Miracle-dz/UniFAR-FTUs huggingface dataset:
Engineering characterization of Vetiver system for shallow slope stabilization

The Vetiver system (VS) has proven to be very effective in mitigating erosion and shallow slope instability, provided it is applied correctly. Nevertheless, quantitative studies of how much contribution the VS really provides to slope stabilization in the field are still relatively scarce. This study outlines an approach for such purposes, including laboratory tests, field observation as well as numerical analysis. Laboratory tests include root & soil mechanics characterization, such as tensile strength, as well as direct-shear testing and permeability tests of root-reinforced soil sample. This, together with field observation of root quantity by means of mini-rhizotron, enables quantification of root-area ratio and the root cohesion that exists in actual slopes. It can be seen from a test site in Surathani, South Thailand, that after the Vetiver was invaded by the local species and its roots decayed, the root area ratio decreased significantly leading to loss in root cohesion and decreased factor of safety. This thus emphasizes the importance of frequent maintenance of the Vetiver in practice in order to sustain long-term slope stability. Also based on studies using numerical simulation, the VS appeared to have mainly beneficial effects for slope of 26 o gradient (1V:2H). However, when applying the VS on very steep slope (>60 o , 2V:1H), practitioner should exercise certain cautions, especially for the case of easily degradable rock such as claystone, since there could be theoretical adverse effect of increased infiltration through root zone, amount to 10% reduction in factor of safety.
An Approach on Soils Management Practices for Red Oxisols under Tropical Savanna in Thailand

An approach on soil management practices for red Oxisols under Tropical Savanna in Thailand was based on consideration derived from the soils properties. A determination of these properties was carried out for 6 representative pedons. These soils were Kandiustox in Northeast Plateau developed on limestone and basalt and had characteristics typical of highly weathered soils. They were very deep (> 2 m depth), clayey, having moderate to strong structures. The soils had low bulk densities (0.95-1.29 Mg m-3). Water quickly drained from these soils at high matric potentials, while at low matric potentials, water contents were relatively high. Amounts of water retained at 33 kPa (field capacity) (ranging 25-38%) and 1500 kPa (permanent wilting point) (ranging 19-29%) by these red Oxisols were both high. Water available to plants was only 3-10 percent. Their pH values were low, ranging 4.3-7.6 and CEC’s were also low ranging 5.4-20.4 cmol kg-1. Organic matter contents were in a range of 2.1-41.3 g kg-1. They had high amount of available P and K in the surface horizon which decreased sharply with depth. Pak Chong series (Pc) had low to medium fertility whereas Chok Chai series (Ci) had low fertility status. The approach for effective management practices includes irrigation, fertilizer and organic matter management, respectively.
Strong Coupling of Exciton-Polaritons in a Bulk 
GaN
 Planar Waveguide: Quantifying the Coupling Strength

We investigate the demonstration and quantification of the strong coupling between excitons and guided photons in a GaN slab waveguide. The dispersions of waveguide polaritons are measured from T=6~K to 300~K through gratings. They are carefully analyzed within four models based on different assumptions, in order to assess the strong coupling regime. We prove that the guided photons and excitons are strongly coupled at all investigated temperatures, with a small $(11 \%)$ dependence on the temperature. However the values of the Rabi splitting strongly vary among the four models: the "coupled oscillator" model over-estimates the coupling; the analytical "Elliott-Tanguy" model precisely describes the dielectric susceptibility of GaN near the excitonic transition, leading to a Rabi splitting equal to $82 \pm 10 \ meV$ for TE0 modes; the experimental ellipsometry-based model leads to smaller values of $55 \pm 6 \ meV. $ We evidence that for waveguides including active layers with large oscillator strengths, as required for room-temperature polaritonic devices, a strong bending of the modes dispersion is not necessarily the signature of the strong-coupling, which requires for its reliable assessment a precise analysis of the material dielectric susceptibility.
Analytical solutions to some generalized and polynomial eigenvalue problems

Abstract It is well-known that the finite difference discretization of the Laplacian eigenvalue problem −Δu = λu leads to a matrix eigenvalue problem (EVP) Ax =λx where the matrix A is Toeplitz-plus-Hankel. Analytical solutions to tridiagonal matrices with various boundary conditions are given in a recent work of Strang and MacNamara. We generalize the results and develop analytical solutions to certain generalized matrix eigenvalue problems (GEVPs) Ax = λBx which arise from the finite element method (FEM) and isogeometric analysis (IGA). The FEM matrices are corner-overlapped block-diagonal while the IGA matrices are almost Toeplitz-plus-Hankel. In fact, IGA with a correction that results in Toeplitz-plus-Hankel matrices gives a better numerical method. In this paper, we focus on finding the analytical eigenpairs to the GEVPs while developing better numerical methods is our motivation. Analytical solutions are also obtained for some polynomial eigenvalue problems (PEVPs). Lastly, we generalize the eigenvector-eigenvalue identity (rediscovered and coined recently for EVPs) for GEVPs and derive some trigonometric identities.
Relaxed Queues and Stacks from Read/Write Operations

Considering asynchronous shared memory systems in which any number of processes may crash, this work identifies and formally defines relaxations of queues and stacks that can be non-blocking or wait-free while being implemented using only read/write operations. Set-linearizability and Interval-linearizability are used to specify the relaxations formally, and precisely identify the subset of executions which preserve the original sequential behavior. The relaxations allow for an item to be returned more than once by different operations, but only in case of concurrency; we call such a property multiplicity. The stack implementation is wait-free, while the queue implementation is non-blocking. Interval-linearizability is used to describe a queue with multiplicity, with the additional relaxation that a dequeue operation can return weak-empty, which means that the queue might be empty. We present a read/write wait-free interval-linearizable algorithm of a concurrent queue. As far as we know, this work is the first that provides formalizations of the notions of multiplicity and weak-emptiness, which can be implemented on top of read/write registers only.
Interpretable Goal-based Prediction and Planning for Autonomous Driving

We propose an integrated prediction and planning system for autonomous driving which uses rational inverse planning to recognise the goals of other vehicles. Goal recognition informs a Monte Carlo Tree Search (MCTS) algorithm to plan optimal maneuvers for the ego vehicle. Inverse planning and MCTS utilise a shared set of defined maneuvers and macro actions to construct plans which are explainable by means of rationality principles. Evaluation in simulations of urban driving scenarios demonstrate the system’s ability to robustly recognise the goals of other vehicles, enabling our vehicle to exploit non-trivial opportunities to significantly reduce driving times. In each scenario, we extract intuitive explanations for the predictions which justify the system’s decisions.
Quench Dynamics of Collective Modes in Fractional Quantum Hall Bilayers.

We introduce different types of quenches to probe the nonequilibrium dynamics and multiple collective modes of bilayer fractional quantum Hall states. We show that applying an electric field in one layer induces oscillations of a spin-1 degree of freedom, whose frequency matches the long-wavelength limit of the dipole mode. On the other hand, oscillations of the long-wavelength limit of the quadrupole mode, i.e., the spin-2 graviton, as well as the combination of two spin-1 states, can be activated by a sudden change of band mass anisotropy. We construct an effective field theory to describe the quench dynamics of these collective modes. In particular, we derive the dynamics for both the spin-2 and the spin-1 states and demonstrate their excellent agreement with numerics.
λ And: a post-main-sequence wind from a solar-mass star

We investigate the wind of λ And, a solar-mass star that has evolved off the main sequence becoming a subgiant. We present spectropolarimetric observations and use them to reconstruct the surface magnetic field of λ And. Although much older than our Sun, this star exhibits a stronger (reaching up to 83 G) large-scale magnetic field, which is dominated by the poloidal component. To investigate the wind of λ And, we use the derived magnetic map to simulate two stellar wind scenarios, namely a ‘polytropic wind’ (thermally driven) and an ‘Alfven-wave-driven wind’ with turbulent dissipation. From our 3D magnetohydrodynamics simulations, we calculate the wind thermal emission and compare it to previously published radio observations and more recent Very Large Array observations, which we present here. These observations show a basal sub-mJy quiescent flux level at ∼5 GHz and, at epochs, a much larger flux density (>37 mJy), likely due to radio flares. By comparing our model results with the radio observations of λ And, we can constrain its mass-loss rate $\dot{M}$. There are two possible conclusions. (1) Assuming the quiescent radio emission originates from the stellar wind, we conclude that λ And has $\dot{M} \simeq 3 \times 10^{-9}$ M⊙ yr −1, which agrees with the evolving mass-loss rate trend for evolved solar-mass stars. (2) Alternatively, if the quiescent emission does not originate from the wind, our models can only place an upper limit on mass-loss rates, indicating that $\dot{M} \lesssim 3 \times 10^{-9}$ M⊙ yr −1.
On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow

We consider the maximum mean discrepancy ($\mathrm{MMD}$) GAN problem and propose a parametric kernelized gradient flow that mimics the min-max game in gradient regularized $\mathrm{MMD}$ GAN. We show that this flow provides a descent direction minimizing the $\mathrm{MMD}$ on a statistical manifold of probability distributions. We then derive an explicit condition which ensures that gradient descent on the parameter space of the generator in gradient regularized $\mathrm{MMD}$ GAN is globally convergent to the target distribution. Under this condition, we give non asymptotic convergence results of gradient descent in MMD GAN. Another contribution of this paper is the introduction of a dynamic formulation of a regularization of $\mathrm{MMD}$ and demonstrating that the parametric kernelized descent for $\mathrm{MMD}$ is the gradient flow of this functional with respect to the new Riemannian structure. Our obtained theoretical result allows ones to treat gradient flows for quite general functionals and thus has potential applications to other types of variational inferences on a statistical manifold beyond GANs. Finally, numerical experiments suggest that our parametric kernelized gradient flow stabilizes GAN training and guarantees convergence.
Probing Multilingual BERT for Genetic and Typological Signals

We probe the layers in multilingual BERT (mBERT) for phylogenetic and geographic language signals across 100 languages and compute language distances based on the mBERT representations. We 1) employ the language distances to infer and evaluate language trees, finding that they are close to the reference family tree in terms of quartet tree distance, 2) perform distance matrix regression analysis, finding that the language distances can be best explained by phylogenetic and worst by structural factors and 3) present a novel measure for measuring diachronic meaning stability (based on cross-lingual representation variability) which correlates significantly with published ranked lists based on linguistic approaches. Our results contribute to the nascent field of typological interpretability of cross-lingual text representations.
Extracting Chemical–Protein Interactions via Calibrated Deep Neural Network and Self-training

The extraction of interactions between chemicals and proteins from several biomedical articles is important in many fields of biomedical research such as drug development and prediction of drug side effects. Several natural language processing methods, including deep neural network (DNN) models, have been applied to address this problem. However, these methods were trained with hard-labeled data, which tend to become over-confident, leading to degradation of the model reliability. To estimate the data uncertainty and improve the reliability, “calibration” techniques have been applied to deep learning models. In this study, to extract chemical–protein interactions, we propose a DNN-based approach incorporating uncertainty information and calibration techniques. Our model first encodes the input sequence using a pre-trained language-understanding model, following which it is trained using two calibration methods: mixup training and addition of a confidence penalty loss. Finally, the model is re-trained with augmented data that are extracted using the estimated uncertainties. Our approach has achieved state-of-the-art performance with regard to the Biocreative VI ChemProt task, while preserving higher calibration abilities than those of previous approaches. Furthermore, our approach also presents the possibilities of using uncertainty estimation for performance improvement.
Belief Propagation on the random k-SAT model

Corroborating a prediction from statistical physics, we prove that the Belief Propagation message passing algorithm approximates the partition function of the random $k$-SAT model well for all clause/variable densities and all inverse temperatures for which a modest absence of long-range correlations condition is satisfied. This condition is known as "replica symmetry" in physics language. From this result we deduce that a replica symmetry breaking phase transition occurs in the random $k$-SAT model at low temperature for clause/variable densities below but close to the satisfiability threshold.
Booster: An Accelerator for Gradient Boosting Decision Trees Training and Inference

Recent breakthroughs in machine learning (ML) have sparked hardware innovation for efficient execution of the emerging ML workloads. For instance, due to recent refine-ments and high-performance implementations, well-established gradient boosting decision tree (GBT) models (e.g., XGBoost) have demonstrated their dominance in commercially-important contexts, such as table-based datasets (e.g., relational databases and spreadsheets). Unfortunately, GBT training and inference are time-consuming (e.g., several hours of training for large datasets). Despite their importance, GBTs have not been targeted for hardware acceleration as much as neural networks. We propose Booster, a novel accelerator for GBTs based on their unique characteristics. We observe that the dominant steps of GBT training and inference (accounting for 90-98% of time) involve simple, fine-grained, independent operations on small-footprint data structures (e.g., histograms and shallow trees) - i.e., GBT is on-chip memory bandwidth-bound. Unfortunately, existing multicores and GPUs do not support massively-parallel data structure accesses that are irregular and data-dependent. By employing a scalable sea-of-small-SRAMs approach and an SRAM bandwidth-preserving mapping of data record fields to the SRAMs called group-by-field mapping, Booster achieves significantly more parallelism (e.g., 3200-way parallelism) than multicores and GPUs. In addition, Booster employs a redun-dant data representation that significantly lowers the memory bandwidth demand. Our simulations reveal that Booster achieves 11.4x and 6.4x speedups for training, and 45x and 22x (21x and 11x) speedups for offline (online) inference, over an ideal 32-core multicore and an ideal GPU, respectively. Based on ASIC synthesis of FPGA-validated RTL using 45 nm technology, we estimate a Booster chip to occupy 60 mm2 of area and dissipate 23 W when operating at 1-G Hz clock speed.
Multi-View Mouse Social Behaviour Recognition With Deep Graphic Model

Home-cage social behaviour analysis of mice is an invaluable tool to assess therapeutic efficacy of neurodegenerative diseases. Despite tremendous efforts made within the research community, single-camera video recordings are mainly used for such analysis. Because of the potential to create rich descriptions for mouse social behaviors, the use of multi-view video recordings for rodent observations is increasingly receiving much attention. However, identifying social behaviours from various views is still challenging due to the lack of correspondence across data sources. To address this problem, we here propose a novel multi-view latent-attention and dynamic discriminative model that jointly learns view-specific and view-shared sub-structures, where the former captures unique dynamics of each view whilst the latter encodes the interaction between the views. Furthermore, a novel multi-view latent-attention variational autoencoder model is introduced in learning the acquired features, enabling us to learn discriminative features in each view. Experimental results on the standard CRMI13 and our multi-view Parkinson’s Disease Mouse Behaviour (PDMB) datasets demonstrate that our proposed model outperforms the other state of the arts technologies, has lower computational cost than the other graphical models and effectively deals with the imbalanced data problem.
Online Observer-Based Inverse Reinforcement Learning

In this letter, a novel approach to the output-feedback inverse reinforcement learning (IRL) problem is developed by casting the IRL problem, for linear systems with quadratic cost functions, as a state estimation problem. Two observer-based techniques for IRL are developed, including a novel observer method that re-uses previous state estimates via history stacks. Theoretical guarantees for convergence and robustness are established under appropriate excitation conditions. Simulations demonstrate the performance of the developed observers and filters under noisy and noise-free measurements.
Bursting and excitability in neuromorphic resonant tunneling diodes

The dynamics of quantum nanoelectronic resonant tunneling diodes (RTDs) as excitable neuromorphic spike generators is studied. We disclose the mechanisms by which the RTD creates excitable all-or-nothing spikes and we identify a regime of bursting in which the RTD emits a random number of closely packed spikes. The control of the latter is paramount for applications in event-activated neuromorphic sensing and computing. Finally, we discuss a regime of multi-stability in which the RTD behaves as a memory.
CoT-AMFlow: Adaptive Modulation Network with Co-Teaching Strategy for Unsupervised Optical Flow Estimation

The interpretation of ego motion and scene change is a fundamental task for mobile robots. Optical flow information can be employed to estimate motion in the surroundings. Recently, unsupervised optical flow estimation has become a research hotspot. However, unsupervised approaches are often easy to be unreliable on partially occluded or texture-less regions. To deal with this problem, we propose CoT-AMFlow in this paper, an unsupervised optical flow estimation approach. In terms of the network architecture, we develop an adaptive modulation network that employs two novel module types, flow modulation modules (FMMs) and cost volume modulation modules (CMMs), to remove outliers in challenging regions. As for the training paradigm, we adopt a co-teaching strategy, where two networks simultaneously teach each other about challenging regions to further improve accuracy. Experimental results on the MPI Sintel, KITTI Flow and Middlebury Flow benchmarks demonstrate that our CoT-AMFlow outperforms all other state-of-the-art unsupervised approaches, while still running in real time. Our project page is available at https://sites.google. com/view/cot-amflow.
Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning

Uncontrolled growth of weeds can severely affect the crop yield and quality. Unrestricted use of herbicide for weed removal alters biodiversity and cause environmental pollution. Instead, identifying weed-infested regions can aid selective chemical treatment of these regions. Advances in analyzing farm images have resulted in solutions to identify weed plants. However, a majority of these approaches are based on supervised learning methods which requires huge amount of manually annotated images. As a result, these supervised approaches are economically infeasible for the individual farmer because of the wide variety of plant species being cultivated. In this paper, we propose a deep learning-based semi-supervised approach for robust estimation of weed density and distribution across farmlands using only limited color images acquired from autonomous robots. This weed density and distribution can be useful in a site-specific weed management system for selective treatment of infected areas using autonomous robots. In this work, the foreground vegetation pixels containing crops and weeds are first identified using a Convolutional Neural Network (CNN) based unsupervised segmentation. Subsequently, the weed infected regions are identified using a fine-tuned CNN, eliminating the need for designing hand-crafted features. The approach is validated on two datasets of different crop/weed species (1) Crop Weed Field Image Dataset (CWFID), which consists of carrot plant images and the (2) Sugar Beets dataset. The proposed method is able to localize weed-infested regions a maximum recall of 0.99 and estimate weed density with a maximum accuracy of 82.13%. Hence, the proposed approach is shown to generalize to different plant species without the need for extensive labeled data.
Learning from Forecast Errors: A New Approach to Forecast Combinations

This paper studies forecast combination (as an expert system) using the precision matrix estimation of forecast errors when the latter admit the approximate factor model. This approach incorporates the facts that experts often use common sets of information and hence they tend to make common mistakes. This premise is evidenced in many empirical results. For example, the European Central Bank's Survey of Professional Forecasters on Euro-area real GDP growth demonstrates that the professional forecasters tend to jointly understate or overstate GDP growth. Motivated by this stylized fact, we develop a novel framework which exploits the factor structure of forecast errors and the sparsity in the precision matrix of the idiosyncratic components of the forecast errors. The proposed algorithm is called Factor Graphical Model (FGM). Our approach overcomes the challenge of obtaining the forecasts that contain unique information, which was shown to be necessary to achieve a "winning" forecast combination. In simulation, we demonstrate the merits of the FGM in comparison with the equal-weighted forecasts and the standard graphical methods in the literature. An empirical application to forecasting macroeconomic time series in big data environment highlights the advantage of the FGM approach in comparison with the existing methods of forecast combination.