Do Predictive Brain Implants Threaten Patient's Autonomy or Authenticity?

The development of predictive brain implant (PBI) technology that is able to forecast specific neuronal events and advise and/or automatically administer appropriate therapy for diseases of the brain raises a number of ethical issues. Provided that this technology satisfies basic safety and functionality conditions, one of the most pressing questions to address is its relation to the autonomy of patients. As Frederic Gilbert in his article asks, if autonomy implies a certain idea of freedom, or self-government, how can an individual be considered to decide freely if the implanted device stands at the inception of the causal chain producing his decisions? He claims that PBIs threaten persons’ autonomy by diminishing their post-operative experience of self-control. In this commentary, I wish to discuss this claim. Contrary to Gilbert, I will suggest that PBIs do not pose a significant threat to patient’s autonomy, as self-control, but rather to his/her sense of authenticity. My claim is that the language of authenticity, already introduced in the recent bioethical literature, may offer a better way to voice some of the concerns with PBIs that Gilbert recognized

Two-body recombination in a quantum mechanical lattice gas: Entropy generation and probing of short-range magnetic correlations

We study entropy generation in a one-dimensional (1D) model of bosons in an optical lattice experiencing two-particle losses. Such heating is a major impediment to observing exotic low temperature states, and "simulating" condensed matter systems. Developing intuition through numerical simulations, we present a simple empirical model for the entropy produced in this 1D setting. We also explore the time evolution of one and two particle correlation functions, showing that they are robust against two-particle loss. Because of this robustness, induced two-body losses can be used as a probe of short range magnetic correlations.Comment: 6 pages, 3 figures - v4, published versio

Subnanometer Translation of Microelectromechanical Systems Measured by Discrete Fourier Analysis of CCD Images

Abstract—In-plane linear displacements of microelectromechanical systems are measured with subnanometer accuracy by observing the periodic micropatterns with a charge-coupled device camera attached to an optical microscope. The translation of the microstructure is retrieved from the video by phase-shift computation using discrete Fourier transform analysis. This approach is validated through measurements on silicon devices featuring steep-sided periodic microstructures. The results are consistent with the electrical readout of a bulk micromachined capacitive sensor, demonstrating the suitability of this technique for both calibration and sensing. Using a vibration isolation table, a standard deviation of σ = 0.13 nm could be achieved, enabling a measurement resolution of 0.5 nm (4σ) and a subpixel resolution better than 1/100 pixel. [2010-0170

High-Performance Shuffle Motor Fabricated by Vertical Trench Isolation Technology

Shuffle motors are electrostatic stepper micromotors that employ a built-in mechanical leverage to produce large output forces as well as high resolution displacements. These motors can generally move only over predefined paths that served as driving electrodes. Here, we present the design, modeling and experimental characterization of a novel shuffle motor that moves over an unpatterned, electrically grounded surface. By combining the novel design with an innovative micromachining method based on vertical trench isolation, we have greatly simplified the fabrication of the shuffle motors and significantly improved their overall performance characteristics and reliability. Depending on the propulsion voltage, our motor with external dimensions of 290 μm × 410 mm displays two distinct operational modes with adjustable step sizes varying respectively from 0.6 to 7 nm and from 49 to 62 nm. The prototype was driven up to a cycling frequency of 80 kHz, showing nearly linear dependence of its velocity with frequency and a maximum velocity of 3.6 mm/s. For driving voltages of 55 V, the device had a maximum travel range of ±70 μm and exhibited an output force of 1.7 mN, resulting in the highest force and power densities reported so far for an electrostatic micromotor. After five days of operation, it had traveled a cumulative distance of more than 1.5 km in 34 billion steps without noticeable deterioration in performance.\u

Topological Phase Separation In Trapped Ultracold Fermionic Gases

We investigate the harmonically trapped 2D fermionic systems with a effective spin-orbit coupling and intrinsic s-wave superfluidity under the local density approximation, and find that there is a critical value for Zeeman field. When the Zeeman field larger than the critical value, the topological superfluid phases emerge and coexist with the normal superfluid phase, topological phase separation, in the trapped region. Otherwise, the superfluid phase is topologically trivial.Comment: 6 pages, 3 figure

Decentralized Massive MIMO Processing Exploring Daisy-chain Architecture and Recursive Algorithms

Algorithms for Massive MIMO uplink detection and downlink precoding typically rely on a centralized approach, by which baseband data from all antenna modules are routed to a central node in order to be processed. In the case of Massive MIMO, where hundreds or thousands of antennas are expected in the base-station, said routing becomes a bottleneck since interconnection throughput is limited. This paper presents a fully decentralized architecture and an algorithm for Massive MIMO uplink detection and downlink precoding based on the Stochastic Gradient Descent (SGD) method, which does not require a central node for these tasks. Through a recursive approach and very low complexity operations, the proposed algorithm provides a good trade-off between performance, interconnection throughput and latency. Further, our proposed solution achieves significantly lower interconnection data-rate than other architectures, enabling future scalability.Comment: Manuscript accepted for publication in IEEE Transactions on Signal Processin

Graphlet-based Characterization of Directed Networks

We are flooded with large-scale, dynamic, directed, networked data. Analyses requiring exact comparisons between networks are computationally intractable, so new methodologies are sought. To analyse directed networks, we extend graphlets (small induced sub-graphs) and their degrees to directed data. Using these directed graphlets, we generalise state-of-the-art network distance measures (RGF, GDDA and GCD) to directed networks and show their superiority for comparing directed networks. Also, we extend the canonical correlation analysis framework that enables uncovering the relationships between the wiring patterns around nodes in a directed network and their expert annotations. On directed World Trade Networks (WTNs), our methodology allows uncovering the core-broker-periphery structure of the WTN, predicting the economic attributes of a country, such as its gross domestic product, from its wiring patterns in the WTN for up-to ten years in the future. It does so by enabling us to track the dynamics of a country’s positioning in the WTN over years. On directed metabolic networks, our framework yields insights into preservation of enzyme function from the network wiring patterns rather than from sequence data. Overall, our methodology enables advanced analyses of directed networked data from any area of science, allowing domain-specific interpretation of a directed network’s topology