A PBW basis for Lusztig's form of untwisted affine quantum groups

Let $\mathfrak{g}$ be an untwisted affine Kac-Moody algebra over the field $K \,$, and let $U_q(\mathfrak{g})$ be the associated quantum enveloping algebra; let $\mathfrak{U}_q(g)$ be the Lusztig's integer form of $U_q(\mathfrak{g}) \,$, generated by $q$-divided powers of Chevalley generators over a suitable subring $R$ of $K(q) \,$. We prove a Poincar\'e-Birkhoff-Witt like theorem for $\mathfrak{U}_q(\mathfrak{g}) \,$, yielding a basis over $R$ made of ordered products of $q$-divided powers of suitable quantum root vectors.Comment: 22 pages, AMS-TeX C, Version 2.1c. This is the author's final version, corresponding to the printed journal versio

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 2-categorical extension of Etingof–Kazhdan quantisation

Let k be a field of characteristic zero. Etingof and Kazhdan constructed a quantisation U_h(b) of any Lie bialgebra b over k, which depends on the choice of an associator Phi. They prove moreover that this quantisation is functorial in b. Remarkably, the quantum group U_h(b) is endowed with a Tannakian equivalence F_b from the braided tensor category of Drinfeld-Yetter modules over b, with deformed associativity constraints given by Phi, to that of Drinfeld-Yetter modules over U_h(b). In this paper, we prove that the equivalence F_b is functorial in b.Comment: Small revisions in Sections 2 and 6. An appendix added on the equivalence between admissible Drinfeld-Yetter modules over a QUE and modules over its quantum double. To appear in Selecta Math. 71 page

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

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