nils-herrmann/clean_scirepeval huggingface dataset:
Discrimination of Dysplastic Nevi from Common Melanocytic Nevi by Cellular and Molecular Criteria.
Venue: The Journal of investigative dermatology

Dysplastic nevi (DNs) also known as Clark's nevi or atypical moles, are distinguished from common melanocytic nevi by variegation in pigmentation and clinical appearance, as well as differences in tissue patterning. However, cellular and molecular differences between DNs and common melanocytic nevi are not completely understood. Using cDNA microarray, quantitative RT PCR, and immunohistochemistry, we molecularly characterized DNs and analyzed the difference between DNs and common melanocytic nevi. A total of 111 probesets (91 annotated genes, fold change 2.0 and false discovery rate 0.25) were differentially expressed between the two lesions. An unexpected finding in DNs was altered differentiation and activation of epidermal keratinocytes with increased expression of hair follicle related molecules (keratin 25, trichohyalin, ribonuclease, RNase A family, 7) and inflammation related molecules (S100A7, S100A8) at both genomic and protein levels. The immune microenvironment of DNs was characterized by an increase of T helper type 1 (IFNg) and T helper type 2 (IL13) cytokines as well as an upregulation of oncostatin M and CXCL1. DUSP3, which regulates cellular senescence, was identified as one of the disease discriminative genes between DNs and common melanocytic nevi by three independent statistical approaches and its altered expression was confirmed by immunohistochemistry. The molecular and cellular changes in which the epidermal melanin unit undergoes follicular differentiation as well as upregulation of defined cytokines could drive complex immune, epidermal, and pigmentary alterations.
Unsupervised learning in probabilistic neural networks with multi state metal oxide memristive synapses
Venue: Nature communications

In an increasingly data rich world the need for developing computing systems that cannot only process, but ideally also interpret big data is becoming continuously more pressing. Brain inspired concepts have shown great promise towards addressing this need. Here we demonstrate unsupervised learning in a probabilistic neural network that utilizes metal oxide memristive devices as multi state synapses. Our approach can be exploited for processing unlabelled data and can adapt to time varying clusters that underlie incoming data by supporting the capability of reversible unsupervised learning. The potential of this work is showcased through the demonstration of successful learning in the presence of corrupted input data and probabilistic neurons, thus paving the way towards robust big data processors.
Improved probabilistic neural networks with self adaptive strategies for transformer fault diagnosis problem

Probabilistic neural network has successfully solved all kinds of engineering problems in various fields since it is proposed. In probabilistic neural network, Spread has great influence on its performance, and probabilistic neural network will generate bad prediction results if it is improperly selected. It is difficult to select the optimal manually. In this article, a variant of probabilistic neural network with self adaptive strategy, called self adaptive probabilistic neural network, is proposed. In self adaptive probabilistic neural network, Spread can be self adaptively adjusted and selected and then the best selected Spread is used to guide the self adaptive probabilistic neural network train and test. In addition, two simplified strategies are incorporated into the proposed self adaptive probabilistic neural network with the aim of further improving its performance and then two versions of simplified self adaptive probabilistic neural network (simplified self adaptive probabilistic neural networks 1 and 2) are proposed. The variants of self adaptive probabilistic neural networks are further applied to solve the transformer fault diagnosis problem. By comparing them with basic probabilistic neural network, and the traditional back propagation, extreme learning machine, general regression neural network, and self adaptive extreme learning machine, the results have experimentally proven that self adaptive probabilistic neural networks have a more accurate prediction and better generalization performance when addressing the transformer fault diagnosis problem.
Natural Parameter Networks: A Class of Probabilistic Neural Networks
Venue: NIPS

Neural networks (NN) have achieved state of the art performance in various applications. Unfortunately in applications where training data is insufficient, they are often prone to overfitting. One effective way to alleviate this problem is to exploit the Bayesian approach by using Bayesian neural networks (BNN) Another shortcoming of NN is the lack of flexibility to customize different distributions for the weights and neurons according to the data, as is often done in probabilistic graphical models. To address these problems, we propose a class of probabilistic neural networks, dubbed natural parameter networks (NPN) as a novel and lightweight Bayesian treatment of NN. NPN allows the usage of arbitrary exponential family distributions to model the weights and neurons. Different from traditional NN and BNN, NPN takes distributions as input and goes through layers of transformation before producing distributions to match the target output distributions. As a Bayesian treatment, efficient backpropagation (BP) is performed to learn the natural parameters for the distributions over both the weights and neurons. The output distributions of each layer, as byproducts, may be used as second order representations for the associated tasks such as link prediction. Experiments on real world datasets show that NPN can achieve state of the art performance.
Wormhole attack detection in wireless sensor networks
Venue: 2016 SAI Computing Conference (SAI)

A wireless sensor network (WSN) consists of a large number of sensor nodes with limited batteries, the sensoring devices are deployed randomly on a zone to collect data. WSNs are threatened by several malicious behaviors caused by some nodes. The impact of such behaviors can be serious, even fatal, due to the collaborative nature of nodes in a network without fixed infrastructure. Then, in this work we have referred to the need for a secure network communication network with mechanisms that take into account the limited resources of the nodes. In order to achieve such a security, the network can be split into sectors, and mobile agents (MAs) can be used to reject traffic intruders caused by Wormhole attacks taking into account energy constraint. Wormhole attack is a denial of service attack launched by malicious nodes by creating a tunnel through which the packets are replayed to malicious nodes disrupting the communication channel and corrupting network routing. In order to evaluate the performance of our proposal, we have carried out several simulation tests using the SINALGO simulator. The results obtained show that our proposal extends the life of the network, in terms of energy consumption and the rate of packet delivery.
Wormhole Attack Detection in Zigbee Wireless Sensor Networks using Intrusion Detection System

Wormhole attack is a serious threat to Wireless Sensor Networks (WSN) due to its wireless broadcast nature of communication, multi hop nature and resource constraints. Hence, to enhance the security and energy efficiency of a WSN, energy efficient Intrusion Detection System (IDS) is needed to detect and mitigate the attacks. In this paper, an Energy Efficient Intrusion Detection System (EE IDS) has been proposed for IEEE 802.15.4 based WSN to detect and mitigate the effect of wormhole attacks.  The wormhole attack is detected by using the optimized watchdog system. The optimized watchdog mechanism is a trust based method which is used to evaluate the validity of all the nodes of the network. In proposed approach, the optimization has been done in the selection of watchdog nodes, in order to consume less energy compared to that of the existing approaches. The detection of wormhole attack is also completely relies on the three main factors like trustworthiness, packet delivery ratio and end to end delay.  The proposed EE IDS is examined through extensive simulations by considering static and mobility model, and then it is compared with the existing IDS for detecting wormhole attacks. The performance of zigbee WSN is analyzed in terms of metrics such as packet delivery ratio, average end to end delay and energy consumption for static model. In mobility model, the Proposed EE IDS is evaluated in terms of detection rate, false positive rate and detection time.  The proposed EE IDS is used to detect the wormhole attack and improve the energy efficiency in zigbee based wireless sensor networks, which can be used for military, environmental, health and commercial applications.
On the usage of active learning for SHM

The key element of this work is to demonstrate a strategy for using pattern recognition algorithms to investigate correlations between feature variables for Structural Health Monitoring (SHM) The task will take advantage of data from a bridge. An informative chain of artificial intelligence tools will allow an active learning interaction between the unfolded shapes of the manifold of online data by characterising the physical shape between variables. In many data mining and machine learning applications, there is a significant supply of unlabelled data but an important undersupply of labelled data. Semi supervised active learning, which combines both labelled and unlabelled data can offer serious access to useful information and may be the crucial element in successful decision making, regarding the health of structures.
On the Limits of LoRaWAN Channel Access
Venue: 2016 International Conference on Engineering and Telecommunication (EnT)

The rising tide of the Internet of Things has brought to the surface numerous low power, long range and low bitrate wireless network technologies. One of them, LoRaWAN, is being intensely popularized as a solution for sensor networks, however, its potential and limitations are unclear, because there is still neither accurate study nor massive LoRaWAN deployment. This paper surveys and analyzes LoRaWAN operation, focusing on performance evaluation of its channel access as the most crucial component for massive machine type communication. We reveal and point out weaknesses of the LoRaWAN specification and propose solutions to improve LoRaWAN performance.
Catalytic decomposition of carbon based liquid phase chemical hydrogen storage materials for hydrogen generation under mild conditions
Venue: Applied Petrochemical Research

The increasing demand of energy requires the development of sufficient and sustainable energy sources. However, the upcoming hydrogen economy is suffering from challenges which need to be solved. In this context, the search for liquid hydrogen storage materials and successful utilisation is crucial due to the high gravimetric and volumetric hydrogen densities, easy recharging, low capital investment, and low potential risks. In this review, we survey the progress made in hydrogen generation from carbon based liquid phase chemical hydrogen storage materials, focusing mainly on the catalytic decomposition of formic acid for hydrogen production.
Development of the ReaxFFCBN reactive force field for the improved design of liquid CBN hydrogen storage materials.
Venue: Physical chemistry chemical physics PCCP

Liquid CBN (carbon boron nitrogen) hydrogen storage materials such as 3 methyl 1,2 BN cyclopentane have the advantage of being easily accessible for use in current liquid fuel infrastructure. To develop practical liquid CBN hydrogen storage materials, it is of great importance to understand the reaction pathways of hydrogenation/dehydrogenation in the liquid phase, which are difficult to discover by experimental methods. Herein, we developed a reactive force field (ReaxFFCBN) from quantum mechanical (QM) calculations based on density functional theory for the storage of hydrogen in BN substituted cyclic hydrocarbon materials. The developed ReaxFFCBN provides similar dehydrogenation pathways and energetics to those predicted by QM calculations. Moreover, molecular dynamics (MD) simulations with the developed ReaxFFCBN can predict the stability and dehydrogenation behavior of various liquid CBN hydrogen storage materials. Our simulations reveal that a unimolecular dehydrogenation mechanism is preferred in liquid CBN hydrogen storage materials. However, as the temperature in the simulation increases, the contribution of a bimolecular dehydrogenation mechanism also increases. Moreover, our ReaxFF MD simulations show that in terms of thermal stability and dehydrogenation kinetics, liquid CBN materials with a hexagonal structure are more suitable materials than those with a pentagonal structure. We expect that the developed ReaxFFCBN could be a useful protocol in developing novel liquid CBN hydrogen storage materials.
Entanglement hamiltonians in two dimensional conformal field theory

We enumerate the cases in 2d conformal field theory where the logarithm of the reduced density matrix (the entanglement or modular hamiltonian) may be written as an integral over the energy momentum tensor times a local weight. These include known examples and new ones corresponding to the time dependent scenarios of a global and local quench. In these latter cases the entanglement hamiltonian depends on the momentum density as well as the energy density. In all cases the entanglement spectrum is that of the appropriate boundary CFT. We emphasize the role of boundary conditions at the entangling surface and the appearance of boundary entropies as universal O(1) terms in the entanglement entropy.
Minimal lectures on two dimensional conformal field theory

We provide a brief but self contained review of two dimensional conformal field theory, from the basic principles to some of the simplest models. From the representations of the Virasoro algebra on the one hand, and the state field correspondence on the other hand, we deduce Ward identities and Belavin Polyakov Zamolodchikov equations for correlation functions. We then explain the principles of the conformal bootstrap method, and introduce conformal blocks. This allows us to define and solve minimal models and Liouville theory. In particular, we study their three and four point functions, and discuss their existence and uniqueness. In appendices, we introduce the free boson theory (with an arbitrary central charge) and the modular bootstrap in minimal models.
Quasiconformal Teichmuller theory as an analytical foundation for two dimensional conformal field theory

The functorial mathematical definition of conformal field theory was first formulated approximately 30 years ago. The underlying geometric category is based on the moduli space of Riemann surfaces with parametrized boundary components and the sewing operation. We survey the recent and careful study of these objects, which has led to significant connections with quasiconformal Teichmuller theory and geometric function theory. In particular we propose that the natural analytic setting for conformal field theory is the moduli space of Riemann surfaces with so called Weil Petersson class parametrizations. A collection of rigorous analytic results is advanced here as evidence. This class of parametrizations has the required regularity for CFT on one hand, and on the other hand are natural and of interest in their own right in geometric function theory.
Pseudo HE images derived from CARS/TPEF/SHG multimodal imaging in combination with Raman spectroscopy as a pathological screening tool
Venue: BMC Cancer

BackgroundDue to the steadily increasing number of cancer patients worldwide the early diagnosis and treatment of cancer is a major field of research. The diagnosis of cancer is mostly performed by an experienced pathologist via the visual inspection of histo pathological stained tissue sections. To save valuable time, low quality cryosections are frequently analyzed with diagnostic accuracies that are below those of high quality embedded tissue sections. Thus, alternative means have to be found that enable for fast and accurate diagnosis as the basis of following clinical decision making.MethodsIn this contribution we will show that the combination of the three label free non linear imaging modalities CARS (coherent anti Stokes Raman scattering) TPEF (two photon excited autofluorescence) and SHG (second harmonic generation) yields information that can be translated into computational hematoxylin and eosin (HE) images by multivariate statistics. Thereby, a computational HE stain is generated resulting in pseudo HE overview images that allow for identification of suspicious regions. The latter are analyzed further by Raman spectroscopy retrieving the tissue's molecular fingerprint.ResultsThe results suggest that the combination of non linear multimodal imaging and Raman spectroscopy possesses the potential as a precise and fast tool in routine histopathology.ConclusionsAs the key advantage, both optical methods are non invasive enabling for further pathological investigations of the same tissue section, e.g. a direct comparison with the current pathological gold standard.
A flexible and dynamic mission planning architecture for UAV swarm coordination
Venue: 2016 International Conference on Unmanned Aircraft Systems (ICUAS)

In this paper a scalable and flexible Architecture for real time mission planning and dynamic agent to task assignment for a swarm of Unmanned Aerial Vehicles (UAV) is presented. The proposed mission planning architecture consists of a Global Mission Planner (GMP) which is responsible of assigning and monitoring different high level missions through an Agent Mission Planner (AMP) which is in charge of providing and monitoring each task of the mission to each UAV in the swarm. The objective of the proposed architecture is to carry out high level missions such as autonomous multi agent exploration, automatic target detection and recognition, search and rescue, and other different missions with the ability of dynamically re adapt the mission in real time. The proposed architecture has been evaluated in simulation and real indoor flights demonstrating its robustness in different scenarios and its flexibility for real time mission re planning and dynamic agent to task assignment.
Investigating gated recurrent neural networks for speech synthesis

Recently, recurrent neural networks (RNNs) as powerful sequence models have re emerged as a potential acoustic model for statistical parametric speech synthesis (SPSS) The long short term memory (LSTM) architecture is particularly attractive because it addresses the vanishing gradient problem in standard RNNs, making them easier to train. Although recent studies have demonstrated that LSTMs can achieve significantly better performance on SPSS than deep feed forward neural networks, little is known about why. Here we attempt to answer two questions: a) why do LSTMs work well as a sequence model for SPSS; b) which component (e.g. input gate, output gate, forget gate) is most important. We present a visual analysis alongside a series of experiments, resulting in a proposal for a simplified architecture. The simplified architecture has significantly fewer parameters than an LSTM, thus reducing generation complexity considerably without degrading quality.
Investigating gated recurrent networks for speech synthesis
Venue: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Recently, recurrent neural networks (RNNs) as powerful sequence models have re emerged as a potential acoustic model for statistical parametric speech synthesis (SPSS) The long short term memory (LSTM) architecture is particularly attractive because it addresses the vanishing gradient problem in standard RNNs, making them easier to train. Although recent studies have demonstrated that LSTMs can achieve significantly better performance on SPSS than deep feedforward neural networks, little is known about why. Here we attempt to answer two questions: a) why do LSTMs work well as a sequence model for SPSS; b) which component (e.g. input gate, output gate, forget gate) is most important. We present a visual analysis alongside a series of experiments, resulting in a proposal for a simplified architecture. The simplified architecture has significantly fewer parameters than an LSTM, thus reducing generation complexity considerably without degrading quality.
New Parents' Psychological Adjustment and Trajectories of Early Parental Involvement.
Venue: Journal of marriage and the family

Trajectories of parental involvement time (engagement and child care) across 3, 6, and 9 months postpartum and associations with parents' own and their partners' psychological adjustment (dysphoria, anxiety, and empathic personal distress) were examined using a sample of dual earner couples experiencing first time parenthood (N 182 couples) Using time diary measures that captured intensive parenting moments, hierarchical linear modeling analyses revealed that patterns of associations between psychological adjustment and parental involvement time depended on the parenting domain, aspect of psychological adjustment, and parent gender. Psychological adjustment difficulties tended to bias the 2 parent system toward a gendered pattern of "mother step in" and "father step out," as father involvement tended to decrease, and mother involvement either remained unchanged or increased, in response to their own and their partners' psychological adjustment difficulties. In contrast, few significant effects were found in models using parental involvement to predict psychological adjustment.
Developing cross cultural awareness through foreign immersion programs: Implications of university study abroad research for global competency development

The purpose of this paper is to investigate the efficacy of foreign immersion programs in terms of increasing cross cultural awareness among university students in business, accounting, human resources and agriculture. The authors extrapolate from their population to the practice of developing business professionals on international assignments.  This paper presents findings of a four year, government sponsored university exchange program involving 40 professional management and agriculture science students from four US and Brazilian top research universities who participated in a semester long study abroad experience. Pre departure and post exchange data were collected using the well established Intercultural Development Inventory (IDI) In addition, the authors collected academic performance data and verbal mid and end project personal assessments. Two of the authors of this paper served as project directors, the third as evaluation specialist.  Despite
"You look like them" Drawing on Counselling Theory and Practice to Reflexively Negotiate Cultural Difference in Research Relationships
Venue: International journal for the advancement of counseling

Located within a context of intercultural counselling research, this paper highlights the pertinence of the researcher's reflexivity and cultural awareness in relation to research relationships. It draws on an excerpt between a white European interviewer and an Asian trainee counsellor discussing the latter's experience of intercultural counselling practice. A reflexive analysis of a short passage aims to demonstrate how explicit negotiation of cultural difference within the interview setting advanced the researcher's understanding of the participant's experience, which was being investigated. The interrelated challenges, but also the importance of the interviewer's preparedness to explore power imbalances in the research relationship are also examined, as are some key limitations of such endeavours. This paper underlines the usefulness of counselling skills in qualitative research, hoping to function as an invitation for more practitioner involvement in therapeutic inquiry.