Miracle-dz/UniFAR-FTUs huggingface dataset:
QJM Severe hepatic dysfunction in pregnancy

Acute liver disease in pregnancy may have fatal consequences. Pre-eclampsia, HELLP syndrome and acute fatty liver of pregnancy form a spectrum of disease that range from mild symptoms to severe life-threatening multi-organ dysfunction. Early recognition of signs and prognostic indicators may enable prompt referral to specialist centres providing the multidisciplinary support required to reduce maternal and perinatal morbidity and mortality. We review the common causes of acute hepatic failure associated with pregnancy, and current management practices.
From whom, about what, and reason why Capturing public's perception using Natural Language Processing (NLP)

In dealing with various crisis situations or other mundane events such as an introduction of a new medicine, it is important to understand the changing nature of the public’s perception to ensure that proper and relevant information is communicated amongst the public. This monitoring functionality can ensure the public’s continued support for a just cause. It is also important to assess the impact of potential misinformation, which is injected intentionally or unintentionally by the various interest bearing parties to delineate their intentions and their potential future activities.
Computation of Bifurcation Boundaries for Power Systems: a New

This paper is devoted to the problems of finding the load flow feasibility saddle node, and Hopf bifurcation boundaries in the space of power system parameters. The first part contains a review of the existing relevant approaches including not-so-well-known contributions from Russia. The second part presents a new robust method for finding the power system load flow feasibility boundary on the plane defined by any three vectors of dependent variables (nodal voltages), called the /spl Delta/ plane. The method exploits some quadratic and linear properties of the load flow equations and state matrices written in rectangular coordinates. An advantage of the method is that it does not require an iterative solution of nonlinear equations (except the eigenvalue problem). In addition to benefits for visualization, the method is a useful tool for topological studies of power system multiple solution structures and stability domains. Although the power system application is developed, the method can be equally efficient for any quadratic algebraic problem.
Collaborative environments for digital publishing

This work presents a software platform designed and implemented to support the creation of digital publishing organizations (DPOs) and to mediate the collaborative interaction among participants of the digital publishing process. We posit that as digital publications become the norm, the organization of publishing entities is expected to evolve and to differ significantly from current print-oriented publishers. Our major goal is to support the entire digital publishing process, which may vary from DPO to DPO. Distinctive features of our design include facilities for users to define the structure, participants and genres handled by DPOs, as well as tools to support the processes involved in authoring, reviewing, publishing and using digital materials.
Robotic Dexterity by Multi-fingered Hand with Soft Fingertip

This paper deals with the dexterous manipulation using a multi-fingered robotic hand. Robotic dexterity is still much inferior to that of human hand. Among many open problems, we focus two and propose solutions. Firstly, a method of tactile sensing using soft-fingertip is proposed and validated with experiments. Secondly, a method of coordinated motion of the fingers and the arm is described. Simulation results show effectiveness of the coordinated motion. Keywords— multi-fingered robotic hand, soft fingertip, dexterous manipulation, tactile sensing.
Design of a robot joint with variable stiffness

A robot joint with a variable stiffness unit is presented. The variable stiffness unit (VSU) is composed of a motor, two rings that consist of arc-shaped magnets separated by spacers, and a linear guide to change the cross-sectional area of the two rings. Angular displacement between two rings causes the magnets to generate torque, which acts as a nonlinear spring. The stiffness of the joint is varied via changing the overlapping area of the magnets. The VS J exhibits nearly zero stiffness, which enables robot manipulator to be harmless to humans at a wide range of operating speed. Connected to a joint motor in series, the stiffness by the VSU and the position of the joint are controlled independently by two motors. The torque generated by the magnets is analyzed. Using dynamics of the joint, feedback linearization method is adopted to control the VSJ. In addition to feedback linearization, an integral controller is augmented in order to reduce the effect of model uncertainty and disturbances.
Traffic-aware hybrid routing algorithm for mobile ad-hoc networks

Wireless ad-hoc networks have many different characteristics from previous centralized networks. While the infrastructure based networks usually employ the simple routing protocol because every node can reach the coordinator in a single hop, some specific situations, i.e., unplanned dynamic links and multi hop environment, were required in ad-hoc networks. All nodes engaging in communication under the ad-hoc networks should be operated as a router. For this, two routing approaches are previously designed for ad-hoc networks; proactive and reactive routing, and they differently have pros and cons from each other. Moreover, to integrate their advantages in case by case, hybrid approach is consistently researched, and Zone Routing Protocol (ZRP) was motivating many recent hybrid protocols. ZRP uses proactive routing to the node located within the zone defined by the specific number of hops, while it uses reactive routing to other nodes. However, proactive routing is applied only within the zone defined by the number of hops. In this, the zone is formed regardless of whether real data communication occurred frequently or not. In this paper, we propose a new routing scheme called Traffic Aware Dynamic Zone Routing (TA-DZR) protocol which employs the zone method but forms customized zone using traffic load, by a new decision method named Dynamic Zone Decision (DZD). The DZD selects nodes that are good to use proactive routing based on the traffic load. The customized proactive routing zone is formed around the route path from source to the selected destination. By considering traffic awareness, TA-DZR can decrease the number of transmissions and can reduce the total energy consumption. After the description of TA-DZR, the performance is demonstrated with simulation results which show that the number of transmissions decreases in overall network, and that the energy consumption is also reduced. Additionally, we validate that the end-to-end delivery delay is reduced and that the packet delivery ratio (PDR) is improved. These enhancements are caused by reducing overall network congestion.
A mathematical model for the primary tumor of mCRC and analysis of clinical data based on 5-FU and bevacizumab regimen

The growth of the primary tumor of metastatic colorectal cancer (mCRC) and associated angiogenesis is modeled by a system of ordinary differential equations for basic quantities which summarize the number of proliferating and hypoxic cancer cells, the degree of angiogenesis and the density of VEGF. The system is controlled by the action of 5-FU with or without bevacizumab. Randomizing certain parameters we compute progression free survival (PFS) medians and Kaplan-Meier curves which show a behavior comparable with clinical data.
OpinioNetIt: understanding the opinions-people network for politically controversial topics

The wikileaks documents or the economic crises in Ireland and Portugal are some of the controversial topics being played on the news everyday. Each of these topics has many different aspects, and there is no absolute, simple truth in answering questions such as: should the EU guarantee the financial stability of each member country, or should the countries themselves be solely responsible? To understand the landscape of opinions, it would be helpful to know which politician or other stakeholder takes which position - support or opposition - on these aspects of controversial topics. In this paper, we describe our system, named OpinioNetIt (pronounced similar to "opinionated"), which aims to automatically derive a map of the opinions-people network from news and other Web documents. We build this network as follows. First, we make use of a small number of generic seeds to identify controversial phrases from text. These phrases are then clustered and organized into a hierarchy of topics. Second, opinion holders are identified for each topic and their opinions (either supporting or opposing the topic) are extracted. Third, the known topics and people are used to construct a lexicon phrases indicating support or opposition. Finally, the lexicon is uses to identify more opinion holders, opinions and topics. Our system currently consists of approximately 30000 person-opinion-topic triples. Our evaluation shows that OpinioNetIt has high accuracy.
AN IMMUNE AGENTS SYSTEM FOR NETWORK INTRUSIONS DETECTION

With the development growing of network technology, computer networks became increasingly wide and opened. This evolution gave birth to new techniques allowing accessibility of networks and information systems with an aim of facilitating the transactions. Consequently, these techniques gave also birth to new forms of threats. In this article, we present the utility to use a system of intrusion detection through a presentation of these characteristics. Using as inspiration the immune biological system, we propose a model of artificial immune system which is integrated in the behavior of distributed agents on the network in order to ensure a good detection of intrusions. We also present the internal structure of the immune agents and their capacity to distinguish between self and not self. The agents are able to achieve simultaneous treatments, are able to auto-adaptable to environment evolution and have also the property of distributed coordination.
Marginal abatement cost curves and the budgetary impact of CO2 taxation in Portugal

The objective of this paper is to study CO2 taxation in its dual role as a climate and fiscal policy instrument. It develops marginal abatement cost curves for CO2 emissions using a dynamic general equilibrium model of the Portuguese economy which highlights the mechanisms of endogenous growth and includes a detailed modeling of the public sector. It also considers complementary cost curves corresponding to the impact of CO2 taxes on GDP and on the public budget. Simulation results show that a tax of 17.00 Euros per tCO2 has the capacity to limit emissions to 62.6 Mt CO2 in 2020, consistent with the existing climate policy target for Portugal. In turn, changes in tax revenues, together with reductions in public spending, lead to a 2.7% decline in public debt. These desirable outcomes come at the cost of a 0.7% reduction in GDP. In general, stricter emission targets imply greater equilibrium CO2 tax levels and larger GDP losses, although these are accompanied by greater reductions in public debt. Finally, the paper highlights the importance of public spending behavior for projecting the impact of CO2 taxes on public revenues and the public account and designing policies to promote fiscal consolidation.
Regenerating Gene Protein as a Novel Autoantigen in the Pathogenesis of Sjögren’s Syndrome

Sjogren’s syndrome, an autoimmune disease characterized by exocrine gland dysfunction leading to dry mouth and dry eye diseases, is typified by lymphoplasmacytic infiltrations and a progressive destruction of the salivary and lacrimal glands. Despite an ever-increasing focus on identifying the underlying etiology of Sjogren’s syndrome, the factors that initiate this autoimmune disease and the mechanisms that cause the subsequent exocrine gland dysfunction remain a mystery. The original explanatory concept for the pathogenesis of Sjogren’s syndrome proposed a specific, self-perpetuating, immune-mediated loss of acinar and ductal cells as the principal cause of salivary gland dysfunction. We highlight the possible involvement of regenerating gene (Reg) in the regeneration and destruction of salivary gland acinar and ductal cells in Sjogren’s syndrome. The Reg gene was originally isolated as a gene specifically overexpressed in regenerating pancreatic islets and constitutes a growth factor family (Reg family). We describe how salivary gland dysfunction is initiated and maintained and how it can be regenerated or progressed, mediated by the Reg gene, Reg protein, and anti-REG autoantibodies in Sjogren’s syndrome.
Improving Efficiency in Training of Artificial Neural Networks using Information-rich Data

Artificial Neural Networks (ANNs) are classified as a data-driven technique, which implies that their learning improves as more and more training data are presented. This observation is based on the premise that a longer time series of training samples will contain more events of different types, and hence, the generalization ability of the ANN will improve. However, a longer time series need not necessarily contain more information. If there is considerable repetition of the same type of information, the ANN may not become “wiser”, and one may be just wasting computational effort and time. This study assumes that there are segments in a long time series that contain a large quantum of information. The reason behind this assumption is that the information contained in any hydrological series is not uniformly distributed, and it may be cyclic in nature. If an ANN is trained using these segments rather than the whole series, the training would be the same or better based on the information contained in the series. A pre-processing can be used to select information-rich data for training. However, most of the conventional pre-processing methods do not perform well due to large variation in magnitude, scale and many zeros in the data series. Therefore, it is not very easy to identify these information-rich segments in long time series with large variation in magnitude and many zeros. In this study, the data depth function was used as a tool for the identification of critical (information) segments in a time series, which does not depend on large variation in magnitude, scale or the presence of many zeros in data. Data from two gauging sites were used to compare the performance of ANN trained on the whole data set and just the data from critical events. Selection of data for critical events was done by two methods, using the depth function (identification of critical events (ICE) algorithm) and using random selection. Inter-comparison of the performance of the ANNs trained using the complete data sets and the pruned data sets shows that the ANN trained using the data from critical events, i.e., information-rich data (whose length could be one third to half of the series), gave similar results as the ANN trained using the complete data set. However, if the data set is pruned randomly, the performance of the ANN degrades significantly. The concept of this paper may be very useful for training data-driven models where the training time series is incomplete.
The voting power approach : a theory of measurement. A response to Max Albert

Max Albert (2003) has recently argued that the theory of power indices “should not . be considered as part of political science” and that “[v]iewed as a scientific theory, it is a branch of probability theory and can safely be ignored by political scientists”. Albert’s argument rests on a particular claim concerning the theoretical status of power indices, namely that the theory of power indices is not a positive theory, i.e. not one that has falsifiable implications. I re-examine the theoretical status of power indices and argue that it would be unwise for political scientists to ignore such indices. Although I agree with Albert that the theory of power indices is not a positive theory, I suggest that it is a theory of measurement that can usefully supplement other positive and normative socialscientific theories.
Dynamic Adaptation of Fragment-Based and Context-Aware Business Processes

We propose a comprehensive framework for adaptivity of service-based applications, which exploits the concept of process fragments as a way to model reusable process knowledge and to allow for the dynamic, incremental, context-aware composition of such fragments into adaptable service-based applications. The framework provides a set of adaptation mechanisms that, combined through adaptation strategies, are able to solve complex adaptation problems. An implementation of the proposed solution is presented and evaluated on a real world scenario from the logistics domain.
Identifying Recurring Faulty Functions in Field Traces of a Large Industrial Software System

Software maintainers use the traces of field failures to understand and diagnose faulty functions that cause the system to fail. Despite their usefulness, traces from the field can be quite overwhelming, especially for software systems with a vast client base. In the execution of realistic applications, many of them being millions of lines of code, there are just too many traces that are generated. In addition, traces are known to be extraordinarily large, which further complicates matters. Fortunately, not all field failures are caused by new faults. In fact, previous studies showed that 50% to 90% of field failures are due to previously known faults. In this paper, we propose a machine learning approach that automatically detects recurring faulty functions in the traces of new field failures. We achieve our goal by training decision trees on earlier resolved traces of system failures from the current and prior releases of the system. When applied to a large industrial system with 20 million lines of code and 200,000 functions, our approach was able to detect recurring faulty functions in the traces of field failures with an accuracy of 90%, to even 97% in some cases.
Find my mug: Efficient object search with a mobile robot using semantic segmentation

In this paper, we propose an efficient semantic segmentation framework for indoor scenes, tailored to the application on a mobile robot. Semantic segmentation can help robots to gain a reasonable understanding of their environment, but to reach this goal, the algorithms not only need to be accurate, but also fast and robust. Therefore, we developed an optimized 3D point cloud processing framework based on a Randomized Decision Forest, achieving competitive results at sufficiently high frame rates. We evaluate the capabilities of our method on the popular NYU depth dataset and our own data and demonstrate its feasibility by deploying it on a mobile service robot, for which we could optimize an object search procedure using our results.
The Politics of Second Generation Discrimination in American Indian Education: Incidence, Explanation, and Mitigating Strategies

Preface Introduction A Political History of American Indian Education A Theoretical Model of Second Generation Discrimination Research Design and Measurement of Variables The Incidence of Second Generation Discrimination Among American Indians Representation in Educational Policymaking: The Impact of American Indian Representation on Second Generation Discrimination Mitigating Second Generation Discrimination: Lessons from Case Studies Conclusion and Suggestions for Future Research Appendix Bibliography Index
Algebraic Multigrid for Discrete Differential Forms

Discrete differential forms arise in scientific disciplines ranging from computational electromagnetics to computer graphics. Examples include stable discretizations of the eddy-current problem, topological methods for sensor network coverage, visualization of complex flows, surface parameterization, and the design of vector fields on meshes. In this thesis we describe efficient and scalable numerical solvers for discrete k-form problems. Our approach is based on the principles of algebraic multigrid (AMG) which is designed to solve large-scale linear systems with optimal, or near-optimal efficiency. Since the k-form problems to be solved are arbitrarily large, the need for scalable numerical solvers is clear.
Strong stabilization of MIMO systems with restricted zeros in the unstable region

The strong stabilization problem (i.e., stabilization by a stable feedback controller) is considered for a class of finite dimensional linear, time-invariant, multi-input multi-output plants. It is assumed that the plant satisfies the parity interlacing property, which is a necessary condition for the existence of strongly stabilizing controllers. Furthermore, the plant class under consideration has no restrictions on the poles, on the zeros in the open left-half complex plane, on the zeros at the origin or at infinity; but only one finite positive real zero is allowed. A systematic strongly stabilizing controller design procedure is proposed that applies to any plant in the class, whereas alternative approaches may work for larger class of plants but only under certain sufficient conditions. The freedom available in the design parameters may be used for additional performance objectives although the only goal here is strong stabilization. In the special case of single-input single-output plants in the class considered, the proposed stable controllers have order one less than the order of the plant.
Variational phasor mean field model for Markov random fields

We show that a variational phasor mean field approximation for Markov random fields can well represent marginal distribution as well as correlation among the sites. The network is represented by complex valued equations, which consist of phase equations and variational mean-field equations; the correlation coefficient between two sites on stochastic machines can be given by the cosine of the phase differences. This enables to compute the correlation between two units directly in a deterministic manner. The variational correlation network improves the accuracy of the mean field approximation for graphical models. Unlike the conventional elaborated mean field methods the efficient training method can be implemented on this model. The model gives a much more efficient learning recipe for pattern recognition or image processing than Markov network models.
Unravelling Prion Diseases Using Molecular Dynamics Simulations

Human prion diseases such as Creutzfeldl-Jakob disease are neurodegenerative diseases and present as infectious, sporadic and genetic disorders. Association of bovine spongiform encephalopathy (BSE) or 'mad cow disease' with human variant Creutzfeldt-Jakob disease (vCJD) have increased the attention on these diseases. Here we review and summarizes the use of molecular dynamics simulation to investigate the possible conformational transformation pathways of the prion protein (PrP) from its normal isoforms (PrPc) to a diseased scrapie isoforms (PrPsc). The review showed that although molecular dynamics simulations have been used in a variety of ways using various conditions such as pH temperatures with diverse starting structures, the results showed consensus only on the creation of beta-sheets within the flexible N terminal, which is one possible characteristic of the PrPC to PrPC conversion. There is also a trend showing a research refocus from the flexible N terminal segment to the C terminal globular domain especially on second a-helix (H2), a segment linked to a cluster of point mutations.