Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward an Understanding of Observational Advantages in Information Visualization

Visualizations act as cognitive aids by making reasoning tractable. To choose an appropriate visualization, designers need to know about the cognitive advantages and disadvantages of different visualization techniques. While considerable research has focused on low-level perceptual issues related to visual encodings and judgments, less research has focused on the cognitive operations that are supported by visualization techniques. We conducted a pilot study using mixed methods to uncover properties of some common visualization techniques that allow propositional statements to be directly observed rather than indirectly inferred. We describe the results of our study and discuss potential benefits of this line of research to visualization designers and researchers.
Formal approaches to language : The case of Generative Transformational Grammar

Στο άρθρο αυτό επιχειρούμε να δείξουμε ότι ο βασικός στόχος της Γενετικής Μετασχηματιστικής Γραμματικής (Chomsky 1981, 1986, 1995 κ. εξ.) είναι να αποκαλύψει τις ιδιότητες του γραμματικού σχεδιασμού της γλώσσας και επομένως να κατανοήσει καλύτερα αυτή τη μοναδική ανθρώπινη ικανότητα. Εξετάζουμε τις δύο πιο αμφισβητούμενες πτυχές αυτής της προσέγγισης, την αυτονομία της γραμματικής και το έμφυτο των βασικών της στοιχείων και υποστηρίζουμε τη γενική τους ισχύ, παρέχοντας στοιχεία από τις ιδιότητες της Ελληνικής αλλά και από τη γλωσσική κατάκτηση. ΛΕΞΕΙΣ-ΚΛΕΙ∆ΙΑ: Generative Transformational Grammar, formalism, functionalism, innateness, language acquisition
Battery-Free Smart Objects Based on RFID Backscattering

The Internet of Things era has witnessed an explosion of smart objects. As we move toward connecting the next billion wireless devices to the Internet, however, the use of batteries to power them will become unworkable, with significant repercussions on health and the environment if improperly disposed. Hence, the need for more eco-friendly technologies. This article shows how radio-frequency identification technology enables the re-design of personal wireless computing devices in a battery-less manner, representing a major leap forward in moving beyond chargers, cords, and dying devices. Specifically, we study the development of various battery-free devices, and identify the types of devices that can be handled today and what is future work. We describe testbed experiments that clearly demonstrate the feasibility of the devices we built, presenting performance comparable to commercial battery-powered counterparts.
Numerical Simulation of Atmospheric-pressure Non-equilibrium Plasmas : Status and Prospects

We present a brief review of recent work on the numerical simulation of atmospheric-pressure nonequilibrium plasma. In order to simulate plasma at atmospheric pressure, a theoretical model and numerical scheme are presented for solving Poisson’s equation and a set of continuity equations using plasma properties estimated from an electron energy distribution function. Numerical methods, including determination of the computational domain and adoption of correction methods to minimize numerical divergence, are proposed and discussed, and recent work on simulating corona and dielectric barrier discharge is presented. Finally, we assess the prospects of using numerical simulation to develop new insights into the fundamental mechanisms behind atmospheric-pressure non-equilibrium plasmas for improved practical use. Keywords—Atmospheric-pressure non-equilibrium plasmas, simulation, corona discharge, dielectric barrier
Challenges in Searching for Zika Therapeutics

Copyright: © 2016 Zhao Y. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Zika virus was discovered nearly 70 years ago, but received little attention prior to 2015, as the virus infection causes no or only a mild, self-limiting illness without need for treatment. However, large scale outbreaks in South and Central America and the Caribbean in 2015 strongly suggested Zika epidemic may have associations with the increased cases of birth defect called microcephaly. Microcephaly is a condition in which a person’s head is significantly smaller than normal for their age and sex, and occurs most often when a baby’s brain has not developed properly during pregnancy. The microcephalus babies may have life-long intellectual or physical disabilities. Now, a compilation of evidences solidified the link between the Zika virus infection in pregnant mothers and microcephaly of their babies. As the Zika virus has ‘explosive’ pandemic potential in Africa, Southeast Asia, the Pacific Islands and the Americas, the World Health Organization (WHO) declared Zika virus a Public Health Emergency of International Concern in early 2016.
GraphSAINT: Graph Sampling Based Inductive Learning Method

Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs.To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the “neighbor explosion” problem during minibatch training. Here we propose GraphSAINT, a graph sampling based inductive learning method that improves training efficiency in a fundamentally different way. By a change of perspective, GraphSAINT constructs minibatches by sampling the training graph, rather than the nodes or edges across GCN layers. Each iteration, a complete GCN is built from the properly sampled subgraph. Thus, we ensure fixed number of well-connected nodes in all layers. We further propose normalization technique to eliminate bias, and sampling algorithms for variance reduction. Importantly, we can decouple the sampling process from the forward and backward propagation of training, and extend GraphSAINT with other graph samplers and GCN variants. Comparing with strong baselines using layer sampling, GraphSAINT demonstrates superior performance in both accuracy and training time on four large graphs.
VITICULTURAL ZONING IN EUROPE : INTERANNUAL VARIABILITY

1 Department of Environmental Studies, Southern Oregon University, 1250 Siskiyou Blvd, ASHLAND, Oregon 97520, USA 2 Centre for Research and Technology of Agro-Environment and Biological Sciences (CITAB), University of Trás-os-Montes e Alto Douro, 5001-801 Quinta dos Prados, VILA REAL, Portugal 3 Institute for Geophysics and Meteorology, University of Cologne, 50923 Kerpener Str. , COLOGNE, Germany *Corresp. author: Gregory V. Jones, 541-552-6758, 541-552-6439, Email: gjones@sou.edu
Interactive comment on "HIRHAM–NAOSIM 2.0: The upgraded version of the coupled regional atmosphere-ocean-sea ice model for Arctic climate studies"

1. The two component models HIRHAM5 and NAOSIM have already been used in previous studies that also include their evaluation. Since the setups of the component models have been left unchanged for the present coupled model version as far as possible, the primary task is to demonstrate that the new coupling procedure with the aid of YAC is technically working properly and that interactions between the component models are actually represented in an acceptable way.
Design and implementation of teaching quality evaluation system based on web

To solve the networked problem of the teaching quality evaluation,functional requirements and an architecture of teaching quality evaluation system based on web are proposed according to the construction demand of digital campus.The background database and system function modules are designed.The practicality,compatibility and scalability are guaranteed.Then,a method of common data collection oriented to the integrated management system in digital campus and a safety design of system are studied,so that the integrated problem between quality evaluation system and other operational systems is solved.Finally,performances of the system are demonstrated by the practical test.
G 6-2 Characterizing Areas of Spatial Networks Based on Mixing Patterns

In this paper, we propose a new method for classifying a road network automatically into some areas and characterizing them based on the tri-mixing patterns of node degrees and the K-medoids clustering algorithm. In our experiments, using the three road networks of three cities in Shizuoka prefecture collected from OpenStreetMap (OSM), we show that our method can produce some promising results.
Quantum message compression with applications

We present a new scheme for the compression of one-way quantum messages, in the setting of coherent entanglement assisted quantum communication. For this, we present a new technical tool that we call the convex split lemma, which is inspired by the classical compression schemes that use rejection sampling procedure. As a consequence, we show new bounds on the quantum communication cost of single-shot entanglement-assisted one-way quantum state redistribution task and for the sub-tasks quantum state splitting and quantum state merging . Our upper and lower bounds are tight upto a constant and hence stronger than previously known best bounds for above tasks. Our protocols use explicit quantum operations on the sides of Alice and Bob , which are diﬀerent from the decoupling by random unitaries approach used in previous works. As another application, we present a port-based teleportation scheme which works when the set of input states is restricted to a known ensemble, hence potentially saving the number of required ports. Furthermore, in case of no prior knowledge about the set of input states, our average success ﬁdelity matches the known average success ﬁdelity, providing a new port-based teleportation scheme with similar performance as appears in literature.
Lynceus: Long-Sighted, Budget-Aware Online Tuning of Cloud Applications

—Over the past years, modern cloud providers have widely incremented the heterogeneity of the products they offer, be it virtual machines or remote storage systems. With such a huge variety of machines to choose from, several conﬁgurations – sets of machines – can be formed. These conﬁgurations may yield similar performances with identical costs. Therefore, users have a hard time ﬁnding the best one for deploying their speciﬁc workloads. This paper presents Lynceus, a long-sighted, budget-aware system for self-tuning of cloud applications, which uses a roll-out technique to search the space of possible conﬁgurations for the next best conﬁguration to try, without the overhead of a brute force search. We present numerical experiments demonstrating the cost savings achieved by our system when compared to state-of-the-art systems that neglect the cost of the exploration phase.
Prospect of Sustainable Rural Development through Solar Electrification in Bangladesh

Despite rapid economic growth in Bangladesh, overall prevalence of electrification for households is 60% for the nation but much lower for rural areas at only 42.5%. In this context, this study aims to examine the potential of renewable energy technologies like Solar Home System to meet the unprecedented energy demand and thereby contribute in uplifting the rural living standard. By extracting the research concentrations and findings of pertinent studies, this paper attempts to identify the gap in present literature on the significance of solar energy usage to promote sustainable rural development. The study proceeds further by synchronizing qualitative and quantitative information collected from key informant interviews, peer reviewed journals and reports issued by regulatory organizations. Based on the theoretical arguments presented in this qualitative research, the study signifies the resulting socio-economic benefits and environmental impacts of disseminating SHS in rural communities. The paper concludes with an emphasis on multilateral collaboration among various stakeholders to enhance the rural development initiatives. Thus, it adds value to the existing literature on assessing the impact of rural electrification in a developing country like Bangladesh that sets an impressive example in resilience and innovation in the face of possible climate change adversity.
Top-Down Parsing with Parsing Contexts A Simple Approach to Context-Sensitive Parsing

The domain of context-free languages has been extensively explored and there exist numerous techniques for parsing (all or a subset of) context-free languages. Unfortunately, some programming languages are not context-free. Using standard context-free parsing techniques to parse a context-sensitive programming language poses a considerable challenge. Implementors of programming language parsers have adopted various techniques, such as hand-written parsers, special lexers, or post-processing of an ambiguous parser output to deal with that challenge. In this paper we suggest a simple extension of a top-down parser with contextual information. Contrary to the traditional approach that uses only the input stream as an input to a parsing function, we use a parsing context that provides access to a stream and possibly to other context-sensitive information. At a same time we keep the context-free formalism so a grammar definition stays simple without mind-blowing context-sensitive rules. We show that our approach can be used for various purposes such as indent-sensitive parsing, a high-precision island parsing or XML (with arbitrary element names) parsing. We demonstrate our solution with PetitParser, a parsing-expression grammar based, top-down, parser combinator framework written in Smalltalk.
The role of outlier detection and replacement in practical seasonal adjustment *

The particular role of outlier detection and replacement in seasonal adjustment of a time series is to allow an unbiased estimation of typical seasonal and calendar effects. Only if abrupt, strong and atypical changes are explicitly modelled, is it possible to estimate typical calendar influences and seasonal effects, which recur with similar intensity in the same season each year. The seasonal and calendar components are then eliminated from the unadjusted data in order to obtain the seasonally adjusted results. It is very likely that problems will arise if outliers are not modelled. It may be the case, for example, that initial estimates of seasonally adjusted figures will identify turning points too late and be misleading over a period of months – compared with the final results, which are established years later. Furthermore, one year after mistakenly deciding not to identify outliers, problems may arise at the current end of the time series because parts of the oneoff special effects have been erroneously ascribed to the seasonal component as recurring each year. This would result in inaccurate movements of the seasonally adjusted series which have to be corrected at a later date. In order to prevent such problems and the corresponding revisions, it is advisable to treat abrupt, strong and atypical events as outliers in the process of seasonal adjustment. The reasoning behind this is illustrated using empirical examples such as the circulation of Argentine currency during and after the Argentine crisis in 2001-2002 as well as building permits granted for residential construction in western Germany during and after discussions about phasing out the grant for homebuyers. It is shown that applying the Guidelines on Seasonal Adjustment of the European Statistical System helps to avoid flawed modelling of the seasonal and calendar components and, thus, of the seasonally adjusted results. 1 The role of outlier modelling in seasonal adjustment The goal of seasonally adjusted data is to reveal the “news” contained in a time series (cf. Eurostat, 2009, item 0). Seasonally adjusted results should show changes in long-term growth trends or in the business cycle as well as the impact of special effects such as strikes, large orders, atypical weather conditions or natural disasters. In terms of methodology, “news” is filtered out of unadjusted data using time series analysis tools. As a rule, it is assumed that the time series of the unadjusted values Yt can be decomposed into three unobservable components * This paper represents the authors’ personal opinions and does not necessarily reflect the views of the Deutsche Bundesbank or its staff. 1 Int. Statistical Inst. : Proc. 58th World Statistical Congress, 2011, Dublin (Session STS030) p.2726
Algorithm for Distributed Constraint Optimization Problems with Low Constraint Density

Many challenges in multi-agent coordination can be modeled as Distributed Constraint Optimization Problems(DCOPs). Aiming at DCOPs with low constraint density,this paper proposes a distributed algorithm based on the idea of greed and backjumping.In this algorithm,each agent makes decisions according to the greedy principle that the most assignment combinations in the problems with low constraint density occur at a zero cost,and the backjumping mechanism among the agents ensures the success of this algorithm,even when this greedy principle leads to a local optimum.In contrast with the existing mainstream DCOP algorithms,this algorithm can solve problems with low constraint density with fewer messages while keeping the polynomial message length and space complexity.The correctness of the key mechanisms in this algorithm has been proved,and those advantages in performance have been verified by experiments.
A Data Mining Application to Leukemia Microarray Gene Expression Data Analysis

Microarray technology makes biologists be capable of monitoring expression of thousands of genes in a single experiment on a small chip. How to extract knowledge and information from these microarray gene expression datasets has attracted great attention of many molecular biologist, statisticians and computer scientists. Recently, data mining has become a synonym for the process of extracting the hidden and useful information from datasets. In this paper, we developed a methodology to analyze the gene expression data by using the techniques in data mining such as feature selection and classification. We are given the leukemia microarry gene expression dataset that consists of 72 samples. Each sample has 7129 gene expression levels and comes with one of three class labels: AML, B-cell ALL and T-cell ALL. We selected 30 genes out of 7129 by using feature selection technique and prescribe a procedure to classify every sample into the correct category.
Slot-Based Co-operative Driver Guidance System for Public Events

Public events in urban environments often suffer from large amounts of congestion and unpredictable journey times due to the high volume of drivers in the surrounding road network e.g. concerts. To address this problem, the co-operative slot-based driver guidance system is proposed. This system applies traffic shaping measures in an attempt to organise the flow of traffic, leading to more accurate journey time predictions and alleviating congestion. This project explores whether this slot-based system is a more beneficial form of driving, rather than the current unco-operative approach. The system that was developed focused on analysing the feasibility and potential benefits gained from incorporating this driver guidance into event management plans. The potential benefits include more accurate journey time predictions and an overall better-organised flow of traffic. Existing research has been completed for slot-based driving scenarios for highway on-ramp merging and intersections. However, this project will specifically target public events in an urban environment, particularly around the entry and exit routes to parking lots. The slot-based solution is similar to time division multiple access (TDMA) in computer networking. In this instance, the road is the channel to be divided up and drivers are allocated time slots on demand to travel within towards their destination. Each driver is provided with real-time guidance information in order to maintain their position within the slot. An implementation of this system has been developed and tested using PTV Vissim, a traffic simulation software. Initial results indicate that the overall throughput of vehicles in the vicinity of the event is increased and journey times are significantly more consistent during varying traffic volume levels. These slot-based results are in comparison to the results generated from the simulations run using the Wiedemann 74 human driver model within Vissim.
An Improved-Water Filling Algorithm Power Allocation for DFFR Network MIMO

In wireless systems, interference is a major factor that limits the total network capacity. Power allocation is one of the effective techniques that has garnered interest in Network MIMO system and improved the efficiency of wireless systems. This study presents the development of a new power allocation algorithm based on water filling. This algorithm combines the Dynamic Fractional Frequency Reuse (DFFR) with a Network MIMO to maximise the performance of cell edge users. Simulation results show that the proposed algorithm provides more ergodic capacity, compared with the existing power allocation strategies. In addition, it improves the network throughput, while ensuring fairness for cell edge users in the LTE-A system. When the total transmit power is 100W, the proposed algorithm offers 50% capacity, 37.5% throughput and 38% fairness advantage over the conventional water-filling algorithm.
A Comparison of Methods Used to Census Mantled Howlers in the Dry Tropical Forest of Costa Rica

Symposium: Critical Issues in Captive Care.7 . Primate-Focused Ecotourism: Proceed with Caution, by C. Russell.7 . Rehabilitating orangutans (Pongo pygmaeus): Behavioral Competence, by A. E. Russon.8 . Captive Care in Primates: Application to a Chimpanzee Release Program (Pan troglodytes troglodytes) in the Republic of Congo, by B. Goossens, M. Ancrenaz, C. Vidal, and A. Jamart.10 . Potential Solutions to Managing a Population of Surplus Research Chimpanzees, by F. L. Dolins.11
ΟΙΚΟΝΟΜΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ ΠΡΟΓΡΑΜΜΑ ΜΕΤΑΠΤΥΧΙΑΚΩΝ ΣΠΟΥ∆ΩΝ ΣΤΗΝ ΕΠΙΣΤΗΜΗ ΤΩΝ ΥΠΟΛΟΓΙΣΤΩΝ ∆ιπλωματική Εργασία Μεταπτυχιακού ∆ιπλώματος Ειδίκευσης «Adaptive Rate Allocation in Distributed Stream Processing Systems under Bursty Workloads»

In the recent years we have witnessed a proliferation of distributed stream processing systems that need to operate under bursty workloads. Examples include network traffic monitoring, processing of financial feeds and real-time sensor data analysis systems. Meeting the QoS requirements of the stream processing systems under burstiness is a challenging process. In this paper we present our approach for adaptive rate allocation within the distributed stream processing system to meet the end-to-end execution time and rate demands of the applications. Our algorithm determines the rates of the application components at runtime, with respect to the QoS constraints, to compensate for delays experienced by the components or to react to sudden bursts of load. Our technique is distributed and low-cost. Our detailed experimental results over our Synergy middleware illustrate that our approach is practical, depicts good performance and has low resource overhead.
Applying Compiler Techniques to Reachability Analysis of High-Level Models

Using a tool for high-level Petri nets as an example, this article shows how techniques familiar from compilers can make reachability analysers more powerful. Syntax transformations can be applied to extend the modelling language with convenient short-hand notations, such as universal and existential quantification and multi-set summation. The process of reachability analysis can be dramatically sped up by compiling models to executable machine code that performs all model-dependent tasks, such as computing the successors of a state. This work describes a code generator implementation and the way the generated code is linked with the reachability analyser code.