Miracle-dz/UniFAR-FTUs huggingface dataset:
A generative probabilistic approach to visualizing sets of symbolic sequences

There is a notable interest in extending probabilistic generative modeling principles to accommodate for more complex structured data types. In this paper we develop a generative probabilistic model for visualizing sets of discrete symbolic sequences. The model, a constrained mixture of discrete hidden Markov models, is a generalization of density-based visualization methods previously developed for static data sets. We illustrate our approach on sequences representing web-log data and chorals by J.S. Bach.
Design and implementation of a high quality TRNG in FPGA

This paper focuses on the design and implementation of a high quality true-random number generator (TRNG) in FPGA. Various practical issues which we encountered are highlighted and the influence of the various parameters on the functioning of the TRNG are discussed. We also propose a few values for the parameters which use the minimum amount of the resources but still pass common random number generator test batteries such as DieHard and TestU01.
Delta-compressed caching for overcoming the write bandwidth limitation of hybrid main memory

Limited PCM write bandwidth is a critical obstacle to achieve good performance from hybrid DRAM/PCM memory systems. The write bandwidth is severely restricted in PCM devices, which harms application performance. Indeed, as we show, it is more important to reduce PCM write traffic than to reduce PCM read latency for application performance. To reduce the number of PCM writes, we propose a DRAM cache organization that employs compression. A new delta compression technique for modified data is used to achieve a large compression ratio. Our approach can selectively and predictively apply compression to improve its efficiency and performance. Our approach is designed to facilitate adoption in existing main memory compression frameworks. We describe an instance of how to incorporate delta compression in IBM's MXT memory compression architecture when used for DRAM cache in a hybrid main memory. For fourteen representative memory-intensive workloads, on average, our delta compression technique reduces the number of PCM writes by 54.3%, and improves IPC performance by 24.4%.
The principle of discrimination: Investigating perceptions of soldiers

The principle of discrimination states that soldiers are legitimate targets of violence in war, whereas civilians are not. Is this prescriptive rule reflected in the descriptive judgments of laypeople? In two studies (Ns = 300, 229), U.S. Mechanical Turk workers were asked to evaluate the character traits of either a soldier or a civilian. Participants also made moral judgments about scenarios in which the target individual (soldier or civilian) killed or was killed by the enemy in war. Soldiers were consistently viewed as more dangerous and more courageous than civilians (Study 1). Participants also viewed killing by (and of) soldiers as more permissible than killing by (and of) civilians, in line with the principle of discrimination (Study 1). Altering the war context to involve a clearly just and unjust side (in Study 2) did not appear to moderate the principle of discrimination in moral judgment, although soldiers and civilians on the just side were evaluated more positively overall. However, the soldiers on the unjust side of the war were not attributed greater courage than were civilians on the unjust side. Theoretical and practical implications of these descriptive findings are discussed.
Kernel collaborative representation-based classifier for face recognition

Recent research has shown that collaborative representation-based classifier (CRC) can lead to promising results for the classification of face images. However, CRC is conducted in the original image space rather than the nonlinear high dimensional feature space in which features belonging to the same class are better grouped together and thus can be easily separable. To address this problem, this paper presents a novel classifier, Kernel Collaborative Representation-based Classifier (KCRC), by incorporating the kernel trick into the framework of CRC. Extensive experiments on both the AT&T and the FERET face databases demonstrate the priority of KCRC to CRC and several state-of-the-art methods.
Scalable parallel geometric algorithms for coarse grained multicomputers

Whereas most of the literature assumes that the number of processors p is a function of the problem size n, in scalable algorithms p becomes a parameter of the time complexity. This is a more realistic modelisation of real parallel machines and yields optimal algorithms, for the case that n H p, where H is a function depending on the architecture of the interconnexion network. In this paper we present scalable algorithms for a number of geometric problems, namely lower envelope of line segments, 2D-nearest neighbour, 3D-maxima, 2D-weighted dominance counting area of the union of rectangles, 2D-convex hull. The main idea of these algorithms is to decompose the problem in p subproblems of size 0(F(n;p) + f(p)), with f(p) 2 F(n;p) , which can be solved independently using optimal sequential algorithms. For each problem we present a spatial decomposition scheme based on some geometric observations. The decomposition schemes have in common that they can be computed by globally sorting the entire data set at most twice. The data redundancy of f(p) duplicates of data elements per processor does not increase the asymptotic time complexity and ranges for the algorithms presented in this paper, from p to p2. The algorithms do not depend on a specific architecture,they are easy to implement and in practice efficient as experiments show.
A taxonomy of everyday grasps in action

Grasping has been well studied in the robotics and human subjects literature, and numerous taxonomies have been developed to capture the range of grasps employed in work settings or everyday life. But how completely do these taxonomies capture grasping actions that we see every day? We asked two subjects to monitor every action that they performed with their hands during a typical day, as well as to role-play actions important for self-care, rehabilitation, and various careers and then to classify all grasping actions using existing taxonomies. While our subjects were able to classify many grasps, they also found a collection of grasps that could not be classified. In addition, our subjects observed that single entries in the taxonomy captured not one grasp, but many. When we investigated, we found that these grasps were distinguished by features related to the grasping action, such as intended motion, force, and stiffness - properties also needed for robot control. We suggest a format for augmenting grasp taxonomies that includes features of motion, force, and stiffness using a language that can be understood and expressed by subjects with light training, as would be needed, for example, for annotating examples or coaching a robot. This paper describes our study, the results, and documents our annotated database.
Lithium Substitution and the Distribution of Sodium in the Rat Tail Artery

The exchange of lithium (Li) with sodium (Na) was explored in an effort to quantify cellular Na in the rat tail artery. Two phases of cellular Na were demonstrated by the kinetics of exchange at 37°C. Cellular Na, was rapidly lost following cell disruption and increased in proportion to potassium (K) loss during conventional Na enrichment in a K-free medium. Cooling to 2°C almost abolished the transmembrane movement of Li; therefore, a simple 30-minute incubation in cold Li medium removed extracellular but not cellular Na. To prove that the residual Na after such a wash in cold Li was cellular, we demonstrated that Na exchanges readily with Li even at 2°C after cells are disrupted, increases slowly in proportion to K loss during prolonged cooling, and increases rapidly in a precise one-to-one ratio with K loss when active Na transport is stopped by incubation in a K-free medium. Cellular Na in the normal artery was about 20–25 mmoles/kg dry weight when cellular K was about 225 mmoles/kg dry weight. In arteries from rats with deoxycorticosterone acetate-induced hypertension of 8 weeks duration, a 13% fall in cellular K was balanced one to one by an increase in cellular Na.
A Co-Integration Analysis Between Electricity Consumption and Economic Development in Hebei Province

Electricity is a convenient and clean energy which can provide strong support for the development of all walks of life. The power development should maintain coordination with the regional economic development, in which power development may advance sometimes. Hebei province is a typical resource-based area in China, and its power consumption is closely related to economic development, which makes it important to study the relationship between electricity and economic development in Hebei. In order to select the economic factors affecting electricity demand, the gray correlation analysis is used to analyse the correlation among different factors. And then, a long-term equilibrium model between electricity consumption and economic factors is proposed through co-integration analysis. The analysis result showed that the electricity consumption, GDP, the level of residential consumption, efficiency levels and economic structures have a long-run equilibrium relationship in Hebei Province, in which the economic structure has the strongest impact on the electricity consumption, followed by GDP, energy consumption intensity and the residential consumption level. Currently, the impact of the economic restructuring on electricity demand in Hebei cannot be overlooked. Furthermore, this model can be used to give a reference to Hebei Electric Power Planning.
A Spatio-temporal Regression Model for the Analysis of Functional MRI Data

The standard method for analyzing functional magnetic resonance imaging (fMRI) data applies the general linear model to the time series of each voxel separately. Such a voxelwise approach, however, does not consider the spatial autocorrelation between neighboring voxels in its model formulation and parameter estimation. We propose a spatio-temporal regression analysis for detecting activation in fMRI data. Its main features are that (1) each voxel has a regression model that involves the time series of the neighboring voxels together with its own, (2) the regression coefficient assigned to the center voxel is estimated so that the time series of these multiple voxels will best fit the model, (3) a generalized least squares (GLS) method was employed instead of the ordinary least squares (OLS) to put intrinsic autocorrelation structures into the model, and (4) the underlying spatial and temporal correlation structures are modeled using a separable model which expresses the combined correlation structures as a product of the two. We evaluated the statistical power of our model in comparison with voxelwise OLS/GLS models and a multivoxel OLS model. Our model's power to detect clustered activation was higher than that of the two voxelwise models and comparable to that of the multivoxel OLS. We examined the usefulness and goodness of fit of our model using real experimental data. Our model successfully detected neural activity in expected brain regions and realized better fit than the other models. These results suggest that our spatio-temporal regression model can serve as a reliable analysis suited for the nature of fMRI data.
Evaluation of interest point detectors for image information extraction

Interest points extraction and matching is a common task in many computer vision based application, which are used in different domains, such as 3D reconstruction, object recognition, or tracking. We present an evaluation of current state of the art about interest point extraction algorithms to measure several parameters, such as detection quality, invariance to rotation and scale transformation, and computational efficiency.
LAICOS: an open source platform for personalized social web search

In this paper, we introduce LAICOS, a social Web search engine as a contribution to the growing area of Social Information Retrieval (SIR). Social information and personalization are at the heart of LAICOS. On the one hand, the social context of documents is added as a layer to their textual content traditionally used for indexing to provide Personalized Social Document Representations. On the other hand, the social context of users is used for the query expansion process using the Personalized Social Query Expansion framework (PSQE) proposed in our earlier works. We describe the different components of the system while relying on social bookmarking systems as a source of social information for personalizing and enhancing the IR process. We show how the internal structure of indexes as well as the query expansion process operated using social information.
Intervening into mechanisms: Prospects and challenges

In contemporary philosophy of science, the consensus view seems to be that scientific explanations describe mechanisms responsible for the phenomena to be explained. Two kinds of explanatory relevance figure in mechanistic accounts of explanation: causal (etiological) and constitutive. Following prominent accounts, it seems natural to analyze both these relations in terms of systematic interventions into some factor X with respect to another factor Y. However, such interventions are tailored to uncover causal relations only. Construing the constitutive relationship between parts and wholes in terms of interventions thus raises metaphysical, conceptual, and epistemological questions. We here review the barriers that intervention ‐ based inquiry into mechanisms encounters and consider some solutions.
Trusted user authentication scheme combining password with fingerprint for mobile devices

Mobile equipment (ME) playing an important role of bridge between wireless network and mobile user has been facing more and more security threats. Trusted mobile platform (TMP) was proposed by TCG (trusted computing group) as a new mechanism to enhance the security of the resource-constrained ME. In this paper, we embark a new study on constructing a TMP according to ME's feature, and performing mutual authentication in mobile user domain. A smart-phone's processor is used as an example to demonstrate the constructing of TMP, along with which three methods for adding trusted platform module (TPM) in ME are presented respectively. In the framework of TMP, we also propose a user authentication scheme combining password and fingerprint with the USIM (universal subscriber identity module). The proposed scheme is validated through a performance analysis and experimental test. The validation result shows that our approach offers better efficiency and advanced security over the authentication scheme presented in TMP's draft standard. It also outperforms TCG's user authorization scheme by providing improved security, flexibility and universality.
Differential Induction in Telomerase Activity among Bladder Cancer Patients and Controls on γ-Radiation

Expression of telomerase is one of the hallmarks of tumor cells and has been used as a diagnostic biomarker and a therapeutic target in cancer. Novel findings have shown that telomerase activation in normal human epithelial cells may affect expression of several cancer-related genes, such as growth-related genes and c-myc gene, suggesting a possible role of telomerase in tumor initiation. Therefore, we hypothesized that individuals who are sensitive to mutagen challenge in terms of induced telomerase activity might have increased cancer risk. We tested this hypothesis in a bladder cancer case-control study (51 cases and 51 matched controls) by measuring baseline and γ-radiation–induced telomerase activities in peripheral blood lymphocytes. We found a significantly higher γ-radiation–induced telomerase activity in bladder cancer cases compared with the controls (1.34 versus 1.23; P = 0.044). A similar finding was also observed using the normalized telomerase activity (ratio of γ-radiation induced versus baseline; 1.49 versus 1.19; P < 0.001). In further categorizing the telomerase activity using 75% of the normalized value in the controls as a cutoff point, we found a significantly increased risk for bladder cancer associated with higher induced telomerase activity (adjusted odds ratio, 3.62; 95% confidence interval, 1.38-9.51). In quartile analysis, a dose-response association was noted between the induced telomerase activity and increased bladder cancer risk (Ptrend = 0.005). Our findings provide the first evidence linking the mutagen-induced telomerase activity in peripheral blood lymphocytes to the risk of bladder cancer, which warrants further investigation in large-sized studies and other cancer types. (Cancer Epidemiol Biomarkers Prev 2007;16(3):606–9)
Cyclohexylmethyl Flavonoids Suppress Propagation of Breast Cancer Stem Cells via Downregulation of NANOG

Breast cancer stem cells (CSCs) are highly tumorigenic and possess the capacity to self-renew. Recent studies indicated that pluripotent gene NANOG involves in regulating self-renewal of breast CSCs, and expression of NANOG is correlated with aggressiveness of poorly differentiated breast cancer. We initially confirmed that breast cancer MCF-7 cells expressed NANOG, and overexpression of NANOG enhanced the tumorigenicity of MCF-7 cells and promoted the self-renewal expansion of CD24−/lowCD44+ CSC subpopulation. In contrast, knockdown of NANOG significantly affected the growth of breast CSCs. Utilizing flow cytometry, we identified five cyclohexylmethyl flavonoids that can inhibit propagation of NANOG-positive cells in both breast cancer MCF-7 and MDA-MB231 cells. Among these flavonoids, ugonins J and K were found to be able to induce apoptosis in non-CSC populations and to reduce self-renewal growth of CD24−/lowCD44+ CSC population. Treatment with ugonin J significantly reduced the tumorigenicity of MCF-7 cells and efficiently suppressed formation of mammospheres. This suppression was possibly due to p53 activation and NANOG reduction as either addition of p53 inhibitor or overexpression of NANOG can counteract the suppressive effect of ugonin J. We therefore conclude that cyclohexylmethyl flavonoids can possibly be utilized to suppress the propagation of breast CSCs via reduction of NANOG.
Comparing decision fusion paradigms using -NN based classifiers, decision trees and logistic regression in a multi-modal identity verification ap plication

The contribution of this paper is threefold: (1) to formulate a decision fusion problem encountered in the design of a multi-modal identity verification system as a particular classification problem, (2) to propose three simple classifiers to solve this problem, (3) to compare the relative performances of the proposed classifiers. The multi-modal identity verification system under consideration is built of d modalities in parallel, each one delivering as output a scalar number, called score, stating how well the claimed identity is verified. A fusion module receiving as input the d scores has to take a binary decision: acceptor reject identity. This fusion problem has been solved using three different classifiers, respectively based on the k-nearestneighbor (k-NN) classifier, decision trees and logistic regression. The performances of these different fusion modules have been evaluated and compared on a multi-modal database, containing both vocal and visual modalities.
Doubly Differential Communication Assisted with Cooperative Relay

Doubly differential modem turns out to be a promising technology for coping with unknown frequency offsets with the pay of signal-to-noise ratio (SNR). In this paper, we propose to compensate the SNR loss by employing the detection-forward cooperative relay. The receiver can employ two kind of combiners to attain the achievable spatial diversity-gain. Performance analysis is carefully investigated for the Rayleigh-fading channel. It is shown that the SNR-compensation is satisfied for the large-SNR range.
A Declarative Framework for the Analysis of Routing Protocols

Researchers have recently shown that declarative database query languages, such as Datalog, could naturally be used to specify and implement network protocols and services. This recognition has opened new avenues for analyzing routing protocols given that the semantics of Datalog rules are well understood, and different forms of declarative programming have been developed to solve NP-hard problems and could be applied. In this paper, we present a declarative framework to implement routing protocols and analyze desirable properties. The main components for the analysis include a protocol model, where the protocol execution is specified, a network communication model, and an analysis model, which specifies a network configuration and the set of properties to verify. We demonstrate the generality of our framework by implementing three broad classes of protocols – a path vector, a link state, and a recently proposed Mobile Ad-Hoc Networks (MANET) routing protocol – and showing how different properties, including convergence, loopfree, and disjoint path routing, can be verified.
Cache-enabled small cell networks with local user interest correlation

In this paper we study cache-enabled small cell networks (SCNs) with local regularly requested content sampling to take into account local user interests for the cache decisions. We consider Zipf-like local content popularity with variables indicating the correlation level of user interests in the same region. Based on stochastic spatial models for the small cell base station (SCBS) and user distribution, we give analytical result on the cache service probability which is the probability of an arbitrary user to find its requested content cached in its nearby SCBSs. The tradeoff between the service probability and the sampling cost is discussed with the optimal sampling distance given by maximizing the service probability under the cost constraint. Numerical results are shown with different correlation levels of local user interests, which validate our analysis on the service-cost tradeoff.
Understanding the Evolution of Multimorbidity: Evidences from the North West Adelaide Health Longitudinal Study (NWAHS)

Objective The aim of this study is to describe the evolution of multimorbidity. Study Design and Setting Data from 1854 South Australians who participated in the North West Adelaide longitudinal Health Study(NWAHS) was collected between baseline (2000–2002) and follow-up (2008–2010). Status for eight chronic diseases (CDs) was determined by biomedical measurement or self-report. Chronic disease (CD) mean age of occurrence and order of appearance was investigated. Results The prevalence of multimorbidity increased from 32% to 64% during the 7.8±1.1 years of follow-up. The estimated mean age of onset of a new CD was significantly older for hypertension, cardiovascular disease (CVD) and chronic obstructive pulmonary disease (COPD) and younger for hypercholesterolemia, asthma and other mental problem. Hypercholesterolemia was more likely to develop as a first than as a subsequent CD (39%vs.16%, p<0.0001) while CVD (1%vs.5%, p<0.0001), diabetes (5%vs.11%, p<0.001) and COPD (6%vs.16%, p<0.0001) were less likely. The presence of mood disorders at baseline was associated with an increased risk of developing other mental disorders (36%vs.12%, p<0.0001), diabetes (18%vs.9%, p<0.01) and asthma (30%vs.21%, p<0.05). Conclusion Longitudinal data could be used to study the evolution of multimorbidity and could provide information on CDs mean age of occurrence, order of appearance and impact on the development of future CDs.