Miracle-dz/UniFAR-FTUs huggingface dataset:
OPERATION OF DFIG UNDER UNBALANCED AND DISTORTED SYSTEM

The interest towards renewable energy resources like wind and solar are increasing over a decade due to the concern over the nature, economic and long run operation and many such. Among all these type of sources, wind energy based renewable energy resource with doubly fed induction generator (DFIG) seems to be more promising due to low converter cost, better operation during distorted, unbalanced grid, better fault ride through and more over effective control of real and reactive power. However, such DFIG system faces challenges because of unbalanced voltage operation because load or grid disturbances or due to transmission line perturbations. This paper gives a rough idea of present literature survey about fault ride through strategies, power control and unbalanced and distorted DFIG operation. The paper also gives brief picture on the challenges of DFIG grid connected system and future scope for extending the research in these areas.
Morphological variation of male A. arguta plants affects their flowering potential and pollen efficiency

Actinidia arguta (Siebold et Zucc.) Planch. ex Miq. is functionally a cryptic dioecious plant and successful pollination is crucial for achieving high quality fruit. The extent and bases of morphological variability in female genotypes have been well studied, but here we focus on the males. Mature plants of seven male A. arguta genotypes were studied in 2016 and 2017 during which a suite of phenological and morphological features was measured on ten randomly chosen one-year-old canes on each plant. This analysis was complemented by two other, specialized measures potentially related to pollination efficiency, namely pollen quality, and quantity. The time of bud break was similar each year across all genotypes and the pollen quality was uniformly very high (viability – 95.0–99.9% and germination – 89.0–97.3%). However, the ten genotypes exhibited high variability in both the number of flowers per inflorescence and pollen quantity, indicating marked differences in their flowering potential and efficiency as pollinizers. The male kiwiberry indicators developed in this study – flowering potential and pollen efficiency, appear to be effective tools for the comparative evaluation of male A. arguta genotypes.
Retrieval of microphysical dust particle properties from SALTRACE lidar observations: Case studies

Abstract. Saharan dust is a major natural atmospheric aerosol component with significant impact on the Earth radiation budget. In this work we determine the microphysical properties of dust particles after a long-range transport over the Atlantic Ocean, using input from three depolarization channels of a multi-wavelength polarization Raman lidar. The measurements were performed at Barbados in the framework of the Saharan Aerosol Long-Range Transport and Aerosol–Cloud-Interaction Experiment (SALTRACE) in the summers of 2013 and 2014. The microphysical retrievals are performed with the software tool SphInX (Spheroidal Inversion Experiments) which uses regularization for the inversion process and a new two-dimensional (2-D) extension of the Mie model approximating dust with spheroids. The method allows us to simultaneously retrieve shape- and size-dependent particle distributions. Because dust particles are mostly non-spherical this software tool fills the gap in estimating the non-spherical particle fraction. Two cases measured on 10 July 2013 and 20 June 2014 are discussed. 2-D radius-bimodal shape-size distribution are retrieved. The ratio of spherical-to-non-spherical contributions to the particle number concentration was found to be about 3/7. A volume-weighted effective aspect ratio of 1.1 was obtained, indicating slightly prolate particles. The total effective radius for the two cases in the preselected radius range from 0.01–2.2 μm was found to be, on average, 0.75 μm. The stronger dust event (10 July 2013) showed about 24 % higher values for the total surface-area and volume concentration. Finally, we compare our results with the ones from the polarization lidar-photometer networking (POLIPHON) method and ground-based photometers as well as with airborne in situ particle counters. Considering all differences in these independent approaches, we find a qualitatively good agreement between the different results and a consistent description of the dust cases. Such an extensive comparison is a novel and fruitful exercise and corroborates that the mathematical retrieval based on Raman lidar data of particle backscattering, extinction, and depolarization is a powerful tool even in the case of dust particles.
Monitoring Depression in Bipolar Disorder using Circadian Measures from Smartphone Accelerometers

Current management of bipolar disorder relies on self-reported questionnaires and interviews with clinicians. The development of objective measures of deteriorating mood may also allow for early interventions to take place to avoid transitions into depressive states. The objective of this study was to use acceleration data recorded from smartphones to predict levels of depression in a population of participants diagnosed with bipolar disorder. Data were collected from 52 participants, with a mean of 37 weeks of acceleration data with a corresponding depression score recorded per participant. Time varying hidden Markov models were used to extract weekly features of activity, sleep and circadian rhythms. Personalised regression achieved mean absolute errors of 1.00(0.57) from a possible scale of 0 to 27 and was able to classify depression with an accuracy of 0.84(0.16). The results demonstrate features derived from smartphone accelerometers are able to provide objective markers of depression. Low barriers for uptake exist due to the widespread use of smartphones, with personalised models able to account for differences in the behaviour of individuals and provide accurate predictions of depression.
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition

Lying at the core of human intelligence, relational thinking is characterized by initially relying on innumerable unconscious percepts pertaining to relations between new sensory signals and prior knowledge, consequently becoming a recognizable concept or object through coupling and transformation of these percepts. Such mental processes are difficult to model in real-world problems such as in conversational automatic speech recognition (ASR), as the percepts (if they are modelled as graphs indicating relationships among utterances) are supposed to be innumerable and not directly observable. In this paper, we present a Bayesian nonparametric deep learning method called deep graph random process (DGP) that can generate an infinite number of probabilistic graphs representing percepts. We further provide a closed-form solution for coupling and transformation of these percept graphs for acoustic modeling. Our approach is able to successfully infer relations among utterances without using any relational data during training. Experimental evaluations on ASR tasks including CHiME-2 and CHiME-5 demonstrate the effectiveness and benefits of our method.
Electronically Steered Nyquist Metasurface Antenna

Mobile devices, climate science, and autonomous vehicles all require advanced microwave antennas for imaging, radar, and wireless communications. The cost, size, and power consumption of existing technology, however, has hindered the ubiquity of electronically steered systems. Here, we propose a metasurface antenna design paradigm that enables electronic beamsteering from a passive lightweight circuit board with varactor-tuned elements. Distinct from previous metasurfaces (which require dense element spacing), the proposed design uses Nyquist spatial sampling of half a wavelength. We detail the design of this Nyquist metasurface antenna and experimentally validate its ability to electronically steer in two directions. Nyquist metasurface antennas can realize high performance without costly and power hungry phase shifters, making them a compelling technology for future antenna hardware.
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification

Extreme multi-label text classification (XMTC) is a task for tagging a given text with the most relevant labels from an extremely large label set. We propose a novel deep learning method called APLC-XLNet. Our approach fine-tunes the recently released generalized autoregressive pretrained model (XLNet) to learn a dense representation for the input text. We propose Adaptive Probabilistic Label Clusters (APLC) to approximate the cross entropy loss by exploiting the unbalanced label distribution to form clusters that explicitly reduce the computational time. Our experiments, carried out on five benchmark datasets, show that our approach has achieved new state-of-the-art results on four benchmark datasets. Our source code is available publicly at this https URL.
Overlaying Spaces and Practical Applicability of Complex Geometries

Recently, non-Euclidean spaces became popular for embedding structured data. Following hyperbolic and spherical spaces, more general product spaces have been proposed. However, searching for the best configuration of a product space is a resource-intensive procedure, which reduces the practical applicability of the idea. We introduce a novel concept of overlaying spaces that does not have the problem of configuration search and outperforms the competitors in structured data embedding tasks, when the aim is to preserve all distances. On the other hand, for local loss functions (e.g., for ranking losses), the dot-product similarity, which is often overlooked in graph embedding literature since it cannot be converted to a metric, outperforms all metric spaces. We discuss advantages of the dot product over proper metric spaces.
Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media

Social networks enable people to interact with one another by sharing information, sending messages, making friends, and having discussions, which generates massive amounts of data every day, popularly called as the user-generated content. This data is present in various forms such as images, text, videos, links, and others and reflects user behaviours including their mental states. It is challenging yet promising to automatically detect mental health problems from such data which is short, sparse and sometimes poorly phrased. However, there are efforts to automatically learn patterns using computational models on such user-generated content. While many previous works have largely studied the problem on a small-scale by assuming uni-modality of data which may not give us faithful results, we propose a novel scalable hybrid model that combines Bidirectional Gated Recurrent Units (BiGRUs) and Convolutional Neural Networks to detect depressed users on social media such as Twitter-based on multi-modal features. Specifically, we encode words in user posts using pre-trained word embeddings and BiGRUs to capture latent behavioural patterns, long-term dependencies, and correlation across the modalities, including semantic sequence features from the user timelines (posts). The CNN model then helps learn useful features. Our experiments show that our model outperforms several popular and strong baseline methods, demonstrating the effectiveness of combining deep learning with multi-modal features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media.
Massive Galaxies Impede Massive Outflows

A set of 66 3D hydrodynamical simulations explores how galactic stellar mass affects three-phase, starburst-driven outflows. Simulated velocities are compared to two basic analytic models: with and without a gravitational potential. For stellar mass <1010M⊙, simulated velocities match those of both analytical models and are unaffected by the potential; above they reduce significantly as expected from the analytic model with gravity. Gravity also affects total outflow mass and each of the three phases differently. Outflow masses in the hot, warm, and cold phases each scale with stellar mass as −0.25, −0.97, and −1.70, respectively. Thus, the commonly used Chevalier & Clegg analytic model should be modified to include gravity when applied to higher-mass galaxies. In particular, using M82 as the canonical galaxy to interpret hydrodynamical simulations of starburst-driven outflows from higher-mass galaxies will underestimate the retarding effect of gravity. Using the analytic model of Johnson & Axford with realistic thermalization efficiency and mass loading, I find that only galaxy masses ≲1011.5M⊙ can outflow.
Photo-excitation production of medically interesting isomers using high-intensity {\gamma}-ray source

Photon-induced nuclear excitation (i.e. photo-excitation) can be used for production of nuclear isomers, which have potential applications in astrophysics, energy storing, and medical diagnosis and treatment. This paper presents a feasibility study on production of four nuclear isomers ({99m}^Tc, {103m}^Rh and {113m, 115m}^In) using high-intensity {\gamma}-ray source based on laser-electron Compton scattering (LCS), for use in the medical diagnosis and treatment. The decay properties and the medical applications of these nuclear isomers were reviewed. The cross-section curves, simulated yields and activity of product of each photo-excitation process were calculated. The cutoff energy of LCS {\gamma}-ray beam is optimized by adjusting the electron energy in order to maximize the yields as well as the activities of photo-excitation products. It is found that the achievable activity of above-mentioned isomers can exceed 10 mCi for 6-hour target irradiation at an intensity of the order of 10^{13} {\gamma}/s. Such magnitude of activity satisfies the dose requirement of medical diagnosis. Our simulation results suggest the prospect of producing medically interesting isomers with photo-excitation using the state-of-art LCS {\gamma}-ray beam facility.
Genetic Diversity in Egyptian Tilapia Species Using PCR-RFLP of D-loop Mitochondrial DNA Gene.

Genetic differentiation within and among three populations of Egyptian tilapia species (Oreochromis niloticus, Oreochromis aureus and Tilapia zillii) were examined using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) of D-loop mitochondrial DNA gene. A total of 16 phenotypes corresponding to 11 different haplotypes were defined. Analysis of molecular variance (AMOVA) showed a significantly different genetic variation among tilapia species (ΦFst=0.2966, P=0.00). Gene flow (Nm) was high and ranged from 0.3046 to 8.5448. The value of net nucleotide divergence (dA) among species varied from 5.5000 to 8.8000%. A UPGMA tree based on the distance matrix of net interpopulation nucleotide divergence (dA) and haplotype of mtDNA D-loop divided tilapia species into two clades. Oreochromis aureus and T. zillii Qarun belonged to the first cluster whereas O. niloticus and T. zillii Manzalah aggregated to the second cluster. The neutrality and mismatch distribution tests explained that tilapia populations do not undergone population expansion. A high genetic diversity and significant genetic differentiation were observed between tilapia
Strong-coupling Bose polarons in one dimension: Condensate deformation and modified Bogoliubov phonons

We discuss the interaction of a quantum impurity with a one-dimensional degenerate Bose gas forming a Bose-polaron. In three spatial dimensions the quasiparticle is typically well described by the extended Frohlich model, in full analogy with the solid-state counterpart. This description, which assumes an undepleted condensate, fails however in 1D, where the backaction of the impurity on the condensate leads to a self-bound mean-field polaron for arbitrarily weak impurity-boson interactions. We present a model that takes into account this backaction and describes the impurity-condensate interaction as coupling to phonon-like excitations of a deformed condensate. A comparison of polaron energies and masses to diffusion quantum Monte-Carlo simulations shows very good agreement already on the level of analytical mean-field solutions and is further improved when taking into account quantum fluctuations.
Improving Chinese Segmentation-free Word Embedding With Unsupervised Association Measure

Recent work on segmentation-free word embedding(sembei) developed a new pipeline of word embedding for unsegmentated language while avoiding segmentation as a preprocessing step. However, too many noisy n-grams existing in the embedding vocabulary that do not have strong association strength between characters would limit the quality of learned word embedding. To deal with this problem, a new version of segmentation-free word embedding model is proposed by collecting n-grams vocabulary via a novel unsupervised association measure called pointwise association with times information(PATI). Comparing with the commonly used n-gram filtering method like frequency used in sembei and pointwise mutual information(PMI), the proposed method leverages more latent information from the corpus and thus is able to collect more valid n-grams that have stronger cohesion as embedding targets in unsegmented language data, such as Chinese texts. Further experiments on Chinese SNS data show that the proposed model improves performance of word embedding in downstream tasks.
Multi-Armed Bandit-Based Client Scheduling for Federated Learning

By exploiting the computing power and local data of distributed clients, federated learning (FL) features ubiquitous properties such as reduction of communication overhead and preserving data privacy. In each communication round of FL, the clients update local models based on their own data and upload their local updates via wireless channels. However, latency caused by hundreds to thousands of communication rounds remains a bottleneck in FL. To minimize the training latency, this work provides a multi-armed bandit-based framework for online client scheduling (CS) in FL without knowing wireless channel state information and statistical characteristics of clients. Firstly, we propose a CS algorithm based on the upper confidence bound policy (CS-UCB) for ideal scenarios where local datasets of clients are independent and identically distributed (i.i.d.) and balanced. An upper bound of the expected performance regret of the proposed CS-UCB algorithm is provided, which indicates that the regret grows logarithmically over communication rounds. Then, to address non-ideal scenarios with non-i.i. d. and unbalanced properties of local datasets and varying availability of clients, we further propose a CS algorithm based on the UCB policy and virtual queue technique (CS-UCB-Q). An upper bound is also derived, which shows that the expected performance regret of the proposed CS-UCB-Q algorithm can have a sub-linear growth over communication rounds under certain conditions. Besides, the convergence performance of FL training is also analyzed. Finally, simulation results validate the efficiency of the proposed algorithms.
Meta-SAC: Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient

Exploration-exploitation dilemma has long been a crucial issue in reinforcement learning. In this paper, we propose a new approach to automatically balance between these two. Our method is built upon the Soft Actor-Critic (SAC) algorithm, which uses an "entropy temperature" that balances the original task reward and the policy entropy, and hence controls the trade-off between exploitation and exploration. It is empirically shown that SAC is very sensitive to this hyperparameter, and the follow-up work (SAC-v2), which uses constrained optimization for automatic adjustment, has some limitations. The core of our method, namely Meta-SAC, is to use metagradient along with a novel meta objective to automatically tune the entropy temperature in SAC. We show that Meta-SAC achieves promising performances on several of the Mujoco benchmarking tasks, and outperforms SAC-v2 over 10% in one of the most challenging tasks, humanoid-v2.
Potential applications of remotely sensed vegetation greenness to habitat analysis and the conservation of dispersive fauna

Remotely sensed (satellite) time-series estimates of GPP (gross primary productivity) provide potentially useful information about vegetation-based habitat resources. These data are now available for the entire continent at a range of scales commensurate with those of dispersive animals, particularly bird species with large scale and irregular patterns of movement. The physical basis by which GPP can be estimated from satellite sensed vegetation greenness (NDVI) is explained, and previous habitat applications of NDVI reviewed. The results of GPP time-series analyses are presented based on a 10-year continental AVHRR data set for Australia and a five-year MODIS sequence for a portion of the Channel Country. The relationship between GPP and other trophic levels is discussed, along with possible applications to conservation biology.
Model Distillation for Revenue Optimization: Interpretable Personalized Pricing

Data-driven pricing strategies are becoming increasingly common, where customers are offered a personalized price based on features that are predictive of their valuation of a product. It is desirable to have this pricing policy be simple and interpretable, so it can be verified, checked for fairness, and easily implemented. However, efforts to incorporate machine learning into a pricing framework often lead to complex pricing policies which are not interpretable, resulting in mixed results in practice. We present a customized, prescriptive tree-based algorithm that distills knowledge from a complex black box machine learning algorithm, segments customers with similar valuations and prescribes prices in such a way that maximizes revenue while maintaining interpretability. We quantify the regret of a resulting policy and demonstrate its efficacy in applications with both synthetic and real-world datasets.
Online NEAT for Credit Evaluation - a Dynamic Problem with Sequential Data

In this paper, we describe application of Neuroevolution to a P2P lending problem in which a credit evaluation model is updated based on streaming data. We apply the algorithm Neuroevolution of Augmenting Topologies (NEAT) which has not been widely applied generally in the credit evaluation domain. In addition to comparing the methodology with other widely applied machine learning techniques, we develop and evaluate several enhancements to the algorithm which make it suitable for the particular aspects of online learning that are relevant in the problem. These include handling unbalanced streaming data, high computation costs, and maintaining model similarity over time, that is training the stochastic learning algorithm with new data but minimizing model change except where there is a clear benefit for model performance
Black-box Adversarial Example Generation with Normalizing Flows

Deep neural network classifiers suffer from adversarial vulnerability: well-crafted, unnoticeable changes to the input data can affect the classifier decision. In this regard, the study of powerful adversarial attacks can help shed light on sources of this malicious behavior. In this paper, we propose a novel black-box adversarial attack using normalizing flows. We show how an adversary can be found by searching over a pre-trained flow-based model base distribution. This way, we can generate adversaries that resemble the original data closely as the perturbations are in the shape of the data. We then demonstrate the competitive performance of the proposed approach against well-known black-box adversarial attack methods.