A quantum homogeneous space of nilpotent matrices

A quantum deformation of the adjoint action of the special linear group on the variety of nilpotent matrices is introduced. New non-embedded quantum homogeneous spaces are obtained related to certain maximal coadjoint orbits, and known quantum homogeneous spaces are revisited.Comment: 12 page

Test, Reliability and Functional Safety Trends for Automotive System-on-Chip

This paper encompasses three contributions by industry professionals and university researchers. The contributions describe different trends in automotive products, including both manufacturing test and run-time reliability strategies. The subjects considered in this session deal with critical factors, from optimizing the final test before shipment to market to in-field reliability during operative life

A short history of the 5-HT2C receptor: from the choroid plexus to depression, obesity and addiction treatment

This paper is a personal account on the discovery and characterization of the 5-HT2C receptor (first known as the 5- HT1C receptor) over 30 years ago and how it translated into a number of unsuspected features for a G protein-coupled receptor (GPCR) and a diversity of clinical applications. The 5-HT2C receptor is one of the most intriguing members of the GPCR superfamily. Initially referred to as 5-HT1CR, the 5-HT2CR was discovered while studying the pharmacological features and the distribution of [3H]mesulergine-labelled sites, primarily in the brain using radioligand binding and slice autoradiography. Mesulergine (SDZ CU-085), was, at the time, best defined as a ligand with serotonergic and dopaminergic properties. Autoradiographic studies showed remarkably strong [3H]mesulergine-labelling to the rat choroid plexus. [3H]mesulergine-labelled sites had pharmacological properties different from, at the time, known or purported 5-HT receptors. In spite of similarities with 5-HT2 binding, the new binding site was called 5-HT1C because of its very high affinity for 5-HT itself. Within the following 10 years, the 5-HT1CR (later named 5- HT2C) was extensively characterised pharmacologically, anatomically and functionally: it was one of the first 5-HT receptors to be sequenced and cloned. The 5-HT2CR is a GPCR, with a very complex gene structure. It constitutes a rarity in theGPCR family: many 5-HT2CR variants exist, especially in humans, due to RNA editing, in addition to a few 5-HT2CR splice variants. Intense research led to therapeutically active 5-HT2C receptor ligands, both antagonists (or inverse agonists) and agonists: keeping in mind that a number of antidepressants and antipsychotics are 5- HT2CR antagonists/inverse agonists. Agomelatine, a 5-HT2CR antagonist is registered for the treatment of major depression. The agonist Lorcaserin is registered for the treatment of aspects of obesity and has further potential in addiction, especially nicotine/ smoking. There is good evidence that the 5-HT2CR is involved in spinal cord injury-induced spasms of the lower limbs, which can be treated with 5-HT2CR antagonists/inverse agonists such as cyproheptadine or SB206553. The 5-HT2CR may play a role in schizophrenia and epilepsy. Vabicaserin, a 5-HT2CR agonist has been in development for the treatment of schizophrenia and obesity, but was stopped. As is common, there is potential for further indications for 5-HT2CR ligands, as suggested by a number of preclinical and/or genome-wide association studies (GWAS) on depression, suicide, sexual dysfunction, addictions and obesity. The 5-HT2CR is clearly affected by a number of established antidepressants/antipsychotics and may be one of the culprits in antipsychotic-induced weight gain

A multi-element psychosocial intervention for early psychosis (GET UP PIANO TRIAL) conducted in a catchment area of 10 million inhabitants: study protocol for a pragmatic cluster randomized controlled trial

Multi-element interventions for first-episode psychosis (FEP) are promising, but have mostly been conducted in non-epidemiologically representative samples, thereby raising the risk of underestimating the complexities involved in treating FEP in 'real-world' services

Markers biologici di risposta clinica alla clozapina in soggetti schizofrenici resistenti al trattamento : il ruolo degli antigeni del sistema HLA

It has been suggested that the individual susceptibility to develop agranulocytosis and the patients' responsiveness to clozapine are both Human Leukocyte Antigen (HLA) associated. The study below was designed to re-examine the role of HLA antigens in influencing the clinical outcome of clozapine treatment in schizophrenic patients refractory to treatment. 112 Italian schizophrenic outpatients were treated with clozapine for 8 months. BPRS was used to assess clinical symptomatology at baseline (T0) and after 8 months' clozapine therapy (T1). HLA were serologically typed on peripheral blood lymphocytes at T1. None of the patients developed agranulocytosis or leucopenia. 23 patients (20.53%) were rated as high clozapine responders (as defined by a BPRS score reduced by =60% from T0 to T1) and 46 patients (41.06%) as low clozapine responders (as defined by a BPRS score reduced by <40% from T0 to T1). Looking at the frequencies of HLA antigens between high and low clozapine responders, our results show that HLA B35 seems to be a strong predictor of a high response to the drug (high responders vs low clozapine responders OR = 3.65, p = 0.042). Our results suggest that HLA-B35 antigen behaves like strong and independent predictor of clozapine responsiveness in Italian schizophrenic patients refractory to treatment

Linear instability characteristics of incompressible coaxial jets

The present study deals with the local linear instability of axisymmetric coaxial jets with a duct wall separating the two streams. The flow is assumed to be locally parallel, inviscid and incompressible. The objective of the work is to understand how the various parameters describing this flow geometry (i.e. boundary layers thicknesses at the exit, velocity ratio, wall thickness) may influence the instability of the flow and, in particular, the convective/absolute instability transition. A specific family of profiles is chosen for the modelling of the mean undisturbed flow and a spatial stability analysis is performed in order to identify the unstable modes and to assess how they are affected by the wake region behind the wall. An absolutely unstable mode is found, and its characteristics, depending on the velocity ratio and shear layers thicknesses, are determined. Results show that the absolute unstable mode is present only for a limited range of velocity ratios and that the corresponding frequency is almost constant if normalized with the mean velocity and wake thickness. This frequency value and the extension of the range of velocity ratios is similar to those found in the experiments on a similar geometry. Finally, a specific velocity ratio is found that maximizes the region at the jet exit for which an absolute instability behind the wall is present. This may increase the possibility for the onset of a global mode that may sustain the instability of the whole jet, enhancing considerably the mixing and entrainment characteristics between the two streams

Applicazioni di Intelligenza Artificiale alla difesa dai terremoti

During the last ten years the Research Unit has carried on its investigations relative to the utilisation of AI methodologies for seismic protection. Interesting results have been obtained, due to the possibilities the same methodologies offer to: • Supply operative tools for activities on field, in general Expert Systems; • Supply a user friendly environment, also dealing with large amount of information coming from various sources: survey and evaluation of data, decisions, design provisions, priorities, code specifications…; • Manage uncertain data and/or heuristic or empirical information, as well as to deal with semantic value of linguistic sources; The research results have been illustrated in the works indicated in the bibliography. Seismic risk is the common connotation unifying the different questions dealt with. Further applications in the frame of Civil Engineering and Architecture are possible, too. The investigations regard: • Priority choices in interventions on monuments under seismic risk. The proposed tool: Expert System EXPRIM • Utilisation of "Fuzzy Set" theory for the interpretation of historical data regarding seismic damages • Postearthquake usability assessment of buildings damaged by an earthquake or by a seismic crisis. The proposed tool: Expert System AMADEUS • Definition of an Informative System and of a Knowledge Based System for monuments and/or historical building heritage that should guide in data supply and management, as well as in safeguard and risk mitigation interventions. In the course of the investigation different kinds of informatics tools have been used, such as normal programming languages, PROLOG and the shell NEXPERT OBJECT. The exigency to have at disposal a tool able to use the Fuzzy logic, as well as the problems inherent to the same investigations has led the Research Unit to set up an original shell called KSBT.Published4.4. Scenari e mitigazione del rischio ambientaleope

A Fast Reliability Analysis of Image Segmentation Neural Networks Exploiting Statistical Fault Injections

The reliability of hardware running deep neural networks (DNNs) is becoming the object of multiple research works. Fault injections (FIs) are one of the most used solutions to determine the reliability of DNN models. However, defining how many faults to inject in the model is not a trivial task. An exhaustive FI campaign requires injecting, in modern DNNs, billions or trillions of parameters. On the other hand, random FI campaigns do not offer a practical measure of the accuracy of the result. A different approach is to perform a statistical FI: the number of faults to inject is decided based on the number of possible faults and by fixing an error margin and a confidence level on the measured output metric. While the statistical approach offers the best of both worlds, it requires a proper setup to guarantee its statistically significance. In this work, a study on the statistical fault injection procedure on an image segmentation neural network is proposed. In particular, the study compares results from a random FI campaign and an improperly-defined statistical FI campaign, and shows how they fail at highlighting some of the critical aspects of U-Net, a state-of-the-art DNN used for image segmentation. The proposed approach, by injecting only the 0.07% of all the possible faults, accurately measures both the criticality of each layer and of the parameters' bit with an error margin of 1% and a confidence level of 99%

On the resilience of representative and novel data formats in CNNs

In recent years, a wide range of data type representations have been employed for training and storing the parameters of Deep Neural Networks (DNNs). The decision to employ a particular data type over another is influenced by various requirements, including the desire to enhance training accuracy or reduce data size to minimize memory usage, energy and power consumption. However, opting for one data type over another inevitably impacts the reliability of the model. This work studies the impact of different data representations on the reliability of LeNet-5, a popular Convolutional Neural Network (CNN) used for image classification tasks.An investigation is performed to evaluate the efficacy of the Average Bit-Flip Distance (ABFD) in predicting the criticality of bit positions in the data representation. The data type under analysis are FP32, POSIT32, POSIT16 and INT8. Together with the widely adopted metrics, this work proposes a new metric, called Soft SDC-n, to measure the percentage of faults that cause a change in the order of the top-n output elements. Experimental results shows that POSIT is not as reliable as FP32, while indicating that the most reliable data type is INT8. Furthermore, the same results confirm the presence of a relationship between the ABFD and the criticality of a bit in all the data representations under analysis