Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Regional Ventilation Distribution: Comparison of Vibration Response Imaging (VRI) with Electrical Impedance Tomography (EIT)

Background Vibration response imaging (VRI) is a bedside technology to monitor ventilation by detecting lung sound vibrations. It is currently unknown whether VRI is able to accurately monitor the local distribution of ventilation within the lungs. We therefore compared VRI to electrical impedance tomography (EIT), an established technique used for the assessment of regional ventilation. Methodology/Principal Findings Simultaneous EIT and VRI measurements were performed in the healthy and injured lungs (ALI; induced by saline lavage) at different PEEP levels (0, 5, 10, 15 mbar) in nine piglets. Vibration energy amplitude (VEA) by VRI, and amplitudes of relative impedance changes (rel.ΔZ) by EIT, were evaluated in seven regions of interest (ROIs). To assess the distribution of tidal volume (VT) by VRI and EIT, absolute values were normalized to the VT obtained by simultaneous spirometry measurements. Redistribution of ventilation by ALI and PEEP was detected by VRI and EIT. The linear correlation between pooled VT by VEA and rel. ΔZ was R2 = 0.96. Bland-Altman analysis showed a bias of −1.07±24.71 ml and limits of agreement of −49.05 to +47.36 ml. Within the different ROIs, correlations of VT-distribution by EIT and VRI ranged between R2 values of 0.29 and 0.96. ALI and PEEP did not alter the agreement of VT between VRI and EIT. Conclusions/Significance Measurements of regional ventilation distribution by VRI are comparable to those obtained by EIT.
Encoding Information in Clustered-Dot Halftones

While barcodes are a popular means for encoding information for printed matter, they add unsightly overt content. If an image is already part of the composition of a printed label or page, hiding information in that image is an attractive alternative to barcodes. This paper offers a new method for encoding information in the halftone of an image. We focus on the class of techniques that perform clustered-dot halftoning, as commonly used in both dry toner and liquid toner electrophotographic processes. The method takes as input any grayscale image and a payload of data to be encoded and produces a bitonal clustered-dot halftone of that image with selected halftone clusters shifted to carry varying numbers of bits from the payload. The resulting data-bearing steganographic halftone is referred to as a "Stegatone". Because of the small size and large number of clustered-dot cells in printed halftones the bit density is quite high - over 2000 bytes/square-inch. Scans of test printed stegatones from a number of printers support the robustness of the method with high recovery rates.
Demographic noise can lead to the spontaneous formation of species

When a collection of phenotypically diverse organisms compete with each other for limited resources, the population can evolve into tightly localised clusters. Past studies have neglected the effects of demographic noise and studied the population on a macroscopic scale, where cluster formation is found to depend on the shape of the curve describing the decline of competition strength with phenotypic distance. Here we show how including the effects of demographic noise leads to a radically different conclusion. Two situations are identified: a weak-noise regime in which the population exhibits patterns of fluctuation around the macroscopic description, and a strong-noise regime where clusters appear spontaneously even in the case that all organisms have equal fitness.
Dynamics of short polymer chains in solution

We present numerical and analytical results describing the effect of hydrodynamic interactions on the dynamics of a short polymer chain in solution. A molecular dynamics algorithm for the polymer is coupled to a direct simulation Monte Carlo algorithm for the solvent. We give an explicit expression for the velocity autocorrelation function of the centre of mass of the polymer which agrees well with numerical results if Brownian dynamics, hydrodynamic correlations and sound wave scattering are included.
Magnification Control in Relational Neural Gas

Prototype-based clustering algorithms such as the Self Orga- nizing Map (SOM) or Neural Gas (NG) offer powerful tools for automated data inspection. The distribution of prototypes, however, does not co- incide with the underlying data distribution and magnification control is necessary to obtain information theoretic optimum maps. Recently, sev- eral extensions of SOM and NG to general non-vectorial dissimilarity data have been proposed, such as Relational NG (RNG). Here, we derive a magnification control scheme for RNG based on localized learning, and we demonstrate its applicability for various data sets.
A static pessimistic scheme for handling replicated databases

A replicated database system may partition into isolated groups in the presence of node and link failures. When the system has partitioned, a pessimistic scheme maintains availability and consistency of replicated data by ensuring that updates occur in at most one group. A pessimistic scheme is called a static scheme if these distinguished groups are determined only by the membership of different groups in the partitioned system. In this paper, we present a new static scheme that is more powerful than voting. In this scheme, the set of distinguished groups, called an acceptance set, is chosen at design time. To commit an update, a node checks if its enclosing group is a member of this acceptance set. Using an encoding scheme for groups, this check is implemented very efficiently. Another merit of the proposed scheme is that the problem of determining an optimal acceptance set is formulated as a sparse 0-1 linear programming problem. Hence, the optimization problem can be handled using the very rich class of existing techniques for solving such problems. Based on our experiments, we feel that this optimization approach is feasible for systems containing up to 10 nodes (copies).
A novel self oscillating power electronics for contactless energy transfer and frequency shift keying modulation

This paper deals with a combination of an inductive based contactless energy transfer system and a data transfer from the primary side to the secondary side of this system. Therefore, an extension of a self-oscillating power electronics offering the possibility to change the capacity of the resonant circuit during energy transmission is presented. An additional switchable capacitor in the compensation network leads to an immediate shift of the resonance frequency of the circuit. On the basis of this frequency shift a frequency shift keying modulation is implemented to encode binary data in the energy signal.
Greedy Structure Search for Sum-Product Networks

Sum-product networks (SPNs) are rooted, directed acyclic graphs (DAGs) of sum and product nodes with well-defined probabilistic semantics. Moreover, exact inference in the distribution represented by an SPN is guaranteed to take linear time in the size of the DAG. In this paper we introduce an algorithm that learns the structure of an SPN using a greedy search approach. It incorporates methods used in a previous SPN structure-learning algorithm, but, unlike the previous algorithm, is not limited to learning tree-structured SPNs. Several proven ideas from circuit complexity theory along with our experimental results provide evidence for the advantages of SPNs with less-restrictive, nontree structures.
Recovering technologies that account for generalized managerial preferences: an application to non-risk neutral banks

The authors suggest that risk plays an important role in managerial production decisions. Managers make implicit and explicit decisions related to risk, return, and cost in setting target market, product, pricing and delivery decisions. Standard models of production and cost do not explicitly account for risk, assuming that managers are neutral toward risk. This simplification may undermine the model's usefulness when applied to an industry such as banking where risk plays an important economic role in the business. The standard model would label risk-averse banks at best, allocatively inefficient and at worst, technically inefficient. The authors attempt to maximize a managerial utility function, defined over profit, inputs, and outputs with respect to the mix of inputs and with respect to profit subject to the production constraint that the input mix must produce the given output vector. The solution to this utility maximization problem gives the manager's most preferred production plan. To the extent that managers have favored inputs whose employment they will increase at the expense of profit, the most preferred production plan will not be allocatively efficient. In fact, it may not even be technically efficient. The cost function that follows from the utility-maximizing production plan is sufficiently general to incorporate non-neutrality toward risk and to allow other managerial objectives in addition to profit maximization. Formulating the production plan from a model of constrained utility maximization suggests that the functional forms needed to implement the model can be derived by analogy to those of consumer theory. The Almost Ideal (AI) Demand System, adapted to accommodate generalized managerial preferences, yields input share equations and a profit (cost) function that, in the case of cost minimization, are identical to the translog cost function and input share equations. The model is estimated using 1989 and 1990 data from U.S. banks whose assets equal or exceed $1 billion. The AI System fits the data well; risk neutrality is conclusively rejected. The measure of scale economies obtained from the most preferred cost function is considerably larger than those obtained from more conventional cost functions, and it increases with bank asset size, suggesting that the diversification economies enjoyed by larger banks allow them to reduce the share of resources used to control risk which magnifies the diversification economies. When risk neutrality is imposed and financial capital is deleted from the model, the measure of scale elasticity drops considerably and resembles the magnitudes generally found in studies that employ the standard framework. Thus, this evidence provides some explanation of the seeming inconsistency between the actual merger wave that has been occurring and the standard literature that finds little motivation from cost savings.
MatLM: a Matrix Formulation for Probabilistic Language Models

Probabilistic language models are widely used in Information Retrieval (IR) to rank documents by the probability that they generate the query. However, the implementation of the probabilistic representations with programming languages that favor matrix calculations is challenging. In this paper, we utilize matrix representations to reformulate the probabilistic language models. The matrix representation is a superstructure for the probabilistic language models to organize the calculated probabilities and a potential formalism for standardization of language models and for further mathematical analysis. It facilitates implementations by matrix friendly programming languages. In this paper, we consider the matrix formulation of conventional language model with Dirichlet smoothing, and two language models based on Latent Dirichlet Allocation (LDA), i.e., LBDM and LDI. We release a Java software package--MatLM--implementing the proposed models. Code is available at: this https URL
A Load Distribution Method Based on Distributed Hashing for P2P Sensor Data Stream Delivery System

Due to the prevalence of sensors such as security cameras or environmental monitoring, sensor data stream delivery which means delivering the observed data continuously attracts great attention. Currently, the methods to distribute communication loads by relay nodes in the case of delivering the sensor data streams that have different data delivery cycles are proposed. However, in the existing methods, since the specific node builds delivery paths and notifies to the related nodes, the delivery paths are necessary to be built and notified again when nodes join or leave. Therefore, in this paper, we propose a method which determines relay nodes based on distributed hashing and constructs delivery paths autonomously by each node. We evaluated the effectiveness of proposal in simulation.
Contemporary Issues of Ethical E-therapy

© 2010 Journal of Ethics in Mental Health (ISSN: 1916-2405) internet-accessible devices have eroded barriers that once stood in the way of consumers’ access to information and/or mental health services (Pew Internet & American Life Project, 2009). Th e explosion in technological advances and cultural shift towards internet-based communication has also supported the acceptance of e-therapy. E-therapy is defi ned in this paper as an internetbased modality in delivering psychological support that can be synchronous (simultaneous) or asynchronous (time-delayed) communications. Beginning in the 1980’s, e-therapy became an emerging delivery method within the practice of counseling psychology (Alleman, 2002; Oravec, 2000; Skinner & Zack, 2004). Th is modality of communication is mediated primarily through a computer-like device between a mental health professional and client (Alcaniz, Botella, Banos, Zaragoza, & Guixeres, 2009; Grohol, 1999).
Use of structural magnetic resonance imaging to predict who will get Alzheimer's disease

We used magnetic resonance imaging (MRI) measurements to determine whether persons in the prodromal phase of Alzheimer's disease (AD) could be accurately identified before they developed clinically diagnosed dementia. Normal subjects (n = 24) and those with mild memory difficulty (n = 79) received an MRI scan at baseline and were then followed annually for 3 years to determine which individuals subsequently met clinical criteria for AD. Patients with mild AD at baseline were also evaluated (n = 16). Nineteen of the 79 subjects with mild memory difficulty “converted” to a diagnosis of probable AD after 3 years of follow‐up. Baseline MRI measures of the entorhinal cortex, the banks of the superior temporal sulcus, and the anterior cingulate were most useful in discriminating the status of the subjects on follow‐up examination. The accuracy of discrimination was related to the clinical similarity between groups. One hundred percent (100%) of normal subjects and patients with mild AD could be discriminated from one another based on these MRI measures. When the normals were compared with the individuals with memory impairments who ultimately developed AD (the converters), the accuracy of discrimination was 93%, based on the MRI measures at baseline (sensitivity = 0.95; specificity = 0.90). The discrimination of the normal subjects and the individuals with mild memory problems who did not progress to the point where they met clinical criteria for probable AD over the 3 years of follow‐up (the “questionables”) was 85% and the discrimination of the questionables and converters was 75%. The apolipoprotein E genotype did not improve the accuracy of discrimination. The specific regions selected for each of these discriminations provides information concerning the hierarchical fashion in which the pathology of AD may affect the brain during its prodromal phase. Ann Neurol 2000;47:430–439.
Impaired membrane resealing and autoimmune myositis in synaptotagmin VII–deficient mice

Members of the synaptotagmin family have been proposed to function as Ca2+ sensors in membrane fusion. Syt VII is a ubiquitously expressed synaptotagmin previously implicated in plasma membrane repair and Trypanosoma cruzi invasion, events which are mediated by the Ca2+-regulated exocytosis of lysosomes. Here, we show that embryonic fibroblasts from Syt VII–deficient mice are less susceptible to trypanosome invasion, and defective in lysosomal exocytosis and resealing after wounding. Examination of mutant mouse tissues revealed extensive fibrosis in the skin and skeletal muscle. Inflammatory myopathy, with muscle fiber invasion by leukocytes and endomysial collagen deposition, was associated with elevated creatine kinase release and progressive muscle weakness. Interestingly, similar to what is observed in human polymyositis/dermatomyositis, the mice developed a strong antinuclear antibody response, characteristic of autoimmune disorders. Thus, defective plasma membrane repair in tissues under mechanical stress may favor the development of inflammatory autoimmune disease.
Stellar Collisions and Blue Straggler Formation

We review recent 3D hydrodynamic calculations of stellar collisions using the Smoothed Particle Hydrodynamics (SPH) method, and we discuss the implications of the results for the formation and evolution of blue stragglers in globular clusters. We also discuss the construction of simple analytic models for merger remnants, approximating the mass loss, shock heating, mixing, and angular momentum transport during a collision with simple algorithms that can be calibrated using our 3D hydrodynamic results. The thermodynamic and chemical composition profiles predicted by these simple models are compared with those from our SPH simulations, demonstrating that our new models provide accurate representations of true collisional merger remnants.
Image Reconstruction in Functional Optical Neuroimaging The modelling and separation of the scalp blood flow: A research proposal

Optical imaging is an emerging modality to image biological tissue non-invasively. Its use for brain imaging function is known as functional Near Infrared Spectroscopy (fNIRS). Its use as a functional neuroimaging modality demands attending numerous factors many which are still being investigated. One of these factors is the image reconstruction, particularly critical for brain understanding. The main problem to investigate in this research is the inherent inverse problem of the optical neuroimage reconstruction in which several sources of noise exist aggravating the ill-posed nature of the problem. Popular methods address this problem with strong assumptions leading to a reconstruction departing from reality. In order to alleviate this situation, this research, aims to develop a reconstruction based on a precise model of the forward problem, the quantification of the noise due to the blood irrigation in the scalp as one of the leading artefacts affecting reconstruction and the use of this information as an a priori knowledge in order to improve reconstruction. The main scientific contributions will be; the better understanding of the image formation process in the optical neuroimage domain, the more accurate forward model developed, the quantification of noise from scalp irrigation and the solution to the inverse problem informed with the a priory knowledge. Preliminary results shows that reconstruction is more viable when the source of noise is avoided.
Inforex – a web-based tool for text corpus management and semantic annotation

The aim of this paper is to present a system for semantic text annotation called Inforex. Inforex is a web-based system designed for managing and annotating text corpora on the semantic level including annotation of Named Entities (NE), anaphora, Word Sense Disambiguation (WSD) and relations between named entities. The system also supports manual text clean-up and automatic text pre-processing including text segmentation, morphosyntactic analysis and word selection for word sense annotation. Inforex can be accessed from any standard-compliant web browser supporting JavaScript. The user interface has a form of dynamic HTML pages using the AJAX technology. The server part of the system is written in PHP and the data is stored in MySQL database. The system make use of some external tools that are installed on the server or can be accessed via web services. The documents are stored in the database in the original format ― either plain text, XML or HTML. Tokenization and sentence segmentation is optional and is stored in a separate table. Tokens are stored as pairs of values representing indexes of first and last character of the tokens and sets of features representing the morpho-syntactic information.
Corporate governance challenges accompanying the privatisation of public education in Qatar

Research on corporate governance in different institutional and national contexts is important because of the peculiarity of differing institutional environments, and the extent to which governance models are transferred or copied from one environment to another. Thus, research in each area enriches our understanding of corporate governance in general. Despite the fact that governments globally have placed privatisation at the top of the policy agenda in recent years, there are some sectors that have experienced very little or limited privatisation, for example education. The purpose of this thesis is to give a critical account of the introduction and development of the privatised education system in Qatar following consultancy advice from RAND1 in 2001 and to explore the model of corporate governance used for that delivery. In presenting and reviewing, these developments in Qatar, this thesis will serve three purposes: 1) To challenge some findings of prior research; 2) To suggest a corporate governance mix for this case that encompasses a broader view of governance than has been considered in prior research; and 3) To identify gaps in past research that represent promising opportunities for future study. The guiding question raised in this thesis is, ―what are the roles and challenges of corporate governance structures (mechanisms, approaches), in contributing to the development and establishment of Qatar‘s unique privatised education system? ‖ The researcher chose this question because of the relative neglect of corporate governance aspects in this instance. This research revealed that corporate governance aspects came as an afterthought to decision makers in Qatar. As an informed observer and participant, being a member of the Supreme Education Council for Education, the researcher was in a position to access valuable and relevant information.
A REVIEW PAPER ON EARLY DIAGNOSIS OF ALZHEIMER’S DISEASE (AD) THROUGH PROFILING OF HUMAN BODY PARAMETERS

Geratology deals with the many clinical problems that are common in the elderly population, and many of these follow the orthodox pattern of clinical practice. Patients characteristically have poor insight and often attribute their early symptoms of amnesia to normal ageing. Alzheimer’s disease (AD) is common form of senile dementia. There are several causes for the disease. Although our understanding of the key steps underlying neurodegeneration in Alzheimer’s disease (AD) is incomplete, it is clear that it begins long before symptoms are noticed by patient. Any disease – modifying treatments which are developed are most likely to be successful if initiated early in the process, and this requires that we develop reliable, validated and economical ways to diagnose Alzheimer’s−type pathology. In this case, the use of advanced biomedical engineering technology will definitely helpful for making diagnosis successfully. Profiling of human body parameter using computers can be utilised for the successful early diagnosis of Alzheimer’s disease. There are several neuroimaging techniques used in clinical practice for the diagnosis of Alzheimer’s – type pathology. Prominent of them are Magnetic Resonance Imaging Scan (MRI), Positron Emission Tomography (PET) and Single−Proton CT Scanning (SPECT). In this research work, it is planned to investigate techniques for the early diagnosis of Alzheimer’s disease (AD) with the help of various laboratory tests and neuroimaging techniques.