Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review

Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor technology currently in place for ground and airborne applications and either have satellite concepts under development (e.g., HySPIRI/SBG (Surface Biology and Geology), Sentinel-8, HiTeSEM in the TIR) or are subject to satellite missions recently launched or scheduled within the next years (i.e., EnMAP and PRISMA (PRecursore IperSpettrale della Missione Applicativa, launched on March 2019) in the VNIR/SWIR, Fluorescence Explorer (FLEX) in the SIF). Identification of plant water stress or drought is of utmost importance to guarantee global water and food supply. Therefore, knowledge of crop water status over large farmland areas bears large potential for optimizing agricultural water use. As plant responses to water stress are numerous and complex, their physiological consequences affect the electromagnetic signal in different spectral domains. This review paper summarizes the importance of water stress-related applications and the plant responses to water stress, followed by a concise review of water-stress detection through remote sensing, focusing on TIR without neglecting the comparison to other spectral domains (i.e., VNIR/SWIR and SIF) and multi-sensor approaches. Current and planned sensors at ground, airborne, and satellite level for the TIR as well as a selection of commonly used indices and approaches for water-stress detection using the main multi-/hyperspectral remote sensing imaging techniques are reviewed. Several important challenges are discussed that occur when using spectral emissivity, temperature-based indices, and physically-based approaches for water-stress detection in the TIR spectral domain. Furthermore, challenges with data processing and the perspectives for future satellite missions in the TIR are critically examined. In conclusion, information from multi-/hyperspectral TIR together with those from VNIR/SWIR and SIF sensors within a multi-sensor approach can provide profound insights to actual plant (water) status and the rationale of physiological and biochemical changes. Synergistic sensor use will open new avenues for scientists to study plant functioning and the response to environmental stress in a wide range of ecosystems.
MuST-C: a Multilingual Speech Translation Corpus

Current research on spoken language translation (SLT) has to confront with the scarcity of sizeable and publicly available training corpora. This problem hinders the adoption of neural end-to-end approaches, which represent the state of the art in the two parent tasks of SLT: automatic speech recognition and machine translation. To fill this gap, we created MuST-C, a multilingual speech translation corpus whose size and quality will facilitate the training of end-to-end systems for SLT from English into 8 languages. For each target language, MuST-C comprises at least 385 hours of audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations. Together with a description of the corpus creation methodology (scalable to add new data and cover new languages), we provide an empirical verification of its quality and SLT results computed with a state-of-the-art approach on each language direction.
Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data

Cropland maps are useful for the management of agricultural fields and the estimation of harvest yield. Some local governments have documented field properties, including crop type and location, based on site investigations. This process, which is generally done manually, is labor-intensive, and remote-sensing techniques can be used as alternatives. In this study, eight crop types (beans, beetroot, grass, maize, potatoes, squash, winter wheat, and yams) were identified using gamma naught values and polarimetric parameters calculated from TerraSAR-X (or TanDEM-X) dual-polarimetric (HH/VV) data. Three indices (difference (D-type), simple ratio (SR), and normalized difference (ND)) were calculated using gamma naught values and m-chi decomposition parameters and were evaluated in terms of crop classification. We also evaluated the classification accuracy of four widely used machine-learning algorithms (kernel-based extreme learning machine, support vector machine, multilayer feedforward neural network (FNN), and random forest) and two multiple-kernel methods (multiple kernel extreme learning machine (MKELM) and multiple kernel learning (MKL)). MKL performed best, achieving an overall accuracy of 92.1%, and proved useful for the identification of crops with small sample sizes. The difference (raw or normalized) between double-bounce scattering and odd-bounce scattering helped to improve the identification of squash and yams fields.
Parallel Streaming Implementation of Online Time Series Correlation Discovery on Sliding Windows with Regression Capabilities

This paper addresses the problem of continuously finding highly correlated pairs of time series over the most recent time window and possibly use the discovered correlations to select features for training a regression model for prediction. The implementation builds upon the ParCorr parallel method for online correlation discovery and is designed to run continuously on top of the UPM-CEP data streaming engine through efficient streaming operators.
Transaction cost analytics for corporate bonds

Electronic platforms have been increasingly popular for executing large corporate bond orders by asset managers, who in turn have to assess the quality of their executions via Transaction Cost Analysis (TCA). One of the challenges in TCA is to build a realistic benchmark for the expected transaction cost and to characterize the price impact of each individual trade with given bond characteristics and market conditions. Taking the viewpoint of retail investors, this paper presents an analytical methodology for TCA of corporate bond trading. Our analysis is based on the TRACE Enhanced dataset; and starts with estimating the initiator of a bond transaction, followed by estimating the bid-ask spread and the mid-price dynamics. With these estimations, the first part of our study is to identify key features for corporate bonds and to compute the expected average trading cost. This part is on the time scale of weekly transactions, and is by applying and comparing several regularized regression models. The second part of our study is using the estimated mid-price dynamics to investigate the amplitude of its price impact and the decay pattern of individual bond transaction. This part is on the time scale of each transaction of liquid corporate bonds, and is by applying a transient impact model to estimate the price impact kernel using a non-parametric method. Our benchmark model allows for identifying abnormal transactions and for enhancing counter-party selections. A key discovery of our study is the price impact asymmetry between customer-buy orders and consumer-sell orders.
Near Neighbor: Who is the Fairest of Them All?

$\newcommand{\ball}{\mathbb{B}}\newcommand{\dsQ}{\mathcal{Q}}\newcommand{\dsS}{\mathcal{S}}$In this work we study a fair variant of the near neighbor problem. Namely, given a set of $n$ points $P$ and a parameter $r$, the goal is to preprocess the points, such that given a query point $q$, any point in the $r$-neighborhood of the query, i.e., $\ball(q,r)$, have the same probability of being reported as the near neighbor. We show that LSH based algorithms can be made fair, without a significant loss in efficiency. Specifically, we show an algorithm that reports a point in the $r$-neighborhood of a query $q$ with almost uniform probability. The query time is proportional to $O\bigl( \mathrm{dns}(q.r) \dsQ(n,c) \bigr)$, and its space is $O(\dsS(n,c))$, where $\dsQ(n,c)$ and $\dsS(n,c)$ are the query time and space of an LSH algorithm for $c$-approximate near neighbor, and $\mathrm{dns}(q,r)$ is a function of the local density around $q$. Our approach works more generally for sampling uniformly from a sub-collection of sets of a given collection and can be used in a few other applications. Finally, we run experiments to show performance of our approach on real data.
Pulse Phase Variation of the Cyclotron Line in HMXB 4U 1907+09 with AstroSat LAXPC

We present timing and spectral analysis of data from an observation of the high mass X-ray binary pulsar 4U 1907+09 with the Large Area X-ray Proportional Counter instrument onboard AstroSat. The light curve consisted of a flare at the beginning of the observation, followed by persistent emission. The pulsar continues to spin down, and the pulse profile is found to be double-peaked up to 16 keV with the peaks separated by a phase of ∼0.45. Significant energy dependence of the pulse profile is seen with diminishing amplitude of the secondary peak above 16 keV, and increasing amplitude of the main peak up to 40 keV with a sharp decline after that. We confirm earlier detections of the cyclotron resonance scattering feature (CRSF) in 4U 1907+09 at ∼18.5 ± 0.2 keV in the phase-averaged spectrum with a high detection significance. An intensity resolved spectral analysis of the initial flare in the light curve shows that the CRSF parameters do not change with a change in luminosity by a factor of 2.6. We also performed pulse phase-resolved spectral analysis with 10 independent phase bins. The energy and the strength of the cyclotron line show pulse phase dependence that is in agreement with previous measurements. Two features from the current observation (different energy dependence of the two pulse peaks and a strong CRSF only around the secondary peak) both indicate a deviation from a dipole geometry of the magnetic field of the neutron star, or complex beaming pattern from the two poles.
GCCNet: Global Context Constraint Network for Semantic Segmentation

The state-of-the-art semantic segmentation tasks can be achieved by the variants of the fully convolutional neural networks (FCNs), which consist of the feature encoding and the deconvolution. However, they struggle with missing or inconsistent labels. To alleviate these problems, we utilize the image-level multi-class encoding as the global contextual information. By incorporating object classification into the objective function, we can reduce incorrect pixel-level segmentation. Experimental results show that our algorithm can achieve better performance than other methods on the same level training data volume.
Graph Embedding-Based Wireless Link Scheduling With Few Training Samples

Link scheduling in device-to-device (D2D) networks is usually formulated as a non-convex combinatorial problem, which is generally NP-hard and difficult to get the optimal solution. Traditional methods to solve this problem are mainly based on mathematical optimization techniques, where accurate channel state information (CSI), usually obtained through channel estimation and feedback, is needed. To overcome the high computational complexity of the traditional methods and eliminate the costly channel estimation stage, machine leaning (ML) has been introduced recently to address the wireless link scheduling problems. In this article, we propose a novel graph embedding based method for link scheduling in D2D networks. We first construct a fully-connected directed graph for the D2D network, where each D2D pair is a node while interference links among D2D pairs are the edges. Then we compute a low-dimensional feature vector for each node in the graph. The graph embedding process is based on the distances of both communication and interference links, therefore without requiring the accurate CSI. By utilizing a multi-layer classifier, a scheduling strategy can be learned in a supervised manner based on the graph embedding results for each node. We also propose an unsupervised manner to train the graph embedding based method to further reinforce the scalability and develop a K-nearest neighbor graph representation method to reduce the computational complexity. Extensive simulation demonstrates that the proposed method is near-optimal compared with the existing state-of-art methods but is with only hundreds of training network layouts. It is also competitive in terms of scalability and generalizability to more complicated scenarios.
Shrinking Japanese Morphological Analyzers With Neural Networks and Semi-supervised Learning

For languages without natural word boundaries, like Japanese and Chinese, word segmentation is a prerequisite for downstream analysis. For Japanese, segmentation is often done jointly with part of speech tagging, and this process is usually referred to as morphological analysis. Morphological analyzers are trained on data hand-annotated with segmentation boundaries and part of speech tags. A segmentation dictionary or character n-gram information is also provided as additional inputs to the model. Incorporating this extra information makes models large. Modern neural morphological analyzers can consume gigabytes of memory. We propose a compact alternative to these cumbersome approaches which do not rely on any externally provided n-gram or word representations. The model uses only unigram character embeddings, encodes them using either stacked bi-LSTM or a self-attention network, and independently infers both segmentation and part of speech information. The model is trained in an end-to-end and semi-supervised fashion, on labels produced by a state-of-the-art analyzer. We demonstrate that the proposed technique rivals performance of a previous dictionary-based state-of-the-art approach and can even surpass it when training with the combination of human-annotated and automatically-annotated data. Our model itself is significantly smaller than the dictionary-based one: it uses less than 15 megabytes of space.
CellTree: A New Paradigm for Distributed Data Repositories

We present CellTree, a new architecture for distributed data repositories. The repository allows data to be stored in largely independent, and highly programmable cells, which are “assimilated” into a tree structure. The data in the cells are allowed to change over time, subject to each cell’s own policies; a cell’s policies also govern how the policies themselves can evolve. A design goal of the architecture is to let a CellTree evolve organically over time, and adapt itself to multiple applications. Different parts of the tree may be maintained by different sets of parties interested in the respective parts, and the core mechanisms used for maintaining the tree can also vary across the tree and over time. We present provable guarantees of liveness, correctness and consistency (the last one being a generalization of the typical blockchain guarantee of “persistence,” when data is dynamic), when the CellTree architecture is instantiated using a simple set of modules. These properties can be guaranteed for individual cells that satisfy requisite trust assumptions, even if these trust assumptions do not hold for other cells in the tree. We also discuss several features of a CellTree that can be exploited by applications. We leave it for future work to develop full-fledged applications on top of this powerful architecture.
A cryptographic approach to black box adversarial machine learning

We propose an ensemble technique for converting any classifier into a computationally secure classifier. We define a simpler security problem for random binary classifiers and prove a reduction from this model to the security of the overall ensemble classifier. We provide experimental evidence of the security of our random binary classifiers, as well as empirical results of the adversarial accuracy of the overall ensemble to black-box attacks. Our construction crucially leverages hidden randomness in the multiclass-to-binary reduction.
Opacity distribution functions for stellar spectra synthesis

Context. Stellar spectra synthesis is essential for the characterization of potential planetary hosts. In addition, comprehensive stellar variability calculations with fast radiative transfer are needed to disentangle planetary transits from stellar magnetically driven variability. The planet-hunting space telescopes, such as CoRoT, Kepler, and TESS, bring vast quantities of data, rekindling the interest in fast calculations of the radiative transfer. Aims. We revisit the opacity distribution functions (ODF) approach routinely applied to speed up stellar spectral synthesis. To achieve a considerable speedup relative to the state of the art, we further optimize the approach and search for the best ODF configuration. Furthermore, we generalize the ODF approach for fast calculations of flux in various filters often used in stellar observations. Methods. In a parameter-sweep fashion, we generated ODF in the spectral range from UV to IR with different setups. The most accurate ODF configuration for each spectral interval was determined. We adapted the wavelength grid based on the transmission curve for calculations of the radiative fluxes through filters before performing the normal ODF procedure. Results. Our optimum ODF configuration allows for a three-fold speedup, compared to the previously used ODF configurations. The ODF generalization to calculate fluxes through filters results in a speedup of more than two orders of magnitude.
Prediction of Workplace Injuries

Workplace injuries result in substantial human and financial losses. As reported by the International Labour Organization (ILO), there are more than 374 million work-related injuries reported every year. In this study, we investigate the problem of injury risk prediction and prevention in a work environment. While injuries represent a significant number across all organizations, they are rare events within a single organization. Hence, collecting a sufficiently large dataset from a single organization is extremely difficult. In addition, the collected datasets are often highly imbalanced which increases the problem difficulty. Finally, risk predictions need to provide additional context for injuries to be prevented. We propose and evaluate the following for a complete solution: 1) several ensemble-based resampling methods to address the class imbalance issues, 2) a novel transfer learning approach to transfer the knowledge across organizations, and 3) various techniques to uncover the association and causal effect of different variables on injury risk, while controlling for relevant confounding factors.
Unifying Data and Replica Placement for Data-intensive Services in Geographically Distributed Clouds

The increased reliance of data management applications on cloud computing technologies has rendered research in identifying solutions to the data placement problem to be of paramount importance. The objective of the classical data placement problem is to optimally partition, while also allowing for replication, the set of data-items into distributed data centers to minimize the overall network communication cost. Despite significant advancement in data placement research, replica placement has seldom been studied in unison with data placement. More specifically, most of the existing solutions employ a two-phase approach: 1) data placement, followed by 2) replication. Replication should however be seen as an integral part of data placement, and should be studied as a joint optimization problem with the latter. In this paper, we propose a unified paradigm of data placement, called CPR, which combines data placement and replication of data-intensive services into geographically distributed clouds as a joint optimization problem. Underneath CPR, lies an overlapping correlation clustering algorithm capable of assigning a data-item to multiple data centers, thereby enabling us to jointly solve data placement and replication. Experiments on a real-world trace-based online social network dataset show that CPR is effective and scalable. Empirically, it is  35% better in efficacy on the evaluated metrics, while being up to 8 times faster in execution time when compared to state-of-the-art techniques.
Deep Compositional Spatial Models

Abstract Spatial processes with nonstationary and anisotropic covariance structure are often used when modeling, analyzing, and predicting complex environmental phenomena. Such processes may often be expressed as ones that have stationary and isotropic covariance structure on a warped spatial domain. However, the warping function is generally difficult to fit and not constrained to be injective, often resulting in “space-folding. ” Here, we propose modeling an injective warping function through a composition of multiple elemental injective functions in a deep-learning framework. We consider two cases; first, when these functions are known up to some weights that need to be estimated, and, second, when the weights in each layer are random. Inspired by recent methodological and technological advances in deep learning and deep Gaussian processes, we employ approximate Bayesian methods to make inference with these models using graphics processing units. Through simulation studies in one and two dimensions we show that the deep compositional spatial models are quick to fit, and are able to provide better predictions and uncertainty quantification than other deep stochastic models of similar complexity. We also show their remarkable capacity to model nonstationary, anisotropic spatial data using radiances from the MODIS instrument aboard the Aqua satellite.
Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory

Recently, there has been a great deal of research attention on understanding the convergence behavior of first-order methods. One line of this research focuses on analyzing the convergence behavior of first-order methods using tools from continuous dynamical systems such as ordinary differential equation and different inclusions. These research results shed lights on better understanding first-order methods from a non-optimization point of view. The alternating direction method of multipliers (ADMM) is a widely used firstorder method for solving optimization problems arising from machine learning and statistics, and it is important to investigate its behavior using these new techniques from dynamical systems. Existing works along this line have been mainly focusing on problems with smooth objective functions, which excludes many important applications that are traditionally solved by ADMM variants. In this paper, we analyze some well-known and widely used ADMM variants for nonsmooth optimization problems using the tools of differential inclusions. In particular, we analyze the convergence behavior of linearized ADMM and gradient-based ADMM for nonsmooth problems and show their connections with dynamical systems. We anticipate that these results will provide new insights on understanding ADMM for solving nonsmooth problems.