Photonic Delay Systems as Machine Learning Implementations

Nonlinear photonic delay systems present interesting implementation platforms for machine learning models. They can be extremely fast, offer great degrees of parallelism and potentially consume far less power than digital processors. So far they have been successfully employed for signal processing using the Reservoir Computing paradigm. In this paper we show that their range of applicability can be greatly extended if we use gradient descent with backpropagation through time on a model of the system to optimize the input encoding of such systems. We perform physical experiments that demonstrate that the obtained input encodings work well in reality, and we show that optimized systems perform significantly better than the common Reservoir Computing approach. The results presented here demonstrate that common gradient descent techniques from machine learning may well be applicable on physical neuro-inspired analog computers

Sinus Sigmoideus Thrombosis Secondary to Graves' Disease: A Case Description

Cerebral venous thrombosis (CVT) is a distinct cerebrovascular condition that represents 0.5-1% of all strokes in the general population. Because of its procoagulant and antifibrinolytic effects [Horne et al.: J Clin Endocrinol Metab 2004;89:4469-4473], hyperthyroidism has been proposed as a predisposing factor for CVT [Saposnik et al.: Stroke 2011;42:1158-1192]. For the first time, we describe a 22-year-old right-handed woman with a sinus sigmoideus thrombosis due to Graves' disease. Although subclinical hyperthyroidism had been detected 2 years before the onset of neurological symptoms, she did not receive any medical follow-up. Early recognition, diagnosis and treatment are of crucial importance, as Graves' disease is a risk factor for CVT and stroke

Hartle-Hawking state is a maximum of entanglement entropy

It is shown that the Hartle-Hawking state of a scalar field is a maximum of entanglement entropy in the space of pure quantum states satisfying the condition that backreaction is finite. In other words, the Hartle-Hawking state is a curved-space analogue of the EPR state, which is also a maximum of entanglement entropy.Comment: Latex, 4 pages, Some comments are added on the "small backreaction condition

Automated design of complex dynamic systems

Several fields of study are concerned with uniting the concept of computation with that of the design of physical systems. For example, a recent trend in robotics is to design robots in such a way that they require a minimal control effort. Another example is found in the domain of photonics, where recent efforts try to benefit directly from the complex nonlinear dynamics to achieve more efficient signal processing. The underlying goal of these and similar research efforts is to internalize a large part of the necessary computations within the physical system itself by exploiting its inherent non-linear dynamics. This, however, often requires the optimization of large numbers of system parameters, related to both the system's structure as well as its material properties. In addition, many of these parameters are subject to fabrication variability or to variations through time. In this paper we apply a machine learning algorithm to optimize physical dynamic systems. We show that such algorithms, which are normally applied on abstract computational entities, can be extended to the field of differential equations and used to optimize an associated set of parameters which determine their behavior. We show that machine learning training methodologies are highly useful in designing robust systems, and we provide a set of both simple and complex examples using models of physical dynamical systems. Interestingly, the derived optimization method is intimately related to direct collocation a method known in the field of optimal control. Our work suggests that the application domains of both machine learning and optimal control have a largely unexplored overlapping area which envelopes a novel design methodology of smart and highly complex physical systems

Teacher-training, ICT, creativity, MOOC, Moodle - What pedagogy?

The paper discusses learning theories and pedagogical approaches that inform the design of a teacher-training MOOC implementing creativity techniques and ICT tools. The article describes different versions of the course that applies Learning Design Studio as a course format and The First Principles of Instruction as an approach to structure the content and learning activities. It is claimed that the course needs to accommodate the learning profiles based on learning styles, learning locus of control and behavioral patterns as identified by MOOC research.European Commission, Project Number: 531086-LLP-1-2012-1-ES-KA3-KA3MP Agreement Number: 2012-4275 / 001-00

Entanglement between a diamond spin qubit and a photonic time-bin qubit at telecom wavelength

We report on the realization and verification of quantum entanglement between an NV electron spin qubit and a telecom-band photonic qubit. First we generate entanglement between the spin qubit and a 637 nm photonic time-bin qubit, followed by photonic quantum frequency conversion that transfers the entanglement to a 1588 nm photon. We characterize the resulting state by correlation measurements in different bases and find a lower bound to the Bell state fidelity of F = 0.77 +/- 0.03. This result presents an important step towards extending quantum networks via optical fiber infrastructure

Fluid drag reduction by magnetic confinement

A solid interface with a viscous liquid flow results in large drag, related to frictional forces. Reducing the drag by more than a few tens of percent remains elusive. Here, we use magnetic forces to stabilize a ferrofluid encapsulating a transported immiscible viscous liquid. This liquid-in-liquid flow exhibits drag reduction from 80% to more than 99%, tuneable by the viscosity ratio between the two liquids, over a range where the transported liquid has a viscosity larger or smaller than that of the encapsulating liquid. Our findings are explained by a laminar flow model that matches data for our whole range of experimental conditions.Comment: MS- 13 pages, 5 figure

Photonic delay systems as machine learning implementations

Nonlinear photonic delay systems present interesting implementation platforms for machine learning models. They can be extremely fast, offer great degrees of parallelism and potentially consume far less power than digital processors. So far they have been successfully employed for signal processing using the Reservoir Computing paradigm. In this paper we show that their range of applicability can be greatly extended if we use gradient descent with backpropagation through time on a model of the system to optimize the input encoding of such systems. We perform physical experiments that demonstrate that the obtained input encodings work well in reality, and we show that optimized systems perform significantly better than the common Reservoir Computing approach. The results presented here demonstrate that common gradient descent techniques from machine learning may well be applicable on physical neuro-inspired analog computers.P.B., M.H. and J.D. acknowledge support by the interuniversity attraction pole (IAP) Photonics@be of the Belgian Science Policy Office, the ERC NaResCo Starting grant and the European Union Seventh Framework Programme under grant agreement no. 604102 (Human Brain Project). M.C.S. and I.F. acknowledge support by MINECO (Spain), Comunitat Autónoma de les Illes Balears, FEDER, and the European Commission under Projects TEC2012-36335 (TRIPHOP), and Grups Competitius. M.H. and I.F. acknowledge support from the Universitat de les Illes Balears for an Invited Young Researcher Grant.Peer Reviewe

Trainable hardware for dynamical computing using error backpropagation through physical media

Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation-a crucial step for tuning such systems towards a specific task-can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers

Hydrogen peroxide in exhaled air is increased in stable asthmatic children

Exhaled air condensate provides a noninvasive means of obtaining samples from the lower respiratory tract. Hydrogen peroxide (H2O2) in exhaled air has been proposed as a marker of airway inflammation. We hypothesized that in stable asthmatic children the H2O2 concentration in exhaled air condensate may be elevated as a result of airway inflammation. In a cross-sectional study, 66 allergic asthmatic children (of whom, 41 were treated with inhaled steroids) and 21 healthy controls exhaled through a cold trap. The resulting condensate was examined fluorimetrically for the presence of H2O2. All subjects were clinically stable, nonsmokers, without infection. The median H2O2 level in the exhaled air condensate of the asthmatic patients was significantly higher than in healthy controls (0.60 and 0.15 micromol, respectively; p<0.05), largely because of high values in the stable asthmatic children who did not use anti-inflammatory treatment (0.8 micromol; p<0.01 compared to controls). We conclude that hydrogen peroxide is elevated in exhaled air condensate of children with stable asthma, and may reflect airway inflammation