Monte Carlo studies of the properties of the Majorana quantum error correction code: is self-correction possible during braiding?

The Majorana code is an example of a stabilizer code where the quantum information is stored in a system supporting well-separated Majorana Bound States (MBSs). We focus on one-dimensional realizations of the Majorana code, as well as networks of such structures, and investigate their lifetime when coupled to a parity-preserving thermal environment. We apply the Davies prescription, a standard method that describes the basic aspects of a thermal environment, and derive a master equation in the Born-Markov limit. We first focus on a single wire with immobile MBSs and perform error correction to annihilate thermal excitations. In the high-temperature limit, we show both analytically and numerically that the lifetime of the Majorana qubit grows logarithmically with the size of the wire. We then study a trijunction with four MBSs when braiding is executed. We study the occurrence of dangerous error processes that prevent the lifetime of the Majorana code from growing with the size of the trijunction. The origin of the dangerous processes is the braiding itself, which separates pairs of excitations and renders the noise nonlocal; these processes arise from the basic constraints of moving MBSs in 1D structures. We confirm our predictions with Monte Carlo simulations in the low-temperature regime, i.e. the regime of practical relevance. Our results put a restriction on the degree of self-correction of this particular 1D topological quantum computing architecture.Comment: Main text: 20 pages, Supplementary Material: 66 pages. Short version: arXiv:1505.0371

Human–Robot Role Arbitration via Differential Game Theory

The industry needs controllers that allow smooth and natural physical Human-Robot Interaction (pHRI) to make production scenarios more flexible and user-friendly. Within this context, particularly interesting is Role Arbitration, which is the mechanism that assigns the role of the leader to either the human or the robot. This paper investigates Game-Theory (GT) to model pHRI, and specifically, Cooperative Game Theory (CGT) and Non-Cooperative Game Theory (NCGT) are considered. This work proposes a possible solution to the Role Arbitration problem and defines a Role Arbitration framework based on differential game theory to allow pHRI. The proposed method can allow trajectory deformation according to human will, avoiding reaching dangerous situations such as collisions with environmental features, robot joints and workspace limits, and possibly safety constraints. Three sets of experiments are proposed to evaluate different situations and compared with two other standard methods for pHRI, the Impedance Control, and the Manual Guidance. Experiments show that with our Role Arbitration method, different situations can be handled safely and smoothly with a low human effort. In particular, the performances of the IMP and MG vary according to the task. In some cases, MG performs well, and IMP does not. In some others, IMP performs excellently, and MG does not. The proposed Role Arbitration controller performs well in all the cases, showing its superiority and generality. The proposed method generally requires less force and ensures better accuracy in performing all tasks than standard controllers. Note to Practitioners—This work presents a method that allows role arbitration for physical Human-Robot Interaction, motivated by the need to adjust the role of leader/follower in a shared task according to the specific phase of the task or the knowledge of one of the two agents. This method suits applications such as object co-transportation, which requires final precise positioning but allows some trajectory deformation on the fly. It can also handle situations where the carried obstacle occludes human sight, and the robot helps the human to avoid possible environmental obstacles and position the objects at the target pose precisely. Currently, this method does not consider external contact, which is likely to arise in many situations. Future studies will investigate the modeling and detection of external contacts to include them in the interaction models this work addresses

Absence of spontaneous magnetic order of lattice spins coupled to itinerant interacting electrons in one and two dimensions

We extend the Mermin-Wagner theorem to a system of lattice spins which are spin-coupled to itinerant and interacting charge carriers. We use the Bogoliubov inequality to rigorously prove that neither (anti-) ferromagnetic nor helical long-range order is possible in one and two dimensions at any finite temperature. Our proof applies to a wide class of models including any form of electron-electron and single-electron interactions that are independent of spin. In the presence of Rashba or Dresselhaus spin-orbit interactions (SOI) magnetic order is allowed and intimately connected to equilibrium spin currents. However, in the special case when Rashba and Dresselhaus SOIs are tuned to be equal, magnetic order is excluded again. This opens up a new possibility to control magnetism in magnetic semiconductors electrically

EMG-based visual-haptic biofeedback: a tool to improve motor control in children with primary dystonia.

New insights suggest that dystonic motor impairments could also involve a deficit of sensory processing. In this framework, biofeedback, making covert physiological processes more overt, could be useful. The present work proposes an innovative integrated setup which provides the user with an electromyogram (EMG)-based visual-haptic biofeedback during upper limb movements (spiral tracking tasks), to test if augmented sensory feedbacks can induce motor control improvement in patients with primary dystonia. The ad hoc developed real-time control algorithm synchronizes the haptic loop with the EMG reading; the brachioradialis EMG values were used to modify visual and haptic features of the interface: the higher was the EMG level, the higher was the virtual table friction and the background color proportionally moved from green to red. From recordings on dystonic and healthy subjects, statistical results showed that biofeedback has a significant impact, correlated with the local impairment, on the dystonic muscular control. These tests pointed out the effectiveness of biofeedback paradigms in gaining a better specific-muscle voluntary motor control. The flexible tool developed here shows promising prospects of clinical applications and sensorimotor rehabilitation

Safe obstacle avoidance for industrial robot working without fences

Abstract-Until now, the presence of fences is a technological barrier for the adoption of robots in Small Medium Enterprises (SME). The work deals with the definition of an intrinsically safe algorithm to avoid collisions between an industrial manipulator and obstacles in its workspace (Standard ISO 10218-1). The suggested strategy aims to offer an industrial solution to the problem: an off-line analysis of the workspace is performed to have an exhaustive and intrinsically description of the static obstacles and a safe spatial grid of "pass-through points" is calculated; an on-line algorithm, based on an enhanced Artificial Potential Field evaluates the most suitable points to avoid collisions against obstacles and perform a realtime replanning the path of the robot. A Matlab toolbox that elaborates STL CAD files has been developed to obtain a full description of the workcell, and the avoidance algorithm has been designed and implemented in a standard industrial controller. Various experimental results are reported by using a COMAU NS16 arm manipulator

Robotic manipulation for the shoe-packaging process

[EN] This paper presents the integration of a robotic system in a human-centered environment, as it can be found in the shoe manufacturing industry. Fashion footwear is nowadays mainly handcrafted due to the big amount of small production tasks. Therefore, the introduction of intelligent robotic systems in this industry may contribute to automate and improve the manual production steps, such us polishing, cleaning, packaging, and visual inspection. Due to the high complexity of the manual tasks in shoe production, cooperative robotic systems (which can work in collaboration with humans) are required. Thus, the focus of the robot lays on grasping, collision detection, and avoidance, as well as on considering the human intervention to supervise the work being performed. For this research, the robot has been equipped with a Kinect camera and a wrist force/ torque sensor so that it is able to detect human interaction and the dynamic environment in order to modify the robot¿s behavior. To illustrate the applicability of the proposed approach, this work presents the experimental results obtained for two actual platforms, which are located at different research laboratories, that share similarities in their morphology, sensor equipment and actuation system.This work has been partly supported by the Ministerio de Economia y Competitividad of the Spanish Government (Key No.: 0201603139 of Invest in Spain program and Grant No. RTC-2016-5408-6) and by the Deutscher Akademischer Austauschdienst (DAAD) of the German Government (Projekt-ID 54368155).Gracia Calandin, LI.; Perez-Vidal, C.; Mronga, D.; Paco, JD.; Azorin, J.; Gea, JD. (2017). Robotic manipulation for the shoe-packaging process. The International Journal of Advanced Manufacturing Technology. 92(1-4):1053-1067. https://doi.org/10.1007/s00170-017-0212-6S10531067921-4Pedrocchi N, Villagrossi E, Cenati C, Tosatti LM (2017) Design of fuzzy logic controller of industrial robot for roughing the uppers of fashion shoes. 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Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks

BACKGROUND: The design of an optimal neuroprostheses controller and its clinical use presents several challenges. First, the physiological system is characterized by highly inter-subjects varying properties and also by non stationary behaviour with time, due to conditioning level and fatigue. Secondly, the easiness to use in routine clinical practice requires experienced operators. Therefore, feedback controllers, avoiding long setting procedures, are required. METHODS: The error mapping controller (EMC) here proposed uses artificial neural networks (ANNs) both for the design of an inverse model and of a feedback controller. A neuromuscular model is used to validate the performance of the controllers in simulations. The EMC performance is compared to a Proportional Integral Derivative (PID) included in an anti wind-up scheme (called PIDAW) and to a controller with an ANN as inverse model and a PID in the feedback loop (NEUROPID). In addition tests on the EMC robustness in response to variations of the Plant parameters and to mechanical disturbances are carried out. RESULTS: The EMC shows improvements with respect to the other controllers in tracking accuracy, capability to prolong exercise managing fatigue, robustness to parameter variations and resistance to mechanical disturbances. CONCLUSION: Different from the other controllers, the EMC is capable of balancing between tracking accuracy and mapping of fatigue during the exercise. In this way, it avoids overstressing muscles and allows a considerable prolongation of the movement. The collection of the training sets does not require any particular experimental setting and can be introduced in routine clinical practice

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

Background: The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. Materials and methods: We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. Results: A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. Conclusion: AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice

Epigenetic regulation of S100 protein expression

S100 proteins are small, calcium-binding proteins whose genes are localized in a cluster on human chromosome 1. Through their ability to interact with various protein partners in a calcium-dependent manner, the S100 proteins exert their influence on many vital cellular processes such as cell cycle, cytoskeleton activity and cell motility, differentiation, etc. The characteristic feature of S100 proteins is their cell-specific expression, which is frequently up- or downregulated in various pathological states, including cancer. Changes in S100 protein expression are usually characteristic for a given type of cancer and are therefore often considered as markers of a malignant state. Recent results indicate that changes in S100 protein expression may depend on the extent of DNA methylation in the S100 gene regulatory regions. The range of epigenetic changes occurring within the S100 gene cluster has not been defined. This article reviews published data on the involvement of epigenetic factors in the control of S100 protein expression in development and cancer