nils-herrmann/clean_scirepeval huggingface dataset:
Attention U Net: Learning Where to Look for the Pancreas
Venue: ArXiv
Year: 2018

We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs) AGs can be easily integrated into standard CNN architectures such as the U Net model with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed Attention U Net architecture is evaluated on two large CT abdominal datasets for multi class image segmentation. Experimental results show that AGs consistently improve the prediction performance of U Net across different datasets and training sizes while preserving computational efficiency. The code for the proposed architecture is publicly available.
Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections
Year: 2018

The paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology, and discusses research issues that are related to the various disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models, as well as conduct simulation to examine whether the estimators in their theories on estimation and hypothesis testing have good size and high power. Thereafter, academics and practitioners could apply theory to analyse some interesting issues in the seven disciplines and cognate areas.
Understanding Deformable Alignment in Video Super Resolution
Venue: AAAI
Year: 2021

Deformable convolution, originally proposed for the adaptation to geometric variations of objects, has recently shown compelling performance in aligning multiple frames and is increasingly adopted for video super resolution. Despite its remarkable performance, its underlying mechanism for alignment remains unclear. In this study, we carefully investigate the relation between deformable alignment and the classic flow based alignment. We show that deformable convolution can be decomposed into a combination of spatial warping and convolution. This decomposition reveals the commonality of deformable alignment and flow based alignment in formulation, but with a key difference in their offset diversity. We further demonstrate through experiments that the increased diversity in deformable alignment yields better aligned features, and hence significantly improves the quality of video super resolution output. Based on our observations, we propose an offset fidelity loss that guides the offset learning with optical flow. Experiments show that our loss successfully avoids the overflow of offsets and alleviates the instability problem of deformable alignment. Aside from the contributions to deformable alignment, our formulation inspires a more flexible approach to introduce offset diversity to flow based alignment, improving its performance.
Convolutional Neural Network based Automated Attendance System by using Facial Recognition Domain
Venue: 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS)
Year: 2020

This project aims to recognize faces in an image, video, or via live camera using a deep learning based Convolutional Neural Network model that is fast as well as accurate. Face recognition is a process of identifying faces in an image and has practical applications in a variety of domains, including information security, biometrics, access control, law enforcement, smart cards, and surveillance system. Deep Learning uses numerous layers to discover interpretations of data at different extraction levels. It has improved the landscape for performing research in facial recognition. The state of the art implementation has been bettered by the introduction of deep learning in face recognition and has stimulated success in practical applications. Convolutional neural networks, a kind of deep neural network model has been proven to achieve success in the face recognition domain. For real time systems, sampling must be done before using CNNs. On the other hand, complete images (all the pixel values) are passed as the input to Convolutional Neural Networks. The following steps: feature selection, feature extracti on, and training are performed in each step. This might lead to the assumption, where convolutional neural network implementation has a chance to get complicated and time consuming.
Analytical approximations to the core radius and energy of magnetic vortex in thin ferromagnetic disks
Year: 2013

The energy of magnetic vortex core and its equilibrium radius in thin circular cylinder were first presented by Usov and Peschany in 1994. Yet, the magnetostatic function, entering the energy expression, is hard to evaluate and approximate. Here, precise and explicit analytical approximations to this function (as well as equilibrium vortex core radius and energy) are derived in terms of elementary functions. Also, several simplifying approximations to the magnetic Hamiltonian and their impact on theoretical stability of magnetic vortex state are discussed.
Radial basis function model for vegetable price prediction
Venue: 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering
Year: 2013

The Agricultural sector needs more support for its development in developing countries like India. Price prediction helps the farmers and also the Government to make effective decision. Based on the complexity of vegetable price prediction, making use of the characteristics of data mining classification technique like neural networks such as self adapt, self study and high fault tolerance, to build up the model of Back propagation neural network (BPNN) and Radial basis function neural network (RBF) to predict vegetable price. A prediction models were set up by applying the BPNN and RBF neural networks. Taking tomato as an example, the parameters of the model are analysed through experiment. Compare the two neural network forecast results. The result shows that the RBF neural network is more efficient and accurate than Back propagation neural network.
Diseno de un Algoritmo MAC para la Asignacion Equitativa de Espectro en Redes Inalambricas de Radio Cognitiva
Year: 2016

En este proyecto, se presenta una propuesta de un algoritmo de capa MAC para la fase de comparticion de espectro o "spectrum sharing" que busca permitir el acceso de la manera mas justa posible a los usuarios secundarios sin dejar de lado la busqueda del maximo aprovechamiento de las bandas libres, ademas se introdujo una medida de justicia cuantificable para poder medir el desempeno del sistema, se utilizaron dos tecnicas (ANFIS y FAHP) para la clasificacion de usuarios secundarios al momento de asignarles canales y se compararon los resultados de cada tecnica frente a la otra y frente a un algoritmo de asignacion de canales que no realiza clasificacion alguna. Se implemento una simulacion de los algoritmos en Matlab suponiendo que las demas fases del esquema CR (sensado de espectro, caracterizacion de UP, decision especral y movilidad espectral) funcionan perfectamente con el fin de que errores en dichas fases no afectaran en la evaluacion del algoritmo.
Efficient sequencing of Anuran mtDNAs and a mitogenomic exploration of the phylogeny and evolution of frogs.
Venue: Molecular biology and evolution
Year: 2013

Anura (frogs and toads) constitute over 88% of living amphibian diversity but many important questions about their phylogeny and evolution remain unresolved. For this study, we developed an efficient method for sequencing anuran mitochondrial DNAs (mtDNAs) by amplifying the mitochondrial genome in 12 overlapping fragments using frog specific universal primer sets. Based on this method, we generated 47 nearly complete, new anuran mitochondrial genomes and discovered nine novel gene arrangements. By combining the new data and published anuran mitochondrial genomes, we assembled a large mitogenomic data set (11,007 nt) including 90 frog species, representing 39 of 53 recognized anuran families, to investigate their phylogenetic relationships and evolutionary history. The resulting tree strongly supported a paraphyletic arrangement of archaeobatrachian =nonneobatrachian) frogs, with Leiopelmatoidea branching first, followed by Discoglossoidea, Pipoidea, and Pelobatoidea. Within Neobatrachia, the South African Heleophrynidae is the sister taxon to all other neobatrachian frogs and the Seychelles endemic Sooglossidae is recovered as the sister taxon to Ranoidea. These phylogenetic relationships agree with many nuclear gene studies. The chronogram derived from two Bayesian relaxed clock methods (MultiDivTime and BEAST) suggests that modern frogs (Anura) originated in the early Triassic about 244 Ma and the appearance of Neobatrachia took place in the late Jurassic about 163 Ma. The initial diversifications of two species rich superfamilies Hyloidea and Ranoidea commenced 110 and 133 Ma, respectively. These times are older than some other estimates by approximately 30 40 My. Compared with nuclear data, mtDNA produces compatible time estimates for deep nodes >150 Ma) but apparently older estimates for more shallow nodes. Our study shows that, although it evolves relatively rapidly and behaves much as a single locus, mtDNA performs well for both phylogenetic and divergence time inferences and will provide important reference hypotheses for the phylogeny and evolution of frogs.
Perceptions of undergraduate pharmacy students of Ahmadu Bello University Zaria, Nigeria on the introduction of the Pharm.D. degree to Nigerian pharmacy schools
Venue: Bayero Journal of Pure and Applied Sciences
Year: 2019

As a result of the increasing scope of Pharmacy practice over the years, many countries (Nigeria inclusive) are introducing or undertaking major transformations in pharmacy education. The main aim of this work is to explore the knowledge and perceptions of selected 4th and 5th year undergraduate students of Ahmadu Bello University, Zaria on the introduction of the proposed Pharm. D. Degree to Nigerian pharmacy schools. The study was qualitative, and used focus group discussions (FGDs) to collect data. Two focus group discussions with ten students (Five participants per group) lasting an average of one hour were audiotaped in June 2017. Data obtained from the FGDs was then transcribed and checked for accuracy and the transcripts analyzed using summative qualitative content analysis. Most participants highlighted both advantages and disadvantages to introducing the new degree. Advantages identified included the potential for specialization and better career prospects. On the other hand, they expressed worries that the degree change would be disadvantageous to people interested in other (non clinical) areas of pharmacy practice. Several participants also felt that the degree change was going to be unsuccessful because of several factors within our environment including the unharmonious working relationship between healthcare professionals, inadequate infrastructure in pharmacy schools and the unwillingness of several lecturers to embrace the new degree. In conclusion, participants believed that the proposed Pharm.D. Degree was both potentially advantageous and problematic. Their recommendations include increasing the awareness of both staff and prospective students on what the degree entailed, and allowing concurrent coexistence of both B.Pharm and Pharm.D. Degrees Keywords Focus group, Curriculum change, Students, Nigeria, Pharm.D. Perceptions,
Hybrid Generative Adversarial Networks for Deep MR to CT Synthesis Using Unpaired Data
Venue: MICCAI
Year: 2019

Many different methods have been proposed for generation of synthetic CT from MR images. Most of these methods depend on pairwise aligned MR and CT training images of the same patient, which are difficult to obtain. 2D cycle consistent Generative Adversarial Networks (2D cGAN) have been explored before for generating synthetic CTs from MR images but the results are not satisfied due to spatial inconsistency. There exists attempt to develop 3D cycle GAN (3D cGAN) for image translation but its training requires large number of data which may not be always available. In this paper, we introduce two novel mechanisms to address above mentioned problems. First, we introduce a hybrid GAN (hGAN) consisting of a 3D generator network and a 2D discriminator network for deep MR to CT synthesis using unpaired data. We use 3D fully convolutional networks to form the generator, which can better model the 3D spatial information and thus could solve the discontinuity problem across slices. Second, we take the results generated from the 2D cGAN as weak labels, which will be used together with an adversarial training strategy to encourage the generator's 3D output to look like a stack of real CT slices as much as possible. Experimental results demonstrated that our approach achieved better results than the state of the art when limited number of unpaired data are available.
Expected utility or prospect theory: Which better fits agent based modeling of markets?
Venue: J. Comput. Sci.
Year: 2016

Agent based simulations may be a way to model human society behavior in decisions under risk. However, it is well known in economics that Expected Utility Theory (EUT) is flawed as a descriptive model. In fact, there are some models based on prospect theory (PT) that try to provide a better description. If people behave according to PT in finance environments, it is arguable that PT based agents may be a better choice for such environments. We investigate this idea in a specific risky environment, a financial market. We propose an architecture for PT based agents. Due to some limitations of the original PT, we use an extension of PT called Smooth Prospect Theory (SPT) We simulate artificial markets with PT and traditional (TRA) agents using historical data of many different assets over a period of 20 years. The results showed that SPT based agents provided behavior that is closer to real market data than TRA agents, and that the improvement when using SPT rather than TRA agents is statistically significant. It supports the idea that PT based agents may be a better pick to model the behaviour of agents in risky environments.
Structure aided drug development of potential neuraminidase inhibitors against pandemic H1N1 exploring alternate binding mechanism
Venue: Molecular diversity
Year: 2019

The rate of mutability of pathogenic H1N1 influenza virus is a threat. The emergence of drug resistance to the current competitive inhibitors of neuraminidase, such as oseltamivir and zanamivir, attributes to a need for an alternative approach. The design and synthesis of new analogues with alternate approach are particularly important to identify the potential neuraminidase inhibitors which may not only have better anti influenza activity but also can withstand challenge of resistance. Five series of scaffolds, namely aurones (1a 1e) pyrimidine analogues (2a 2b) cinnamic acid analogues (3a 3k) chalcones (4a 4h) and cinnamic acid linkages (5a 5c) were designed based on virtual screening against pandemic H1N1 virus. Molecular modelling studies revealed that the designed analogues occupied 430 loop cavity of neuraminidase. Docking of sialic acid in the active site preoccupied with the docked analogues, i.e. in 430 loop cavity, resulted in displacement of sialic acid from its native pose in the catalytic cavity. The favourable analogues were synthesized and evaluated for the cytotoxicity and cytopathic effect inhibition by pandemic H1N1 virus. All the designed analogues resulting in displacement of sialic acid suggested alternate binding mechanism. Overall results indicated that aurones can be measured best among all as potential neuraminidase inhibitor against pandemic H1N1 virus.
Which group should policies target? Effects of incentive policies and product cognitions for electric vehicle adoption among Chinese consumers
Year: 2019

As a sustainable innovation in transport sector, electric vehicles (EVs) have shown great potential in reducing carbon emissions and pollution. Understanding the factors affecting consumers' adoption intentions of EVs is vital to the diffusion of EVs. This study aims to identify factors that can encourage consumers to adopt EVs from both internal and external perspectives. We examined three types of policies for EVs formulated by the Chinese government since 2010. Aiming to understand consumers' perceptions of these policies and their impacts on adoption intentions, a survey was sent to potential vehicle consumers in urban China. The survey asked respondents about their cognitions of EVs, including environmental performance and vehicle property, and their perceptions toward three types of incentive policies and their adoption intentions. Results show that consumers' adoption intentions of EVs are determined by product cognition toward EVs and perceptions of incentive policies. Respondents' perceptions of policies are also diverse when grouped according to different demographic characteristics. Women, the elders, and those with lower income exhibit higher acceptance and interest in the policies than their counterparts. Our results are meaningful to policy makers and companies interested in targeting at certain groups and guiding consumers toward clean vehicle technologies.
2 Halogenoimidazolium salt catalyzed aza Diels Alder reaction through halogen bond formation.
Venue: Organic letters
Year: 2015

2 Halogenoimidazolium salts are found to catalyze aza Diels Alder reaction of aldimines with Danishefsky diene in an efficient manner. Comparative studies and titration experiments support the formation of halogen bonding between imines and catalysts.
Supporting and training female necessity entrepreneurs
Year: 2017

This chapter contributes to the field of entrepreneurship by shedding light on the challenges female necessity entrepreneurs face and the nature of their relationship with their business counselors. It provides policy makers with clear guidance on how to best provide support and training to the female necessity entrepreneur, in light of the challenges they face throughout their entrepreneurial process, using customized support skills and programs unique to this very special type of entrepreneur. The chapter draws on large research project that addressed necessity entrepreneurship among unemployed individuals in France. The project collected narratives of 15 necessity entrepreneurs and 10 business consultants. Supporting female necessity entrepreneurs requires special skills. Thus, active listening, empathy, psychological support, hermeneutic techniques and psychotherapy can solve the complexity of phenomenon. The importance of the network concept in entrepreneurial environment is central. Some consultants were aware of the isolation of female necessity entrepreneurs and felt sympathy because the entrepreneurs experienced chaotic pathways on personal and professional levels.
A Systematic Literature Review on Online Medical Services in Malaysia
Venue: Int. J. Online Biomed. Eng.
Year: 2020

The aim of the study reviews online medical services in Malaysia. The paper reviews the improvement in Malaysian Medical services in the past five years. In this paper, we systematically reviewed literature from the year 2014 to 2018. We selected the web of science database for the literature search to make the process more transparent and clearer, every process is recorded on the excel sheets and all studies were screened through rigorous inclusion and exclusion criteria. Final 48 papers were selected for this systematic literature review and all irrelevant papers are excluded from the study. For the selection and exclusion process, PRISMA 2009 is used. The results revealed that Literature is heavily focusing on the medical services towards the urban areas, but rural areas were largely neglected. Many studies are discussing the importance of online medical services; However, implementation mechanisms have not been discussed. The systematic review recommends that future research should be focused more on holistic patterns of implementations of the online medical services in Malaysia.
Energy efficient neighborhood design under residential zoning regulations in Shanghai
Year: 2017

In the current pursuit of eco city development in China, energy efficient residential neighborhood development becomes one of the focuses. While current codes and practices have been paying attention to green building design, building energy is influenced by not only building design, but also complex urban form at a larger spatial scale, which was not well discussed. At the same time, neighborhood design is always regulated by residential zoning codes, which restrict the density measures for economic considerations and layout to ensure safety, privacy, fire prevention and sunlight. What neighborhood design can achieve high energy performance under those regulations becomes a critical question for urban design practices. This study explores three questions: How density influences residential building energy efficiency? How different building heights affects building energy? And how different neighborhood layouts impact the building energy? Those questions are examined by computational simulation experiments on parametric design scenarios in the case study of a hypothetical neighborhood in Shanghai. The comparisons of the simulation results suggest that density and energy performance has a complex relation with a threshold density that has the best performance, and building height and energy use have a segmented relation with the same density due to elevator design codes. The energy performance ranges with the same FAR and with the same number of floors show the great impact of the building height and limited influence of neighborhood layout on building energy use. The simulation results also suggest the optimal design with best energy performance with different density settings. The findings can inform urban designers and policy makers on more energy efficient residential neighborhood design under current zoning regulations.
Evaluation of Classical Machine Learning Techniques towards Urban Sound Recognitionon Embedded Systems
Venue: Applied Sciences
Year: 2019

Automatic urban sound classification is a desirable capability for urban monitoring systems, allowing real time monitoring of urban environments and recognition of events. Current embedded systems provide enough computational power to perform real time urban audio recognition. Using such devices for the edge computation when acting as nodes of Wireless Sensor Networks (WSN) drastically alleviates the required bandwidth consumption. In this paper, we evaluate classical Machine Learning (ML) techniques for urban sound classification on embedded devices with respect to accuracy and execution time. This evaluation provides a real estimation of what can be expected when performing urban sound classification on such constrained devices. In addition, a cascade approach is also proposed to combine ML techniques by exploiting embedded characteristics such as pipeline or multi thread execution present in current embedded devices. The accuracy of this approach is similar to the traditional solutions, but provides in addition more flexibility to prioritize accuracy or timing.