STOCHASTIC PSEUDOSPIN NEURAL NETWORK WITH TRIDIAGONAL SYNAPTIC CONNECTIONS

Context. To reduce the computational resource time in the problems of diagnosing and recognizing distorted images based on a fully connected stochastic pseudospin neural network, it becomes necessary to thin out synaptic connections between neurons, which is solved using the method of diagonalizing the matrix of synaptic connections without losing interaction between all neurons in the network. Objective. To create an architecture of a stochastic pseudo-spin neural network with diagonal synaptic connections without loosing the interaction between all the neurons in the layer to reduce its learning time. Method. The paper uses the Hausholder method, the method of compressing input images based on the diagonalization of the matrix of synaptic connections and the computer mathematics system MATLAB for converting a fully connected neural network into a tridiagonal form with hidden synaptic connections between all neurons. Results. We developed a model of a stochastic neural network architecture with sparse renormalized synaptic connections that take into account deleted synaptic connections. Based on the transformation of the synaptic connection matrix of a fully connected neural network into a Hessenberg matrix with tridiagonal synaptic connections, we proposed a renormalized local Hebb rule. Using the computer mathematics system “WolframMathematica 11.3”, we calculated, as a function of the number of neurons N, the relative tuning time of synaptic connections (per iteration) in a stochastic pseudospin neural network with a tridiagonal connection Matrix, relative to the tuning time of synaptic connections (per iteration) in a fully connected synaptic neural network. Conclusions. We found that with an increase in the number of neurons, the tuning time of synaptic connections (per iteration) in a stochastic pseudospin neural network with a tridiagonal connection Matrix, relative to the tuning time of synaptic connections (per iteration) in a fully connected synaptic neural network, decreases according to a hyperbolic law. Depending on the direction of pseudospin neurons, we proposed a classification of a renormalized neural network with a ferromagnetic structure, an antiferromagnetic structure, and a dipole glass.

A QoS-Aware Dynamic Bandwidth Allocation Algorithm for Passive Optical Networks with Non-Zero Laser Tuning Time

The deployment of new 5G services and future demands for 6G make it necessary to increase the performance of access networks. This challenge has prompted the development of new standardization proposals for Passive Optical access Networks (PONs) that offer greater bandwidth, greater reach and a higher rate of aggregation of users per fiber, being Time- and Wavelength-Division Multiplexing (TWDM) a promising technological solution for increasing the capacity by up to 40 Gbps by using several wavelengths. This solution introduces tunable transceivers into the Optical Network Units (ONUs) for switching from one wavelength to the other, thus addressing the ever-increasing bandwidth demands in residential broadband and mobile fronthaul networks based on Fiber to the Home (FTTH) technology. This adds complexity and sources of inefficiency, such as the laser tuning time (LTT) delay, which is often ignored when evaluating the performance of Dynamic Bandwidth Allocation (DBA) mechanisms. We present a novel DBA algorithm that dynamically handles the allocation of bandwidth and switches the ONUs’ lasers from one wavelength to the other while taking LTT into consideration. To optimize the packet delay, we introduce a scheduling mechanism that follows the Longest Processing Time first (LPT) scheduling discipline, which is implemented over the Interleaved Polling with Adaptive Cycle Time (IPACT) DBA. We also provide quality of service (QoS) differentiation by introducing the Max-Min Weighted Fair Share Queuing principle (WFQ) into the algorithm. The performance of our algorithm is evaluated through simulations against the original IPACT algorithm, which we have extended to support multi-wavelengths. With the introduction of LPT, we obtain an improved performance of up to 73% reduction in queue delay over IPACT while achieving QoS differentiation with WFQ.

NN Search based on Non-uniform Space Partition on Air

It is one of most important challenges in information services based on location to support a huge number of clients to process the NN query for a given query point. A scheme of NN query processing based on wireless data broadcasting is an effective way to overcome the challenge. In this paper, we propose an indexing scheme NSPI (Non-uniform Space Partition Index) for quick NN search over wireless data broadcasting. For implementing the aim, we use a non-uniform spatial partition and provide an index based on the partition for equidistance between indexes on the wireless channel. The index scheme enables the clients to process NN quickly by lessening the time for the clients to meet the index on the channel. To show the effectiveness of the proposed scheme, we compare the access time and tuning time of the clients with existing indexing schemes by simulation studies. The proposed NSPI shows shorter access time than the other existing scheme. Also, NSPI outperforms in the aspect of tuning time than the others.

A QoS-Aware Dynamic Bandwidth Allocation Algorithm for Passive Optical Networks with Non-Zero Laser Tuning Time

Time- and Wavelength-Division Multiplexing (TWDM) increases the capacity of Passive Optical Networks by up to 40 Gbps by using several wavelengths (typically four). It introduces tunable transceivers into the Optical Network Units (ONUs) for switching from one wavelength to the other, thus addressing the ever-increasing bandwidth demands in residential broadband and mobile fronthaul networks based on Fiber to the Home (FTTH) technology. This adds complexity and sources of inefficiency, such as the laser tuning time (LTT) delay, which is often ignored when evaluating the performance of Dynamic Bandwidth Allocation (DBA) mechanisms. We present a novel DBA algorithm that dynamically handles the allocation of bandwidth and switches the ONUs’ laser from one wavelength to the other while taking LTT into consideration. To optimize the packet delay, we introduce a scheduling mechanism that follows the Longest Processing Time first (LPT) scheduling discipline, which is implemented over the Interleaved Polling with Adaptive Cycle Time (IPACT) DBA. We also provide quality of service (QoS) differentiation by introducing the Max-Min Weighted Fair Share Queuing principle (WFQ) into the algorithm. The performance of our algorithm is evaluated through simulations against the original IPACT algorithm, which we have extended to support multi-wavelengths. We obtain an improved performance of up to 73% and 33% reduction in queue delay in, respectively, IPACT and WFQ.

Dataset Sensitive Autotuning of Multi-versioned Code Based on Monotonic Properties

Functional languages allow rewrite-rule systems that aggressively generate a multitude of semantically-equivalent but differently-optimized code versions. In the context of GPGPU execution, this paper addresses the important question of how to compose these code versions into a single program that (near-)optimally discriminates them across different datasets. Rather than aiming at a general autotuning framework reliant on stochastic search, we argue that in some cases, a more effective solution can be obtained by customizing the tuning strategy for the compiler transformation producing the code versions.We present a simple and highly-composable strategy which requires that the (dynamic) program property used to discriminate between code versions conforms with a certain monotonicity assumption. Assuming the monotonicity assumption holds, our strategy guarantees that if an optimal solution exists it will be found. If an optimal solution doesn’t exist, our strategy produces human tractable and deterministic results that provide insights into what went wrong and how it can be fixed.We apply our tuning strategy to the incremental-flattening transformation supported by the publicly-available Futhark compiler and compare with a previous black-box tuning solution that uses the popular OpenTuner library. We demonstrate the feasibility of our solution on a set of standard datasets of real-world applications and public benchmark suites, such as Rodinia and FinPar. We show that our approach shortens the tuning time by a factor of $$6\times $$ 6 × on average, and more importantly, in five out of eleven cases, it produces programs that are (as high as $$10\times $$ 10 × ) faster than the ones produced by the OpenTuner-based technique.

Actuating Voltage Waveform Optimization of Piezoelectric Inkjet Printhead for Suppression of Residual Vibrations

After a piezoelectric inkjet printhead jets the first droplet, the actuating membrane still vibrates, creating residual vibrations in the ink channel, which can degrade the inkjet printhead performance. For suppressing these vibrations, an optimized actuating voltage waveform with two pulses must be obtained, of which the first pulse is used for jetting and the second pulse is used to suppress the residual vibrations. In this study, the pressure history within the ink channel of a recirculating piezoelectric inkjet printhead was first acquired using lumped element modeling. Then, for suppressing residual vibrations, a bipolar voltage waveform was optimized via analysis of the tuning time (tt ), dwell time (td2), rising time (tr2), falling time (tf2), and voltage amplitude of the second pulse. Two voltage waveforms, Waveform 01 and Waveform 02, were optimized thereafter. In Waveform 01, tt=2 μs, td2=2 μs, and tr2 and tf2=1 μs were finalized as the optimal parameters; in the case of another waveform, the optimal parameters of td2, tr2, and tf2 were found to be 4, 1, and 1 μs, respectively. The optimal voltage amplitude of the second pulse was found to be 1/3 the amplitude of the first pulse. On the basis of our analysis, the tuning time in Waveform 01 is the most sensitive parameter, and the performance yielded is even poorer than that yielded by standard waveform, if not optimized. Therefore, the other waveform is recommended for the suppression of residual vibrations.

Aural Speed-Reading: Some Historical Bookmarks

Together they are writing a book entitled “Tuning Time: Histories of Sound and Speed.” The authors would like to thank the Epistemes of Modern Acoustics group at the Max Planck Institute for the History of Science, the Alexander von Humboldt Foundation, Jason Camlot, Iben Have, Burç Kostem, and Shafeka Hashash.We write the history of aural speed-reading and time-stretching technology in two tracks, taking a cue from Annemarie Mol's The Body Multiple: Ontology in Medical Practice, with its “upper text” and “subtext” that invite readers “to invent a way of reading that works for them from scratch” (ix). In the spirit of the story that opens track 1, on the left, we decided to jimmy the format of the PMLA page. To differing degrees, each track provides context, describes events, raises questions, and applies analytic frames. Track 1 is our narration of a series of events recalled by Harvey Lauer to Mara Mills; the insights derive from his professional expertise and personal reading experiences. Track 2, on the right, does not benefit from the kind of omniscient sight known as hindsight; it reads alongside. Think of these tracks as an animated and mostly asynchronous conversation among people who care about instruments of sound and reading in distinct but similarly fanatical ways. For a cluster of historical recordings associated with this essay, tune in to the Sound and Science: Digital Histories database: acoustics.mpiwg-berlin.mpg.de/sets/clusters/aural-speed-reading.