Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal transmission policies for energy harvesting nodes with partial information of energy arrivals

When energy is harnessed by wireless nodes from renewable sources, its availability becomes uncertain and its use for communications must be carefully designed. While the optimal power allocation has been derived in previous works when energy availability is fully (a priori) known, practical algorithms are needed when only causal and statistical information is available. In this paper, we study the optimal transmission policy when only the statistical distribution of the energy arrival intervals is known and no information is available on the amount of energy that will be harnessed. We firstly obtain an exact solution for the case of a step-wise transmission power profile. This result is then extended to the time-continuous case. Within energy arrival intervals, the obtained power profile is shown to be non increasing as a function of time and non decreasing as a function of the residual energy. Numerical results are finally provided focusing on an exponentially distributed energy arrival process as a case study.
Using RSS with difference method in localization algorithm for sensor networks

In this paper we present a scalable range-free algorithm for localization in wireless sensor network. Accurate and distributed localization algorithms are needed for a wide variety of wireless sensor network applications. Our algorithm finds neighbor nodes by comparing RSS and estimates the location from these neighbors' location. We also provide an evaluation of our algorithm on a simulated sensor network.
Data Hiding on 3D Meshes Based on Dither Modulation

In this paper, data hiding on 3D meshes is investigated based on dither modulation. We present two algorithms to embed the data imperceptibly into polygonal meshes by modulating the distance from the mesh surface to mesh centroid and the one from a vertex to the centroid of its traversed neighbors, respectively. In these two algorithms, the embedded data are invariant to rotation, uniformly scaling and translation, as well as slight distortion of the cover mesh to a certain degree. On the other hand, if the cover mesh is maliciously modified, the embedded data will be changed accordingly so that the proposed algorithms can be utilized for authentication applications. Further, to improve the security of using dither modulation, a component is added into the modulation to make the quantization step statistically undetectable. The two algorithms are compared in terms of complexity, capacity and security, and their common characteristics are discussed as well. Experimental results show their efficacies.
Plant Location Modelling for the Concrete and Asphalt Industries

Abstract The Auckland concrete and asphalt industries currently face substantial change in the supply of their principal ingredient, aggregate rock, as key aggregate quarries have been mined to completion and are scheduled to close. Consequent economic and environmental conditions mean a number of Auckland concrete and asphalt plants are in turn expected to close, with new plants being built in more suitable locations, and possible modifications made to remaining plants for increased capacity. This paper discusses a transportation and blending model used to assist two Auckland companies: Firth Industries with regards to the location of their ready-mix concrete plants, and Works Infrastructure for the location of their asphalt plants. The model provides simulation and analysis of the expected costs involved with each of the various scenarios of plant locations currently under consideration by the respective companies, accounting for the raw material, blending and transportation costs. The choice of plant location is shown to impact decisions throughout the industry supply chain, influencing the selection of raw material suppliers, along with the assignment of customer orders to plants for final delivery.
Metformin combined with sodium dichloroacetate promotes B leukemic cell death by suppressing anti-apoptotic protein Mcl-1

Metformin and the mitochondrial targeting dichloroacetate (DCA) have recently received attention due to their ability to inhibit anaerobic glycolysis, which renders most cancer cells resistant to apoptosis induction. We observed that Metformin alone exhibited a dose-dependent anti-leukemic activity in both B leukemic cell lines and primary B-chronic lymphocytic leukemia (B-CLL) patients' cells and its anti-leukemic activity was enhanced when used in combination with DCA. In order to overcome the problems of poor bioavailability and cellular uptake, which limit DCA efficacy, we have designed and synthetized cocrystals consisting of Metformin and DCA (Met-DCA) at different stoichiometric ratios. Of note, the MetH2++•2DCA− cocrystal exhibited enhanced in vitro anti-leukemic activity, with respect to the treatment with the mix consisting of Metformin plus DCA. In particular, the treatment with the cocrystal MetH2++•2DCA− induced a synergistic apoptotic cell death coupled to a marked down-modulation of the anti-apoptotic Mcl-1 protein. Taken together, our data emphasize that innovative compounds based on Metformin-DCA combination merit to be further evaluated as chemotherapeutic agents for the treatment of B-CLL.
Trust and Deterrence

This paper presents results from a laboratory experiment on the channels through which different law enforcement strategies deter cartel formation. With leniency policies offering immunity to the first reporting party a high fine is the main determinant of deterrence, having a strong effect even when the probability of exogenous detection is zero. Deterrence appears then mainly driven by 'distrust', the fear of partners deviating and reporting. Absent leniency, the probability of detection and the expected fine matter the most, and low fines are exploited to punish defections. The results appear relevant to several other crimes sharing cartels' strategic features, including corruption and financial fraud.
Granular location-based cluster in wireless sensor networks

The capability of sensor nodes to collect ambient data makes environmental monitoring as one of the prime applications of wireless sensor networks. Mobile sink traverses an area of interest in collecting data. With the help of virtual infrastructure (VI), an area is divided into cluster. However, the nature of the structure causes various sizes of localized cluster; coarser as a cluster is farther from the center (distance dependent). This causes inconsistency and approximate in cluster-based localization. Distance independent clustering provides consistent granularity which creates almost equal size of clusters called enhanced VI (eVI) is proposed. Applications that can benefit from this granularity include accuracy in aggregation function, and shorter safe path charting. Our previous work intelligently handled out-of-bound sensor nodes that reside outside an area of interest. However, the excluded region varies due to the structure. Distance independent clustering can enhance the exclusion process further for efficient data collection of the queried area through dividing the cluster into smaller sub-cluster appropriately. Additionally, utilizing the eVI for autonomous navigation enables mobile sink to detect more precise location of dangerous object and thus result shorter safe travelled distance. Simulation result demonstrates that the granular clusters perform better than coarse-grained clusters.
The Widening Canada-US Productivity Gap in Manufaturing

In this article, Jeffrey I. Bernstein of Carleton University, Richard G. Harris from Simon Fraser University, and Andrew Sharpe from the Centre for the Study of Living Standards provide a comprehensive analysis of the widening of the Canada-US manufacturing productivity gap. Since 1994, labour productivity growth in manufacturing in the United States has greatly exceeded that recorded in Canada. Output per hour in Canada fell 20 percentage points from 87 per cent of the US level in 1994 to 67 per cent in 2001. This development has been responsible for most of the widening of the aggregate Canada-US labour productivity gap. The authors find that the growth in the gap largely reflects the acceleration of productivity growth in US high-tech manufacturing sector. The Canadian high-sector is smaller than its US counterpart and experienced much weaker productivity growth. It is estimated that these two factors themselves account for 70 per cent of the widening of the gap over the 1994-2000 period. Faster growth in capital intensity of production in the United States also played a complementary role in the growth of the gap, a development in part fostered by the greater increase in the price of labour relative to that of investment goods in the United States than in Canada. This was due to slower labour compensation growth and, to a lesser extent, a smaller decline in the price of investment goods in Canada. The depreciation of the value of the Canadian dollar relative to the US dollar played some role in this latter development. The authors conclude that Canadian economic policies have not directly contributed in any significant manner to the widening of the gap.
Endophilin Is Critically Required for Synapse Formation and Function in Drosophila melanogaster

Studies in cell-free systems and the lamprey giant synapse have implicated crucial roles for amphiphysin and endophilin in synaptic transmission. However, null mutants at the amphiphysinlocus of Drosophila are viable and have no demonstrable synaptic vesicle-recycling defect. This has necessitated a re-examination of the role of Src homology 3 domain-containing proteins in synaptic vesicle recycling. In this report, we show that endophilin-deficient eye clones in Drosophila have an altered electroretinogram. A characteristic of this defect is its aggravation during heightened visual stimulation. It is shown that endophilin is primarily required in the nervous system. Decreased endophilin activity results in alterations in the neuromuscular junction structure and physiology. Immunofluorescence studies show colocalization of endophilin with dynamin consistent with a possible role in synaptic vesicle recycling.
Adaptive Management of Computing and Network Resources for Spacecraft Systems

It is likely that NASA's future spacecraft systems will consist of distributed processes which will handle dynamically varying workloads in response to perceived scientific events, the spacecraft environment, spacecraft anomalies and user commands. Since all situations and possible uses of sensors cannot be anticipated during pre-deployment phases, an approach for dynamically adapting the allocation of distributed computational and communication resources is needed. To address this, we are evolving the DeSiDeRaTa adaptive resource management approach to enable reconfigurable ground and space information systems. The DeSiDeRaTa approach embodies a set of middleware mechanisms for adapting resource allocations, and a framework for reasoning about the real-time performance of distributed application systems. The framework and middleware will be extended to accommodate (1) the dynamic aspects of intra-constellation network topologies, and (2) the complete real-time path from the instrument to the user. We are developing a ground-based testbed that will enable NASA to perform early evaluation of adaptive resource management techniques without the expense of first deploying them in space. The benefits of the proposed effort are numerous, including the ability to use sensors in new ways not anticipated at design time; the production of information technology that ties the sensor web together; the accommodation of greater numbers of missions with fewer resources; and the opportunity to leverage the DeSiDeRaTa project's expertise, infrastructure and models for adaptive resource management for distributed real-time systems.
Non-Conventional Method for Friction Compensation in DC Drive Position Tracking

Friction, especially its nonlinear components, may degrade the tracking performance of any servo drive systems. Focused on high precision positioning of DC electrical drive the paper proposes an easy method to compensate for the friction by using a non-model based compensator developed around a simple control structure. The approach to inhibit the friction influence is based on a compensation signal provided by the mathematical model of the servo positioning DC drive in which the load torque (including friction component) is considered zero. Build on the difference between the command signals of real servo tracking drive and, respectively, of the same servo drive, but no load status, the compensation variable depends only on the usual and measurable states of an electrical drive (current and velocity) The control problem of the servo drive with friction is solved via the gain scheduling of a P controller parameter as function of position reference scale. Good results concerning the tracking errors are achieved for large range of reference test signals, including the micrometer scale.
Perceptual proximity-based approach to structural complexity estimate of simplicial complex in the framework of Q-analysis holistic methodology

In this paper, we introduce the scheme to construct numerical estimate (with possible further verbal assessment) in reference to structural complexity ψ(K) of system's model in the form of simplicial complex(-es) K. The approach uses the notion of feature vectors meant for obtaining aforesaid ψ(K) based on proximity measures. Additional examination of aspects related to the way humans percept, represent and use information for classification and reasoning may develop into close partnering of cognitive psychology, fuzzy logic and Q-analysis methods.
Seamless Merging of Hypertext and Algorithm Animation

Online learning material that students use by themselves is one of the typical usages of algorithm animation (AA). Thus, the integration of algorithm animations into hypertext is seen as an important topic today to promote the usage of algorithm animation in teaching. This article presents an algorithm animation viewer implemented purely using HTML and JavaScript. The viewer is capable of viewing animations in Xaal (eXtensible Algorithm Animation Language), a language designed to allow easy transformation of AAs between various formats. This solution is extremely suited for use in hypertext learning material due to the advanced interaction possibilities between learning material (HTML) and the animation.
Extending the Logic IM-SPDL with Impulse and State Rewards

This report presents the logic SDRL (Stochastic Dynamic Reward Logic), an extension of the stochastic logic IM-SPDL, which supports the specication of complex performance and dependability requirements. SDRL extends IM-SPDL with the possibility to express impulse- and state reward measures. The logic is interpreted over extended action-based Markov reward model (EMRM), i.e. transition systems containing both immediate and Markovian transitions, where additionally the states and transitions can be enriched with rewards. We define ne the syntax and semantics of the new logic and show that SDRL provides powerful means to specify path-based properties with timing and reward-based restrictions. In general, paths can be characterised by regular expressions, also called programs, where the executability of a program may depend on the validity of test formulae. For the model checking of SDRL time- and reward-bounded path formulae, a deterministic program automaton is constructed from the requirement. Afterwards the product transition system between this automaton and the EMRM is built and subsequently transformed into a continuous time Markov reward model (MRM) on which numerical analysis is performed.
Share-and-Chat: Achieving Human-Level Video Commenting by Search and Multi-View Embedding

Video has become a predominant social media for the booming live interactions. Automatic generation of emotional comments to a video has great potential to significantly increase user engagement in many socio-video applications (e.g., chat bot). Nevertheless, the problem of video commenting has been overlooked by the research community. The major challenges are that the generated comments are to be not only as natural as those from human beings, but also relevant to the video content. We present in this paper a novel two-stage deep learning-based approach to automatic video commenting. Our approach consists of two components. The first component, similar video search, efficiently finds the visually similar videos w.r. t. a given video using approximate nearest-neighbor search based on the learned deep video representations, while the second dynamic ranking effectively ranks the comments associated with the searched similar videos by learning a deep multi-view embedding space. For modeling the emotional view of videos, we incorporate visual sentiment, video content, and text comments into the learning of the embedding space. On a newly collected dataset with over 102K videos and 10.6M comments, we demonstrate that our approach outperforms several state-of-the-art methods and achieves human-level video commenting.
Rethinking Democratic Accountability

Traditionally, American government has created detailed, formal procedures to ensure that its agencies and employees are accountable for finances and fairness. Now in the interest of improved performance, we are asking our front-line workers to be more responsive, we are urging our middle managers to be innovative, and we are exhorting our public executives to be entrepreneurial. Yet what is the theory of democratic accountability that empowers public employees to exercise such discretion while still ensuring that we remain a government of laws? How can government be responsive to the needs of individual citizens and still remain accountable to the entire polity? In Rethinking Democratic Accountability , Robert D. Behn examines the ambiguities, contradictions, and inadequacies in our current systems of accountability for finances, fairness, and performance. Weaving wry observations with political theory, Behn suggests a new model of accountability uwith "compacts of collective, mutual responsibility" uto address new paradigms for public management.
BOTTLENECK : A Computer Program for Detecting Recent Reductions in the Effective Population Size Using Allele Frequency Data

BOTTLENECK (current version 1.2) is a population genetics computer program that conducts four tests for identifying populations that have recently experienced a severe reduction in effective population size (Ne). ‘‘Recently’’ is defined as within approximately the past 2Ne–4Ne generations, depending on several factors such as the severity of the bottleneck and the mutation rate of the loci being studied (Cornuet and Luikart 1996). The program runs on Windows 95y. It requires allele frequency data obtained from one sample of individuals (e.g., 20–30 diploid individuals) and at least four polymorphic loci. Significant deviations from population mutation-drift equilibrium (e.g., bottlenecks) are important to detect because equilibrium is an assumption required for numerous analyses of population genetics data (e.g., see Nei 1987, p. 251). Bottlenecks are important to detect in conservation biology because they can increase the risk of population extinction. Founderflush events (i.e., short but severe bottlenecks) are important to detect because they may play a role in some modes of speciation [for reviews see Harrison (1991) and Howard (1993)].
An Elastic Middleware Platform for Concurrent and Distributed Cloud and Map-Reduce Simulation-as-a-Service

Middleware platforms empower the developers and users with an additional layer, enhancing scalability and performance. Cloud computing offers variety of resources and involves a tremendous amount of entities. Due to the limited access, along with varying availability of cloud resources, prototypes are often tested against cloud simulation environments. While computing has been evolving with multicore programming, MapReduce paradigms, and middleware platforms, cloud and MapReduce simulations still fail to exploit these developments themselves. This paper describes the research for the design, development and evaluation of a complete fully parallel and distributed cloud simulation platform (CloudSim), which tries to fill the gap between current simulations and the actual technology that they are trying to simulate. First, CloudSim provides a concurrent, distributed and elastic cloud simulator, by extending CloudSim cloud simulator, using Hazelcast in-memory key-value store. Then, it also provides an assessment of the MapReduce implementations of Hazelcast and Infinispan, as well as elastically distributing their execution in a cluster, providing means of simulating MapReduce executions. A dynamic scaler solution scales out the cloud and MapReduce simulations to multiple nodes running Hazelcast and Infinispan, based on load. The distributed execution model and adaptive scaling solution could further be leveraged as a general purpose auto-scaler middleware for a multi-tenant deployment. ı̈ż£
On the bias of BFS

Breadth First Search (BFS) and other graph traversal techniques are widely used for measuring large unknown graphs, such as online social networks. It has been empirically observed that an incomplete BFS is biased toward high degree nodes. In contrast to more studied sampling techniques, such as random walks, the precise bias of BFS has not been characterized to date. In this paper, we quantify the degree bias of BFS sampling. In particular, we calculate the node degree distribution expected to be observed by BFS as a function of the fraction of covered nodes, in a random graph $RG(p_k)$ with a given degree distribution $p_k$. Furthermore, we also show that, for $RG(p_k)$, all commonly used graph traversal techniques (BFS, DFS, Forest Fire, and Snowball Sampling) lead to the same bias, and we show how to correct for this bias. To give a broader perspective, we compare this class of exploration techniques to random walks that are well-studied and easier to analyze. Next, we study by simulation the effect of graph properties not captured directly by our model. We find that the bias gets amplified in graphs with strong positive assortativity. Finally, we demonstrate the above results by sampling the Facebook social network, and we provide some practical guidelines for graph sampling in practice.
MSSM Braneworld Inflation

We consider a Minimal Supersymmetric Standard Model (MSSM) inflation in the context of the Randall-Sundrum type-2 braneworld model. We apply the slow-roll approximations, in high-energy limit, to constraint various inflationary spectrum parameters in relation with recent WMAP9 data. We show that, for some values of the number of efolding N, the MSSM braneworld inflation provides the best fit results according to recent observations.