Recommending Tasks in Online Judges using Autoencoder Neural Networks

Programming contests such as International Olympiads in Informatics (IOI) and ACM International Collegiate Programming Contest (ICPC) are becoming increasingly popular in recent years. To train for these contests, there are several Online Judges available, in which users can test their skills against a usually large set of programming tasks. In the literature, so far few papers have addressed the problem of recommending tasks in online judges. Most notably, as opposed with traditional Recommender Systems, since the learners improve their skills as they solve more problems, there is an intrinsic dynamic dimension that has to be considered: when recommending movies or books, it is likely that the preferences of the users are more or less stable, whilst in recommending tasks this does not hold true. In order to help the learners, it is crucial to recommend them tasks that are challenging but not unsolvable compared with their current set of skills. In this paper we present a Recommender System (RS) for Online Judges based on an Autoencoder (Artificial) Neural Network (ANN). We also discuss the results of an experimental evaluation of our approach in both the scenarios in which we consider, or not, the intrinsic dynamic dimension of the problem. The ANNs are trained with the dataset of all the submissions in the Italian National Online Judge, used to train students for the Italian Olympiads in Informatics

A rare case of melanosis of the hard palate mucosa in a patient with chronic myeloid leukemia

Imatinib Mesylate, also known as Gleevec or ST1-571, is a tyrosine-kinase inhibitor used as the gold standard medication for the chronic myeloid leukemia (CML); Imatinib has indeed deeply revolutionized the CML therapy allowing most patients to have a good quality of life. Despite its beneficial effects, Imatinib has significant side effects such as mucosal pigmentation. A 72-year-old female having an Imatinib induced mucosal pigmentation is presented: she has been treated with Imatinib since 2003 and only in 2014 discovered, during a routine dental visit, having a pigmented lesion on her hard palate mucosa. Histopathologically, the lesion shows the deposition of fine dark brown spherical bodies within the lamina propria and cloaked in between the collagen fibers. There was no sign of inflammation, hyperplasia, or hemorrhage in the tissu

Complexity-based partitioning of CSFI problem instances with Transformers

In this paper, we propose a two-steps approach to partition instances of the Conjunctive Normal Form (CNF) Syntactic Formula Isomorphism problem (CSFI) into groups of different complexity. First, we build a model, based on the Transformer architecture, that attempts to solve instances of the CSFI problem. Then, we leverage the errors of such model and train a second Transformer-based model to partition the problem instances into groups of different complexity, thus detecting the ones that can be solved without using too expensive resources. We evaluate the proposed approach on a pseudo-randomly generated dataset and obtain promising results. Finally, we discuss the possibility of extending this approach to other problems based on the same type of textual representation

Weighted-distance sliding windows and cooccurrence graphs for supporting entity-relationship discovery in unstructured text

The problem of Entity relation discovery in structured data, a well covered topic in literature, consists in searching within unstructured sources (typically, text) in order to find connections among entities. These can be a whole dictionary, or a specific collection of named items. In many cases machine learning and/or text mining techniques are used for this goal. These approaches might be unfeasible in computationally challenging problems, such as processing massive data streams. A faster approach consists in collecting the cooccurrences of any two words (entities) in order to create a graph of relations - a cooccurrence graph. Indeed each cooccurrence highlights some grade of semantic correlation between the words because it is more common to have related words close each other than having them in the opposite sides of the text. Some authors have used sliding windows for such problem: they count all the occurrences within a sliding windows running over the whole text. In this paper we generalise such technique, coming up to a Weighted-Distance Sliding Window, where each occurrence of two named items within the window is accounted with a weight depending on the distance between items: a closer distance implies a stronger evidence of a relationship. We develop an experiment in order to support this intuition, by applying this technique to a data set consisting in the text of the Bible, split into verses

Leaf protein availability in food: Significance of the binding of phenolic compounds to ribulose-1,5-diphosphate carboxylase

Abstract The binding of phenolic compounds to ribulose-1,5-diphosphate carboxylase (RubisCO) is known to give rise to some digestive problems in human beings. In fact, the biological value of protein and hence the Protein Efficiency Ratio and Net Protein Utilization decrease drastically. For this reason the binding of phenolic compounds (e.g. rutin and chlorogenic acid) to ribulose-1,5-diphosphate carboxylase (RubisCO) was studied by means of ultrafiltration techniques in order to better elucidate the nature of this interaction and the factors influencing it in an attempt to limit or avoid it. RubisCO behaviour was also compared with that of Bovine Serum Albumin. A multivariate approach was used to determine the most influencing variables and their effects on binding. A classical binding study with the aim of determining the binding stoichiometry was also carried out. pH was found to be the most important variable affecting the binding of rutin to RubisCO as well as rutin to Bovine Serum Albumin while contact time became relevant when operating in sub-alkaline pH conditions. Classical binding analysis was carried out at pH 7.0 to 7.3 by both direct partition and diafiltration methods. A total number of five binding sites was determined, with two kinds of binding mechanisms, one of which was hydrophobic. The diafiltration method can be considered a useful tool when high affinity interactions are studied; RubisCO protein stability was disturbed by stirring, but this allowed an increased affinity of aggregated RubisCO to chlorogenic acid to be noted. This might have important consequences on RubisCO extraction technology since the most critical phase of phenolic contamination is the crystallization-precipitation step

Detecting Moral Features in TV Series with a Transformer Architecture through Dictionary-Based Word Embedding

Moral features are essential components of TV series, helping the audience to engage with the story, exploring themes beyond sheer entertainment, reflecting current social issues, and leaving a long-lasting impact on the viewers. Their presence shows through the language employed in the plot description. Their detection helps regarding understanding the series writers’ underlying message. In this paper, we propose an approach to detect moral features in TV series. We rely on the Moral Foundations Theory (MFT) framework to classify moral features and use the associated MFT dictionary to identify the words expressing those features. Our approach combines that dictionary with word embedding and similarity analysis through a deep learning SBERT (Sentence-Bidirectional Encoder Representations from Transformers) architecture to quantify the comparative prominence of moral features. We validate the approach by applying it to the definition of the MFT moral feature labels as appearing in general authoritative dictionaries. We apply our technique to the summaries of a selection of TV series representative of several genres and relate the results to the actual content of each series, showing the consistency of results

Measuring Injectors Fouling in Internal Combustion Engines through Imaging

Abstract The use of liquid fuels derived from biomass in internal combustion engines, based on direct fuel injection, involves the formation of a large amount of carbon deposits on the tip of injectors which significantly influence emissions and engine performance. Currently most of the research activities are focused on the physical and chemical evaluation of deposits, using GC/MS (gas chromatography/mass spectrometry) analysis of alcoholic solutions with dissolved samples and FESEM (Field Emission Scanning Electron Microscopy) and EDS (Energy Dispersive X-ray Spectroscopy) analysis to characterize their microstructures. There are few methodologies to quantify the temporal fouling on the injectors in order to define a correlation between fouling, fuel and engine performance. The development of a methodology to compare the different effects of fouling obtained diversifying the fuel input of a direct injection engine is the aim of this work. The methodology is based on photography and post-processing of images to obtain a pixel count linked to a fouling index. The effect of lighting and visual angle is taken into account and a preliminary qualitative evaluation of the performance of the methodology is carried out. This methodology was also carried out to determine the minimum number of photos required to quantify the deposit independently by the orientation

geometry optimization of a commercial annular rql combustor of a micro gas turbine for use with natural gas and vegetal oils

Abstract A new annular RQL combustion chamber of an 80 kWel Elliott TA80R micro gas turbine was designed and validated by means of CFD simulations of natural gas combustion on modified geometries to overcome known failures at low running hours (around 2500 hrs) caused by overheating. This work provides the results of the design optimization on some geometrical parameters for fuel injection, air-fuel mixing and mixture combustion. Moreover, the new design considered simplified manufacturability and flow optimization to reduce emission while maintaining similar temperatures and efficiencies. The new combustor can easily be built with affordable overall gross costs € guaranteeing similar TIT with respect to the original geometry and with a considerable reduction of NOx emission

editorial preface ati 2018 energy procedia

Abstract The 73rd Conference of the Italian Thermal Machines Engineering Association (ATI) was held in Pisa (Italy) on September 12-14, 2018. The conference was organized by ATI and the University of Pisa. The main topic of this conference edition was: "Innovation and research for a sustainable energy future" In the last 10 years, the share of renewable sources in the energy mix of several countries has increased at a steady pace. This led to a revolution in the way energy conversion is conceived and distributed in comparison to a fossil based system. Nowadays, thinking about a 100% renewable energy system is no more just a dream. Nevertheless, the transition to this future implies several critical decisions from the technical and economic point of view. The 73rd ATI conference was the opportunity to discuss these topics, present new frontiers of the energy engineering research and promote the cooperation between researchers. The topics of the conference were: Heat Transfer and Fluid Dynamics, Energetics of Buildings, Air Conditioning and Refrigeration, Environmental Aspects of Energy Conversion, Renewable Energy, Advanced Energy Conversion and Storage Systems, Innovative Propulsion Systems and Internal Combustion Engines, Turbomachinery, Combustion and Fuels, Fluid Power. This issue of Energy Procedia includes all the papers presented at the conference, 143 in oral form and 20 in the poster session reserved to PhD students. The conference also included a plenary session and two panel sessions with outstanding keynote speakers from both academy and industry. The Guest Editors of the 73rd Conference of the Italian Thermal Machines Engineering Associatio

Enhanced selective sonosensitizing efficacy of ultrasound-based anticancer treatment by targeted gold nanoparticles

partially_open9noThis study investigates cancer targeted gold nanoparticles as ultrasound sensitizers for the treatment of cancer.his study investigates cancer targeted gold nanoparticles as ultrasound sensitizers for the treatment of cancer. Methods: The ultrasound sensitizer activity of folate-PEG decorated gold nanoparticles (FA-PEG-GNP) has been studied on human cancer cell lines that overexpress folate receptors (KB and HCT-116) and another that does not (MCF7), at two ultrasound energy densities (8 × 10-6 J cm-2 and 8 × 10-5 J cm-2, for 5 min at 1.866 MHz). Results: FA-PEG-GNP selectively targeted KB and HCT-116 cells and a remarkable reduction in cancer cell growth was observed upon ultrasound exposure, along with significant reactive oxygen species generation and increase in necrotic cells. Conclusion: The combined use of targeting capacity and the ultrasound sensitizing effect, make FA-PEG-GNP promising candidates for the site-specific cancer treatment. © 2016 Future Medicine Ltd.partially_openBrazzale, Chiara; Canaparo, Roberto; Racca, Luisa; Foglietta, Federica; Durando, Giovanni; Fantozzi, Roberto; Caliceti, Paolo; Salmaso, Stefano; Serpe, LoredanaBrazzale, Chiara; Canaparo, Roberto; Racca, Luisa; Foglietta, Federica; Durando, Giovanni; Fantozzi, Roberto; Caliceti, Paolo; Salmaso, Stefano; Serpe, Loredan