Miracle-dz/UniFAR-FTUs huggingface dataset:
Inflammatory mechanisms linking periodontal diseases to cardiovascular diseases.

AIMS In this article, inflammatory mechanisms that link periodontal diseases to cardiovascular diseases are reviewed. METHODS This article is a literature review. RESULTS Studies in the literature implicate a number of possible mechanisms that could be responsible for increased inflammatory responses in atheromatous lesions due to periodontal infections. These include increased systemic levels of inflammatory mediators stimulated by bacteria and their products at sites distant from the oral cavity, elevated thrombotic and hemostatic markers that promote a prothrombotic state and inflammation, cross-reactive systemic antibodies that promote inflammation and interact with the atheroma, promotion of dyslipidemia with consequent increases in pro-inflammatory lipid classes and subclasses, and common genetic susceptibility factors present in both disease leading to increased inflammatory responses. CONCLUSIONS Such mechanisms may be thought to act in concert to increase systemic inflammation in periodontal disease and to promote or exacerbate atherogenesis. However, proof that the increase in systemic inflammation attributable to periodontitis impacts inflammatory responses during atheroma development, thrombotic events or myocardial infarction or stroke is lacking.
A Classification Based Framework for Concept Summarization

In this paper we propose a novel classification based framework for finding a small number of images that summarize a given concept. Our method exploits metadata information available with the images to get category information using Latent Dirichlet Allocation. Using this category information for each image, we solve the underlying classification problem by building a sparse classifier model for each concept. We demonstrate that the images that specify the sparse model form a good summary. In particular, our summary satisfies important properties such as likelihood, diversity and balance in both visual and semantic sense. Furthermore, the framework allows users to specify desired distributions over categories to create personalized summaries. Experimental results on seven broad query types show that the proposed method performs better than state-of-the-art methods.
Aggregate homotopy method for semi-supervised SVMs

Semi-supervised Support Vector Machines is an appealing method for using unlabeled data in classification. Based on a smooth approximation function named as aggregate function, a global aggregate homotopy method is presented in this paper. Compared to some existing algorithms, the new method is superior in no need of introducing extra variables or solving a sequence of subproblems. Moreover, the global convergence can make better local minima and then result in better prediction accuracy. Final numerical results reveals the efficiency of the method.
Industrial ecology, life cycles, supply chains: differences and interrelations

Within recent years, various concepts have arisen in environmental management that directly address the flow of material (and information) along life cycles or supply chains and thereby relate to inter-organizational management aspects. These include industrial ecology (IE), life-cycle management, closed-loop supply chains, integrated chain management and green/environmental or sustainable supply chain management. It is not clear how these concepts relate to each other and whether or how they are different. Starting with sustainable development three criteria are identified that allow the comparison of the four concepts. Building on definitions the concepts are discussed and analysed using the three criteria while also identifying a distinctive feature of each approach. The criteria reveal that the concepts take a specific approach to study material flows in their particular system boundaries. This also relates to the time frame usually applied within the concept as well as the relevant actor network taken into account. Beyond these differences, it arises that the concepts have their strengths on different levels, which leads to a framework for the interrelation of the concepts. Copyright © 2004 John Wiley & Sons, Ltd and ERP Environment.
Empty Categories in Hindi Dependency Treebank: Analysis and Recovery

In this paper, we first analyze and classify the empty categories in a Hindi dependency tree-bank and then identify various discovery procedures to automatically detect the existence of these categories in a sentence. For this we make use of lexical knowledge along with the parsed output from a constraint based parser. Through this work we show that it is possible to successfully discover certain types of empty categories while some other types are more difficult to identify. This work leads to the state-of-the-art system for automatic insertion of empty categories in the Hindi sentence.
Measuring Thematic Fit with Distributional Feature Overlap

In this paper, we introduce a new distributional method for modeling predicate-argument thematic fit judgments. We use a syntax-based DSM to build a prototypical representation of verb-specific roles: for every verb, we extract the most salient second order contexts for each of its roles (i.e. the most salient dimensions of typical role fillers), and then we compute thematic fit as a weighted overlap between the top features of candidate fillers and role prototypes. Our experiments show that our method consistently outperforms a baseline re-implementing a state-of-the-art system, and achieves better or comparable results to those reported in the literature for the other unsupervised systems. Moreover, it provides an explicit representation of the features characterizing verb-specific semantic roles.
Efficient Personalized Recommendation of Mobile Web Content Using an EM-Based Clustering Method

Many applications recommend personalized content to users based on their interests. However, personalized recommendation is time and memory consuming especially for commercial systems that have huge numbers of users, requests and big data that require complex computation. Since users do not totally have unique interests, we can cluster similar users then recommend the same items to users belonging in the same cluster. Even though clustering-based recommendations are efficient, the recommendation items to users may not be accurate. We present an Expectation-Maximization (EM) based personalized recommendation method for selecting the appropriate items efficiently and accurately. We use a browser log dataset to compare our method with personalized, k-means, and EM-based recommendations according to average rank, novelty, diversity, and time performance. Results show that based on average rank, novelty and diversity, our proposed method performs close to that of personalized, however it is less efficient than k-means. Since k-means has the worst average rank, novelty and diversity, our method is the best overall.
Serial one-step majority logic decoder for EG-LDPC code

In the modern digital system design the reliability and security of memories are essential considerations. As technology scales, memory devices become larger and more powerful error correction codes are needed to protect memories from soft errors. Low Density Parity Check (LDPC) Codes are the class of linear block codes which provide near capacity performance on large collection of data transmission channels while simultaneously feasible for implementable decoders. One specific type of LDPC codes, namely EG- LDPC are used due to their fault secure detection capability, higher reliability and lower area overhead. One of the existing methods for error detection in EG-LDPC is the one step Majority Logic Decoder (MLD) method used to detect the error in memory device itself.
Disease progression modeling using Hidden Markov Models

The development of novel treatments for many slowly progressing diseases, such as Alzheimer's disease (AD), is dependent on the ability to monitor and detect changes in disease progression. In some diseases the distinct clinical stages of the disease progress far too slowly to enable a quick evaluation of the efficacy of a given proposed treatment. To help improve the assessment of disease progression, we propose using Hidden Markov Models (HMM's) to model, in a more granular fashion, disease progression as compared to the clinical stages of the disease. Unlike many other applications of Hidden Markov Models, we train our HMM in an unsupervised way and then evaluate how effective the model is at uncovering underlying statistical patterns in disease progression by considering HMM states as disease stages. In this study, we focus on AD and show that our model, when evaluated on the cross validation data, can identify more granular disease stages than the three currently accepted clinical stages of “Normal”, “MCI” (Mild Cognitive Impairment), and “AD”.
Role of Alveolar Macrophages in Respiratory Transmission of Visna/Maedi Virus

ABSTRACT A major route of transmission of Visna/maedi virus (VMV), an ovine lentivirus, is thought to be via the respiratory tract, by inhalation of either cell-free or cell-associated virus. In previous studies, we have shown that infection via the lower respiratory tract is much more efficient than via upper respiratory tissues (T. N. McNeilly, P. Tennant, L. Lujan, M. Perez, and G. D. Harkiss, J. Gen. Virol. 88:670-679, 2007). Alveolar macrophages (AMs) are prime candidates for the initial uptake of virus in the lower lung, given their in vivo tropism for VMV, abundant numbers, location within the airways, and role in VMV-induced inflammation. Furthermore, AMs are the most likely cell type involved in the transmission of cell-associated virus. In this study, we use an experimental in vivo infection model that allowed the infection of specific segments of the ovine lung. We demonstrate that resident AMs are capable of VMV uptake in vivo and that this infection is associated with a specific up-regulation of AM granulocyte-macrophage colony-stimulating factor mRNA expression (P < 0.05) and an increase in bronchoalveolar lymphocyte numbers (P < 0.05), but not a generalized inflammatory response 7 days postinfection. We also demonstrate that both autologous and heterologous VMV-infected AMs are capable of transmitting virus after lower, but not upper, respiratory tract instillation and that this transfer of virus appears not to involve the direct migration of virus-infected AMs from the airspace. These results suggest that virus is transferred from AMs into the body via an intermediate route. The results also suggest that the inhalation of infected AMs represents an additional mechanism of virus transmission.
A Bayesian Method to Mine Spatial Data Sets to Evaluate the Vulnerability of Human Beings to Catastrophic Risk

Vulnerability of human beings exposed to a catastrophic disaster is affected by multiple factors that include hazard intensity, environment, and individual characteristics. The traditional approach to vulnerability assessment, based on the aggregate‐area method and unsupervised learning, cannot incorporate spatial information; thus, vulnerability can be only roughly assessed. In this article, we propose Bayesian network (BN) and spatial analysis techniques to mine spatial data sets to evaluate the vulnerability of human beings. In our approach, spatial analysis is leveraged to preprocess the data; for example, kernel density analysis (KDA) and accumulative road cost surface modeling (ARCSM) are employed to quantify the influence of geofeatures on vulnerability and relate such influence to spatial distance. The knowledge‐ and data‐based BN provides a consistent platform to integrate a variety of factors, including those extracted by KDA and ARCSM to model vulnerability uncertainty. We also consider the model's uncertainty and use the Bayesian model average and Occam's Window to average the multiple models obtained by our approach to robust prediction of the risk and vulnerability. We compare our approach with other probabilistic models in the case study of seismic risk and conclude that our approach is a good means to mining spatial data sets for evaluating vulnerability.
Parallel-Trellis Turbo Equalizers for Sparse-Coded Transmission over SISO and MIMO Sparse Multipath Channels

This paper describes a low-complexity turbo equalizer for coded sparse multipath channels whose length can span hundreds of symbol intervals. First, for single-input-single-output (SISO) systems, an existing parallel-trellis framework, which consists of a bank of identical regular trellises, is exploited to construct the maximum a posteriori (MAP) equalizer in the turbo equalizer. This MAP equalizer, when combined with prefiltering, can equalize a broad selection of sparse multipath channels, including those with nonminimum phase, by using only M-state trellises, where M is the constellation size. For multiple-input-multiple-output (MIMO) systems, a MIMO prefilter and a bank of M-state parallel-trellis MAP equalizers are deployed according to a layering structure. The total number of states needed is only NTM, where NT is the number of transmit antennae. For both SISO and MIMO systems, a class of binary convolutional and turbo codes having sparse generator polynomials are chosen as the coding schemes. These codes partially integrate channel interleaving with encoding, which allows a simple channel interleaver with low latency and memory storage requirement to be employed. The MAP decoders for these codes can also be implemented with the same parallel-trellis framework as the MAP equalizers. Overall, parallel processing is supported throughout the turbo equalizers. The amount of computational time reduction when compared to a turbo equalizer using a single-trellis MAP equalizer and decoder is proportional to the number of trellises in the parallel-trellis MAP equalizer and decoder. The performance of the parallel-trellis turbo equalizers is evaluated on static and Rayleigh fading sparse multipath channels via Monte Carlo simulations
TD-learning with exploration

We introduce exploration in the TD-learning algorithm to approximate the value function for a given policy. In this way we can modify the norm used for approximation, “zooming in” to a region of interest in the state space. We also provide extensions to SARSA to eliminate the need for numerical integration in policy improvement. Construction of the algorithm and its analysis build on recent general results concerning the spectral theory of Markov chains and positive operators.
Termination Analysis of C Programs Using Compiler Intermediate Languages

Modeling the semantics of programming languages like C for the automated termination analysis of programs is a challenge if complete coverage of all language features should be achieved. On the other hand, low-level intermediate languages that occur during the compilation of C programs to machine code have a much simpler semantics since most of the intricacies of C are taken care of by the compiler frontend. It is thus a promising approach to use these intermediate languages for the automated termination analysis of C programs. In this paper we present the tool KITTeL based on this approach. For this, programs in the compiler intermediate language are translated into term rewrite systems (TRSs), and the termination proof itself is then performed on the automatically generated TRS. An evaluation on a large collection of C programs shows the effectiveness and practicality of KITTeL on "typical" examples.
Negotiating the Construction and Reconstruction of Organisational Memories

This paper describes an approach to capturing organisational memory, which serves to ground an analysis of human issues that knowledge management (KM) technologies raise. In the approach presented, teams construct graphical webs of the arguments and documents relating to key issues they are facing. This supports collaborative processes which are central to knowledge work, and provides a group memory of this intellectual investment. This approach emphasises the centrality of negotiation in making interdisciplinary decisions in a changing environment. Discussion in the paper focuses on key human dimensions to KM technologies, including the cognitive and group dynamics set up by an approach, the general problem of preserving contextual cues, and the political dimensions to formalising knowledge processes and products. These analyses strongly motivate the adoption of participatory design processes for KM systems. In order to operationalise the concept of Knowledge Management (KM), numerous disciplines are now trying to analyse the processes and products of organisational knowledge, in order to clarify what tangible representations future knowledge managers might work with. These representations of the domain facilitate viewpoints and analyses of particular information-types from particular perspectives. This paper describes one form of KM technology that has been developed over several years, which throws into relief a spectrum of human issues which are intrinsic to the process of designing and implementing KM representations -- computer-supported or otherwise instantiated. This is particularly germane to the application of artificial intelligence (AI) techniques to KM, currently one of the most strongly represented disciplines in KM research, since the success of such approaches rests heavily on finding appropriate representations for knowledge modelling, ontology design, knowledge-based system building, and the subsequent reasoning that these activities are intended to support.
Learning with Guaranteed Label Quality

In supervised learning, label quality is crucial for learning performance. However, noise is ubiquitous in labels provided by oracles in active learning. To rule out its negative influence, multiple-oracles have been proposed. However, unrealistic assumptions (such as the evenly distributed noise level of oracles) have been made to restrict the learning algorithms for real-world applications. In this paper, we propose a learning algorithm, c-certainty, to guarantee the label quality, and allow the noise level of oracles to be example-dependent. Furthermore, we develop an effective learning algorithm which is able to select the more accurate oracles to query. The experiment results show that the learning strategy developed in this paper outperforms other learning algorithms significantly.
Corpora and exemplars in phonology

This chapter reviews the role of corpora in phonological research, as well as the role of exemplars in phonological theory. We begin with illustrating the importance of corpora for phonological research as a source of data. We then present an overview of speech corpora, and discuss the kinds of problems that arise when corpus data have to be transcribed and analyzed. The enormous variability in the speech signal that emerges from speech corpora, in combination with current experimental evidence, calls for more sophisticated theories of phonology than those developed in the early days of generative grammar. The importance of exemplars for accurate phonological generalization is discussed in detail, as well as the characteristics of and challenges to several types of abstractionist, exemplar, and hybrid models.
Beyond life streams: activities and intentions for managing personal digital memories

In this paper, we expose a set of initial ideas related to an innovative way of structuring and organizing personal information. Indeed, users have to deal with a huge amount of information either coming from social connections, collected on the Web or generated by them. This phenomenon leads to new research challenges. In particular, how to structure, organize, and classify this personal information in order to better manage the user's digital memory ? In this position paper, we present the concepts of activities and intentions as means for the user to structure efficiently all his past information, but also help him in the future, for example by suggesting relevant events, anticipating his information needs or providing opportunities to satisfy latent desires.
A Deadly Consensus Worker Safety and Regulatory Degradation under New Labour

This paper documents the vulnerability of the UK workplace safety regime to ‘regulatory degradation’. Following a brief overview of this regime, the paper examines the dominant arguments within academic literature on appropriate and feasible regulatory enforcement, arguing that the approaches to regulation thereby advocated have been easily degraded as a result of their compatibility with neo-liberal economic strategy. A subsequent analysis of empirical trends within safety enforcement reveals a virtual collapse of formal enforcement, as political and resource pressures have taken their toll on the regulatory authority. Finally, the paper indicates that the increasing impunity with which employers can kill and injure is particularly problematic as we enter sustained economic recession, and underlines the urgent need for regulatory alternatives.
Evaluation of Policy Gradient Methods and Variants on the Cart-Pole Benchmark

In this paper, we evaluate different versions from the three main kinds of model-free policy gradient methods, i.e., finite difference gradients, 'vanilla' policy gradients and natural policy gradients. Each of these methods is first presented in its simple form and subsequently refined and optimized. By carrying out numerous experiments on the cart pole regulator benchmark we aim to provide a useful baseline for future research on parameterized policy search algorithms. Portable C++ code is provided for both plant and algorithms; thus, the results in this paper can be reevaluated, reused and new algorithms can be inserted with ease
Evaluating Non-deterministic Multi-threaded Commercial Workloads

Full-system simulation is increasingly used to evaluate the performance of commercial workloads on future multiprocessor designs. However, challenges such as simulation slowdown, sizing constraints, and workload tuning impede the development of commercial workloads for timing simulators. We describe how we address these challenges in our development of four commercial workload benchmarks. This paper introduces non-deterministic workload behavior as another potential challenge in timing simulation. Non-determinism refers to the sensitivity of the system’s timing to small changes in its parameters. This problem is nearly universally ignored because most simulators (including ours) are deterministic: they produce the same timing result every time, for given a workload and system parameters. However, we find that small changes in the memory latency can cause large changes in run-time (nearly 10%). We propose a methodology that uses pseudo-random perturbations and standard statistical techniques to compensate for these non-deterministic effects. Finally, we provide evidence that commercial workloads have different characteristics over time, further supporting a sampled simulation method-