On Termination for Faulty Channel Machines

A channel machine consists of a finite controller together with several fifo channels; the controller can read messages from the head of a channel and write messages to the tail of a channel. In this paper, we focus on channel machines with insertion errors, i.e., machines in whose channels messages can spontaneously appear. Such devices have been previously introduced in the study of Metric Temporal Logic. We consider the termination problem: are all the computations of a given insertion channel machine finite? We show that this problem has non-elementary, yet primitive recursive complexity

A Neural Model of Biased Oscillations in Aplysia Head-Waving Behavior

A long-term bias in the exploratory head-waving behavior of Aplysia can be induced using bright lights as an aversive stimulus: coupling onset of the lights with head movements to one side results in a bias away from that side (Cook & Carew, 1986). This bias has been interpreted as a form of operant conditioning, and has previously been simulated with a neural network model based on associative synaptic facilitation (Raymond, Baxter, Buonomano, & Byrne, 1992). In this article we simulate the head-waving behavior using a recurrent gated dipole, a nonlinear dynamical neural model that has previously been used to explain various data including oscillatory behavior in biological pacemakers. Within the recurrent gated dipole, two channels operate antagonistically to generate oscillations, which drive the side-to-side head waving. The frequency of oscillations depends on transmitter mobilization dynamics, which exhibit both short- and long-term adaptation. We assume that light onset results in a nonspecific increase in arousal to both channels of the dipole. Repeated pairing of arousal increments with activation of one channel (the "reinforced" channel) of the dipole leads to a bias in transmitter dynamics, which causes the oscillation to last a shorter time on the reinforced channel than on the non-reinforced channel. Our model provides a parsimonious explanation of the observed behavior, and it avoids some of the unexpected results obtained with the Raymond et al. model. In addition, our model makes predictions concerning the rate of onset and extinction of the biases, and it suggests new lines of experimentation to test the nature of the head-waving behavior.Office of Naval Research (N00014-92-J-4015, N00014-91-J-4100, N0014-92-J-1309); Air Force Office of Scientific Research (F49620-92-J-0499); A.P. Sloan Foundation (BR-3122

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as well

Multi-channel whole-head OPM-MEG: Helmet design and a comparison with a conventional system

© 2020 The Authors Magnetoencephalography (MEG) is a powerful technique for functional neuroimaging, offering a non-invasive window on brain electrophysiology. MEG systems have traditionally been based on cryogenic sensors which detect the small extracranial magnetic fields generated by synchronised current in neuronal assemblies, however, such systems have fundamental limitations. In recent years, non-cryogenic quantum-enabled sensors, called optically-pumped magnetometers (OPMs), in combination with novel techniques for accurate background magnetic field control, have promised to lift those restrictions offering an adaptable, motion-robust MEG system, with improved data quality, at reduced cost. However, OPM-MEG remains a nascent technology, and whilst viable systems exist, most employ small numbers of sensors sited above targeted brain regions. Here, building on previous work, we construct a wearable OPM-MEG system with ‘whole-head’ coverage based upon commercially available OPMs, and test its capabilities to measure alpha, beta and gamma oscillations. We design two methods for OPM mounting; a flexible (EEG-like) cap and rigid (additively-manufactured) helmet. Whilst both designs allow for high quality data to be collected, we argue that the rigid helmet offers a more robust option with significant advantages for reconstruction of field data into 3D images of changes in neuronal current. Using repeat measurements in two participants, we show signal detection for our device to be highly robust. Moreover, via application of source-space modelling, we show that, despite having 5 times fewer sensors, our system exhibits comparable performance to an established cryogenic MEG device. While significant challenges still remain, these developments provide further evidence that OPM-MEG is likely to facilitate a step change for functional neuroimaging

Influence of bed elevation discordance on flow patterns and head losses in an open-channel confluence

Confluences play a major role in the dynamics of networks of natural and man-made open channels, and field measurements on river confluences reveal that discordance in bed elevation is common. Studies of schematized confluences with a step at the interface between the tributary and the main channel bed reveal that bed elevation discordance is an important additional control for the confluence hydrodynamics. This study aimed to improve understanding of the influence of bed elevation discordance on the flow patterns and head losses in a right-angled confluence of an open channel with rectangular cross-sections. A large eddy simulation (LES)-based numerical model was set up and validated with experiments by others. Four configurations with different bed discordance ratios were investigated. The results confirm that, with increasing bed elevation discordance, the tributary streamlines at the confluence interface deviate less from the geometrical confluence angle, the extent of the recirculation zone (RZ) gets smaller, the ratio of the water depth upstream to that downstream of the confluence decreases, and the water level depression reduces. The bed elevation discordance also leads to the development of a large-scale structure in the lee of the step. Despite the appearance of the large-scale structure, the reduced extent of the RZ and associated changes in flow deflection/contraction reduce total head losses experienced by the main channel with an increase of the bed discordance ratio. It turns out that bed elevation discordance converts the lateral momentum from the tributary to streamwise momentum in the main channel more efficiently. (C) 2019 Hohai University. Production and hosting by Elsevier B.V

Data Transmission with Reduced Delay for Distributed Acoustic Sensors

This paper proposes a channel access control scheme fit to dense acoustic sensor nodes in a sensor network. In the considered scenario, multiple acoustic sensor nodes within communication range of a cluster head are grouped into clusters. Acoustic sensor nodes in a cluster detect acoustic signals and convert them into electric signals (packets). Detection by acoustic sensors can be executed periodically or randomly and random detection by acoustic sensors is event driven. As a result, each acoustic sensor generates their packets (50bytes each) periodically or randomly over short time intervals (400ms~4seconds) and transmits directly to a cluster head (coordinator node). Our approach proposes to use a slotted carrier sense multiple access. All acoustic sensor nodes in a cluster are allocated to time slots and the number of allocated sensor nodes to each time slot is uniform. All sensor nodes allocated to a time slot listen for packet transmission from the beginning of the time slot for a duration proportional to their priority. The first node that detect the channel to be free for its whole window is allowed to transmit. The order of packet transmissions with the acoustic sensor nodes in the time slot is autonomously adjusted according to the history of packet transmissions in the time slot. In simulations, performances of the proposed scheme are demonstrated by the comparisons with other low rate wireless channel access schemes.Comment: Accepted to IJDSN, final preprinted versio

On the Stability of Random Multiple Access with Stochastic Energy Harvesting

In this paper, we consider the random access of nodes having energy harvesting capability and a battery to store the harvested energy. Each node attempts to transmit the head-of-line packet in the queue if its battery is nonempty. The packet and energy arrivals into the queue and the battery are all modeled as a discrete-time stochastic process. The main contribution of this paper is the exact characterization of the stability region of the packet queues given the energy harvesting rates when a pair of nodes are randomly accessing a common channel having multipacket reception (MPR) capability. The channel with MPR capability is a generalized form of the wireless channel modeling which allows probabilistic receptions of the simultaneously transmitted packets. The results obtained in this paper are fairly general as the cases with unlimited energy for transmissions both with the collision channel and the channel with MPR capability can be derived from ours as special cases. Furthermore, we study the impact of the finiteness of the batteries on the achievable stability region.Comment: The material in this paper was presented in part at the IEEE International Symposium on Information Theory, Saint Petersburg, Russia, Aug. 201

An Explorative Study on Sales Distribution in M-commerce

Despite the proliferation of studies on the sales distribution in e-commerce, little research has been conducted on the sales distribution in the m-commerce channel. This study empirically examines the sales distribution of various product categories in the mobile channel, using the large transaction data from a leading e-marketplace in Korea. Overall, transactions in the mobile channel are more concentrated to head products compared to the PC channel sales, but the pattern is inconsistent across product categories. Transactions in product categories of high average price (e.g., computers) and low purchase frequency rate (e.g., health care products) are less concentrated to head products in the mobile channel than the PC channel. The revenue distribution, however, shows the opposite. Head products generate relatively less revenue in the mobile channel than the PC channel. We provide explanations why the mixing results appear across product categories and between the distribution types