Miracle-dz/UniFAR-FTUs huggingface dataset:
State Capacity, Inequality and Inter-Group Violence in Sub-Saharan Africa: 1989–2011

Abstract Most studies on internal armed conflict focus on the dyadic interaction between the state and a rebel group, leaving less attention to inter-group fighting. Addressing this gap in the literature, this study argues that the interplay between economic and political inequality and weak state capacity increases the risk of non-state conflict. An empirical analysis of 178 non-state conflicts in Sub-Saharan Africa between 1989 and 2011 provides support for the theorized conditional effect, but only for the role of economic inequality. The effect of political exclusion in the context of a weak state is not confirmed, suggesting that such conditions may be more prone to violence of another kind (i.e., mobilization against the state). Overall, these findings highlight the importance of a functioning state for maintaining peaceful inter-group relations, while they also lend support to earlier research that reports divergent effects of economic and political inequalities on civil conflict risk.
Foundation of Neuro-Fuzzy Systems and an Engineering Application

This chapter discusses the foundation of neuro-fuzzy systems. First, we introduce Takagi, Sugeno, and Kang (TSK) fuzzy model [l,2] and its difference from the Mamdani model. Under the idea of TSK fuzzy model, we discuss a neuro-fuzzy system architecture: Adaptive Network-based Fuzzy Inference System (ANFIS) that is developed by Jang [3]. This model allows the fuzzy systems to learn the parameters adaptively. By using a hybrid learning algorithm, the ANFIS can construct an input-output mapping based on both human knowledge and numerical data. Finally, the ANFIS architecture is employed for an engineering example an IC fabrication time estimation. The result is compared with other different algorithms: Gauss-Newton-based Levenberg-Marquardt algorithm (GN algorithm), and backpropagation of neural network (BPNN) algorithm. Comparing these two methods, the ANFIS algorithm gives the most accurate prediction result at the expense of the highest computation cost. Besides, the adaptation of fuzzy inference system provides more physical insights for engineers to understand the relationship between the parameters.
Around Inverse Reinforcement Learning and Score-based Classification

Inverse reinforcement learning (IRL) aims at estimating an unknown reward function optimized by some expert agent from interactions between this expert and the system to be controlled. One of its major application fields is imitation learning, where the goal is to imitate the expert, possibly in situations not encountered before. A classic and simple way to handle this problem is to see it as a classification problem, mapping states to actions. The potential issue with this approach is that classification does not take naturally into account the temporal structure of sequential decision making. Yet, many classification algorithms consist in learning a score function, mapping state-action couples to values, such that the value of the action chosen by the expert is higher than the others. The decision rule of the classifier maximizes the score over actions for a given state. This is curiously reminiscent of the state-action value function in reinforcement learning, and of the associated greedy policy. Based on this simple statement, we propose two IRL algorithms that incorporate the structure of the sequential decision making problem into some classifier in different ways. The first one, SCIRL (Structured Classification for IRL), starts from the observation that linearly parameterizing a reward function by some features imposes a linear parametrization of the Q-function by a so-called feature expectation. SCIRL simply uses (an estimate of) the expert feature expectation as the basis function of the score function. The second algorithm, CSI (Cascaded Supervised IRL), applies a reversed Bellman equation (expressing the reward as a function of the Q-function) to the score function outputted by any scorebased classifier, which reduces to a simple (and generic) regression step. These two algorithms come with theoretical guarantees and perform competitively on toy problems.
Developing Organizational Agility through IT and Supply Chain Capability

Organizations have increasingly invested money on information technology (IT) in order to improve firms' agility. It is generally believed that companies with greater IT investment tend to be more agile to respond to environmental changes. Yet, the issue of whether IT is an enabler or impeder of organizational agility still remain unresolved. Drawing upon the resource-based view theory, the information systems (IS) and supply chain management literature, we develop and test a hypothesized model that integrates IT capability, supply chain capability and organizational agility. We propose that IT capability enables the development of a higher level of IT-based supply chain capability which is embedded within inter-firm processes and in turn enhances organizational agility. Structural equation modeling is employed to test our theoretical conceptualization of 310 Australian fast-growth small-to-medium enterprises across different industrial sectors. The results show that IT capability does contribute to firm agility through enhancing inter-firm supply chain processes such as integration, information sharing and coordination. This research highlights the role of IT-enabled intermediated processes and the ways in which IT is used by firms to enhance core business processes.
Optimizing the Power Consumption of Mobile Networks Based on Traffic Prediction

Nowadays, mobile networks approach a steady growth in traffic demand. As a result, mobile network providers continuously expand their network infrastructure mainly by installing more base stations. Currently, there is a huge number of base stations serving mobile users all over the world, and this number is expected to double in the coming few years, which leads to a larger wastage of energy during low demand times. Exploiting the possibility of turning off base stations at low demand times is one of the promising approaches for saving energy and reducing CO2 emissions. Here, an early and accurate estimation of the traffic is crucial for managing resources proactively. Therefore, in this paper, we introduce a Power Management System that applies a global provisioning policy to base stations for enabling network reconfigurations in terms of power efficiency. This system is based on a Hybrid Traffic Prediction Model that forecasts the workload of base stations by utilizing historic traffic traces. A simulator is implemented to evaluate the proposed management system, which is fed with real data provided by the Open Mobile Network project. The experimental results show the possibility of turning off 49% of the base stations at some times of the day without degrading the QoS.
Point: Steady Progress and Current Challenges in Clinical Islet Transplantation

The field of β-cell replacement therapies has evolved substantially over the last decades. The lesson learned from recent islet transplantation trials in patients with unstable type 1 diabetes is that primary goals are the achievement of stable, normalized glycemic control in the absence of severe hypoglycemic episodes with improvement of quality of life and the prevention of progressive, chronic diabetes complications. Insulin independence, although desirable, should not be considered the main objective, particularly in light of the sustained positive effects achieved even with a “marginal” functional islet mass via restoration of C-peptide secretion and reduction of insulin requirements. As present limitations of islet transplantation are progressively overcome, the clinical application will greatly expand from the currently limited indication in controlled clinical research trials to more widely available cellular therapies and regenerative medicine solutions that will eventually be offered as standard treatment to the majority of patients with insulin-requiring diabetes. Vantyghem et al. (1) in the article in this issue of Diabetes Care evaluated the predictive value of primary graft function on long-term clinical outcomes of islet transplantation alone (ITA). Surrogate measures have been proposed to monitor or predict β-cell function, but they are not yet fully validated (2–4). In this report, the use of the β-score in the early posttransplant period allowed to quantify primary graft function that, when “optimal,” was associated with prolonged graft survival and better metabolic control following islet transplantation (1). In agreement with previous reports using the “Edmonton Protocol” (5–10), this trial resulted in a significant improvement of metabolic control and long-term graft function (∼70% having measurable C-peptide at 5 years). Importantly, the investigators also showed prolonged insulin independence in 57% of the patients at 5 years, with the subjects with optimal primary graft …
Trauma Disclosure on Social Networking Websites and Psychological Well-being

Trauma disclosure is associated with mental health; it could also facilitate trauma recovery. Social networking websites are transforming social interactions and becoming a big part of everyday social life. The aim of the study was to analyze how trauma disclosure on social network sites is associated with psychological well-being. Methods. A total of 70 participants, 37.1% (n = 26) men, aged from 18 to 65, participated in this study. The Life Event Checklist (LEC) was used to assess traumatic experiences. The Psychological Well-being Questionnaire was used to assess psychological well-being. Questions measuring disclosure behavior on social network sites developed by the authors of this study were also included. Results. Participants that reported disclosing and participants that reported never disclosing their traumatic experiences on social networking sites did not differ in their psychological well-being. Conclusions. Results suggest that the majority of study participants did not disclose traumatic experiences on social networking sites. The disclosure of traumatic events on social network websites was not associated with psychological well-being.
A user-microprogrammable, local host computer with low-level parallelism

This paper describes the architecture of a dynamically microprogrammable computer with low-level parallelism, called QA-2, which is designed as a high-performance, local host computer for laboratory use. The architectural principle of the QA-2 is the marriage of high-speed, parallel processing capability offered by four powerful Arithmetic and Logic Units (ALUs) with architectural flexibility provided by large scale, dynamic user-microprogramming. By changing its writable control storage dynamically, the QA-2 can be tailored to a wide spectrum of research-oriented applications covering high-level language processing and real-time processing. The QA-2 employs four identical ALUs which are independently controlled by the different fields of a 256-bit microinstruction. Sharing one uniform register file (6-kBytes), the ALUs can perform mutually independent/dependent operations on four sets of variable length operands (max. 4-Bytes × 4). In main memory accessing, four sets of variable length data (max. 4-Bytes × 4) can be simultaneously read from or written into different/continuous locations. In addition, structured microprogramming is possible because the QA-2 supports natural control structures such as an IF f(logic variables) THEN (CALL, GOTO or RETURN) ELSE (CALL,GOTO or RETURN) statement. All these features combine to yield an extensive microprogramming capability with which the user can tailor the QA-2 to his problems at hand.
Location-Aware Online Learning for Top-k Hashtag Recommendation

In this paper we investigate the problem of recommending Twitter hashtags for users with known GPS location, learning online from the stream of geo-tagged tweets. Our method learns the relevance of regions in a geographical hierarchy, combined with the local popularity of the hashtag. Unlike in typical collaborative ltering settings, trends and geolocation turns out to be more important than personalized user preferences. We evaluate in a time-aware setting, where evaluation is cumbersome by traditional measures, since we have dierent top recommendations at dierent times. We describe a time-aware framework based on individual item discounted gain.
Animation of Volcanic Smoke using CML

The animation of the volcanic smoke is useful for natural disaster simulations, entertainments, etc. In this paper, we propose a model to generate realistic animations of the volcanic smoke. The model is designed by taking the variety of the atmospheric density along the height and the loss of the pyroclasts (fragments of magma). Thus, the model is based on the dynamics of the volcanic smoke. Moreover, we solve the Navier-Stokes equations of the model by using the Coupled Map Lattice (CML) that is an efficient solver. Hence, in our system, the behavior of the volcanic smoke can be calculated in practical calculation time, and various shapes of the volcanic smoke can be generated by only changing some parameters. Therefore, realistic volcanic smoke animations can be created by our system efficiently.
MultiStage Cascading of Multiple Classifiers: One Man's Noise is Another Man's Data

For building implementable and industryvaluable classification solutions, machine learning methods must focus not only on accuracy but also on computational and space complexity. We discuss a multistage method, namely cascading, where there is a sequence of classifiers ordered in terms of increasing complexity and specificity such that early classifiers are simple and general whereas later ones are more complex and specific, being localized on patterns rejected by the previous classifiers. We present the technique and its rationale and validate its use by comparing it with the individual classifiers as well as the widely accepted ensemble methods bagging and Adaboost on eight data sets from the UCI repository. We do see that cascading increases accuracy without the concomitant increase in complexity and cost.
Background subtraction methods in video streams: A review

Background subtraction is one of the most important parts in image and video processing field. There are some unnecessary parts during the image or video processing, and should be removed, because they lead to more execution time or required memory. Several subtraction methods have been presented for the time being, but find the best-suited method is an issue, which this study is going to address. This paper presents a comparative study of several existing background subtraction methods which have been investigated from simple background subtraction to more complex statistical techniques. The goal of this study is to provide a view of the strengths and drawbacks of the widely used methods. The methods are compared based on their memory requirement, the computational time and their robustness of different videos. Finally, a comparison between the existing methods has been employed with some factors like computational time or memory requirements.
Support Detection for SAR Tomographic Reconstructions from Compressive Measurements

The problem of detecting and locating multiple scatterers in multibaseline Synthetic Aperture Radar (SAR) tomography, starting from compressive measurements and applying support detection techniques, is addressed. Different approaches based on the detection of the support set of the unknown sparse vector, that is, of the position of the nonzero elements in the unknown sparse vector, are analyzed. Support detection techniques have already proved to allow a reduction in the number of measurements required for obtaining a reliable solution. In this paper, a support detection method, based on a Generalized Likelihood Ratio Test (Sup-GLRT), is proposed and compared with the SequOMP method, in terms of probability of detection achievable with a given probability of false alarm and for different numbers of measurements.
Q-Cop: Avoiding bad query mixes to minimize client timeouts under heavy loads

In three-tiered web applications, some form of admission control is required to ensure that throughput and response times are not significantly harmed during periods of heavy load. We propose Q-Cop, a prototype system for improving admission control decisions that considers a combination of the load on the system, the number of simultaneous queries being executed, the actual mix of queries being executed, and the expected time a user may wait for a reply before they or their browser give up (i.e., time out). Using TPC-W queries, we show that the response times of different types of queries can vary significantly depending not just on the number of queries being processed but on the mix of other queries that are running simultaneously. We develop a model of expected query execution times that accounts for the mix of queries being executed and integrate this model into a three-tiered system to make admission control decisions. Our results show that this approach makes more informed decisions about which queries to reject and as a result significantly reduces the number of requests that time out. Across the range of workloads examined an average of 47% fewer requests are unsuccessful than the next best approach.
Amplify-and-Forward Relaying in Two-Hop Diffusion-Based Molecular Communication Networks

This paper studies a three-node network in which an intermediate nano-transceiver, acting as a relay, is placed between a nano-transmitter and a nano-receiver to improve the range of diffusion- based molecular communication. Motivated by the relaying protocols used in traditional wireless communication systems, we study amplify-and- forward (AF) relaying with fixed and variable amplification factor for use in molecular communication systems. To this end, we derive a closed-form expression for the expected end-to-end error probability. Furthermore, we derive a closed-form expression for the optimal amplification factor at the relay node for minimization of an approximation of the expected error probability of the network. Our analytical and simulation results show the potential of AF relaying to improve the overall performance of nano-networks.
Mobile PC Platforms Enabled with Intel ® Centrino TM Mobile Technology

Mobile PC platforms enabled with Intel CentrinoTM mobile technology incorporate innovative technologies to significantly increase the mobility of notebook PCs. In this paper, we describe the following innovations: • How to reduce interference between wireless communication technologies using the Intel Wireless Coexistence System (Intel WCS). • Two techniques for significantly extending average platform battery life. • Simple thermal solutions for thinner and lighter form factors. Finally, we outline a comprehensive platform-level validation process that helps deliver a stable platform with Intel quality.
Collaboration : Towards Automated Study Guides for MOOCs

One of the biggest challenges of delivering education with massively open online courses (MOOCs) is the high student to instructor ratio, which is further compounded in courses where students don’t arrive at traditional term boundaries. In the extreme, untended courses may not have any active teaching staff. Without support, students fend for themselves when they don’t understand a concept or need to review for a test. Our team is building a system to autonomously help students enrolled in unattended courses; this will enhance, not replace, human instruction and improve the support for all learners. For example, when a forum post indicates that the posting learner is confused, we wish to offer automatically identified related learning resources. We define learning resources broadly — they might be pointers into relevant video snippets, Wikipedia articles, past homework assignments, or forum answers from learners who took the course in the past. As online learning is enriched beyond the current video lecture model, we expect more types of resources to emerge. Finding such related resources requires that (a) the many topics that comprise the course are automatically identified and (b) resources are associated with each topic. Finally (c), the forum post in question must be associated with one or more of those topics, so that appropriate resources are identified. We break (a) the identification of topics into two steps. First we have worked to identify topics constituent to an online course on compilers from the closed caption files of the associated instructional videos. We began by automatically identifying words and phrases that primarily describe concepts important to the course. The result is much like the index at the end of a book, though referencing video snippets rather than pages. In the second step (b), we have been attempting to group the index words into clusters large enough to each describe a topic with which we can associate learning resources in the future. While this work is not mature, and we do not yet have results, we briefly describe our approaches to both these steps. 2 Keyword Extraction
Liposomal Encapsulation Enhances In Vivo Near Infrared Imaging of Exposed Phosphatidylserine in a Mouse Glioma Model

We have previously demonstrated that exposed phosphatidylserine (PS) on tumor vascular endothelial cells is highly tumor specific, and development of the PS targeted near infrared (NIR) optical probe enables successful in vivo optical imaging of U87 gliomas in a mouse model. Liposomes have been widely used as a nanovector for delivery of chemotherapeutics and imaging contrast agents due to their high payload and longer circulation time. In the current study, we have fabricated PS-targeted liposomal nanoprobes encapsulating a NIR dye, IRDye® 800CW, aiming to enhance PS-targeted tumor imaging. Hydrophilic 800CW dye was packed into the core of polyethylene glycol (PEG)-coated liposomes functionalized with F(ab’)2 fragments of PGN635, a fully human monoclonal antibody that binds PS. As expected, in vivo dynamic NIR imaging revealed significantly improved tumor/normal contrast (TNR = 20 ± 3; p < 0.01) of subcutaneous U87 gliomas in mice after injection of the liposomal nanoprobes. Markedly enhanced TNR was observed after the tumors were irradiated to increase PS exposure (TNR = 48 ± 6; p < 0.05). Intriguingly, the liposomal nanoprobes, PGN-L-800CW showed distinct biodistribution and pharmacokinetics compared to the 800CW-PGN probes used in our previous study. Our data further suggest the usefulness of PS-targeted imaging probes for sensitive tumor detection and the potential of utilizing liposomal platform for glioma theranostics.
Self-efficacy of perceived performance of leader behaviors under varied group sex compositions: Towards an understanding of sex differences in leadership.

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Using petal-decompositions to build a low stretch spanning tree

We prove that any graph G=(V,E) with n points and m edges has a spanning tree T such that ∑(u,v)∈ E(G)dT(u,v) = O(m log n log log n). Moreover such a tree can be found in time O(m log n log log n). Our result is obtained using a new petal-decomposition approach which guarantees that the radius of each cluster in the tree is at most 4 times the radius of the induced subgraph of the cluster in the original graph.