High Level Tracker Triggers for CMS

Two fast trigger algorithms based on 3 innermost hits in the CMS Inner Tracker are presented. One of the algorithms will be applied at LHC low luminosity to select B decay channels. Performance of the algorithm is demonstrated for the decay channel Bs->Ds+pi. The second algorithm will be used to select tau-jets at LHC high luminosity.Comment: 10 pages, 10 figures, to be published in the Vertex 2001 Conference Proceedin

A general approach to the encapsulation of glycoenzymes chains inside calcium alginate gel beads

In this work an enzyme encapsulation general approach, based on the use of calcium alginate hydrogels, is reported. Alginate gels are biodegradable and low cost and have been found to provide a good matrix for the entrapment of sensitive biomolecules. Alginate is an anionic polymer whose gelation occurs by an exchange of sodium ions from the polymer chains with multivalent cations, resulting in the formation of a three dimensional gel network. For gelation alginate is dripped into a calcium chloride solution. The cations diffuse from the continuous phase to the interior of the alginate droplets and form a gelled matrix. By means of this “external gelation method” beads with a diameter of few millimeters can be obtained (see figure 1). The entrapment of enzymes in alginate beads suffers some disadvantages, like as low enzyme loading efficiency with reduction of the immobilization yields and reusability, related to the enzyme leakage from the large beads pores (cut off of about 100 kDa). Please click Additional Files below to see the full abstract

Domain Generalization for Crop Segmentation with Knowledge Distillation

In recent years, precision agriculture has gradually oriented farming closer to automation processes to support all the activities related to field management. Service robotics plays a predominant role in this evolution by deploying autonomous agents that can navigate fields while performing tasks without human intervention, such as monitoring, spraying, and harvesting. To execute these precise actions, mobile robots need a real-time perception system that understands their surroundings and identifies their targets in the wild. Generalizing to new crops and environmental conditions is critical for practical applications, as labeled samples are rarely available. In this paper, we investigate the problem of crop segmentation and propose a novel approach to enhance domain generalization using knowledge distillation. In the proposed framework, we transfer knowledge from an ensemble of models individually trained on source domains to a student model that can adapt to unseen target domains. To evaluate the proposed method, we present a synthetic multi-domain dataset for crop segmentation containing plants of variegate shapes and covering different terrain styles, weather conditions, and light scenarios for more than 50,000 samples. We demonstrate significant improvements in performance over state-of-the-art methods and superior sim-to-real generalization. Our approach provides a promising solution for domain generalization in crop segmentation and has the potential to enhance a wide variety of precision agriculture applications

Letter: chronic hepatitis C genotype 3 infection - still a hurdle toward a direct-acting anti-viral-induced HCV cure?

Letter: chronic hepatitis C genotype 3 infection – still a hurdle toward a direct-acting anti-viral-induced HCV cur

Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation

Single-Image Super-Resolution can support robotic tasks in environments where a reliable visual stream is required to monitor the mission, handle teleoperation or study relevant visual details. In this work, we propose an efficient Generative Adversarial Network model for real-time Super-Resolution. We adopt a tailored architecture of the original SRGAN and model quantization to boost the execution on CPU and Edge TPU devices, achieving up to 200 fps inference. We further optimize our model by distilling its knowledge to a smaller version of the network and obtain remarkable improvements compared to the standard training approach. Our experiments show that our fast and lightweight model preserves considerably satisfying image quality compared to heavier state-of-the-art models. Finally, we conduct experiments on image transmission with bandwidth degradation to highlight the advantages of the proposed system for mobile robotic applications

Supervision of an Humanoid Robot

In this master thesis, the problem of supervision of an humanoid robot will be addressed. First, a model of the robot will be developed. Then, a fault detection and isolation scheme will be implemented using the linear parameter varying (LPV) approach. Finally, a fault tolerant scheme will be implemented to compensate the faulty effect, once the fault has been detected and isolated. The proposed approach will be tested in simulation and on a real humanoid platform.Incomin

Local Planners with Deep Reinforcement Learning for Indoor Autonomous Navigation

Autonomous indoor navigation requires an elab- orated and accurate algorithmic stack, able to guide robots through cluttered, unstructured, and dynamic environments. Global and local path planning, mapping, localization, and decision making are only some of the required layers that undergo heavy research from the scientific community to achieve the requirements for fully functional autonomous navigation. In the last years, Deep Reinforcement Learning (DRL) has proven to be a competitive short-range guidance system solution for power-efficient and low computational cost point-to-point local planners. One of the main strengths of this approach is the possibility to train a DRL agent in a simulated environment that encapsulates robot dynamics and task constraints and then deploy its learned point-to-point navigation policy in a real setting. However, despite DRL easily integrates complex mechanical dynamics and multimodal signals into a single model, the effect of different sensor data on navigation performance has not been investigated yet. In this paper, we compare two different DRL navigation solutions that leverage LiDAR and depth camera information, respectively. The agents are trained in the same simulated environment and tested on a common benchmark to highlight the strengths and criticalities of each technique

Autonomous Navigation in Rows of Trees and High Crops with Deep Semantic Segmentation

Segmentation-based autonomous navigation has recently been proposed as a promising methodology to guide robotic platforms through crop rows without requiring precise GPS localization. However, existing methods are limited to scenarios where the centre of the row can be identified thanks to the sharp distinction between the plants and the sky. However, GPS signal obstruction mainly occurs in the case of tall, dense vegetation, such as high tree rows and orchards. In this work, we extend the segmentation-based robotic guidance to those scenarios where canopies and branches occlude the sky and hinder the usage of GPS and previous methods, increasing the overall robustness and adaptability of the control algorithm. Extensive experimentation on several realistic simulated tree fields and vineyards demonstrates the competitive advantages of the proposed solution

Action Transformer: A Self-Attention Model for Short-Time Human Action Recognition

Deep neural networks based purely on attention have been successful across several domains, relying on minimal architectural priors from the designer. In Human Action Recognition (HAR), attention mechanisms have been primarily adopted on top of standard convolutional or recurrent layers, improving the overall generalization capability. In this work, we introduce Action Transformer (AcT), a simple, fully self-attentional architecture that consistently outperforms more elaborated networks that mix convolutional, recurrent, and attentive layers. In order to limit computational and energy requests, building on previous human action recognition research, the proposed approach exploits 2D pose representations over small temporal windows, providing a low latency solution for accurate and effective real-time performance. Moreover, we open-source MPOSE2021, a new large-scale dataset, as an attempt to build a formal training and evaluation benchmark for real-time short-time human action recognition. Extensive experimentation on MPOSE2021 with our proposed methodology and several previous architectural solutions proves the effectiveness of the AcT model and poses the base for future work on HAR