Miracle-dz/UniFAR-FTUs huggingface dataset:
Necessary and Sufficient Conditions for Consensus of Second-Order Multiagent Systems Under Directed Topologies Without Global Gain Dependency

The consensus problem for second-order multiagent systems with absolute velocity damping under directed topologies is investigated. In contrast to the existing results, which rely on a sufficiently large common absolute velocity damping gain above a lower bound dependent on global information, this paper focuses on novel algorithms to overcome this limitation. A novel consensus algorithm, where different agents use different absolute velocity damping gains, is first proposed. In the absence of delays, based on a system transformation method, the consensus problem for second-order multiagent systems is converted into that for first-order multiagent systems with the agent number doubled. Necessary and sufficient conditions are then derived under directed topologies by relating the topologies associated with the doubled number of agents and the original team of agents. In the presence of multiple constant delays, based on a further system transformation method, the consensus problem for second-order multiagent systems is converted into the stability problem for corresponding systems. Necessary and sufficient conditions are presented to guarantee consensus under a directed fixed topology. For systems with a uniform constant delay, more concrete necessary and sufficient conditions on how large the delay can be to guarantee consensus is given. Numerical simulations are provided to illustrate the effectiveness of the obtained theoretical results.
The feasibility of atlas‐based automatic segmentation of MRI for H&N radiotherapy planning

Atlas‐based autosegmentation is an established tool for segmenting structures for CT‐planned head and neck radiotherapy. MRI is being increasingly integrated into the planning process. The aim of this study is to assess the feasibility of MRI‐based, atlas‐based autosegmentation for organs at risk (OAR) and lymph node levels, and to compare the segmentation accuracy with CT‐based autosegmentation. Fourteen patients with locally advanced head and neck cancer in a prospective imaging study underwent a T1‐weighted MRI and a PET‐CT (with dedicated contrast‐enhanced CT) in an immobilization mask. Organs at risk (orbits, parotids, brainstem, and spinal cord) and the left level II lymph node region were manually delineated on the CT and MRI separately. A ‘leave one out’ approach was used to automatically segment structures onto the remaining images separately for CT and MRI. Contour comparison was performed using multiple positional metrics: Dice index, mean distance to conformity (MDC), sensitivity index (Se Idx), and inclusion index (Incl Idx). Automatic segmentation using MRI of orbits, parotids, brainstem, and lymph node level was acceptable with a DICE coefficient of 0.73−0.91, MDC 2.0−5.1 mm, Se Idx 0.64−0.93, Incl Idx 0.76−0.93. Segmentation of the spinal cord was poor (Dice coefficient 0.37). The process of automatic segmentation was significantly better on MRI compared to CT for orbits, parotid glands, brainstem, and left lymph node level II by multiple positional metrics; spinal cord segmentation based on MRI was inferior compared with CT. Accurate atlas‐based automatic segmentation of OAR and lymph node levels is feasible using T1‐MRI; segmentation of the spinal cord was found to be poor. Comparison with CT‐based automatic segmentation suggests that the process is equally as, or more accurate, using MRI. These results support further translation of MRI‐based segmentation methodology into clinical practice. PACS number(s): 87.55. de
Modelling determinants, impact, and space-time risk of age-specific mortality in rural South Africa: integrating methods to enhance policy relevance.

BACKGROUND There is a lack of reliable data in developing countries to inform policy and optimise resource allocation. Health and socio-demographic surveillance sites (HDSS) have the potential to address this gap. Mortality levels and trends have previously been documented in rural South Africa. However, complex space-time clustering of mortality, determinants, and their impact has not been fully examined. OBJECTIVES To integrate advanced methods enhance the understanding of the dynamics of mortality in space-time, to identify mortality risk factors and population attributable impact, to relate disparities in risk factor distributions to spatial mortality risk, and thus, to improve policy planning and resource allocation. METHODS Agincourt HDSS supplied data for the period 1992-2008. Advanced spatial techniques were used to identify significant age-specific mortality 'hotspots' in space-time. Multivariable Bayesian models were used to assess the effects of the most significant covariates on mortality. Disparities in risk factor profiles in identified hotspots were assessed. RESULTS Increasing HIV-related mortality and a subsequent decrease possibly attributable to antiretroviral therapy introduction are evident in this rural population. Distinct space-time clustering and variation (even in a small geographic area) of mortality were observed. Several known and novel risk factors were identified, and population impact was quantified. Significant differences in the risk factor profiles of the identified 'hotspots' included ethnicity; maternal, partner, and household deaths; household head demographics; migrancy; education; and poverty. CONCLUSIONS A complex interaction of highly attributable multilevel factors continues to demonstrate differential space-time influences on mortality risk (especially for HIV). High-risk households and villages displayed differential risk factor profiles. This integrated approach could prove valuable to decision makers. Tailored interventions for specific child and adult high-risk mortality areas are needed, such as preventing vertical transmission, ensuring maternal survival, and improving water and sanitation infrastructure. This framework can be applied in other settings within the region. Appendices available online under Reading Tools.
Manifold Partition Discriminant Analysis

We propose a novel algorithm for supervised dimensionality reduction named manifold partition discriminant analysis (MPDA). It aims to find a linear embedding space where the within-class similarity is achieved along the direction that is consistent with the local variation of the data manifold, while nearby data belonging to different classes are well separated. By partitioning the data manifold into a number of linear subspaces and utilizing the first-order Taylor expansion, MPDA explicitly parameterizes the connections of tangent spaces and represents the data manifold in a piecewise manner. While graph Laplacian methods capture only the pairwise interaction between data points, our method captures both pairwise and higher order interactions (using regional consistency) between data points. This manifold representation can help to improve the measure of within-class similarity, which further leads to improved performance of dimensionality reduction. Experimental results on multiple real-world data sets demonstrate the effectiveness of the proposed method.
Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders

Multimodal neuroimaging is increasingly used in neuroscience research, as it overcomes the limitations of individual modalities. One of the most important applications of multimodal neuroimaging is the provision of vital diagnostic data for neuropsychiatric disorders. Multimodal neuroimaging computing enables the visualization and quantitative analysis of the alterations in brain structure and function, and has reshaped how neuroscience research is carried out. Research in this area is growing exponentially, and so it is an appropriate time to review the current and future development of this emerging area. Hence, in this paper, we review the recent advances in multimodal neuroimaging (MRI, PET) and electrophysiological (EEG, MEG) technologies, and their applications to the neuropsychiatric disorders. We also outline some future directions for multimodal neuroimaging where researchers will design more advanced methods and models for neuropsychiatric research.
Sociodemographic predictors of multiple non-communicable disease risk factors among older adults in South Africa.

Background and objective Unhealthy lifestyle behaviours are important risk factors of morbidity and mortality. This study aimed to explore the sociodemographic predictors of multiple non-communicable disease (NCD) risk factors experienced by elderly South Africans. Methods We conducted a national population-based cross-sectional survey with a sample of 3,840 individuals aged 50 years or above in South Africa in 2008. The outcome variable was the co-existence of multiple NCD risk factors (tobacco use, alcohol, physical inactivity, fruit and vegetable intake, overweight or obesity, and hypertension) in each individual. The exposure variables were sociodemographic characteristics, namely, age, gender, education, wealth status, population group, marital status, and residence. Multivariate linear regression was used to assess the association between sociodemographic variables and multiple NCD risk factors. Results The mean number of NCD risk factors among all participants was three (95% confidence interval: 2.81-3.10). Multivariate linear regression analysis revealed that being female, being in the age group of 60-69 years, and being from the Coloured and Black African race were associated with a higher number of NCD risk factors. Marital status, educational level, wealth, and residence were not significantly associated with the number of NCD risk factors experienced. Conclusions The co-existence of multiple lifestyle NCD risk factors among the elderly is a public health concern. Comprehensive health-promotion interventions addressing the co-existence of multiple NCD risk factors tailored for specific sociodemographic groups are needed.
Securing SIFT: Privacy-Preserving Outsourcing Computation of Feature Extractions Over Encrypted Image Data

Advances in cloud computing have greatly motivated data owners to outsource their huge amount of personal multimedia data and/or computationally expensive tasks onto the cloud by leveraging its abundant resources for cost saving and flexibility. Despite the tremendous benefits, the outsourced multimedia data and its originated applications may reveal the data owner's private information, such as the personal identity, locations, or even financial profiles. This observation has recently aroused new research interest on privacy-preserving computations over outsourced multimedia data. In this paper, we propose an effective and practical privacy-preserving computation outsourcing protocol for the prevailing scale-invariant feature transform (SIFT) over massive encrypted image data. We first show that the previous solutions to this problem have either efficiency/security or practicality issues, and none can well preserve the important characteristics of the original SIFT in terms of distinctiveness and robustness. We then present a new scheme design that achieves efficiency and security requirements simultaneously with the preservation of its key characteristics, by randomly splitting the original image data, designing two novel efficient protocols for secure multiplication and comparison, and carefully distributing the feature extraction computations onto two independent cloud servers. We both carefully analyze and extensively evaluate the security and effectiveness of our design. The results show that our solution is practically secure, outperforms the state-of-the-art, and performs comparably with the original SIFT in terms of various characteristics, including rotation invariance, image scale invariance, robust matching across affine distortion, and addition of noise and change in 3D viewpoint and illumination.
Benchmarking of Traditional Method of Genetic Algorithm with the Real Coded Method with a modified type of 2-Point Crossover (F-Crossover)

A benchmarking using cellular neural networks is performed between the traditional method of Genetic algorithm (using binary population of random chromosomes) with a real coded approach of genetic algorithm. The benchmarking was done with various image processing operations and it is shown that in most of the image processing operations, real coded appraoch converges faster. Real numbers population prevents the repeated encoding and decoding of chromosomes. Also the sizes of chromosomes are relatively smaller. Moreover, a modified type of 2-point crossover (F-Crossover) is introduced which decreases the convergence time of the genetic algorithm and eliminates the need of mutation.
Combining User-Based and Item-Based Models for Collaborative Filtering Using Stacked Regression

Collaborative filtering can be classified into user-based or item-based methods according to different assumptions. Our experiment results show that both userbased and item-based methods can reach a certain degree of accuracy, but the recommendation coverage of these two methods is significantly different. This paper qualitatively analyzes the advantages and disadvantages of such two methods, and found that user-based methods advantage in recommending popular items while item-based methods perform better at suggesting long tail items. Based on this analysis, we proposes a novel machine learning framework to systematically combine both. We train a model for each user-item pair for discovering the local preference of the user or item over both methods, and effectively combine user-based and item-based predictions using the preference information. Experiment results show that our approach can significantly improve the recommendation quality, and to a certain extend, alleviate the data sparsity problem.
Snap to it.

A training pack for A&E staff in spotting and treating whiplash injury has been issued by the British Association for A&E Medicine. Sponsored by the Association of British Insurers, it is being made available free of charge to A&E departments. Whiplash injuries cost insurers an estimated £700 million a year.
The Analysis of Some Selective-Repeat ARQ Schemes with Finite Receiver Buffer

In high bit rate data transmission systems with ARQ error control, the throughput efficiency is a function of bit error rate, block or packet size, and the effect of significant round trip delays such as may be experienced in satellite communication systems. The selective-repeat ARQ scheme is capable of providing superior throughput performance independent of round trip delay, but requires excessively large receiver buffers; as a result the inferior GoBack N procedure is commonly adopted. This paper analyzes a class of mixed-mode ARQ protocol models which incorporate a selectiverepeat mode with finite receiver buffer. The protocol models are shown to be amenable to exact throughput analysis, but do assume that the round trip delay is constant and known, blocks are of constant length, and the ACK/NAK signals are returned error free. These assumptions might create difficulties for practical implementation. However, the analytical model results highlight those aspects of ARQ protocols which affect throughput performance as round trip delays increase. The results show that it is desirable for best throughput performance in practical systems that at least the first retransmission of a block following an error should be in the selective-repeat mode to obtain superior performance over GoBack N schemes. Furthermore, alternative secondary retransmission modes are considered which ensure that reliable transmission can be achieved without receiver buffer overflow, even if the selective-repeat mode retransmissions fail. It is shown that the choice of secondary mode does not have a significant effect on the throughput efficiency but has a bearing on complexity.
Floating Gate Synapses With

This paper describes a single transistor floating-gate synapse device that can be used to store a weight in a nonvolatile manner, compute a biological EPSP, and demonstrate biolog- ical learning rules such as Long-Term Potentiation, LTD, and spike-time dependent plasticity. We also describe a highly scalable architecture of a matrix of synapses to implement the described learning rules. Parameters for weight update in the 0.35 um process have been extracted and can be used to predict the change in weight based on time difference between pre- and post-synaptic spike times.
Missing Data Analysis in Practice

Basic Concepts Introduction Definition of Missing Values Missing Data Pattern Missing Data Mechanism Problems with Complete-Case Analysis Analysis Approaches Basic Statistical Concepts A Chuckle or Two Weighting Methods Motivation Adjustment Cell Method Response Propensity Model Example Impact of Weights on Population Mean Estimates Post-Stratification Survey Weights Alternative to Weighted Analysis Inverse Probability Weighting Imputation Generation of Plausible Values Hot Deck Imputation Model Based Imputation Example Sequential Regression Imputation Multiple Imputation Introduction Basic Combining Rule Multivariate Hypothesis Testing Combining Test Statistics Basic Theory of Multiple Imputation Extended Combining Rules Some Practical Issues Revisiting Examples Example: St. Louis Risk Research Project Regression Analysis General Observations Revisiting St. Louis Risk Research Example Analysis of Variance Survival Analysis Example Longitudinal Analysis with Missing Values Introduction Imputation Model Assumption Example Practical Issues Weighting Methods Binary Example Nonignorable Missing Data Mechanisms Modeling Framework EM-Algorithm Inference under Selection Model Inference under Mixture Model Example Practical Considerations Other Applications Measurement Error Combining Information from Multiple Data Sources Bayesian Inference from Finite Population Causal Inference Disclosure Limitation Other Topics Uncongeniality and Multiple Imputation Multiple Imputation for Complex Surveys Missing Values by Design Replication Method for Variance Estimation Final Thoughts Bibliography Index Bibliographic Notes and Exercises appear at the end of each chapter.
ITALIAN LITERACY TUTOR tools and technologies for individuals with cognitive disabilities

The Italian Literacy Tutor (ILT) is a project designed to implement a program of individualized, computer-aided reading instruction with the potential to dramatically improve reading achievement and learning from text in Italian, especially considering L2 teaching/learning frameworks or working with children with reading disabilities. The Italian Literacy Tutor is the Italian counterpart of the “Colorado Literacy Tutor” (CLT), a project developed at CSLR (Center for Spoken Language Research, Colorado University Boulder) for English. The ILT will mainly integrate two sets of literacy tools, the first one based on speech and animation technology, and the second based on language comprehension technology.
Siloseer: A Visual Content Extraction System

The Web contains an enormous amount of information which is presented in some sort of structured manner formatted for human users. There are lots of potential uses in extracting such information. We present Siloseer, a visual content extraction system that allows for fast and accurate extraction of data from any web site. It tackles two challenges in supervised content extraction systems 1. Labelling examples requires a fair amount of time and effort from the user and 2. Wrappers tend to break down due to site design changes. We address these challenges by incorporating a hybrid extraction engine which uses both supervised and unsupervised content extraction methodologies. We also include a crowdsourced platform for users to share, edit, update and rate wrappers. Finally our system has a wrapper error correction system that can fix wrappers that have broken down without any human intervention.
A.M.B.E.R. Shark-Fin: An Unobtrusive Affective Mouse

Analysing, measuring, recognising and exploiting emotion is an attractive agenda in designing computer games. The current devices for imputing physiological modalities are usually awkward to wear or handle. Here we propose a Shark-fin Mouse which streams three signals in real-time: pulse, electrodermal activity (EDA, also known as galvanic skin response or GSR) and skin temperature. All sensors are embedded into a fully functional computer mouse and positioned so as to ensure maximum robustness of the signals. No restriction of the mouse operation is imposed apart from the user having to place the tip of their middle finger into the Shark-fin hub. Boundary tests and experiments with a simple bespoke computer game demonstrate that the Shark-fin Mouse is capable of streaming clean and useful signals.
Presenting an Efficient Method for Vehicle Detection using Edge Detection and Morphological Operations

Identifying vehicles in highway inspection is a critical task. In the proposed method, automatic real time vehicle detection is performed using adaptive background subtraction, Sobel filter, morphological operations for removing irrelevant objects and noise, and finally finding the centroid of remaining objects which are vehicles. Using these methods together provides excellent capabilities to detect and track vehicles in highway inspection. We consider the average total of 14 frames before the current frame as the background model. When a new object enters the frame, our algorithm detects it using the subtraction of background model from the current frame. Experimental results show the high accuracy of the proposed method with maintaining its low time complexity and Computational complexity.
Improving Efficiency ofSpaceMapping Optimization ofMicrowave Structures andDevices

Anew implementation of spacemapping optimization andmodeling procedures formicrowave design is presented. We usetheoptimization capability ofthe"coarse model"simulator, Agilent ADS,tosignificantly reducethe computational costofsolving theparameter extraction and surrogate optimization sub-problems. Thisallowssubstantial reduction oftheoverall optimization timeforthespacemapping algorithm. Illustration examples areprovided. IndexTerms-Computer-aided design (CAD), EM optimization, spacemapping, surrogate modeling.
Algebraic Programming with Magma II: An Introduction to the Magma Categories

This book, written in two volumes, is an introductory manual for Magma, a programming language designed for the investigation of algebraic, geometric and combinatorial structures, or "magmas". It presumes no knowledge of computer programming, and its examples are chosen to illustrate language and algorithmic features as simply as possible. Magma is a sophisticated tool for experimentation, education, and computer-aided proof, useful for both students and professional mathematicians. Volume I explains the language and user environment in detail, whereas Volume II deals with the major algebraic, geometrical and combinatorial structures implemented in the system.
Modeling Lexicon-Syntax Interaction with Catenae

The paper discusses the interaction between the lexicon and syntactic relations in texts. It considers the encoding of the language units by means of catenae. It also shows that catenae representation together with valence information can provide a good way of handling Multiword Expressions and compounds1 in the lexicon and syntax. The paper introduces a strategy for mapping noun/verb compounds with their counterpart syntactic phrases2. Thus, the investigation presents two research directions: (1) realization of lexical units from lexicons to texts, and (2) relations between identical semantic units with differing morphosyntactic properties. Although the provided examples come from Bulgarian, the proposed mechanism is language independent.