Miracle-dz/UniFAR-FTUs huggingface dataset:
A Flexible Modeling Approach Using Dirichlet Process Mixtures: Application to Multi-Level Railway Grade Crossing Crash Data

This paper introduces a new approach to addressing two of the most challenging issues in road safety research, namely, how to account for unobserved heterogeneity and how to identify latent subpopulations in data. Compared to the approaches of applying random effects/parameters models and finite mixtures, the proposed approach employs a Bayesian semi-parametric methodology based on Dirichlet process mixtures. Our method has four noteworthy advantages: (i) it allows examining the robustness of distributional assumptions in random effects/parameters models; (ii) it allows identifying latent clusters in data; (iii) it enables identification of outliers (extreme observations) while allowing accommodating them in analyses without compromising the quality of estimates; and (iv) it is capable of estimating the number of latent clusters in data using an elegant mathematical structure. In this paper, we evaluate the proposed method on a railway grade crossing crash dataset with hierarchical (multilevel) structure, at municipality level, from Canada for the years 2008 to 2013. We use cross-validation predictive densities and pseudo Bayes factor for Bayesian model selection. While confirming the need for the multilevel modeling approach, the results pointed out the inadequacy of the parametric assumption. In fact, our proposed method improved model fitting significantly for the municipality-level data. In a fully probabilistic framework, we also identified the expected number of latent clusters with similar unknown/unmeasured features among 81 Canadian municipalities. It is possible thus to further investigate the reasons behind such similarities and dissimilarities, which could have important policy implications in terms of safety management process.
Intersectionality and Black Women’s Health: Making Room for Rurality

ABSTRACT Black women have poorer health compared to their White counterparts in a range of health outcomes, including breast cancer, diabetes, HIV/AIDS, and heart disease. The health disparities literature has largely treated women as a monolithic group, assuming that health practices and treatments are equally applicable and effective for all women. This approach, which places too much emphasis on gender, risks masking the unique experiences of various women based on other social categories. This article argues that in order to advance Black women’s health, an intersectionality approach should be incorporated into health research and practice. This approach, however, should go beyond the usual intersection of race and gender to include rurality. The article builds this argument on the fact that Black women living in rural areas have unique experiences that intersect with their gender, race, and class status. Benefits for embracing the intersectionality approach are discussed.
RepuScore: Collaborative Reputation Management Framework for Email Infrastructure

We propose RepuScore, a collaborative reputation management framework over email infrastrucure, which allows participating organizations to establish sender accountability on the basis of senders' past actions. RepuScore's generalized design can be deployed with any Sender Authentication technique such as SPF, SenderID and DKIM. With RepuScore, participating organizations collect information on sender reputation locally from users or existing spam classification mechanisms and submit it to a central RepuScore authority. The central authority generates a global reputation summary which can be used to enforce sender accountability. We present the algorithms for reputation score calculation and share our findings from experiments based on a RepuScore prototype using a) our simulation logs and b) a 20 day log from a non-profit organization with five collaborating domains.
An approach of orthology detection from homologous sequences under minimum evolution

In the field of phylogenetics and comparative genomics, it is important to establish orthologous relationships when comparing homologous sequences. Due to the slight sequence dissimilarity between orthologs and paralogs, it is prone to regarding paralogs as orthologs. For this reason, several methods based on evolutionary distance, phylogeny and BLAST have tried to detect orthologs with more precision. Depending on their algorithmic implementations, each of these methods sometimes has increased false negative or false positive rates. Here, we developed a novel algorithm for orthology detection that uses a distance method based on the phylogenetic criterion of minimum evolution. Our algorithm assumes that sets of sequences exhibiting orthologous relationships are evolutionarily less costly than sets that include one or more paralogous relationships. Calculation of evolutionary cost requires the reconstruction of a neighbor-joining (NJ) tree, but calculations are unaffected by the topology of any given NJ tree. Unlike tree reconciliation, our algorithm appears free from the problem of incorrect topologies of species and gene trees. The reliability of the algorithm was tested in a comparative analysis with two other orthology detection methods using 95 manually curated KOG datasets and 21 experimentally verified EXProt datasets. Sensitivity and specificity estimates indicate that the concept of minimum evolution could be valuable for the detection of orthologs.
An Architecture Framework for Experimentations with Self-Adaptive Cyber-physical Systems

Recent advances in embedded devices capabilities and wireless networks paved the way for creating ubiquitous Cyber-Physical Systems (CPS) grafted with self-configuring and self-adaptive capabilities. As these systems need to strike a balance between dependability, open-endedness and adaptability, and operate in dynamic and opportunistic environments, their design and development is particularly challenging. We take an architecture-based approach to this problem and advocate the use of component-based abstractions and related machinery to engineer self-adaptive CPS. Our approach is structured around DEECo -- a component framework that introduces the concept of component ensembles to deal with the dynamicity of CPS at the middleware level. DEECo provides the architecture abstractions of autonomous components and component ensembles on top of which different adaptation techniques can be deployed. This makes DEECo a vehicle for seamless experiments with self-adaptive systems where the physical distribution and mobility of nodes, and the limited data availability play an important role.
Total Variation Regularization and L-curve method for the selection of regularization parameter

The objective of this project is three fold. First, is to present a concise and self-constrained overview on the L-curve method for the selection of regularization parameter. Second, presents an elaborate overview of total variation (TV) images restoration models and few existing numerical methods for solving. Third, is to present a brief discussion on the mathematical background of the TV method and results of my implementation of fixed-point iterative TV regularization. Most of the reconstruction algorithms smooth out the edges of the reconstructed images but total variation regularization method preserve the edge information without any prior knowledge about the blurred image geometric details. The regularization parameter is another important criterion that should be carefully chosen for a good regularized output. The approach towards the objective involves a comprehensive review of various ways of implementing total variation regularization that are applicable to Tikhonov regularization and Truncated Singular Value Decomposition (TSVD). The L-curve methodology and its criterion for the selection of regularization parameters in comparison to other methods is also evaluated in order to select the regularization parameters and compares advantages and limitations. The fixed-point iteration method for obtaining total variation regularization is selected and is applied to an image that is corrupted with Gaussian noise and whose Point Spread Function (PSF) is also Gaussian. The outcome is comprised of a reconstructed image from a noisy, blurred image using the total variation regularization method. A least square method output is also presented.
Initial assessment for K-12 English language support in six countries: revisiting the validity–reliability paradox

ABSTRACT It is common practice for K-12 schools to assess multilingual students’ language proficiency to determine language support program placement. Because such programs can provide essential scaffolding, the policies guiding these assessments merit careful consideration. It is well accepted that quality assessments must be valid (representative of the constructs of interest) and reliable (error-free and consistent). However, a tension exists between validity and reliability, known as the attenuation paradox. Validity is strengthened when the range and depth of the assessed construct align with the target domain. Yet, increased domain coverage can introduce construct-irrelevant variance and greater potential for error, negatively impacting reliability. On the other hand, narrowing the assessed construct, which tends to increase reliability, also weakens validity due to construct-underrepresentation. In this paper, we revisit the validity–reliability paradox by examining initial assessment policies for K-12 English language support programs in six nations. We report on each nation's policies for language placement assessment and the associated language support programs and funding mechanisms. We compare the assessment policies on the validity and reliability spectrum, framed by Bachman's assessment use argument heuristic. We conclude with a discussion of implications related to educational equity.
The Perceptron Algorithm with Uneven Margins

The perceptron algorithm with margins is a simple, fast and eﬀective learning algorithm for linear classiﬁers; it produces decision hyperplanes within some constant ratio of the maximal margin. In this paper we study this algorithm and a new variant: the perceptron algorithm with uneven margins, tailored for document categorisation problems (i.e. problems where classes are highly unbalanced and performance depends on the ranking of patterns). We discuss the interest of these algorithms from a theoretical point of view, provide a generalisation of Novikoﬀ’s theorem for uneven margins, give a geometrically description of these algorithms and show experimentally that both algorithms yield equal or better performances than support vector machines, while reducing training time and sparsity, in classiﬁcation (USPS) and document categorisation (Reuters) problems.
Soluble dominant‐negative receptor uncovers essential roles for fibroblast growth factors in multi‐organ induction and patterning

Despite a wealth of experimental data implicating fibroblast growth factor (FGF) signaling in various developmental processes, genetic inactivation of individual genes encoding specific FGFs or their receptors (FGFRs) has generally failed to demonstrate their role in vertebrate organogenesis due to early embryonic lethality or functional redundancy. Here we show that broad mid‐gestational expression of a novel secreted kinase‐deficient receptor, specific for a defined subset of the FGF superfamily, caused agenesis or severe dysgenesis of kidney, lung, specific cutaneous structures, exocrine and endocrine glands, and craniofacial and limb abnormalities reminiscent of human skeletal disorders associated with FGFR mutations. Analysis of diagnostic molecular markers revealed that this soluble dominant‐negative mutant disrupted early inductive signaling in affected tissues, indicating that FGF signaling is required for growth and patterning in a broad array of organs and in limbs. In contrast, transgenic mice expressing a membrane‐tethered kinase‐deficient FGFR were viable. Our results demonstrate that secreted FGFR mutants are uniquely effective as dominant‐negative agents in vivo, and suggest that related soluble receptor isoforms expressed in wild‐type mouse embryos may help regulate FGF activity during normal development.
WiFi iLocate: WiFi based indoor localization for smartphone

In recent years, the increasing popularity of smartphones has promoted the development of location-aware applications. However, highly accurate indoor localization by smartphones remains an open problem. In this paper, we present WiFi iLocate - a system that can help track the location and movement of a smartphone user in indoor environments. The system applies Gaussian process regression to train the collected WiFi received signal strength (RSS) dataset, and particle filter for the estimation of the smartphone user's location and movement. Simulations were conducted in MATLAB to test the performance and provide more insights of the proposed approach. The experiments carried with an iOS device in typical library environment illustrate that our system is an accurate, real-time, press-to-go system.
An HTML5-based ECG viewer

Hospitals and health care systems usually need to install appropriate, ad-hoc software in every computer that requires access to an electrocardiogram (ECG). Other solutions provide the ECGs as images, hindering thereby advanced features. One step beyond is the use of applets, which provide a richer web environment, but this still requires additional software (e.g. a Java interpreter). To avoid supplementary installations while preserving advanced capabilities, emergent web standards can be used. This paper presents a web-based ECG viewer able to perform advanced actions over the waveform by using the fifth revision of the HyperText Markup Language (HTML) and JavaScript. Different features were designed and tested in different browsers, including desktop, tablet, smartphone, and television set devices. Some features were not fully supported by all browsers, but these deficiencies will eventually be resolved, as browsers completely adapt HTML5. As a result, a platform-independent, web-based ECG viewer in compliance with the latest web standards was designed, developed and tested in different browsers. The use of the chosen technologies facilitates the creation of complex applications displaying high-performance graphics that can be visualized anywhere with an internet connection and in different types of devices.
Visualization of Cortical Projection Neurons with Retrograde TET-Off Lentiviral Vector

We are interested in identifying and characterizing various projection neurons that constitute the neocortical circuit. For this purpose, we developed a novel lentiviral vector that carries the tetracycline transactivator (tTA) and the transgene under the TET Responsive Element promoter (TRE) on a single backbone. By pseudotyping such a vector with modified rabies G-protein, we were able to express palmitoylated-GFP (palGFP) or turboFP635 (RFP) in corticothalamic, corticocortical, and corticopontine neurons of mice. The high-level expression of the transgene achieved by the TET-Off system enabled us to observe characteristic elaboration of neuronal processes for each cell type. At higher magnification, we were able to observe fine structures such as boutons and spines as well. We also injected our retrograde TET-Off vector to the marmoset cortex and proved that it can be used to label the long-distance cortical connectivity of millimeter scale. In conclusion, our novel retrograde tracer provides an attractive option to investigate the morphologies of identified cortical projection neurons of various species.
Applications of Entropy Measures in Field of Queuing Theory

— In the present communication, we have studied different variations in the entropy measures in the different states of queueing processes. In case of steady state queuing process, it has been shown that as the arrival rate increases, the uncertainty increases whereas in the case of non-steady birth-death process, it is shown that the uncertainty varies differently. In this pattern, it first increases and attains its maximum value and then with the passage of time, it decreases and attains its minimum value.
Low Power Intelligent Wearable Cardiac Sensor Using Discrete Wavelet Compression

Claiming 17.1 million lives a year, cardiovascular diseases are one of the major causes of death in the world especially in rural India. Conventional ECG monitoring instruments are quite bulky and if we are able to miniaturize them, then they can be used to collect data in scenarios that were not possible with the traditional systems. The system proposed here describes a low cost, low power wearable wireless sensor which can be used for issuing early warnings of cardiac disease to doctors, who are mostly mobile or who are away from the patient's location. The knowledge of early symptoms of a cardiac disease will provide an opportunity for the doctor to deliver real-time instructions to a relative/care taker for giving urgent medical care to the patient. Power optimisation has been achieved by using wavelet based compression technique (DWT) and also different modes of operation based on the risk factor of the patient. An algorithm based on discrete wavelet compression for power optimisation is developed and it is simulated in MATLAB. The results obtained shows that this technique achieves a power reduction of 22%. The proposed system can be used to deliver better healthcare to rural India, where availability of experienced doctors is almost scarce compared to urban India.
Application of Artificial Intelligence for Bridge Deterioration Model

The deterministic bridge deterioration model updating problem is well established in bridge management, while the traditional methods and approaches for this problem require manual intervention. An artificial-intelligence-based approach was presented to self-updated parameters of the bridge deterioration model in this paper. When new information and data are collected, a posterior distribution was constructed to describe the integrated result of historical information and the new gained information according to Bayesian theorem, which was used to update model parameters. This AI-based approach is applied to the case of updating parameters of bridge deterioration model, which is the data collected from bridges of 12 districts in Shanghai from 2004 to 2013, and the results showed that it is an accurate, effective, and satisfactory approach to deal with the problem of the parameter updating without manual intervention.
A novel uncertainty measure on rough sets: A mean-variance approach

Uncertainty measure is an important implement for characterizing the degree of uncertainty in rough set theory. It has been extensively applied in pattern recognition and data clustering. However, this paper reveals the issue that classical uncertainty measures are sensitive to disturbances or noises. Therefore, a novel uncertainty measure, called mean-variance measure (MVM), is proposed to characterize the degree of uncertainty of rough sets. Since it takes fully information in the boundary region into account, MVM is more robust and effective than classical uncertainty measures in depressing disturbances and noises.
Theory of Network Communication

The goal of this lecture is to give an introduction to the state of the art in the theory of network communication. It is a widely accepted fact that algorithmic advances in the area of computer science are only useful to society if they are based on models that truthfully reflect the restrictions and requirements of the corresponding applications. This is certainly also true for network communication. For example, in general any infrastructure connecting processing units with each other may be called a network, but certain infrastructures such as a dedicated line between any pair of nodes are certainly unrealistic, because they are too expensive to build. Thus, messages may have to traverse several units to reach their destination, causing (among other problems) route selection and scheduling problems. Also, we will not just study network communication out of context, but we will also look at various topics that require or support efficient network communication such as distributed sorting, load balancing, data management, or design strategies for overlay networks. In this section we start with an introduction to graph theory followed by a simple routing example. Afterwards, we will speak shortly about various communication layers and modes and give an introduction to network flows and parameters.
Optimal allocation of power — Graphical interpretation of some scheduling problem

We consider a practical makespan minimization problem that arises in a multiprocessor computer system where some processors may be shut down during computation to save an amount of shared power. The system consists of m processors driven by a common power source. The processors are modeled as a set of identical parallel machines. Moreover, we consider a set of n independent, nonpreemptive jobs which model a given set of computational tasks. Each job requires a machine and an amount of power for its processing. A processing time of a jobs is unknown a priori. Instead of this, each job is characterized by a size determined by a number of CPU cycles necessary to complete this job. The processing rate of a job depends on an amount of power allotted to this job at a moment. This relation is expressed by a non-decreasing processing rate function. Power is treated as a renewable continuous resource available in a constant amount. Each machine may be in one of two states: “power on”, and “power off”. In the “power on” state an idle machine uses a constant amount of power. A machine uses no power in the power off state. We assume that time of switch between states may be neglected and brings no additional costs. We identify some cases of optimal solutions for the simplest situation of two machines and jobs characterized by the same processing rate function.
Effect of Varying the Energy Density of Protein-adequate Diets on Nutrient Metabolism, Clinical Chemistry, Immune Response and Growth of Muzaffarnagari Lambs

Effects of varied dietary energy densities on immune response and performance of Muzzafarnagari lambs were ascertained in a 180-d study. Animals (n = 24), in three groups, were fed diets providing 100% (100E), 80% (80E) or 70% (70E) of their metabolizable energy requirement. Mean nutrient digestibilities varied significantly among treatments. Nitrogen intake was lower (p<0.01) in the 70E. Nitrogen retention, was reduced (p<0.001) in 80E and 70E vs 100E. The average daily gain (p<0.001) was 47.01±4.23, 13.54±1.72 and -16.67±8.24 g for 100E, 80E and 70E, respectively. Hemoglobin concentration, haematocrit, total and differential leukocyte counts were lower (p<0.001) for 80E and 70E than for 100E with a similar trend (p<0.05) for serum glucose and total protein. Serum cortisol was reduced (p<0.001) with decreased energy availability. Antibody titre to Brucella abortus S19 showed an initial reduction in 80E and 70E vs 100E. Delayed-type hypersensitivity response was lower (p<0.001) in 80E and 70E vs 100E, accompanying a lower (p<0.001) nitric oxide production by the peripheral lymphocytes. It is concluded that the reduced dietary energy density significantly affects the growth performance and immune response of lambs.