Possible Poecilogony Due to Discontinuous Multifactorial Inheritance in Some Mediterranean Species of Raphitoma (Mollusca, Conoidea, Raphitomidae)

At least 10 pairs of similar, most probably closely related, species of Raphitoma are often sampled in the same Mediterranean localities. In each pair, one member bears a planktotrophic protoconch and the other a lecithotrophic one. We propose that the phenomenon may be attributed to a simple gene that functions in conjunction with others and environmental factors to exhibit a discontinuous multifactorial inheritance leading to poecilogony. Below a threshold, the animals may produce fewer and larger germ cells, giving rise to fewer and larger eggs and large lecithotrophic embryos with large paucispiral protoconch I, while above that threshold, more and smaller germ cells leading to smaller eggs and to planktotrophic larvae with small protoconch I and large multispiral protoconch II. Preliminary measurements are in support of our hypothesis. Analysis of mitochondrial DNA markers as well as interbreeding experiments could bring an end to the existing confusion

Preliminary search for a νirus in Dacus oleae Gmel. populations in Northern Greece

Στην περίοδο από Ιούλιο έως Δεκέμβριο 1984 συλλέχΟησαν 4.5 g ακμαίων, 8.5 g υγιών προνυμφών και 0.45 g νεκρών προνυμφών του εντόμου Dacus oleae Gmel. από περιοχές της Βόρειας Ελλάδας, που χαρακτηρίζονταν από Βαριά προσβολή των ελαιοδένδρων από δάκο, στα οποία δεν εφαρμόστηκε χημική καταπολέμηση. Τα δείγματα εξετάσθηκαν για εγκλεισμένους και μη εγκλεισμένους ιούς με τη χρησιμοποίηση φυγοκεντρήσεων, οπτικού και ηλεκτρονικού μικροσκοπίου, ανάλυσης νουκλεϊ νικών οξέων KUI πειράματα μολυσματικότητας. Στα δείγματα των νεκρών προνυμφών, και σε αντίθεση με εκείνα των υγιών προνυμφών και των ακμαίων, εντοπίσθηκαν και απομονώθηκαν ιόμορφα σωμάτια. Τα σωμάτια αυτά είχαν διάμετρο περίπου 35 nm και μερικά ήταν άδεια, όπως φάνηκε από τη διείσδυση της χρωστικής κατά την αρνητική χρώση. Δεν κατέστη δυνατός ο παραπέρα χαρακτηρισμός των «ιοσωματίων» για το λόγο έλλειψης αρκετής ποσότητας δείγματος, ενώ προσπάθειες πολλαπλασιασμούτους σε προνύμφες του λεπιδόπτερου Galleria mellonella και σε καλλιέργειες κυττάρων Drosophila melanogastcr αποδείχθησαν ανεπιτυχείς. Αν και τα μικρά ιόμορφα σωμάτια ήταν το μοναδικό πιθανό παθογόνο αίτιο που αναγνωρίστηκε στις νεκρές προνύμφες, φαίνεται κάπως απίθανο να αποτελούν και το μοναδικό αίτιο του θανάτου για το λόγο του σχετικά μικρού αριθμού τους. Πάντως αν καταστεί δυνατό να πολλαπλασιασθούν οι «ιοί» αυτοί σε εκτροφές του δάκου της ελιάς ή σε καλλιέργειες κυττάρων ιστών του ίδιου εντόμου, ίσως να αποτελέσουν στο μέλλον ένα βιολογικό μέσο καταπολέμησης του.A large number of larvae of Dacus oleae were collected from infested olives in Northern Greece, and a small proportion of these were found to be dead. Adult flies were caught in McPhail traps at the same locations. The larvae and adults were fractionated by a series of steps designed to identify occluded and nonoccluded viruses. Virus-like particles were identified in small amounts only in the dead larvae

Enabling Efficient and General Subpopulation Analytics in Multidimensional Data Streams

Today’s large-scale services (e.g., video streaming platforms, data centers, sensor grids) need diverse real-time summary statistics across multiple subpopulations of multidimensional datasets. However, state-of-the-art frameworks do not offer general and accurate analytics in real time at reasonable costs. The root cause is the combinatorial explosion of data subpopulations and the diversity of summary statistics we need to monitor simultaneously. We present Hydra, an efficient framework for multidimensional analytics that presents a novel combination of using a “sketch of sketches” to avoid the overhead of monitoring exponentially-many subpopulations and universal sketching to ensure accurate estimates for multiple statistics. We build Hydra as an Apache Spark plugin and address practical system challenges to minimize overheads at scale. Across multiple real-world and synthetic multidimensional datasets, we show that Hydra can achieve robust error bounds and is an order of magnitude more efficient in terms of operational cost and memory footprint than existing frameworks (e.g., Spark, Druid) while ensuring interactive estimation times

Deep Learning for Energy Time-Series Analysis and Forecasting

Energy time-series analysis describes the process of analyzing past energy observations and possibly external factors so as to predict the future. Different tasks are involved in the general field of energy time-series analysis and forecasting, with electric load demand forecasting, personalized energy consumption forecasting, as well as renewable energy generation forecasting being among the most common ones. Following the exceptional performance of Deep Learning (DL) in a broad area of vision tasks, DL models have successfully been utilized in time-series forecasting tasks. This paper aims to provide insight into various DL methods geared towards improving the performance in energy time-series forecasting tasks, with special emphasis in Greek Energy Market, and equip the reader with the necessary knowledge to apply these methods in practice.Comment: 13 papges, 4 figure

Enabling efficient and general subpopulation analytics in multidimensional data streams

Today's large-scale services ( e.g. , video streaming platforms, data centers, sensor grids) need diverse real-time summary statistics across multiple subpopulations of multidimensional datasets. However, state-of-the-art frameworks do not offer general and accurate analytics in real time at reasonable costs. The root cause is the combinatorial explosion of data subpopulations and the diversity of summary statistics we need to monitor simultaneously. We present Hydra, an efficient framework for multidimensional analytics that presents a novel combination of using a "sketch of sketches" to avoid the overhead of monitoring exponentially-many subpopulations and universal sketching to ensure accurate estimates for multiple statistics. We build Hydra as an Apache Spark plugin and address practical system challenges to minimize overheads at scale. Across multiple real-world and synthetic multidimensional datasets, we show that Hydra can achieve robust error bounds and is an order of magnitude more efficient in terms of operational cost and memory footprint than existing frameworks (e.g., Spark, Druid) while ensuring interactive estimation times.Red Hat; CNS-2107086 - National Science Foundation; CNS-2106946 - National Science Foundation; CNS-2132643 - National Science FoundationPublished versio

Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management

Financial portfolio management describes the task of distributing funds and conducting trading operations on a set of financial assets, such as stocks, index funds, foreign exchange or cryptocurrencies, aiming to maximize the profit while minimizing the loss incurred by said operations. Deep Learning (DL) methods have been consistently excelling at various tasks and automated financial trading is one of the most complex one of those. This paper aims to provide insight into various DL methods for financial trading, under both the supervised and reinforcement learning schemes. At the same time, taking into consideration sentiment information regarding the traded assets, we discuss and demonstrate their usefulness through corresponding research studies. Finally, we discuss commonly found problems in training such financial agents and equip the reader with the necessary knowledge to avoid these problems and apply the discussed methods in practice

Unified management of multi-model data : (Vision paper)

Peer reviewe

Integrated Management of European Cherry Fruit Fly Rhagoletis cerasi (L.): Situation in Switzerland and Europe

Abstract: The European cherry fruit fly, Rhagoletis cerasi (L.) (Diptera: Tephritidae), is a highly destructive pest. The low tolerance for damaged fruit requires preventive insecticide treatments for a marketable crop. The phase-out of old insecticides threatens cherry production throughout the European Union (EU). Consequently, new management techniques and tools are needed. With the increasing number of dwarf tree orchards covered against rain to avoid fruit splitting, crop netting has become a viable, cost-effective method of cherry fruit fly control. Recently, a biocontrol method using the entomopathogenic fungus Beauveria bassiana has been developed for organic agriculture. However, for most situations, there is still a lack of efficient and environmentally sound insecticides to control this pest. This review summarizes the literature from over one hundred years of research on R. cerasi with focus on the biology and history of cherry fruit fly control as well as on antagonists and potential biocontrol organisms. We will present the situation of cherry fruit fly regulation in different European countries, give recommendations for cherry fruit fly control, show gaps in knowledge and identify future research opportunities