Miracle-dz/UniFAR-FTUs huggingface dataset:
Successful Recovery Performance Guarantees of SOMP Under the $\ell _{2}$-Norm of Noise

The simultaneous orthogonal matching pursuit (SOMP) is a popular, greedy approach for common support recovery of a row-sparse matrix. However, compared to the noiseless scenario, the performance analysis of noisy SOMP is still nascent, especially in the scenario of unbounded noise. In this article, we present a new study based on the mutual incoherence property (MIP) for performance analysis of noisy SOMP when the measurement matrix and sparse signal are deterministic. Specifically, when noise is bounded, we provide the condition on which the exact support recovery is guaranteed in terms of the MIP. When noise is unbounded, we instead derive a bound on the successful recovery probability (SRP) that depends on the specific distribution of the $\ell _{2}$-norm of the noise matrix. Then we focus on the typical case when noise is random Gaussian, and show that the lower bound of SRP follows Tracy-Widom law distribution. The analysis reveals the number of measurements, noise level, the number of sparse vectors, and the value of mutual coherence that are required to guarantee a predefined recovery performance. Theoretically, we show that the mutual coherence of the measurement matrix must decrease proportionally to the noise standard deviation, and the number of sparse vectors needs to grow proportionally to the noise variance. Finally, we extensively validate the derived analysis through numerical simulations.
A Reliable Low-area Low-power PUF-based Key Generator

This paper reports the implementation of a lowarea low-power 128-bit PUF-based key generation module which exploits a novel Two-Stage IDentification (TSID) cell showing a higher noise immunity then a standard SRAM cell. In addition, the pre-selection technique introduced in [1] is applied. This results in a stable PUF response in spite of process and environmental variations thus requiring a low cost error correction algorithm in order to generate a reliable key. The adopted PUF cell array includes 1056 cells and shows a power consumption per bit of 4:2 W at 100MHz with an area per bit of 2:4 m2. In order to evaluate reliability and unpredictability of the generated key, extensive tests have been performed both on the raw PUF data and on the final key. The raw PUF data after pre-selection show a worst case intra-chip Hamming distance below 0:7%. After a total of more than 5 109 key reconstructions, no single fail has been detected.
Wind Energy at Nygårdsfjellet – Norway. Wind field characterization and modelling

The research focuses on the characterization of wind flow over complex terrain of Nygårdsfjell and numerical wind flow modelling. In the context to research focus, the main contributions of this research thesis are within the following areas: Characterization of wind flow at the Nygårdsfjell wind farm, situated above the Arctic circle in Norway. Wind data is collected from the anemometers installed on the wind turbines and thereafter analysed for understanding the existing wind patterns. The analysis established existence of bi-directional high winds at Nygårdsfjell wind farm particularly during colder periods. Computational fluid dynamic (CFD) solver FLUENT is used to model the wind fields over the complex terrain of Nygårdsfjell. Overall, the shear stress transport model gives good simulation results that explains the speed up effects at the turbine location, which makes the wind farm a suitable site for wind energy. Local terrain effects on the wind flow over Nygårdsfjell are modelled by coupling meso-micro scale models. One set of meso-scale winds are generated fromWeather Research and Forecasting model, whereas the other set is taken from the Modern-Era Retrospective Analysis for Research and Applications dataset. CFD based numerical solver, WindSim, is used as micro-scale coupling partner. One of the proposed coupled models achieved improvements in wind speed modelling. A basic method for preliminary wind resource assessment at remote sites is proposed. The method is a combination of interpolation and extrapolation of wind data from the surrounding sites to the potential wind farm site. However, due to large disparities between the terrains and conditions, the results from this project does not contribute directly to the main research area.
Traffic Flow Prediction Based on Stream Tensor Analysis

In intelligent transportation system (ITS), traffic flow prediction can provide data support for route planning, traffic management and public safety. Prediction algorithms based on machine learning or deep learning usually need a large number of unabridged historical data to conduct parameter training. However, data will be missing and abnormal in practice, which will affect the accuracy of prediction. In this paper, we propose the stream tensor analysis (STA) algorithm for traffic flow prediction. First, dynamic tensor stream of four dimensions of space, time, day and week is constructed to better mine the multi-mode correlation between traffic flow data. Second, the first few columns with the largest norm are selected to update the projection matrix by the tracking projection matrix algorithm. The experimental results show that the STA algorithm has low complexity, and also achieve good prediction performance in random missing and extreme missing patterns.
A Review on Technology Adoption in Precision Agriculture: The Behavior and Use Acceptance

This research gives a systematic literature review on technology adoption for the farmers that recognized precision agriculture. This research aims to identify the operational elements in relevant technology adoption precision agriculture and comprehend the relation between the following operational elements (Farm Size, Performance Expectancy, Effort Expectancy, Social Influence, Facilitation Condition, Attitude Toward, Behaviour Intention, Use Behaviour). In addition, this research aims to inspire research on technology adoption in precision agriculture and develop the proposed theoretical model in the future. This research is based on a systematic literature review on technology adoption with precision agriculture, especially precision agriculture, published in international journals between 2015-2020. This literature study shows that there is only a little research about technology adoption in precision agriculture.
A Segmented Adaptive Router for Near Energy-Proportional Networks-on-Chip

A Network-on-Chip (NoC) is an essential component of a chip multiprocessor (CMP) which however contributes to a large fraction of system energy. The unpredictability of traffic across a NoC frequently involves an expensive over-sizing of NoC resources which in turn leads to a significant contribution to the CMP power consumption. There exists a body of work addressing this issue, however so far solutions fall short when aiming for power reduction whilst maintaining high NoC performance. This paper proposes to combine router architecture optimizations with smart resource management to overcome this limitation. Based on a fully segmented architecture, we present an online adaptive router adjusting its active routing resources to meet the current traffic demand. This enhanced power-gating strategy significantly decreases both static and dynamic power consumption of the NoC, up to 70% for synthetic traffic patterns and up to 58% for real traffic workloads, while preserving NoC latency and throughput. Thanks to these adaptive power-saving mechanisms the proposed segmented NoC router provides near energy-proportional operation across the range of used benchmarks.
Insight into mobility patterns under COVID-19: A time-sequence-based approach

Various measures have been taken to prevent the spread of COVID-19. Even with widespread vaccination, the control of the epidemic is still difficult due to the mutation of the virus. When an epidemic breaks out, the simplest and most efficient method of control is still social isolation, which greatly affects daily lives and mobility patterns. To study mobility patterns, we leveraged mobile base station data in Shulan, China, during the epidemic. Our main discoveries are as follows: (1) With the development of COVID-19, travel volumes and the scopes of trips were gradually reduced. (2) In addition to the government’s prevention policy, media coverage of COVID-19 had a huge impact on mobility patterns. (3) Previous studies focused on morning and evening rush hours. However, our results show that humans tend to intensively travel at noon. (4) The travel network was significantly more active in the early stages of the COVID-19 outbreak; hence, the possibility of disease transmission was greater. (5) With the development of the epidemic, travel intervals became increasingly longer, and the number of contacts between base stations decreased. (6) By analyzing the temporal path length, we found that some nodes were still active during the epidemic.
An Exploratory Study of Potential Freeway Level-of-Service Measures by Simulation Approach and Statistical Analysis

Traffic operations analysis for freeway management includes Level of Service (LOS) methodology. Conventionally, density is adopted as the LOS measure for basic freeway segment. To account for different vehicle types in mixed freeway flows, the current method transforms trucks into passenger car equivalent (PCE) values. This practice has raised questions regarding its effectiveness because drivers of different vehicle types perceive the quality of service differently even under a same density. Therefore, "whether the PCE-based adjustment for a single LOS threshold for all vehicle types is reasonable" is subjected to investigation. So this paper proposes two measures and evaluates their applicability to major vehicle types: acceleration noise (AN); average lane change rate (ALCR). Their respective relationship to traffic and operational factors was investigated through simulation approach, and totally 108 simulation scenarios resulted from the factorial design. AN showed promising results as it exhibited differentiable monotonous tendency, with the flow rate changing, for each of different vehicle types, while ALCR showed the vulnerability, namely the lack of monotonous and differentiable tendency, regarding the service quality measurement for different vehicle types. This study provides some inspiring insights into further researching and developing freeway LOS measures for different vehicle types in burgeoning big-data environments.
DeCloud GAN: An Advanced Generative Adversarial Network for Removing Cloud Cover in Optical Remote Sensing Imagery

Optical Remote Sensing imagery has several applications in monitoring the states of natural and man-made features around the globe. However, due to clouds and other climatic conditions, information extracted from the imagery retrieved is very limited. Deep learning has often been used in several image processing and remote sensing tasks. In this work, we propose the usage of generative adversarial networks to remove clouds and other climatic interference from high-resolution remote sensing imagery. We have trained and tested upon the Remote sensing Image Cloud rEmoving dataset (RICE). The novel network(DeCloud GAN) we propose, makes use of residual UNets and pixel shuffle layers in the generator, which yield high quality cloudless satellite images. We have tested 4 methods for comparison, and have found that DeCloudGAN achieves the best performance on two main metrics, peak signal to noise ratio (PSNR) and structural similarity index (SSIM), to measure similarity in visual perception of the produced and target images.
Lubricated viscous gravity currents of power-law fluids. Part 2. Stability analysis

Abstract We examine the stability of radially spreading, gravity-driven thin films of power-law fluids, lubricated from below by another power-law viscous fluid. Such flows are susceptible to a viscous fingering instability, also known as a non-porous viscous fingering instability, when a less viscous fluid intrudes beneath a more viscous fluid. In contrast to the Saffman–Taylor instability, such instabilities originate from a jump in hydrostatic pressure gradient across the intrusion front, associated with gradients in the upper surface. These are stabilised by buoyancy forces associated with the lower layer near its nose, and all instabilities are suppressed above a critical density difference. We find that shear-thinning flows are more prone to instability than Newtonian and shear-thickening flows. Lower consistency ratios are sufficient for the onset of instability of shear-thinning flows, and the stabilising influences of buoyancy forces are suppressed. As such, higher density differences are required to suppress the instability completely.
Nonlinear evolution and low-frequency acoustic radiation of ring-mode coherent structures on subsonic turbulent circular jets

Abstract By adapting the triple decomposition of an instantaneous turbulent flow into a time-averaged mean field, large-scale coherent motion and fine-scale random fluctuations, and treating the coherent motion as instability modes on the mean flow, a mathematical theory is developed to describe the nonlinear spatial–temporal modulation and acoustic radiation of a coherent structure (CS) on a circular jet in the form of a wavepacket consisting of an axisymmetric (ring) mode and its sideband components. The effect of fine-scale turbulence on the CS is characterised via a gradient closure model, and the non-parallelism due to the axial variation and radial velocity of the mean flow is taken into account. By employing the matched asymptotic expansion and multi-scale techniques, a strongly nonlinear system is derived, which governs the envelope of the CS and its vorticity and temperature in the critical layer. Numerical solutions to the evolution system show that the theory captures the nonlinear amplitude attenuation and vorticity roll-up as observed in experiments. The large-distance asymptotic properties of the CS allow us to describe and predict its acoustic radiation on the basis of first principles. The CS is trapped within the jet, but its self-interaction generates a temporally and axially modulated mean-flow distortion, which acts as the emitter to radiate low-frequency sound waves, with the Reynolds stresses driving this mean-flow distortion being identified unambiguously to be the physical source in the present context. An equivalent source in the Lighthill type of acoustic analogy is also identified. For the present ring-mode CS in the fully developed region of a circular jet, the equivalent source can be determined before the acoustic field is, and the intensity of the radiated sound waves is found to be $O(\epsilon ^3)$, where $\epsilon$ measures the magnitude of the CS. The directivity and spectrum of the acoustic far field are calculated for representative parameters, and the predicted features resemble experimental measurements.
Can Surface Soil Moisture Information Identify Evapotranspiration Regime Transitions?

The transition of evapotranspiration between energy‐ and water‐limitation regimes also denotes a nonlinear change in surface water and energy coupling strength. The regime transitions are primarily dominated by available moisture in the soil, although other micro‐meteorological factors also play a role. Remotely sensed soil moisture is frequently used for detecting evapotranspiration regime transitions during inter storm dry downs. However, its sampling depth does not include the entire soil profile, over which water uptake is dominated by plant root distribution. We use flux tower, surface (θs; observations at 5 cm), and vertically integrated in situ soil moisture ( θv ${\theta }_{v}$ ; 0–50 cm) observations to address the question: Can surface soil moisture robustly identify evapotranspiration regime transitions? Results demonstrate that θs and θv are hydraulically linked and have synchronized evapotranspiration regime transitions. As such, θs and θv capture comparable statistics of evapotranspiration regime prevalence, which supports the utility of remote‐sensing θs for large‐scale land‐atmosphere exchange analysis.
An improved ultrasonic computerized tomography (UCT) technique for damage localization based on compressive sampling (CS) theory

The traditional ultrasonic computerized tomography (UCT) method needs numerous measurements to detect the inside conditions of concrete. Considering the damage is sparsely distributed in the reinforced concrete, compressive sampling (CS) is applied to advance the UCT method at both the measurement stage and the imaging stage to localize damage part in the structure. The proposed detection algorithm requires much fewer measurements compared to the traditional UCT with the same accuracy. In the measurement stage, the pitch–catch paths are randomly selected to capture the travel time of ultrasonic wave from one side to the other side in the dense measurement net. When ultrasonic waves propagate in the structure, they will interact with the damaged part, prolong the propagation length, and delay the time of flight (ToF). In the imaging stage, the whole specimen is divided into small pixels based on the desired accuracy. The conventional Rando equation is advanced based on ℓ1‐minimization algorithm from CS method to solve the slowness of each pixel to reconstruct the damaged part. The functionality of the proposed algorithm is validated via both numerical model and experimental testing on a reinforced concrete beam in the laboratory. The results reveal the UCT based on CS is more efficient for localizing and imaging the damage with much fewer measurements, which has the immense potential in the development of structural health monitoring (SHM).
Large mammalian herbivores modulate plant growth form diversity in a tropical rainforest

The world’s terrestrial biomes are broadly classified according to the dominant plant growth forms that define ecosystem structure and processes. Although the abundance and distribution of different plant growth forms can be strongly determined by factors such as climate and soil composition, large mammalian herbivores have a strong impact on plant communities, thus defaunation (the local or functional extinction of large animals) has the potential to alter the compositional structure of plant growth forms in natural ecosystems. Tropical rainforests sustain a high diversity of growth forms, including trees, palms, lianas, shrubs, herbs and bamboos, all of which play important ecosystem functions. Here, we experimentally evaluate how large mammalian herbivores affect the dominance, diversity and coexistence of these major tropical forest plant growth forms, by monitoring communities of saplings on the understorey in 43 paired exclusion plots in a long‐term replicated exclusion experiment in the understorey of the Atlantic forest of Brazil. Over the course of 10 years large herbivore exclusion decreased diversity among growth forms, increased the absolute abundance of palms and trees (22% and 38% respectively) and increased the diversity of species within these two groups, to the detriment of other growth forms. Furthermore, all pairwise relationships between growth forms were positive on plots where herbivores had access, whereas several strong negative relationships emerged in plots where herbivores were excluded. This occurred despite strong background directional temporal trends affecting plant communities in both experimental treatments across the region. Synthesis. Our work indicates that the defaunation alters growth form dominance by favouring palms and trees while eroding diversity among growth forms and coexistence on a temporal scale. Large herbivore mammals promote diversity among growth forms, preventing the hyper‐dominance of trees and palms, yet without supressing the diversity of species within growth forms. We argue that large herbivore mammals affect growth forms through several non‐mutually exclusive mechanisms, including herbivory, seed dispersal and physical disturbance, as well as differential effects linked to the morphological and physiological adaptations of growth forms. We conclude that defaunation might lead to profound impacts on important ecosystem functions underpinned by growth form diversity, and result in vertical and horizontal structural simplification of tropical rainforests.
Learning and Expression of Dopaminergic Reward Prediction Error via Plastic Representations of Time

Dopamine (DA) releasing neurons in the midbrain learn response patterns that represent reward prediction error (RPE). Typically, models proposing a mechanistic explanation for how dopamine neurons learn to exhibit RPE are based on temporal difference (TD) learning, a machine learning algorithm. However, mechanistic models motivated by TD learning face two significant hurdles. First, TD based models typically require rather unrealistic components, such as long and robust temporal chains of feature specific neurons which tile, a priori, each interval from a given stimulus to a given reward. Secondly, various predictions of TD clash with experimental observations of how RPE evolves over learning. Here, we present a biophysically plausible plastic network model of spiking neurons, that learns RPEs and can replicate results observed in multiple experiments. This model, coined FLEX ( F lexibly L earned E rrors in E x pected Reward), learns feature specific representations of time, allowing for neural representations of stimuli to adjust their timing and relation to rewards in an online manner. Following learning, model dopamine neurons in FLEX report a distribution of response types, as observed experimentally and as used in machine learning. Dopamine firing in our model reflects an RPE before and after learning but not necessarily during
The Delta variant SARS-CoV-2 spike protein uniquely promotes aggregation of pseudotyped viral particles

Individuals infected with the SARS-CoV-2 Delta variant, lineage B.1. 617.2, exhibit faster initial infection with a higher viral load than prior variants, and pseudotyped particles bearing the SARS-CoV-2 Delta variant spike protein induce a faster initial infection rate of target cells compared to those bearing other SARS-CoV-2 variant spikes. Here, we show that pseudotyped particles bearing the Delta variant spike form unique aggregates, as evidenced by negative stain and cryogenic electron microscopy (EM), flow cytometry, and nanoparticle tracking analysis. Viral particles pseudotyped with other SARS-CoV-2 spike variants do not show aggregation by any of these criteria. The contribution to infection kinetics of the Delta spike’s unique property to aggregate is discussed with respect to recent evidence for collective infection by other viruses. Irrespective of this intriguing possibility, spike-dependent aggregation is a new functional parameter of spike-expressing viral particles to evaluate in future spike protein variants.