Theoretical and computational analysis of second- and third-harmonic generation in periodically patterned graphene and transition-metal dichalcogenide monolayers

Remarkable optical and electrical properties of two-dimensional (2D) materials, such as graphene and transition-metal dichalcogenide (TMDC) monolayers, offer vast technological potential for novel and improved optoelectronic nanodevices, many of which relying on nonlinear optical effects in these 2D materials. This article introduces a highly effective numerical method for efficient and accurate description of linear and nonlinear optical effects in nanostructured 2D materials embedded in periodic photonic structures containing regular three-dimensional (3D) optical materials, such as diffraction gratings and periodic metamaterials. The proposed method builds upon the rigorous coupled-wave analysis and incorporates the nonlinear optical response of 2D materials by means of modified electromagnetic boundary conditions. This allows one to reduce the mathematical framework of the numerical method to an inhomogeneous scattering matrix formalism, which makes it more accurate and efficient than previously used approaches. An overview of linear and nonlinear optical properties of graphene and TMDC monolayers is given and the various features of the corresponding optical spectra are explored numerically and discussed. To illustrate the versatility of our numerical method, we use it to investigate the linear and nonlinear multiresonant optical response of 2D-3D heteromaterials for enhanced and tunable second- and third-harmonic generation. In particular, by employing a structured 2D material optically coupled to a patterned slab waveguide, we study the interplay between geometric resonances associated to guiding modes of periodically patterned slab waveguides and plasmon or exciton resonances of 2D materials.Comment: 28 pages, 21 figure

Fast and Robust Quantum State Tomography from Few Basis Measurements

Quantum state tomography is a powerful but resource-intensive, general solution for numerous quantum information processing tasks. This motivates the design of robust tomography procedures that use relevant resources as sparingly as possible. Important cost factors include the number of state copies and measurement settings, as well as classical postprocessing time and memory. In this work, we present and analyze an online tomography algorithm designed to optimize all the aforementioned resources at the cost of a worse dependence on accuracy. The protocol is the first to give provably optimal performance in terms of rank and dimension for state copies, measurement settings and memory. Classical runtime is also reduced substantially and numerical experiments demonstrate a favorable comparison with other state-of-the-art techniques. Further improvements are possible by executing the algorithm on a quantum computer, giving a quantum speedup for quantum state tomography

Multiple Criteria Evaluation of Transportation Performance for Selected Agribusiness Companies

AbstractThis paper presents the analysis of transportation activities carried out in different agribusiness entities and resulting in the overall ranking of transportation units operating in the considered agribusiness companies. It is assumed that all these units utilize their own fleet and thus arrange transportation services, by themselves, as the company's internal activities. The data for analysis is obtained from the survey research carried out on a sample of transportation units operating in 10 agribusiness companies. The authors define a consistent family of criteria that allows to evaluate transportation activity in an agribusiness industry, including both universal merits and industry specific transportation features. The evaluation matrix is constructed and the ranking of 10 transportation units is generated. It is based on a subjective model of preferences defined by the decision maker (DM) - the management teams of the analyzed agribusiness companies. The defined model of preferences includes the interests of different stakeholders, such as: customers, employees (in particular drivers) and the society. In the computational phase a multiple criteria ranking procedure called Analytic Hierarchy Process (AHP) method is applied. A series of computational experiments is carried out. As a result a company featured by the most desirable transportation performance is selected

Energy management and guidelines to digitalisation of integrated natural gas distribution systems equipped with expander technology

In a swirling dynamic interaction, digital innovation, environment and anthropological evolution are swiftly shaping the smart grid scenario. Integration and flexibility are the keywords in this emergent picture characterised by a low carbon footprint. Digitalisation, within the natural limits imposed by the thermodynamics, seems to offer excellent opportunities for these purposes. Of course, here starts a new challenge: how digital technologies should be employed to achieve these objectives? How would we ensure a digital retrofit does not lead to a carbon emission increase? In author opinion, as long as it remains a generalised question, none answer exists: the need to contextualise the issue emerges from the variety of the characteristics of the energy systems and from their interactions with external processes. To address these points, in the first part of this research, the author presented a collection of his research contributions to the topic related to the energy management in natural gas pressure reduction station equipped with turbo expander technology. Furthermore, starting from the state of the art and the author's previous research contributions, the guidelines for the digital retrofit for a specific kind of distributed energy system, were outlined. Finally, a possible configuration of the ideal ICT architecture is extracted. This aims to achieve a higher level of coordination involving, natural gas distribution and transportation, local energy production, thermal user integration and electric vehicles charging. Finally, the barriers and the risks of a digitalisation process are critically analysed outlining in this way future research needs

A Literature Review on Electricity Transmission Expansion Planning: The Mexican Case

Numéro de référence interne originel : a1.1 g 113

BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability

Adversarial defenses protect machine learning models from adversarial attacks, but are often tailored to one type of model or attack. The lack of information on unknown potential attacks makes detecting adversarial examples challenging. Additionally, attackers do not need to follow the rules made by the defender. To address this problem, we take inspiration from the concept of Applicability Domain in cheminformatics. Cheminformatics models struggle to make accurate predictions because only a limited number of compounds are known and available for training. Applicability Domain defines a domain based on the known compounds and rejects any unknown compound that falls outside the domain. Similarly, adversarial examples start as harmless inputs, but can be manipulated to evade reliable classification by moving outside the domain of the classifier. We are the first to identify the similarity between Applicability Domain and adversarial detection. Instead of focusing on unknown attacks, we focus on what is known, the training data. We propose a simple yet robust triple-stage data-driven framework that checks the input globally and locally, and confirms that they are coherent with the model's output. This framework can be applied to any classification model and is not limited to specific attacks. We demonstrate these three stages work as one unit, effectively detecting various attacks, even for a white-box scenario

Learning-based methods for planning and control of humanoid robots

Nowadays, humans and robots are more and more likely to coexist as time goes by. The anthropomorphic nature of humanoid robots facilitates physical human-robot interaction, and makes social human-robot interaction more natural. Moreover, it makes humanoids ideal candidates for many applications related to tasks and environments designed for humans. No matter the application, an ubiquitous requirement for the humanoid is to possess proper locomotion skills. Despite long-lasting research, humanoid locomotion is still far from being a trivial task. A common approach to address humanoid locomotion consists in decomposing its complexity by means of a model-based hierarchical control architecture. To cope with computational constraints, simplified models for the humanoid are employed in some of the architectural layers. At the same time, the redundancy of the humanoid with respect to the locomotion task as well as the closeness of such a task to human locomotion suggest a data-driven approach to learn it directly from experience. This thesis investigates the application of learning-based techniques to planning and control of humanoid locomotion. In particular, both deep reinforcement learning and deep supervised learning are considered to address humanoid locomotion tasks in a crescendo of complexity. First, we employ deep reinforcement learning to study the spontaneous emergence of balancing and push recovery strategies for the humanoid, which represent essential prerequisites for more complex locomotion tasks. Then, by making use of motion capture data collected from human subjects, we employ deep supervised learning to shape the robot walking trajectories towards an improved human-likeness. The proposed approaches are validated on real and simulated humanoid robots. Specifically, on two versions of the iCub humanoid: iCub v2.7 and iCub v3