Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Influencing Urban Adolescents’ Eating Behavior, Ratchaburi Province, Thailand: an Application of the Theory of Planned Behavior

Background: Cardiovascular disease (CVD) has been the leading cause of premature death in adults in Thailand. Promoting healthy eating in adolescents is one way of helping to maintain health and lower risk of CVD in later life. This quantitative study aimed to identify the factors influencing urban adolescents’ eating behavior, so that they could be used in the future to improve and promote a healthy eating intervention program. Methods: The theory of planned behavior (TPB) was used as the theoretical framework to identify factors influencing adolescents’ eating behavior and dietary intake. One hundred and eighty four urban adolescents from public high schools in Ratchaburi Province, Thailand were recruited to complete the Thai Eating Questionnaire (TEQ) developed based on the TPB. It included a brief food frequency questionnaire (FFQ). Stepwise regression analyses were undertaken with SPSS software to assess how well the variables of the TPB predicted eating intention and behavior. Results: Urban adolescents’ eating behavior was predicted by perceived behavioral control (PBC) (R2=.050), while no association was found between eating intention and eating behavior. Urban adolescents’ eating intention was together predicted by attitude toward eating behavior, and subjective norm regarding eating behavior (R2=.274). Whereas attitude toward eating behavior predicted 24% of the variance (p<0.001), and subjective norm regarding eating behavior add another 3% (p<0.005). Conclusions: This study supports the application of the TPB to the prediction of eating intention and eating behavior among urban adolescents. The results indicate that urban adolescents would benefit from interventions designed to increase facilitator-based and self-efficacy opportunities, as well as decreasing barriers to healthy eating.
ActivFORMS: active formal models for self-adaptation

Self-adaptation enables a software system to deal autonomously with uncertainties, such as dynamic operating conditions that are difficult to predict or changing goals. A common approach to realize self-adaptation is with a MAPE-K feedback loop that consists of four adaptation components: Monitor, Analyze, Plan, and Execute. These components share Knowledge models of the managed system, its goals and environment. To provide guarantees of the adaptation goals, state of the art approaches propose using formal models of the knowledge. However, less attention is given to the formalization of the adaptation components themselves, which is important to provide guarantees of correctness of the adaptation behavior (e.g., does the execute component execute the plan correctly? ). We propose Active FORmal Models for Self-adaptation (ActivFORMS) that uses an integrated formal model of the adaptation components and knowledge models. The formal model is directly executed by a virtual machine to realize adaptation, hence active model. The contributions of ActivFORMS are: (1) the approach assures that the adaptation goals that are verified offline are guaranteed at runtime, and (2) it supports dynamic adaptation of the active model to support changing goals. We show how we have applied ActivFORMS for a small-scale robotic system.
A hardware architecture for implementing protection rings

Protection of computations and information is an important aspect of a computer utility. In a system which uses segmentation as a memory addressing scheme, protection can be achieved in part by associating concentric rings of decreasing access privilege with a computation. This paper describes hardware processor mechanisms for implementing these rings of protection. The mechanisms allow cross-ring calls and subsequent returns to occur without trapping to the supervisor. Automatic hardware validation of references across ring boundaries is also performed. Thus, a call by a user procedure to a protected subsystem (including the the supervisor) is identical to a call to a companion user procedure. The mechanisms of passing and referencing arguments are the same in both cases as well.
Stabilization of networked control systems under feedback-based communication

We study the stabilization of a networked control system (NCS) in which multiple sensors and actuators of a physical plant share a communication medium to exchange information with a remote controller. The plant's sensors and actuators are allowed only limited access to the controller at any one time, in a way that is decided on-line using a feedback-based communication policy. Our NCS model departs from those in previous formulations in that the controller and plant handle communication disruptions by "ignoring" sensors and actuators that are not actively communicating. We present an algorithm that provides a complete and straightforward method for simultaneously determining stabilizing gains and communication policies and avoids the computational complexity and limitations associated with some previously proposed models. We introduce three feedback-based scheduling policies that quadratically stabilize the closed-loop NCS while achieving various objectives related to the system's rate of convergence, the priorities of different sensors and actuators, and the avoidance of chattering.
Scaling-law Optimal Training and Scheduling in the SIMO Uplink

In fading MIMO channels, there is a tradeoff between the time (or energy) spent gathering CSI and the remaining time in which to transmit data before the channel loses coherence. The tradeoff is more pronounced in multiuser systems as the number of usershence the number of channel vectors to be estimatedincreases, and is inherently coupled with multiuser scheduling. We consider a multiple access block fading channel with coherence time T , n independent users, each with one transmit antenna and the same average power constraint ρavg, and a base station with M receive antennas and no a priori channel state information. We construct a training-based communication scheme and jointly optimize the training and user selection: we find the optimal number of users to be trained, Lopt, and the optimal number to be scheduled for transmission out of those trained, in order to maximize sum rate. The optimal duration of training is shown to be equal to Lopt symbol times. We also show the necessary and sufficient condition for training sequences to satisfy. This optimized training-based scheme achieves the same scaling law with increasing SNR as the noncoherent capacity of a single user n×M MIMO channel: Lopt ( 1 − Lopt T ) log2(ρavg) + O(1) as ρavg → ∞, where Lopt = min(n, M, b2 c). We show this is also the scaling law of the sum capacity of the associated non-coherent SIMO uplink, hence our scheme is scaling-law optimal. Finally, the asymptotic behavior of sum rate and throughput per user under increasing n, M or T is explored.
Detection of epileptiform activity in human EEG signals using Bayesian neural networks

In this paper, we investigate the application of neural networks to the problem of detecting inter-ictal epileptiform activity in the electroencephalogram (EEG). The proposed detector consists of a segmentation, feature extraction and classification stage. For the feature extraction stage, coefficients of the discrete wavelet transform (DWT), real and imaginary parts of the fast Fourier transform and raw EEG data were all found to be well-suited to EEG classification. Principal component analysis was used to reduce the dimensionality of the features. For the classification stage, multi-layer perceptron neural networks were implemented according to maximum likelihood and Bayesian learning formulations. The latter was found to make better use of training data and consequently produced better trained neural networks. Rejection thresholds of 0.9 were applied to the network output as a doubt level in order to ensure that only reliable classification decisions are made. A maximum classifier accuracy of 95,10% was achieved with 24,97% of patterns not being classified. Bayesian moderated outputs could not improve on these classification predictions significantly enough to warrant their added computational overhead.
Web Crawling : A Generic Framework for Specifying the User Interest and for Adaptive Crawling Strategies

Compared to the standard web search engines, focused crawlers yield good recall as well as good precision by restricting themselves to a limited domain. In this paper, we do not introduce another focused crawler, but we introduce a generic framework for focused crawling consisting of two major components: (1) Specification of the user interest and measuring the resulting relevance of a given web page. The proposed method of specifying the user interest by a formula combining atomic topics significantly improves the expressive power of the user. (2) Crawling strategy. Ordering the links at the crawl frontier is a challenging task since pages of a low relevance may be on a path to highly relevant pages. Thus, tunneling may be necessary. The explicit specification of the user interest allows us to define topic-specific strategies for tunneling. Our system Ariadne is a prototype implementation of the proposed framework. An experimental evaluation of different crawling strategies demonstrates the performance gain obtained by focusing a crawl and by dynamically adapting the focus.
Experimental study of the frequency correlation of space-time entangled photons

Space-time entangled photons generated from a continuous-wave parametric downconverter have a well defined sum-frequency despite having individual broad bandwidths. The narrowband frequency correlation that results from this well defined sum-frequency is examined experimentally. The measurements use degenerate, 1.55 μm photon pairs that are also suitable for fiber-based quantum communication protocols. Techniques for optimizing the pair generation rate, the detector and coincidence circuit parameters and the fiber coupling of downconverted light are also presented. A strong frequency correlation is observed using ~0.5 nm bandpass filters to measure the frequencies of entangled photons with >100 nm individual bandwidths. Thesis Supervisor: Franco N. C. Wong Title: Senior Research Scientist, Research Laboratory of Electronics
Potential prevention of medical errors in casualty surgery by using information technology

Recent studies on adverse events in medicine have shown that errors in medicine are not rare and may cause severe harm. Quality problems in discharge letters may be a source of medical error. We have analyzed 150 discharge letters of an outpaitient clinic for casualty surgery in order to identify and to classify typical mistakes. A Failure Mode and Effect Analysis has been initiated in order to estimate the risk associated with different failure types. Possible IT solutions to prevent the identified problems have been assessed, focusing on expected effects and on feasibility. Our analyses have shown that there is a need to improve the quality of discharge letters, and that IT support based on the frequency and severity of certain error types has a good potential. We plan to introduce both pre-structured discharge letters and reminders in order to prevent the observed errors. They could improve both documentation quality and, if used during the patient visit, quality of treatment. Moreover, they could produce training effects on less experienced physicians. To be able to rapidly integrate such an adapted IT support into a comprehensive Healthcare Information System (HIS), it is important to establish a responsive IT infrastructure.
Towards a sub-decimetric georeferencing of ground-based mobile mapping systems in urban areas : Matching ground-based and aerial-based imagery using roadmarks

We propose a novel image-based strategy for a sub-decimetre quality georeferencing of Mobile Mapping Systems in urban areas. The presented approach uses very high resolution multiple-view calibrated aerial images to extract a database of 3D invariant objects, namely road marks. These objects are also extracted in 3D in the ground-based imagery and are matched with the aerial data source. These road features can then be used as Ground Control Objects for a photogrammetric resection to finely register the images of a terrestrial mobile mapping system. All the results in this paper will be shown on a "zebra crossing" road mark. Nevertheless, the process we describe could be applied to any other road mark.
Towards Finding Valuable Topics

Enterprises depend on their information workers finding valuable information to be productive. However, existing enterprise search and recommendation systems can exploit few studies on the correlation between information content and information workers’ productivity. In this paper, we combine content, social network and revenue analysis to identify computational metrics for finding valuable information content in people’s electronic communications within a large-scale enterprise. Specifically, we focus on two questions: (1) how are the topics extracted from such content correlate with information workers’ performance? and (2) how to find valuable topics with potentially high impact on employee performance? For the first question, we associate the topics with the corresponding workers’ productivity measured by the revenue they generate. This allows us to evaluate the topics’ influence on productivity. We further verify that the derived topic values are consistent with human assessor subjective evaluation. For the second question, we identify and evaluate a set of significant factors including both content and social network factors. In particular, the social network factors are better in filtering out low-value topics, while content factors are more effective in selecting a few top high-value topics. In addition, we demonstrate that a Support Vector regression model that combines the factors can already effectively find valuable topics. We believe that our results provide significant insights towards scientific advances to find valuable information.
htmlQuantitative bounds for convergence ratesof continuous time Markov processesbyGareth

We develop quantitative bounds on rates of convergence for continuous-time Markov processes on general state spaces. Our methods involve coupling and shift-coupling, and make use of minorization and drift conditions. In particular, we use auxiliary coupling to establish the existence of small (or pseudo-small) sets. We apply our method to some diiusion examples. We are motivated by interest in the use of Langevin diiusions for Monte Carlo simulation.
Characterization of Antimicrobial Resistance Dissemination across Plasmid Communities Classified by Network Analysis

The global clustering of gene families through network analysis has been demonstrated in whole genome, plasmid, and microbiome analyses. In this study, we carried out a plasmidome network analysis of all available complete bacterial plasmids to determine plasmid associations. A blastp clustering search at 100% aa identity cut-off and sharing at least one gene between plasmids, followed by a multilevel community network analysis revealed that a surprisingly large number of the plasmids were connected by one largest connected component (LCC), with dozens of community sub-groupings. The LCC consisted mainly of Bacilli and Gammaproteobacteria plasmids. Intriguingly, horizontal gene transfer (HGT) was noted between different phyla (i.e., Staphylococcus and Pasteurellaceae), suggesting that Pasteurellaceae can acquire antimicrobial resistance (AMR) genes from closely contacting Staphylococcus spp. , which produce the external supplement of V-factor (NAD). Such community network analysis facilitate displaying possible recent HGTs like a class 1 integron, str and tet resistance markers between communities. Furthermore, the distribution of the Inc replicon type and AMR genes, such as the extended-spectrum ß-lactamase (ESBL) CTX-M or the carbapenemases KPC NDM-1, implies that such genes generally circulate within limited communities belonging to typical bacterial genera. Thus, plasmidome network analysis provides a remarkable discriminatory power for plasmid-related HGT and evolution.
JML : a Java Modeling

JML is a behavioral interface speciication language tailored to Java. It also allows assertions to be intermixed with Java code, as an aid to veriication and debugging. JML is designed to be used by working software engineers, and requires only modest mathematical training. To achieve this goal, JML uses Eiiel-style assertion syntax combined with the model-based approach to speciications typiied by VDM and Larch. However, JML supports quantiiers, speciication-only variables, frame conditions, and other enhancements that make it more expressive for speciication than Eiiel. This paper discusses the goals of JML, the overall approach, and prospects for giving JML a formal semantics through a veriication logic.
On the equivalence of a RBF-like network to TS fuzzy systems: a GA approach for TS-network

Functional equivalence of radial basis function (RBF) networks and a class of fuzzy inference systems is considered. The class of fuzzy systems based on the Takagi-Sugeno model is referred to as TS-model of fuzzy inference. From the abstract mathematical viewpoint the functional equivalence between radial basis function networks and fuzzy inference systems is already shown. However, from the viewpoint of realisation of the models with data, the difference between the model performances is observed. The research makes comparisons between an RBF model and its fuzzy model counterpart and qualifications on the equivalence being observed are reported.
Dominating and Admissible Mse-Bounds Using Saddle-Point Methods

We treat the problem of evaluating the performance of estimators for estimating a deterministic parameter vector x, with the mean-squared error (MSE) as the performance measure. Since the MSE depends on the unknown vector x, direct comparison between estimators is a difficult problem. Here we consider a framework for examining the MSE of different approaches based on the concepts of admissible and dominating estimators. Using a saddle-point framework we reduce these abstract concepts to a concrete convex optimization problem, which can then by analyzed by utilizing the machinery of convex optimization. Our development considers both the case of linear estimation in linear models as well as more general nonlinear models
Anonymous communication for encouraging the reporting of wrongdoing in social groups

We propose an application of DC-net called Layered DC-net for encouraging the reporting of wrongdoing in social groups. A social group is a set of individuals, structured into layered subgroups. Individuals are divided into subgroups called lower groups. One of the individuals in each lower group is a representative who has authority and responsibility for the lower group. The set of all representatives from all lower groups is called the upper group. In social groups, wrongdoing (e.g., bullying in schools) occasionally occurs. If individuals in a lower group discover wrongdoing, they should report it to their representative. Meanwhile, all representatives are responsible for settling wrongdoing. However, once individuals reporting such wrongdoing and their representative are identified, the payoffs for both are smaller than those for individuals who do not report it. We focus on reporting bullying in a school as an example of reporting wrongdoing in social groups. By using game theory, we formulate a game among bystanders of bullying in a lower group and another game among teachers in the upper group. We evaluate the games with and without the Layered DC-net. The results show that the Layered DC-net implements the Nash equilibria that encourage the reporting of wrongdoing in both lower and upper groups and settles wrongdoing in the social group.
An Effective Indexing and Retrieval Approach for Temporal Cases

In this work D-HS, a novel, effective indexing technique for cases with temporal attributes is presented. It is shown to be more competent, and efficient than the widely used F-Index approach based on a series of experiments using 5 synthetically generated case-bases. Additional benefits include its scalability and its suitability for indexing both temporal and non-temporal attributes alike.
A study of the edge detection for road lane

This article presents an investigation of an optimum algorithm for edge detection in order to use in the road lane detection process. The main issues, including the speed, the accuracy, and the limited resources, were taken to consider for the realization on the FPGA technology. The edge detection algorithms of Canny, Prewitt, Sobel and Roberts were compared using MATLAB. A number of road images were captured by a video camera with the image size of 640x480 pixels and the frame rate of 30 fps. In addition, a mask filter was applied to remove red, green, and blue values to help the edge detection process be more efficient. From the experimental results, the Canny algorithm was the most time consuming process, and gave too many lines outside the road lane. Among these, the Roberts algorithm is not only the smallest size, but also gained the fastest speed (3.14 times faster than the Canny algorithm) and the most accurate one to detect the lines of the actual road lanes.
Correlplot: a collection of functions for plotting correlation matrices

This documents gives some instructions on how to create plots of correlation matrices in the statistical environment R (R Core Team, 2013) using the package Correlplot. The outline of this guide is as follows: Section 2 explains how to install this package and Section 3 shows how to use the functions of the package for creating pictures like correlograms and biplots for a given correlation matrix. Computation of goodness-of-fit statistics is also illustrated. If you appreciate this software then please cite the following paper in your work:
Semi-supervised learning of deep metrics for stereo reconstruction

Deep-learning metrics have recently demonstrated extremely good performance to match image patches for stereo reconstruction. However, training such metrics requires large amount of labeled stereo images, which can be difficult or costly to collect for certain applications. The main contribution of our work is a new semi-supervised method for learning deep metrics from unlabeled stereo images, given coarse information about the scenes and the optical system. Our method alternatively optimizes the metric with a standard stochastic gradient descent, and applies stereo constraints to regularize its prediction. Experiments on reference data-sets show that, for a given network architecture, training with this new method without ground-truth produces a metric with performance as good as state-of-the-art baselines trained with the said ground-truth. This work has three practical implications. Firstly, it helps to overcome limitations of training sets, in particular noisy ground truth. Secondly it allows to use much more training data during learning. Thirdly, it allows to tune deep metric for a particular stereo system, even if ground truth is not available.