Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulations of global evapotranspiration using semiempirical and mechanistic schemes of plant hydrology

Some of the plant hydrology schemes implemented in global land surface models (LSMs) are relatively simple. This is despite evidence that simulated carbon, water, and energy fluxes are sensitive to both the availability of soil moisture and the formulation of plant hydrology. The current study introduces a mechanistic scheme of plant hydrology (soil‐plant‐atmosphere (SPA)) into a global LSM (Joint U.K. Land Environmental Simulator (JULES)) in order to compare with a traditional, semiempirical plant hydrology scheme (SiB) based on the Ball‐Berry stomatal relation. The SPA scheme simulates explicitly the physical processes which change leaf water potential and account for the flow of water through the soil‐plant‐atmosphere media. Using both plant hydrology schemes, the annually averaged global evaporation‐to‐precipitation ratio is 0.58±0.9. The annually averaged global transpiration‐to‐precipitation ratio is 0.22±0.09 and 0.28±0.08 using the SPA and SiB plant hydrology schemes, respectively. The output from the two plant hydrology schemes typically differs less than the systematic errors associated with the different observational data sets (eddy covariance fluxes, continental runoff, etc.) employed to calibrate and validate the LSM. However, SPA is more conservative with respect to plant water use efficiency compared to SiB. This is partly due to the exhaustion of stored leaf water (not accounted for with the SiB scheme) which acts to limit afternoon transpiration when diurnal vapor pressure deficit is greatest. The trend in global runoff simulated with both plant hydrology schemes for the latter half of the 20th century (−1% per 50 years) agrees well with recent observational estimates that sample 80% of the global runoff network.
A survey on conventional and learning‐based methods for multi‐view stereo

3D reconstruction of scenes using multiple images, relying on robust correspondence search and depth estimation, has been thoroughly studied for the two‐view and multi‐view scenarios in recent years. Multi‐view stereo (MVS) algorithms aim to generate a rich, dense 3D model of the scene in the form of a dense point cloud or a triangulated mesh. In a typical MVS pipeline, the robust estimations for the camera poses along with the sparse points obtained from structure from motion (SfM) are used as input. During this process, the depth of generally every pixel of the scene is to be calculated. Several methods, either conventional or, more recently, learning‐based have been developed for solving the correspondence search problem. A vast amount of research exists in the literature using local, global or semi‐global stereomatching approaches, with the PatchMatch algorithm being among the most popular and efficient conventional ones in the last decade. Yet, and despite the widespread evolution of the algorithms, yielding complete, accurate and aesthetically pleasing 3D representations of a scene remains an open issue in real‐world and large‐scale photogrammetric applications. This work aims to provide a concrete survey on the most widely used MVS methods, investigating underlying concepts and challenges. To this end, the theoretical background and relative literature are discussed for both conventional and learning‐based approaches, with a particular focus on close‐range 3D reconstruction applications.
Introduction to Compressed Sensing with Python

SUMMARY Compressed sensing is a rapidly growing research ﬁeld in signal and image processing, machine learning, statistics, and systems control. In this survey paper, we provide a review of the theoretical foundations of compressed sensing and present state-of-the-art algorithms for solving the corresponding optimization problems. Additionally, we discuss several practical applications of compressed sensing, such as group testing, sparse system identiﬁcation, and sparse feedback gain design, and demonstrate their eﬀectiveness through Python programs. This survey paper aims to contribute to the advancement of compressed sensing research and its practical applications in various scientiﬁc disciplines.
SkiLL: Skipping Color and Label Landscape: Self Supervised Design Representations for Products in E-commerce

Understanding the design of a product without human supervision is a crucial task for e-commerce services. Such a capability can help in multiple downstream e-commerce tasks like product recommendations, design trend analysis, image-based search, and visual information retrieval, etc. For this task, getting fine-grain label data is costly and not scalable for the e-commerce product. In this paper, we leverage knowledge distillation based self-supervised learning (SSL) approach to learn design representations. These representations do not require human annotation for training and focus on only design related attributes of a product and ignore attributes like color, orientation, etc. We propose a global and task specific local augmentation space which captures the desired image information and provides robust visual embedding. We evaluated our model for the three highly diverse datasets, and also propose and measure a quantitative metric to evaluate the model’s color invariant feature learning ability. In all scenarios, our proposed approach outperforms the recent SSL model by upto 8.6% in terms of accuracy.
MixRep: Hidden Representation Mixup for Low-Resource Speech Recognition

In this paper, we present MixRep, a simple and effective data augmentation strategy based on mixup for low-resource ASR. MixRep interpolates the feature dimensions of hidden representations in the neural network that can be applied to both the acoustic feature input and the output of each layer, which generalizes the previous MixSpeech method. Further, we propose to combine the mixup with a regularization along the time axis of the input, which is shown as complementary. We apply MixRep to a Conformer encoder of an E2E LAS architecture trained with a joint CTC loss. We experiment on the WSJ dataset and subsets of the SWB dataset, covering reading and telephony conversational speech. Experimental results show that MixRep consistently outperforms other regularization methods for low-resource ASR. Compared to a strong SpecAugment baseline, MixRep achieves a +6.5\% and a +6.7\% relative WER reduction on the eval92 set and the Callhome part of the eval'2000 set.
Insights into end-to-end audio-to-score transcription with real recordings: A case study with saxophone works

Neural end-to-end Audio-to-Score (A2S) transcription aims to retrieve a score that encodes the music content of an audio recording in a single step. Due to the recentness of this formulation, the existing works have exclusively addressed controlled scenarios with synthetic data that fail to provide conclusions applicable to real-world cases. In response to this gap in the literature, this work introduces a novel assortment of real saxophone recordings—together with their digital scores—and poses several experimental scenarios involving real and synthetic data. The obtained results confirm the adequacy of this A2S framework to deal with real data as well as proving the relevance of leveraging synthetic interpretations to improve the recognition rate in scenarios with real-data scarcity.
A Method of Multiple Targets DOA Tracking with Vector Hydrophone Based on Unite Processing in Time and Frequency Domain

Acoustic pressure and particle velocity is narrowband filtered to obtain multi-band signal. High-order correlation equations composed of acoustic pressure and particle velocity are solved by optimization algorithm and DOA (Direction of arrive) distribution for different signal in multi-band is counted. Then the DOA of multiple targets can be distinguished and tracked. In order to improve the accuracy, the least square method is used to fit the measurement results and a prediction model is established, and then DOA trajectory is optimized by Kalman filter. The experimental results indicate that DOA of three targets can be distinguished and trajectory tracked, DOA of interference source is also observed by the pseudo color picture of DOA distribution, the accuracy of DOA estimation is less than 5°.
Evaluating Usability and User Experience Amid COVID-19: The Case of Innovative Digital Retailers

This research evaluates e-commerce platforms (ECP) amid the COVID-19 pandemic and investigates the relationship between usability and user experience (UX) and e-commerce platforms performance (ECPP) by testing online customer's interactions with online retailers. Exploratory and confirmatory factor analyses assessed the research constructs' dimensions, dimensionality, validity, and composite reliability, while structural path analysis tested the hypothesized model relationships. The empirical results indicate that six usability dimensional attributes are strategically significant antecedents to UX and ECPP: navigation, information architecture, value of content, satisfaction, aesthetics, and consistency and functionality. An integrated framework of usability and UX is recommended for retailers running their businesses on digital platforms. This research investigates usability, UX, and ECPP through understanding digital marketing frameworks in e-commerce stores in the COVID-19 context, shedding light on the strategic role of usability attributes and how they affect UX digital performance.
Temporal Feature Extraction and Machine Learning for Classification of Sleep Stages Using Telemetry Polysomnography

Accurate sleep stage detection is crucial for diagnosing sleep disorders and tailoring treatment plans. Polysomnography (PSG) is considered the gold standard for sleep assessment since it captures a diverse set of physiological signals. While various studies have employed complex neural networks for sleep staging using PSG, our research emphasises the efficacy of a simpler and more efficient architecture. We aimed to integrate a diverse set of feature extraction measures with straightforward machine learning, potentially offering a more efficient avenue for sleep staging. We also aimed to conduct a comprehensive comparative analysis of feature extraction measures, including the power spectral density, Higuchi fractal dimension, singular value decomposition entropy, permutation entropy, and detrended fluctuation analysis, coupled with several machine-learning models, including XGBoost, Extra Trees, Random Forest, and LightGBM. Furthermore, data augmentation methods like the Synthetic Minority Oversampling Technique were also employed to rectify the inherent class imbalance in sleep data. The subsequent results highlighted that the XGBoost classifier, when used with a combination of all feature extraction measures as an ensemble, achieved the highest performance, with accuracies of 87%, 90%, 93%, 96%, and 97% and average F1-scores of 84.6%, 89%, 90.33%, 93.5%, and 93.5% for distinguishing between five-stage, four-stage, three-stage, and two distinct two-stage sleep configurations, respectively. This combined feature extraction technique represents a novel addition to the body of research since it achieves higher performance than many recently developed deep neural networks by utilising simpler machine-learning models.
Novel Double Mode Dual-Stator Wound Rotor Synchronous Machine for Variable Speed Applications

This paper offers a novel dual-mode double stator wound rotor synchronous machine for variable speed applications. The proposed motor integrates the benefits of both the traditional wound rotor synchronous machine (WRSM) and brushless wound rotor synchronous machine (BL-WRSM). A constant torque can be attained in the maximum torque per ampere region by operating the proposed machine as a traditional WRSM in Mode I, and a constant power can be attained in the field-weakening region by operating it as a BL-WRSM in Mode II. Moreover, due to the dual-stator structure, the proposed machine exhibits improved performance in terms of high torque density as compared to the existing single stator BL-WRSM. By using a special stator winding arrangement to achieve the sub-harmonic component of the stator magnetomotive force, the brushless operation of the proposed machine is achieved. The additional sub-harmonic component induces a voltage in the harmonic winding placed on the rotor, which is then rectified and provided a DC current to field winding for brushless excitation. In order to validate the effectiveness of the proposed machine, a two-dimensional finite element analysis (FEA) is carried out.
A System for Dense Monocular Mapping with a Fisheye Camera

We introduce a novel dense mapping system that uses a single monocular fisheye camera as the sole input sensor and incrementally builds a dense surfel representations of the scene’s 3D geometry. We extend an existing hybrid sparse-dense monocular SLAM system, reformulating the mapping pipeline in terms of the Kannala-Brandt fisheye camera model. Each frame is processed in its original undistorted fisheye form, with no attempt to remove distortion. To estimate depth, we introduce a new version of the PackNet depth estimation neural network adapted for fisheye inputs. We reformulate PackNet’s multi-view stereo self-supervised loss in terms of the Kannala-Brandt fisheye camera model. To encourage the network to learn metric depth during training, the pose network is weakly supervised with the camera’s ground-truth inter-frame velocity. To improve overall performance, we additionally provide sparse depth supervision from dataset LiDAR and SICK laser scanners. We demonstrate our system’s performance on the realworld KITTI-360 benchmark dataset. Our experimental results show that our system is capable of accurate, metric camera tracking and dense surface reconstruction within local windows. Our system operates within real-time processing rates and in challenging conditions. We direct the reader to the following video where the system can be seen in operation: https://youtu.be/Y-9q_wfqocs.
Energy Constrained Data Collection in Multi-UAV-Assisted IoT

Benefit to the advantages of low cost, strong security, flexibility and high line-of-sight (LoS), UAVs constitute a promising platform to accomplish data collection for the IoT networks. However, it has to be admitted that the constrained energy consumption of UAVs has become the main challenge for the UAV-assisted IoT data collection. The existed works are mainly based on the assumption that the amount of data uploaded by different devices are the same, which makes the energy consumption of UAVs deviating from the real situation. Therefore, in this paper, for the sake of practical consideration, the amount of data uploaded varies from different devices in a multi-UAV-assisted IoT data collection scenario. To solve the problem of minimizing the total energy consumption of UAVs, we decouple it into two subproblems, devices clustering and UAVs trajectory planning, and propose an iterative optimization algorithm with energy and data volume constraints. Numerical results show that the performance of the proposed method outperforms the comparison schemes significantly in terms of saving total UAVs energy consumption and reducing the standard deviation of UAVs energy consumption.
Uplink Power Allocation for RSMA-aided User-centric Cell-free Massive MIMO Systems

This paper tackles the problem of maximizing the minimum spectral efficiency (SE) via allocation of the uplink optimal transmission power in a rate splitting multiple access (RSMA)-aided user-centric (UC) cell-free (CF) massive MIMO (mMIMO) system. It is shown that the optimization problem is non-convex. The problem is then transformed into a second-order cone programming problem which is solved iteratively by leveraging the bisection method. Numerical results demonstrate the effectiveness of the RSMA technique to improve the 95%-likely SE gain by up to 332% compared to a UC CF mMIMO system with no power control.
Incentive-Driven Fog-Edge Computation Offloading and Resource Allocation for 5G-NR V2X-Based Vehicular Networks

Vehicular fog-edge computing (VFEC) is a new paradigm that has significant advantages in expanding the resource capacity of mobile edge computing (MEC) servers in vehicular networks. This work studies an incentive-based vehicular fog-edge computational offloading, and resource allocation schemes for vehicular networks using 5G-NR V2X communications. Here, smart vehicles in a given area are assumed to have computationally intensive tasks which can offload to the roadside unit (RSU) with a MEC server. To expand the resource capacity of the MEC server, in this work, we propose an incentive-based vehicular fog-edge computational offloading, and resource allocation scheme to maximize the unified utility function of the system with delay and incentive satisfaction of the entities under deadline and incentive constraints. This results in the mixed integer non-linear programming problem that is solved using continuous relaxation and an alternating optimization between convex sub-problems. Simulations are provided to demonstrate the effectiveness of the proposed scheme.
Molecular Cloning and Gene Expression of Type I Suppressors of Cytokine Signaling 6 and 7 (SOCS6 and SOCS7) in Whiteleg Shrimp (Litopenaeus vannamei)

Suppressor of cytokine signaling (SOCS) genes are essential negative regulators that modulate cytokine signaling and play key roles in numerous biological processes, including immune responses. In this study, we cloned the complementary DNA (cDNA) sequences of two SOCS genes, designated as LvSOCS6 and LvSOCS7, from the whiteleg shrimp, Litopenaeus vannamei. LvSOCS6 encoded a polypeptide of 463 amino acids (aas), spanning 1392 base pairs (bps), while LvSOCS7 encoded a significantly larger polypeptide of 955 aas, encompassing 2868 bps. Both LvSOCS proteins exhibited conserved domains associated with SOCS, including a centralized Src homology 2 (SH2) domain and a C-terminal SOCS box. Phylogenetic analysis revealed that the deduced aa sequences of LvSOCS6 and LvSOCS7 clustered within the invertebrate type I SOCS family, indicating their evolutionary relatedness. Tissue distribution analysis demonstrated ubiquitous expression of both LvSOCS genes across all examined tissues, with LvSOCS6 showing heightened expression in the gills and LvSOCS7 in the gills and stomach. Notably, mRNA expression patterns of LvSOCS genes following LPS and poly (I:C) stimulations exhibited significant upregulations, while PGN stimulation yielded incongruous results across the examined tissues. Interestingly, concurrent with the diminished expression of LvSOCS6 and LvSOCS7, there was significant elevation in mRNA expression levels of LvSTAT, a vital component of the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway. These findings shed light on the potential involvement of the newly discovered type I SOCS genes in regulating the JAK/STAT pathways and playing pivotal roles in orchestrating the innate immune responses in L. vannamei defense mechanisms.
Quantitative Assessment of Penetration Rates of CCAM Applications on GHG Emissions in EU27

Due to significant climate change in the past decades, the reduction of Greenhouse Gas (GHG) has been set as the top priority. In recent years, many Connected, Cooperative and Automated Mobility (CCAM) applications have been proposed and developed to improve the network and increase traffic efficiency. By following the net-zero emission reduction by 2050 in European Union (EU), a slowly increased percentage of connected vehicles (penetration rate) should be considered, and yet, most studies only show benefits of CCAM at 100% penetration rate, i.e., under assumption that all vehicles are connected. Therefore, we study the effect of different penetration rates for CCAM applications in the urban scenario. In this work, some representative German cities are chosen and classified into three Tiers based on the population. Then the urban scenarios is built in Simulation of Urban MObility (SUMO), and the CCAM applications control the vehicles via Traffic Control Interface (TraCI). Simulation results show a significant reduction in CO2 emissions and an increase in vehicle speed. Then, these results are fed into a Machine Learning (ML)-based estimator, called Random Forest Regressor (RFR), to predict further improvements of CCAM at the 27 European Union Countries (EU27)-level. The predicted results can act as references or guidelines for developing or deploying connected vehicles to reduce GHG and improve traffic efficiency.
Establishing Compliance between Spectral, Colourimetric and Photometric Indicators in Resazurin Reduction Test

The resazurin reduction test is one of the basic tests for bacterial culture viability and drug resistance endorsed by the World Health Organisation. At the same time, conventional spectrophotometric and spectrofluorimetric methods demand rather bulky and expensive equipment. This induces a challenge for developing simpler approaches to sensor systems that are portable and applicable in resource-limited settings. In this work, we address two such alternative approaches, based on the colour processing of the microbiological plate’s photographic images and single-channel photometry with a recently developed portable microbiological analyser. The key results consist of establishing a sequential linear correspondence between the concentration of resorufin produced due to the reduction of resazurin by viable bacteria as determined by the UV-Vis studies, the intensity of the a* channel of the CIE L*a*b* colour space and the transmitted light intensity registered by a luxmeter under the LED illumination with a yellow colour filter. This route is illustrated with the chemical system “Hydrazine hydrate – resazurin”, isolating the target colour change-inducing reaction and the test of determining the minimal inhibition concentration of the antibacterial first-line drug isoniazid acting on the culture of the H37Rv strain of M. tuberculosis.
Wi-Five: Optimal Placement of Wi-Fi Routers in 5G Networks for Indoor Drone Navigation

In the near future, unmanned aerial vehicles (UAVs) will be used to automate the logistics between organizations and their customers by relying on high accuracy localization. In this paper, we propose a framework that leverages the multi radio access technology (RAT) 5G network to solve the cellular positioning problem for such high mobility targets. For indoors, we utilize wireless fidelity (Wi-Fi) routers, denoted as Wi-Five dots, to improve positioning accuracy where the 5G signal is weaker compared with outdoor environments. These anchor points will use the 5G backhaul to report to the same location management function (LMF) as the cellular 5G access network. The primary contribution of this work is showing how the geometry of these indoor Wi-Five dots significantly affects positioning accuracy. Through a novel optimization algorithm based on the Evolutionary Algorithm (EA) class, we solve the NP-Hard problem of finding the optimal placement of Wi-Five dots for three-dimensional high-accuracy positioning of a mobile target. Our results show that the final positioning accuracy is the product of both the ranging errors and the geometric dilution of precision (GDOP). We experimentally verify that for the latter, the error stems primarily from the Z-axis estimations rather than the errors in the X − Y plane. Finally, we evaluate the results of our optimal placement to show it drastically improves positioning estimations in a three-dimensional space compared with arbitrary beacon placement.