NFU-Enabled FASTA: moving bioinformatics applications onto wide area networks

Abstract Background Advances in Internet technologies have allowed life science researchers to reach beyond the lab-centric research paradigm to create distributed collaborations. Of the existing technologies that support distributed collaborations, there are currently none that simultaneously support data storage and computation as a shared network resource, enabling computational burden to be wholly removed from participating clients. Software using computation-enable logistical networking components of the Internet Backplane Protocol provides a suitable means to accomplish these tasks. Here, we demonstrate software that enables this approach by distributing both the FASTA algorithm and appropriate data sets within the framework of a wide area network. Results For large datasets, computation-enabled logistical networks provide a significant reduction in FASTA algorithm running time over local and non-distributed logistical networking frameworks. We also find that genome-scale sizes of the stored data are easily adaptable to logistical networks. Conclusion Network function unit-enabled Internet Backplane Protocol effectively distributes FASTA algorithm computation over large data sets stored within the scaleable network. In situations where computation is subject to parallel solution over very large data sets, this approach provides a means to allow distributed collaborators access to a shared storage resource capable of storing the large volumes of data equated with modern life science. In addition, it provides a computation framework that removes the burden of computation from the client and places it within the network

Numerical investigation of the scale effects of pump-jet propulsor with a pre-swirl stator

In this study, the performance of a pump-jet propulsor (PJP) with pre-swirl stator in open water is numerically investigated. Both full-scale and model-scale configurations are considered. The Reynolds-averaged Navier–Stokes equations and shear stress transport\ua0\u1d458−\u1d714 turbulence model are used in the numerical calculation. The computational domain is discretized using structured grids, and a rotating grid is affixed to the rotor to deal with the relative motion between the rotor and stationary components. The mesh quality is determined based on a grid uncertainty analysis. The numerical method is validated using model-scale experimental data. The simulation results reveal the influences of the scale size on the hydrodynamic performance and the distributions of the velocity, pressure and vorticity under three advance coefficients. With the increase in the advance coefficients, the scale influences on the efficiency become more obvious, and the efficiency of the full-scale PJP is always higher than that of the model-scale PJP. The full-scale configuration is found with a more significant instability in the gap vortex development, because it presents larger interaction between tip leakage vortex (TLV) and the inner wall of the duct. As the main velocity increases, the TLV shedding is delayed. Finally, the development process of gap vortices is analyzed for the difference operation conditions

Research on the strength prediction model of Na based bentonite filling body based on ultrasonic transverse wave testing

As the core unit of backfill mining method, the strength of backfill is an important indicator to ensure safe mining. Na-based bentonite features a high methylene blue adsorption capacity and green compressive strength, making it a high-quality additive for preparing filling materials. However, there are few studies probing into the relationship between the dosage and the strength of the filling materials. This paper analyzes the changes in shear wave velocity, dominant frequency amplitude, amplitude attenuation coefficient, and waveform fractal dimension of filling materials with different Na-based bentonite dosages at different ages through ultrasonic testing technology and uniaxial compression tests. Combined with sensitivity analysis, we selected the most sensitive acoustic parameters to changes in compressive strength. Furthermore, this study establishes a strength prediction model for backfill with different Na-bentonite contents and combining significance testing and comparative analysis. The research findings can serve as a valuable reference for theoretical research and engineering applications related to predicting the uniaxial compressive strength of backfill materials

MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction

Predicting the future behavior of agents is a fundamental task in autonomous vehicle domains. Accurate prediction relies on comprehending the surrounding map, which significantly regularizes agent behaviors. However, existing methods have limitations in exploiting the map and exhibit a strong dependence on historical trajectories, which yield unsatisfactory prediction performance and robustness. Additionally, their heavy network architectures impede real-time applications. To tackle these problems, we propose Map-Agent Coupled Transformer (MacFormer) for real-time and robust trajectory prediction. Our framework explicitly incorporates map constraints into the network via two carefully designed modules named coupled map and reference extractor. A novel multi-task optimization strategy (MTOS) is presented to enhance learning of topology and rule constraints. We also devise bilateral query scheme in context fusion for a more efficient and lightweight network. We evaluated our approach on Argoverse 1, Argoverse 2, and nuScenes real-world benchmarks, where it all achieved state-of-the-art performance with the lowest inference latency and smallest model size. Experiments also demonstrate that our framework is resilient to imperfect tracklet inputs. Furthermore, we show that by combining with our proposed strategies, classical models outperform their baselines, further validating the versatility of our framework.Comment: Accepted by IEEE Robotics and Automation Letters. 8 Pages, 9 Figures, 9 Tables. Video: https://www.youtube.com/watch?v=XY388iI6sP

A modified J model for efficiently calculating the electromagnetic fields of ReBCO no-insulation pancake coils using an explicit-implicit hybrid algorithm

Rare-earth (Re)Ba2Cu3O7-x (ReBCO) no-insulation (NI) coil is widely concerned due to its excellent electromagnetic and thermal properties. However, the presence of the turn-to-turn shunts in NI coils leads to that complexity of numerical simulation is increased. In this paper, a modified J model is proposed and the corresponding explicit-implicit hybrid algorithm is designed to calculate NI coils. The numerical results are in good agreement with the experimental data and the circuit model. The homogenization model is also proposed to simulate the large-scale NI coils in the background magnets. The modified J model has good accuracy and fast calculation speed, which can also be used to solve electromagnetic fields of insulation coils efficiently

Observations and Interpretation of a Low Coronal Shock Wave Observed in the EUV by the SDO/AIA

Taking advantage of both the high temporal and spatial resolution of the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO), we studied a limb coronal shock wave and its associated extreme ultraviolet (EUV) wave that occurred on 2010 June 13. Our main findings are (1) the shock wave appeared clearly only in the channels centered at 193 \AA and 211 \AA as a dome-like enhancement propagating ahead of its associated semi-spherical CME bubble; (2) the density compression of the shock is 1.56 according to radio data and the temperature of the shockis around 2.8 MK; (3) the shock wave first appeared at 05:38 UT, 2 minutes after the associated flare has started and 1 minute after its associated CME bubble appeared;(4) the top of the dome-like shock wave set out from about 1.23 R\odot and the thickness of the shocked layer is ~ 2\times10^4 km; (5) the speed of the shock wave is consistent with a slight decrease from about 600 km/s to 550 km/s; (6) the lateral expansion of the shock wave suggests a constant speed around 400 km/s, which varies at different heights and directions. Our findings support the view that the coronal shock wave is driven by the CME bubble, and the on-limb EUV wave is consistent with a fast wave or at least includes the fast wave component.Comment: 24 pages,8 Figures and 6 movies. It is scheduled for publication on the Astrophysical Journal on the August 1, 2011, Issue 736 -

Satellite-based estimate of the variability of warm cloud properties associated with aerosol and meteorological conditions

Aerosol-cloud interaction (ACI) is examined using 10 years of data from the MODIS/Terra (morning orbit) and MODIS/Aqua (afternoon orbit) satellites. Aerosol optical depth (AOD) and cloud properties retrieved from both sensors are used to explore in a statistical sense the morning-to-afternoon variation of cloud properties in conditions with low and high AOD, over both land and ocean. The results show that the interaction between aerosol particles and clouds is more complex and of greater uncertainty over land than over ocean. The variation in d(Cloud_X), defined as the mean change in cloud property Cloud_X between the morning and afternoon overpasses in high-AOD conditions minus that in low-AOD conditions, is different over land and ocean. This applies to cloud droplet effective radius (CDR), cloud fraction (CF) and cloud top pressure (CTP), but not to cloud optical thickness (COT) and cloud liquid water path (CWP). Both COT and CWP increase over land and ocean after the time step, irrespective of the AOD. However, the initial AOD conditions can affect the amplitude of variation of COT and CWP. The effects of initial cloud fraction and meteorological conditions on the change in CF under lowand high-AOD conditions after the 3 h time step over land are also explored. Two cases are considered: (1) when the cloud cover increases and (2) when the cloud cover decreases. For both cases, we find that almost all values of d(CF) are positive, indicating that the variations of CF are larger in high AOD than that in low AOD after the 3 h time step. The results also show that a large increase in cloud fraction occurs when scenes experience large AOD and stronger upward motion of air parcels. Furthermore, the increase rate of cloud cover is larger for high AOD with increasing relative humidity (RH) when RH is larger than 20 %. We also find that a smaller increase in cloud fraction occurs when scenes experience larger AOD and larger initial cloud cover. Overall, the analysis of the diurnal variation of cloud properties provides a better understanding of aerosol-cloud interaction over land and ocean.Peer reviewe

Plug-and-Play Knowledge Injection for Pre-trained Language Models

Injecting external knowledge can improve the performance of pre-trained language models (PLMs) on various downstream NLP tasks. However, massive retraining is required to deploy new knowledge injection methods or knowledge bases for downstream tasks. In this work, we are the first to study how to improve the flexibility and efficiency of knowledge injection by reusing existing downstream models. To this end, we explore a new paradigm plug-and-play knowledge injection, where knowledge bases are injected into frozen existing downstream models by a knowledge plugin. Correspondingly, we propose a plug-and-play injection method map-tuning, which trains a mapping of knowledge embeddings to enrich model inputs with mapped embeddings while keeping model parameters frozen. Experimental results on three knowledge-driven NLP tasks show that existing injection methods are not suitable for the new paradigm, while map-tuning effectively improves the performance of downstream models. Moreover, we show that a frozen downstream model can be well adapted to different domains with different mapping networks of domain knowledge. Our code and models are available at https://github.com/THUNLP/Knowledge-Plugin.Comment: ACL 202

Use of Electroencephalography for the Study of Gain–Loss Asymmetry in Intertemporal Decision-Making

Intertemporal decision-making refers to the process whereby an individual evaluates and selects among competing alternatives based on the cost and benefit over time. While most previous studies on temporal discounting focused their attention on the gain context, only a few explored the loss context. In the present study, both the event-related potentials (ERPs) and the graph theory analysis were employed to investigate the differences in intertemporal decision-making between the gain and loss frameworks. Our results suggested that participants preferred the short latency/small amount (SS) alternatives and exhibited a smaller discount rate in a loss context compared to a gain framework. Furthermore, our ERP data indicated that the P200 component could constitute a preliminary assessment of the decision-making, related to gain and loss. In contrast, the N2 component was associated with negative emotions and showed significantly bigger amplitudes in the loss context, when compared to the gain framework. Further analyses of brain networks suggested the loss decision-making brain network to have a larger small-worldness index given individuals' loss aversion. Taken together, intertemploral decision-making in a loss context was accompanied by a greater brain response due to the negative emotions linked to loss aversion

Detection of neural connections with ex vivo MRI using a ferritin-encoding trans-synaptic virus

The elucidation of neural networks is essential to understanding the mechanisms of brain functions and brain disorders. Neurotropic virus-based trans-synaptic tracing tools have become an effective method for dissecting the structure and analyzing the function of neural-circuitry. However, these tracing systems rely on fluorescent signals, making it hard to visualize the panorama of the labeled networks in mammalian brain in vivo. One MRI method, Diffusion Tensor Imaging (DTI), is capable of imaging the networks of the whole brain in live animals but without information of anatomical connections through synapses. In this report, a chimeric gene coding for ferritin and enhanced green fluorescent protein (EGFP) was integrated into Vesicular stomatitis virus (VSV), a neurotropic virus that is able to spread anterogradely in synaptically connected networks. After the animal was injected with the recombinant VSV (rVSV), rVSV-Ferritin-EGFP, into the somatosensory cortex (SC) for four days, the labeled neural-network was visualized in the postmortem whole brain with a T2-weighted MRI sequence. The modified virus transmitted from SC to synaptically connected downstream regions. The results demonstrate that rVSV-Ferritin-EGFP could be used as a bimodal imaging vector for detecting synaptically connected neural-network with both ex vivo MRI and fluorescent imaging. The strategy in the current study has the potential to longitudinally monitor the global structure of a given neural-network in living animals