Vulnerability to Poverty in select Central Asian Countries

In the extant literature either income or consumption expenditures as measured over short periods of time have been regarded as a proxy for the material well-being of households. However, economists have long recognized that a household's sense of well-being depends not just on its average income or expenditures, but also on the risks it faces and its ability to deal with these risks. Hence vulnerability is a more satisfactory measure of welfare. In this study we used the concept of vulnerability as expected poverty to assess the household vulnerability to poverty in four Central Asian countries: Azerbaijan, Kazakhstan, Kyrgyzstan, and Tajikistan. Except for Tajikistan, headcount poverty and vulnerability rates are significantly different. We also find that vulnerability differs significantly across households by location and selected household characteristics. In this paper we use a simple empirical measurement that allows estimating the headcount vulnerability to poverty using cross-section data. This measurement is based on the strong assumption that households have the same conditional distribution of consumption in a stationary environment. While this approach cannot capture all dimensions of vulnerability, it at least begins to raise the policy issue that vulnerability should be considered alongside poverty.Poverty ; Vulnerability ; Cross-section data ; Central Asia

Vulnerability to Poverty in select Central Asian Countries

In the extant literature either income or consumption expenditures as measured over short periods of time have been regarded as a proxy for the material well-being of households. However, economists have long recognized that a household's sense of well-being depends not just on its average income or expenditures, but also on the risks it faces and its ability to deal with these risks. Hence vulnerability is a more satisfactory measure of welfare. In this study we used the concept of vulnerability as expected poverty to assess the household vulnerability to poverty in four Central Asian countries: Azerbaijan, Kazakhstan, Kyrgyzstan, and Tajikistan. Except for Tajikistan, headcount poverty and vulnerability rates are significantly different. We also find that vulnerability differs significantly across households by location and selected household characteristics. In this paper we use a simple empirical measurement that allows estimating the headcount vulnerability to poverty using cross-section data. This measurement is based on the strong assumption that households have the same conditional distribution of consumption in a stationary environment. While this approach cannot capture all dimensions of vulnerability, it at least begins to raise the policy issue that vulnerability should be considered alongside poverty

Higher SLA Satisfaction in Datacenters with Continuous Placement Constraints

National audienceIn a virtualized datacenter, the Service Level Agreement for an application restricts the Virtual Machines (VMs) placement. An algorithm is in charge of maintaining a placement compatible with the stated constraints. Conventionally, when a placement algorithm computes a schedule of actions to re-arrange the VMs, the constraints ignore the intermediate states of the datacenter to only restrict the resulting placement. This situation may lead to temporary constraint violations. In this thesis, we present the causes of these violations. We then advocate for continuous placement constraints to restrict also the action schedule. We discuss why the development of continuous constraints requires more attention but how the extensible placement algorithm BtrPlace can address this issue

Predicting pedestrian trajectories at different densities: A multi-criteria empirical analysis

Predicting human trajectories is a challenging task due to the complexity of pedestrian behavior, which is influenced by external factors such as the scene's topology and interactions with other pedestrians. A special challenge arises from the dependence of the behaviour on the density of the scene. In the literature, deep learning algorithms show the best performance in predicting pedestrian trajectories, but so far just for situations with low densities. In this study, we aim to investigate the suitability of these algorithms for high-density scenarios by evaluating them on different error metrics and comparing their accuracy to that of knowledge-based models that have been used since long time in the literature. The findings indicate that deep learning algorithms provide improved trajectory prediction accuracy in the distance metrics for all tested densities. Nevertheless, we observe a significant number of collisions in the predictions, especially in high-density scenarios. This issue arises partly due to the absence of a collision avoidance mechanism within the algorithms and partly because the distance-based collision metric is inadequate for dense situations. To address these limitations, we propose the introduction of a novel continuous collision metric based on pedestrians' time-to-collision. Subsequently, we outline how this metric can be utilized to enhance the training of the algorithms

Replay detection and multi-stream synchronization in CS:GO game streams using content-based Image retrieval and Image signature matching

In GameStory: The 2019 Video Game Analytics Challenge, two main tasks are nominated to solve in the challenge, which are replay detection - multi-stream synchronization, and game story summarization. In this paper, we propose a data-driven based approach to solve the first task: replay detection - multi-stream synchronization. Our solution aims to determine the replays which lie between two logo-transitional endpoints and synchronize them with their sources by extracting frames from videos, then applying image processing and retrieval remedies. In detail, we use the Bag of Visual Words approach to detect the logo-transitional endpoints, which contains multiple replays in between, then employ an Image Signature Matching algorithm for multi-stream synchronization and replay boundaries refinement. The best configuration of our proposed solution manages to achieve the second-highest scores in all evaluation metrics of the challenge

LIFER 2.0: discovering personal lifelog insights using an interactive lifelog retrieval system

This paper describes the participation of the Organiser Team in the ImageCLEFlifelog 2019 Solve My Life Puzzle (Puzzle) and Lifelog Moment Retrieval (LMRT) tasks. We proposed to use LIFER 2.0, an enhanced version of LIFER, which was an interactive retrieval system for personal lifelog data. We utilised LIFER 2.0 with some additional visual features, obtained by using traditional visual bag-of-words, to solve the Puzzle task, while with the LMRT, we applied LIFER 2.0 only with the provided information. The results on both tasks confirmed that by using faceted filter and context browsing, a user can gain insights from their personal lifelog by employing very simple interactions. These results also serve as baselines for other approaches in the ImageCLEFlifelog 2019 challenge to compare with

Comparing the Effectiveness of Phrase-Focused Exercises. A Partial Replication of Boers, Demecheleer, Coxhead, and Webb (2014)

In a recent article, Boers, Demecheleer, Coxhead, and Webb (2014) deplored the lack of effectiveness for the learning of verb-noun collocations of a number of exercise formats which they sampled from EFL textbooks and put to the test in a series of quasi-experimental trials. The authors called for further investigations into possible improvements to such exercise formats. The present article is a response to that call. It also addresses methodological issues which may have affected Boers et al.’s (2014) findings and which rendered their conclusions tentative. In the quasi-experiment reported here, EFL learners were given fill-in-the-blank exercises on verb-noun phrases in one of three formats: (1) choose the appropriate verb, (2) complete the verb by using a first-letter cue, and (3) choose the appropriate intact phrase. A delayed post-test gauged the learners’ ability to recall the meaning of the phrases as well as their verb-noun partnership. In both regards the exercise where learners worked with intact phrases generated the best results. We then evaluate the extent to which exercises for phrase learning in ten recent EFL textbooks accord with recommendations that follow from the quasi-experimental findings