Miracle-dz/UniFAR-FTUs huggingface dataset:
Composition Semantics for Component-Based Embedded Software

With the increasing complexity of embedded systems, Component-Based Software Development (CBSD) is regarded as a beneficial approach for embedded systems. For component technology, applications are built by assembling interacting components and interfaces are the means by which components interact. Embedded systems are usually safety-critical applications. There is a real need for clear and sound definition of operational semantics for the composition of components such that they may be used to direct the construction of the desired system and help to make analysis of behaviors and properties. This paper describes the composition semantics of component-based embedded software architecture in terms of the Communicating Sequential Language (CSP). Three types of interfaces: procedure call, data access and event are classified. And the composition semantics for each type is presented. The overall objective is to provide a soundly based notation for specifying and verifying embedded software architectures.
Western medicine : an illustrated history

I From the hippocratic corpus to twentieth-century medicine: Martin Kemp, Medicine in View - Art and Visual Representation James Longrigg, Medicine in the Classical World Emilie Savage-Smith, Europe and Islam Michael McVaugh, Medicine in the Middle Ages c.500-1350 Katherine Park, Medicine and the Renaissance Harold Cook, From the Scientific Revolution to Germ Theory E.M. Tansey, From Germ Theory to 1945 Stephen Lock, From 1950 to the Present. II Medicine in context: Lisa Rosner, The Growth of Medical Education and the Medical Profession Ulrich Trohler, Cay-Rudiger Prull, The Rise of the Modern Hospital Mary Dobson, Epidemics and the Geography of Disease Anne Summers, Nurses and Ancilliaries in the Christian Era Irvine Loudon, Childbirth J.A. Walker-Smith, Children in Hospital 1850-1990 Michael Neve, Medicine and the Mind Michael Worboys, The Spread of Western Medicine Margaret Pelling, Unofficial and Unorthodox Medicine Jane Lewis, Medicine, Politics, and the State Anne Digby, The Patient's View.
Multiple scaling factors based Semi-Blind watermarking of grayscale images using OS-ELM neural network

In this paper, a multiple scaling factor based Semi-Blind watermarking scheme for grayscale image watermarking using Online Sequential Extreme Learning Machine (OS-ELM) is proposed. Four-level DWT is applied on three standard test images of size 512 × 512. LL4 sub-band coefficients are chosen for watermark embedding. OS-ELM is initially tuned with a fixed number of training data used in its initial phase and size of data block learned in each iteration. The training set is developed by combining the quantized and desired LL4 sub-band coefficients. This training set is fed to OS-ELM for training. The output of OS-ELM is a sequence of predicted coefficients which is sorted and divided into three equal parts. Multiple Scaling Factor (MSF) scheme is used for embedding a binary watermark in a Semi-Blind manner. Results show good visual quality of signed images and good similarity between the extracted and original watermarks from signed and attacked images. The PSNR and BER are found to be well optimized both in case of signed and attacked images. The time for embedding and extraction is in milliseconds, which makes the proposed technique effective for developing real time watermarking applications.
MPEG-4 FGS video streaming with constant-quality rate control and differentiated forwarding

Streaming video over IP is studied by integrating the error resilient scalable source codec with constant quality rate adaptation and prioritized network transmission. The scalable codec is based on the standardized ISO/IEC MPEG-4 fine granular scalable (FGS) coding scheme, which allows arbitrary truncation according to the given rate budget. To maximize the end-to-end visual quality, constant quality video rate control (CQVRC) is proposed to preserve smooth video quality when there is a significant amount of variation in the video source, the available bandwidth or both. The proposed CQVRC attempts to provide smooth video quality for both the base layer (BL) and the enhancement layer (EL). The video stream is then prioritized for priority dropping, where rate adaptation is dynamically performed to meet the time-varying available bandwidth. This kind of prioritized stream can benefit from QoS networks such as the IP differentiated service (DiffServ). All key components, including FGS encoding, CQVRC based rate adaptation and packetization, error resilient decoding, and differentiated forwarding, are seamlessly integrated into one system. By focusing on the end-to-end quality, we set both source and network parameters properly to achieve a superior performance of FGS video streaming.
Understanding Disease Susceptibility through Population Genomics

Genetic epidemiology studies have established that the natural variation of gene expression profiles is heritable and has genetic bases. A number of proximal and remote DNA variations, known as expression quantitative trait loci (eQTLs), that are associated with the expression phenotypes have been identified, first in Epstein-Barr virus-transformed lymphoblastoid cell lines and later expanded to other cell and tissue types. Integration of the eQTL information and the network analysis of transcription modules may lead to a better understanding of gene expression regulation. As these network modules have relevance to biological or disease pathways, these findings may be useful in predicting disease susceptibility.
Image Annotation System Using Visual and Textual Features

We present an automated Image Annotation system called I-Tag which uses both visual and textual information of the images and recommends relevant tags for them. The automatic generation of metadata would allow image searches and content-based image retrieval (CBIR) to be more effective. We use state of the art tools on text based retrieval and image content based retrieval to retrieve similar images. We use an open source image retrieval engine called FIRE [8], to perform content based image search. We use the visual features as well as the title of the image to recommend tags for the image with high relevance. Experiments are conducted on approximately 5000 images downloaded from FLICKR to compare the accuracy of the recommended tags. Our experiments show that the mean average precision of the recommended tags is 69%.
Control and random searching with multiple robots

This paper discusses the elements of modeling and simulation methodology for the study of system performance analysis in minefield reconnaissance and object mapping in very shallow water environments. Crawling and swimming vehicles are considered, although the focus is on the first. Vehicle locomotion models are proposed. Wave and current models are discussed by reference to other ongoing research. The modeling of object detection sensors, and vehicle navigation sensors are also given. Using these principles given above, reference is made to the importance of two types of simulator graphics based visualization simulator that views the interactive behavior of robots and environment objects, and a Monte Carlo low resolution simulator that allows the study of system effectiveness. Also, other control parameters are studied including the effect of changes in the detection range of the primary sensor.
Ensemble Learningfor Hidden Markov

The standard method for training Hidden Markov Models optimizes a point estimate of the model parameters. This estimate, which can be viewed as the maximum of a posterior probability density over the model parameters, may be susceptible to over-tting, and contains no indication of parameter uncertainty. Also, this maximummay be unrepresentative of the posterior probability distribution. In this paper we study a method in which we optimize an ensemble which approximates the entire posterior probability distribution. The ensemble learning algorithm requires the same resources as the traditional Baum{Welch algorithm. The traditional training algorithm for hidden Markov models is an expectation{ maximization (EM) algorithm (Dempster et al. 1977) known as the Baum{Welch algorithm. It is a maximum likelihood method, or, with a simple modiication, a penalized maximum likelihood method, which can be viewed as maximizing a posterior probability density over the model parameters. Recently, Hinton and van Camp (1993) developed a technique known as ensemble learning (see also MacKay (1995) for a review). Whereas maximum a poste-riori methods optimize a point estimate of the parameters, in ensemble learning an ensemble is optimized, so that it approximates the entire posterior probability distribution over the parameters. The objective function that is optimized is a vari-ational free energy (Feynman 1972) which measures the relative entropy between the approximating ensemble and the true distribution. In this paper we derive and test an ensemble learning algorithm for hidden Markov models, building on Neal
Seroprevalence of Pertussis in Senegal: A Prospective Study

Background Pertussis, also known as whooping cough, is a vaccine-preventable respiratory disease caused by Bordetella pertussis infection, against which Senegalese children are immunized with the diphtheria-tetanus-whole cell pertussis vaccine (DTwP). Seroepidemiology of pertussis has been widely described in industrialized countries, but rare are the studies referring to it in developing countries. Methods We conducted a longitudinal survey in Northern Senegal to investigate the epidemiology of B. pertussis by evaluating the IgG antibody (Ab) response against pertussis toxin (PT). A cohort of 410 children aged 1 to 9 from five villages in the Middle Senegal River Valley were followed-up for 18 months. During that period, five visits were made to assess the immunological status of the children. Principal Findings PT-specific IgG responses were significantly different according to age. Until the age of 3, there was a decrease in the Ab response, which then increased in the older groups. Assessment of IgG antibodies to PT (IgG-PT) suggested evidence of recent exposures to the pathogen. Surprisingly, in one of the five villages the average Ab response to PT was very low at all ages during the first 6 months of the study. At the third visit, IgG-PT concentrations peaked to very high levels, to slightly decline at the end of the survey. This indicates an outbreak of B. pertussis, whereas in the other villages a pertussis endemic profile could be observed. Conclusions Pertussis is endemic in Northern Senegal despite the introduction of vaccination. The circulation of the bacteria seems to differ between geographic locations and over time. A more complete understanding of the epidemiology of pertussis and its environmental determinants could provide information to adapt vaccination programs.
Modeling The U.S. Sweetener Sector: An Application To The Analysis Of Policy Reform

This report documents a modeling framework for the U.S. sweeteners industry developed in the Specialty Crops Branch (SCB) of the Economic Research Service (ERS). Several innovations not seen in other studies include the regional modeling of sugar processing capacity adjustments and detailed sectoral analysis of sweeteners demand, using an Almost Ideal Demand Systems (AIDS) approach. The report describes the use of the framework to evaluate the benefits and costs of the current U.S. sugar program.
On clustering heterogeneous networks

One of the primary contributing factors for increasing complexity in network analysis is data heterogeneity. Multiple types of data are available from a diverse range of sources and scientists need new capabilities to integrate information from these diverse data types in order to gain a deeper and holistic understanding of a naturally built system. In this paper, we visit the problem of clustering networks built out of heterogeneous data. Given a k-partite network, where each of the k vertex partitions represents a distinct vertex (data) type and the edges represent inter-type relationships, the problem is one of identifying subsets of vertices of one type that are strongly linked to subsets of vertices of one or more of the other types. While the idea of modeling heterogeneity as a graph problem is not new, algorithm development efforts have been more recent1,2,3. While most of these methods have shown appreciable efficacy in clustering or scaling, they all make one or both of the following assumptions: that the number of output clusters is known a priori ; or that the network has a particular topology. On the contrary, many real world use-cases exist (e.g., Figure 1a) where the number of output clusters is unknown and the network can be of an arbitrary k-partite structure with or without intra-type edge information.
The 2008 Crisis from the Neurofinance Perspective: Investor Humor and Market Sentiment

The world is still facing a financial crisis that started in mid 2007 and up to moment it is unsolved. Stock markets around the world reacted badly and the real time news has never played such an important role to investors as seen in previous crises. The impact of the media deepened the bear dynamics of markets around the world amplifying their volatility. Neurofinances is a new field of inquiring that has the purpose of studying decision making taking into consideration the role played by emotion. Here, we use these notions to develop a neurofinance modelling of the Brazilian stock market assuming that the investor humor is dependent on the ratio between perceived benefit and risk, as well as market sentiment. Market sentiment, in turn, is proposed to be sensitive to the kind of news delivered by the media. The model is used to study the BMFBovespa index (Ibov) evolution from January, 2003 to September, 2010 in order to test if these hypotheses hold and whether market sentiment is sensitive to an index of Good/Bad news about Ibov. Results supported present propositions.
Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition

In this paper, parameter estimation of a state-space model of noise or noisy speech cepstra is investigated. A blockwise EM algorithm is derived for the estimation of the state and observation noise covariance from noise-only input data. It is supposed to be used during the offline training mode of a speech recognizer. Further a sequential online EM algorithm is developed to adapt the observation noise covariance on noisy speech cepstra at its input. The estimated parameters are then used in model-based speech feature enhancement for noise-robust automatic speech recognition. Experiments on the AURORA4 database lead to improved recognition results with a linear state model compared to the assumption of stationary noise.
An oncofetal antigen, IMP-3-derived long peptides induce immune responses of both helper T cells and CTLs

ABSTRACT Insulin-like growth factor II mRNA-binding protein 3 (IMP-3), an oncofetal antigen identified using genome-wide cDNA microarray analyses, is overexpressed in several malignancies. IMP-3-derived cytotoxic T lymphocyte (CTL) epitopes have been used for peptide-based immunotherapies against various cancers. In addition to CTLs, induction of tumor-associated antigen (TAA)-specific helper T (Th) cells is crucial for establishment of effective antitumor immunity. In this study, we aimed to identify IMP-3-derived long peptides (IMP-3-LPs) carrying CTL and promiscuous Th-cell epitopes for use in cancer immunotherapy. IMP-3-derived Th-cell epitopes that bind to multiple HLA-class II molecules were predicted by in silico analysis, and their immunogenicity was determined by utilizing human T cells. We identified two highly immunogenic IMP-3-LPs presented by multiple HLA-class II molecules. One of the IMP-3-LPs encompassed two CTL epitopes that have been used for peptide-vaccine immunotherapy in ongoing clinical trials. IMP-3-LPs-specific Th cells responded to autologous dendritic cells (DCs) loaded with the recombinant IMP-3 proteins, suggesting that these s (LPs) can be naturally processed and presented. The IMP-3-LPs and specific Th cells augmented the expansion of IMP-3-specific CTLs, which was further enhanced by programmed cell death-1 (PD-1) blockade. In addition, IMP-3-LP encapsulated in liposomes was efficiently cross-presented in vitro, and this LP successfully cross-primed CTLs in HLA-A2 transgenic mice (Tgm) in vivo. Furthermore, one of the IMP-3-LPs induced IMP-3-specific Th cells from peripheral blood mononuclear cells (PBMCs) of head-and-neck malignant tumor (HNMT) patients. These findings suggest the potential usefulness of IMP-3-LPs in propagating both Th cells and CTLs and may have implications for IMP-3-LPs-based cancer immunotherapy.
Discovering transcription factor regulatory targets using gene expression and binding data

MOTIVATION Identifying the target genes regulated by transcription factors (TFs) is the most basic step in understanding gene regulation. Recent advances in high-throughput sequencing technology, together with chromatin immunoprecipitation (ChIP), enable mapping TF binding sites genome wide, but it is not possible to infer function from binding alone. This is especially true in mammalian systems, where regulation often occurs through long-range enhancers in gene-rich neighborhoods, rather than proximal promoters, preventing straightforward assignment of a binding site to a target gene. RESULTS We present EMBER (Expectation Maximization of Binding and Expression pRofiles), a method that integrates high-throughput binding data (e.g. ChIP-chip or ChIP-seq) with gene expression data (e.g. DNA microarray) via an unsupervised machine learning algorithm for inferring the gene targets of sets of TF binding sites. Genes selected are those that match overrepresented expression patterns, which can be used to provide information about multiple TF regulatory modes. We apply the method to genome-wide human breast cancer data and demonstrate that EMBER confirms a role for the TFs estrogen receptor alpha, retinoic acid receptors alpha and gamma in breast cancer development, whereas the conventional approach of assigning regulatory targets based on proximity does not. Additionally, we compare several predicted target genes from EMBER to interactions inferred previously, examine combinatorial effects of TFs on gene regulation and illustrate the ability of EMBER to discover multiple modes of regulation. AVAILABILITY All code used for this work is available at http://dinner-group.uchicago. edu/downloads.html.
Testing of Flow-Based Microfluidic Biochips: Fault Modeling, Test Generation, and Experimental Demonstration

Recent advances in flow-based microfluidics have led to the emergence of biochemistry-on-a-chip as a new paradigm in clinical diagnostics and biomolecular recognition. However, a potential roadblock in the deployment of microfluidic biochips is the lack of test techniques to screen defective devices before they are used for biochemical analysis. Defective chips lead to repetition of experiments, which is undesirable due to high reagent cost and limited availability of samples. Prior work on fault detection in biochips has been limited to digital (“droplet”) microfluidics and other electrode-based technology platforms. The paper proposes the first approach for automated testing of flow-based microfluidic biochips that are designed using membrane-based valves for flow control. The proposed test technique is based on a behavioral abstraction of physical defects in microchannels and valves. The flow paths and flow control in the microfluidic device are modeled as a logic circuit composed of Boolean gates, which allows test generation to be carried out using standard automatic test pattern generation tools. The tests derived using the logic circuit model are then mapped to fluidic operations involving pumps and pressure sensors in the biochip. Feedback from pressure sensors can be compared to expected responses based on the logic circuit model, whereby the types and positions of defects are identified. We show how a fabricated biochip can be tested using the proposed method, and demonstrate experimental results for two additional fabricated chips.
The Doors of Perception: Unlocking Hispanic Access to Higher Education Faculty Positions in New Mexico

This study investigates the perceptions of Hispanic tenure track faculty in New Mexico regarding their educational experiences, family background, and life experiences that empowered them to become professors. It is a qualitative study in which Hispanic tenure and/or tenure track faculty were interviewed. This study identifies institutional, group, and individual barriers to minority faculty positions in academia as well as assets that can be attributed to success. The literature review identifies barriers and assets in detail and provides extensive documentation regarding the continual difficulties that institutions of higher education have in diversifying the faculty. A conceptual model, entitled the Doors of Perception, was developed, and the lenses of cultural density, cultural capital, and cultural fluidity are used to conceptualize the experiences of Hispanic faculty who are from high-density cultural environments and who attended high-density minority schools. Unlocking Hispanic Access vii Table of
Role of convergent activation of glutamatergic and dopaminergic systems in the nucleus accumbens in the development of methamphetamine psychosis and dependence.

Methamphetamine (Meth) abuse can result in long-lasting psychosis and dependence. The nucleus accumbens (NAc), which controls psychomotor and reward behaviours, is an important interface between the limbic system and receives convergent projections from dopaminergic and glutamatergic terminals. This study investigated the involvements of dopaminergic and glutamatergic transmission in the development of Meth psychosis and dependence by using tyrosine hydroxylase heterozygous mutant (TH+/-) mice and N-methyl-d-aspartate receptor knockout (NR2A-/-) mice. Repeated treatment with Meth (1 mg/kg s.c.) for 7 d in wild-type mice led to the development of behavioural abnormalities such as hyperactivity, sensory motor gating deficits and place preference. Associated with the behavioural changes, repeated treatment with Meth led to protein kinase A activation and phosphorylation of Ca2+/calmodulin kinase II and cyclic AMP response element binding protein in the NAc. In contrast, TH+/- and NR2A-/- mice displayed neither behavioural abnormalities nor activation of intracellular signalling pathways in the NAc. These results suggest that both dopaminergic and glutamatergic transmission play a crucial role in the development of Meth psychosis and dependence, which are associated with convergent activation of intracellular signalling pathways in the NAc.
RISK ESTIMATION OF HEART FAILURE PATIENTS USING WEKA

Two to three percent of the adult population suffer from heart failure and half of all patients with this diagnosis die within four years. To minimize life-threatening situations and costs of treatment, it is interesting to predict if a patient with known heart failure could die soon. For these reasons risk estimation of heart failure patients on the basis of collected data is described in this paper. The risk estimation makes use of some data mining techniques which are implemented in open source software tool Weka Waikato Environment for Knowledge Analysis. A description of used risk estimation models based on data mining techniques and experimental results showing the performance of these models are also given.