COVID-19-Related Social Isolation Predispose to Problematic Internet and Online Video Gaming Use in Italy

COVID-19 pandemic and its related containment measures have been associated with increased levels of stress, anxiety and depression in the general population. While the use of digital media has been greatly promoted by national governments and international authorities to maintain social contacts and healthy lifestyle behaviors, its increased access may also bear the risk of inappropriate or excessive use of internet-related resources. The present study, part of the COVID Mental hEalth Trial (COMET) study, aims at investigating the possible relationship between social isolation, the use of digital resources and the development of their problematic use. A cross sectional survey was carried out to explore the prevalence of internet addiction, excessive use of social media, problematic video gaming and binge watching, during Italian phase II (May-June 2020) and III (June-September 2020) of the pandemic in 1385 individuals (62.5% female, mean age 32.5 ± 12.9) mainly living in Central Italy (52.4%). Data were stratified according to phase II/III and three groups of Italian regions (northern, central and southern). Compared to the larger COMET study, most participants exhibited significant higher levels of severe-to-extremely-severe depressive symptoms (46.3% vs. 12.4%; p < 0.01) and extremely severe anxiety symptoms (77.8% vs. 7.5%; p < 0.01). We also observed a rise in problematic internet use and excessive gaming over time. Mediation analyses revealed that COVID-19-related general psychopathology, stress, anxiety, depression and social isolation play a significant role in the emergence of problematic internet use, social media addiction and problematic video gaming. Professional gamers and younger subjects emerged as sub-populations particularly at risk of developing digital addictions. If confirmed in larger and more homogenous samples, our findings may help in shedding light on possible preventive and treatment strategies for digital addictions

Mind the Gap: Transitions Between Concepts of Information in Varied Domains

The concept of 'information' in five different realms – technological, physical, biological, social and philosophical – is briefly examined. The 'gaps' between these conceptions are dis‐ cussed, and unifying frameworks of diverse nature, including those of Shannon/Wiener, Landauer, Stonier, Bates and Floridi, are examined. The value of attempting to bridge the gaps, while avoiding shallow analogies, is explained. With information physics gaining general acceptance, and biology gaining the status of an information science, it seems rational to look for links, relationships, analogies and even helpful metaphors between them and the library/information sciences. Prospects for doing so, involving concepts of complexity and emergence, are suggested

Real-time self-adaptive deep stereo

Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to regress dense disparity maps from stereo pairs. These models, however, suffer from a notable decrease in accuracy when exposed to scenarios significantly different from the training set (e.g., real vs synthetic images, etc.). We argue that it is extremely unlikely to gather enough samples to achieve effective training/tuning in any target domain, thus making this setup impractical for many applications. Instead, we propose to perform unsupervised and continuous online adaptation of a deep stereo network, which allows for preserving its accuracy in any environment. However, this strategy is extremely computationally demanding and thus prevents real-time inference. We address this issue introducing a new lightweight, yet effective, deep stereo architecture, Modularly ADaptive Network(MADNet), and developing a Modular ADaptation (MAD) algorithm, which independently trains sub-portions of the network. By deploying MADNet together with MAD we introduce the first real-time self-adaptive deep stereo system enabling competitive performance on heterogeneous datasets. Our code is publicly available at https://github.com/CVLAB-Unibo/Real-time-self-adaptive-deep-stereo

Learning confidence measures in the wild

Confidence measures for stereo earned increasing popularity in most recent works concerning stereo, being effectively deployed to improve its accuracy. While most measures are obtained by processing cues from the cost volume, top-performing ones usually leverage on random-forests or CNNs to predict match reliability. Therefore, a proper amount of labeled data is required to effectively train such confidence measures. Being such ground-truth labels not always available in practical applications, in this paper we propose a methodology suited for training confidence measures in a self-supervised manner. Leveraging on a pool of properly selected conventional measures, we automatically detect a subset of very reliable pixels as well as a subset of erroneous samples from the output of a stereo algorithm. This strategy provides labels for training confidence measures based on machine-learning technique without ground-truth labels. Compared to state-of-the-art, our method is neither constrained to image sequences nor to image content. Experimental results on three challenging datasets with three stereo algorithms and three state-of-the-art confidence measures based on machine-learning techniques confirm the effectiveness of our proposal for self-supervised training

Urgent psychiatric consulting in general hospital

During the year 2003, 728 urgent psychiatric consultations were carried out by our unit. The activity concerned 202 males (mean age: 45.1 years) and 203 females (mean age: 43.4 years). The highest number of visits has been requested by the infectious diseases, emergency and medicine units. The distribution for disorders appears similiar to previous years, with a predominance of alcoholism and substance abuse in males, depression, anxiety disorders and eating disorders in females. Data show that the focus of urgent intervention is first screening diagnosis and psychopharmacological intertion rather than integrated treatment

Serum magnesium profile in heroin addicts: according to psychiatric comorbidity

Psychiatric comorbidity in heroin addiction can modify both the biological pattern and clinical course of this disorder. Because of the role of magnesium in neurotransmission and its specific patterns in some psychiatric conditions, such as depression and schizophrenia, we studied a sample of heroin dependent subjects, with and without psychiatric comorbidity. A sample of 162 drug addicts (123 men and 39 women, mean age 32.3 +/- 6.7) was diagnosed for the presence of psychiatric comorbidity with DSM W criteria. They were subsequently divided in 4 subgroups: No comorbidity, Anxiety Disorders, Mood Disorders, Personality Disorders. Differences in serum magnesium level between the groups were analysed with the Anova method, with age as covariate. Results show that seritin Mg++ levels are significantly higher in patients with heroin dependence and personality disorders compared to patients with depression comorbidity and without comorbidity. Psychiatric codiagnosis significantly modifies Mg++ levels in this drug dependent sample. Gender modifies Mg levels in no comorbid subjects so that females show significantly lower Mg++ levels compared to males. The presence of psychiatric comorbidity abates this difference