Miracle-dz/UniFAR-FTUs huggingface dataset:
Depressor and Tachypneic Responses to Chemical Stimulation of the Ventral Respiratory Group Are Reduced by Ablation of Neurokinin-1 Receptor-Expressing Neurons

Our goal was to investigate whether the neurokinin-1 receptor (NK1R)-expressing cells of the rostral ventrolateral medulla (RVLM) regulate respiration and arterial pressure (AP). We examined the consequences of their ablation on the cardiorespiratory responses [phrenic nerve discharge (PND) and AP] caused by injectingdl-homocysteic acid (DLH) into the ventral respiratory group (VRG). In intact rats, DLH produced tachypnea only when injected into the pre-Bötzinger complex (pre-BötC). Injections into pre-BötC and rostral VRG (rVRG) caused hypotension, whereas injections into the Bötzinger region elevated AP. Selective unilateral ablation of RVLM NK1R-immunoreactive cells (97% loss within the pre-BötC and rVRG without loss of catecholaminergic neurons) was done by injecting saporin (SAP) conjugated with a selective NK1R agonist [Sar9, Met(O2)11]-substance P (SSP). Free SAP produced no lesion. Resting AP was normal in SAP- and SSP-SAP-treated rats, but the PND rate was slightly elevated in SSP-SAP-treated rats. The response of SAP-treated rats to DLH injection into VRG was normal and identical on each side, but tachypnea could not be elicited in the pre-BötC of SSP-SAP-treated rats on the toxin-injected side, and DLH caused a long-lasting apnea on the untreated side. The hypotension produced by DLH injection into pre-BötC and rVRG of SSP-SAP-treated rats was reduced on the lesioned side only. In conclusion, NK1R-expressing cells of the rostral ventrolateral medulla control both respiratory rhythm and blood pressure. However, there is no evidence yet that these two functions are regulated by the same NK1R-expressing neurons.
Exploiting Temporal Correlation of Speech for Error Robust and Bandwidth Flexible Distributed Speech Recognition

In this paper, the temporal correlation of speech is exploited in front-end feature extraction, client-based error recovery, and server-based error concealment (EC) for distributed speech recognition. First, the paper investigates a half frame rate (HFR) front-end that uses double frame shifting at the client side. At the server side, each HFR feature vector is duplicated to construct a full frame rate (FFR) feature sequence. This HFR front-end gives comparable performance to the FFR front-end but contains only half the FFR features. Second, different arrangements of the other half of the FFR features creates a set of error recovery techniques encompassing multiple description coding and interleaving schemes where interleaving has the advantage of not introducing a delay when there are no transmission errors. Third, a subvector-based EC technique is presented where error detection and concealment is conducted at the subvector level as opposed to conventional techniques where an entire vector is replaced even though only a single bit error occurs. The subvector EC is further combined with weighted Viterbi decoding. Encouraging recognition results are observed for the proposed techniques. Lastly, to understand the effects of applying various EC techniques, this paper introduces three approaches consisting of speech feature, dynamic programming distance, and hidden Markov model state duration comparison
A robust fusion method for RGB-D SLAM

RGB-D cameras are becoming more and more popular in the areas of Simultaneous Localisation and Mapping (SLAM). Visual Feature matching and dense point cloud ICP are the main methods to estimate camera pose in the existing RGB-D SLAM system. Only using visual information, feature matching method can hardly obtain accurate enough result as the appearance features are sparse. Making use of depth information alone, ICP method cannot avoid the result converging to an incorrect local minimum when the environment has poor 3D geometry. In this paper, we propose a robust fusion method, which acquires accurate and robust pose estimation via combining visual information with depth information. The method can obtain relative transformation by minimizing a robust error function, which integrates matching errors from both visual features and dense point clouds. In addition, a loop closure detection mechanism and a pose graph optimizing method are utilized to estimate the globally consistent pose. Therefore the 3D environment can be well reconstructed. Extensive experiments demonstrate that the method can robustly and accurately estimate the camera trajectory for the RGB-D SLAM system, even in the environment with poor 3D geometry or the area with sparse texture.
The Influence Factors for Visitor Loyalty on Taipei International Travel Fair

The purpose of this study is to understand and analyze the impacts of visitor background variable, travel fair attraction, experience, and satisfaction on visitor loyalty at the Taipei International Travel Fair. Subjects were visitors who attended the 2010 Taipei International Travel Fair. Convenience sampling was conducted at the fair exit. A total of 150 surveys were performed each day. Out of a total of 450 surveys performed, 423 were valid, with a valid response rate of 94%. In the hierarchical regression analysis, following the entry of four blocks into the model, study results show that background variables in the model has no significant explained variance predicting visitor loyalty. In contrast, with attraction, experience, and satisfaction being added to the model, the model provides a statistically significant explained variance predicting visitor loyalty. The study concludes that attraction, experience, and satisfaction are important influence factors for visitor loyalty.
Adding custom widgets to model-driven GUI generation

For usable and acceptable Graphical User Interfaces (GUIs) in practice, predefined widgets of some given toolkit are often insufficient. An example is flight booking, where special widgets for selecting seats are common in today's applications. Model-driven GUI generation uses predefined widgets as building blocks, and also needs to include such special widgets customized for a given application. The problem is how to allow a GUI designer other than a framework developer to include such special widgets in the automated generation process. In this paper, we present a new approach for adding such special widgets to model-driven GUI generation. It does so by including custom widgets already during the automated generation, in order to make the result persistent also in case of re-generation. We explain our extensions to a generation framework for that purpose, so that a GUI designer using it for automated GUI generation can integrate custom widgets into the generated GUI, without having its source code or in-depth knowledge of the generation framework. This involves specifying them in Custom Widget Templates including design-time variability, so that they can be integrated in the design-time generation process starting from the highest level of abstraction.
Finding representative and diverse community contributed images to create visual summaries of geographic areas

This paper presents an automatic approach that uses community-contributed images to create representative and diverse visual summaries of specific geographic areas. Complex relations between images, extracted visual features, text associated with the images as well as users and their social network are modeled using a multimodal graph. To compute affinities between nodes in the graph we rely on the proven concept of random walk with restarts. The novelty of our approach lies in its use of the multimodal graph to create a diverse, yet representative, image set. Further, we introduce an edge-weighting mechanism for the fusion of heterogeneous modalities. We evaluate our summaries with a new protocol that tests for representativeness and diversity using image geo-coordinates and is independent of the need for human evaluators. The experiments, performed on a set of Flickr images, demonstrate the effectiveness of our approach.
A column matching based algorithm for target self-localization using beacon nodes

In this work, an algorithm is proposed for self-localization of a target node using power measurements from beacon nodes transmitting from known locations. The geographical area is overlaid with a virtual grid, and the problem is treated as one of testing overlapping subsets of grid cells for the presence of the target node. The proposed algorithm is validated both by Monte Carlo simulations as well as using experimental data collected from commercially-off-the-shelf bluetooth low energy (BLE) beacon nodes.
The mathfrakphBcFunctionin lambda-K hBcConversion Universal Turing Machine

Many experts would agree that, had it not been for amphibious algorithms, the emulation of consistent hashing might never have occurred. In this position paper, we argue the synthesis of XML, which embodies the significant principles of cryptography. In our research we investigate how public-private key pairs can be applied to the improvement of robots.
The Subtle Balance between Lipolysis and Lipogenesis: A Critical Point in Metabolic Homeostasis

Excessive accumulation of lipids can lead to lipotoxicity, cell dysfunction and alteration in metabolic pathways, both in adipose tissue and peripheral organs, like liver, heart, pancreas and muscle. This is now a recognized risk factor for the development of metabolic disorders, such as obesity, diabetes, fatty liver disease (NAFLD), cardiovascular diseases (CVD) and hepatocellular carcinoma (HCC). The causes for lipotoxicity are not only a high fat diet but also excessive lipolysis, adipogenesis and adipose tissue insulin resistance. The aims of this review are to investigate the subtle balances that underlie lipolytic, lipogenic and oxidative pathways, to evaluate critical points and the complexities of these processes and to better understand which are the metabolic derangements resulting from their imbalance, such as type 2 diabetes and non alcoholic fatty liver disease.
Modelling the Strategic Alignment of Software Requirements using Goal Graphs

This paper builds on existing Goal Oriented Requirements Engineering (GORE) research by presenting a methodology with a supporting tool for analysing and demonstrating the alignment between software requirements and business objectives. Current GORE methodologies can be used to relate business goals to software goals through goal abstraction in goal graphs. However, we argue that unless the extent of goal-goal contribution is quantified with verifiable metrics and confidence levels, goal graphs are not sufficient for demonstrating the strategic alignment of software requirements. We introduce our methodology using an example software project from Rolls-Royce. We conclude that our methodology can improve requirements by making the relationships to business problems explicit, thereby disambiguating a requirement's underlying purpose and value.
Building up resources in the relationship between work–family conflict and burnout among firefighters: moderators of guanxi and emotion regulation strategies

ABSTRACT This study investigated the moderating effects of supervisor–subordinate “guanxi” alongside relevant emotional regulation strategies, including cognitive reappraisal and expressive suppression, undertaken by 402 firefighters experiencing burnout caused by work–family conflict, as revealed through the application and analysis of the job demands–resources model. According to the regression model for the moderating effects, high levels of supervisor–subordinate guanxi may improve the relationship between work–family conflict and burnout, while frequent suppression of expressivity may enhance this relationship. Moreover, when supervisor–subordinate guanxi is high, cognitive reappraisal may effectively mitigate the relationship between work–family conflict and burnout. To this end, both guanxi and cognitive reappraisal are crucial for supporting emotional regulation in the workplaces of ethnic Chinese societies.
Analysis of two approaches for Association Rules Mining in Vertically Partitioned Databases1

Privacy concerns have become an important issue in Data Mining. This paper deals with the problem of association rule mining from distributed vertically partitioned data with the goal of preserving the confidentiality of each database. Each site holds some attributes of each transaction, and the sites wish to work together to find globally valid association rules without revealing individual transaction data. This problem occurs, for example, when the same users access several electronic shops purchasing different items in each. We present two algorithms for discovering frequent item sets and for calculating the confidence of the rules. We then analyze the algorithms privacy properties, and compare them to other published algorithms.
Explaining the Familial Colorectal Cancer Risk Associated with Mismatch Repair (MMR)-Deficient and MMR-Stable Tumors

Purpose: There is a paucity of data quantifying the familial risk of colorectal cancer associated with mismatch repair (MMR)-deficient and MMR-stable tumors. To address this, we analyzed a population-based series of 1,042 colorectal cancer probands with verified family histories. Experimental Design: Constitutional DNA from probands was systematically screened for MYH variants and those with cancers displaying microsatellite instability (MSI) for germ-line MMR mutations; diagnoses of familial adenomatous polyposis and juvenile polyposis were established based on clinical phenotype and mutational analysis. Familial colorectal cancer risks were enumerated from age-, sex-, and calendar-specific population incidence rates. Segregation analysis was conducted to derive a model of the residual familial aggregation of colorectal cancer. Results: Germ-line predisposition to colorectal cancer was identified in 37 probands [3.4%; 95% confidence interval (95% CI), 2.4-4.6]: 29 with MLH1/MSH2 mutations, 2 with familial adenomatous polyposis, 1 with juvenile polyposis, and 5 with biallelic MYH variants. The risk of colorectal cancer in first-degree relatives of probands with MSI and MMR-stable cancers was increased 5.01-fold (95% CI, 3.73-6.59) and 1.31-fold (95% CI, 1.07-1.59), respectively. MSH2/MLH1 mutations were responsible for 50% of the overall excess familial risk and 80% of the risk associated with MSI cancers but 32% of the familial risk was unaccounted for by known loci. Genetic models based on major gene loci did not provide a better explanation of the residual familial aggregation than a simple polygenic model. Conclusions: The information from our analyses should be useful in quantifying familial risks in clinical practice and in the design of studies to identify novel disease alleles.
On the Number of 2-Protected Nodes in Tries and Suffix Trees

We use probabilistic and combinatorial tools on strings to discover the average number of 2-protected nodes in tries and in suffix trees. Our analysis covers both the uniform and non-uniform cases. For instance, in a uniform trie with $n$ leaves, the number of 2-protected nodes is approximately 0.803$n$, plus small first-order fluctuations. The 2-protected nodes are an emerging way to distinguish the interior of a tree from the fringe.
Space and time-dependant bus accessibility: A case study in Rome

The study of the accessibility in the city has always been appealing for urban planners and public trasportation companies. Nowadays, thanks to the availability of tracking devices on public transportation devices, it is possible to evaluate such accessibility very accurately, and derive useful performance measures. In this paper, we propose a complete methodological process to analysize and measure the accessibility of a city using bus GPS traces. Firstly, the process involves the application of recent results in spatio-temporal data mining in order to detect bus lines and bus stops from the traces dataset. Then an agent-based algorithm is used to simulate human mobility in the network, in order to study how the accessibility of the city changes over time, and starting from different locations in the city. Finally, the methodology is applied to bus traces collected for the city of Rome and both the detection process and the accessibility study are deeply investigate.
From Informal Project Management Artifacts to Formal System Models

Software systems are specified with formal artifacts such as requirements or architecture models. However, informal project artifacts such as bug reports, tasks or discussion threads also include relevant information about the respective software systems and their development. It is beneficial to externalize such information in formalized representations, e.g. to increase the automation of development activities. In this paper we describe a model that integrates formal system models and informal artifacts of software development projects. We show how this integration eases the manual transition of information from project management artifacts to system models, and discuss how this transition can be automated. To facilitate this transition we propose an approach for the automated identification of informal management artifacts, which contain information about functional requirements and other system specifications such as classes.
The Impact of Product Market Competition on Employment and Wages

Standard economic wisdom generally stresses the benefits of increased competition on the product market. This paper proposes a model of monopolistic competition with an endogenous determination of workers flows in and out of unemployment, where wages are determined according to an efficiency wage mechanism. We show that an increase in product market competition boosts the hiring rate as well as the separation rate. Hence, the efficiency wage schedule compatible with more competition shifts upward. An adverse effect on workers’ incentive is at work which pushes real wages up to the point that increased competition may indeed generate employment losses rather than gains.
Robust joint space control of a 6 DOF parallel robot

The purpose of this study is to apply robust inverse dynamics control for a 6 DOF parallel robot. The implementation is performed considering a simplification of the inverse dynamics control law by assuming the control law matrices as constants, and by using a joint control structure to avoid the forward kinematics calculation. The robust control strategy is applied in the outer loop of the inverse dynamic control to counteract the effects of imperfect compensation due to this simplification. Additionally, the control strategy is designed using the Lyapunov stability theory, and the dynamic model in joint coordinates is briefly presented. Numerical results are shown in order to validate the controller performance.
MNFS-FPM: A novel memetic neuro-fuzzy system based financial portfolio management

Portfolio management consists of deciding what assets to include in a portfolio given the investor's objectives and changing market and economic conditions. The always difficult selection process includes identifying which assets to purchase, how much, and when. This paper presents a novel memetic neuro-fuzzy system for financial portfolio management (MNFS-FPM) which emulates the thinking process of a rational investor and generates the optimal portfolio from a collection of assets based on a chosen investment style. The system consists mainly of two modules: the generic self-organizing fuzzy neural network realizing Yager inference (GenSoFNN-Yager), to predict the expected return of each asset, and a memetic algorithm using simplex local searches (MA-NM/SMD) to determine the optimal investment weight allocation for all assets in the portfolio. Experimental results on Dow Jones industrial average (DJIA) stocks show that the proposed system yields better performance compared to that of existing financial models: statistical mean-variance analysis and capital asset pricing model (CAPM).
Reconfigurable service-oriented architecture for autonomic computing

Almost a decade has passed since the objectives and benefits of autonomic computing were stated, yet even the latest system designs and deployments exhibit only limited and isolated elements of autonomic functionality. In previous work, we identified several of the key challenges behind this delay in the adoption of autonomic solutions, and proposed a generic framework for the development of autonomic computing systems that overcomes these challenges. In this article, we describe how existing technologies and standards can be used to realise our autonomic computing framework, and present its implementation as a service-oriented architecture. We show how this implementation employs a combination of automated code generation, model-based and object-oriented development techniques to ensure that the framework can be used to add autonomic capabilities to systems whose characteristics are unknown until runtime. We then use our framework to develop two autonomic solutions for the allocation of server capacity to services of different priorities and variable workloads, thus illustrating its application in the context of a typical data-centre resource management problem.
Workload Predicting-Based Automatic Scaling in Service Clouds

Service platforms have disadvantages such as they have long construction periods, low resource utilizations and isolated constructions. Migrating service platforms into clouds can solve these problems. The scalability is an important characteristic of service clouds. With the scalability, the service cloud can offer on-demand capacities to different services. In order to achieve the scalability, we need to know when and how to scale virtual resources assigned to different services. In this paper, a linear regression model is used to predict the workload. Based on this predicted workload, an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds. The automatic scaling mechanism combines the real-time scaling and the pre-scaling. Finally experimental results are provided to demonstrate that our approach can satisfy the user SLA while keeping scaling costs low.
A summary review of vibration-based damage identification methods

This paper provides an overview of methods to detect, locate, and characterize damage in structural and mechanical systems by examining changes in measured vibration response. Research in vibration-based damage identification has been rapidly expanding over the last few years. The basic idea behind this technology is that modal parameters (notably frequencies, mode shapes, and modal damping) are functions of the physical properties of the structure (mass, damping, and stiffness). Therefore, changes in the physical properties will cause detectable changes in the modal properties. The motivation for the development of this technology is presented. The methods are categorized according to various criteria such as the level of damage detection provided, model-based versus non-model-based methods, and linear versus nonlinear methods. The methods are also described in general terms including difficulties associated with their implementation and their fidelity. Past, current, and future-planned applications of this technology to actual engineering systems are summarized. The paper concludes with a discussion of critical issues for future research in the area of vibration-based damage identification.