Interpretable multiclass classification by MDL-based rule lists

Interpretable classifiers have recently witnessed an increase in attention from the data mining community because they are inherently easier to understand and explain than their more complex counterparts. Examples of interpretable classification models include decision trees, rule sets, and rule lists. Learning such models often involves optimizing hyperparameters, which typically requires substantial amounts of data and may result in relatively large models. In this paper, we consider the problem of learning compact yet accurate probabilistic rule lists for multiclass classification. Specifically, we propose a novel formalization based on probabilistic rule lists and the minimum description length (MDL) principle. This results in virtually parameter-free model selection that naturally allows to trade-off model complexity with goodness of fit, by which overfitting and the need for hyperparameter tuning are effectively avoided. Finally, we introduce the Classy algorithm, which greedily finds rule lists according to the proposed criterion. We empirically demonstrate that Classy selects small probabilistic rule lists that outperform state-of-the-art classifiers when it comes to the combination of predictive performance and interpretability. We show that Classy is insensitive to its only parameter, i.e., the candidate set, and that compression on the training set correlates with classification performance, validating our MDL-based selection criterion

Real-time image detection for edge devices: a peach fruit detection application

Within the scope of precision agriculture, many applications have been developed to support decision making and yield enhancement. Fruit detection has attracted considerable attention from researchers, and it can be used offline. In contrast, some applications, such as robot vision in orchards, require computer vision models to run on edge devices while performing inferences at high speed. In this area, most modern applications use an integrated graphics processing unit (GPU). In this work, we propose the use of a tensor processing unit (TPU) accelerator with a Raspberry Pi target device and the state-of-the-art, lightweight, and hardware-aware MobileDet detector model. Our contribution is the extension of the possibilities of using accelerators (the TPU) for edge devices in precision agriculture. The proposed method was evaluated using a novel dataset of peaches with three cultivars, which will be made available for further studies. The model achieved an average precision (AP) of 88.2% and a performance of 19.84 frames per second (FPS) at an image size of 640 × 480. The results obtained show that the TPU accelerator can be an excellent alternative for processing on the edge in precision agriculture.info:eu-repo/semantics/publishedVersio

Vitamin D-related polymorphisms and vitamin D levels as risk biomarkers of COVID-19 disease severity

© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Vitamin D is a fundamental regulator of host defences by activating genes related to innate and adaptive immunity. Previous research shows a correlation between the levels of vitamin D in patients infected with SARS-CoV-2 and the degree of disease severity. This work investigates the impact of the genetic background related to vitamin D pathways on COVID-19 severity. For the first time, the Portuguese population was characterized regarding the prevalence of high impact variants in genes associated with the vitamin D pathways. This study enrolled 517 patients admitted to two tertiary Portuguese hospitals. The serum concentration of 25 (OH)D, was measured in the hospital at the time of patient admission. Genetic variants, 18 variants, in the genes AMDHD1, CYP2R1, CYP24A1, DHCR7, GC, SEC23A, and VDR were analysed. The results show that polymorphisms in the vitamin D binding protein encoded by the GC gene are related to the infection severity (p = 0.005). There is an association between vitamin D polygenic risk score and the serum concentration of 25 (OH)D (p = 0.04). There is an association between 25 (OH)D levels and the survival and fatal outcomes (p = 1.5e-4). The Portuguese population has a higher prevalence of the DHCR7 RS12785878 variant when compared with its prevalence in the European population (19% versus 10%). This study shows a genetic susceptibility for vitamin D deficiency that might explain higher severity degrees in COVID-19 patients. These results reinforce the relevance of personalized strategies in the context of viral diseases.This project was supported by the “Fundação para a Ciência e Tecnologia”, program “Research 4 Covid-19 Apoio especial a projetos de implementação rápida para soluções inovadoras de resposta à pandemia de COVID-19”. It was also partially supported by each institution.info:eu-repo/semantics/publishedVersio

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Interpersonal psychotherapy versus sertraline for women with posttraumatic stress disorder following recent sexual assault: a randomized clinical trial

Background: Sexual assault often triggers posttraumatic stress disorder (PTSD), a potentially chronic severe mental disorder. Most guidelines recommend selective serotonin reuptake inhibitors (SSRIs) and trauma-focused psychotherapies as treatment options. Interpersonal Psychotherapy (IPT), adapted for PTSD (IPT-PTSD), focuses on interpersonal consequences of trauma rather than confronting the trauma itself. Studies have found IPT-PTSD efficaciously reduced PTSD symptoms with limited attrition. No efficacy trials have compared IPT-PTSD and SSRI. We hypothesized IPT would reduce PTSD, anxiety, and depressive symptoms more than sertraline among women with PTSD following a recent sexual assault. Objectives: To compare the efficacy of IPT-PTSD to SSRI sertraline in a 14-week randomized clinical trial for women with PTSD following a recent sexual assault. Methods: Seventy-four women with PTSD who had suffered sexual assault in the last six months were randomly assigned to 14 weeks of IPT-PTSD (n = 39) or sertraline (n = 35). Instruments assessed PTSD, anxiety, and depressive symptoms. This randomized clinical trial was conducted in São Paulo, Brazil, using the Clinician-Administered PTSD Scale-5 (CAPS-5) as the primary outcome measure. Results: Both treatments significantly reduced PTSD, anxiety, and depressive symptoms, without between-group outcome differences. CAPS-5 mean decreased from 42.5 (SD = 9.4) to 27.1 (SD = 15.9) with sertraline and from 42.6 (SD = 9.1) to 29.1 (SD = 15.5) with IPT-PTSD. Attrition was high in both arms (p = .40). Conclusions: This trial showed within-group improvements without differences between IPT-PTSD and sertraline treatment of PTSD. Our findings suggest that non-exposure-based psychotherapies may benefit patients with PTSD, although we did not directly compare these treatments to an exposure therapy. Brazilian Clinical Trials Registry RBR-3z474z

MICHE Competitions: A Realistic Experience with Uncontrolled Eye Region Acquisition

People struggle every day with authentication to access a protected service or location, or simply aimed at protecting one’s own devices. This spurs a growing demand for self-handled authentication strategies. The increasing number of remote services of various kinds corresponds to an increasing number of passwords to use and remember, and also to the growth of the password theft risk, due to the increasing value of the protected resources. The other core element in present authentication scenarios is the ubiquity of mobile equipment. Smartphones add a “whatever” dimension to the possible uses of the mobile devices whenever and wherever that include storing/transferring multimedia information, often personal and often sensitive. Biometrics can both enforce and simplify authentication in controlled environments. Mobile biometrics in uncontrolled settings, where there is no operator to guide the capture of a “good-quality” sample on a mobile device, is the new frontier for secure use of data and services. The iris is among the best candidates for biometric recognition. It is extremely discriminative: Right and left irises of the same person are so different to hinder a correct matching, because randotypic elements largely overcome genotypic ones in individual development. However, self-acquired samples often suffer from poor quality, due, e.g., to reflections, motion blurring, out of focus, or bad image framing. Mobile setting and especially the inherent problems related to uncontrolled iris image acquisition are addressed in the two challenges of the MICHE project, whose results are the core topic of this chapter