PyPlutchik: Visualising and comparing emotion-annotated corpora

The increasing availability of textual corpora and data fetched from social networks is fuelling a huge production of works based on the model proposed by psychologist Robert Plutchik, often referred simply as the “Plutchik Wheel”. Related researches range from annotation tasks description to emotions detection tools. Visualisation of such emotions is traditionally carried out using the most popular layouts, as bar plots or tables, which are however sub-optimal. The classic representation of the Plutchik’s wheel follows the principles of proximity and opposition between pairs of emotions: spatial proximity in this model is also a semantic proximity, as adjacent emotions elicit a complex emotion (a primary dyad) when triggered together; spatial opposition is a semantic opposition as well, as positive emotions are opposite to negative emotions. The most common layouts fail to preserve both features, not to mention the need of visually allowing comparisons between different corpora in a blink of an eye, that is hard with basic design solutions. We introduce PyPlutchik the Pyplutchik package is available as a Github repository (http://github.com/alfonsosemeraro/pyplutchik) or through the installation commands pip or conda. For any enquiry about usage or installation feel free to contact the corresponding author, a Python module specifically designed for the visualisation of Plutchik’s emotions in texts or in corpora. PyPlutchik draws the Plutchik’s flower with each emotion petal sized after how much that emotion is detected or annotated in the corpus, also representing three degrees of intensity for each of them. Notably, PyPlutchik allows users to display also primary, secondary, tertiary and opposite dyads in a compact, intuitive way. We substantiate our claim that PyPlutchik outperforms other classic visualisations when displaying Plutchik emotions and we showcase a few examples that display our module’s most compelling features

Screening of endocrine organ-specific humoral autoimmunity in 47,XXY Klinefelter's syndrome reveals a significant increase in diabetes-specific immunoreactivity in comparison with healthy control men.

The aim of this study was to evaluate the frequency of humoral endocrine organ-specific autoimmunity in 47,XXY Klinefelter’s syndrome (KS) by investigating the autoantibody profile specific to type 1 diabetes (T1DM), Addison’s disease (AD), Hashimoto thyroiditis (HT), and autoimmune chronic atrophic gastritis (AG). Sixty-one adult Caucasian 47,XXY KS patients were tested for autoantibodies specific to T1DM (Insulin Abs, GAD Abs, IA-2 Abs, Znt8 Abs), HT (TPO Abs), AD (21-OH Abs), and AG (APC Abs). Thirty-five of these patients were not undergoing testosterone replacement therapy TRT (Group 1) and the remaining 26 patients started TRT before the beginning of the study (Group 2). KS autoantibody frequencies were compared to those found in 122 control men. Six of 61 KS patients (9.8 %) were positive for at least one endocrine autoantibody, compared to 6.5 % of controls. Interestingly, KS endocrine immunoreactivity was directed primarily against diabetes-specific autoantigens (8.2 %), with a significantly higher frequency than in controls (p = 0.016). Two KS patients (3.3 %) were TPO Ab positive, whereas no patients were positive for AD- and AG-related autoantigens. The autoantibody endocrine profile of untreated and treated KS patients was not significantly different. Our findings demonstrate for the first time that endocrine humoral immunoreactivity is not rare in KS patients and that it is more frequently directed against type 1 diabetes-related autoantigens, thus suggesting the importance of screening for organ-specific autoimmunity in clinical practice. Follow-up studies are needed to establish if autoantibody-positive KS patients will develop clinical T1D

自記式「健康チェック票THI」による健康に及ぼす喫煙の影響評価－地方都市と大都市の学生間の比較－

キャンパスが群馬県内（Ｉキャンパス）、東京都内（Ｔキャンパス）および愛知県内（Ｎキャンパス）にあるＡ私立大学の学生347人（男子121人、女子226人）を対象に、喫煙が健康に及ぼす影響について、自記式「健康チェック票THI」によって評価した。対象学生の喫煙率は全国平均と比較して、Ｉキャンパスの男子学生がやや高かったことを除くと、ほぼ同程度であった。健康状態は1.2万人の標準グループから得られた尺度得点の分布をもとに、パーセンタイル値で評価した。喫煙者の健康状態は非喫煙者と比較して、身体面およびメンタル面の両方において劣る傾向がみられ、特に、女子学生の方が男子学生より顕著であった。さらに、Ｉキャンパスの学生より、ＴキャンパスやＮキャンパスの学生の方が、メンタル面の症状レベルが高い傾向が認められた。これらの結果は、喫煙が身体面の健康状態に悪影響を及ぼすことを確認するとともに、その影響は地方都市より大都会で学ぶ学生に強く出やすい可能性を示している

Technologies and techniques offering new interpretations of the landscape evolution

The need for specific documents in terms of environment and landscape has its origin in late eighties, when in Italy both at European and Regional level several dispositions related to the improvement of the landscape quality came out to guarantee the compatibility of territorial transformations. Within this traditional context, how innovative, mainly BIM-oriented, technologies can be useful in offering new ways of interpretation for the landscape planning and safeguarding? The aim of the present work is to read and translate landscape elements using digital BIM-oriented platforms, in order to develop an optimized procedure to collect/organize/implement data and define guidelines for landscape and visual impact assessment; furthermore, the aim is to understand the limits that are still present within available tools. After having read the features of the territory another key point is the creation of digital parametric libraries to represent environmental mitigation works. As far as visual mitigation works are concerned, visibility analysis of the infrastructure is possible through Virtual and Mixed Reality. Once the modeling of specific components is performed, they can be located within the contextual model, where outputs related to the “whole” can be extracted and updated afterwards. New digital technologies and techniques undoubtedly offer new interpretation scenarios of the landscape evolution; nevertheless, there are still strong limits related to the interactions among these tools. The present work provides a valid methodology to involve landscape and urban planning in the BIM process. The research also proposes a series of BIM oriented tools and software to develop a typical output (landscape report) required during the design process, useful for landscape architects

Multi-temporal images and 3D dense models for archaeological site monitoring in Hierapolis of Phrygia (TK)

and range-based measurement systems have become increasingly interesting in excavation processes for monitoring purposes and large scale mapping, both from a terrestrial and aerial point of view. The paper will focus on the great challenge of monitoring sites over time, integrating and conforming multiple data coming from previous metric survey projects and image data collected in the past for different purposes. The test-site was the complex archaeological landscape of the ancient city of Hierapolis in Phrygia on which the MAIER – Italian Archaeological Mission of Hierapolis has operated since the 1960s and where the Politecnico di Torino conducted several survey campaigns. A set of multi-temporal datasets acquired in a series of campaigns in 1997, 2002, 2007, 2012, 2015 are presented, as well as their 3D multi-sensor models; the older dense models generated with archival images are intended to be compared and integrated with newer models generated by the LiDAR scans in 2012 and the UAV systems employed in the last mission in 2015. In particular, the case study was the massive complex of the ancient Bath-Church in the northern part of the city below the Northern Necropolis, and Building A of the Apollo Sanctuary, in the central Sacred Area near the Ancient Theatre. In these sites, many different sensors have been experimented with over the years and preliminary multi-temporal data integration has been tested in order to up-date and improve older archival records based on collected images and related to newer and updated documentation projects

Improvement of surface flatness in high precision milling

The use of high precision micro components has increased in various industrial fields in recent years. Repeatable techniques are needed to face very tight tolerances and make micro fabrication processes industrially feasible against current micro machining limitation. Improving surface flatness in high precision milling is the main target of the present research. Critical issues such as machining strategy, spindle thermal transient management and tool wear compensation were considered for machining operations on a representative part

A general aspect-term-extraction model for multi-criteria recommendations

In recent years, increasingly large quantities of user reviews have been made available by several e-commerce platforms. This content is very useful for recommender systems (RSs), since it reflects the users' opinion of the items regarding several aspects. In fact, they are especially valuable for RSs that are able to exploit multi-faceted user ratings. However, extracting aspect-based ratings from unstructured text is not a trivial task. Deep Learning models for aspect extraction have proven to be effective, but they need to be trained on large quantities of domain-specific data, which are not always available. In this paper, we explore the possibility of transferring knowledge across domains for automatically extracting aspects from user reviews, and its implications in terms of recommendation accuracy. We performed different experiments with several Deep Learning-based Aspect Term Extraction (ATE) techniques and Multi-Criteria recommendation algorithms. Results show that our framework is able to improve recommendation accuracy compared to several baselines based on single-criteria recommendation, despite the fact that no labeled data in the target domain was used when training the ATE model