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We propose a filtering feature selection framework that considers subsets of features as paths in a graph, where a node is a feature and an edge indicates pairwise (customizable) relations among features, dealing with relevance and redundancy principles. By two different interpretations (exploiting properties of power series of matrices and relying on Markov chains fundamentals) we can evaluate the values of paths (i.e., feature subsets) of arbitrary lengths, eventually go to infinite, from which we dub our framework Infinite Feature Selection (Inf-FS). Going to infinite allows to constrain the computational complexity of the selection process, and to rank the features in an elegant way, that is, considering the value of any path (subset) containing a particular feature. We also propose a simple unsupervised strategy to cut the ranking, so providing the subset of features to keep. In the experiments, we analyze diverse settings with heterogeneous features, for a total of 11 benchmarks, comparing against 18 widely-known comparative approaches. The results show that Inf-FS behaves better in almost any situation, that is, when the number of features to keep are fixed a priori, or when the decision of the subset cardinality is part of the process., Comment: TPAMI PREPRINT 2020

Illumination is important for well-being, productivity and safety across several environments, including offices, retail shops and industrial warehouses. Current techniques for setting up lighting require extensive and expert support and need to be repeated if the scene changes. Here we propose the first fully automated light management system which measures lighting in real-time, leveraging an RGBD sensor and a radiosity-based light propagation model. Thanks to the integration of light distribution and perception curves into the radiosity, we outperform a commercial software (Relux) on a newly introduced dataset. Furthermore, our proposed light management system is the first to estimate both the presence and the attention of the people in the environment, as well as their light perception. Our new light management system adapts lighting to the scene and human activity and it is capable of energy savings of up to 66%, as we experimentally quantify, without compromising the lighting quality.

Objective To assess the impact on women’s reproductive outcomes of barbed sutures to repair uterine breaches during laparoscopic myomectomy compared with traditional smooth sutures. Design Retrospective, monocentric cohort study, with information on subsequent pregnancies prospectively acquired for some women. Setting Tertiary-level academic referral center. Patient(s) Women older than 18 years who had undergone a laparoscopic myomectomy and had sought pregnancy afterward, divided into two groups based on type of suture used to repair the uterine wall: group A (nonbarbed) and group B (barbed). Intervention(s) Laparoscopic removal of FIGO types 3, 4, 5, and 6 uterine leiomyomas by use of either only barbed sutures or only traditional smooth sutures to reconstruct the uterine defect. Main Outcome Measure(s) Pregnancy achievement rates, delivery modes, main pregnancy complications, perioperative complications for both kinds of suture, and the trend of the use of barbed sutures over time at our center. Result(s) Of 164 patients included, 83 were in group A and 81 in group B. Ninety-one patients (55.5%) experienced at least one postoperative pregnancy, with no differences between the groups (group A 60.5%; group B 50.6%). Of the 103 recorded postoperative pregnancies, 70 (68%) resulted in live births, 29 (28.1%) in first-trimester miscarriages, and 4 (3.9%) were ongoing. Conclusion(s) Barbed sutures have a similar impact on reproductive outcomes as smooth conventional threads, both in terms of pregnancy and obstetric complication rates, after laparoscopic myomectomy.

In this paper we discuss a transition model from commonly adopted models of data gathering, transfer and management for environmental monitoring towards more sophisticated ones based on Artificial Intelligence and IoT. The transition model is based on the paradigm of multiple agent systems. The adoption of this transition model is motivated by the need to improve effectiveness, efficiency and interoperability of environmental monitoring by simultaneously guaranteeing its sustainability in economic terms.

Background: There is a growing awareness that nutritional habits may influence risk of several inflammatory and immune-mediated disorders, including autoimmune diseases, through various mechanisms. The aim of the present study was to investigate dietary habits and their relationship with redox homeostasis in the setting of thyroid autoimmunity. Materials and Methods: Two hundred subjects (173 females and 27 males; median age, 37 years) were enrolled. None were under any pharmacological treatment. Exclusion criteria were any infectious/inflammatory/autoimmune comorbidity, kidney failure, diabetes, and cancer. In each subject, serum thyrotropin (TSH), free thyroxine, antithyroid antibodies, and circulating oxidative stress markers were measured. A questionnaire on dietary habits, evaluating the intake frequencies of food groups and adherence to the Mediterranean diet, was submitted to each participant. Results: Among the 200 recruited subjects, 81 (71 females and 10 males) were diagnosed with euthyroid Hashimoto's thyroiditis (HT); the remaining 119 (102 females and 17 males) served as controls. In questionnaires, HT subjects reported higher intake frequencies of animal foods (meat, p = 0.0001; fish, p = 0.0001; dairy products, p = 0.004) compared with controls, who reported higher intake frequencies of plant foods (legumes, p = 0.001; fruits and vegetables, p = 0.030; nuts, p = 0.0005). The number of subjects who preferentially consumed poultry instead of red/processed meat was lower in HT subjects than in controls (p = 0.0141). In logistic regression analysis, meat consumption was associated with increased odds ratio of developing thyroid autoimmunity, while the Mediterranean diet traits were protective. In HT subjects, serum advanced glycation end products (markers of oxidative stress) were significantly higher (p = 0.0001) than in controls, while the activity of glutathione peroxidase and thioredoxin reductase, as well as total plasma antioxidant activity, were lower (p = 0.020, p = 0.023, and p = 0.002, respectively), indicating a condition of oxidative stress. Stepwise regression models demonstrated a significant dependence of oxidative stress parameters on consumption of animal foods, mainly meat. Conclusions: The present study suggests a protective effect of low intake of animal foods toward thyroid autoimmunity and a positive influence of such nutritional patterns on redox balance and potentially on oxidative stress-related disorders.

This paper presents DOHMO, an embedded computer vision system where multiple sensors, including intelligent cameras, are connected to actuators that regulate illumination and doors. The system aims at assisting elderly and impaired people in co-housing scenarios, in accordance with privacy design principles. The paper provides details of two core elements of the system: The first one is the BOX-IO controller, a fully scalable and customizable hardware and software IoT ecosystem that can collect, control, and monitor data, operational flows and business scenarios, whether indoor or outdoor. The second one is the embedded 3DEverywhere intelligent camera, a device composed of an embedded system that receives input data provided by a 3D/2D camera, analyzes it, and returns the metadata of this analysis. We illustrate how they can be connected and how simple decision mechanisms can be implemented in such a framework. In particular, illumination can be triggered on and off by the detected presence of people, overcoming the limitations of typical sensors, while doors can be opened or closed based on person trajectories in an intelligent manner. To substantiate the proposed system, numerous experiments are performed in a lab and a co-housina scenario.

Introduction Hereditary angioedema due to C1-INH deficiency (C1-INH-HAE) or with normal C1-INH is characterized by recurrent swellings due to uncontrolled production of vasoactive mediators, among which bradykinin (BK) is crucial. Through the binding and activation of the two human BK-receptors, kinins may have dual beneficial and deleterious effects in vascular and inflammation physiopathology by inducing oxidative stress. We aimed to assess the serum concentrations of advanced glycation end products (AGEs) and advanced oxidation protein products (AOPPs) in patients affected by HAE. Material and methods Blood samples were collected to measure the serum concentrations of AGEs and AOPPs by spectrofluorimetric and spectrophotometric methods in patients affected by C1-INH-HAE and FXII-HAE during the remission state. Results We showed that the circulating levels of AOPPs observed on control group (0.94 (0.36) nmol/mg) were significantly lower than those observed on the C1-INH-HAE group (1.68 (0.47) nmol/mg; p = 0.002) and FXII-HAE (1.50 (0.27) nmol/mg; p = 0.001). Moreover, the circulating levels of AGEs were significantly higher in C1-INH-HAE group (211.58 (151.05) AU/g; p = 0.02) than the FXII group (141.48 (89.59) AU/g), thus demonstrating a state of heightened oxidative stress. Conclusions Our observations show additional underlying events involved in HAE and are of central importance for further investigations of differences in bradykinin receptors signaling among the two disease subgroups.

Flickr allows its users to tag the pictures they like as “favorite”. As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-the art of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits).

In this paper, we propose the use of a new modality characterized by a richer information content, namely acoustic images, for the sake of audio-visual scene understanding. Each pixel in such images is characterized by a spectral signature, associated to a specific direction in space and obtained by processing the audio signals coming from an array of microphones. By coupling such array with a video camera, we obtain spatio-temporal alignment of acoustic images and video frames. This constitutes a powerful source of self-supervision, which can be exploited in the learning pipeline we are proposing, without resorting to expensive data annotations. However, since 2D planar arrays are cumbersome and not as widespread as ordinary microphones, we propose that the richer information content of acoustic images can be distilled, through a self-supervised learning scheme, into more powerful audio and visual feature representations. The learnt feature representations can then be employed for downstream tasks such as classification and cross-modal retrieval, without the need of a microphone array. To prove that, we introduce a novel multimodal dataset consisting in RGB videos, raw audio signals and acoustic images, aligned in space and synchronized in time. Experimental results demonstrate the validity of our hypothesis and the effectiveness of the proposed pipeline, also when tested for tasks and datasets different from those used for training.

Advanced glycation end products (AGEs) are increased in conditions of oxidative stress and promote inflammation by interacting with their receptor RAGE on cell membrane. By contrast, the soluble receptor sRAGE exerts protective effects by competing with RAGE for ligand binding. AGEs/sRAGEs interaction is involved in the pathogenesis of several diseases related to oxidative stress. In the present study, we evaluated the AGEs/sRAGEs oxidative balance in Hashimoto’ thyroiditis (HT). We measured the levels of sRAGE, by ELISA, and AGEs, by spectrophotometric method, in the serum of 50 HT patients (5 M, 45 F; mean age 38.5 ± 12 years) and 50 age-, sex- and BMI-matched healthy controls. All subjects were euthyroid at recruitment and none was on LT-4 therapy. Serum sRAGEs were significantly lower (median 424 vs 738 pg/ml; p = 0.001) and AGEs higher (205 vs 114 AU/g prot; p = 0.001) in HT patients compared to controls, and the two parameters were inversely correlated (p = 0.016). Accordingly, the AGEs/sRAGEs ratio was threefold higher in HT patients than controls (0.48 vs 0.15; p = 0.0001). In regression analysis models, serum TPO-Ab were the main predictors for AGEs and sRAGEs levels and AGEs/sRAGEs ratio (p

Data anonymization is the process of making information contained in a group of data such that it is not possible to identify unique references to single elements in the group after the process. This action, when conducted onto datasets used to make statistical inference is bound to have ananlogous behaviours on certain indices before and after the process itself. In this paper we study the pipeline of anonymization process for datasets, when this pipeline is managed on cloud technology, where cryptography is not applicable at all, for datasets being available in an open setting. We examine the open problems, and devise a method to address these problems in a logical framework.

Agents use channels to communicate publicly. An agent announcing a statement on a communication channel poses non trivial questions about the nature of such a statement as well as about the attitude of the agent herself. Does the agent know whether the statement is true? Is this agent announcing that statement or its contrary in any other channel?

Some wearable solutions exploiting on-body acceleration sensors have been proposed to recognize Freezing of Gait (FoG) in people affected by Parkinson Disease (PD). Once a FoG event is detected, these systems generate a sequence of rhythmic stimuli to allow the patient restarting the gait. While these solutions are effective in detecting FoG events, they are unable to predict FoG to prevent its occurrence. This paper fills in the gap by presenting a machine learning-based approach that classifies accelerometer data from PD patients, recognizing a pre-FOG phase to further anticipate FoG occurrence in advance. Gait was monitored by three tri-axial accelerometer sensors worn on the back, hip and ankle. Gait features were then extracted from the accelerometer's raw data through data windowing and non-linear dimensionality reduction. A k-nearest neighbor algorithm (k-NN) was used to classify gait in three classes of events: pre-FoG, no-FoG and FoG. The accuracy of the proposed solution was compared to state-of-the-art approaches. Our study showed that: (i) we achieved performances overcoming the state-of-the-art approaches in terms of FoG detection, (ii) we were able, for the very first time in the literature, to predict FoG by identifying the pre-FoG events with an average sensitivity and specificity of, respectively, 94.1% and 97.1%, and (iii) our algorithm can be executed on resource-constrained devices. Future applications include the implementation on a mobile device, and the administration of rhythmic stimuli by a wearable device to help the patient overcome the FoG.

Background: Advanced glycation end-products (AGEs) and their cell receptor (RAGE) are involved in the pathophysiology of cardio-metabolic diseases. Interaction of AGEs with RAGE results in increased generation of oxygen radicals and pro-inflammatory cytokines. Circulating soluble RAGE (sRAGE) interacts with AGEs in order to counterbalance the negative effects of AGEs-RAGE interaction. Objectives: To define factors influencing AGEs, sRAGE, AGEs/sRAGE-ratio, and advanced oxidation-protein products (AOPPs) levels and to investigate changes in oxidative balance among overweight/obese children. Materials and methods: Cross-sectional, one Center, case-control study included 41 overweight and obese children aged between 5 and 16 years and 36 lean matched controls. Inclusion criteria were: BMI ≥ 1 SD; term birth; no genetic or endocrine causes of obesity; no associated chronic diseases neither chronic therapies. All patients underwent clinical and biochemical investigations (lipid and glucose profiles, liver, renal and thyroid function tests, uric acid, C-reactive protein (CRP), AGEs, sRAGE, and AOPPs serum concentrations). Significance was established at 0.050. Results: AOPPs, AGEs/sRAGE-ratio, HOMA-IR, triglycerides, triglycerides/HDL-ratio, total cholesterol (TC)/HDL-ratio, atherogenic-index of plasma (AIP), uric acid, CRP were significantly higher, whereas sRAGE and HDL were significantly lower in overweight/obese children than controls. sRAGE was significantly negatively correlated with BMI SD, TC/HDL-ratio, CRP, AOPPs, and positively with HDL. AGE/sRAGE-ratio and AOPPs were significantly positively correlated with BMI SD, TC/HDL-ratio, AIP, CRP, and negatively with HDL. BMI SD was independently associated with AGEs/sRAGE-ratio (B = 0.06; p = 0.008), AOPPs (B = 0.13; p = 0.02), and sRAGE (B = -73.18; p = 0.000). Conclusions: We demonstrated, for the first time in a pediatric cohort, a significant higher value of AGEs/sRAGE-ratio among overweight/obese children, expression of a relative shift to oxidant from anti-oxidant factors, suggesting an AGE/RAGE-related oxidative homeostasis dysregulation that could enhance susceptibility to oxidative/inflammatory tissues damage. Severity of overweight, influencing the increase of oxidative stress in human organism and even in children, may contribute to the pathogenesis of long-term cardiovascular and metabolic alterations.

In this paper we introduce a simplified approach to sentiment analysis: a lexicon-driven method based upon only adjectives and adverbs. This method is compared in cross-validation with other known techniques and then compared directly to the gold standard, a sample of human subjects asked to deliver the same class of judgments computed by the method. We prove that the method is similar in accuracy and precision with the other methods. We finally argue that the approach we employ is more valid than others for it is scalable, and exportable to languages other than English.

Introduction : Oxidative stress is a result of an imbalance between endogenous production of free reactive oxygen species and reduced effectiveness of antioxidant defence mechanisms. Advanced glycation end products (AGEs) and advanced oxidation protein products (AOPPs) are compounds formed by transformation of macromolecules, including proteins which can serve as markers of oxidative stress and inflammation in several diseases. Aim : To investigate the role of AGEs and AOPPs as new markers of oxidative stress and inflammation in patients with chronic spontaneous urticaria (CSU). Material and methods : Advanced glycation end products and AOPP levels were determined in the sera of 85 patients with CSU and 64 healthy controls, using spectrofluorimetry and spectrophotometry, respectively. Results : Advanced oxidation protein products levels in patients were statistically higher than those in controls. These levels were not affected by the presence of positive autologous serum test results or autologous plasma test results. No statistically significant differences were found between AGE levels in patients and controls. Conclusions : Formation of AGEs and AOPPs may be accelerated in immunological and allergic disorders. Depending on the sites evaluated, the presence or absence of oxidative stress in chronic urticaria is controversial. To our knowledge, this is the first study showing the possible involvement of AOPPs in CSU. The different behaviour observed for these two biomarkers is very likely due to the activation of specific related biochemical pathways associated with the condition under study.

Serious multiple traumatic injuries may rapidly become fatal or be complicated by a life-threatening sequelae leading to a significant increase of the mortality rate. Trauma scoring systems are used to evaluate the critical status of the patient and recently many different biomarkers have been taken into account to better estimate the potential clinical outcome. The aim of the present study is to analyse the expression pattern of high-mobility group box-1 (HMGB1), oxidative stress markers and nuclear factor erythroid 2-related (Nrf2) in critically ill traumatic patients (at hospital admittance and after 6 and 24 h), in order to find out their potential role as early post-traumatic predictors markers. Forty-seven patients admitted for multiple trauma and 15 healthy participants were prospectively recruited. Eight patients (17%) died within 92 h of admission; this subgroup of patients presented the highest severity scores and their HMGB1 expression levels were significantly correlated with ISS, whereas patients with higher ISS exhibited higher levels of HMGB1 ( P

Questions arise as to what will happen when a foreign investor sues a Member State or even the EU, given the new European Union (EU) competence over foreign direct investment (FDI) from the Lisbon Treaty, Articles 206–207 of the Treaty on the Functioning of the European Union (TFEU). In particular, financial responsibility of the EU resulting from an investor-state arbitration is now becoming a politically sensitive topic, particularly regarding the on-going negotiations between EU and third countries for the conclusion of investmentrelated international agreements. While the EU bears, in principle, international responsibility for the breach of any provision to which it is bound under international law, it is possible, under EU law, to provide for the allocation of financial responsibility between the EU and its Member States. This paper analyses the recent EU Regulation on financial responsibility in investor-State arbitration, putting it in the broader framework of the creation of an EU investment poli...

In this paper, we illustrate a combinatorial approach to the problem of defining an ontology used for detecting multiple authorship and roles in weblogs and social network literature production. The investigation aims at providing a model of workflows and therefore traits to be recognised in these documents.

The detection of groups of individuals is attracting the attention of many researchers in diverse fields, from automated surveillance to human-computer interaction, with a growing number of approaches published every year. Unexpectedly, the evaluation metrics for this problem are not consolidated, with some measures inherited from the people detection field, other from clustering, other designed specifically for a particular approach, thus lacking in generalization and making the comparisons between different approaches hard to be carried out. Moreover, most of the existent metrics are scarcely expressive, addressing groups as they are atomic entities, ignoring that they may have different cardinalities, and that group detection approaches may fail in capturing the exact number of individuals that compose it. This paper fills this gap presenting the GROup DEtection (GRODE) metrics, which formally define precision and recall on the groups, including the group cardinality as a variable. This gives the possibility to investigate aspects never considered so far, such as the tendency of a method of over- or under-segmenting, or of better dealing with specific group cardinalities. The GRODE metrics have been evaluated first on controlled scenarios, where the differences with alternative metrics are evident. Then, the metrics have been applied to eight approaches of group detection, on eight public datasets, providing a fresh-new panorama of the state-of-the-art, discovering interesting strengths and pitfalls of the recent approaches.

Linear Logic and Defeasible Logic have been adopted to formalise different features of knowledge representation: consumption of resources, and non monotonic reasoning in particular to represent exceptions. Recently, a framework to combine sub-structural features, corresponding to the consumption of resources, with defeasibility aspects to handle potentially conflicting information, has been discussed in literature, by some of the authors. Two applications emerged that are very relevant: energy management and business process management. We illustrate a set of guide lines to determine how to apply linear defeasible logic to those contexts., Comment: In Proceedings DICE-FOPARA 2019, arXiv:1908.04478. arXiv admin note: substantial text overlap with arXiv:1809.03656

In this paper we propose an image segmentation method specifically designed to detect crystalline grains in microscopic images. We build on the watershed segmentation approach; we propose a preprocessing pipeline to generate a topographic map exploiting the physical nature of the incoming data (i.e. Atomic Force Microscopy) to emphasize grain boundaries and generate seeds for basins. Experimental results show the effectiveness of the proposed method against grain segmentation implementations available in commercial software on a new labelled dataset with an average improvement of over 20% in precision and recall over the standard implementation of watershed segmentation.

Text normalisation is an important task in the context of Natural Language Processing. By normalisation, free text is mapped into dictionaries, i.e. indexed collections of locutions recognised as typical of a particular jaergon. In general, technical dictionaries are difficult to build and validate. They are typically constructed by hand on the basis of everyday human work and they are agreement-based. This is indubitably time consuming and the approach requires a strong human supervision and does not provide a general methodology. In this paper, we perform the first steps towards the to automatic building of a dictionary for Italian journalistic lexicon, called NewsDict, based on sub dictionaries able to characterise main topics occurring in newspaper articles. We exploit a dataset of annotated documents from some Italian newspapers and a statistical techniques based on the Mutual Information Principle. Documents contains information such as the release date and the topic of the article and has been directly annotated by the author. To check the accuracy of the dictionary we built, we develop an initial test. We normalise a control set of journal article into NewsDict. Crossing results presented in this paper against the human annotation, we provide a fist measure of performances of the described methodology.

In this work, we explore the correlation between people trajectories and their head orientations. We argue that people trajectory and head pose forecasting can be modelled as a joint problem. Recent approaches on trajectory forecasting leverage short-term trajectories (aka tracklets) of pedestrians to predict their future paths. In addition, sociological cues, such as expected destination or pedestrian interaction, are often combined with tracklets. In this paper, we propose MiXing-LSTM (MX-LSTM) to capture the interplay between positions and head orientations (vislets) thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation. We additionally exploit the head orientations as a proxy for the visual attention, when modeling social interactions. MX-LSTM predicts future pedestrians location and head pose, increasing the standard capabilities of the current approaches on long-term trajectory forecasting. Compared to the state-of-the-art, our approach shows better performances on an extensive set of public benchmarks. MX-LSTM is particularly effective when people move slowly, i.e. the most challenging scenario for all other models. The proposed approach also allows for accurate predictions on a longer time horizon., Comment: Accepted at IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019. arXiv admin note: text overlap with arXiv:1805.00652

Object pose estimation is important for systems and robots to interact with the environment where the main challenge of this task is the complexity of the scene caused by occlusions and clutters. A key challenge is performing pose estimation leveraging on both RGB and depth information: prior works either extract information from the RGB image and depth separately or use costly post-processing steps, limiting their performances in highly cluttered scenes and real-time applications. Traditionally, the pose estimation problem is tackled by matching feature points between 3D models and images. However, these methods require rich textured models. In recent years, the raising of deep learning has offered an increasing number of methods based on neural networks, such as DSAC++, PoseCNN, DenseFusion and SingleShotPose. In this work, we present a comparison between two recent algorithms, DSAC++ and DenseFusion, focusing on computational cost, performance and applicability in the industry.

We present SIMCO, the first agnostic multi-class object counting approach. SIMCO starts by detecting foreground objects through a novel Mask RCNN-based architecture trained beforehand (just once) on a brand-new synthetic 2D shape dataset, InShape; the idea is to highlight every object resembling a primitive 2D shape (circle, square, rectangle, etc.). Each object detected is described by a low-dimensional embedding, obtained from a novel similarity-based head branch; this latter implements a triplet loss, encouraging similar objects (same 2D shape + color and scale) to map close. Subsequently, SIMCO uses this embedding for clustering, so that different types of objects can emerge and be counted, making SIMCO the very first multi-class unsupervised counter. Experiments show that SIMCO provides state-of-the-art scores on counting benchmarks and that it can also help in many challenging image understanding tasks., Accepted at ICPR 2020

Capturing the essence of a textile image in a robust way is important to retrieve it in a large repository, especially if it has been acquired in the wild (by taking a photo of the textile of interest). In this paper we show that a texel-based representation fits well with this task. In particular, we refer to Texel-Att, a recent texel-based descriptor which has shown to capture fine grained variations of a texture, for retrieval purposes. After a brief explanation of Texel-Att, we will show in our experiments that this descriptor is robust to distortions resulting from acquisitions in the wild by setting up an experiment in which textures from the ElBa (an Element-Based texture dataset) are artificially distorted and then used to retrieve the original image. We compare our approach with existing descriptors using a simple ranking framework based on distance functions. Results show that even under extreme conditions (such as a down-sampling with a factor of 10), we perform better than alternative approaches., Comment: ICIAP - International Conference on Image Analysis and Processing

Lighting design and modelling or industrial applications like luminaire planning and commissioning rely heavily on time consuming manual measurements or on physically coherent computational simulations. Regarding the latter,standard approaches are based on CAD modeling simulations and offline rendering, with long processing times and therefore inflexible workflows. Thus, in this paper we pro-pose a computer vision based system to measure lighting with just a single RGBD camera. The proposed method uses both depth data and images from the sensor to provide a dense measure of light intensity in the field of view of the camera. We evaluate our system on novel ground truth data and compare it to state-of-the-art commercial light-planning software. Our system provides improved performance, while being completely automated, given that the CAD model is extracted from the depth and the albedo estimated with the support of RGB images. To the best of our knowledge, this is the first automatic framework for the estimation of lighting in general indoor scenarios from RGBDinput., 10 pages, 9 figures, this manuscript is accepted in WACV 2019

Background Kounis syndrome (KS) has been described as the coincidental occurrence of acute coronary syndromes during an allergic reaction with cardiac anaphylaxis. It is caused by inflammatory mediators released after exposure to drugs, food, environmental and other triggers. Oxidative stress occurring in various inflammatory disorders causes molecular damage with the production of advanced oxidation products (AOPPs) and advanced glycation end products (AGEs). Case presentation Markers of oxidative stress were evaluated in a patient who had experienced KS after antibiotic administration in order to investigate the possible role of these molecules in KS. No data, up to now, are available on biomarkers of oxidative stress in patients with drug-induced KS. Conclusions AOPPs, but not AGEs, were significantly increased in the KS affected patient compared to controls as already reported in mastocytosis affected patients.

This study highlights variations in HEV serological status inpatients with ESRD undergoing HD.To assess the dynamics of HEV infection in thispopulation, we prospectively evaluated individuals with end-stagerenal disease (ESRD) undergoing HD at a private hospital in Cordoba, Argentina, between November 2014 and November 2017. Fil: Pisano, María Belén. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cordoba. Facultad de Medicina. Instituto de Virología; Argentina Fil: Leathers, James. Vanderbilt University School Of Medicine; Estados Unidos Fil: Balderramo, Domingo. Hospital Privado Centro Médico de Córdoba; Argentina Fil: Diehl, Fernando. Hospital Privado Centro Médico de Córdoba; Argentina Fil: Bolomo, Andrea. Hospital Privado Centro Médico de Córdoba; Argentina Fil: Fernández, Pehuen. Hospital Privado Centro Médico de Córdoba; Argentina Fil: Martínez Wassaf, Maribel. Laboratorios Clínicos Especializados- Lace; Argentina Fil: Cristani, Carla. Universidad Católica de Córdoba. Facultad de Ciencias Químicas; Argentina Fil: Di Cola Bucciarelli, Guadalupe. Universidad Nacional de Cordoba. Facultad de Medicina. Instituto de Virología; Argentina. Universidad Católica de Córdoba. Facultad de Ciencias Químicas; Argentina Fil: De la Fuente, Jorge. Hospital Privado Centro Médico de Córdoba; Argentina Fil: Debes, José D.. Universidad de Minnesota; Estados Unidos Fil: Ré, Viviana Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cordoba. Facultad de Medicina. Instituto de Virología; Argentina

Diagnosis is widely used in many different disciplines to identify the nature and cause of a certain phenomenon. We present \(t\mathsf {L}\), a new logical framework able to formalise diagnostic reasoning, i.e., an hybrid learning technique based both on deduction and experiments. In this paper we introduce tL, a Labeled Modal Logic, garnishing with temporal and statistical information and a basic propositional language.

Interleukin-37 (IL-37), member of the IL-1 family, is a natural suppressor of immune and inflammatory responses. Increased serum IL-37 levels were observed in several autoimmune diseases, including Graves’ disease. To our knowledge, no data on Hashimoto’s thyroiditis (HT) are available in the literature. Aim of our study was to measure serum IL-37 levels and evaluate their relationship, if any, with oxidative stress markers in HT patients. We enrolled 45 euthyroid HT patients (5 M e 40 F, median age 40 years) and 50 age- and sex-matched healthy controls. None was under l-thyroxine therapy. Serum IL-37 levels were measured by ELISA. Specific serum tests, such as derived reactive oxygen metabolites (d-ROMs), and biological anti-oxidant potential (BAP) test were performed in all subjects to investigate the changes in oxidative balance, and advanced glycation end products (AGEs) were determined as a specific marker of oxidative stress. IL-37 levels were significantly higher in HT than in controls (median 475 vs. 268 pg/ml, P = 0.018). In the same patients, serum oxidants (d-ROMs) were increased and anti-oxidants (BAP) decreased compared with controls (P = 0.011 and

Agents negotiate the meaning of terms in numerous real-life situations. When they behave so, they can be used as the basis for providing an emulation paradigm for software agents, habilitating therefore socio-technical systems to perform meaning negotiation. In this paper we focus upon two methods for meaning negotiation in defeasible logic and provide room for an analysis of how the proposed approaches perform the aforementioned process. Finally we also provide a computational analysis of the process automation problem.

In this paper we show the importance of the head pose estimation in the task of trajectory forecasting. This cue, when produced by an oracle and injected in a novel socially-based energy minimization approach, allows to get state-of-the-art performances on four different forecasting benchmarks, without relying on additional information such as expected destination and desired speed, which are supposed to be know beforehand for most of the current forecasting techniques. Our approach uses the head pose estimation for two aims: 1) to define a view frustum of attention, highlighting the people a given subject is more interested about, in order to avoid collisions; 2) to give a shorttime estimation of what would be the desired destination point. Moreover, we show that when the head pose estimation is given by a real detector, though the performance decreases, it still remains at the level of the top score forecasting systems.

The head pose is an important cue for computer vision. Traditionally considered in human computer interaction applications, it becomes very hard to model in surveillance scenarios, due to the tiny head size. Additionally, no public dataset contains continuous head pose annotations in open scenery, making the challenge even harder to face. Here we present a framework based on Faster RCNN, which introduces a branch in the network architecture related to the head pose estimation. The key idea is to leverage the presence of the people body to better infer the head pose, through a joint optimization process. Additionally, we enrich the Town Center dataset with head pose labels, promoting further study on this topic. Results on this novel benchmark and ablation studies on other task-specific datasets promote our idea and confirm the importance of the body cues to contextualize the head pose estimation.

Lighting design in indoor environments is of primary importance for at least two reasons: 1) people should perceive an adequate light; 2) an effective lighting design means consistent energy saving. We present the Invisible Light Switch (ILS) to address both aspects. ILS dynamically adjusts the room illumination level to save energy while maintaining constant the light level perception of the users. So the energy saving is invisible to them. Our proposed ILS leverages a radiosity model to estimate the light level which is perceived by a person within an indoor environment, taking into account the person position and her/his viewing frustum (head pose). ILS may therefore dim those luminaires, which are not seen by the user, resulting in an effective energy saving, especially in large open offices (where light may otherwise be ON everywhere for a single person). To quantify the system performance, we have collected a new dataset where people wear luxmeter devices while working in office rooms. The luxmeters measure the amount of light (in Lux) reaching the people gaze, which we consider a proxy to their illumination level perception. Our initial results are promising: in a room with 8 LED luminaires, the energy consumption in a day may be reduced from 18585 to 6206 watts with ILS (currently needing 1560 watts for operations). While doing so, the drop in perceived lighting decreases by just 200 lux, a value considered negligible when the original illumination level is above 1200 lux, as is normally the case in offices.

We present the new methodology ONTO-PLC to deliver software programs on system-on-chip or single-board computers used to control industrial plants, as substitutes for programmable logic control technologies. The methodology is ontology-driven based on the abstract description of the plant at a level in which the plant itself is viewed as a set of instruments, each instrument being a set of machineries coordinated in functional terms by a control system, formed by sensors and actuators, under the control of an abstract model of behavior delivered by means of an extended finite state machine.

Recent approaches on trajectory forecasting use tracklets to predict the future positions of pedestrians exploiting Long Short Term Memory (LSTM) architectures. This paper shows that adding vislets, that is, short sequences of head pose estimations, allows to increase significantly the trajectory forecasting performance. We then propose to use vislets in a novel framework called MX-LSTM, capturing the interplay between tracklets and vislets thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation. At the same time, MX-LSTM predicts the future head poses, increasing the standard capabilities of the long-term trajectory forecasting approaches. With standard head pose estimators and an attentional-based social pooling, MX-LSTM scores the new trajectory forecasting state-of-the-art in all the considered datasets (Zara01, Zara02, UCY, and TownCentre) with a dramatic margin when the pedestrians slow down, a case where most of the forecasting approaches struggle to provide an accurate solution., Comment: 10 pages, 3 figures to appear in CVPR 2018

We present a novel framework to directly estimate body fat percentage from depth images of human subjects and to visually evaluate salient points of the body shape related to the fat distribution. For this purpose, we created a novel, publicly available dataset including front and back depth images of a set of subjects with specific features (active young men or professional sportsmen) with associated ground truth fat values estimated with dual-energy x-ray absorptiometry (DXA) scanning. These depth images were obtained with depth rendering of an available dataset of whole body scans, simulating low-cost depth sensor acquisitions. We customized a ResNet-50 regressor to estimate fat percentage values directly from the front/back scans, achieving promising accuracy (standard errors of estimate SEE less than 2.1 on the depth renderings and 2.5 on a small set of real depth scans. We also demonstrate that, using a custom perturbation-based procedure for analyzing deep networks, it is possible to highlight, on subjects' depth images, the specific body areas related to fat accumulation (typically neck, shoulders, hip, and abdomen) and those characterizing skinny subjects (chest and abdomen).

Lexical classification is one of the most widely investigated fields in (computational) linguistic and Natural language Processing. Adjectives play a significant role both in classification tasks and in applications as sentiment analysis. In this paper a simple algorithm for lexical classification of comparable adjectives, called MORE (coMparable fORm dEtector), is proposed. The algorithm is efficient in time. The method is a specific unsupervised learning technique. Results are verified against a reference standard built from 80 manually annotated lists of adjective. The algorithm exhibits an accuracy of 76%.

This position paper discusses the definition and implementation of Web-Physical (WP) user profiles, which allow the creation of personalized recommendations and innovative behavioral predictions in particular scenarios, i.e., fairs. The nature of a WP profile builds upon two different worlds: the Web (social networks and web applications) and the Physical one, each one of them being explored through (big) data collection platforms. These two platforms collect radically different information: on the one hand, information of appreciation towards a particular product or service (web domain) together with other metadata; on the other, the leases (x, y) of users in the exhibition space (physical domain). In this scenario, our research idea consists in identifying how the information in the two domains can be merged in a whole entity under a theoretical point of view: this will unleash tangible repercussions in terms of personalized recommendations and effective behavioral predictions, where with personalized recommendation we mean a suggestion to a user in physical terms (eg a pavilion to visit) and / or in web terms (eg a site to visit) and with behavioral prediction a prediction of where a user can go in the future, even in a multimedia perspective (physical + web).

In this paper we summarize the results obtained so far in the communities interested in the development of automated processing techniques as applied to business documents, and devise a few evolutions that are demanded by the current stage of either those techniques by themselves or by collateral sector advancements. It emerges a clear picture of a field that has put an enormous effort in solving problems that changed a lot during the last 30 years, and is now rapidly evolving to incorporate document processing into workflow management systems on one side and to include features derived by the introduction of cloud computing technologies on the other side. We propose an architectural schema for business document processing that comes from the two above evolution lines.

We propose a topological categorisation of agents that makes use of the multiple-channel logic (MCL) framework, a recently developed model of reasoning about agents. We firstly introduce a complete formalization of prejudices on agents’ attitudes and propose an extension of the rules of the MCL framework. We then use RCC-5 (the Region Connection Calculus) to categorize different agents in Multi-Agent Systems (MAS) based on the collaboration, competence, and honesty of agents. We discuss the possibility of using RCC-3 and RCC-8 and generalize our results to define an upper bound in the number of different types of agents in MAS. Finally, we apply our topological categorization to a specific MAS that describes a Cyber-Physical System, for which we define, categorize and discuss the resulting attack states.

Clothing conveys a strong communicative message in terms of social signals, influencing the impression and behaviour of others towards a person; unfortunately, the nature of this message is not completely clear, and social signal processing approaches are starting to consider this problem. Wearable computing devices offer a unique perspective in this scenario, capturing fine details of clothing items in the same way we do during a social interaction, through ego-centered points of views. These clothing characteristics can be then employed to unveil statistical relations with personal impressions. This position paper investigates this novel research direction, individuating the main objectives, the possible problems, viable research strategies, techniques and expected results. This analysis gives birth to brand-new concepts such as clothing saliency, that is, those parts of garments more relevant for triggering personal impressions.