategies are employed: one based on domain adversarial training (DAT) and the other one based on cross gradient training (CGT) Besides the conventional data set, we also evaluate our proposed models on a much larger publicly available emotion data set with 250 speakers. Evaluation results show that on IEMOCAP, DAT and CGT provides 5.6% and 7.4% improvement respectively, over a baseline system without speaker invariant representation learning on 5 fold cross validation. On the larger emotion data set, while CGT fails to yield better results than baseline, DAT can still provide 9.8% relative improvement on a standalone test set.
Application of MVC Platform in Bank E CRM
Year: 2013

Customer relationship management (CRM) has been as important to the banking industry at the start of the 21st century as it has been to any other industry. Banks have used E CRM tools to acquire more customers and to improve relationships with them. Model View Controller (MVC) pattern is a software design pattern that is suitable for interactive system. This paper analyzes the CRM characteristics: interactive, expansibility, the complexity of data and integration. To these characteristics of CRM, it provides a system design scheme on MVC pattern, and describes the advantages of function and structure in the system. Today, many businesses such as banks, insurance companies, and other service providers realize the importance of Customer Relationship Management (CRM) and its potential to help them acquire new customers retain existing ones and maximize their lifetime value. At this point, close relationship with customers will require a strong coordination between IT and marketing departments to provide a long term retention of selected customers. CRM is an integration of information technology and management method. Design and development of CRM is a key step of solution of enterprise's CRM. More competition and increased regulation made it more difficult for banks to stand out from the crowd. However, the development of CRM gave proactive banks access to technology that helped them improve customer retention by using customer feedback to offer conveniences like ATMs and online banking. Banks can also use CRM tools to improve customer loyalty by using data collected through customer sign ups, transactions and feedback processes. E CRM is defined as a mean of selling, serving, or communicating with customer through the web. Further he takes E CRM as a subset of CRM, which means that E CRM is one of the channels that a company can use to deploy its CRM strategies (1) The goal of E CRM system is to improve customer service, retain valuable customers and to aid in providing analytical capabilities within an organization (2) This paper deals with the role of Customer Relationship Management in banking sector and the need for Customer Relationship Management and provides a Design of CRM System Based on MVC Pattern according to the CRM characteristics. 2. The characteristics of E CRM CRM is a broad approach for creating, maintaining and expanding customer relationships. CRM is the business strategy that aims to understand, anticipate, manage and personalize the needs of an organization's current and potential customers. At the heart of a perfect strategy is the creation of mutual value for all parties involved in the business process. It is about creating a sustainable competitive advantage by being the best at understanding, communicating, and delivering and developing existing customer relationships in addition to
Effect of Government Expenditure on Human Capital Development in Kaduna State (1990 2015) An Application of ARDL Model
Year: 2018

This study investigates the effect of government expenditure on human capital development in Kaduna state from 1990 to 2015. Secondary data were obtained from Kaduna state Ministry of Budget and Planning, Cooperate Affairs Commission, National Youth Service Corps and Kaduna state Ministry of Health on; government expenditure on education and health, creative citizens, number of graduates and life expectancy at birth respectively. Also an index for human capital development was created using three pillars; creative citizens, number of graduates and healthy citizens using principal component analysis (PCA) The Auto Regressive Distributed Lag (ARDL) was adopted for the study. Findings of the work suggests that in the shortrun, increase in government spending on education does not stimulate an increase in human capital development which is significant at 5 percent level of significance (t 4.37, p<0.05; t= 2.44, p<0.05) However, it was confirmed that in the long run, there is a positive effect as a 1 percent increase in education expenditure stir up an increase to human capital by 2.79 percent which was found to be significant at 5 percent (t=3.789, p<0.05) Health expenditure was found to had a positive and a statistically significant effect on human capital development in the short run covering the first year and second year (t =3.88, p< 0.05; t=3.56, p<0.005) However, the long run evidence has showed that a 1 percent increase in health expenditure does not increase but rather decrease human capital development by 2.9 percent. The study concludes that government expenditure directly affects human capital development. The under study recommends that the state government should increase its spending on education and health in other to rapidly develop human capital since it is crucial to economic prosperity.
Convolutional neural network with structural input for visual object tracking
Venue: SAC
Year: 2019

Numerous deep learning approaches have been applied to visual object tracking owing to their capabilities to leverage huge training data for performance improvement. Most of these approaches have limitations with regard to learning target specific information rich features and therefore observe reduced accuracy in the presence of different challenges such as occlusion, scale variations, rotation and clutter. We proposed a deep neural network that takes input in the form of two stacked patches and regresses both the similarity and the dis similarity scores in single evaluation. Image patches are concatenated depth wise and fed to a six channel input of the network. The proposed network is generic and exploits the structural differences between the two input patches to obtain more accurate similarity and dissimilarity scores. Online learning is enforced via short term and long term updates to improve the tracking performance. Extensive experimental evaluations have been performed on OTB2015 and TempleColor128 benchmark datasets. Comparisons with state of the art methods indicate that the proposed framework has achieved better tracking performance. The proposed tracking framework has obtained improved accuracy in different challenges including occlusion, background clutter, in plane rotation and scale variations.
Business Closures, Stay at Home Restrictions, and COVID 19 Testing Outcomes in New York City
Venue: Preventing chronic disease
Year: 2020

In response to the coronavirus disease 2019 (COVID 19) pandemic, New York City closed all nonessential businesses and restricted the out of home activities of residents as of March 22, 2020. This order affected different neighborhoods differently, as stores and workplaces are not randomly distributed across the city, and different populations may have responded differently to the out of home restrictions. This study examines how the business closures and activity restrictions affected COVID 19 testing results. An evaluation of whether such actions slowed the spread of the pandemic is a crucial step in designing effective public health policies.  Daily data on the fraction of COVID 19 tests yielding a positive result at the zip code level were analyzed in relation to the number of visits to local businesses (based on smartphone location) and the number of smartphones that stayed fixed at their home location. The regression model also included vectors of fixed effects for the day of the week, the calendar date, and the zip code of residence.  A large number of visits to local businesses increased the positivity rate of COVID 19 tests, while a large number of smartphones that stayed at home decreased it. A doubling in the relative number of visits increases the positivity rate by about 12.4 percentage points (95% CI, 5.3 to 19.6) A doubling in the relative number of stay at home devices lowered it by 2.0 percentage points (95% CI, 2.9 to 1.2) The business closures and out of home activity restrictions decreased the positivity rate, accounting for approximately 25% of the decline observed in April and May 2020.  Policy measures decreased the likelihood of positive results in COVID 19 tests. These specific policy tools may be successfully used when comparable health crises arise in the future.
WASTE TO WEALTH CONVERSION OF POULTRY LITTER FROM RAW FORM TO PELLETED ORGANIC FERTILIZER
Year: 2019

A review of the use of pelletized poultry litter for creation of wealth through use as organic fertilizer was done, using information from literature and data from field observation. Poultry wastes, which are a menace to the environment, are sources of wealth creation, especially for organic farming. There is an abundance of poultry wastes in Nigeria, as about 932.5 metric tonnes of poultry manure is produced annually from the well established poultry houses which keep expanding at the rate of 8% per year. Also, global trend is shifting to organic fertilizer as the use of chemical fertilizer over the years has destroyed our soil. Organic fertilizer has much organic content, which can rejuvenate the already destroyed soil. Not only that, organic fertilizer is safe for human health. Pelletized poultry litter would compete successfully as a soil conditioner or soil amendment. It would also be priced competitively, as it provides a unique mixture of nutrients and conditioning properties. Consequently, there is more poultry farming profitability, efficient resources utilization and conservation of environment due to waste management.
Cases from the Immune Related Adverse Event Tumor Board: Diagnosis and Management of Immune Checkpoint Blockade Induced Diabetes.
Venue: The oncologist
Year: 2020

The addition of immune checkpoint inhibitors to the armamentarium of cancer therapies has resulted in unprecedented improvement in clinical outcomes for a vast range of malignancies. Because they interfere with the physiologic function of immune checkpoints, such as programmed cell death protein 1 or cytotoxic T lymphocyte associated protein 4, to promote self tolerance, these agents are associated with a unique spectrum of immune related adverse events (irAEs) Immune mediated endocrinopathies are among the most commonly noted irAEs. Immune mediated diabetes is an uncommon irAE but can be associated with significant morbidity if it is not recognized and treated in a time sensitive manner. In this manuscript, we present a case based discussion and review of the literature pertaining to immune mediated diabetes associated with immune checkpoint blockade.  Immune checkpoint inhibitor associated diabetes mellitus often resembles type 1 diabetes mellitus (DM) in its pathophysiology and clinical manifestations. However, some patients may present with type 2 DM or worsening hyperglycemia in the setting of pre existent DM. Early recognition and management is key to preventing life threatening events such as diabetic ketoacidosis. Endocrinology referral and interdisciplinary management should be considered for every patient to optimize glycemic control and to ensure optimal monitoring for long term microvascular complications.
Health Consequences of Witnessing Domestic Violence Among Children:A Review
Year: 2018

Domestic violence, being one of the most bitter realities of Indian society, have affected the health and well being of women. But, the health of children is not unaffected and isolated from the psychological environment of the family The present work aimed at critically look at the previous literature in order to find out how witnessing domestic violence in a regular manner, too, can affect the physical and psychological health status of children. The study revealed that experiencing physical, sexual, or emotional abuse during pregnancy leads to certain direct health hazards among neonates and results in problems like low birth weight and premature delivery. Besides that, psychological consequences like post traumatic symptoms and conduct problems among children are also discussed. The study also draws attention to future research endeavours in this context.
Study Protocol for an Online Questionnaire Survey on Symptoms/Signs, Protective Measures, Level of Awareness and Perception Regarding COVID 19 Outbreak among Dentists. A Global Survey
Venue: International journal of environmental research and public health
Year: 2020

The Centres for Disease Control and Prevention and the World Health Organization have developed preparedness and prevention checklists for healthcare professionals regarding the containment of COVID 19. The aim of the present protocol is to evaluate the impact of the COVID 19 outbreak among dentists in different countries where various prevalence of the epidemic has been reported. Several research groups around the world were contacted by the central management team. The online anonymous survey will be conducted on a convenience sample of dentists working both in national health systems and in private or public clinics. In each country/area, a high ~5 20% proportion of dentists working there will be invited to participate. The questionnaire, developed and standardized previously in Italy, has four domains: (1) personal data; (2) symptoms/signs relative to COVID 19; (3) working conditions and PPE (personal protective equipment) adopted after the infection's outbreak; (4) knowledge and self perceived risk of infection. The methodology of this international survey will include translation, pilot testing, and semantic adjustment of the questionnaire. The data will be entered on an Excel spreadsheet and quality checked. Completely anonymous data analyses will be performed by the central management team. This survey will give an insight into the dental profession during COVID 19 pandemic globally.
AclNet: efficient end to end audio classification CNN
Venue: ArXiv
Year: 2018

We propose an efficient end to end convolutional neural network architecture, AclNet, for audio classification. When trained with our data augmentation and regularization, we achieved state of the art performance on the ESC 50 corpus with 85:65% accuracy. Our network allows configurations such that memory and compute requirements are drastically reduced, and a tradeoff analysis of accuracy and complexity is presented. The analysis shows high accuracy at significantly reduced computational complexity compared to existing solutions. For example, a configuration with only 155k parameters and 49:3 million multiply adds per second is 81:75% exceeding human accuracy of 81:3% This improved efficiency can enable always on inference in energy efficient platforms.