nils-herrmann/clean_scirepeval huggingface dataset:
Impact of the Sub Resting Membrane Potential on Accurate Inference in Spiking Neural Networks
Venue: Scientific Reports
Year: 2020

Spiking neural networks (SNNs) are considered as the third generation of artificial neural networks, having the potential to improve the energy efficiency of conventional computing systems. Although the firing rate of a spiking neuron is an approximation of rectified linear unit (ReLU) activation in an analog valued neural network (ANN) there remain many challenges to be overcome owing to differences in operation between ANNs and SNNs. Unlike actual biological and biophysical processes, various hardware implementations of neurons and SNNs do not allow the membrane potential to fall below the resting potential in other words, neurons must allow the sub resting membrane potential. Because there occur an excitatory post synaptic potential (EPSP) as well as an inhibitory post synaptic potential (IPSP) negatively valued synaptic weights in SNNs induce the sub resting membrane potential at some time point. If a membrane is not allowed to hold the sub resting potential, errors will accumulate over time, resulting in inaccurate inference operations. This phenomenon is not observed in ANNs given their use of only spatial synaptic integration, but it can cause serious performance degradation in SNNs. In this paper, we demonstrate the impact of the sub resting membrane potential on accurate inference operations in SNNs. Moreover, several important considerations for a hardware SNN that can maintain the sub resting membrane potential are discussed. All of the results in this paper indicate that it is essential for neurons to allow the sub resting membrane potential in order to realize high performance SNNs.
A Case for a Philosophical History of Psychology: An Interview with Saulo de Freitas Araujo at the Centenary of the Death of Wilhelm Wundt (1832 1920)
Year: 2020

On the occasion of the centenary of Wilhelm Wundt's death (1832 1920) we had a conversation with Saulo de Freitas Araujo on the works and influence of the German author. After a brief introduction, the conversation begins with a reflection on the aims and objectives of Araujo's work on the history and philosophy of Wundt's psychology. A philosophical approach to the history of science and of psychology is then described. After considering the social and intellectual context of the revival of Wundt scholarship during the 1970s, Wundt's philosophical and psychological project is discussed. The conversation ends with general reflections on Wundt's legacy to recent and contemporary psychology.
Three dimensional elastic wave speeds in the northern Chile subduction zone: variations in hydration in the supraslab mantle
Year: 2016

We use seismic tomography to investigate the state of the supraslab mantle beneath northern Chile, a part of the Nazca South America Plate boundary known for frequent megathrust earthquakes and active volcanism. We performed a joint inversion of arrival times from earthquake generated body waves and phase delay times from ambient noise generated surface waves recorded by a combined 360 seismic stations deployed in northern Chile at various times over several decades. Our preferred model shows an increase in Vp/Vs by as much as 3 per cent from the subducting slab into the supraslab mantle throughout northern Chile. Combined with low values of both Vp and Vs at depths between 40 and 80 km, we attribute this increase in Vp/Vs to the serpentinization of the supraslab mantle in this depth range. The region of high Vp/Vs extends to 80 120 km depth within the supraslab mantle, but Vp and Vs both increase to normal to high values. This combination, along with the greater abundance of ambient seismicity and higher temperatures at these depths, suggest that conversion from basalt to eclogite in the slab accelerates and that the fluids expelled into the supraslab mantle contribute to partial melt. The corresponding maximum melt fraction is estimated to be about 1 per cent. Both the volume of the region affected by hydration and size of the wave speed contrasts are significantly larger north of ~21degS. This latitude also delimits large coastal scarps and the eruption of ignimbrites in the north. Ambient seismicity is more abundant north of 21degS, and the seismic zone south of this latitude is offset to the east. The high Vp/Vs region in the north may extend along the slab interface to depths as shallow as 20 km, where it corresponds to a region of reduced seismic coupling and overlaps the rupture zone of the recent 2014 M8.2 Pisagua earthquake. A potential cause of these contrasts is enhanced hydration of the subducting oceanic lithosphere related to a string of seamounts located on the Iquique Ridge of the Nazca Plate.
MicroRNA, a new target for engineering new crop cultivars
Venue: Bioengineered
Year: 2016

Global sustainable development depends on, at least partially, the sustainable development of crop reproduction that provides food, cloth and bioenergy as well certain drugs. During thousands of years of human history, many crops have been domesticated for feeding the world. Perfectly, in the past 2 decades, scientists have innovated biotechnological tools for improving crop yield and quality. For sustainable development, more targets and tools are needed to develop. Among these, microRNA (miRNA) is becoming an emerging target for engineering new crop cultivars with high yield and quality as well tolerance to environmental abiotic and biotic stresses. miRNAs are an extensive class of small regulatory RNAs, which play essential roles in all plant biological and metabolic processes, not only in plant development and growth but also in compound biosynthesis and response to various environmental stress. miRNA based biotechnology is becoming a new strategy for crop improvement, which will play important role in future agricultural sustainable development.
Effect of non surgical periodontal therapy on insulin resistance in patients with type II diabetes mellitus and chronic periodontitis, as assessed by C peptide and the Homeostasis Assessment Index+
Venue: Journal of investigative and clinical dentistry
Year: 2017

A bidirectional relationship exists between diabetes and periodontitis. In the present clinical trial, we evaluated the effects of non surgical periodontal therapy (NSPT) on insulin resistance in patients with type II diabetes mellitus (DM) and chronic periodontitis.  Forty chronic periodontitis patients with type II DM were selected and equally allocated to case and control groups. All patients were assessed for periodontal parameters and systemic parameters. The case group received NSPT, and both groups were re evaluated after 3 months.  All periodontal parameters were found to be significantly improved in the case group compared to the control group 3 months after NSPT. The mean differences in systemic parameters, such as fasting serum C peptide, Homeostasis Assessment (HOMA) Index insulin resistance, and HOMA insulin sensitivity, from baseline to 3 months for the case group were 0.544 0.73, 0.54 0.63, and 25.44 36.81, respectively; for the control group, they were significant at 1.66 1.89, 1.48 1.86, and 31.42 38.82 respectively (P 0.05) There was a significant decrease in fasting blood glucose and glycosylated hemoglobin A1c from baseline to 3 months in the case group (P 0.05)  The present study showed that periodontal inflammation could affect glycemic control and insulin resistance. Effective periodontal therapy reduced insulin resistance and improved periodontal health status and insulin sensitivity in patients with type II DM and chronic periodontitis.
A Method for Automatic and Rapid Mapping of Water Surfaces from Sentinel 1 Imagery
Venue: Remote. Sens.
Year: 2018

Reliable information about the spatial distribution of surface waters is critically important in various scientific disciplines. Synthetic Aperture Radar (SAR) is an effective way to detect floods and monitor water bodies over large areas. Sentinel 1 is a new available SAR and its spatial resolution and short temporal baselines have the potential to facilitate the monitoring of surface water changes, which are dynamic in space and time. While several methods and tools for flood detection and surface water extraction already exist, they often comprise a significant manual user interaction and do not specifically target the exploitation of Sentinel 1 data. The existing methods commonly rely on thresholding at the level of individual pixels, ignoring the correlation among nearby pixels. Thus, in this paper, we propose a fully automatic processing chain for rapid flood and surface water mapping with smooth labeling based on Sentinel 1 amplitude data. The method is applied to three different sites submitted to recent flooding events. The quantitative evaluation shows relevant results with overall accuracies of more than 98% and F measure values ranging from 0.64 to 0.92. These results are encouraging and the first step to proposing operational image chain processing to help end users quickly map flooding events or surface waters.
Netzob un outil pour la retro conception de protocoles de communication
Year: 2012

In this paper, we present Netzob, an opensource tool which supports the expert in its operations of reverse engineering, evaluation and simulation of communication protocols. Its main goals are to help security evaluators to assess the robustness of proprietary or unknown protocols implementation, simulate realistic communications to test third party products (IDS, firewalls, etc. or even create an open source implementation of a proprietary or unknown protocol. Netzob supports the expert in a semi automatic manner. It includes the necessaries to passively learn the vocabulary of a protocol and to actively infer its grammar. In addition, it integrates a stochastic and statefull model to represent any statefull communication protocol. The definition of the model can be shared and loaded in a dedicated component of Netzob, its simulator. Therefore, it becomes easy to simulate multiple actors (servers and clients) which communicate according to the infered protocol, and to apply advanced fuzzing.
Mediating effects of bullying involvement on the relationship of body mass index with social phobia, depression, suicidality, and self esteem and sex differences in adolescents in Taiwan.
Venue: Child abuse neglect
Year: 2014

The aims of this study were to examine the mediating effect of bullying involvement on the relationships between body mass index (BMI) and mental health problems, including social phobia, depression, suicidality, and low self esteem among adolescents in Taiwan. The moderation effect of sex on the mediating role of bullying involvement was also examined. Five thousand two hundred and fifty two students of high schools completed the questionnaires. Victimization and perpetration of passive and active bullying were assessed using the Chinese version of the School Bullying Experience Questionnaire. BMI was calculated from self reported weight and height measurements. The Social Phobia Inventory, the Mandarin Chinese version of the Center for Epidemiological Studies Depression Scale, the suicidality related questionnaire from the epidemiological version of the Kiddie Schedule for Affective Disorders and Schizophrenia, and the Rosenberg Self Esteem Scale were applied to assess social phobia, depression, suicidality, and low self esteem, respectively. The mediating effect of bullying involvement on the associations between increased BMI and mental health problems was examined by the Sobel test. The moderation effect of sex on the mediating role of bullying involvement was tested by the multiple group structural equation model. Victimization of passive and active bullying and perpetration of passive bullying, but not perpetration of active bullying, had a mediating effect on the relationships between increased BMI and all four mental health problems. Sex did not have a significant moderation effect on the mediating role of bullying involvement. Bullying involvement should be a target of prevention and intervention in developing a strategy to improve mental health among adolescents with increased BMI.
Statistical Analysis of Risk Factors of Drug Addiction of Youth in Debre Berhan Town, Ethiopia
Year: 2020

Drugs are damaging by their nature. These substances produce changes in behavior function by altering the chemistry of the brain. Once brain function is altered, a person experiences physical, psychological, and behavioral changes as a direct result. Changes in physical and psychological functioning cause damage to the mind, body, behavior and can harm the social relationships. According United Nations Office for Drug Control and Crime Prevention report, the use of substances such as alcohol, khat, and tobacco has become one of the rising major public health and socioeconomic problem worldwide. The main objective of this study is to identify the risk factors for drug addiction of youth in debre berhan town. In general, this study is expected to be useful for the parents, the youth themselves, government, nongovernmental organization, and city administration in providing primary information and assist the concerned bodies to come up with appropriate intervention strategies that can help to curb the drug problem. The data is primary and cross sectional. Descriptive statistics and the binary logistic regression analysis is applied to examine the association between the binary outcome dependent variable and explanatory variables. The binary logistic model revealed that the numbers of youth those are addicted in the age group 20 25 are much greater than from the youth those are addicted in the age group 15 19 and 25 30, youths with family members who use drugs are more likely to be drug addicts, and also those who are orthodox religion followers are more likely to be addicted than Muslim, protestant and other religion follower, additionally youth who are single are more drug user than married, divorced and widowed.
SISTEM MONITORING PENGAIRAN OTOMATIS DENGAN METODE LOGIKA FUZZY
Year: 2019

Rice plant which is a source of food for the people, it needs enough temperature, air humidity and high water for maximum growth. The irrigation system is a major requirement in the field of agriculture, especially for rice plant. Some constraints in conventional irrigation, so they need irrigation system automatically. Some previous studies about automatic irrigation were only used one or two parameters and only used fuzzy or IoT. The method offered in this study uses fuzzy logic using 3 inputs and combines the monitoring system in real time based on IoT. The purpose of this study is to determine the effectiveness of fuzzy logic using three inputs to control the automatic irrigation system byreal time monitoring usingIoT. Data obtained by testing in themorning, afternoon, evening, night and using heat and rain treatment then compared using Matlab calculation. From the tool testing, the average precision of the tool comparison using the calculation is 77.13%
Selection and optimization of transfection enhancer additives for increased virus like particle production in HEK293 suspension cell cultures
Venue: Applied Microbiology and Biotechnology
Year: 2015

The manufacturing of biopharmaceuticals in mammalian cells typically relies on the use of stable producer cell lines. However, in recent years, transient gene expression has emerged as a suitable technology for rapid production of biopharmaceuticals. Transient gene expression is particularly well suited for early developmental phases, where several potential therapeutic targets need to be produced and tested in vivo. As a relatively new bioprocessing modality, a number of opportunities exist for improving cell culture productivity upon transient transfection. For instance, several compounds have shown positive effects on transient gene expression. These transfection enhancers either facilitate entry of PEI/DNA transfection complexes into the cell or nucleus or increase levels of gene expression. In this work, the potential of combining transfection enhancers to increase Gag based virus like particle production levels upon transfection of suspension growing HEK 293 cells is evaluated. Using Plackett Burman design of experiments, it is first tested the effect of eight transfection enhancers: trichostatin A, valproic acid, sodium butyrate, dimethyl sulfoxide (DMSO) lithium acetate, caffeine, hydroxyurea, and nocodazole. An optimal combination of compounds exhibiting the highest effect on gene expression levels was subsequently identified using a surface response experimental design. The optimal consisted on the addition of 20 mM lithium acetate, 3.36 mM valproic acid, and 5.04 mM caffeine which increased VLP production levels 3.8 fold, while maintaining cell culture viability at 94
Learning regularized hoeffding trees from data streams
Venue: SAC
Year: 2019

Learning from data streams is a hot topic in machine learning that targets the learning and update of predictive models as data becomes available for both training and query. Due to their simplicity and convincing results in a multitude of applications, Hoeffding Trees are, by far, the most widely used family of methods for learning decision trees from streaming data. Despite the aforementioned positive characteristics, Hoeffding Trees tend to continuously grow in terms of nodes as new data becomes available, i.e. they eventually split on all features available, and multiple times on the same feature; thus leading to unnecessary complexity. With this behavior, Hoeffding Trees lose the ability to be human understandable and computationally efficient. To tackle these issues, we propose a regularization scheme for Hoeffding Trees that (i) uses a penalty factor to control the gain obtained by creating a new split node using a feature that has not been used thus far; and (ii) uses information from previous splits in the current branch to determine whether the gain observed indeed justifies a new split. The proposed scheme is combined with both standard and adaptive variants of Hoeffding Trees. Experiments using real world, stationary and drifting synthetic data show that the proposed method prevents both original and adaptive Hoeffding Trees from unnecessarily growing while maintaining impressive accuracy rates. As a byproduct of the regularization process, significant improvements in processing time, model complexity, and memory consumption have also been observed, thus showing the effectiveness of the proposed regularization scheme.
EVALUATION OF GREEN LOGISTICS SYSTEM OF SOLID WASTE AT PORTS BASED ON ANALYTIC HIERARCHY PROCESS
Year: 2019

This paper attempts to realize the green logistics of solid waste at ports, enabling the harmless treatment, reduction and recycling of such waste through the entire life cycle. For this purpose, the life cycle assessment and the analytic hierarchy process (AHP) were applied to evaluate the green logistics of solid waste at ports. Firstly, the solid waste logistics system was examined from the perspective of logistics system. Drawing on life cycle evaluation, the author analyzed how the solid waste logistics system affects the society, economy and environment. Meanwhile, an AHP based evaluation system was established to quantify the overall impacts of solid waste. On this basis, the solid waste logistics system of a port was optimized, and subjected to life cycle assessment. The evaluation results show that the optimized system has much lower social, economic and environmental impacts than the original system. The research findings shed important new light on solid waste logistics at ports.
Business Process Improvement through Data Mining Techniques: An Experimental Approach
Year: 2016

The chapter proposes a general methodology on how to use data mining techniques to support total quality management especially related to the quality tools. The effectiveness of the proposed general methodology is demonstrated through their application. The goal of this chapter is to build the 7 new quality tools based on the rules that are "hidden" in the raw data of a database and to propose solutions and actions that will lead the organization under study to improve its business processes by evaluating the results. Four popular data mining approaches (rough sets, association rules, classification rules and Bayesian networks) were applied on a set of 12.477 case records concerning vehicles damages. The set of rules and patterns that was produced by each algorithm was used as input in order to dynamically form each of the quality tools. This would enable the creation of the quality tools starting from the raw data and passing through the stage of data mining, using automatic software was employed.
Information processing with large scale optical integrated circuits
Venue: 2016 IEEE International Conference on Rebooting Computing (ICRC)
Year: 2016

Photonic integrated circuits (PICs) offer an enticing platform for further advances in computation. Photonic communications hardware is already widely used within datacenters and is now reaching into the board and chip level. This trend is driving the development of more complex PICs that are more tightly integrated into computing systems. This PIC technology could be attractive for building photonic computational accelerators and for incorporating all optical signal processing tasks into photonic communications and sensing. At Hewlett Packard Labs, we are using a silicon photonics platform to build complex PICs with many hundreds of components, including nonlinear components. We use these PICs to test various approaches to photonic computation, including neuromorphic approaches as well as traditional logic circuits. For example, we are currently fabricating a circuit to solve the so called Ising problem, a classic problem of solid state physics that turns out to be equivalent to a number of combinatorial optimization problems. The circuit is closely related to Hopfield neural networks. In parallel, we are investigating PICs based on photonic crystals in an InGaAs platform. These PICs offer radically reduced power consumption compared to CMOS circuits, potentially consuming less than 1 fJ per elementary operation.
Advancing the social identity theory of leadership: A meta analytic review of leader group prototypicality
Year: 2020

This research advances a social identity approach to leadership through a meta analysis examining four novel hypotheses that clarify the nature and impact of leader group prototypicality (the extent to which a leader is perceived to embody shared social identity) A random effects meta analysis (k 128, N 32,834) reveals a moderate to large effect of prototypicality that holds across evaluative and behavioral outcomes. The effect is stronger (a) when prototypicality is conceptualized as the ideal type rather than the average group member, (b) for stronger prototypes (indexed by group longevity) and (c) for group members in formal rather than nonformal leadership roles. The effect is not contingent on group prototypicality entailing differentiation from other (out)groups. Additionally, results provide meta analytic evidence of widely examined key factors: follower group identification (which enhances the relationship) and leader group serving behavior (which attenuates the relationship) Building on these findings, we outline the implications for the next wave of theoretical and empirical work.