Greek Economy: Back to the Past

The Greek economy has been characterized by successive phases of growth and recession with marked changes in its macroeconomic data over the last twenty years, which have affected the social reality of the country. The growth phase resulted in the deadlocks of 2009 (deficits, debt and EU rules) and the recession phase, which was determined by the memorandum programs through internal devaluation, resulted in a drastic reduction in national income (28%). During the last two years, the country has been led back to a downward path (recession – pandemic) with unspecified final consequences for the economy and society. In typical terms, the performance of the Greek economy refers to the past (90s), and in this case there are strong doubts about its strategy and objectives. Keywords: EU, Greece, crisi

SECONDARY EDUCATION TEACHERS' VIEWS ON ISSUES RELATED TO WASTEWATER AND SOLID WASTE MANAGEMENT

This research investigated the views of secondary education teachers in Greece regarding waste management. A questionnaire was administered to 332 in-service secondary education teachers from various specialties to collect data on their views related to their ability and willingness to implement waste management topics, knowledge of waste management terms, and prioritization of waste management actions. The findings revealed that secondary education teachers expressed a moderate to low level of ability to implement educational activities related to waste management, with a wide distribution of views. They express that they felt more confident in implementing Environmental Education Programs (EEP) related to solid waste management than wastewater management. Teachers showed interest in participating in and organizing environmental programs, particularly for solid waste management. While teachers demonstrated a moderate level of basic knowledge of waste management, their understanding varied across different topics. Some terms were well-known, while others were less familiar. Teachers had a limited understanding of waste management principles and struggled to correctly prioritize waste management actions, with a significant percentage prioritizing recycling over waste minimization. These findings highlight the need for further education and training for secondary education teachers in Greece regarding waste management and environmental education. Improvements are necessary to bridge the gap between teachers' knowledge and understanding of waste management principles. This study emphasizes the importance of integrating waste management topics into teacher education programs and providing ongoing professional development opportunities. This research contributes to the literature by identifying areas for improvement in environmental education practices among secondary education teachers in Greece. The findings provide insights for curriculum development and teacher training programs. Future research should evaluate the effectiveness of environmental education initiatives aimed at enhancing teachers' knowledge and understanding of waste management principles.  Article visualizations

Parameterized Compilation Lower Bounds for Restricted CNF-formulas

We show unconditional parameterized lower bounds in the area of knowledge compilation, more specifically on the size of circuits in decomposable negation normal form (DNNF) that encode CNF-formulas restricted by several graph width measures. In particular, we show that - there are CNF formulas of size $n$ and modular incidence treewidth $k$ whose smallest DNNF-encoding has size $n^{\Omega(k)}$, and - there are CNF formulas of size $n$ and incidence neighborhood diversity $k$ whose smallest DNNF-encoding has size $n^{\Omega(\sqrt{k})}$. These results complement recent upper bounds for compiling CNF into DNNF and strengthen---quantitatively and qualitatively---known conditional low\-er bounds for cliquewidth. Moreover, they show that, unlike for many graph problems, the parameters considered here behave significantly differently from treewidth

Learning to detect video events from zero or very few video examples

In this work we deal with the problem of high-level event detection in video. Specifically, we study the challenging problems of i) learning to detect video events from solely a textual description of the event, without using any positive video examples, and ii) additionally exploiting very few positive training samples together with a small number of ``related'' videos. For learning only from an event's textual description, we first identify a general learning framework and then study the impact of different design choices for various stages of this framework. For additionally learning from example videos, when true positive training samples are scarce, we employ an extension of the Support Vector Machine that allows us to exploit ``related'' event videos by automatically introducing different weights for subsets of the videos in the overall training set. Experimental evaluations performed on the large-scale TRECVID MED 2014 video dataset provide insight on the effectiveness of the proposed methods.Comment: Image and Vision Computing Journal, Elsevier, 2015, accepted for publicatio

Directed Hamiltonicity and Out-Branchings via Generalized Laplacians

We are motivated by a tantalizing open question in exact algorithms: can we detect whether an $n$-vertex directed graph $G$ has a Hamiltonian cycle in time significantly less than $2^n$? We present new randomized algorithms that improve upon several previous works: 1. We show that for any constant $0<\lambda<1$ and prime $p$ we can count the Hamiltonian cycles modulo $p^{\lfloor (1-\lambda)\frac{n}{3p}\rfloor}$ in expected time less than $c^n$ for a constant $c<2$ that depends only on $p$ and $\lambda$. Such an algorithm was previously known only for the case of counting modulo two [Bj\"orklund and Husfeldt, FOCS 2013]. 2. We show that we can detect a Hamiltonian cycle in $O^*(3^{n-\alpha(G)})$ time and polynomial space, where $\alpha(G)$ is the size of the maximum independent set in $G$. In particular, this yields an $O^*(3^{n/2})$ time algorithm for bipartite directed graphs, which is faster than the exponential-space algorithm in [Cygan et al., STOC 2013]. Our algorithms are based on the algebraic combinatorics of "incidence assignments" that we can capture through evaluation of determinants of Laplacian-like matrices, inspired by the Matrix--Tree Theorem for directed graphs. In addition to the novel algorithms for directed Hamiltonicity, we use the Matrix--Tree Theorem to derive simple algebraic algorithms for detecting out-branchings. Specifically, we give an $O^*(2^k)$-time randomized algorithm for detecting out-branchings with at least $k$ internal vertices, improving upon the algorithms of [Zehavi, ESA 2015] and [Bj\"orklund et al., ICALP 2015]. We also present an algebraic algorithm for the directed $k$-Leaf problem, based on a non-standard monomial detection problem

Erythropoiesis-stimulating agents significantly delay the onset of a regular transfusion need in nontransfused patients with lower-risk myelodysplastic syndrome

Background The EUMDS registry is an unique prospective, longitudinal observational registry enrolling newly diagnosed patients with lower‐risk myelodysplastic syndrome (MDS) from 17 European countries from both university hospitals and smaller regional hospitals. Objective The aim of this study was to describe the usage and clinical impact of erythropoiesis‐stimulating agents (ESAs) in 1696 patients enrolled between 2008 and 2014. Methods The effects of ESAs on outcomes were assessed using proportional hazards models weighting observations by propensity to receive ESA treatment within a subset of anaemic patients with or without a regular transfusion need. Results ESA treatment (median duration of 27.5 months, range 0–77 months) was administered to 773 patients (45.6%). Outcomes were assessed in 897 patients (484 ESA treated and 413 untreated). ESA treatment was associated with a nonsignificant survival benefit (HR 0.82, 95% CI: 0.65–1.04, P = 0.09); this benefit was larger amongst patients without prior transfusions (P = 0.07). Amongst 539 patients for whom response to ESA treatment could be defined, median time to first post‐ESA treatment transfusion was 6.1 months (IQR: 4.3–15.9 months) in those transfused before ESA treatment compared to 23.3 months (IQR: 7.0–47.8 months) in patients without prior transfusions (HR 2.4, 95% CI: 1.7–3.3, P < 0.0001). Responding patients had a better prognosis in terms of a lower risk of death (HR 0.65, 95% CI: 0.45–0.893, P = 0.018), whereas there was no significant effect on the risk of progression to acute myeloid leukaemia (HR 0.71, 95% CI: 0.39–1.29, P = 0.27). Conclusion Appropriate use of ESAs can significantly delay the onset of a regular transfusion need in patients with lower‐risk MDS

Strategies for PS processing of large sentinel-1 datasets

Several advanced DInSAR techniques have been used to map surface deformations due to volcanism, active tectonics, landslides, subsidence, and uplift as well as to monitor the deformation of critical infrastructure such as bridges and dams. Recently, studies have explored the potential of these techniques to be integrated into a permanently operating monitoring system. ESA’s Sentinel-1 satellites have been providing SAR images for such a purpose since 2014. Nowadays, it is easy to access more than 230 SAR images of any area of interest, and update this dataset every six days with a new image. Due to the high frequency of the data acquisition, the question arises on how to best handle such a dataset. Is it suitable to always consider the whole available dataset or would a partial processing of the dataset and combining the results at a later point be more appropriate? To answer these questions, three different processing strategies are investigated in this paper. The first is a continuously growing dataset and for the second and third strategy, the dataset was divided into sub-stacks with and without overlap. In this study, the key parameters of each strategy are analyzed. In addition, the size of the sub-stacks is varied and the results are compared

Performance over Random

This paper proposes a new evaluation approach for video summarization algorithms. We start by studying the currently established evaluation protocol; this protocol, defined over the ground-truth annotations of the SumMe and TVSum datasets, quantifies the agreement between the user-defined and the automatically-created summaries with F-Score, and reports the average performance on a few different training/testing splits of the used dataset. We evaluate five publicly-available summarization algorithms under a large-scale experimental setting with 50 randomly-created data splits. We show that the results reported in the papers are not always congruent with their performance on the large-scale experiment, and that the F-Score cannot be used for comparing algorithms evaluated on different splits. We also show that the above shortcomings of the established evaluation protocol are due to the significantly varying levels of difficulty among the utilized splits, that affect the outcomes of the evaluations. Further analysis of these findings indicates a noticeable performance correlation among all algorithms and a random summarizer. To mitigate these shortcomings we propose an evaluation protocol that makes estimates about the difficulty of each used data split and utilizes this information during the evaluation process. Experiments involving different evaluation settings demonstrate the increased representativeness of performance results when using the proposed evaluation approach, and the increased reliability of comparisons when the examined methods have been evaluated on different data splits

Ground deformation and associated hazards in NW peloponnese (Greece)

In the last decades, ground deformations were investigated, analysed and monitored using several methods. As a consequence of a spreading urbanization, several phenomena, e.g. landslide and subsidence, were emphasized or triggered causing not only socio-economic damages, but, in some cases, also casualties. The investigation and mapping of these phenomena are important for both local authorities and civil protection in order to promote a higher conscientious urban planning and to highlight the more hazardous areas. Furthermore, the information are a key point for social development connected to the awareness of the environment and the related risk. The Achaia prefecture, in the north-eastern Peloponnese (Greece), close to the Gulf of Patras, is an area strongly affected by subsidence and landslides. Furthermore, this is an earthquake-prone area, a factor that can trigger some mass movements. For this region, a landslide inventory was realized with the help of the interpretation of Persistent Scatterers data, for the period 1992–2008, and high-resolution optical satellite images, available until 2016, in addition to the investigation of the landslide State of Activity. Moreover, for the coastal area, a section was investigated to evidence subsidence