From source to target and back: symmetric bi-directional adaptive GAN

The effectiveness of generative adversarial approaches in producing images according to a specific style or visual domain has recently opened new directions to solve the unsupervised domain adaptation problem. It has been shown that source labeled images can be modified to mimic target samples making it possible to train directly a classifier in the target domain, despite the original lack of annotated data. Inverse mappings from the target to the source domain have also been evaluated but only passing through adapted feature spaces, thus without new image generation. In this paper we propose to better exploit the potential of generative adversarial networks for adaptation by introducing a novel symmetric mapping among domains. We jointly optimize bi-directional image transformations combining them with target self-labeling. Moreover we define a new class consistency loss that aligns the generators in the two directions imposing to conserve the class identity of an image passing through both domain mappings. A detailed qualitative and quantitative analysis of the reconstructed images confirm the power of our approach. By integrating the two domain specific classifiers obtained with our bi-directional network we exceed previous state-of-the-art unsupervised adaptation results on four different benchmark datasets

New insights in Microbial Fuel Cells: Novel solid phase anolyte

For the development of long lasting portable microbial fuel cells (MFCs) new strategies are necessary to overcome critical issues such as hydraulic pump system and the biochemical substrate retrieval overtime to sustain bacteria metabolism. The present work proposes the use of a synthetic solid anolyte (SSA), constituted by agar, carbonaceous and nitrogen sources dissolved into diluted seawater. Results of a month-test showed the potential of the new SSA-MFC as a long lasting low energy consuming system

OpenPatch: a 3D patchwork for Out-Of-Distribution detection

Moving deep learning models from the laboratory setting to the open world entails preparing them to handle unforeseen conditions. In several applications the occurrence of novel classes during deployment poses a significant threat, thus it is crucial to effectively detect them. Ideally, this skill should be used when needed without requiring any further computational training effort at every new task. Out-of-distribution detection has attracted significant attention in the last years, however the majority of the studies deal with 2D images ignoring the inherent 3D nature of the real-world and often confusing between domain and semantic novelty. In this work, we focus on the latter, considering the objects geometric structure captured by 3D point clouds regardless of the specific domain. We advance the field by introducing OpenPatch that builds on a large pre-trained model and simply extracts from its intermediate features a set of patch representations that describe each known class. For any new sample, we obtain a novelty score by evaluating whether it can be recomposed mainly by patches of a single known class or rather via the contribution of multiple classes. We present an extensive experimental evaluation of our approach for the task of semantic novelty detection on real-world point cloud samples when the reference known data are synthetic. We demonstrate that OpenPatch excels in both the full and few-shot known sample scenarios, showcasing its robustness across varying pre-training objectives and network backbones. The inherent training-free nature of our method allows for its immediate application to a wide array of real-world tasks, offering a compelling advantage over approaches that need expensive retraining efforts

Hallucinating Agnostic Images to Generalize Across Domains

The ability to generalize across visual domains is crucial for the robustness of artificial recognition systems. Although many training sources may be available in real contexts, the access to even unlabeled target samples cannot be taken for granted, which makes standard unsupervised domain adaptation methods inapplicable in the wild. In this work we investigate how to exploit multiple sources by hallucinating a deep visual domain composed of images, possibly unrealistic, able to maintain categorical knowledge while discarding specific source styles. The produced agnostic images are the result of a deep architecture that applies pixel adaptation on the original source data guided by two adversarial domain classifier branches at image and feature level. Our approach is conceived to learn only from source data, but it seamlessly extends to the use of unlabeled target samples. Remarkable results for both multi-source domain adaptation and domain generalization support the power of hallucinating agnostic images in this framework

Transient-fault-aware design and training to enhance DNNs reliability with zero-overhead

Deep Neural Networks (DNNs) enable a wide series of technological advancements, ranging from clinical imaging, to predictive industrial maintenance and autonomous driving. However, recent findings indicate that transient hardware faults may corrupt the models prediction dramatically. For instance, the radiation-induced misprediction probability can be so high to impede a safe deployment of DNNs models at scale, urging the need for efficient and effective hardening solutions. In this work, we propose to tackle the reliability issue both at training and model design time. First, we show that vanilla models are highly affected by transient faults, that can induce a performances drop up to 37%. Hence, we provide three zero-overhead solutions, based on DNN re-design and re-train, that can improve DNNs reliability to transient faults up to one order of magnitude. We complement our work with extensive ablation studies to quantify the gain in performances of each hardening component

Concomitant trauma of brain and upper cervical spine: lessons in injury patterns and outcomes

Purpose: The literature on concomitant traumatic brain injury (TBI) and traumatic spinal injury is sparse and a few, if any, studies focus on concomitant TBI and associated upper cervical injury. The objective of this study was to fill this gap and to define demographics, patterns of injury, and clinical data of this specific population. Methods: Records of patients admitted at a single trauma centre with the main diagnosis of TBI and concomitant C0-C1-C2 injury (upper cervical spine) were identified and reviewed. Demographics, clinical, and radiological variables were analyzed and compared to those of patients with TBI and: (i) C3-C7 injury (lower cervical spine); (ii) any other part of the spine other than C1-C2 injury (non-upper cervical); (iii) T1-L5 injury (thoracolumbar). Results: 1545 patients were admitted with TBI and an associated C1-C2 injury was found in 22 (1.4%). The mean age was 64 years, and 54.5% were females. Females had a higher rate of concomitant upper cervical injury (p = 0.046 vs non-upper cervical; p = 0.050 vs thoracolumbar). Patients with an upper cervical injury were significantly older (p = 0.034 vs lower cervical; p = 0.030 vs non-upper cervical). Patients older than 55 years old had higher odds of an upper cervical injury when compared to the other groups (OR = 2.75). The main mechanism of trauma was road accidents (RAs) (10/22; 45.5%) All pedestrian injuries occurred in the upper cervical injured group (p = 0.015). ICU length of stay was longer for patients with an upper cervical injury (p = 0.018). Four patients died in the upper cervical injury group (18.2%), and no death occurred in other comparator groups (p = 0.003). Conclusions: The rate of concomitant cranial and upper cervical spine injury was 1.4%. Risk factors were female gender, age ≥ 55, and pedestrians. RAs were the most common mechanism of injury. There was an association between the upper cervical injury group and longer ICU stay as well as higher mortality rates. Increased understanding of the pattern of concomitant craniospinal injury can help guide comprehensive diagnosis, avoid missed injuries, and appropriate treatment

Instantaneous limit equilibrium back analyses of major rockslides triggered during the 2016–2017 central Italy seismic sequence

Among the almost 1400 landslides triggered by the shocks of the 2016–2017 central Italy seismic sequence, only a limited number, all classifiable as rockslides, involved volumes larger than 1000 m3 . Four of these failures, including the three largest among the documented landslides, were described in terms of structural and geomechanical investigations in a previous paper. In this study, the estimated acceleration time histories at the rockslide sites were evaluated through a 2D simplified numerical model accounting for the attenuation phenomena and for the topographic effect of the rock cliffs from which the slide detached. Instantaneous stability analyses were carried out to obtain insights into the variability of the instantaneous margin of safety along the motion, over the entire spectrum of mechanisms that could be activated. Finally, some general suggestions on the pseudostatic verification method for 3D cases are proposed, which represent useful indications to hazard evaluation at local and regional scales

The ligand-receptor interactions based on silicon technology

We explored the use of porous silicon (pSi) technology for the construction of a biotechnological device, in which the ligand-receptor interactions are revealed by means of laser optical measurements. Here we report the settling of chemical procedures for the functionalization of the silicon wafers and for the subsequent anchoring of biological molecules such as a purified murine monoclonal antibody (UN1 mAb), an antibody anti-P8 protein of M13 phage and an antibody anti-A20 murine lymphoma cell line. The optical analysis of the interaction on the biochips between the bound biomolecules and their corresponding ligands indicated that the pSi is suitable for thi

Contribution of MUTYH variants to male breast cancer risk: results from a multicenter study in Italy

Inherited mutations in BRCA1, and, mainly, BRCA2 genes are associated with increased risk of male breast cancer (MBC). Mutations in PALB2 and CHEK2 genes may also increase MBC risk. Overall, these genes are functionally linked to DNA repair pathways, highlighting the central role of genome maintenance in MBC genetic predisposition. MUTYH is a DNA repair gene whose biallelic germline variants cause MUTYH-associated polyposis (MAP) syndrome. Monoallelic MUTYH variants have been reported in families with both colorectal and breast cancer and there is some evidence on increased breast cancer risk in women with monoallelic variants. In this study, we aimed to investigate whether MUTYH germline variants may contribute to MBC susceptibility. To this aim, we screened the entire coding region of MUTYH in 503 BRCA1/2 mutation negative MBC cases by multigene panel analysis. Moreover, we genotyped selected variants, including p.Tyr179Cys, p.Gly396Asp, p.Arg245His, p.Gly264Trpfs*7, and p.Gln338His, in a total of 560 MBC cases and 1,540 male controls. Biallelic MUTYH pathogenic variants (p.Tyr179Cys/p.Arg241Trp) were identified in one MBC patient with phenotypic manifestation of adenomatous polyposis. Monoallelic pathogenic variants were identified in 14 (2.5%) MBC patients, in particular, p.Tyr179Cys was detected in seven cases, p.Gly396Asp in five cases, p.Arg245His and p.Gly264Trpfs*7 in one case each. The majority of MBC cases with MUTYH pathogenic variants had family history of cancer including breast, colorectal, and gastric cancers. In the case-control study, an association between the variant p.Tyr179Cys and increased MBC risk emerged by multivariate analysis [odds ratio (OR) = 4.54; 95% confidence interval (CI): 1.17-17.58; p = 0.028]. Overall, our study suggests that MUTYH pathogenic variants may have a role in MBC and, in particular, the p.Tyr179Cys variant may be a low/moderate penetrance risk allele for MBC. Moreover, our results suggest that MBC may be part of the tumor spectrum associated with MAP syndrome, with implication in the clinical management of patients and their relatives. Large-scale collaborative studies are needed to validate these findings