HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action Representations

Self-paced learning has been beneficial for tasks where some initial knowledge is available, such as weakly supervised learning and domain adaptation, to select and order the training sample sequence, from easy to complex. However its applicability remains unexplored in unsupervised learning, whereby the knowledge of the task matures during training. We propose a novel HYperbolic Self-Paced model (HYSP) for learning skeleton-based action representations. HYSP adopts self-supervision: it uses data augmentations to generate two views of the same sample, and it learns by matching one (named online) to the other (the target). We propose to use hyperbolic uncertainty to determine the algorithmic learning pace, under the assumption that less uncertain samples should be more strongly driving the training, with a larger weight and pace. Hyperbolic uncertainty is a by-product of the adopted hyperbolic neural networks, it matures during training and it comes with no extra cost, compared to the established Euclidean SSL framework counterparts. When tested on three established skeleton-based action recognition datasets, HYSP outperforms the state-of-the-art on PKU-MMD I, as well as on 2 out of 3 downstream tasks on NTU-60 and NTU-120. Additionally, HYSP only uses positive pairs and bypasses therefore the complex and computationally-demanding mining procedures required for the negatives in contrastive techniques. Code is available at https://github.com/paolomandica/HYSP.Comment: Accepted at ICLR 202

About latent roles in forecasting players in team sports

Forecasting players in sports has grown in popularity due to the potential for a tactical advantage and the applicability of such research to multi-agent interaction systems. Team sports contain a significant social component that influences interactions between teammates and opponents. However, it still needs to be fully exploited. In this work, we hypothesize that each participant has a specific function in each action and that role-based interaction is critical for predicting players' future moves. We create RolFor, a novel end-to-end model for Role-based Forecasting. RolFor uses a new module we developed called Ordering Neural Networks (OrderNN) to permute the order of the players such that each player is assigned to a latent role. The latent role is then modeled with a RoleGCN. Thanks to its graph representation, it provides a fully learnable adjacency matrix that captures the relationships between roles and is subsequently used to forecast the players' future trajectories. Extensive experiments on a challenging NBA basketball dataset back up the importance of roles and justify our goal of modeling them using optimizable models. When an oracle provides roles, the proposed RolFor compares favorably to the current state-of-the-art (it ranks first in terms of ADE and second in terms of FDE errors). However, training the end-to-end RolFor incurs the issues of differentiability of permutation methods, which we experimentally review. Finally, this work restates differentiable ranking as a difficult open problem and its great potential in conjunction with graph-based interaction models. Project is available at: https://www.pinlab.org/aboutlatentrolesComment: AI4ABM@ICLR2023 Worksho

Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection

Anomalies are rare and anomaly detection is often therefore framed as One-Class Classification (OCC), i.e. trained solely on normalcy. Leading OCC techniques constrain the latent representations of normal motions to limited volumes and detect as abnormal anything outside, which accounts satisfactorily for the openset'ness of anomalies. But normalcy shares the same openset'ness property, since humans can perform the same action in several ways, which the leading techniques neglect. We propose a novel generative model for video anomaly detection (VAD), which assumes that both normality and abnormality are multimodal. We consider skeletal representations and leverage state-of-the-art diffusion probabilistic models to generate multimodal future human poses. We contribute a novel conditioning on the past motion of people, and exploit the improved mode coverage capabilities of diffusion processes to generate different-but-plausible future motions. Upon the statistical aggregation of future modes, anomaly is detected when the generated set of motions is not pertinent to the actual future. We validate our model on 4 established benchmarks: UBnormal, HR-UBnormal, HR-STC, and HR-Avenue, with extensive experiments surpassing state-of-the-art results.Comment: Accepted at ICCV202

Adversarial Branch Architecture Search for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) is a key issue in visual recognition, as it allows to bridge different visual domains enabling robust performances in the real world. To date, all proposed approaches rely on human expertise to manually adapt a given UDA method (e.g. DANN) to a specific backbone architecture (e.g. ResNet). This dependency on handcrafted designs limits the applicability of a given approach in time, as old methods need to be constantly adapted to novel backbones. Existing Neural Architecture Search (NAS) approaches cannot be directly applied to mitigate this issue, as they rely on labels that are not available in the UDA setting. Furthermore, most NAS methods search for full architectures, which precludes the use of pre-trained models, essential in a vast range of UDA settings for reaching SOTA results. To the best of our knowledge, no prior work has addressed these aspects in the context of NAS for UDA. Here we tackle both aspects with an Adversarial Branch Architecture Search for UDA (ABAS): i. we address the lack of target labels by a novel data-driven ensemble approach for model selection; and ii. we search for an auxiliary adversarial branch, attached to a pre-trained backbone, which drives the domain alignment. We extensively validate ABAS to improve two modern UDA techniques, DANN and ALDA, on three standard visual recognition datasets (Office31, Office-Home and PACS). In all cases, ABAS robustly finds the adversarial branch architectures and parameters which yield best performances.Comment: Accepted at WACV 202

Staged Contact-Aware Global Human Motion Forecasting

Scene-aware global human motion forecasting is critical for manifold applications, including virtual reality, robotics, and sports. The task combines human trajectory and pose forecasting within the provided scene context, which represents a significant challenge. So far, only Mao et al. NeurIPS'22 have addressed scene-aware global motion, cascading the prediction of future scene contact points and the global motion estimation. They perform the latter as the end-to-end forecasting of future trajectories and poses. However, end-to-end contrasts with the coarse-to-fine nature of the task and it results in lower performance, as we demonstrate here empirically. We propose a STAGed contact-aware global human motion forecasting STAG, a novel three-stage pipeline for predicting global human motion in a 3D environment. We first consider the scene and the respective human interaction as contact points. Secondly, we model the human trajectory forecasting within the scene, predicting the coarse motion of the human body as a whole. The third and last stage matches a plausible fine human joint motion to complement the trajectory considering the estimated contacts. Compared to the state-of-the-art (SoA), STAG achieves a 1.8% and 16.2% overall improvement in pose and trajectory prediction, respectively, on the scene-aware GTA-IM dataset. A comprehensive ablation study confirms the advantages of staged modeling over end-to-end approaches. Furthermore, we establish the significance of a newly proposed temporal counter called the "time-to-go", which tells how long it is before reaching scene contact and endpoints. Notably, STAG showcases its ability to generalize to datasets lacking a scene and achieves a new state-of-the-art performance on CMU-Mocap, without leveraging any social cues. Our code is released at: https://github.com/L-Scofano/STAGComment: 15 pages, 7 figures, BMVC23 ora

Bivalirudin in patients undergoing pci: state of art and future perspectives

Acute coronary syndrome (ACS) represents the most common cause of death worldwide. Percutaneous coronary intervention (PCI) is the management of choice in patients with ACS and occurrence of intra procedural thrombotic complications are an independent predictor of mortality and other major adverse cardiovascular events in patients undergoing PCI. According to current guideline, anticoagulation therapy is indicated during PCI in order to reduce the risk of thrombotic complications such as stent thrombosis. Among currently available anticoagulant drugs, bivalirudin demonstrates a lower incidence of bleeding risk, despite it is associated with an increased risk of stent thrombosis. The aim of this paper is to discuss the pharmacology of bivalirudin and the clinical evidences of its use in patients undergoing PCI for ACS

Effects of physical activity on endothelial progenitor cells (EPCs)

Physical activity has a therapeutic role in cardiovascular disease (CVD), through its beneficial effects on endothelial function and cardiovascular system. Circulating endothelial progenitor cells (EPCs) are bone marrow (BM) derived cells that represent a novel therapeutic target in CVD patients, because of their ability to home to sites of ischemic injury and repair the damaged vessels. Several studies show that physical activity results in a significant increase in circulating EPCs, and, in particular, there are some evidence of the beneficial exercise-induced effects on EPCs activity in CVD settings, including coronary artery disease (CAD), heart failure (HF), and peripheral artery disease (PAD). The aim of this paper is to review the current evidence about the beneficial effects of physical exercise on endothelial function and EPCs levels and activity in both healthy subjects and patients with CVD

RC infilled building performance against the evidence of the 2016 EEFIT Central Italy post-earthquake reconnaissance mission:empirical fragilities and comparison with the FAST method

Damage data on low-to-mid-rise Reinforced Concrete (RC) buildings, collected during the UK Earthquake Engineering Field Investigation Team post-earthquake reconnaissance mission on the August 24 Central Italy earthquake, are employed to derive empirical fragility relationships. Given the small dataset, the new data distributions are used for the Bayesian update of fragility functions derived for the L’Aquila earthquake (same seismic region and similar construction typologies). Other properties such as number of storeys, age of construction and shape in plan of the buildings are also analyzed. This information is employed to assess the ability of the FAST method to predict damage states in non-regular infilled RC buildings for the municipalities of Amatrice, Accumoli, Arquata del Tronto and Norcia, all severely affected by the 2016 Central Italy sequence. FAST is a spectral-based method to derive capacity curves and peak ground acceleration damage state thresholds for buildings. It is a dedicated methodology for regular RC frame buildings with masonry infills, first calibrated on damage data from the 2009 L’Aquila earthquake and applied to the 2011 Lorca (Spain), the 2012 Emilia (Italy) events for damage back-analyses. The new data from the August 2016 Central Italy earthquake provide a test-bed for FAST further employments in case of less homogenous building samples. The application of FAST presented here accounts for different shake-maps produced by both the United States Geological Survey and the Italian National Institute of Geophysics and Volcanology which are significantly different and representative of different refinements of the demand scenario. For the area of Amatrice, where the two shake-maps provide similar estimates and the buildings considered match reasonably well the typology for which FAST is calibrated, the comparison between damage level observed and as provided by FAST is very satisfactory. For other structural typologies like RC industrial structures and dwellings with non-hollow-clay-bricks as infills, FAST needs further calibration

Lipoprotein(a) levels and risk of adverse events after myocardial infarction in patients with and without diabetes

Introduction: The aim of this study was to evaluate the association of lipoprotein(a) [Lp(a)] levels with long-term outcome in patients with recent history of myocardial infarction (MI), and to investigate if diabetes may influence this association. Methods: Consecutive MI patients who underwent urgent/emergent coronary angiography from February 2013 to June 2019 were prospectively collected. The primary outcome was the composite of MI recurrence and all-cause death. The propensity score weighting technique was used to account for covariates potentially influencing the relationship between Lp(a) levels and the study outcomes. Results: The study population consisted of 1018 post-MI patients (median age 63 years). Diabetes was reported in 280 patients (27.5%), who showed lower Lp(a) levels than patients without diabetes (p = 0.026). At a median follow-up of 1121 days, the primary outcome was reported in 182 patients (17.9%). At univariable Cox regression analysis, Lp(a) was associated with the risk of the primary outcome in the overall population and in non-diabetic patients, but not in diabetics. The adjusted Cox regression analysis confirmed the independent association between Lp(a) values and the primary outcome in non-diabetic patients, but not in diabetics. Lp(a) levels > 70 mg/dL were independently associated with the risk of the primary outcome in non-diabetic patients (adjusted HR: 2.839; 95% CI, 1.382-5.832), but not in diabetics. Conclusions: In this real-world post-MI population, increasing Lp(a) levels were significantly associated with the risk of recurrent MI and all-cause death, and very high Lp(a) serum concentration independently predicted long-term outcome in non-diabetic patients, but not in diabetics