QKD based on symmetric entangled Bernstein-Vazirani

This paper introduces a novel entanglement-based QKD protocol, that makes use of a modified symmetric version of the Bernstein-Vazirani algorithm, in order to achieve a secure and efficient key distribution. Two variants of the protocol, one fully symmetric and one semi-symmetric, are presented. In both cases, the spatially separated Alice and Bob share multiple EPR pairs, one qubit of the pair each. The fully symmetric version allows both parties to input a secret key from the irrespective location and, finally, acquire in the end a totally new and original key, an idea which was inspired by the Diffie-Hellman key exchange protocol. In the semi-symmetric version, Alice sends her chosen secret key to Bob (or vice versa). Furthermore, their performance against an eavesdropper's attack is analyzed. Finally, in order to illustrate the operation of the protocols in practice, two small scale but detailed examples are given.Comment: 16 pages, 8 figure

Neurological modeling of what experts vs. non-experts find interesting

The P3 and related ERP's have a long history of use to identify stimulus events in subjects as part of oddball-style experiments. In this work we describe the ongoing development of oddball style experiments which attempt to capture what a subject finds of interest or curious, when presented with a set of visual stimuli i.e. images. This joint work between Dublin City University (DCU) and the European Space Agency's Advanced Concepts Team (ESA ACT) is motivated by the challenges of autonomous space exploration where the time lag for sending data back to earth for analysis and then communicating an action or decision back to the spacecraft means that decision-making is slow. Also, when extraterrestrial sensors capture data, the determination of what data to send back to earth is driven by an expertly devised rule set, that is scientists need to determine apriori what will be of interest. This cannot adapt to novel or unexpected data that a scientist may find curious. Our work is attempting to determine if it is possible to capture what a scientist (subject) finds of interest (curious) in a stream of image data through EEG measurement. One of the our challenges is to determine the difference between an expert and a lay subject response to stimulus. To investigate the theorized difference, we use a set of lifelog images as our dataset. Lifelog images are first person images taken by a small wearable camera which continuously records images whilst it is worn. We have devised two key experiments for use with this data and two classes of subjects. Our subjects are a person who has worn the personal camera, from which our collection of lifelog images is taken and who becomes our expert, and the remaining subjects are people who have no association with the captured images. Our first experiment is a traditional oddball experiment where the oddballs are people having coffee, and can be thought of as a directed information seeking task. The second experiment is to present a stream of lifelog images to the subjects and record which images cause a stimulus response. Once the data from these experiments has been captured our task is to compare the responses between the expert and lay subject groups, to determine if there are any commonalities between these groups or any distinct differences. If the latter outcome is the case the objective is then to investigate methods for capturing properties of images which cause an expert to be interested in a presented image. Further novelty is added to our work by the fact we are using entry-level off-the-shelf EEG devices, consisting of 4 nodes with a sampling rate of 255Hz

Evolutionary swarm robotics: a theoretical and methodological itinerary from individual neuro-controllers to collective behaviours

In the last decade, swarm robotics gathered much attention in the research community. By drawing inspiration from social insects and other self-organizing systems, it focuses on large robot groups featuring distributed control, adaptation, high robustness, and flexibility. Various reasons lay behind this interest in similar multi-robot systems. Above all, inspiration comes from the observation of social activities, which are based on concepts like division of labor, cooperation, and communication. If societies are organized in such a way in order to be more efficient, then robotic groups also could benefit from similar paradigms

Separate Microcircuit Modules of Distinct V2a Interneurons and Motoneurons Control the Speed of Locomotion

SummarySpinal circuits generate locomotion with variable speed as circumstances demand. These circuits have been assumed to convey equal and uniform excitation to all motoneurons whose input resistance dictates their activation sequence. However, the precise connectivity pattern between excitatory premotor circuits and the different motoneuron types has remained unclear. Here, we generate a connectivity map in adult zebrafish between the V2a excitatory interneurons and slow, intermediate, and fast motoneurons. We show that the locomotor network does not consist of a uniform circuit as previously assumed. Instead, it can be deconstructed into three separate microcircuit modules with distinct V2a interneuron subclasses driving slow, intermediate, or fast motoneurons. This modular design enables the increase of locomotor speed by sequentially adding microcircuit layers from slow to intermediate and fast. Thus, this principle of organization of vertebrate spinal circuits represents an intrinsic mechanism to increase the locomotor speed by incrementally engaging different motor units

Curiosity cloning: neural analysis of scientific knowledge

Event-related potentials (ERPs) are indicators of brain activity related to cognitive processes. They can be de- tected from EEG signals and thus constitute an attractive non-invasive option to study cognitive information pro- cessing. The P300 wave is probably the most celebrated example of an event-related potential and it is classically studied in connection to the odd-ball paradigm experi- mental protocol, able to consistently provoke the brain wave. We propose the use of P300 detection to identify the scientific interest in a large set of images and train a computer with machine learning algorithms using the subject’s responses to the stimuli as the training data set. As a first step, we here describe a number of experiments designed to relate the P300 brain wave to the cognitive processes related to placing a scientific judgment on a picture and to study the number of images per seconds that can be processed by such a system

Evolving homogeneous neuro-controllers for a group of heterogeneous robots: coordinated motion, cooperation, and acoustic communication

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Implicit Retrieval Of Salient Images Using Brain Computer Interface

Space missions are often equipped with several high definition sensors that can autonomously collect a potentially enormous amount of data. The bottleneck in retrieving these often precious datasets is the onboard data storing capability and the communication bandwidth, which limit the amount of data that can be sent back to Earth. In this paper, we propose a method based on the analysis of brain electrical activity to identify the scientific interest of experts towards a given image in a large set of images. Such a method can be used to efficiently create an abundant training set (images and whether they are scientifically interesting) with a considerably faster image presentation rate that can go beyond expert consciousness, with less interrogation time for experts and relatively high performance

Implicit retrieval of salient images using brain computer interface

ABSTRACT Space missions are often equipped with several high definition sensors that can autonomously collect a potentially enormous amount of data. The bottleneck in retrieving these often precious datasets is the onboard data storing capability and the communication bandwidth, which limit the amount of data that can be sent back to Earth. In this paper, we propose a method based on the analysis of brain electrical activity to identify the scientific interest of experts towards a given image in a large set of images. Such a method can be used to efficiently create an abundant training set (images and whether they are scientifically interesting) with a considerably faster image presentation rate that can go beyond expert consciousness, with less interrogation time for experts and relatively high performance