Parallel Streams Research Articles

Sunspot Group Detection and Classification by Dual Stream Convolutional Neural Network Method

The automatic detection and analysis of sunspots play a crucial role in understanding solar dynamics and predicting space weather events. This paper proposes a novel method for sunspot group detection and classification called the dual stream Convolutional Neural Network with Attention Mechanism (DSCNN-AM). The network consists of two parallel streams each processing different input data allowing for joint processing of spatial and temporal information while classifying sunspots. It takes in the white light images as well as the corresponding magnetic images that reveal both the optical and magnetic features of sunspots. The extracted features are then fused and processed by fully connected layers to perform detection and classification. The attention mechanism is further integrated to address the “edge dimming” problem which improves the model’s ability to handle sunspots near the edge of the solar disk. The network is trained and tested on the SOLAR-STORM1 data set. The results demonstrate that the DSCNN-AM achieves superior performance compared to existing methods, with a total accuracy exceeding 90%.

Redzone stream compaction: removing k items from a list in parallel O(k) time

Stream compaction, the parallel removal of selected items from a list, is a fundamental building block in parallel algorithms. It is extensively used, both in computer graphics, for shading, collision detection, and ray tracing, as well as in general computing, such as for tree traversal and database selection. In this article, we present Redzone stream compaction, the first parallel stream compaction algorithm to remove k items from a list with n ≥ k elements in O(k) rather than O(n) time. Based on our benchmark experiments on both GPU and CPU, if k is proportionally small ( k ≪ n ), Redzone outperforms existing parallel stream compaction by orders of magnitude, while if k is close to n , it underperforms by a constant factor. Redzone removes items in-place and needs only O(1) auxiliary space. However, unlike current O(n) algorithms, it is unstable (i.e., the order of elements is not preserved) and it needs a list of the items to be removed.

Primate brain: A unique connection between dorsal and ventral visual cortex

In humans and other primates, vision is subserved by at least two parallel processing streams that are interconnected through a pathway known as the vertical occipital fasciculus. New research reveals that this white matter pathway may be a unique feature of the primate brain.

A prominent vertical occipital white matter fasciculus unique to primate brains

Vision in humans and other primates enlists parallel processing streams in the dorsal and ventral visual cortex, known to support spatial and object processing, respectively. These streams are bridged, however, by a prominent white matter tract, the vertical occipital fasciculus (VOF), identified in both classical neuroanatomy and recent diffusion-weighted magnetic resonance imaging (dMRI) studies. Understanding the evolution of the VOF may shed light on its origin, function, and role in visually guided behaviors. To this end, we acquired high-resolution dMRI data from the brains of select mammalian species, including anthropoid and strepsirrhine primates, a tree shrew, rodents, and carnivores. In each species, we attempted to delineate the VOF after first locating the optic radiations in the occipital white matter. In all primate species examined, the optic radiation was flanked laterally by a prominent and coherent white matter fasciculus recognizable as the VOF. By contrast, the equivalent analysis applied to four non-primate species from the same superorder as primates (tree shrew, ground squirrel, paca, and rat) failed to reveal white matter tracts in the equivalent location. Clear evidence for a VOF was also absent in two larger carnivore species (ferret and fox). Although we cannot rule out the existence of minor or differently organized homologous fiber pathways in the non-primate species, the results suggest that the VOF has greatly expanded, or possibly emerged, in the primate lineage. This adaptation likely facilitated the evolution of unique visually guided behaviors in primates, with direct impacts on manual object manipulation, social interactions, and arboreal locomotion.

Columns with parallel streams applied to the steam stripping of edible oils

Parastripping involves splitting the vapor flow within the stripping column. This arrangement can reduce the height of the column, utility requirements, and, consequently, overall costs. Nevertheless, studies on parastripping are scarce and do not consider multicomponent mixtures. This work aimed to investigate by rigorous simulation the multicomponent parastripping process applied to the steam deacidification of edible oils. The effects of process variables, such as feed temperature, column pressure, and stripping steam flow rate, which impact final oil acidity, neutral oil loss, and tocopherol recovery, were evaluated using the response surface methodology (RSM). Optimal ranges for the investigated process variables were determined to achieve an oil acidity of 0.03 %w/w of oleic acid. Through RSM, an optimised region for the three independent variables was identified - pressures (< 3.5 mBar), mild temperatures (230–240 ºC) and stripping steam of 0.9 % of the oil load), which reduced operation costs and possibly prevents the formation of glycidyl esters and trans-fatty acids. Within this region an optimised condition of temperature was set (233 ºC), and the effects of pressure and % of stripping were analysed by varying (233 ºC, 3.5 mBar and 0.9 %w/w of stripping steam) on was deeply analysed in terms of successful for reaching a final oil acidity < 0.03 %w/w – a value conforming to industrial standards – with 19 % of tocopherol recovery in the distillate and 0.5 % neutral oil loss. Further analyses were performed on the temperature and molar flow profiles of the oil compounds as a function of the parastripping stages. These results clearly demonstrated that decision-making could be based on this analysis.

Development of a high-throughput dual-stream liquid chromatography-tandem mass spectrometry method to screen for inhibitors of glutamate carboxypeptidase II.

Glutamate carboxypeptidase II (GCPII) catalyzes the hydrolysis of N-acetylaspartylglutamate (NAAG) to yield glutamate (Glu) and N-acetylaspartate (NAA). Inhibition of GCPII has been shown to remediate the neurotoxicity of excess Glu in a variety of cell and animal disease models. A robust high-throughput liquid chromatography-tandem mass spectrometry (LC/MS/MS) method was needed to quantify GCPII enzymatic activity in a biochemical high-throughput screening assay. A dual-stream LC/MS/MS method was developed. Two parallel eluent streams ran identical HILIC gradient methods on BEH-Amide (2×30 mm) columns. Each LC channel was run independently, and the cycle time was 2min per channel. Overall throughput was 1min per sample for the dual-channel integrated system. Multiply injected acquisition files were split during data review, and batch metadata were automatically paired with raw data during the review process. Two LC sorbents, BEH-Amide and Penta-HILIC, were tested to separate the NAAG cleavage product Glu from isobaric interference and ion suppressants in the bioassay matrix. Early elution of NAAG and NAA on BEH-Amide allowed interfering species to be diverted to waste. The limit of quantification was 0.1pmol for Glu. The Z-factor of this assay averaged 0.85. Over 36 000 compounds were screened using this method. A fast gradient dual-stream LC/MS/MS method for Glu quantification in GCPII biochemical screening assay samples was developed and validated. HILIC separation chemistry offers robust performance and unique selectivity for targeted positive mode quantification of Glu, NAA, and NAAG.

Deep Learning-Based Detection of Human Blastocyst Compartments with Fractal Dimension Estimation

In vitro fertilization (IVF) is an efficacious form of aided reproduction to deal with infertility. Human embryos are taken from the body, and these are kept in a supervised laboratory atmosphere during the IVF technique until they exhibit blastocyst properties. A human expert manually analyzes the morphometric properties of the blastocyst and its compartments to predict viability through manual microscopic evaluation. A few deep learning-based approaches deal with this task via semantic segmentation, but they are inaccurate and use expensive architecture. To automatically detect the human blastocyst compartments, we propose a parallel stream fusion network (PSF-Net) that performs the semantic segmentation of embryo microscopic images with inexpensive shallow architecture. The PSF-Net has a shallow architecture that combines the benefits of feature aggregation through depth-wise concatenation and element-wise summation, which helps the network to provide accurate detection using 0.7 million trainable parameters only. In addition, we compute fractal dimension estimation for all compartments of the blastocyst, providing medical experts with significant information regarding the distributional characteristics of blastocyst compartments. An open dataset of microscopic images of the human embryo is used to evaluate the proposed approach. The proposed method also demonstrates promising segmentation performance for all compartments of the blastocyst compared with state-of-the-art methods, achieving a mean Jaccard index (MJI) of 87.69%. The effectiveness of PSF-Net architecture is also confirmed with the ablation studies.

An analysis of information segregation in parallel streams of a multi-stream convolutional neural network.

Visual information is processed in hierarchically organized parallel streams in the primate brain. In the present study, information segregation in parallel streams was examined by constructing a convolutional neural network with parallel architecture in all of the convolutional layers. Although filter weights for convolution were initially set to random values, color information was segregated from shape information in most model instances after training. Deletion of the color-related stream decreased recognition accuracy of animate images, whereas deletion of the shape-related stream decreased recognition accuracy of both animate and inanimate images. The results suggest that properties of filters and functions of a stream are spontaneously segregated in parallel streams of neural networks.

Beyond the microcirculation: sequestration of infected red blood cells and reduced flow in large draining veins in experimental cerebral malaria.

Sequestration of infected red blood cells (iRBCs) in the microcirculation is a hallmark of cerebral malaria (CM) in post-mortem human brains. It remains controversial how this might be linked to the different disease manifestations, in particular brain swelling leading to brain herniation and death. The main hypotheses focus on iRBC-triggered inflammation and mechanical obstruction of blood flow. Here, we test these hypotheses using murine models of experimental CM (ECM), SPECT-imaging of radiolabeled iRBCs and cerebral perfusion, MR-angiography, q-PCR, and immunohistochemistry. We show that iRBC accumulation and reduced flow precede inflammation. Unexpectedly, we find that iRBCs accumulate not only in the microcirculation but also in large draining veins and sinuses, particularly at the rostral confluence. We identify two parallel venous streams from the superior sagittal sinus that open into the rostral rhinal veins and are partially connected to infected skull bone marrow. The flow in these vessels is reduced early, and the spatial patterns of pathology correspond to venous drainage territories. Our data suggest that venous efflux reductions downstream of the microcirculation are causally linked to ECM pathology, and that the different spatiotemporal patterns of edema development in mice and humans could be related to anatomical differences in venous anatomy.

Utilizing Siamese 4D-AlzNet and Transfer Learning to Identify Stages of Alzheimer’s Disease

Alzheimer's disease (AD) is the general form of dementia, leading to a progressive neurological disorder characterized by memory loss due to brain cell damage. Artificial Intelligence (AI) assists in the early identification and prediction of AD patients, determining future risks and benefits for radiologists and doctors to save time and cost. Since deep learning (DL) approaches work well with massive datasets and have recently become helpful for AD detection, there remains an area for improvement in automating detection performance. Present approaches somehow addressed the challenges of limited annotated data samples for binary classification. This contrasts with prior state-of-the-art techniques, which were constrained by their incapacity to capture abstract-level information. In this paper, we proposed a Siamese 4D-AlzNet model comprised of four parallel convolutional neural network (CNN) streams (Five CNN layer blocks) and customized transfer learning models (Frozen VGG-19, Frozen VGG-16, and customized AlexNet). Siamese 4D-AlzNet was vertically and horizontally stored, and the spatial features were passed to the final layer for classification. For experiments, T1-weighted MRI images comprised of four distinct subject classes, normal control (NC), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), and AD, have been employed. Our proposed models achieved outstanding accuracy, with a remarkable 95.05% accuracy distinguishing between normal and AD subjects. The performance across remaining binary class pairs consistently exceeded 90%. We thoroughly compared our model with the latest methods using the same dataset as our reference. Our proposed model improved NC-AD and MCI-AD classification accuracy by 2% 7%.

Dynamic simulation and control of dual-evaporator compression refrigeration systems

The need for refrigeration cooling at two different sub-ambient temperature levels is an important problem in some processes. A modern grocery store is an extremely important global example in which there is a need for a moderate temperature heat sink (5 °C) to keep food cool but also a need for a significantly colder heat sink (− 20 °C) for frozen food products. If the two temperatures are not too different, a single refrigerant can be used. The compression refrigeration configuration then consists of two compressors operating in parallel, a single condenser, a single refrigerant liquid surge drum and multiple evaporators. Liquid refrigerant at high pressure is split into a large number of parallel streams to feed individual evaporators in the cooling and freezing compartments. This paper studies how this multi-variable interacting dynamic system can be modeled and controlled using simple conventional controllers.

Effects of wall cavity and fuel injection pressure on the performance of a non-reacting supersonic combustor

This study numerically investigates the flow field of a non-reacting cavity-configured scramjet (Supersonic Combustion Ramjet) combustor at various fuel injection pressures by solving the 2D Reynolds-Averaged Navier-Stokes (RANS) equations, species transport equations, and Menter SST k-ω model. The aim of this research is to reveal the effects of wall cavity insertion and fuel injection pressure (FIP) on the crucial performance parameters i.e., fuel-air mixing efficiency (MxE), total pressure recovery (TPR), and mass-averaged Mach number (MAMN). Accordingly, two trapezoidal cavities of aspect ratio 7 are introduced on the opposite walls of a rectangular combustor. The combustor entrance is configured with rearward-facing steps and it intakes finite parallel air streams through finite-width inlets. Gaseous hydrogen jets are injected 30 mm downstream from the combustor entrance and 10 mm upstream from the cavity leading edge. FIP is varied according to the fuel-to-freestream pressure ratios (FFPR) of 4.5, 9.0, 13.5, and 18.0. The results of the cavity-configured combustor are then compared with the performance of the combustor in the absence of the wall cavities. The results delineate the change in flow structures with the inclusion of wall cavities and variation in FIP. Insight physics of mixing, total pressure loss, and MAMN in different regions of the combustor are studied and the results are quantified for comparison. MxE in a cavity-configured combustor does not monotonically increase with decreasing FFPR as found in the combustor without wall cavities. The shock-shear layer interactions (SSLIs) play a dominant role in mixing inside the cavity-configured combustor. The results also demonstrate that the insertion of wall cavities can increase fuel-air MxE through the formation of cavity recirculation zones. In the cavity-configured combustor, a maximum of 45% MxE is achieved for FFPR 4.5, which is 4% higher than the value obtained from the combustor without the cavities with an expense of 3% greater total pressure loss.

Parallel Streams of Direct Corticogeniculate Feedback from Mid-level Extrastriate Cortex in the Macaque Monkey.

First-order thalamic nuclei receive feedforward signals from peripheral receptors and relay these signals to primary sensory cortex. Primary sensory cortex, in turn, provides reciprocal feedback to first-order thalamus. Because the vast majority of sensory thalamocortical inputs target primary sensory cortex, their complementary corticothalamic neurons are assumed to be similarly restricted to primary sensory cortex. We upend this assumption by characterizing morphologically diverse neurons in multiple mid-level visual cortical areas of the primate (Macaca mulatta) brain that provide direct feedback to the primary visual thalamus, the dorsal lateral geniculate nucleus (LGN). Although the majority of geniculocortical neurons project to primary visual cortex (V1), a minority, located mainly in the koniocellular LGN layers, provide direct input to extrastriate visual cortex. These "V1-bypassing" projections may be implicated in blindsight. We hypothesized that geniculocortical inputs directly targeting extrastriate cortex should be complemented by reciprocal corticogeniculate circuits. Using virus-mediated circuit tracing, we discovered corticogeniculate neurons throughout three mid-level extrastriate areas: MT, MST, and V4. Quantitative morphological analyses revealed nonuniform distributions of unique cell types across areas. Many extrastriate corticogeniculate neurons had spiny stellate morphology, suggesting possible targeting of koniocellular LGN layers. Importantly though, multiple morphological types were observed across areas. Such morphological diversity could suggest parallel streams of V1-bypassing corticogeniculate feedback at multiple stages of the visual processing hierarchy. Furthermore, the presence of corticogeniculate neurons across visual cortex necessitates a reevaluation of the LGN as a hub for visual information rather than a simple relay.

A study of secure communication with secret sharing method in MIMO eigen stream parallel channels

A study of secure communication with secret sharing method in MIMO eigen stream parallel channels

“The Sense of Wonder”: Gaming Capital and Nostalgia for Inexperience in Magic: The Gathering

Popular culture media is a potent wellspring of nostalgia. From remakes and revivifications to perpetual production, aesthetics of nostalgia are commonplace among fan communities who continually encounter and experience both collective and personal pasts during engagement. Existing research has overlooked the role that fans’ gaming capital holds in the emergence of nostalgia, especially in leisure communities such as the trading card game, Magic: The Gathering. Drawing from qualitative interviews with Magic: The Gathering players in Australia, this paper applies the theory of gaming capital to illustrate how the continual acquisition of knowledge ultimately results in nostalgic attachment to times in which one knew very little. While the experience and knowledge gained by transitioning from novice to adept is seen positively, this paper argues it is nonetheless permeated by a pervading sense of loss. Paradoxically, gaming capital bifurcates into a parallel stream of value, specifically nostalgia for lost wonder and inexperience.

Performance and programmability of GrPPI for parallel stream processing on multi-cores

GrPPI library aims to simplify the burdening task of parallel programming. It provides a unified, abstract, and generic layer while promising minimal overhead on performance. Although it supports stream parallelism, GrPPI lacks an evaluation regarding representative performance metrics for this domain, such as throughput and latency. This work evaluates GrPPI focused on parallel stream processing. We compare the throughput and latency performance, memory usage, and programmability of GrPPI against handwritten parallel code. For this, we use the benchmarking framework SPBench to build custom GrPPI benchmarks and benchmarks with handwritten parallel code using the same backends supported by GrPPI. The basis of the benchmarks is real applications, such as Lane Detection, Bzip2, Face Recognizer, and Ferret. Experiments show that while performance is often competitive with handwritten parallel code, the infeasibility of fine-tuning GrPPI is a crucial drawback for emerging applications. Despite this, programmability experiments estimate that GrPPI can potentially reduce the development time of parallel applications by about three times.

PaCHNOC: Packet and Circuit Hybrid Switching NoC for Real-Time Parallel Stream Signal Processing.

Real-time heterogeneous parallel embedded digital signal processor (DSP) systems process multiple data streams in parallel in a stringent time interval. This type of system on chip (SoC) requires the network on chip (NoC) to establish multiple symbiotic parallel data transmission paths with ultra-low transmission latency in real time. Our early NoC research PCCNOC meets this need. The PCCNOC uses packet routing to establish and lock a transmission circuit, so that PCCNOC is perfectly suitable for ultra-low latency and high-bandwidth transmission of long data packets. However, a parallel multi-data stream DSP system also needs to transmit roughly the same number of short data packets for job configuration and job execution status reports. While transferring short data packets, the link establishment routing delay of short data packets becomes relatively obvious. Our further research, thus, introduced PaCHNOC, a hybrid NoC in which long data packets are transmitted through a circuit established and locked by routing, and short data packets are attached to the routing packet and the transmission is completed during the routing process, thus avoiding the PCCNOC setup delay. Simulation shows that PaCHNOC performs well in supporting real-time heterogeneous parallel embedded DSP systems and achieves overall latency reduction 65% compared with related works. Finally, we used PaCHNOC in the baseband subsystem of a real 5G base station, which proved that our research is the best NoC for baseband subsystem of 5G base stations, which reduce 31% comprehensive latency in comparison to related works.

Estimation of impact of non‐local ion heat flux model on transport simulation for DEMO‐relevant scrape‐off layer plasma

AbstractIn a fusion DEMOnstration reactor (DEMO)‐relevant scrape‐off layer plasma (SOL), whose collisionality is lower than in the SOLs of present‐day tokamaks, kinetic effects are predicted to reduce the plasma heat conductivity along the magnetic field below the value obtained with the classical Spitzer–Harm model. As a part of ongoing efforts to improve the predictive capability of SOL heat transport calculations, we have implemented the non‐local heat flux model proposed by [Luciani, Mora and Virmont, Phys. Rev. Lett. 51 (1983) 1664–1667] (here referred to as the LMV model) in the integrated SOL–divertor simulation code SONIC in order to account for the kinetic effect on the heat conduction due to the parallel streaming of rapidly moving particles. Our transport simulations for the Japanese demonstration tokamak reactor concept, JA DEMO, show that the LMV model yields a significantly reduced ion parallel conductive heat flux density both on the low‐ and high‐field side of the upstream SOL. The heat flux obtained with the LMV model seems to be consistent with earlier kinetic simulations of tokamak ion transport. As a consequence of the reduced heat flux, a significant increase in the ion temperature and a decrease in the density have also been found over a broad area of the upstream SOL.

A feature-level mask self-supervised assisted learning approach based on transformer for remaining useful life prediction

Nowadays, the massive industrial data has effectively improved the performance of the data-driven deep learning Remaining Useful Life (RUL) prediction method. However, there are still problems of assigning fixed weights to features and only coarse-grained consideration at the sequence level. This paper proposes a Transformer-based end-to-end feature-level mask self-supervised learning method for RUL prediction. First, by proposing a fine-grained feature-level mask self-supervised learning method, the data at different time points under all features in a time window is sent to two parallel learning streams with and without random masks. The model can learn more fine-grained degradation information by comparing the information extracted by the two parallel streams. Instead of assigning fixed weights to different features, the abstract information extracted through the above process is invariable correlations between features, which has a good generalization to various situations under different working conditions. Then, the extracted information is encoded and decoded again using an asymmetric structure, and a fully connected network is used to build a mapping between the extracted information and the RUL. We conduct experiments on the public C-MAPSS datasets and show that the proposed method outperforms the other methods, and its advantages are more obvious in complex multi-working conditions.

Magnetic flutter effect on validated edge turbulence simulations

Small magnetic fluctuations ( B1/B0∼10−4 ) are intrinsically present in a magnetic confinement plasma due to turbulent currents. While the perpendicular transport of particles and heat is typically dominated by fluctuations of the electric field, the parallel stream of plasma is affected by fluttering magnetic field lines. In particular through electrons, this indirectly impacts the turbulence dynamics. Even in low beta conditions, we find that E × B turbulent transport can be reduced by more than a factor 2 when magnetic flutter is included in our validated edge turbulence simulations of L-mode ASDEX Upgrade. The primary reason for this is the stabilization of drift-Alfvén-waves, which reduces the phase shifts of density and temperature fluctuations with respect to potential fluctuations. This stabilization can be qualitatively explained by linear analytical theory and appreciably reinforced by the flutter nonlinearity. As a secondary effect, the steeper temperature gradients and thus higher η i increase the impact of the ion-temperature-gradient mode on overall turbulent transport. With increasing beta, the stabilizing effect on E × B turbulence increases, balancing the destabilization by induction, until direct electromagnetic perpendicular transport is triggered. We conclude that including flutter is crucial for predictive edge turbulence simulations.