nils-herrmann/clean_scirepeval huggingface dataset:
Evolving Artificial Neural Networks Using Butterfly Optimization Algorithm for Data Classification
Venue: ICONIP
Year: 2019

One of the most difficult challenges in machine learning is the training process of artificial neural networks, which is mainly concerned with determining the best set of weights and biases. Gradient descent techniques are known as the most popular training algorithms. However, they are susceptible to local optima and slow convergence in training. Therefore, several stochastic optimization algorithms have been proposed in the literature to alleviate the shortcomings of gradient descent approaches. The butterfly optimization algorithm (BOA) is a recently proposed meta heuristic approach. Its inspiration is based on the food foraging behavior of butterflies in the nature. Moreover, it has been shown that BOA is effective in undertaking a wide range of optimization problems and attaining the global optima solutions. In this paper, a new classification method based on the combination of artificial neural networks and BOA algorithm is proposed. To this end, BOA is applied as a new training strategy by optimizing the weights and biases of artificial neural networks. This leads to improving the convergence speed and also reducing the risk of falling into local optima. The proposed classification method is compared with other state of the art methods based on two well known data sets and different evaluation measures. The experimental results ascertain the superiority of the proposed method in comparison with the other methods.
Highly efficient broadband silicon nitride polarization beam splitter incorporating serially cascaded asymmetric directional couplers.
Venue: Optics letters
Year: 2020

The implementation of a polarization beam splitter (PBS) on a silicon nitride platform remains challenging owing to its relatively low index. We therefore propose a silicon nitride PBS that exploits serially cascaded asymmetric directional couplers (ADCs) leading to a high polarization extinction ratio (PER) over a broad bandwidth. The ADC spatially routes incident light through polarization selective mode coupling under a small footprint of 112 um. The proposed PBS does not require an active phase control. It is thus effectively realized via a single step lithography process. The measured transverse electric and transverse magnetic PERs were determined to be above 23 dB and 10 dB over an 80 nm bandwidth, respectively, spanning l=1520 1600nm. The proposed device is thus anticipated to play a key role in providing polarization diversity in photonic integrated circuits.
Implications of landscape genetics and connectivity of snow leopard in the Nepalese Himalayas for its conservation
Venue: Scientific reports
Year: 2020

The snow leopard is one of the most endangered large mammals. Its population, already low, is declining, most likely due to the consequences of human activity, including a reduction in the size and number of suitable habitats. With climate change, habitat loss may escalate, because of an upward shift in the tree line and concomitant loss of the alpine zone, where the snow leopard lives. Migration between suitable areas, therefore, is important because a decline in abundance in these areas may result in inbreeding, fragmentation of populations, reduction in genetic variation due to habitat fragmentation, loss of connectivity, bottlenecks or genetic drift. Here we use our data collected in Nepal to determine the areas suitable for snow leopards, by using habitat suitability maps, and describe the genetic structure of the snow leopard within and between these areas. We also determine the influence of landscape features on the genetic structure of its populations and reveal corridors connecting suitable areas. We conclude that it is necessary to protect these natural corridors to maintain the possibility of snow leopards' migration between suitable areas, which will enable gene flow between the diminishing populations and thus maintain a viable metapopulation of snow leopards.
Some Congruences for Overpartitions with Restriction
Year: 2020

For any given positive integers m and n let pm n denote the number of overpartitions of n with no parts divisible by 4 m and only the parts congruent to m modulo 2 m overlined. In this paper, we prove Ramanujan type congruences modulo 2 for pm n by applying q series and Ramanujan's theta function identities.
Adaptive model predictive control of autonomic distributed parallel computations with variable horizons and switching costs
Venue: Concurr. Comput. Pract. Exp.
Year: 2016

Autonomic computing is a paradigm for building systems capable of adapting their operation when external changes occur, such as workload variations, load surges and changes in the resource availability. The optimal configuration in terms of the number of computing resources assigned to each component must be automatically adjusted to the new environmental conditions. To accomplish the execution goals with the desired Quality of Service, decision making strategies should be in charge of selecting the best reconfigurations by taking into account metrics like performance, efficiency (avoiding wasting resources) number and frequency of reconfigurations, and their amplitude (performing minimal modifications of the current configuration) This paper presents a decision making strategy that merges the potential of Model Predictive Control with a cooperative optimization framework. After a description of our approach, we investigate the effect of different switching costs to model the resource allocation problem. We use a control method in which our proactive decision making strategy (designed to use future prediction horizons) is made adaptive itself by dynamically changing the horizon length on the basis of the prediction errors. Simulations have been used to exemplify our approach and to discuss the effectiveness of the variable horizon strategy in achieving the best trade offs between reconfiguration metrics.
Environmental effects of the Deepwater Horizon oil spill: A review.
Venue: Marine pollution bulletin
Year: 2016

The Deepwater Horizon oil spill constituted an ecosystem level injury in the northern Gulf of Mexico. Much oil spread at 1100 1300m depth, contaminating and affecting deepwater habitats. Factors such as oil biodegradation, ocean currents and response measures (dispersants, burning) reduced coastal oiling. Still, >2100km of shoreline and many coastal habitats were affected. Research demonstrates that oiling caused a wide range of biological effects, although worst case impact scenarios did not materialize. Biomarkers in individual organisms were more informative about oiling stress than population and community indices. Salt marshes and seabird populations were hard hit, but were also quite resilient to oiling effects. Monitoring demonstrated little contamination of seafood. Certain impacts are still understudied, such as effects on seagrass communities. Concerns of long term impacts remain for large fish species, deep sea corals, sea turtles and cetaceans. These species and their habitats should continue to receive attention (monitoring and research) for years to come.
3D printing of hydrogels: Rational design strategies and emerging biomedical applications
Year: 2020

3D printing alias additive manufacturing can transform 3D virtual models created by computer aided design (CAD) into physical 3D objects in a layer by layer manner dispensing with conventional molding or machining. Since the incipiency, significant advancements have been achieved in understanding the process of 3D printing and the relationship of component, structure, property and application of the created objects. Because hydrogels are one of the most feasible classes of ink materials for 3D printing and this field has been rapidly advancing, this Review focuses on hydrogel designs and development of advanced hydrogel based biomaterial inks and bioinks for 3D printing. It covers 3D printing techniques including laser printing (stereolithography, two photon polymerization) extrusion printing (3D plotting, direct ink writing) inkjet printing, 3D bioprinting, 4D printing and 4D bioprinting. It provides a comprehensive overview and discussion of the tailorability of material, mechanical, physical, chemical and biological properties of hydrogels to enable advanced hydrogel designs for 3D printing. The range of hydrogel forming polymers covered encompasses biopolymers, synthetic polymers, polymer blends, nanocomposites, functional polymers, and cell laden systems. The representative biomedical applications selected demonstrate how hydrogel based 3D printing is being exploited in tissue engineering, regenerative medicine, cancer research, in vitro disease modeling, high throughput drug screening, surgical preparation, soft robotics and flexible wearable electronics. Incomparable by thermoplastics, thermosets, ceramics and metals, hydrogel based 3D printing is playing a pivotal role in the design and creation of advanced functional (bio)systems in a customizable way. An outlook on future directions of hydrogel based 3D printing is presented.
MPG Net: Multi Prediction Guided Network for Segmentation of Retinal Layers in OCT Images
Venue: 2020 28th European Signal Processing Conference (EUSIPCO)
Year: 2021

Optical coherence tomography (OCT) is a commonly used method of extracting high resolution retinal information. Moreover there is an increasing demand for the automated retinal layer segmentation which facilitates the retinal disease diagnosis. In this paper, we propose a novel multi prediction guided attention network (MPG Net) for automated retinal layer segmentation in OCT images. The proposed method consists of two major steps to strengthen the discriminative power of a U shape Fully convolutional network (FCN) for reliable automated segmentation. Firstly, the feature refinement module which adaptively re weights the feature channels is exploited in the encoder to capture more informative features and discard information in irrelevant regions. Furthermore, we propose a multi prediction guided attention mechanism which provides pixel wise semantic prediction guidance to better recover the segmentation mask at each scale. This mechanism which transforms the deep supervision to supervised attention is able to guide feature aggregation with more semantic information between intermediate layers. Experiments on the publicly available Duke OCT dataset confirm the effectiveness of the proposed method as well as an improved performance over other state of the art approaches.
Assessing Experiential Learning Styles: A Methodological Reconstruction and Validation of the Kolb Learning Style Inventory
Year: 2013

To understand experiential learning, many have reiterated the need to be able to identify students' learning styles. Kolb's Learning Style Model is the most widely accepted learning style model and has received a substantial amount of empirical support. Kolb's Learning Style Inventory (LSI) although one of the most widely utilized instruments to measure individual learning styles, possesses serious weaknesses. This study transforms the LSI from a type (categorical measure) to a degree (continuous measure) style of learning style measure that is not only more parsimonious but is also easier to use than the existing LSI. Two separate studies using samples of engineering and computer science graduate students (Study 1) and undergraduate and graduate students pursuing quantitative degrees (Study 2) culminating in a corroborative multi sample validation were employed, producing a methodologically sound option to the existing LSI. Implications for future research and guidance for learning and teaching methods are discussed.
Effectiveness of Performance Appraisal System and its Effect on Employee Motivation
Year: 2017

Employee performance has traditionally been accorded prime focus by human resource managers. As a result, a number of performance appraisal techniques have over time been devised to help establish employee's performance. In the contemporary times, the use of performance appraisals has been extended beyond rating of the employee's performance to aspects such as motivation. Accordingly, this study sought to investigate effectiveness of performance appraisal systems and its effect on employee motivation. The study's main objectives pertained to establishing the moderating role of performance appraisal as a motivation tool as well as potential challenges. The study findings show the presence of significant positive outcomes when the organisation uses performance appraisal as a motivation tool. Further, the study finds that the use of more than one appraisal techniques helps yield greater satisfaction and consequently higher motivational levels. The specific aspects of performance appraisal systems (PAS) that help improve motivation include the linking of performance to rewards; using the PAS to help set objectives and benchmarks; as well as the use of PA to help identify employee's strength and weaknesses.
Customer experience and engagement in tourism destinations: the experiential marketing perspective
Venue: Journal of Travel Tourism Marketing
Year: 2019

Tourism destinations are increasingly offering experiential services to promote the development of their competitive advantage. This research investigates the effects of customers' tourism engagement with experiential marketing activities and develops and tests a framework in this area. Findings suggest that customer engagement's dimensions exert differing effects on customer experience and identification, which subsequently affect behavioral intention toward destinations. Findings also suggest the indirect effects of customer engagement dimensions on behavioural intentions via experience and identification. Further, findings propose the significant difference between first time and repeat visitors in terms of the underlying constructs. Theoretical and practical implications of results are discussed.
Building a RAPPOR with the Unknown: Privacy Preserving Learning of Associations and Data Dictionaries
Venue: Proc. Priv. Enhancing Technol.
Year: 2016

Techniques based on randomized response enable the collection of potentially sensitive data from clients in a privacy preserving manner with strong local differential privacy guarantees. A recent such technology, RAPPOR  enables estimation of the marginal frequencies of a set of strings via privacy preserving crowdsourcing. However, this original estimation process relies on a known dictionary of possible strings; in practice, this dictionary can be extremely large and/or unknown. In this paper, we propose a novel decoding algorithm for the RAPPOR mechanism that enables the estimation of "unknown unknowns," i.e. strings we do not know we should be estimating. To enable learning without explicit dictionary knowledge, we develop methodology for estimating the joint distribution of multiple variables collected with RAPPOR. Our contributions are not RAPPOR specific, and can be generalized to other local differential privacy mechanisms for learning distributions of string valued random variables.
Extracellular Vesicle Mediated Cell Cell Communication in the Nervous System: Focus on Neurological Diseases
Venue: International journal of molecular sciences
Year: 2019

Extracellular vesicles (EVs) including exosomes, are membranous particles released by cells into the extracellular space. They are involved in cell differentiation, tissue homeostasis, and organ remodelling in virtually all tissues, including the central nervous system (CNS) They are secreted by a range of cell types and via blood reaching other cells whose functioning they can modify because they transport and deliver active molecules, such as proteins of various types and functions, lipids, DNA, and miRNAs. Since they are relatively easy to isolate, exosomes can be characterized, and their composition elucidated and manipulated by bioengineering techniques. Consequently, exosomes appear as promising theranostics elements, applicable to accurately diagnosing pathological conditions, and assessing prognosis and response to treatment in a variety of disorders. Likewise, the characteristics and manageability of exosomes make them potential candidates for delivering selected molecules, e.g. therapeutic drugs, to specific target tissues. All these possible applications are pertinent to research in neurophysiology, as well as to the study of neurological disorders, including CNS tumors, and autoimmune and neurodegenerative diseases. In this brief review, we discuss what is known about the role and potential future applications of exosomes in the nervous system and its diseases, focusing on cell cell communication in physiology and pathology.
COVID 19, androgens, and androgenic alopecia
Year: 2020

The severity of severe acute respiratory syndrome coronavirus 2 (SARS CoV 2) in males may be due to the higher androgen expression. It is also known that androgen sensitivity is correlated with androgenic alopecia. The "Gabrin sign" identifies those with severe androgen alopecia (Hamilton Norwood scale 3 7) who are at a higher risk for developing severe coronavirus disease 2019 (COVID 19) symptoms. The aim of this review article is to examine the current scientific evidence of the link between androgenic alopecia, androgens, and the severity of COVID 19 and also, to review possible therapeutic targets to impact COVID 19 outcomes.
Investigating Relationships Between Music, Emotions, Personality, and Music Induced Movement
Year: 2013

Listening to music makes us to move in various ways. The characteristics of these movements can be affected by several aspects, such as individual factors, musical features, or the emotional content of the music. In a study in which we presented 60 individuals with 30 musical stimuli representing different genres of popular music and recorded their movement with an optical motion capture system, we found significant correlations 1) between musical characteristics and the exhibited movement, 2) between the perceived emotional content of the music and the movement, and 3) between personality traits of the dancers and the movement. However, such separate analyses are incapable of investigating possible relationships between the different aspects. We describe two multivariate analysis approaches mediation and moderation that enable the simultaneous analysis of relationships between more than two variables. The results of these analyses suggest mediation effects of the perceived emotional content of music on the relationships between different features of music and movement. It can therefore be assumed that musical emotions can (partly) account for the effect of music on movements. However, using personality as moderator between music and movement failed to show a moderation effect in most cases, suggesting that personality does not generally affect existing relationships between music and movement. Hence it can be assumed that musical characteristics and personality are independent factors in relation to music induced movement.
Ancestral Reconstruction of a Pre LUCA Aminoacyl tRNA Synthetase Ancestor Supports the Late Addition of Trp to the Genetic Code
Venue: Journal of Molecular Evolution
Year: 2015

The genetic code was likely complete in its current form by the time of the last universal common ancestor (LUCA) Several scenarios have been proposed for explaining the code's pre LUCA emergence and expansion, and the relative order of the appearance of amino acids used in translation. One co evolutionary model of genetic code expansion proposes that at least some amino acids were added to the code by the ancient divergence of aminoacyl tRNA synthetase (aaRS) families. Of all the amino acids used within the genetic code, Trp is most frequently claimed as a relatively recent addition. We observe that, since TrpRS and TyrRS are paralogous protein families retaining significant sequence similarity, the inferred sequence composition of their ancestor can be used to evaluate this co evolutionary model of genetic code expansion. We show that ancestral sequence reconstructions of the pre LUCA paralog ancestor of TyrRS and TrpRS have several sites containing Tyr, yet a complete absence of sites containing Trp. This is consistent with the paralog ancestor being specific for the utilization of Tyr, with Trp being a subsequent addition to the genetic code facilitated by a process of aaRS divergence and neofunctionalization. Only after this divergence could Trp be specifically encoded and incorporated into proteins, including the TyrRS and TrpRS descendant lineages themselves. This early absence of Trp is observed under both homogeneous and non homogeneous models of ancestral sequence reconstruction. Simulations support that this observed absence of Trp is unlikely to be due to chance or model bias. These results support that the final stages of genetic code evolution occurred well within the "protein world," and that the presence absence of Trp within conserved sites of ancient protein domains is a likely measure of their relative antiquity, permitting the relative timing of extremely early events within protein evolution before LUCA.
Efficient multi scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Venue: Medical Image Anal.
Year: 2017

HIGHLIGHTSAn efficient 11 layers deep, multi scale, 3D CNN architecture.A novel training strategy that significantly boosts performance.The first employment of a 3D fully connected CRF for post processing.State of the art performance on three challenging lesion segmentation tasks.New insights into the automatically learned intermediate representations.  We propose a dual pathway, 11 layers deep, three dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi channel MRI patient data with traumatic brain injuries, brain tumours, and ischemic stroke. We improve on the state of the art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available.
Electronic Nose Technology and its Application A Systematic Survey
Year: 2015

Electronic Nose is one of the most advance device in the field of sensor technology. It is responsible for the automated detection and classification of gases, odors and vapors. This paper reviews the three components of Electronic Nose i.e. sample handling, detection system, data processing system. It outlines the range of sensors their principles, advantages and disadvantages. It describe the data processing through pattern recognition process. It also outlines the enormous benefits of Electronic Nose in the field of food control, clinical diagnosis, cosmetics, environmental factors, garbage control and detection of plant diseases.