Pre-buckling behavior of composite beams: an innovative approach

Fiber-reinforced composite materials have been used over the past years in several different civil structures, acquiring a leading role as structural elements [1-4]. In particular, FRP profiles are manufactured by so-called automated process of pultrusion. From a mechanical point of view, they can be considered as linear elastic, homogeneous and transversely isotropic, with the plane of isotropy being normal to the longitudinal axis (i.e. the axis of pultrusion). It is generally asserted that their mechanical behavior is highly affected by warping strains due to their small thickness. In addition, low shear moduli, more or less the same as those of the polymeric resin, can provoke a non-negligible increase in lateral deflections, thus affecting both the local and global buckling loads. Consequently, FRPs members exhibit significant non classical effects such as transverse shear, warping displacements and non-uniform torsional rigidity that make deformability and stability requirements more relevant than the strength limits in the design process. Recently, experimental studies by Mosallam [5] and Feo et al. [6] showed that the condition of a rigid connection should be replaced by a more appropriate assumption due to the presence of a higher local resin concentration in the connection region between the flange and web. Furthermore, taking into account that pultrusion guarantees very high strength and stiffness along the longitudinal direction of the beam, a deeper investigation of this topic is required. In this paper, which is a continuation of previous ones [7-8], a geometrically nonlinear model for studying the lateral global buckling problem of a generic open/closed composite beam is presented. The model is based on a full second-order deformable beam theory and accounts for both the warping effects and possible displacement discontinuities at the web/flange interface. Equilibrium nonlinear equations are derived from the Principle of Virtual Displacements. A displacement-based one-dimensional finite element model is also developed. Numerical results are obtained for thin-walled composite beams with open and closed section under flexural/torsional loads. The main aim is to investigate the lateral buckling behavior taking into account the effects of shear and web/flange junction deformability as well as the initial geometric imperfections. The reliability of the mechanical model is assured by comparisons with other numerical and experimental results available in literature. Preliminary results show that deformability and stability requirements are fundamental in the safety analysis of such members

SoccER: Computer graphics meets sports analytics for soccer event recognition

Automatic event detection from images or wearable sensors is a fundamental step towards the development of advanced sport analytics and broadcasting software. However, the collection and annotation of large scale sport datasets is hindered by technical obstacles, cost of data acquisition and annotation, and commercial interests. In this paper, we present the Soccer Event Recognition (SoccER) data generator, which builds upon an existing, high quality open source game engine to enable synthetic data generation. The software generates detailed spatio-temporal data from simulated soccer games, along with fine-grained, automatically generated event ground truth. The SoccER software suite includes also a complete event detection system entirely developed and tested on a synthetic dataset including 500 minutes of game, and more than 1 million events. We close the paper by discussing avenues for future research in sports event recognition enabled by the use of synthetic data

PROTOtypical Logic Tensor Networks (PROTO-LTN) for Zero Shot Learning

Semantic image interpretation can vastly benefit from approaches that combine sub-symbolic distributed representation learning with the capability to reason at a higher level of abstraction. Logic Tensor Networks (LTNs) are a class of neuro-symbolic systems based on a differentiable, first-order logic grounded into a deep neural network. LTNs replace the classical concept of training set with a knowledge base of fuzzy logical axioms. By defining a set of differentiable operators to approximate the role of connectives, predicates, functions and quantifiers, a loss function is automatically specified so that LTNs can learn to satisfy the knowledge base. We focus here on the subsumption or isOfClass predicate, which is fundamental to encode most semantic image interpretation tasks. Unlike conventional LTNs, which rely on a separate predicate for each class (e.g., dog, cat), each with its own set of learnable weights, we propose a common isOfClass predicate, whose level of truth is a function of the distance between an object embedding and the corresponding class prototype. The PROTOtypical Logic Tensor Networks (PROTO-LTN) extend the current formulation by grounding abstract concepts as parametrized class prototypes in a high-dimensional embedding space, while reducing the number of parameters required to ground the knowledge base. We show how this architecture can be effectively trained in the few and zero-shot learning scenarios. Experiments on Generalized Zero Shot Learning benchmarks validate the proposed implementation as a competitive alternative to traditional embedding-based approaches. The proposed formulation opens up new opportunities in zero shot learning settings, as the LTN formalism allows to integrate background knowledge in the form of logical axioms to compensate for the lack of labelled examples

Hyperbranched DNA clusters

Taking advantage of the base-pairing specificity and tunability of DNA interactions, we investigate the spontaneous formation of hyperbranched clusters starting from purposely designed DNA tetravalent nanostar monomers, encoding in their four sticky-ends the desired binding rules. Specifically, we combine molecular dynamics simulations and Dynamic Light Scattering experiments to follow the aggregation process of the DNA nanostars at different concentrations and temperatures. At odd with the Flory-Stockmayer predictions, we find that, even when all possible bonds are formed, the system does not reach percolation due to the presence of intracluster bonds. We present an extension of the Flory-Stockmayer theory that properly describes the numerical and the experimental results.Comment: The Supplementary Information is included in the pdf fil

Faster-LTN: a neuro-symbolic, end-to-end object detection architecture

The detection of semantic relationships between objects represented in an image is one of the fundamental challenges in image interpretation. Neural-Symbolic techniques, such as Logic Tensor Networks (LTNs), allow the combination of semantic knowledge representation and reasoning with the ability to efficiently learn from examples typical of neural networks. We here propose Faster-LTN, an object detector composed of a convolutional backbone and an LTN. To the best of our knowledge, this is the first attempt to combine both frameworks in an end-to-end training setting. This architecture is trained by optimizing a grounded theory which combines labelled examples with prior knowledge, in the form of logical axioms. Experimental comparisons show competitive performance with respect to the traditional Faster R-CNN architecture.Comment: accepted for presentation at ICANN 202

Spazi musicali dal podio: conversazione con Francesco Pasqualetti

Intervista col direttore d'orchestra Francesco Pasqualetti sulla progettazione architettonica dei teatr