Attractor Decision Network with Selective Inhibition

The ability to decide swiftly and accurately in an urgent scenario is crucial for an organism's survival. The neural mechanisms underlying the perceptual decision and trade-off between speed and accuracy have been extensively studied in the past few decades. Among several theoretical models, the attractor neural network model has successfully captured both behavioral and neuronal data observed in many decision experiments. However, a recent experimental study revealed additional details that were not considered in the original attractor model. In particular, the study shows that the inhibitory neurons in the posterior parietal cortex of mice are as selective to decision making results as the excitatory neurons, whereas the original attractor model assumes the inhibitory neurons to be unselective. In this study, we investigate a more general attractor model with selective inhibition, and analyze in detail how the computational ability of the network changes with selectivity. We proposed a reduced model for the selective model, and showed that selectivity adds a time-varying component to the energy landscape. This time dependence of the energy landscape allows the selective model to integrate information carefully in initial stages, then quickly converge to an attractor once the choice is clear. This results in the selective model having a more efficient speed-accuracy trade-off that is unreachable by unselective models.

A Hopf physical reservoir computer

Physical reservoir computing utilizes a physical system as a computational resource. This nontraditional computing technique can be computationally powerful, without the need of costly training. Here, a Hopf oscillator is implemented as a reservoir computer by using a node-based architecture; however, this implementation does not use delayed feedback lines. This reservoir computer is still powerful, but it is considerably simpler and cheaper to implement as a physical Hopf oscillator. A non-periodic stochastic masking procedure is applied for this reservoir computer following the time multiplexing method. Due to the presence of noise, the Euler–Maruyama method is used to simulate the resulting stochastic differential equations that represent this reservoir computer. An analog electrical circuit is built to implement this Hopf oscillator reservoir computer experimentally. The information processing capability was tested numerically and experimentally by performing logical tasks, emulation tasks, and time series prediction tasks. This reservoir computer has several attractive features, including a simple design that is easy to implement, noise robustness, and a high computational ability for many different benchmark tasks. Since limit cycle oscillators model many physical systems, this architecture could be relatively easily applied in many contexts.

WOOD AND GENERATIVE ALGORITHMS FOR THE COMPARISON BETWEEN MODELS AND REALITY

This study examines the emblematic case of a test room and its relation to digital modelling. This space is the result of a multi-optimization process that has been physically built for the verification of the initial hypotheses. As a result, it is actually a Physical Twin, designed to be transformable by removing a wall. The same space, on the other hand, has become useful for testing the Digital Twin logic by associating a BIM model with a dynamic representation of the data captured by the sensors. The representation is thus placed at the core of this cyclic phase between reality and representation, with the goal of validating the proposed theories through empirical practice, improving digital computational ability, and identifying pathways for monitoring space's interactions with the environment and those who live in it.

A Pervasive Computational Intelligence based Cognitive Security Co-design Framework for Hype-connected Embedded Industrial IoT

The amplified connectivity of routine IoT entities can expose various security trajectories for cybercriminals to execute malevolent attacks. These dangers are even amplified by the source limitations and heterogeneity of low-budget IoT/IIoT nodes, which create existing multitude-centered and fixed perimeter-oriented security tools inappropriate for vibrant IoT settings. The offered emulation assessment exemplifies the remunerations of implementing context aware co-design oriented cognitive security method in assimilated IIoT settings and delivers exciting understandings in the strategy execution to drive forthcoming study. The innovative features of our system is in its capability to get by with irregular system connectivity as well as node limitations in terms of scares computational ability, limited buffer (at edge node), and finite energy. Based on real-time analytical data, projected scheme select the paramount probable end-to-end security system possibility that ties with an agreed set of node constraints. The paper achieves its goals by recognizing some gaps in the security explicit to node subclass that is vital to our system’s operations.

Acoustically-shaped laser: a machining tool for Industry 4.0

The high versatility of laser direct-write (LDW) systems offers remarkable opportunities for Industry 4.0. However, the inherent serial nature of LDW systems can seriously constrain manufacturing throughput and, consequently, the industrial scalability of this technology. Here we present a method to parallelise LDWs by using acoustically shaped laser light. We use an acousto-optofluidic (AOF) cavity to generate acoustic waves in a liquid, causing periodic modulations of its refractive index. Such an acoustically controlled optical medium diffracts the incident laser beam into multiple beamlets that, operating in parallel, result in enhanced processing throughput. In addition, the beamlets can interfere mutually, generating an intensity pattern suitable for processing an entire area with a single irradiation. By controlling the amplitude, frequency, and phase of the acoustic waves, customised patterns can be directly engraved into different materials (silicon, chromium, and epoxy) of industrial interest. The integration of the AOF technology into an LDW system, connected to a wired-network, results into a cyber-physical system (CPS) for advanced and high-throughput laser manufacturing. A proof of concept for the computational ability of the CPS is given by monitoring the fidelity between a physical laser-ablated pattern and its digital avatar. As our results demonstrate, the AOF technology can broaden the usage of lasers as machine tools for industry 4.0

Lightweight U-Net for cloud detection of visible and thermal infrared remote sensing images

Accurate and rapid cloud detection is exceedingly significant for improving the downlink efficiency of on-orbit data, especially for the microsatellites with limited power and computational ability. However, the inference speed and large model limit the potential of on-orbit implementation of deep-learning-based cloud detection method. In view of the above problems, this paper proposes a lightweight network based on depthwise separable convolutions to reduce the size of model and computational cost of pixel-wise cloud detection methods. The network achieves lightweight end-to-end cloud detection through extracting feature maps from the images to generate the mask with the obtained maps. For the visible and thermal infrared bands of the Landsat 8 cloud cover assessment validation dataset, the experimental results show that the pixel accuracy of the proposed method for cloud detection is higher than 90%, the inference speed is about 5 times faster than that of U-Net, and the model parameters and floating-point operations are reduced to 12.4% and 12.8% of U-Net, respectively.

A new RSA public key encryption scheme with chaotic maps

Public key cryptography has received great attention in the field of information exchange through insecure channels. In this paper, we combine the Dependent-RSA (DRSA) and chaotic maps (CM) to get a new secure cryptosystem, which depends on both integer factorization and chaotic maps discrete logarithm (CMDL). Using this new system, the scammer has to go through two levels of reverse engineering, concurrently, so as to perform the recovery of original text from the cipher-text has been received. Thus, this new system is supposed to be more sophisticated and more secure than other systems. We prove that our new cryptosystem does not increase the overhead in performing the encryption process or the decryption process considering that it requires minimum operations in both. We show that this new cryptosystem is more efficient in terms of performance compared with other encryption systems, which makes it more suitable for nodes with limited computational ability.

MHCOOS: An Offline-Online Certificateless Signature Scheme for M-Health Devices

Current trends of mobile technology have seen a tremendous growth in its application in smart healthcare. This has resulted in the adoption and implementation of mobile health (m-health) systems by providing health assistance to the aging population. Despite its advantageous benefits, its computational complexities cannot be overlooked. M-health devices are portable processing tiny equipment with limited computational capabilities thereby making them complex for the implementation of public key cryptosystems. In spite of this, an Offline-Online signature scheme called the MHCOOS has been proposed to solve the difficulties in the computational ability. The scheme enjoys the following benefits by splitting the signing part into both offline and online phases. The offline phase performs heavy computations when a message is absent, whereas lighter computations are performed at the online stage when a message is present. Secondly, the online computations are extremely fast due to the already computed offline signature value and lighter pairings involved. Our performance analysis demonstrates how the proposed scheme outperforms other schemes. Finally, the hardness of the scheme is proven under the Bilinear Diffie–Hellman (BDH) and Computational Diffie–Hellman (CDH) problem in the random oracle model.

Recent shell-model investigation and its possible role in nuclear structure data study

Up to now, the nuclear shell model is rarely used in the nuclear data study because of several reasons. First, medium and heavy mass nuclei far from the shell-model cores, normally doubly magic nuclei, require a huge amount of calculation resource even in a limited shell-model space. Second, large deformation is difficult to be described in the limited model space, which is based on spherical symmetry. Third, high precision evaluation of nuclear structure data challenges the ability of the shell model. Even so, it is worth starting preliminary nuclear data investigations based on the shell model. With the present computational ability, it is possible to investigate 1000 or more nuclei in the framework of the shell model, which should be helpful for nuclear data study. In the present work, some recent shell-model investigations are briefly introduced. Based on these works, a simple nuclear force is suggested to be used in the systematic nuclear structure data study. The south-west region of 132Sn is taken as an example to show the ability of such a simple nuclear force.