Le nuove regole in materia di contratto a tempo determinato, lavoro somministrato, apprendistato e lavoro a tempo parziale: un contributo per un uso corretto della flessibilità in entrata

Il saggio esamina le regole in materia di flessibilità in entrata a partire dalle modifiche introdotte con la legge n. 92/2012 per arrivare al recente decreto legge n. 34/2014 in fase di conversione. L'autore persegue l'obiettivo di dimostrare l'incisività delle modifiche sullo sviluppo dell'occupazione

Misurazione della rappresentanza, efficacia del contratto collettivo ed esercizio dei diritti sindacali in azienda: il nuovo assetto delle relazioni industriali. Analisi e prospettive

Il saggio ricostruisce il quadro delle relazioni sindacali in Italia partendo dagli accordi separati del 2009 per arrivare ad illustrare gli aspetti qualificanti del recente testo unico sulla rappresentanza del gennaio 2014 (misurazione della rappresentanza ed efficacia del contratto collettivo aziendale e nazionale). L'autore, dopo aver evidenziato i limiti degli accordi interconfederali in ordine all'efficacia generalizzata del contratto collettivo nazionale di lavoro, prospetta l'opportunità di un intervento con legge ordinaria dimostrandone la compatibilità con l'art. 39 Cost

La tutela del lavoratore in caso di licenziamento per giusta causa e giustificato motivo

Esame della normativa sul regime di tutela nel licenziamento individuale introdotta dal d.lgs. 4 marzo 2015 n. 23 accompagnato ad una riflessione di carattere generale sul profondo cambiamento culturale del diritto del lavor

Il recesso ante tempus del datore di lavoro dal contratto a tempo determinato

Indicate le regole fissate dal codice civile in materia di risoluzione anticipata del contratto a termine si argomenta in ordine alla inapplicabilità alla fattispecie della legislazione vincolistica sul licenziamento individuale con una ricognizione di dottrina e giurisprudenza

Un diritto del lavoro per il lavoro che cambia: primi spunti di riflessione = A labor law for changing jobs: first food for thought. WP C.S.D.L.E. “Massimo D’Antona”.IT – 368/2018

The Author'spurpose is to check if job market rules and employment relationships rules are able to adapt themselves to the new way of working that usually is called “industry 4.0”. About the law n. 81/2017 (that concerns self-employment and smart work), in the first part of his considerations, the Author believes that labor rules are absolutely able to meet the new technological challenges. In the second part of his considerations, the Author points out the aspects that must qualify the new direction of Labor Law that identify in: a) protection of self-employment, b) development of the individual freedom of the subordinate worker, c) complementarity of the needs of businesses and workers. About this last aspect he mentioned, for example, the current sanctioning rules in the event of an unjustified dismissal and the new rules of ius variandi on the subject of tasks

Automated Sleep Scoring, Deep Learning and Physician Supervision

Sleep plays a crucial role in human well-being. Polysomnography is used in sleep medicine as a diagnostic tool, so as to objectively analyze the quality of sleep. Sleep scoring is the procedure of extracting sleep cycle information from the whole-night electrophysiological signals. The scoring is done worldwide by the sleep physicians according to the official American Academy of Sleep Medicine (AASM) scoring manual. In the last decades, a wide variety of deep learning based algorithms have been proposed to automatise the sleep scoring task. In this thesis we study the reasons why these algorithms fail to be introduced in the daily clinical routine, with the perspective of bridging the existing gap between the automatic sleep scoring models and the sleep physicians. In this light, the primary step is the design of a simplified sleep scoring architecture, also providing an estimate of the model uncertainty. Beside achieving results on par with most up-to-date scoring systems, we demonstrate the efficiency of ensemble learning based algorithms, together with label smoothing techniques, in both enhancing the performance and calibrating the simplified scoring model. We introduced an uncertainty estimate procedure, so as to identify the most challenging sleep stage predictions, and to quantify the disagreement between the predictions given by the model and the annotation given by the physicians. In this thesis we also propose a novel method to integrate the inter-scorer variability into the training procedure of a sleep scoring model. We clearly show that a deep learning model is able to encode this variability, so as to better adapt to the consensus of a group of scorers-physicians. We finally address the generalization ability of a deep learning based sleep scoring system, further studying its resilience to the sleep complexity and to the AASM scoring rules. We can state that there is no need to train the algorithm strictly following the AASM guidelines. Most importantly, using data from multiple data centers results in a better performing model compared with training on a single data cohort. The variability among different scorers and data centers needs to be taken into account, more than the variability among sleep disorders

DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates

Deep learning is widely used in the most recent automatic sleep scoring algorithms. Its popularity stems from its excellent performance and from its ability to process raw signals and to learn feature directly from the data. Most of the existing scoring algorithms exploit very computationally demanding architectures, due to their high number of training parameters, and process lengthy time sequences in input (up to 12 minutes). Only few of these architectures provide an estimate of the model uncertainty. In this study we propose DeepSleepNet-Lite, a simplified and lightweight scoring architecture, processing only 90-seconds EEG input sequences. We exploit, for the first time in sleep scoring, the Monte Carlo dropout technique to enhance the performance of the architecture and to also detect the uncertain instances. The evaluation is performed on a single-channel EEG Fpz-Cz from the open source Sleep-EDF expanded database. DeepSleepNet-Lite achieves slightly lower performance, if not on par, compared to the existing state-of-the-art architectures, in overall accuracy, macro F1-score and Cohen’s kappa (on Sleep-EDF v1-2013 ±30mins: 84.0%, 78.0%, 0.78; on Sleep-EDF v2-2018 ±30mins: 80.3%, 75.2%, 0.73). Monte Carlo dropout enables the estimate of the uncertain predictions. By rejecting the uncertain instances, the model achieves higher performance on both versions of the database (on Sleep-EDF v1-2013 ±30mins: 86.1.0%, 79.6%, 0.81; on Sleep-EDF v2-2018 ±30mins: 82.3%, 76.7%, 0.76). Our lighter sleep scoring approach paves the way to the application of scoring algorithms for sleep analysis in realtime