Miracle-dz/UniFAR-FTUs huggingface dataset:
Skin contact force information in sensory nerve signals recorded by implanted cuff electrodes

When functional neuromuscular stimulation (FNS) is used to restore the use of paralyzed limbs after a spinal cord injury or stroke, it may be possible to control the stimulation using feedback information relayed by natural sensors in the skin. In this study the authors tested the hypothesis that the force applied on glabrous skin can be extracted from the electroneurographic (ENG) signal recorded from the sensory nerve. They used the central footpad of the cat hindlimb as a model of the human fingertip and recorded sensory activity with a cuff electrode chronically implanted around the tibial nerve. Their results showed that the tibial ENG signal, suitably filtered, rectified, and smoothed carries detailed static and dynamic information related to the force applied on the footpad. The authors derived a mathematical model of the force-ENG relation that provided accurate estimates of the ENG signal for a wide range of force profiles, amplitudes, and frequencies. Once fitted to data obtained in one recording session, the model could be made to fit data obtained in other sessions from the same cat, as well as from other cats, by simply adjusting its overall gain and offset. However, the model was noninvertible; i.e., the force could not be similarly predicted from the ENG signal, unless additional assumptions or restrictions were introduced. The authors discuss the reasons for these findings and their implications on the potential use of nerve signals as a source of continuous force feedback information suitable for closed-loop control of FNS. >
Speech Recognition System and Isolated Word Recognition based on Hidden Markov Model (HMM) for Hearing Impaired

The ability of a reader to recognize written words correctly, virtually and effortlessly is defined as Word Recognition or Isolated Word Recognition. It will recognize each word from their shape. Speech Recognition is the operating system which enablesto convert spoken words to written text which is called as Speech to Text (STT) method. Usual Method used in Speech Recognition (SR) is Neural Network, Hidden Markov Model (HMM) and Dynamic Time Warping (DTW). The widely used technique for Speech Recognition is HMM. Hidden Markov Model assumes that successive acoustic features of a spoken word are state independent. The occurrence of one feature is independent of the occurrence of the others state. Here each single unit of word is considered as state. Based upon the probability of the state it generates possible word sequence for the spoken word. Instead of listening to the speech, the generated sequence of text can be easily viewed. Each word is recognized from their shape. People with hearing impaired can make use of this Speech Recognition.
Factors underlying the effect of organisational downsizing on health of employees: longitudinal cohort study

Abstract Objective: To explore the underlying mechanisms between organisational downsizing and deterioration of health of employees. Design: Longitudinal cohort study. Data were assembled from before downsizing (time 1); during major downsizing affecting some job categories (time 2); and after downsizing (time 3). Contributions of changes in work, support, and health related behaviours between time 1 and time 2 to the relation between downsizing and sickness absence attime 3 were assessed by multilevel modelling. Mean length of follow up was 4.9 years. Setting: Raisio, a town in Finland. Subjects: 64 municipal employees who remained in employment after downsizing. Main outcome measures: Records of absences from work from all causes with medical certificate. Results: Downsizing was associated with negative changes in work, impaired support from spouse, and increased prevalence of smoking. Sickness absence rate from all causes was 2.17 (95% confidence interval 1.54 to 3.07) times higher after major downsizing than after minor downsizing. Adjustment for changes in work (for instance, physical demands, job control, and job insecurity) diminished the relation between downsizing and sickness absence by 49%. Adjustments for impaired social support or increased smoking did not alter the relation between downsizing and sickness absence. The findings were unaffected by sex and income. Conclusions: The exploration of potential mediating factors provides new information about the possible causal pathways linking organisational downsizing and health. Downsizing results in changes in work, social relationships, and health related behaviours. The observed increase in certificated sickness absence was partially explained by concomitant increases in physical demands and job insecurity and a reduction in job control. A considerable proportion of the increase, however, remained unexplained by the factors measured.
Multiagent Reinforcement Learning in Stochastic Games

We adopt stochastic games as a general framework for dynamic noncooperative systems. This framework provides a way of describing the dynamic interactions of agents in terms of individuals' Markov decision processes. By studying this framework, we go beyond the common practice in the study of learning in games, which primarily focus on repeated games or extensive-form games. For stochastic games with incomplete information, we design a multiagent reinforcement learning method which allows agents to learn Nash equilibrium strategies. We show in both theory and experiments that this algorithm converges. From the viewpoint of machine learning research, our work helps to establish the theoretical foundation for applying reinforcement learning, originally deened for single-agent systems, to multiagent systems.
Radiofrequency ablation for locally advanced cancer of the pancreas.

Radiofrequency ablation (RFA) for locally advanced cancer of the pancreas appears an attractive proposition. Many Hepatobiliary Surgical Units currently have the equipment and technical expertise for ablation of non-resectable liver tumours and as RFA becomes established in this setting [1], so the concept of thermal ablation for non-resectable tumours is increasingly an accepted component of hepatobiliary practice. In any given cohort of patients with pancreatic cancer, only a minority are suitable for surgical resection [2, 3]. These individuals with operable disease should undergo surgical resection. An appreciable proportion will be unsuitable for any form of surgical therapy either because of co-morbidity or extent of disease but a small subset will have locally advanced, unresectable cancer without evidence of major co-morbidity or metastatic disease. It is this cohort for whom there is a consideration for the use of RFA. However, there are critical differences between the role of RFA in non-resectable lesions of the liver and a parallel application in pancreatic cancer. These can be considered in three categories: anatomical considerations, factors relating to the tumour biology of pancreas cancer and those relating to the physical properties of pancreatic parenchyma. First, anatomical considerations: liver tumours are surrounded by areas of normal hepatic parenchyma and extension of the zone of ablation beyond the tumour will usually not have adverse consequences. In contrast, lesions in the head of the pancreas will be traversed by the distal common bile duct and be closely related to the duodenum, stomach, transverse colon and portal vein-the risks of inadvertent thermal injury are considerable and made more dangerous when understood in the context of an un-resectable tumour. In this setting, inadvertent thermal necrosis of the duodenum cannot then be retrieved by resection. The second group of considerations relate to tumour biology: hepatic metastases are usually discrete lesions whereas locally advanced pancreatic cancer can be physically diffuse and encase the superior mesenteric artery, extend retroperitoneally or proximally rendering direct ablation of all tumour bulk impractical. The third group of considerations relate to the efficacy of thermal ablation of pancreatic parenchyma. Given the dearth of available information in this area, our group established and validated an ex-vivo porcine model of radiofrequency ablation of the pancreas [4]. In this model, we were able to demonstrate a reproducible, temperature-dependent ablation. As haematoxylin and eosin preparations are inadequate for assessment of ablation, we used the method described by Scudamore [5] relying on loss …
Doing Social Network Research: Network-based Research Design for Social Scientists

The difference with social networks research Fundamental network concepts and ideas Thinking about networks: Research questions and study design Social systems and data structures: relational ties and actor attributes Network observation and measurement The empirical context of network data collection Ethical issues for social networks research Network visualization: What it can and cannot do A review of social network analytic methods Drawing conclusions: Inference, generalization, causality and other weighty matters
Crowdsourcing, innovation and firm performance

Purpose – The purpose of this paper is to identify whether firms that correctly use crowdfunding tools perform significantly better than firms that do not. For that purpose the authors develop a model that relates crowdfunding, innovation distinctive competences and its potential to create value in the firm. The authors want to know if crowdsourcing has more impact on Schumpeterian distinctive competences or continuous distinctive competences. Another aim is to create a new scale to measure the degree of crowdsourcing introduction in a firm. Design/methodology/approach – Within the knowledge intensive industries, the universe selected was the Chinese population of biotechnology and telecommunications industries. The information on the three variables was collected in a primary study, carried out by mail questionnaire. The questionnaire respondent was the manager of the firm. The measuring instrument was pre-tested in 30 firms, 15 from the biotechnology and 15 from the telecommunications industries. The fi.
Business Domain Models in ERP Development

Business domain models play an important role in the context of ERP systems implementations, where e.g. industry specific reference models are used to set up the ERP systems at the end user organisations. In this paper we will focus on another, less researched, part of ERP software practice where business domain models are widely used, namely ERP software development. Our analytical approach is to use the concept of design artefacts to describe the role of the business domain models in the development process. Our investigation is guided by the following questions: How can domain models be designed and used as design artefacts in ERP development? How can the models support the dimensions of construction, cooperation and conception within ERP development and what are the problematic issues? What might be the challenges when designing the design artefact itself? We will present a case of model design and use in ERP development based on empirical studies conducted in a large software development company developing ERP software. On the basis of this study we will point at a number of research topics that need to be further investigated.
Impact of rpoS Deletion onEscherichia coli Biofilms

ABSTRACT Slow growth has been hypothesized to be an essential aspect of bacterial physiology within biofilms. In order to test this hypothesis, we employed two strains of Escherichia coli, ZK126 (ΔlacZ rpoS+) and its isogenic ΔrpoS derivative, ZK1000. These strains were grown at two rates (0.033 and 0.0083 h−1) in a glucose-limited chemostat which was coupled either to a modified Robbins device containing plugs of silicone rubber urinary catheter material or to a glass flow cell. The presence or absence of rpoS did not significantly affect planktonic growth of E. coli. In contrast, biofilm cell density in the rpoS mutant strain (ZK1000), as measured by determining the number of CFU per square centimeter, was reduced by 50% (P < 0.05). Deletion of rpoS caused differences in biofilm cell arrangement, as seen by scanning confocal laser microscopy. In reporter gene experiments, similar levels of rpoS expression were seen in chemostat-grown planktonic and biofilm populations at a growth rate of 0.033 h−1. Overall, these studies suggest thatrpoS is important for biofilm physiology.
USING SUPPORT VECTOR MACHINES FOR BUILDING RECOGNITION IN AERIAL PHOTOGRAPHS

Automated building recognition remains an open research problem in digital photogrammetry. Despite considerable research in this area over the past 15 years, no general methods have been found. Over the same period, considerable advances have occurred in aspects of computer vision that appear to have similar characteristics to the problems in photogrammetry. One approach to object recognition that has not been extensively explored by the photogrammetric community is machine learning. These techniques are based on principles similar to supervised classification, where the computer system is ‘trained’ to recognize certain features in the image space. While these techniques cannot provide all the answers, they have been used successfully in other vision problems to mimic the early stages of visual processing. Most computer vision problems require salient features to be detected in the image as part of the processing strategy. In this application, a simple form of wavelet analysis is used. The images are characterized using the wavelet analysis to provide multiresolution data for the machine learning phase. The learning phase is a binary classification problem, where the program is trained to recognize building and nonbuilding images. The learning in this situation is complicated by the high dimensionality of the image data and its wavelet derivatives. While many different classifiers could be used, Support Vector Machines (SVMs) are a relatively new classification tool that appear well suited to this application. SVMs take advantage of high-dimensional feature space to separate data that might be otherwise inseparable. This paper describes the use wavelet analysis and support vector machines to recognize buildings in aerial photographic images and reports the results of several evaluations that were undertaken.
A Bayesian approach to relative node localization in Wireless Sensor Networks Soma

Wireless sensors find many applications in, including but not limited to, wildlife habitat monitoring, defect monitoring in assembly lines, source localization, intrusion detection, etc. Many of these tasks require knowledge of the sensor locations. Often, we have information about only a few sensors (nodes) and the remaining locations have to be estimated. The process of estimating the node locations (referred to as blindfolded nodes) relative to the nodes (reference nodes), whose exact location is known a priori, is known as relative node localization. In this class project, we develop a Bayesian framework to accomplish this task. We consider the node localization based on received signal strength (RSS) measurements. In this, we follow part of the work Neal Patwari, et al [1]. Ours is a general relative location method. However, throughout, we will illustrate our method with a particular data set, that accompanying the electronic version of Patwari, et al’s paper. The data were collected at a Florida office of Motorola. Sensors, each with a transmitter and a receiver, were placed about the office space. For each pair of sensors, say the ith sensor and the jth sensor, the data consists of the strength of the signal received by the ith sensor from the jth sensor. By convention, a sensor cannot observe itself. We further assume the received signal strength is symmetrical. In other words, the strength of the signal received by the ith sensor from the jth sensor equals that received by the jth from the ith. The data set also includes the actual locations of the sensors, information we will make use of below in evaluating our models.
A variable-order fractional operator based synthesis method for multifractional Gaussian noise

Fractional Gaussian noise (fGn) with a constant Hurst parameter H can be used to more accurately characterize the long memory process than traditional short-range dependent stochastic processes, such as Markov, Poisson or ARMA processes. However, the ability of fGn is limited for modeling the stochastic processes with prescribed local forms. Therefore, the multifractional Gaussian noise (mGn) with local Hölder exponent which varies with a variable t (usually time), become more important both in theory and in practical applications. In this paper, by studying the relationship of white Gaussian noise (wGn), mGn and multifractional Brownian motion (mBm), a synthesis method which is based on variable-order fractional operators for synthesizing mGn is provided. Furthermore, the synthesis method of multifractional α-stable processes, the generalization of mGn, is proposed in the paper in order to more accurately characterize the processes with local scaling characteristic and heavy tailed distribution. Some synthetic examples of mGn and multifractional α-stable noises are provided in the paper.
Measurements of resonance frequencies of clarinet reeds and simulations

A set of 55 clarinet reeds is observed by holography, collecting 2 series of measurements made under 2 different moisture contents, from which the resonance frequencies of the 15 first modes are deduced. A statistical analysis of the results reveals good correlations, but also significant differences between both series. Within a given series, flexural modes are not strongly correlated. A Principal Component Analysis (PCA) shows that the measurements of each series can be described with 3 factors capturing more than $90\%$ of the variance: the first is linked with transverse modes, the second with flexural modes of high order and the third with the first flexural mode. A forth factor is necessary to take into account the individual sensitivity to moisture content. Numerical 3D simulations are conducted by Finite Element Method, based on a given reed shape and an orthotropic model. A sensitivity analysis revels that, besides the density, the theoretical frequencies depend mainly on 2 parameters: $E_L$ and $G_{LT}$. An approximate analytical formula is proposed to calculate the resonance frequencies as a function of these 2 parameters. The discrepancy between the observed frequencies and those calculated with the analytical formula suggests that the elastic moduli of the measured reeds are frequency dependent. A viscoelastic model is then developed, whose parameters are computed as a linear combination from 4 orthogonal components, using a standard least squares fitting procedure and leading to an objective characterization of the material properties of the cane \textit{Arundo donax}.
Some examples of inverse problems in geophysics

Most of the problems arising in geophysics consist in recovering some characteristics of the earth (reflectivity, resistivity, .), from some measurements usually made at the top of the earth (seismic data, electromagnetic data, .). The observations can be seen as the output data of a noisy (linear or non linear) system, that can be known, or unknown. When the system is unknown, it can be estimated, together with the noise variance, by using a stochastic version of EM algorithm. When the system is identified, some adaptations of MCMC techniques allow one to estimate the a posteriori distribution of the input sequence, according to the prior distribution. This prior can take into account the regularity of the input sequence, as well as the presence of abrupt changes in this sequence.
Solving non-linear Horn clauses using a linear solver

Developing an efficient non-linear Horn clause solver is a challenging task since the solver has to reason about the tree structures rather than the linear ones as in a linear solver. In this paper we propose an incremental approach to solving a set of non-linear Horn clauses using a linear Horn clause solver. We achieve this by interleaving a program transformation and a linear solver. The program transformation is based on the notion of tree dimension, which we apply to trees corresponding to Horn clause derivations. The dimension of a tree is a measure of its non-linearity -- for example a linear tree (whose nodes have at most one child) has dimension zero while a complete binary tree has dimension equal to its height. A given set of Horn clauses $P$ can be transformed into a new set of clauses $P^k$ (whose derivation trees are the subset of $P$'s derivation trees with dimension at most $k$). We start by generating $P^k$ with $k=0$, which is linear by definition, then pass it to a linear solver. If $P^k$ has a solution $M$, and is a solution to $P$ then $P$ has a solution $M$. If $M$ is not a solution of $P$, we plugged $M$ to $P^{(k+1)}$ which again becomes linear and pass it to the solver and continue successively for increasing value of $k$ until we find a solution to $P$ or resources are exhausted. Experiment on some Horn clause verification benchmarks indicates that this is a promising approach for solving a set of non-linear Horn clauses using a linear solver. It indicates that many times a solution obtained for some under-approximation $P^k$ of $P$ becomes a solution for $P$ for a fairly small value of $k$.
CF-GeNe: Fuzzy Framework for Robust Gene Regulatory Network Inference

Most Gene Regulatory Network (GRN) studies ignore the impact of the noisy nature of gene expression data despite its significant influence upon inferred results. This paper presents an innovative Collateral-Fuzzy Gene Regulatory Network Reconstruction (CF-GeNe) framework for Gene Regulatory Network (GRN) inference. The approach uses the Collateral Missing Value Estimation (CMVE) algorithm as its core to estimate missing values in microarray gene expression data. CF-GeNe also mimics the inherent fuzzy nature of gene co-regulation by applying fuzzy clustering principles using the well-established fuzzy cmeans algorithm, with the model adapting to the data distribution by automatically determining key parameters, like the number of clusters. Empirical results confirm that the CMVE-based CF-GeNe paradigm infers the majority of co-regulated links even in the presence of large numbers of missing values, compared to other data imputation methods including: Least Square Impute (LSImpute), K-Nearest Neighbour Impute (KNN), Bayesian Principal Component Analysis Impute (BPCA) and ZeroImpute. The statistical significance of this improved performance has been underscored by gene selection and also by applying the Wilcoxon Ranksum Significance Test, with results corroborating the ability of CF-GeNe to successfully infer GRN interactions in noisy gene expression data.