Εμπειρική προσέγγιση της απόλυτης φτώχειας στην Ελλάδα: οι ανάγκες για κατοικία, διατροφή, ένδυση, υπόδηση, μεταφορά

Η έρευνα αυτή αποτελεί μια αντικειμενική αποτύπωση της σύγχρονης κοινωνικής πραγματικότητας αλλά και ένα χρήσιμο εργαλείο για τους σύγχρονους και μελλοντικούς αγώνες της εργατικής τάξης. Παραπέρα, συνεισφέρει στο δημόσιο διάλογο γύρω από το φλέγον ζήτημα των αναγκών, όπως έχουν οριστεί σύγχρονα και κοινωνικά, χρησιμεύοντας ως βάση για την οργάνωση και το σχεδιασμό μιας καλύτερης, πιο ορθολογικής και δίκαιης κοινωνίας. Συμπεραίνεται ότι α) ανεξάρτητα από τον τρόπο μέτρησης το φαινόμενο της απόλυτης φτώχειας φαίνεται να είναι αρκετά διαδεδομένο στην ελληνική κοινωνία, β) το «επίσημο» όριο της φτώχειας (δηλαδή αυτό που μετράται και ανακοινώνεται από την ΕΣΥΕ με βάση τον ορισμό της σχετικής φτώχειας) υποτιμά την πραγματική έκταση του φαινόμενου για όλα τα μεγέθη νοικοκυριών, γ) ότι ο βασικός μισθός δεν επαρκεί για την κάλυψη βασικών αναγκών, ενώ δ) αναδεικνύεται η δύναμη που έχει ένας δείκτης απόλυτης φτώχειας, στην περίπτωση που ο τελευταίος στηρίζεται σε έναν στέρεο θεωρητικό ισχυρισμό σχετικά με την έκταση και το ύψος των σύγχρονων βασικών αναγκών

On Green Scheduling for Desktop Grids

Task scheduling is of paramount importance in a desktop grid environment. Earlier works in the area focused on issues such as: meeting task deadlines, minimizing make-span, monitoring and checkpointing for progress, malicious or erroneous peer discovery and fault tolerance using task replication. More recently energy consumption has been studied from the standpoint of judiciously replicating and assigning tasks to the more power efficient peers. In this paper we tackle another aspect of power efficiency with regards to scheduling, namely greenness of the consumed energy. We give a formulation as a multi-objective optimization problem and propose heuristics to solve it. All the heuristics are evaluated via simulation experiments and conclusions on their merits are drawn

A Distributed Data Allocation Scheme for Autonomous Nodes

The limited computational and storage capabilities of the devices interconnected in Internet of Things (IoT) make them to host only a sub-set of the the collected data. Every device, i.e., an IoT node, should keep only the necessary data locally, thus, it can be able to process them and provide responses in limited time. Nodes can act as a team and cooperate to store the data close to the processing of tasks defined in the form of queries. In this paper, we propose a model for deciding the allocation of data in a set of IoT nodes. Every node decides if the observed data are correlated with the available datasets or they are outliers. We propose an ensemble scheme for multidimensional outliers detection that results, in real time, the final decision. When data are accepted to be locally stored, nodes select their peers where data will be replicated. This way, we keep the data in multiple locations in the network aiming to reduce latency in the provision of responses and support a fault tolerant mechanism. The replication decision is based on the correlation of the incoming data with the present datasets. We analytically describe our model and evaluate it through extensive simulations presenting its pros and cons

Improved Parallel Legalization Schemes for Standard Cell Placement with Obstacles

In standard cell placement, a circuit is given consisting of cells with a standard height, (different widths) and the problem is to place the cells in the standard rows of a chip area so that no overlaps occur and some target function is optimized. The process is usually split into at least two phases. In a first pass, a global placement algorithm distributes the cells across the circuit area, while in the second step, a legalization algorithm aligns the cells to the standard rows of the power grid and alleviates any overlaps. While a few legalization schemes have been proposed in the past for the basic problem formulation, few obstacle-aware extensions exist. Furthermore, they usually provide extreme trade-offs between time performance and optimization efficiency. In this paper, we focus on the legalization step, in the presence of pre-allocated modules acting as obstacles. We extend two known algorithmic approaches, namely Tetris and Abacus, so that they become obstacle-aware. Furthermore, we propose a parallelization scheme to tackle the computational complexity. The experiments illustrate that the proposed parallelization method achieves a good scalability, while it also efficiently prunes the search space resulting in a superlinear speedup. Furthermore, this time performance comes at only a small cost (sometimes even improvement) concerning the typical optimization metrics

Improved Parallel Legalization Schemes for Standard Cell Placement with Obstacles

In standard cell placement, a circuit is given consisting of cells with a standard height, (different widths) and the problem is to place the cells in the standard rows of a chip area so that no overlaps occur and some target function is optimized. The process is usually split into at least two phases. In a first pass, a global placement algorithm distributes the cells across the circuit area, while in the second step, a legalization algorithm aligns the cells to the standard rows of the power grid and alleviates any overlaps. While a few legalization schemes have been proposed in the past for the basic problem formulation, few obstacle-aware extensions exist. Furthermore, they usually provide extreme trade-offs between time performance and optimization efficiency. In this paper, we focus on the legalization step, in the presence of pre-allocated modules acting as obstacles. We extend two known algorithmic approaches, namely Tetris and Abacus, so that they become obstacle-aware. Furthermore, we propose a parallelization scheme to tackle the computational complexity. The experiments illustrate that the proposed parallelization method achieves a good scalability, while it also efficiently prunes the search space resulting in a superlinear speedup. Furthermore, this time performance comes at only a small cost (sometimes even improvement) concerning the typical optimization metrics