nils-herrmann/clean_scirepeval huggingface dataset:
Exponential synchronization of Markovian jumping neural networks with partly unknown transition probabilities via stochastic sampled-data control

Exponential synchronization of Markovian jumping neural networks with partly unknown transition probabilities via stochastic sampled-data control, Neurocomputing, This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  This paper investigates the exponential synchronization for a class of delayed neu-ral networks with Markovian jumping parameters and time varying delays. The considered transition probabilities are assumed to be partially unknown. In addition, the sampling period is assumed to be time-varying that switches between two different values in a random way with given probability. Several delay-dependent synchronization criteria have been derived to guarantee the exponential stability of the error systems and the master systems are stochasti-cally synchronized with the slave systems. By introducing an improved Lyapunov-Krasovskii functional (LKF) including new triple integral terms, free-weighting matrices, partly unknown transition probabilities and combining both the convex combination technique and reciprocal convex technique, a delay-dependent exponential stability criteria is obtained in terms of linear matrix inequalities (LMIs). The information about the lower bound of the discrete time-varying delay is fully used in the LKF. Furthermore, the desired sampled-data synchronization controllers can be solved in terms of the solution to LMIs. Finally, numerical examples are provided to demonstrate the feasibility of the proposed estimation schemes from its gain matrices.
Design of an electronic device for turbidity detection in blood serum in newborns

This work introduces the engineering design of a device capable to detect serum turbidity. We hypothesized that an electronic, portable, and low cost device that can provide objective, quantitative measurements of serum turbidity might have the potential to improve the early detection of neonatal sepsis. The design features, testing methodologies, and the obtained results are described. The final electronic device was evaluated in two experiments. The first one consisted in recording the turbidity value measured by the device for different solutions with known concentrations and different degrees of turbidity. The second analysis demonstrates a positive correlation between visual turbidity estimation and electronic turbidity measurement. Furthermore, our device demonstrated high turbidity in serum from two neonates with sepsis (one with a confirmed positive blood culture; the other one with a clinical diagnosis). We conclude that our electronic device may effectively measure serum turbidity at the bedside. Future studies will widen the possibility of additional clinical implications.
Seaweed extract stimuli in plant science and agriculture

Both micro- and macroalgae have long been used to augment plant productivity and food production in various regions of the world through their beneficial effects when applied to soils. Interactions of algae with the soil community undoubtedly are complex and benefits are dependent on the crop and the local environmental conditions. This has resulted in much speculation as to mechanisms involved as well as the validity of the results reported. It is now 60 years since the first commercial seaweed extract was manufactured for agricultural use. These aqueous extracts allowed for the first time the direct application of soluble seaweed constituents to specific plant organs such as leaves and roots. The earlier concept that benefits of seaweeds and their extracts were due mainly to their manurial value or to their micronutrient suites is no longer tenable. Seaweeds likewise have been used for millennia as fodder supplements to improve animal nutrition and productivity. Recent research is focusing on their mode of action, specific health benefits, and the mechanisms of action in animals. Improved analytical techniques and instrumentation coupled with the use of molecular genetic tools are establishing that seaweed extracts can modify plant and animal responses at a fundamental level. It therefore seems appropriate to review key developments over the years and to remark on novel findings. A new and exciting vista has opened for seaweed extracts in both plant and animal applications.
Direct observation of individual endogenous protein complexes in situ by proximity ligation

Cellular processes can only be understood as the dynamic interplay of molecules. There is a need for techniques to monitor interactions of endogenous proteins directly in individual cells and tissues to reveal the cellular and molecular architecture and its responses to perturbations. Here we report our adaptation of the recently developed proximity ligation method to examine the subcellular localization of protein-protein interactions at single-molecule resolution. Proximity probes—oligonucleotides attached to antibodies against the two target proteins—guided the formation of circular DNA strands when bound in close proximity. The DNA circles in turn served as templates for localized rolling-circle amplification (RCA), allowing individual interacting pairs of protein molecules to be visualized and counted in human cell lines and clinical specimens. We used this method to show specific regulation of protein-protein interactions between endogenous Myc and Max oncogenic transcription factors in response to interferon-γ (IFN-γ) signaling and low-molecular-weight inhibitors.
TODIM and TOPSIS with Z-numbers

In this paper, we present an approach that can handle Z-numbers in the context of multi-criteria decision-making problems. The concept of Z-number as an ordered pair Z=(A, B) of fuzzy numbers A and B is used, where A is a linguistic value of a variable of interest and B is a linguistic value of the probability measure of A. As human beings, we communicate with each other by means of natural language using sentences like “the journey from home to university most likely takes about half an hour.” The Z-numbers are converted to fuzzy numbers. Then the Z-TODIM and Z-TOPSIS are presented as a direct extension of the fuzzy TODIM and fuzzy TOPSIS, respectively. The proposed methods are applied to two case studies and compared with the standard approach using crisp values. The results obtained show the feasibility of the approach.
Performance improvement of optical mouse sensors: Application in a precision planar stage

A cost-effective position measurement system based on optical mouse sensors is presented in this work. The system is intended to be used in a planar positioning stage for microscopy applications and as such, has strict resolution, accuracy, repeatability, and sensitivity requirements. Three techniques which improve the measurement system's performance in the context of these requirements are proposed; namely, an optical magnification of the image projected onto the mouse sensor, a periodic homing procedure to reset the error buildup, and a compensation of the undesired dynamics caused by filters implemented in the mouse sensor chip.
Involvement in home meal preparation is associated with food preference and self-efficacy among Canadian children.

To examine the association between frequency of assisting with home meal preparation and fruit and vegetable preference and self-efficacy for making healthier food choices among grade 5 children in Alberta, Canada.



A cross-sectional survey design was used. Children were asked how often they helped prepare food at home and rated their preference for twelve fruits and vegetables on a 3-point Likert-type scale. Self-efficacy was measured with six items on a 4-point Likert-type scale asking children their level of confidence in selecting and eating healthy foods at home and at school.



Schools (n =151) located in Alberta, Canada.



Grade 5 students (n = 3398).



A large majority (83-93 %) of the study children reported helping in home meal preparation at least once monthly. Higher frequency of helping prepare and cook food at home was associated with higher fruit and vegetable preference and with higher self-efficacy for selecting and eating healthy foods.



Encouraging children to be more involved in home meal preparation could be an effective health promotion strategy. These findings suggest that the incorporation of activities teaching children how to prepare simple and healthy meals in health promotion programmes could potentially lead to improvement in dietary habits.
Emergence of social networks via direct and indirect reciprocity

Many models of social network formation implicitly assume that network properties are static in steady-state. In contrast, actual social networks are highly dynamic: allegiances and collaborations expire and may or may not be renewed at a later date. Moreover, empirical studies show that human social networks are dynamic at the individual level but static at the global level: individuals’ degree rankings change considerably over time, whereas network-level metrics such as network diameter and clustering coefficient are relatively stable. There have been some attempts to explain these properties of empirical social networks using agent-based models in which agents play social dilemma games with their immediate neighbours, but can also manipulate their network connections to strategic advantage. However, such models cannot straightforwardly account for reciprocal behaviour based on reputation scores (“indirect reciprocity”), which is known to play an important role in many economic interactions. In order to account for indirect reciprocity, we model the network in a bottom-up fashion: the network emerges from the low-level interactions between agents. By so doing we are able to simultaneously account for the effect of both direct reciprocity (e.g. “tit-for-tat”) as well as indirect reciprocity (helping strangers in order to increase one’s reputation). This leads to a strategic equilibrium in the frequencies with which strategies are adopted in the population as a whole, but intermittent cycling over different strategies at the level of individual agents, which in turn gives rise to social networks which are dynamic at the individual level but stable at the network level.
Let your fingers do the walking: A unified approach for efficient short-, medium-, and long-distance travel in VR

The tradeoff between speed and precision is one of the challenging problems of travel interfaces. Sometimes users want to travel long distances (e.g., fly) and care less about precise movement, while other times they want to approach nearby objects in a more-precise way (e.g., walk), and care less about how quickly they move. Between these two extremes there are scenarios when both speed and precision become equally important. In real life, we often seamlessly combine these modes. However, most VR systems support a single travel metaphor, which may only be good for one range of travel, but not others. We present a new VR travel framework which supports three separate multi-touch travel techniques, one for each distance range, but that all use the same device. We use a unifying metaphor of the user's fingers becoming their legs for each of the techniques. We are investigating the usability and user acceptance of the fingers-as-legs metaphor, as well as the efficiency and naturalness of switching between the different travel modes. We conducted an experiment focusing on user performance using the three travel modes, and compared our multi-touch, gesture-based approach with a traditional Gamepad travel interface. The results suggest that participants using a Gamepad interface are more time efficient. However, the quality of completing the tasks with the two input devices was similar, while ForcePad user response was faster for switching between travel modes.
Application of Causal Inference to Genomic Analysis: Advances in Methodology

The current paradigm of genomic studies of complex diseases is association and correlation analysis. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), the identified genetic variants by GWAS can only explain a small proportion of the heritability of complex diseases. A large fraction of genetic variants is still hidden. Association analysis has limited power to unravel mechanisms of complex diseases. It is time to shift the paradigm of genomic analysis from association analysis to causal inference. Causal inference is an essential component for the discovery of mechanism of diseases. This paper will review the major platforms of the genomic analysis in the past and discuss the perspectives of causal inference as a general framework of genomic analysis. In genomic data analysis, we usually consider four types of associations: association of discrete variables (DNA variation) with continuous variables (phenotypes and gene expressions), association of continuous variables (expressions, methylations, and imaging signals) with continuous variables (gene expressions, imaging signals, phenotypes, and physiological traits), association of discrete variables (DNA variation) with binary trait (disease status) and association of continuous variables (gene expressions, methylations, phenotypes, and imaging signals) with binary trait (disease status). In this paper, we will review algorithmic information theory as a general framework for causal discovery and the recent development of statistical methods for causal inference on discrete data, and discuss the possibility of extending the association analysis of discrete variable with disease to the causal analysis for discrete variable and disease.
The Body Image Psychological Inflexibility Scale: Development and psychometric properties

Body image disturbance and body dysmorphic disorder (BDD) have been researched from a variety of psychological approaches. Psychological inflexibility, or avoidance of one's own cognitive and affective states at a cost to personal values, may be a useful construct to understand these problems. In an effort to clarify the role of psychological inflexibility in body image disturbance and BDD, a measure was created based on the principles of Acceptance and Commitment Therapy (ACT). The scale was developed by generating new items to represent the construct and revising items from an existing scale measuring aspects of body image psychological inflexibility. The study was conducted with an ethnically diverse undergraduate population using three samples during the validation process. Participants completed multiple assessments to determine the validity of the measure and were interviewed for BDD. The 16-item scale has internal consistency (α = 0.93), a single factor solution, convergent validity, and test re-test reliability (r = 0.90). Data demonstrate a relationship between psychological inflexibility and body image disturbance indicating empirical support for an ACT conceptualization of body image problems and the use of this measure for body image disturbance and BDD.
SiC vs . Si – Evaluation of Potentials for Performance Improvement of Power Electronics Converter Systems by SiC Power Semiconductors

Switching devices based on wide band gap materials as SiC offer a significant performance improvement on the switch level compared to Si devices. A well known example are SiC diodes employed e.g. in PFC converters. In this paper, the impact on the system level performance, i.e. efficiency/power density, of a PFC and of a DC-DC converter resulting with the new SiC devices is evaluated based on analytical optimisation procedures and prototype systems. There, normally-on JFETs by SiCED and normally-off JFETs by SemiSouth are considered.
A hybrid approach to detect spatio-temporal outliers

A spatial outlier is a spatial referenced object whose non-spatial attribute values are significantly different from those of other spatially referenced objects in its spatial neighborhood. It represents locations that are significantly different from their neighborhoods even though they may not be significantly different from the entire population. Here we adopt this definition to spatio-temporal domain and define a spatialtemporal outlier (STO) to be a spatial-temporal referenced object whose thematic attribute values are significantly different from those of other spatially and temporally referenced objects in its spatial or/and temporal neighborhood. Identification of STOs can lead to the discovery of unexpected, interesting, and implicit knowledge, such as local instability. Many methods have been recently proposed to detect spatial outliers, but how to detect the temporal outliers or spatial-temporal outliers has been seldom discussed. In this paper we propose a hybrid approach which integrates several data mining methods such as clustering, aggregation and comparisons to detect the STOs by evaluating the change between consecutive spatial and temporal scales.  Outliers are data objects that appear inconsistent with respect to the remainder of the database (Barnett and Lewis, 1994). While in many cases these can be anomalies or noise, sometimes these represent rare or unusual events to be investigated further. In general, direct methods for outlier detection include distribution-based, depth-based and distancebased approaches. Distribution-based approaches use standard statistical distribution, depth-based technique map data objects into an m-dimensional information space (where m is the number of attribute) and distance-based approaches calculate the proportion of database objects that are a specified distance from a target object (Ng, 2001). A spatial outlier is a spatial referenced object whose non-spatial attribute values are significantly different from those of other spatially referenced objects in its spatial neighborhood. It represents locations that are significantly different from their neighborhoods even though they may not be significantly different from the entire population (Shekhar et al, 2003). Identification of spatial outliers can lead to the discovery of unexpected, interesting, and implicit knowledge, such as local instability. Many methods have been recently proposed to detect spatial outliers by the distributionbased approach. These methods can be broadly classified into two categories, namely 1-D (linear) outlier detection methods and multi-dimensional outlier detection methods (Shekhar et al, 2003). The 1-D outlier detection algorithms consider the statistical distribution of non-spatial attribute values, ignoring the spatial relationships between items.
Dynamic Channel Pruning: Feature Boosting and Suppression

Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources. In this paper, we reduce this cost by exploiting the fact that the importance of features computed by convolutional layers is highly input-dependent, and propose feature boosting and suppression (FBS), a new method to predictively amplify salient convolutional channels and skip unimportant ones at run-time. FBS introduces small auxiliary connections to existing convolutional layers. In contrast to channel pruning methods which permanently remove channels, it preserves the full network structures and accelerates convolution by dynamically skipping unimportant input and output channels. FBS-augmented networks are trained with conventional stochastic gradient descent, making it readily available for many state-of-the-art CNNs. We compare FBS to a range of existing channel pruning and dynamic execution schemes and demonstrate large improvements on ImageNet classification. Experiments show that FBS can respectively provide 5× and 2× savings in compute on VGG-16 and ResNet-18, both with less than 0.6% top-5 accuracy loss.
Self-inflicted burns: a case series.

Self-inflicted burns are regularly admitted to burns units worldwide. Most of these patients are referred to psychiatric services and are successfully treated however some return to hospital with recurrent self-inflicted burns. The aim of this study is to explore the characteristics of the recurrent self-inflicted burn patients admitted to the Royal North Shore Hospital during 2004-2011. Burn patients were drawn from a computerized database and recurrent self-inflicted burn patients were identified. Of the total of 1442 burn patients, 40 (2.8%) were identified as self-inflicted burns. Of these patients, 5 (0.4%) were identified to have sustained previous self-inflicted burns and were interviewed by a psychiatrist. Each patient had been diagnosed with a borderline personality disorder and had suffered other forms of deliberate self-harm. Self-inflicted burns were utilized to relieve or help regulate psychological distress, rather than to commit suicide. Most patients had a history of emotional neglect, physical and/or sexual abuse during their early life experience. Following discharge from hospital, the patients described varying levels of psychiatric follow-up, from a post-discharge review at a local community mental health centre to twice-weekly psychotherapy. The patients who engaged in regular psychotherapy described feeling more in control of their emotions and reported having a longer period of abstinence from self-inflicted burn. Although these patients represent a small proportion of all burns, the repeat nature of their injuries led to a significant use of clinical resources. A coordinated and consistent treatment pathway involving surgical and psychiatric services for recurrent self-inflicted burns may assist in the management of these challenging patients.
Machine Learning for Professional Tennis Match Prediction and Betting

Our project had two main objectives. First, we wanted to use historical tennis match data to predict the outcomes of future tennis matches. Next, we wanted to use the predictions from our resulting model to beat the current betting odds. After setting up our prediction and betting models, we were able to accurately predict the outcome of 69.6% of the 2016 and 2017 tennis season, and turn a 3.3% profit per match.
Application of osteology to forensic medicine.

The four main features of biological identity are sex, age, stature, and ethnic background. The forensic osteologist aims to establish these attributes for an individual from their skeletal remains. Many techniques are available for the osteological determination of sex in the adult but it is one of the most difficult biological factors to ascribe to juvenile remains. Conversely, there are a multitude of markers to estimate age in the young skeleton but ageing becomes less accurate with increasing years. Stature is usually a relatively straightforward parameter to establish in the adult. In the juvenile, it is naturally correlated with age but is complicated by differences in rates of growth both between the sexes and between individuals. Determination of ethnic identity is the least reliable and is hampered by lack of data on many populations. This paper reviews the principal methods used to establish identity and comments on their reliability and accuracy in the forensic context.