Miracle-dz/UniFAR-FTUs huggingface dataset:
War: An Introduction to Theories and Research on Collective Violence

The Onset of Civil War Theories of Collective Violence: The Continuing Rational Actor versus Deprived Actor Debate Civil Wars in International Relations: Patterns, Consequences & Conditions of Termination Cain's Choice: Causes of One-Sided Violence against Civilians Ethnicity Matters, But What Kind? A Review of the Quantitative Literature of Ethnicity & Conflict Macro- & Micro-Level Theories of Violence in Ethnic & Non-Ethnic Civil Wars Civilizations: Value Differences & Conflict Globalization & Insurgent Financing Cursed by Resources? High-Value Natural Resources & Armed Civil Conflict Economic Policies & Armed Conflict: The Promise of Globalization Diasporas & Civil War Adding Fuel to the Flames? Multinational Companies Operating in Zones of Conflict Modern Conflicts: World Systems & Wars in Three Different Military Eras Civil War & the Transnational Diffusion of Domestic Violence The Rivalry Contribution to Conflict: Great Promises & Potential Problems Issues & Conflict Index.
Correlation with Behavioral Activity and Rest Implies Circadian Regulation by SCN Neuronal Activity Levels

The SCN of the hypothalamus contains a major pacemaker, which exhibits 24-h rhythms in electrical impulse frequency. Although it is known that SCN electrical activity is high during the day and low during the night, the precise relationship between electrical activity and behavioral rhythms is almost entirely unknown. The authors performed long-term recordings of SCN multiple unit activity with the aid of implanted microelectrodes in parallel with the drinking activity in freely moving mice. The animals were kept in a 12h:12h light-dark cycle (LD 12:12) and in short-day (LD 8:16) and long-day photoperiods (LD 16:8). Onsets and offsets of behavioral activity occurred when SCN discharge was around half-maximum value. Of the onsets 80%, and of the offsets 62%, occurred when SCN electrical activity differed less than 15% from the half-maximum electrical activity levels. Transitions between rest and activity could be described by a sigmoid shaped probability curve with Hill coefficients of 7.0 for onsets and 5.7 for offsets. The similarity in the onset and offset levels shows an absence of hysteresis in the control of behavioral activity by the SCN. Exposure to short- or long-day photoperiods induced significant alterations in the waveform of electrical activity but did not affect SCN electrical activity levels at which behavioral transitions occurred. In all photoperiods, the SCN signal was skewed with more rapid discharge changes during onsets (19% per hour) than offsets (11% per hour). The precision of the circadian system appears optimized, as transitions between behavioral activity and rest occur when the change in SCN electrical activity is maximal, both during the declining and rising phase. The authors conclude that transitions in behavioral state can be described by a probability function around half-maximum electrical activity levels and that the parameters of the SCN, predicting onset and offset of behavior, are remarkably insensitive to environmental conditions.
The Great Inflation in the United States and the United Kingdom: Reconciling Policy Decisions and Data Outcomes

We argue that the Great Inflation experienced by both the United Kingdom and the United States in the 1970s has an explanation valid for both countries. The explanation does not appeal to common shocks or to exchange rate linkages, but to the common doctrine underlying the systematic monetary policy choices in each country. The nonmonetary approach to inflation control that was already influential in the United Kingdom came to be adopted by the United States during the 1970s. We document our position by examining official policymaking doctrine in the United Kingdom and the United States in the 1970s, and by considering results from a structural macroeconomic model estimated using U.K. data.
Performance of speech services in WCDMA using fixed-beams and transmit diversity systems

The system capacity of speech users in WCDMA system is investigated using a number of different downlink transmit modes. A single antenna sectorized system, a two fixed-beam antenna array, a closed-loop (mode I) and an open-loop space time transmit diversity methods are compared in a dynamic radio network simulator. Frequency selective and flat fading channels are considered. The results show that the diversity gain in flat fading channels is substantial. In frequency selective fading, the benefits of fixed-beam systems are encouraging, whereas the performance of transmit diversity methods (especially the open-loop scheme) are rather unsatisfactory.
The Language Connectome

The field of the neurobiology of language is experiencing a paradigm shift in which the predominant Broca–Wernicke–Geschwind language model is being revised in favor of models that acknowledge that language is processed within a distributed cortical and subcortical system. While it is important to identify the brain regions that are part of this system, it is equally important to establish the anatomical connectivity supporting their functional interactions. The most promising framework moving forward is one in which language is processed via two interacting “streams”—a dorsal and ventral stream—anchored by long association fiber pathways, namely the superior longitudinal fasciculus/arcuate fasciculus, uncinate fasciculus, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, and two less well-established pathways, the middle longitudinal fasciculus and extreme capsule. In this article, we review the most up-to-date literature on the anatomical connectivity and function of these pathways. We also review and emphasize the importance of the often overlooked cortico-subcortical connectivity for speech via the “motor stream” and associated fiber systems, including a recently identified cortical association tract, the frontal aslant tract. These pathways anchor the distributed cortical and subcortical systems that implement speech and language in the human brain.
Surface texture recognition by surface rendering

A model-based texture recognition system that classifies image textures seen from different distances and under different illumination directions is presented. The system works on the basis of a surface model obtained by means of four-source color photometric stereo (CPS), used to generate 2-D image textures as they would have appeared if imaged under different imaging geometries. The proposed recognition system combines cooccurrence matrices for feature extraction with a nearest neighbor classifier. The use of the cooccurrence matrices instead of filtering methods for feature extraction allows us to utilize only pixels for which valid information has been extracted by CPS. The validity of the method is demonstrated by classifying texture images captured under different imaging geometries than the reference images in the database. Moreover, the process of recognition allows one to guess the approximate direction of the illumination used to capture the test image.
A Linux-based Real-Time Operating System

This work describes the design, implementation, and possible applications of Real-Time Linux | a hard real-time version of the Linux operating system. In this system, a standard time-sharing OS and a real-time executive run on the same computer. Interrupt controller emulation is used to guarantee a low maximum interrupt latency independently of the base system. The use of a one-shot timer makes it possible to achieve a low task release jitter without compromising throughput. Lock-free FIFO bu(cid:11)ers are employed for communication between real-time tasks and Linux processes. User-de(cid:12)ned schedulers are allowed as are run-time changes in the scheduling policy. The system is in active use for real-time data acquisition, control, and communications. The software is free and can be obtained by FTP at luz.cs. nmt.edu:/pub/rtlinux or via the WWW at http://luz.cs. nmt.edu/~rtlinux.
Some Political Economy Insights to Multi-Level Government Financing Mechanisms in Hungary

There is a growing literature on the political economy of public finance, however most of it deals with central government behaviour. Observation of the first and second generation of the fiscal federalism literature together with the political economy of intergovernmental grants and the widening literature on political budget cycles makes it obvious that although decentralization is a continuing policy trend in reality there are institutional, political factors that can increase the chances for inefficient policy outcomes. Infrastructure investment finances - at all levels of government – are especially prone to election cycles and corruption, due to high visibility, high expenditures, lobbying by special interests, possible control by politicians, yet they strongly effect long run growth prospects and productivity of a country. As a major political influencing factor affecting public finance decisions can come through the incentives of election cycles, this analysis finds evidence of electoral cycle effects and partisan (political color) considerations in intergovernmental grant distribution and in municipal investment activities – for the case of Hungary. Hypotheses on determinants of investment outlays of local governments and for partisan (political color) effects in grant recipiency chances are tested with linear and Probit panel regressions respectively on a panel dataset comprehensive for all Hungarian local government budgets from 1993-2003, a period bridging three election cycles, linked with some demographic, socioeconomic and local election data.
Adaptive Neural Network Decentralized Backstepping Output-Feedback Control for Nonlinear Large-Scale Systems With Time Delays

In this paper, two adaptive neural network (NN) decentralized output feedback control approaches are proposed for a class of uncertain nonlinear large-scale systems with immeasurable states and unknown time delays. Using NNs to approximate the unknown nonlinear functions, an NN state observer is designed to estimate the immeasurable states. By combining the adaptive backstepping technique with decentralized control design principle, an adaptive NN decentralized output feedback control approach is developed. In order to overcome the problem of “explosion of complexity” inherent in the proposed control approach, the dynamic surface control (DSC) technique is introduced into the first adaptive NN decentralized control scheme, and a simplified adaptive NN decentralized output feedback DSC approach is developed. It is proved that the two proposed control approaches can guarantee that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded, and the observer errors and the tracking errors converge to a small neighborhood of the origin. Simulation results are provided to show the effectiveness of the proposed approaches.
Income Inequality and Rural Poverty in China: Focusing on the Role of Government Transfer Payments

Purpose - – The purpose of this paper is to apply a poverty level decomposition approach to decompose the poverty by income sources and investigate the impact of government transfers on income inequality and rural poverty. Design/methodology/approach - – This paper uses the decomposition method of inequality and the decomposition method of poverty level by resource endowments to decompose the overall inequality and the overall poverty by income sources. Findings - – It is found that unequal income distribution rather than income endowments is mainly responsible for the existence of poverty. Government transfers and relief income, aiming at the poor, help alleviate inequality and poverty, but are not targeting the poorest. Unequal distribution of production subsidies actually lead to higher poverty incidence. Research limitations/implications - – This paper has revealed that the poverty issue cannot be resolved with economic development alone if the issues including the inequality in income distribution are not solved. It is important to make government transfers/subsidies pro-poor. Originality/value - – A poverty level decomposition approach is first used to decompose the poverty by income sources in China.
New framework for Simultaneous Localization and Mapping: Multi map SLAM

The main contribution of this paper arises from the development of a new framework, which has its inspiration in the mechanics of human navigation, for solving the problem of Simultaneous Localization and Mapping (SLAM). The proposed framework has specific relevance to vision based SLAM, in particular, small baseline stereo vision based SLAM and addresses several key issues relevant to the particular sensor domain. Firstly, as observed in the authors' earlier work, the particular sensing device has a highly nonlinear observation model resulting in inconsistent state estimations when standard recursive estimators such as the Extended Kalman Filter (EKF) or the Unscented variants are used. Secondly, vision based approaches tend to have issues related to large feature density, narrow field of view and the potential requirement of maintaining large databases for vision based data association techniques. The proposed Multi Map SLAM solution addresses the filter inconsistency issue by formulating the SLAM problem as a nonlinear batch optimization. Feature management is addressed through a two tier map representation. The two maps have unique attributes assigned to them. The Global Map (GM) is a compact global representation of the robots environment and the Local Map (LM) is exclusively used for low-level navigation between local points in the robot's navigation horizon.
A novel non-invasive ventilation mask to prevent and manage respiratory failure during fiberoptic bronchoscopy, gastroscopy and transesophageal echocardiography

Fiberoptic bronchoscopy (for difficult intubation, bronchoalveolar lavage or biopsies), gastric endoscopies and transesophageal echocardiography (for transfemoral aortic valve replacement, MitraClip or left atrial appendage closure), are widespread diagnostic and therapeutic procedures. Non-invasive ventilation during upper endoscopies can be used to prevent or treat acute respiratory failure especially in high risk or sedated patients. We describe a novel full face mask specifically developed not only for “elective” non-invasive ventilation during upper endoscopies but also for emergent application without probe removal. The mask is formed by two halves fixed only at the upper extremity allowing opening and closure while the probe is in place. Position of the port and shape of the mask allow easy insertion (through the nose or the mouth) and handling of different sized probes. The mask, commercialized as “Janus”, preserves arterial oxygenation during procedures in spontaneously breathing patients with or at risk of hypoxemia (mainly fiberoptic bronchoscopy for guided tracheal intubation or for bronchoalveolar lavage). In patients requiring a true ventilatory support (like patients with neuromuscular disease or those deeply sedated), Janus also allows effective manual or mechanical ventilation. Its use can improve safety, patient’s comfort (as sedation can be titrated to the desired effect without fearing respiratory depression) and efficiency, avoiding time wasting and allowing procedure completion. Prospective trials are required to confirm its effectiveness.
Automatic Construction of the Motion Database which Allows to Search Contents by a Motion Name

In order to reuse the human motion data recorded by the motion capture, we have proposed a motion database construction method named BUYOFU. In this method, captured motion data are divided into a basic motion which is easy to reuse, and the word explaining the motion is used as a search key. Since we have been performing these processes manually at present, if the amount of basic motion data increases registration and maintenance of data will difficult. In order to reuse the captured motion data efficiently, we propose the method of assigning a search key automatically to the divided motion data, and the method of retrieving the target basic motion data from the database by specifying a motion name.
Extracting Compact Sets of Features for Question Classification in Cognitive Systems: A Comparative Study

Question Classification is one of the key tasks of Cognitive Systems based on the Question Answering paradigm. It aims at identifying the type of the possible answer for a question expressed in natural language. Machine learning techniques are typically employed for this task, and exploit a high number of features extracted from labelled questions of benchmark training sets in order to achieve good classification results. However, the high dimensionality of the feature space often limits the possibility of applying more efficient classification approaches, due to high training costs. In this work, more compact sets of lexical and syntactic features are proposed to distinguish question classes. In particular, the widely used unigrams are substituted with a smaller number of features, extracted by modifying typical Natural Language Processing procedures for question analysis. The accuracy values gained on a benchmark dataset by using these different sets of features are compared among them and with the state-of-the-art, taking into account the required complexity at the same time. The new sets of extracted features show a good trade-off between accuracy and complexity.
Occluded Face Recognition Based on Dictionary Learning and Sub-classifier Fusion

Facial recognition is a challenging area of research due to difficulties with robust face recognition (FR) under occlusion and sparse representation-based classification (SRC) only focusing on face global features. To solve these issues, we proposed an occluded FR method based on dictionary learning for sparse representation and sub-classifiers fusion (LSSRC), which efficiently combines local and overall characteristics of face images. First, we partitioned continuous but non-lapped blocks of the face by multi-resolution blocking. Then, for each block, SRC was used for feature extraction and face classification. We established a sub-block dictionary and conducted K-SVD dictionary learning, established sub-classifiers and determined weight. Finally, we conducted sub-classifier fusion recognition using voting rules with weight. Results using the AR and YaleA database showed that our algorithm achieved superior recognition performance to the existing sparse representation classification occluded FR method.
Multiple RFID Tags Access Algorithm

One of the main problems that affect the data integrity of passive RFID systems is the collision between the tags. A popular anticollision algorithm which dominates the standards in HF and UHF passive RFID systems is framed slotted Aloha (FSA) and some variations of FSA. Throughput and average time delay of the RFID system which determines the performance/efficiency of the system are reduced rapidly when the number of tags inside the interrogation zone is increased. Using larger frame sizes is not always the solution. This paper discusses and compares the existing protocols, and proposes a variation of FSA, called progressing scanning (PS) algorithm. The PS algorithm divides the tags in the interrogation zone into smaller groups and gives the reader the ability to communicate with each of them. For performance analysis, the PS algorithm was evaluated with the parameters of a typical passive RFID system at 2.5 GHz. The results showed that the PS algorithm can improve the efficiency of the RFID system and provide a reliable solution for cases with a high density of tags in the area (over 800 tags).
Micro-grid energy management: A computational approach based on simulation and approximate discrete abstraction

We address the problem of designing an energy management system for a small scale micro-grid comprising a chiller plant and a cooling load representing some zone. The energy management task involves distributing the cooling power request among the chillers constituting the chiller plant, and modulating the temperature set-point of the zone so as to save energy while preserving comfort. The problem can be decoupled into a static optimization problem and a dynamic programming problem for a discrete time stochastic hybrid system. The latter one is here addressed by abstracting the stochastic hybrid model to a (finite) controlled Markov chain, where costs associated to transitions are computed by simulating the original model and determining the corresponding energy consumption. A numerical example shows the efficacy of the approach.
La demande de grilles d'Euromillions

Cet article analyse la demande de grilles d’Euromillions, jeu de loto lance en 2004 et joue simultanement dans neuf pays europeens, avec les memes regles et les memes tirages. En utilisant d’abord la methodologie du prix effectif, nous montrons que les elasticites-prix de la demande sont tres variables d’un pays a l’autre. En particulier, l’Espagne et le Portugal se caracterisent par une elasticite tres faible, et notamment une sensibilite reduite aux variations du jackpot. Au contraire, les joueurs irlandais et du Royaume-Uni reagissent fortement a ces memes variations. L’interpretation de ces resultats s’appuie a la fois sur la relative faiblesse du pib par tete dans les deux premiers pays, sur le developpement du jeu syndique en Espagne et sur la taxation differenciee de l’Euromillions par rapport aux autres loteries au Portugal. Concernant le Royaume-Uni et l’Irlande, l’existence d’un marche du jeu tres competitif avec des activites de bookmaking developpees constitue un element d’explication notable des resultats. Nous montrons, par ailleurs, qu’un modele comme la theorie des perspectives de Kahneman et Tversky [1992], tout en permettant de justifier theoriquement la participation a cette loterie, n’ameliore pas la qualite de l’estimation de la fonction de demande du fait du role preeminent des variations du jackpot dans les variations du prix effectif.
A distributed adaptive algorithm for speeding up consensus in networked multi-agent systems

In this paper we provide a distributed scheme for updating the weight links in a weighted graph in order to improve the process of reaching an agreement in multi-agent systems. This concept of reaching an agreement based on local information, known as Consensus, has several quantitative aspects that can characterize the whole network behaviour. Among them, one of the pivotal features, is the consensus time - the time that it takes for the network to reach the agreement. Therefore a distributed adaptive scheme that can improve available consensus protocols in the case of consensus time is suggested.
Safe path planning among multi obstacles

This paper proposed a practical path-planning algorithm for an autonomous vehicle or a car-like robot in an unknown semi-structured (or unstructured) environment, where obstacles are detected online by the vehicle's sensors. The algorithm is based on particle filter, Bézier curves and support vector machine to provide a safe path among various static and moving obstacles and to satisfy the vehicle's curvature constraints. The algorithm has been implemented and verified on the simulation software. Experimental results demonstrate the effectiveness of the proposed method in complicated conditions with existing of multi objects.