Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Three Types of Stress on Fos Expression in the Hypothalamic Paraventricular Nucleus , Hippocampus and Amygdala in Female Rats at Different Stages of Pregnancy

Using immunohistochemistry to reveal the Fos protein (a marker of neuronal activation), the present experiments examined whether there were differences in the responses of the paraventricular nucleus (PVN), hippocampus, and amygdala of pregnant rats exposed to three types of stressors (restraint, immobilization, and communication-box stress), all having inherently different severities, at three pregnancy stages (6 days into pregnancy, or P6, early-pregnancy), P12 (mid-pregnancy) and P18 (late-pregnancy). The parvocellular PVN was activated by all three stressors almost equally at all pregnancy stages. The magnocellular PVN appeared to become more active without stressors in mid-pregnancy than in early-pregnancy. The stress responses of the hippocampal dentate gyrus to immobilization and communicationbox stress, and that of the basolateral amygdala to immobilization stress in pregnant females, were greater in mid-pregnancy than at the other pregnancy stages. The magnocellular PVN was more sensitive to communication-box stress in late-pregnancy than at the other pregnancy stages, while the hippocampal CA3 and medial amygdala were less sensitive to communication-box stress and to restraint stress in late-pregnancy than at the other pregnancy stages, respectively. These results suggest that the responses of the PVN, hippocampus, and amygdala in pregnant female rats to different stressors were variable at different pregnancy
Modeling and control of a doubly fed induction generator for grid integrated wind turbine

This paper presents the modeling, simulation and control of a grid connected doubly fed induction generator (DFIG) coupled with the wind turbine. Among all the renewable energy resources, wind energy is rapidly gaining interest of developed as well as underdeveloped countries. Due to the increase in demand of electrical energy and increase in environmental pollution the use of wind energy to meet the domestic and industrial power demands is essential and inevitable. The main benefit is the production of economically justified and ecofriendly energy in bulk quantity. Because of the irregularity of wind speed, the output of wind turbine varies and that is why, variable speed wind turbines are more practical these days. In order to integrate the variable output of the wind turbine with the power grid, doubly fed induction generators are used due to their inherent advantages like active and reactive power control, absence of large capacitive banks and improved power quality. A DFIG is modeled using the electric equivalent circuit and dq0 transformation along with the stator flux oriented vector control approach is used to decouple active and reactive power. Back to back power converters serve the purpose of controlling active and reactive powers at sub/super synchronous speeds. So the implementation of the proposed scheme ensures the constant output frequency and voltages with the control of active and reactive power and is highly suitable for grid integration in Distributed Generation (DG) Applications. The proposed scheme is implemented in SIMULINK / MATLAB environment and the simulation results validate the efficacy of the proposed scheme.
Reversing and Identifying Overwritten Data Structures for Memory-Corruption Exploit Diagnosis

Exploits diagnosis requires great manual effort and desires to be automated as much as possible. In this paper, we investigate how the syntactic format of program inputs, as well as reverse engineering of data structures, could be used to identify overwritten data structures, and propose a binary-level exploit diagnosis approach, deExploit, that is generic to attack types and effective in identifying key attack steps. In details, we design to use a fine-grained dynamic tainting technique to model how the exploit is dynamically processed during program execution, dynamically reverse corresponding data structures of program input and then identify overwritten data structures by detecting the deviation between dynamic processing of exploit and that of benign input. We implement deExploit and perform it to diagnose multiple exploits in the wild. The results show that deExploit works well to diagnose memory corruption exploits.
Preparing Faculty for Pedagogical Change: Helping Faculty Deal With Fear

How receptive faculty areto changing theirpedagogical approach isa complex issue, but onefactor that impedes change is thefear of taking a risk. Underlying thisfear maybethefear ofloss, fear ofembarrassment, orfear offailure. Address ing these issues canempowerfaculty to be more innovative in theirteaching. Drawing on research literature, personal teaching narratives, and my ownwork infaculty development, I discuss some of these underlyingfears. then offer con crete strategies for working withfaculty to enable them to overcome these emo tional barriers andembrace change.
Fast Updating Algorithms for Latent Semantic Indexing

This paper discusses a few algorithms for updating the approximate singular value decomposition (SVD) in the context of information retrieval by latent semantic indexing (LSI) methods. A unifying framework is considered which is based on Rayleigh--Ritz projection methods. First, a Rayleigh--Ritz approach for the SVD is discussed and it is then used to interpret the Zha and Simon algorithms [SIAM J. Sci. Comput. , 21 (1999), pp. 782--791]. This viewpoint leads to a few alternatives whose goal is to reduce computational cost and storage requirement by projection techniques that utilize subspaces of much smaller dimension. Numerical experiments show that the proposed algorithms yield accuracies comparable to those obtained from standard ones at a much lower computational cost.
PKUTM Participation at TAC 2010 RTE and Summarization Track

This paper describes the systems of PKUTM in Text Analysis Conference (TAC) 2010. We participated in the Recognizing Textual Entailment (RTE) track and the Summarization track. For the RTE track, we propose a method to map every node in the hypothesis to one or more nodes in the text. With the help of named-entities tools, MINIPAR relationships, and regular patterns to recognize temporal and numeric expressions, some nodes are merged into one node. We transform the hypothesis by using semantic knowledge from sources like WordNet, VerbOcean, and LingPipe. In the Summarization track, we propose a unified framework for both kinds of summarization. We employ a manifold-ranking model to select sentences and a novel sentence ordering method to generate final summaries. The underlying idea of the proposed approach is that a good summary is expected to include the sentences with both high biased information richness and high information novelty. The evaluation results show that our proposed two frameworks are very effective for RTE and Summarization tasks, respectively.
Making DPI Engines Resilient to Algorithmic Complexity Attacks

This paper starts by demonstrating the vulnerability of Deep Packet Inspection (DPI) mechanisms, which are at the core of security devices, to algorithmic complexity denial of service attacks, thus exposing a weakness in the first line of defense of enterprise networks and clouds. A system and a multi-core architecture to defend from these algorithmic complexity attacks is presented in the second part of the paper. The integration of this system with two different DPI engines is demonstrated and discussed. The vulnerability is exposed by showing how a simple low bandwidth cache-miss attack takes down the Aho-Corasick (AC) pattern matching algorithm that lies at the heart of most DPI engines. As a first step in the mitigation of the attack, we have developed a compressed variant of the AC algorithm that improves the worst case performance (under an attack). Still, under normal traffic its running-time is worse than classical AC implementations. To overcome this problem, we introduce MCA2-Multi-Core Architecture to Mitigate Complexity Attacks, which dynamically combines the classical AC algorithm with our compressed implementation, to provide a robust solution to mitigate this cache-miss attack. We demonstrate the effectiveness of our architecture by examining cache-miss algorithmic complexity attacks against DPI engines and show a goodput boost of up to 73%. Finally, we show that our architecture may be generalized to provide a principal solution to a wide variety of algorithmic complexity attacks.
Semantic categorization in aphasic patients with impaired language comprehension: An event–related potentials study

Many studies have tackled the question of the organization of our conceptual knowledge in the brain, mainly conducting behaviour studies in healthy and impaired individuals. Animacy and inanimacy are among the most frequently studied categories and there are three main theories or models that explain semantic processing of animate and inanimate objects: the sensory/functional theory, the domain– specific semantic knowledge representation model and the connectionist model of the conceptual structure. Although the event–related potentials (ERP) technique has been used in aphasia research and many studies have used some variation of the semantic categorization task in healthy individuals, to our knowledge there are no studies that were intended to answer the question aboutsemantic categorization in the aphasic population using the ERP technique. The aim of this study is to determine the differences in processing animate and inanimate objects between patients with aphasia with language comprehension difficulties and age, gender and education matched controls using the ERP technique. Results in this study show that the group of aphasic patients with impaired language comprehension have a lower amplitude and a longer latency of the N400 and the LPC amplitude, fewer correct behavioural responses and a slower reaction time on the behavioural categorization task than their controls. It can be concluded that aphasic patients have difficulties in both phases of lexico– semantic processing, the lexical retrieval or recognition phase and the categorization phase. The absence of differences in the processing of animate and inanimate objects in the N400 window and similar topographic distribution for animate and inanimate objects in both groups are consistent with the connectionist model of a single semantic system which claims that the same semantic system is active no matter which category is being processed. However, the observed differences between animate and inanimate objects in the LPC time window in the control group lead to the conclusion that inanimate objects are harder to categorize due to a smaller number of common features needed for the categorization processes.
Relationship between the Decomposition Process of Coarse Woody Debris and Fungal Community Structure as Detected by High-Throughput Sequencing in a Deciduous Broad-Leaved Forest in Japan

We examined the relationship between the community structure of wood-decaying fungi, detected by high-throughput sequencing, and the decomposition rate using 13 years of data from a forest dynamics plot. For molecular analysis and wood density measurements, drill dust samples were collected from logs and stumps of Fagus and Quercus in the plot. Regression using a negative exponential model between wood density and time since death revealed that the decomposition rate of Fagus was greater than that of Quercus. The residual between the expected value obtained from the regression curve and the observed wood density was used as a decomposition rate index. Principal component analysis showed that the fungal community compositions of both Fagus and Quercus changed with time since death. Principal component analysis axis scores were used as an index of fungal community composition. A structural equation model for each wood genus was used to assess the effect of fungal community structure traits on the decomposition rate and how the fungal community structure was determined by the traits of coarse woody debris. Results of the structural equation model suggested that the decomposition rate of Fagus was affected by two fungal community composition components: one that was affected by time since death and another that was not affected by the traits of coarse woody debris. In contrast, the decomposition rate of Quercus was not affected by coarse woody debris traits or fungal community structure. These findings suggest that, in the case of Fagus coarse woody debris, the fungal community structure is related to the decomposition process of its host substrate. Because fungal community structure is affected partly by the decay stage and wood density of its substrate, these factors influence each other. Further research on interactive effects is needed to improve our understanding of the relationship between fungal community structure and the woody debris decomposition process.
Introduction to NMR Quantum Information Processing

1 Liquid-State NMR 31.1 NMR Basics . 31.2 A Brief Survey of NMR QIP . 62 Principles of Liquid-State NMR QIP 72.1 Realizing Qubits . 72.2 One Qubit Gates . 92.3 Two Qubit Gates . 122.4 Turning off the J-Coupling . 132.5 Measurement . 152.6 The Initial State . 202.7 Gradient Fields . 223 Examples of Quantum Algorithms for NMR 243.1 The Controlled-not . 243.2 Creating a Labeled Pseudopure State . 283.3 Quantum Error Correction for Phase Errors . 334 Discussion 364.1 Overview of Contributions to QIP . 364.2 Capabilities of Liquid-State NMR . 374.3 Prospects for NMR QIP . 375 Glossary 421
Real-Time Component Integration Using Runnable Virtual Nodes

We present the concept of runnable virtual nodes (RVNs) as means to achieve predictable integration and temporal error-containment of real-time software components. An RVN exploits the latest techniques for hierarchical scheduling and is intended as a coarse-grained component for single-node deployment, that provides functional and temporal isolations with respect to its environment. It uses a two-level deployment process, i.e. deploying functional entities to RVNs and then deploying RVNs to physical nodes. The two-level deployment process not only gives development benefits with respect to compos ability, system integration, testing, validation and certification but also leverages the hierarchical scheduling to preserve the validity of an RVN's internal temporal behaviour when integrated with other components. We have applied our approach to a simple case study, implemented in the ProCom component-technology executing on top of FreeRTOS-based hierarchical scheduling and present our initial results as a proof-of-concept.
INPRO_iSS: A Component for Just-In-Time Incremental Speech Synthesis

We present a component for incremental speech synthesis (iSS) and a set of applications that demonstrate its capabilities. This component can be used to increase the responsivity and naturalness of spoken interactive systems. While iSS can show its full strength in systems that generate output incrementally, we also discuss how even otherwise unchanged systems may profit from its capabilities.
Parallelized Critical Path Search in Electrical Circuit Designs

For finding the critical path in electrical circuit designs, a shortest-path search must be carried out. This paper introduces a new two-level shortest-path search algorithm specially adapted for parallelization. The proposed algorithm is based on a module-based partitioning algorithm and a shortest-path search parallelized for the usage on multi-core systems. Experimental results show the impact of this approach.
M2M Device Management in LTE Networks

— The driving concept of device management in M2M systems is establishment of a comprehensive end-to-end resource oriented design. In this paper, OMA-DM protocol for device management of M2M devices in 3GPP LTE networks is proposed and discussed. The OMA-DM represents a stable starting point for device management. Utilization of this protocol in M2M requires efficient message formats and transport replacement such as CoAP, Core Link Format, EXI, etc. The additional wake up mechanisms should also be considered for managing the stand by policies.
The use of well-founded argumentation on the conceptual modeling of collaborative ontology development

Divergences in conceptual modeling choices are inherent to the collaborative ontology development. Such divergences have been typically solved through some process of negotiation among the participants of the development process. When negotiating, the participants argue to defend their ideas based on their past experiences. We propose to support and to guide the argumentation using philosophical notions brought by the OntoClean methodology to show the participants the consequences of their conceptual modeling choices. So the participants can choose the modeling option that better represents the intended model of the domain. This paper presents an approach for collaborative ontology development in a distributed way, specifically to support activities of the conceptualization phase and the whole process of discussion to reach consensus, aiming at developing ontologies that better fit the intended models for a domain.
Dealing with challenging robots , environments and missions : operational space control in the context of motor learning

Robotics has evolved from an industrial, repetitive framework to application domains with much more variability of tasks and increasing complexity in uncertain environment. This is clearly the case for Service Robotics where the safety of the interactions is also a major issue. Based on these observations, part of the research activities of the Motion & Perception group at the Intelligent Systems and Robotics Institute (ISIR) from Université Pierre et Marie Curie (UPMC) is dedicated to human motion analysis and to motion generation and control for humanoid robots. This abstract gives a quick overview of the approach we are working on to solve the problem of motion generation and control for humanoid robots. It also tries to show how our approach relates to the existing literature in the domain and why it seems to be a promising alternative within the framework of Service Robotics and more generally in the context of humanoid robots evolving in uncertain environments.
One-year cost-effectiveness of tiotropium versus ipratropium to treat chronic obstructive pulmonary disease

The aim of this paper is to assess the health economic consequences of substituting ipratropium with the new, once-daily bronchodilator tiotropium in patients with a diagnosis of chronic obstructive pulmonary disease (COPD). This prospective cost-effectiveness analysis was performed alongside two 1‐yr randomised, double-blind clinical trials in the Netherlands and Belgium. Patients had a diagnosis of COPD and a forced expiratory volume in one second (FEV1) ≤65% predicted normal. Patients were randomised to tiotropium (18 µg once daily) or ipratropium (2 puffs of 20 µg administered four times daily) in a ratio of 2:1. The mean number of exacerbations was reduced from 1.01 in the ipratropium group (n=175) to 0.74 in the tiotropium group (n=344). The percentages of patients with a relevant improvement on the St. George's Respiratory Questionnaire (SGRQ) were 34.6% and 51.2% respectively. Compared to ipratropium, the number of hospital admissions, hospital days and unscheduled visits to healthcare providers was reduced by 46%, 42% and 36% respectively. Mean annual healthcare costs including the acquisition cost of the study drugs were 1721 (sem 160) in the tiotropium group and 1,541 (SEM 163) in the ipratropium group (difference 180). Incremental cost-effectiveness ratios were 667 per exacerbation avoided and 1084 per patient with a relevant improvement on the SGRQ. Substituting tiotropium for ipratropium in chronic obstructive pulmonary disease patients offers improved health outcomes and is associated with increased costs of 180 per patient per year.
Neuroscience and Biobehavioral Reviews Moving beyond Standard Procedures to Assess Spontaneous Recognition Memory

This review will consider how spontaneous tasks have been applied alongside neuroscientific techniques to test complex forms of recognition memory for objects and their environmental features, e.g. the spatial location of an object or the context in which it is presented. We discuss studies that investigate the roles of the perirhinal cortex and the hippocampus in recognition memory using standard testing paradigms, and consider how these findings contribute to the ongoing debate about whether recognition memory is a single unitary process or multiple processes that can be dissociated anatomically and functionally. Due to the wide use of spontaneous tasks, the need for improved procedures that reduce animal use is acknowledged, with multiple trial paradigms discussed as a novel way of reducing variability and animal numbers in these tasks. The importance of improving translation of animal models to humans is highlighted, with emphasis on a shift away from relying on the phenomenological experience of human subjects.
Knowledge Management System of Intercity Emergency Decision Making

In order to effectively increase the ability of the city's emergency decision-making and reduce the threat of unexpected incidents, by introducing the knowledge management theory, and systematically analyzing the classification and function of knowledge in the intercity emergency decision-making management system, the knowledge management system of intercity emergency decision-making can be established based on the initially establishment of the urban emergency decision-making knowledge base, so as to realize the obtaining, transferring, applying and sharing of the city emergency decision-making knowledge and will improve the utilization rate of knowledge. This scientific and efficient knowledge management is proving accurate information for emergency decision-making in time, which will lead to make more scientific and reasonable decision to rescue rapidly and correctly. The research results show that the construction of knowledge management system oriented to intercity emergency decision-making has a practical significance to the raising of the whole urban emergency management ability.
Using Part-of-Speech Patterns and Domain Ontology to Mine Imprecise Concepts from Text Documents

In the last few years, several works in the literature have addressed the problem of information extraction from text documents. The importance of this problem derives from the fact that, once extracted, the information can be handled in a way similar to instances of a traditional database. But, most of the information extraction systems assume that the texts are having only precise concepts and these restrict the information provider to use only precise concepts to represent their information. In this paper we have presented a system that uses part-of-speech patterns and domain ontology to extract imprecise concepts present in the text documents and hence it allows information provider to describe concepts by using linguistic variables – very, more, light, strong, slightly, quite etc. that are very common with natural languages. We have considered wine documents as case study however it can be applied to any domain for which there is an existing ontology. In this paper, we have shown how a structured knowledgebase can be designed to hold imprecise concept descriptions extracted from text documents. The structured knowledgebase can then be searched efficiently for required information.