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International audience; It has been 35 years since Igbozurike and Raza (1983), and rural communities in Nigeria continue to face many of the challenges identified in the ARMTI seminar. Poverty and rural-urban migration remain widespread in Nigeria. Further issues of security and terrorism have also made their way into the array of problems facing rural communities in Nigeria. Therefore, the aim of this paper is to review the issues affecting the quality of life in 21st century rural Nigeria, in order to ascertain what has changed or remained the same since 1983. In achieving the study aim, the parameters used by Igbozurike and Raza (1983) will be linked with current literature on the quality of life in rural Nigeria. The paper will look at the following parameters: socioeconomic indicators, social services and infrastructure, nutritional status, population structure and mobility, institutional frameworks and the role of Agricultural Development Projects (ADPs).

Grasp planning and most specifically the grasp space exploration is still an open issue in robotics. This article presents a data-driven oriented methodology to model the grasp space of a multi-fingered adaptive gripper for known objects. This method relies on a limited dataset of manually specified expert grasps, and uses variational autoencoder to learn grasp intrinsic features in a compact way from a computational point of view. The learnt model can then be used to generate new non-learnt gripper configurations to explore the grasp space. accepted at SYSID 2021 conference

Introduction The scholarly monograph has been compared to the Hapsburg monarchy in that it seems to have been in decline forever! It was in 2002 that Stephen Greenblatt, in his role as president of the US Modern Language Association, urged his membership to recognise what he called a ‘crisis in scholarly publication’. It is easy to forget now that this crisis, as he then saw it, had nothing to do with the rise of digital technologies, e-publishing, or open access. Indeed, it puts his words in...

Background: Tsetse flies transmit trypanosomes that cause the debilitating diseases human African trypanosomosis (HAT) or sleeping sickness in humans and animal African trypanosomosis (AAT) or nagana in livestock. The riverine tsetse species Glossina palpalis gambiensis Vanderplank (Diptera: Glossinidae) inhabits riparian forests along river systems in West Africa. The Government of Senegal has embarked on a project to eliminate a population of this tsetse species from the Niayes area with the objective to manage AAT in the area. The project is implemented following an area-wide integrated pest management approach with an SIT component. The SIT can only be successful when the sterile males that are released in the field are of high biological quality, i.e. have the same dispersal capacity, survival and competitiveness as their wild counterparts. To date, sterile tsetse males have been released by air using biodegradable cardboard cartons that were manually dropped from a fixed-wing aircraft or gyrocopter. The cardboard boxes are however expensive, and the system is rather cumbersome to implement. Methods: A new prototype of an automated chilled adult release system (Bruno Spreader Innovation, (BSI™)) for tsetse flies was tested for its accuracy (in counting numbers of sterile males as loaded into the machine), release rate consistency and impact on quality of the released males. The impact of the release process was evaluated on several performance indicators of the irradiated male flies such as flight propensity, survival, mating competitiveness, premating and mating duration, and insemination rate of mated females. Results: The BSI TM release system counted with a consistent accuracy and released homogenously tsetse flies at the lowest motor speed (0.6 rpm). In addition, the chilling conditions (6 ± 1 o C) and the release process (passing of flies through the machine) had no significant negative impact on the males' flight propensity. No significant differences were observed between the control males (no irradiation and no exposure to the release process), irradiated males (no exposure to the release process) and irradiated males exposed to the release process with respect to mating competitiveness, premating period and mating duration. Only survival of irradiated males that were exposed to the release process was reduced, irrespective of whether the males were held with or without feeding. Conclusion: Although the release process had a negative effect on survival of the flies, the data of the experiments indicate that the BSI machine holds promise for use in operational tsetse SIT programmes. The promising results of this study will now need to be confirmed under operational field conditions in West Africa.

Telescopes based on the imaging atmospheric Cherenkov technique (IACTs) detect images of the atmospheric showers generated by gamma rays and cosmic rays as they are absorbed by the atmosphere. The much more frequent cosmic-ray events form the main background when looking for gamma-ray sources, and therefore IACT sensitivity is significantly driven by the capability to distinguish between these two types of events. Supervised learning algorithms, like random forests and boosted decision trees, have been shown to effectively classify IACT events. In this contribution we present results from exploratory work using deep learning as an event classification method for the Cherenkov Telescope Array (CTA). CTA, conceived as an array of tens of IACTs, is an international project for a next-generation ground-based gamma-ray observatory, aiming to improve on the sensitivity of current-generation experiments by an order of magnitude and provide energy coverage from 20 GeV to more than 300 TeV. Proceedings of the 35th International Cosmic Ray Conference (ICRC 2017), Busan, Korea. All CTA contributions at arXiv:1709.03483

We consider the fundamental question: how a legacy ``student'' Artificial Intelligent (AI) system could learn from a legacy ``teacher'' AI system or a human expert without re-training and, most importantly, without requiring significant computational resources. Here ``learning'' is broadly understood as an ability of one system to mimic responses of the other to an incoming stimulation and vice-versa. We call such learning an Artificial Intelligence knowledge transfer. We show that if internal variables of the ``student'' Artificial Intelligent system have the structure of an $n$-dimensional topological vector space and $n$ is sufficiently high then, with probability close to one, the required knowledge transfer can be implemented by simple cascades of linear functionals. In particular, for $n$ sufficiently large, with probability close to one, the ``student'' system can successfully and non-iteratively learn $k\ll n$ new examples from the ``teacher'' (or correct the same number of mistakes) at the cost of two additional inner products. The concept is illustrated with an example of knowledge transfer from one pre-trained convolutional neural network to another.

The Helioseismic and Magnetic Imager (HMI) began near-continuous full-disk solar measurements on 1 May 2010 from the Solar Dynamics Observatory (SDO). An automated processing pipeline keeps pace with observations to produce observable quantities, including the photospheric vector magnetic field, from sequences of filtergrams. The primary 720s observables were released in mid 2010, including Stokes polarization parameters measured at six wavelengths as well as intensity, Doppler velocity, and the line-of-sight magnetic field. More advanced products, including the full vector magnetic field, are now available. Automatically identified HMI Active Region Patches (HARPs) track the location and shape of magnetic regions throughout their lifetime. The vector field is computed using the Very Fast Inversion of the Stokes Vector (VFISV) code optimized for the HMI pipeline; the remaining 180 degree azimuth ambiguity is resolved with the Minimum Energy (ME0) code. The Milne-Eddington inversion is performed on all full-disk HMI observations. The disambiguation, until recently run only on HARP regions, is now implemented for the full disk. Vector and scalar quantities in the patches are used to derive active region indices potentially useful for forecasting; the data maps and indices are collected in the SHARP data series, hmi.sharp_720s. Patches are provided in both CCD and heliographic coordinates. HMI provides continuous coverage of the vector field, but has modest spatial, spectral, and temporal resolution. Coupled with limitations of the analysis and interpretation techniques, effects of the orbital velocity, and instrument performance, the resulting measurements have a certain dynamic range and sensitivity and are subject to systematic errors and uncertainties that are characterized in this report. 42 pages, 19 figures, accepted to Solar Physics

Static verification of a program source code correctness is an important element of software reliability. Formal verification of software programs involves proving that a program satisfies a formal specification of its behavior. Many languages use both static and dynamic type checking. With such approach, the static type checker verifies everything possible at compile time, and dynamic checks the remaining. The current state of the Jolie programming language includes a dynamic type system. Consequently, it allows avoidable run-time errors. A static type system for the language has been formally defined on paper but lacks an implementation yet. In this paper, we describe a prototype of Jolie Static Type Checker (JSTC), which employs a technique based on a SMT solver. We describe the theory behind and the implementation, and the process of static analysis. Comment: Modeling and Analysis of Information Systems, 2017

We study a queueing network with a single shared server, that serves the queues in a cyclic order according to the gated service discipline. External customers arrive at the queues according to independent Poisson processes. After completing service, a customer either leaves the system or is routed to another queue. This model is very generic and finds many applications in computer systems, communication networks, manufacturing systems and robotics. Special cases of the introduced network include well-known polling models and tandem queues. We derive exact limits of the mean delays under both heavy-traffic and light-traffic conditions. By interpolating between these asymptotic regimes, we develop simple closed-form approximations for the mean delays for arbitrary loads. The short paper was published in the proceedings of Performance 2011, Amsterdam

In this paper we study the question of life long learning of behaviors from human demonstrations by an intelligent system. One approach is to model the observed demonstrations by a stationary policy. Inverse rein-forcement learning, on the other hand, searches a reward function that makes the observed policy closed to optimal in the corresponding Markov decision process. This approach provides a model of the task solved by the demonstrator and has been shown to lead to better generalization in un-known contexts. However both approaches focus on learning a single task from the expert demonstration. In this paper we propose a feature learn-ing approach for inverse reinforcement learning in which several different tasks are demonstrated, but in which each task is modeled as a mixture of several, simpler, primitive tasks. We present an algorithm based on an al-ternate gradient descent to learn simultaneously a dictionary of primitive tasks (in the form of reward functions) and their combination into an ap-proximation of the task underlying observed behavior. We illustrate how this approach enables efficient re-use of knowledge from previous demon-strations. Namely knowledge on tasks that were previously observed by the learner is used to improve the learning of a new composite behavior, thus achieving transfer of knowledge between tasks.