347 research outputs found
Materiali critici e drammaturgici. La metamorfosi, dal racconto di Franz Kafka. Uno spettacolo di Luca Micheletti
No full textIl volume raccoglie saggi nati intorno alla messa in scena di uno spettacolo tratto dalla Metamorfosi di Franz Kafka, con la regia e la drammaturgia di Luca Micheletti (interpreti, oltre allo stesso Micheletti, Laura Curino, Dario Cantarelli, Claudia Scaravonati). Lo spettacolo è stato prodotto dal Centro Teatrale Bresciano ed è andato in scena al Teatro S. Chiara dal 18 febbraio al 16 marzo 2014. I saggi, che comprendono anche una conversazione fra Luca Micheletti e Franco Rella, sono firmati da Luca Crescenzi, Serafino Corti, Alberti Bentoglio, Laura Curino, Nicola Arrigoni
White matter injury and neurodevelopmental disabilities: A cross-disease (dis)connection
No full text
On the Usage of the Trifocal Tensor in Motion Segmentation
No full textMotion segmentation, i.e., the problem of clustering data in multiple images based on different 3D motions, is an important task for reconstructing and understanding dynamic scenes. In this paper we address motion segmentation in multiple images by combining partial results coming from triplets of images, which are obtained by fitting a number of trifocal tensors to correspondences. We exploit the fact that the trifocal tensor is a stronger model than the fundamental matrix, as it provides fewer but more reliable matches over three images than fundamental matrices provide over the two. We also consider an alternative solution which merges partial results coming from both triplets and pairs of images, showing the strength of three-frame segmentation in a combination with two-frame segmentation. Our real experiments on standard as well as new datasets demonstrate the superior accuracy of the proposed approaches when compared to previous techniques
RobustStateNet: Robust ego vehicle state estimation for Autonomous Driving
No full textControl of an ego vehicle for Autonomous Driving (AD) requires an accurate definition of its state. Implementation of various model-based Kalman Filtering (KF) techniques for state estimation is prevalent in the literature. These algorithms use measurements from IMU and input signals from steering and wheel encoders for motion prediction with physics-based models, and a Global Navigation Satellite System(GNSS) for global localization. Such methods are widely investigated and majorly focus on increasing the accuracy of the estimation. Ego motion prediction in these approaches does not model the sensor failure modes and assumes completely known dynamics with motion and measurement model noises. In this work, we propose a novel Recurrent Neural Network (RNN) based motion predictor that parallelly models the sensor measurement dynamics and selectively fuses the features to increase the robustness of prediction, in particular in scenarios where we witness sensor failures. This motion predictor is integrated into a KF-like framework, RobustStateNet that takes a global position from the GNSS sensor and updates the predicted state. We demonstrate that the proposed state estimation routine outperforms the Model-Based KF and KalmanNet architecture in terms of estimation accuracy and robustness. The proposed algorithms are validated in the modified NuScenes CAN bus dataset, designed to simulate various types of sensor failures
Ensemble clustering via synchronized relabelling
No full textEnsemble clustering is an important problem in unsupervised learning that aims at aggregating multiple noisy partitions into a unique clustering solution. It can be formulated in terms of relabelling and voting, where relabelling refers to the task of finding optimal permutations that bring coherence among labels in input partitions. In this paper we propose a novel solution to the relabelling problem based on permutation synchronization. By effectively circumventing the need for a reference clustering, our method achieves superior performance than previous work under varying assumptions and scenarios, demonstrating its capability to handle diverse and complex datasets
Upregulation of RANKL in Bone Metastatic Breast Cancer Cells after Direct Co-Culture with Osteogenically Differentiated Human MSCs
No full textBone metastases arise in nearly 70 percent of patients with advanced breast cancer, leading
to massive bone lysis. RANKL/RANK/OPG pathway is the key molecular axis for osteoclasts
formation, regulating both normal bone resorption and metastatic bone lysis. This work aims
at investigating the reciprocal interactions between fluorescent human bone metastatic
breast cancer cells (BOKL) and bone-derived cells (MSCs). MSCs were harvested from 3
different donors, cultured for 14 days in osteogenic medium and tested for differentiation.
BOKL were cultured in growth medium (CTR), in direct co-culture and in medium conditioned
by the same MSCs (CM). After enzymatic detachment and FACS sorting of the two cell
types, real time PCR for proliferation and migration related genes was performed. Alizarin
red staining and assay for calcium content confirmed osteogenic differentiation of MSCs.
PCR demonstrated RANKL up-regulation up to 17 fold in BOKL in direct co-culture with
MSCs as compared to CTR. On the contrary, we did non observe significant up or down
regulation for genes of BOKL cultured in CM. Furthermore, RANKL decoy receptor OPG was
2-fold up-regulated in BOKL directly co-cultured with MSCs from each of the 3 patients. In
conclusion, through gene expression analysis in accurately separated cell populations
following direct co-culture, we reliably showed that direct but not indirect co-culture between
BOKL and bone differentiated MSCs increased expression of key genes in metastatic cells.
Thus demonstrating the fundamental role of direct contact between bone metastatic breast
cancer cells and bone cells in the initiation of the vicious cycle causing bone resorption and consequent metastatic cells growth
Assessing the orbital selective Mott transition with variational wave functions
No full textWe study the Mott metal-insulator transition in the two-band Hubbard model with different hopping amplitudes t_1 and t_2 for the two orbitals on the two-dimensional square lattice by using non-magnetic variational wave functions, similarly to what has been considered in the limit of infinite dimensions by dynamical mean-field theory. We work out the phase diagram at half filling (i.e. two electrons per site) as a function of R = t_2/t_1 and the on-site Coulomb repulsion U, for two values of the Hund's coupling J = 0 and J/U = 0.1. Our results are in good agreement with previous dynamical mean-field theory calculations, demonstrating that the non-magnetic phase diagram is only slightly modified from infinite to two spatial dimensions. Three phases are present: a metallic one, for small values of U, where both orbitals are itinerant; a Mott insulator, for large values of U, where both orbitals are localized because of the Coulomb repulsion; and the so-called orbital-selective Mott insulator (OSMI), for small values of R and intermediate U's, where one orbital is localized while the other one is still itinerant. The effect of the Hund's coupling is two-fold: on one side, it favors the full Mott phase over the OSMI; on the other side, it stabilizes the OSMI at larger values of R
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