Miracle-dz/UniFAR-FTUs huggingface dataset:
Release of Hepatic Plasmodium yoelii Merozoites into the Pulmonary Microvasculature

Plasmodium undergoes one round of multiplication in the liver prior to invading erythrocytes and initiating the symptomatic blood phase of the malaria infection. Productive hepatocyte infection by sporozoites leads to the generation of thousands of merozoites capable of erythrocyte invasion. Merozoites are released from infected hepatocytes as merosomes, packets of hundreds of parasites surrounded by host cell membrane. Intravital microscopy of green fluorescent protein–expressing P. yoelii parasites showed that the majority of merosomes exit the liver intact, adapt a relatively uniform size of 12–18 μm, and contain 100–200 merozoites. Merosomes survived the subsequent passage through the right heart undamaged and accumulated in the lungs. Merosomes were absent from blood harvested from the left ventricle and from tail vein blood, indicating that the lungs effectively cleared the blood from all large parasite aggregates. Accordingly, merosomes were not detectable in major organs such as brain, kidney, and spleen. The failure of annexin V to label merosomes collected from hepatic effluent indicates that phosphatidylserine is not exposed on the surface of the merosome membrane suggesting the infected hepatocyte did not undergo apoptosis prior to merosome release. Merosomal merozoites continued to express green fluorescent protein and did not incorporate propidium iodide or YO-PRO-1 indicating parasite viability and an intact merosome membrane. Evidence of merosomal merozoite infectivity was provided by hepatic effluent containing merosomes being significantly more infective than blood with an identical low-level parasitemia. Ex vivo analysis showed that merosomes eventually disintegrate inside pulmonary capillaries, thus liberating merozoites into the bloodstream. We conclude that merosome packaging protects hepatic merozoites from phagocytic attack by sinusoidal Kupffer cells, and that release into the lung microvasculature enhances the chance of successful erythrocyte invasion. We believe this previously unknown part of the plasmodial life cycle ensures an effective transition from the liver to the blood phase of the malaria infection.
Content-based Coding of Videophone Sequences Using Automatic Face Detection

A content–based coding scheme for the transmission of videophone sequences at very low bit rates conforming to the MPEG–4 standard is presented. The goal is to improve the image quality in the facial area of a person, at the expense of a lower quality in the remaining image, which is subjectively less important for the communication partner. In a first step, the face of the talking person is detected automatically. Then, each image is coded and transmitted as two different video object planes (VOP): the face VOP is formed by the facial area, the residual VOP by the remaining image. Thus, a large amount of the available bit rate can be used for coding the face VOP in good quality, while the residual VOP is coded at a lower quality. Using typical videophone sequences and compared to a standard scheme which codes the whole image at the same quality, the proposed scheme shows significant improvements of the image quality in the facial area.
Becoming Resilient: Promoting the Mental Health and Well-Being of Immigrant Women in a Canadian Context

This paper reports on grounded theory findings that are relevant to promoting the mental health and well-being of immigrant women in Canada. The findings illustrate how relationships among settlement factors and dynamics of empowerment had implications for “becoming resilient” as immigrant women and how various health promotion approaches enhanced their well-being. Dimensions of empowerment were embedded in the content and process of the feminist health promotion approach used in this study. Four focus groups were completed in Toronto, Ontario, Canada with 35 racialized immigrant women who represented diverse countries of origin: 25 were from Africa; others were equally represented from South Asia (5), Asia (5), and Central or South America and the Caribbean (5). Participants represented diverse languages, family dynamics, and educational backgrounds. One focus group was conducted in Somali; three were conducted in English. Constructivist grounded theory, theoretical sampling, and a critical feminist approach were chosen to be congruent with health promotion research that fostered women's empowerment. Findings foreground women's agency in the study process, the ways that immigrant women name and frame issues relevant to their lives, and the interplay among individual, family, community, and structural dynamics shaping their well-being. Implications for mental health promotion are discussed.
An adaptive multimicroprocessor array computing structure for radar signal processing applications

This paper describes an array processor designed for signal processing in radar applications. The processor consists of a large number of microprocessor-based processing elements and is designed to be adaptive in real-time processing requirements. The processing problem has been considered to have a quite specific data organization and data rate which can be exploited in the architectural design. Parallel processing is necessary and a pipelined approach was adopted. A generalized pipeline philosophy is described which leads to a description of the hardware structure. The development of a single level control structure with separate generation of opcodes and addresses is noted.
"How do I know what I have to do?": the role of the inquiry culture in requirements communication for distributed software development projects

As software specifications for complex systems are practically never 100% complete and consistent, the recipient of the specification needs domain knowledge in order to decide which parts of the system are specified clearly and which parts are specified ambiguously and thus need inquiry to achieve a more detailed specification. In this paper we classify 16 different situations (states) of requirements communication and analyze, based on a state diagram, how a mature inquiry culture can help to initiate transitions from undesirable states into more desirable states. In a case study the inquiry practices of a very large software development organization are shown. Knowledge networks within the organization play an important role in building up a mature inquiry culture.
The software interface for a cluster interconnect based on the Kautz digraph

The Kautz digraph (Elspas et al., 1968) has been described as the ideal communication network for parallel computers, but it is generally unknown in the engineering community, and has never previously been used in a commercial product. We will define and characterize it in relation to HPC clusters, then discuss some of the implementation issues encountered in developing it as a large-scale cluster interconnect. The software interface to this interconnect is explained with some initial experiences with performance benchmarks.
Mining Specific and General Features in Both Positive and Negative Relevance Feedback

Abstract : User relevance feedback is usually utilized by Web systems to interpret user information needs and retrieve effective results for users. However, how to discover useful knowledge in user relevance feedback and how to wisely use the discovery knowledge are two critical problems. In TREC 2009, we participated in the Relevance Feedback Track and experimented a model consisting of two innovative stages: one for subject-based query expansion to extract pseudo-relevance feedback; one for relevance feature discovery to find useful patterns and terms in relevance judgements to rank documents. In this paper, the detailed description of our model is given, as well as the related discussions for the experimental results.
Predicting rhesus monkey eye movements during natural-image search

There are three prominent factors that can predict human visual-search behavior in natural scenes: the distinctiveness of a location (salience), similarity to the target (relevance), and features of the environment that predict where the object might be (context). We do not currently know how well these factors are able to predict macaque visual search, which matters because it is arguably the most popular model for asking how the brain controls eye movements. Here we trained monkeys to perform the pedestrian search task previously used for human subjects. Salience, relevance, and context models were all predictive of monkey eye fixations and jointly about as precise as for humans. We attempted to disrupt the influence of scene context on search by testing the monkeys with an inverted set of the same images. Surprisingly, the monkeys were able to locate the pedestrian at a rate similar to that for upright images. The best predictions of monkey fixations in searching inverted images were obtained by rotating the results of the model predictions for the original image. The fact that the same models can predict human and monkey search behavior suggests that the monkey can be used as a good model for understanding how the human brain enables natural-scene search.
MSRA at TAC 2011: Entity Linking

The Knowledge Base Population task aims at advancing the state of the art for systems that automatically discover information about named entities and then incorporate this information in a knowledge source. The overall task of populating a knowledge base is decomposed into two related tasks: Entity Linking, where names must be aligned to entities in the KB, and Slot Filling, which involves mining information about entities from text. We participated in the first task. The entity linking task requires either linking entity mentions in the documents to entries in the Knowledge Base (KB) or highlighting these mentions as non-KB (NIL) entries. The task includes particularly difficult cases like ambiguous mentions (e.g George Bush), aliases (e.g Angela Kasner, more known as Angela Merkel) or even examples of both (e.g ABC). In addition, in order to create new KB entries, the task further requires the participating systems to group NIL mentions referring to the same entities together. As has been done in most entity linking systems, our approach consists of two steps: Candidate generation and candidate disambiguation. For candidate generation, we make use of a recall-oriented retrieval model; and for candidate disambiguation, we treat it as a supervised learning problem. As for the details of grouping the NIL mentions, please refer to (Zhang et al., 2011). We jointly participate in KBP 2011 with the I2INUS team. However, in this paper, we only report the results of the MSRA submissions. The joint submission is reported in (Zhang et al., 2011). In our participation, we are interested in answering the following question: Can an retrieval-based method do a good job for candidate generation? If the answer is YES, we can then leverage many research results with the area of information retrieval.
The Influence of IL-10 and TNFα on Chondrogenesis of Human Mesenchymal Stromal Cells in Three-Dimensional Cultures

Chondrogenic differentiated mesenchymal stromal cells (MSCs) are a promising cell source for articular cartilage repair. This study was undertaken to determine the effectiveness of two three-dimensional (3D) culture systems for chondrogenic MSC differentiation in comparison to primary chondrocytes and to assess the effect of Interleukin (IL)-10 and Tumor Necrosis Factor (TNF)α on chondrogenesis by MSCs in 3D high-density (H-D) culture. MSCs were isolated from femur spongiosa, characterized using a set of typical markers and introduced in scaffold-free H-D cultures or non-woven polyglycolic acid (PGA) scaffolds for chondrogenic differentiation. H-D cultures were stimulated with recombinant IL-10, TNFα, TNFα + IL-10 or remained untreated. Gene and protein expression of type II collagen, aggrecan, sox9 and TNFα were examined. MSCs expressed typical cell surface markers and revealed multipotency. Chondrogenic differentiated cells expressed cartilage-specific markers in both culture systems but to a lower extent when compared with articular chondrocytes. Chondrogenesis was more pronounced in PGA compared with H-D culture. IL-10 and/or TNFα did not impair the chondrogenic differentiation of MSCs. Moreover, in most of the investigated samples, despite not reaching significance level, IL-10 had a stimulatory effect on the type II collagen, aggrecan and TNFα expression when compared with the respective controls.
Human Detection Using Wavelet-Based CS-LBP and a Cascade of Random Forests

In this paper, we propose a novel human detection approach combining wavelet-based center symmetric LBP (WCS-LBP) with a cascade of random forests. To detect human regions, we first extract three types of WCS-LBP features from a scanning window of wavelet transformed sub-images to reduce the feature dimension. Then, the extracted WCS-LBP descriptors are applied to a cascade of random forests, which are ensembles of random decision trees. Using a cascade of random forests with WCS-LBP, human detection is performed in near real-time, and the detection accuracy is also increased, as compared to combinations of other features and classifiers. The proposed algorithm is successfully applied to various human and non-human images from the INRIA dataset, and it performs better than other related algorithms.
Loss distributions modeling for motor TPL insurance class using Gaussian Mixture Method and EM Algorithm

The motor insurance is an important branch of non-life insurance in many countries; in some of them, coming first in total premium income category (in Romania, for example). In this paper we present the Gaussian mixture method to model the loss distribution of data from motor compulsory third part liability insurance. The parameters of the mixture are estimated using the Expectation Maximization (EM) algorithm.
Analysis of Energy Efficiency of Compressive Sensing in Wireless Sensor Networks

Improving the lifetime of wireless sensor networks (WSNs) is directly related to the energy efficiency of computation and communication operations in the sensor nodes. Compressive sensing (CS) theory suggests a new way of sensing the signal with a much lower number of linear measurements as compared to the conventional case provided that the underlying signal is sparse. This result has implications on WSN energy efficiency and prolonging network lifetime. In this paper, the effects of acquiring, processing, and communicating CS-based measurements on WSN lifetime are analyzed in comparison to conventional approaches. Energy dissipation models for both CS and conventional approaches are built and used to construct a mixed integer programming framework that jointly captures the energy costs for computation and communication for both CS and conventional approaches. Numerical analysis is performed by systematically sampling the parameter space (i.e., sparsity levels, network radius, and number of nodes). Our results show that CS prolongs network lifetime for sparse signals and is more advantageous for WSNs with a smaller coverage area.
An Energy Efficiency Node Scheduling Model for Spatial-Temporal Coverage Optimization in 3D Directional Sensor Networks

Low-power green communication has become the utmost important and promising research topic for energy efficiency communications. In this paper, we investigated the energy efficiency green communication in 3-D directional sensor networks. Coverage and schedule are two fundamental research issues for energy efficiency in wireless sensor networks. In network lifetime sensitive scenarios, how to consider the network lifetime as the constraint to trade coverage for network lifetime has not been well investigated. Based on the 3-D sensing model of directional sensors, we address this problem of maximizing the spatial-temporal coverage by scheduling sensors' activity after they have been deployed. First, two elementary region generation approaches, grid-based algorithm, and position-based algorithm are proposed. Then, a spatial-temporal coverage optimization scheduling (STCOS) algorithm is designed to obtain the whole network coverage maximized. Finally, a set of simulation experiments are put forward to evaluate the performance of the proposed elementary regions generation schemes and STCOS algorithms. Experimental results show that compared with the grid-based elementary region generation scheme, the position-based scheme significantly reduces the algorithmic complexity and is time consuming while generating elementary regions in network. Further simulation results show that the proposed STCOS scheme effectively enhances the performance of spatial-temporal coverage optimization.
Data Mining on Moving Object Trajectories

Trajectory means a path followed by a moving vehicles or object. Spatio temporal clustering is a process of grouping objects based on their spatial or temporal similarity. It is also known as Trajectory or mobility data. Existing work have mainly focused on moving object is completely based on clustering algorithms which only gives us that at any particular time how many vehicles are present at any particular location, but it does not generate any useful patterns. In this project we deal with the analysis, pre processing and modeling of traffic data in moving object database for traffic management system. And we are using clustering techniques after that we are classifying our data set to generate useful patterns using C 4.5 Algorithm. In clustering we are using Incremental DBSCAN algorithms because as we know moving data always gets updated these data are dynamic in nature.
Data Extrapolation Using Genetic Programming to Matrices Singular Values Estimation

In mathematical models where the dimensions of the matrices are very large, the use of classical methods to compute the singular values is very time consuming and requires a lot of computational resources. In this way, it is necessary to find new faster methods to compute the singular values of a very large matrix. We present a method to estimate the singular values of a matrix based on genetic programming (GP). GP is an approach based on the evolutionary principles of the species. GP is used to make extrapolations of data out of sample data. The extrapolations of data are achieved by irregularity functions which approximate very well the trend of the sample data. GP produces from just simple's functions, operators and a fitness function, complex mathematical expressions that adjust smoothly to a group of points of the form (Xj,yj). We obtain amazing mathematical formulas that follow the behaviour of the sample data. We compare our algorithm with two techniques: the linear regression and non linear regression approaches. Our results suggest that we can predict with some percentage of error the largest singular values of a matrix without computing the singular values of the whole matrix and using only some random selected columns of the matrix.
Code generation from a domain-specific language for C-based HLS of hardware accelerators

As today's computer architectures are becoming more and more heterogeneous, a plethora of options including CPUs, GPUs, DSPs, reconfigurable logic (FPGAs), and other application-specific processors come into consideration for close-to-sensor processing. Especially, in the domain of image processing on mobile devices, among numerous design challenges, a very stringent energy budget is of utmost importance, making embedded GPUs and FPGAs ideal targets for implementation. Recently, the HIPAcc framework was proposed as a means for automatic code generation of image processing algorithms for embedded GPUs, based on a Domain-Specific Language (DSL). Despite of huge advancements in High-Level Synthesis (HLS) for FPGAs, designers are still required to have detailed knowledge about coding techniques and the targeted architecture to achieve efficient solutions. As a remedy, in this work, we propose code generation techniques for C-based HLS from a common high-level DSL description targeting FPGAs. Our approach includes FPGA-specific memory architectures for handling point and local operators, numerous high-level transformations, and automatic test bench generation. We evaluate our approach by comparing the resulting hardware accelerators to existing frameworks in terms of performance and resource requirements. Moreover, we assess the achieved energy efficiency in contrast to software implementations, generated by HIPAcc from the same code base, executed on GPUs.
Investigation of Co-training Views and Variations for Semantic Role Labeling

Co-training, as a semi-supervised learning method, has been recently applied to semantic role labeling to reduce the need for costly annotated data using unannotated data. A main concern in co-training is how to split the problem into multiple views to derive learning features, so that they can effectively train each other. We investigate various feature splits based on two SRL views, constituency and dependency, with different variations of the algorithm. Balancing the feature split in terms of the performance of the underlying classifiers showed to be useful. Also, co-training with a common training set performed better than when separate training sets are used for co-trained classifiers.
Powder Application in Additive Manufacturing of Metallic Parts

This chapter is going to give up-to-date overview of development in the field of addi - tive manufacturing (AM) of metallic components. There will be briefly mentioned input materials and specific requirement for the input materials (powders and wires). General technology process overview will be presented here, and selective laser melting (SLM) technology and beam melting technologies will be described. Advantages of 3D printing technology will be explained in terms of special designs; special properties and gener ally multifunctional components of production possibilities will be shown. Postprinting procedures leading to improvement of mechanical properties of printed components like thermal or thermomechanical treatment will also be mentioned here.
The Aesthetics of Diagrams

Diagrammatic representations are omnipresent and are used in various application domains. One of their major goal, in particular for information visualization, is to make data visual in a way that a spectator can easily understand the graphical encoding to finally derive insights from the data. As we see, there are various different ways to visually depict data by using visual features in various combinations. In this paper we come up with some thoughts about existing diagram styles, for which we first discuss the benefits and drawbacks of each of them focusing on aesthetics based on readability. Additionally, we describe some initial results on the aesthetics of diagrams which we recorded in a web-based experiment. In this, we asked participants to vote for one of two given diagrams of a given repertoire of 70 of them covering all examined aspects which focuses more on aesthetics in the sense of beauty, not readability. The major result of this experiment unhides a trend towards colored, 3D, and radial diagrams which stands somewhat in contrast to readability user studies in information visualization oftentimes tending towards 2D and Cartesian diagrams for data exploration.