Parallel Information Research Articles

ROS-based localization of a race vehicle at high-speed using LIDAR

An approach for LIDAR-based localization at high speeds is presented. In the proposed framework, the laser pose estimation is treated as a parallel redundant information, which is fused in an adjacent Kalman filter. The measurement and motion update step of the ROS-based adaptive Monte Carlo localization package is modified, in order to meet the requirements of a high-speed race scenario. Thereby, the key focus is on computational efficiency and the adaptation to characteristics arising at high speeds and at the limits of handling. An introspective performance evaluation monitors the position estimation process and labels generated outputs for adjacent components accordingly. The effectiveness of the proposed algorithm is illustrated in a real world high-speed experiment, autonomously driving a race vehicle – the DevBot – in a typical race environment.

Application of the Takaga-Sugeno neuro-fuzzy model for determining of engineering structures

The objective of the paper is to show the role of geodetic monitoring and modern methods of measurement data processing with the use of neural-fuzzy systems in identifying changes occurring in engineering structures. Fast methods of measurement and data processing are of key importance whenever rapidly changing phenomena should be captured (e.g. deformations of buildings, fires spreading in forest areas, flood waves, landslides caused by endogenous and exogeonic factors). Processes and phenomena occurring in engineering structures can be modelled based on information obtained by geodetic monitoring. At present, elements of geodetic technologies are being supplemented with artificial intelligence methods, which include neuro-fuzzy systems. These systems can process information that is provided at the input both as numerical and linguistic values. This approach is used for qualitative assessment of the condition of objects and physical quantities and it is characterized by parallel information processing. The paper presents the possibility of using the Takaga-Sugeno neural-fuzzy model for prediction and assessment of horizontal and vertical displacements of engineering structures as illustrated by the example of a historical footbridge in Muskauer Park, located in Germany and Poland.

METHODS FOR IMPROVING THE DATA RELIABILITY IN INFORMATION AND CONTROL SYSTEMS

The paper deals with the questions of analysis and estimation the information reservation validity increase in information-control systems. A device for parallel-serial information reservation has been developed to increase the reliability and validity of data and minimize the limiting factors: reducing the number of parallel reserved information source, information aging time, correlation of random noise, failures and self-restoring failures in information and control systems. The results of mathematical modeling of the device operation are presented, by which it can be concluded that if the parallel and sequential information redundancy is correctly combined, then the number of reserved information source and the number of consecutive checks can be optimally reduced to increase the reliability of the data. The development and use of a device based on a combined reservation to ensure high reliability of information in the information and control systems, as well as a significant reduction in the probability of nondetected and false alarms in alarm dangerous situations systems.

Variability and stability of large-scale cortical oscillation patterns.

Individual differences in brain organization exist at many spatiotemporal scales and underlie the diversity of human thought and behavior. Oscillatory neural activity is crucial for these processes, but how such rhythms are expressed across the cortex within and across individuals is poorly understood. We conducted a systematic characterization of brain-wide activity across frequency bands and oscillatory features during rest and task execution. We found that oscillatory profiles exhibit sizable group-level similarities, indicating the presence of common templates of oscillatory organization. Nonetheless, well-defined subject-specific network profiles were discernible beyond the structure shared across individuals. These individualized patterns were sufficiently stable to recognize individuals several months later. Moreover, network structure of rhythmic activity varied considerably across distinct oscillatory frequencies and features, indicating the existence of several parallel information processing streams embedded in distributed electrophysiological activity. These findings suggest that network similarity analyses may be useful for understanding the role of large-scale brain oscillations in physiology and behavior.

Altered structural and functional brain network overall organization predict human intertemporal decision-making.

Intertemporal decision-making is naturally ubiquitous to us: individuals always make a decision with different consequences occurring at different moments. These choices are invariably involved in life-changing outcomes regarding marriage, education, fertility, long-term well-being, and even public policy. Previous studies have clearly uncovered the neurobiological mechanism of the intertemporal decision in the schemes of regional location or sub-network. However, it still remains unclear how to characterize intertemporal behavior with multimodal whole-brain network metrics to date. Here, we combined diffusion tensor image and resting-state functional connectivity MRI technology, in conjunction with graph-theoretical analysis, to explore the link between topological properties of integrated structural and functional whole-brain networks and intertemporal decision-making. Graph-theoretical analysis illustrated that the participants with steep discounting rates exhibited the decreased global topological organizations including small-world and rich-club regimes in both functional and structural connectivity networks, and reflected the dreadful local topological dynamics in the modularity of functional connectome. Furthermore, in the cross-modalities configuration, the same relationship was predominantly observed for the coupling of structural-functional connectivity as well. Above topological metrics are commonly indicative of the communication pattern of simultaneous global and local parallel information processing, and it thus reshapes our accounts on intertemporal decision-making from functional regional/sub-network scheme to multimodal brain overall organization.

Time, memory, and the legacy of Howard Eichenbaum.

Over the past 15 years, there has been an explosion of new research on the role of the hippocampus in the representation of information about time in memory. Much of this work was inspired by the ideas and research of Howard Eichenbaum, who made major contributions to our understanding of the neurobiology of episodic memory and the neural representation of time. In this article, I will review evidence regarding the role of time in understanding hippocampal function. This review will cover a broad range of evidence from studies of humans and nonhuman animals with a narrative arc that follows Howard's major discoveries. These studies demonstrate that the hippocampus encodes information in relation to an episodic context, and that time, as well as space, serves to define these contexts. Moreover, the research has shown that the hippocampus can encode temporal, spatial, and situational information in parallel. Building on this work, I present a new framework for understanding temporal structure in human episodic memory. I conclude by outlining current controversies and new questions that must be addressed by the field in the years to come.

Rapid 3D image scanning microscopy with multi-spot excitation and double-helix point spread function detection.

Refocusing after Scanning using Helical phase engineering (RESCH) microscopy has previously been demonstrated to provide volumetric information from a single 2D scan. However, the practical application of this method is challenging due to its limited image acquisition speed and spatial resolution. Here, we report on a combination of RESCH and multifocal structured illumination microscopy (MSIM) to improve the image acquisition speed and spatial resolution. A phase mask is introduced to modulate the conventional point spread function (PSF) to the double-helix PSF (DH-PSF), which provides volumetric information, and meanwhile, sparse multifocal illumination patterns are generated by a digital micromirror device (DMD) for parallel 3D subdiffractive imaging information acquisition. We also present a strategy for processing the collected raw data with a Richardson-Lucy deconvolution and pixel reassignment algorithm to improve the spatial resolution of the depth estimation and imaging performance. The proposed 3D image scanning microscopy can record 3D specimen information and the corresponding depth information from a single multi-spot 2D planar scan, which ensures faster data acquisition, larger field of view, and higher spatial resolution than RESCH. Finally, we demonstrate the capability of our system with actual experiments.

Structuring of Abstract Working Memory Content by Fronto-parietal Synchrony in Primate Cortex

How is neuronal activity across distant brain regions orchestrated to allow multiple stimuli to be stored together in working memory, yet maintained separate for individual readout and protection from distractors? Using paired recordings in the prefrontal and parietal cortex of monkeys discriminating numbers of items (numerosities), we found that working memory content is structured by frequency-specific oscillatory synchrony. Parieto-frontal signaling in the beta band carried information about the most recent numerical input. Fronto-parietal coupling in the theta band differentiated between multiple memorized numerosities. Task-relevant and distracting stimuli were nested in spiking activity of single prefrontal neurons, but could be separated by reading out spikes at distinct phases of parietal theta oscillations. The strength of phase-locked, cross-regional memory coding predicted task performance. Frequency-specific communication channels in the fronto-parietal network could enable serial bottom-up and parallel top-down information transmission, providing an important mechanism to protect working memory from interference.

Spike-driven threshold-based learning with memristive synapses and neuromorphic silicon neurons

Biologically plausible neuromorphic computing systems are attracting considerable attention due to their low latency, massively parallel information processing abilities, and their high energy efficiency. To achieve these features neuromorphic silicon neuron circuits need to be integrated with plastic synapse circuits capable of on-line learning and storage of synaptic weights. Within this context, memristive devices play a key role thanks to their non-volatility, scalability, and compatibility with the complementary metal–oxide–semiconductor fabrication process. However, neuro-memristive systems are still facing difficult challenges for implementing efficient learning protocols. Here, we propose and demonstrate in hardware a spike-driven threshold-based learning rule which goes beyond conventional spike-timing dependent plasticity mechanisms, by also taking into account the neuron membrane potential and its firing rate. The mixed memristive–neuromorphic system we demonstrate comprises an oxide-based memristive synapse device placed between two silicon neurons implemented on a neuromorphic chip that comprises the proper interfacing and spike-based learning circuits designed to drive the memristive elements. We show how the system is able to emulate in real-time weight dependent post-synaptic activity and drive synaptic weight updates at the memristive synapse level following the spike-driven learning rule presented. We validate this spike-based learning mechanism with experimental results and quantify the system performance with basic learning experiments.

(Invited) Multi-Terminal Oxide-based Electric-Double-Layer Thin-Film Transistors for Neuromorphic Systems

Our brain is a highly parallel, energy efficient, and event-driven information processing system, which is fundamentally different from traditional von Neumann computers. Inspired by biological neural computing, neuromorphic systems may open up new paradigms to deal with complicated problems such as pattern recognition and decision making. Hardware implementation of neurons and synapses by individual ionic/electronic hybrid device is of great significance for enhancing our understanding of the brain and solving sensory processing and complex recognition tasks. Although two-terminal memristors can perform some basic synaptic and neural functions, our human brain contains many more synapses than neurons. This fact suggests that multi-terminal devices are more favorable for complex neural network emulation. In recent years, multi-terminal electric-double-layer (EDL) thin-film transistors (TFTs) based on interfacial ion-modulation have attracted significant attention in mimicking synaptic dynamic plasticity and neural functions. Here, we provide the proof-of-principle artificial synapses/neurons based on oxide-based EDL TFTs gated by solid electrolytes with multiple driving and modulatory input terminals. Our results provide a new-concept approach for building brain-like cognitive systems.

(Invited) Multi-Terminal Oxide-based Electric-Double-Layer Thin-Film Transistors for Neuromorphic Systems

Our brain is a highly parallel, energy efficient and event-driven information processing system, which is fundamentally different from traditional von Neumann computers. Neuron is the most important building block, and information processing in individual neuron involves the transformation of input synaptic spike train into an appropriate output spike train. Inspired by biological neural computing, neuromorphic systems may open up new paradigms to deal with complicated problems such as pattern recognition, classification and decision making. The idea of building brain-inspired adaptive artificial devices has been around for decades, and hardware implementation of neuron by individual ionic/electronic hybrid device is of great significance for enhancing our understanding of the brain and solving sensory processing and complex recognition tasks. Although two-terminal memristors can performe some basic synaptic and neural functions, our human brain contains many more synapses than neurons. This fact suggests that multi-terminal devices are more favorable for complex neural nwtwork emulation. In recent years, multi-terminal electric-double-layer (EDL) Thin-film transistors (TFTs) based on interfacial ion-modulation have attracted significant attention in mimicking synaptic dynamic plasticity and neural functions. Here, we provide the proof-of-principle artificial synapses/neurons based on solid electrolytes coupled oxide-based EDL TFTs with multiple driving and modulatory inputs terminals. Paired-pulse facilitation, dendritic integration and orientation tuning were successfully emulated. Additionally, neuronal gain control (arithmetic) in the scheme of rate coding is also experimentally demonstrated. Our results provide a new-concept approach for building brain-like cognitive systems.

An improved sine–cosine algorithm based on orthogonal parallel information for global optimization

Many real-life optimization applications are characterized by the presence of some difficulties such as discontinuity, mixing continuity–discontinuity, prohibited zones, non-smooth and non-convex cost functions. In this sense, traditional optimization algorithms may be stuck in local optima when dealing with these natures. Recently, sine–cosine algorithm (SCA) has been introduced as a global optimization technique for solving optimization problems. However, as a new algorithm, the sucking in local optimal may be occurred due to two reasons. The first is that the diversity of solutions may not be maintained efficiently. The second is that no emphasizing strategy is employed to guide the search toward the promising region. In this paper, a novel SCA based on orthogonal parallel information (SCA-OPI) for solving numerical optimization problems is proposed. In SCA-OPI, a multiple-orthogonal parallel information is introduced to exhibit effectively two advantages: the orthogonal aspect of information enables the algorithm to maintain the diversity and enhances the exploration search, while the parallelized scheme enables the algorithm to achieve the promising solutions and emphases the exploitation search. Further, an experience-based opposition direction strategy is presented to preserve the exploration ability. The proposed SCA-OPI algorithm is evaluated and investigated on different benchmark problems and some engineering applications. The results affirmed that the SCA-OPI algorithm can achieve a highly competitive performance compared with different algorithms, especially in terms of optimality and reliability.

Dissociable neural mechanisms track evidence accumulation for selection of attention versus action

Decision-making is typically studied as a sequential process from the selection of what to attend (e.g., between possible tasks, stimuli, or stimulus attributes) to which actions to take based on the attended information. However, people often process information across these various levels in parallel. Here we scan participants while they simultaneously weigh how much to attend to two dynamic stimulus attributes and what response to give. Regions of the prefrontal cortex track information about the stimulus attributes in dissociable ways, related to either the predicted reward (ventromedial prefrontal cortex) or the degree to which that attribute is being attended (dorsal anterior cingulate cortex, dACC). Within the dACC, adjacent regions track correlates of uncertainty at different levels of the decision, regarding what to attend versus how to respond. These findings bridge research on perceptual and value-based decision-making, demonstrating that people dynamically integrate information in parallel across different levels of decision-making.

Rapid Earthquake Discrimination for Earthquake Early Warning: A Bayesian Probabilistic Approach Using Three‐Component Single‐Station Waveforms and Seismicity Forecast

The utility of Earthquake Early Warning (EEW) relies on the robust and rapid classification of near‐site earthquake source signals from noise and teleseismic arrivals. To achieve this goal, we propose using the three‐component acceleration and velocity waveform data and epidemic‐type aftershock sequence (ETAS) seismicity forecast information in parallel, which will produce a posterior prediction by combining the predictions from the heterogeneous sources using a Bayesian probabilistic approach. We collected 2481 three‐component strong‐motion records for training and testing. The rapid prediction is available as quickly as 0.5 s after the trigger at a single station and updates every 0.5 s up to 3.0 s, achieving a precision rate of 94.7% at the first prediction with the classification accuracy increasing with time. The leave‐one‐out cross‐validation method also demonstrates confidence of robust performance for future earthquake signal detections. We compared the method with the τ_c−P_d EEW classification criterion and find that our prediction is 83% faster. Because the method evaluates two independent sources of information simultaneously under an ensemble model, the new strategy has shown fast predictions with promising results and the implementation of this methodology could provide significantly faster and more reliable EEW warnings to regions near the earthquake’s epicenter, where the strongest shaking is observed.

A Formal Model for Compliance Verification of Service Compositions

Business processes design and execution environments increasingly need support from modular services in service compositions to offer the flexibility required by rapidly changing requirements. With each evolution, however, the service composition must continue to adhere to laws and regulations, resulting in a demand for automated compliance checking. Existing approaches, if at all, either offer only verification after the fact or linearize models to such an extent that parallel information is lost. We propose a mapping of service compositions to Kripke structures by using colored Petri nets. The resulting model allows preventative compliance verification using well-known temporal logics and model checking techniques while providing full insight into parallel executing branches and the local next invocation. Furthermore, the mapping causes limited state explosion, and allows for significant further model reduction. The approach is validated on a case study from a telecom company in Australia and evaluated with respect to performance and expressiveness. We demonstrate that the proposed mapping has increased expressiveness while being less vulnerable to state explosion than existing approaches, and show that even large service compositions can be verified preventatively with existing model checking techniques.

Translating Pro-Drop Languages With Reconstruction Models

Pronouns are frequently omitted in pro-drop languages, such as Chinese, generally leading to significant challenges with respect to the production of complete translations. To date, very little attention has been paid to the dropped pronoun (DP) problem within neural machine translation (NMT). In this work, we propose a novel reconstruction-based approach to alleviating DP translation problems for NMT models. Firstly, DPs within all source sentences are automatically annotated with parallel information extracted from the bilingual training corpus. Next, the annotated source sentence is reconstructed from hidden representations in the NMT model. With auxiliary training objectives, in the terms of reconstruction scores, the parameters associated with the NMT model are guided to produce enhanced hidden representations that are encouraged as much as possible to embed annotated DP information. Experimental results on both Chinese-English and Japanese-English dialogue translation tasks show that the proposed approach significantly and consistently improves translation performance over a strong NMT baseline, which is directly built on the training data annotated with DPs.

Three parallel information systems for malaria elimination in Swaziland, 2010-2015: are the numbers the same?

Background: To be able to eliminate malaria, accurate, timely reporting and tracking of all confirmed malaria cases is crucial. Swaziland, a country in the process of eliminating malaria, has three parallel health information systems. Design: This was a cross-sectional study using country-wide programme data from 2010 to 2015. Methods: The Malaria Surveillance Database System (MSDS) is a comprehensive malaria database, the Immediate Disease Notification System (IDNS) is meant to provide early warning and trigger case investigations to prevent onward malaria transmission and potential epidemics, and the Health Management Information Systems (HMIS) reports on all morbidity at health facility level. Discrepancies were stratified by health facility level and type. Results: Consistent over-reporting of 9-85% was noticed in the HMIS, principally at the primary health care level (clinic and/or health centre). In the IDNS, the discrepancy went from under-reporting (12%) to over-reporting (32%); this was also seen at the primary care level. At the hospital level, there was under-reporting in both the HMIS and IDNS. Conclusions: There are considerable discrepancies in the numbers of confirmed malaria cases in the HMIS and IDNS in Swaziland. This may misrepresent the malaria burden and delay case investigation, predisposing the population to potential epidemics. There is an urgent need to improve data integrity in order to guide and evaluate efforts toward elimination.

Electrochemical biosensor system using a CMOS microelectrode array provides high spatially and temporally resolved images

The ability to view biological events in real time has contributed significantly to research in the life sciences. While video capture of real time changes in anatomical relationships is important, it is equally important to visualize real time changes in the chemical communications that drive cell behaviors. This paper describes an electrochemical imaging system capable of capturing changes in chemical gradients in live tissue slices. The system consists of a CMOS microchip with 8192 configurable Pt surface electrodes, on-chip potentiostat, on-chip control logic, and a microfluidic device designed to interface with the CMOS chip to support ex vivo tissue experimentation. All data processing and visualization methods, sensor calibrations, microfluidics fabrication, and tissue preparation and handling procedures are described. Using norepinephrine as a target analyte for proof of concept, the system is capable of differentiating concentrations of norepinephrine as low as 8 µM and up to 1024 µM with a linear response and a spatial resolution of 25.5 µm × 30.4 µm. Electrochemical imaging was tested using murine adrenal tissue as a biological model and successfully showed caffeine-stimulated release of catecholamines from live slices of adrenal tissue with temporal sensitivity. This system successfully demonstrates the use of a high-density microelectrode array for electrochemical analysis with high spatiotemporal resolution to gather chemical gradient information in parallel with optical microscopy recordings.

Deep Ordinal Regression Network for Monocular Depth Estimation.

Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an ill-posed problem. Recent methods have gained significant improvement by exploring image-level information and hierarchical features from deep convolutional neural networks (DCNNs). These methods model depth estimation as a regression problem and train the regression networks by minimizing mean squared error, which suffers from slow convergence and unsatisfactory local solutions. Besides, existing depth estimation networks employ repeated spatial pooling operations, resulting in undesirable low-resolution feature maps. To obtain high-resolution depth maps, skip-connections or multilayer deconvolution networks are required, which complicates network training and consumes much more computations. To eliminate or at least largely reduce these problems, we introduce a spacing-increasing discretization (SID) strategy to discretize depth and recast depth network learning as an ordinal regression problem. By training the network using an ordinary regression loss, our method achieves much higher accuracy and faster convergence in synch. Furthermore, we adopt a multi-scale network structure which avoids unnecessary spatial pooling and captures multi-scale information in parallel. The proposed deep ordinal regression network (DORN) achieves state-of-the-art results on three challenging benchmarks, i.e., KITTI [16], Make3D [49], and NYU Depth v2 [41], and outperforms existing methods by a large margin.

A MODEL for DISTRIBUTED PROCESSING and ANALYSES of NGS DATA under MAP-REDUCE PARADIGM.

Massively parallel sequencing technique, introduced by NGS technology, has resulted in an exponential growth of sequencing data, with greatly reduced cost and increased throughput. This huge explosion of data has introduced new challenges in regard to its storage, integration, processing and analyses. In this paper, we have proposed a novel distributed model under Map-Reduce paradigm to address the NGS big data problem. The architecture of the model involves Map-Reduce based modularized approach involving 3 different phases that support various analytical pipelines. The first phase will generate detailed base level information of various individual genomes, by granulating the alignment data. The other 2 phases independently process this base level information in parallel. One of these 2 phases will provide an integrated DNA profile of multiple individuals, whereas the other phase will generate contigs with similar features in an individual. Each of these 2 phases will generate a repository of genomic information that will facilitate other analytical pipelines. A simulated and real experimental prototypes has been provided as results to show the effectiveness of the model and its superiority over a few existing popular models and tools. A detailed description of the scope of applications of this model is also included in this article.