Miracle-dz/UniFAR-FTUs huggingface dataset:
Landscape Based Agricultural Water Demand Modeling—A Tool for Water Management Decision Making in British Columbia, Canada

Regional water managers face challenges managing water demand and supply in response to climate change. In British Columbia (BC), less than 5% of the total land surface is suitable for cultivation and consequently, urban development and agriculture co-exist on lower elevation sites and compete for water. Surface and ground water supply all uses, affecting in-stream habitats and aquifer levels. To assess water needs, we used a GIS-based water demand model for agricultural water use, with layers for detailed land use, soils, a digital elevation model, sub-basins, aquifers, and socio-political jurisdictional boundaries. The model was driven by gridded daily minimum and maximum temperatures and precipitation at a 500m spatial resolution using historical data (1961-2010) and downscaled climate scenarios (1961-2100) derived from five CMIP5 climate models under two greenhouse gas concentration scenarios (RCP4.5 and RCP8.5). Two case studies were examined: 1. Changes in water use were determined for scenarios of climate change and expanded cropping within the BC Agricultural Land Reserve for 17 agricultural regions. Potential increases in irrigation water demand (IWD) in response to climate change ranged from 21-58% and 30-114% under low and high emissions scenarios respectively. Land use change scenarios resulted in very large potential increases in water demand, up to 2400%. Output from this work forms the basis for a web-based agricultural water licence calculator. 2. Effects of crop production systems on IWD were examined in the Okanagan region. Combinations of pasture and forage crops with inefficient irrigation systems were most vulnerable to drought as indicated by two indices: relative vulnerability index (IWD/ETo) and allocation vulnerability index (IWD/Maximum annual water allocation). In drought years, up to 70% of the irrigated area was vulnerable. A comparison of detailed land use surveys made in 2006 and 2014 indicated a large shift to highly efficient irrigation systems in the horticultural sector from 38% to 68% of the irrigated acreage. Similar shifts for other agricultural sectors may require financial support. On-going development of regional drought management will require collaborative decision making by water suppliers and users.
Success in Achieving the Targets of the 20-Year Infancy-Onset Dietary Intervention: Association With Insulin Sensitivity and Serum Lipids

OBJECTIVE We examined whether success in achieving the key targets of an infancy-onset 20-year dietary intervention associated with insulin sensitivity and serum lipids from early childhood to young adulthood. RESEARCH DESIGN AND METHODS The sample comprised 941 children participating in the prospective, randomized Special Turku Coronary Risk Factor Intervention Project (STRIP). Dietary counseling was given biannually based on the Nordic Nutrition Recommendations with the main aim to improve the quality of dietary fat in children’s diets and the secondary aim to promote intake of vegetables, fruits, and whole-grain products. Food records and serum lipid profile were studied annually from 1 to 20 years of age, and HOMA of insulin resistance (HOMA-IR) was assessed between 7 and 20 years of age. Meeting the intervention targets for quality of dietary fat was defined as the ratio of saturated fatty acids (SAFA) to monounsaturated and polyunsaturated fatty acids (MUFA + PUFA) <1:2 and intake of SAFA <10% of total energy intake (E%). Meeting the target for intake of whole-grain products, fruits, and vegetables was indicated by a fiber intake ≥3 g/MJ. RESULTS Participants in the intervention group had a higher probability of meeting the targets of SAFA/(PUFA + MUFA) <1:2 (risk ratio [RR] 3.91 [95% CI 3.33–4.61]), intake of SAFA <10 E% (RR 3.33 [95% CI 2.99–3.96]), and intake of fiber >3 g/MJ (RR 1.37 [95% CI 1.04–1.80]). Participants who achieved more targets had lower HOMA-IR, lower concentrations of fasting serum glucose, insulin, LDL cholesterol, and non-HDL cholesterol, and a lower ratio of apolipoprotein (Apo) B/ApoA1 (P values all ≤0.003). CONCLUSIONS Achieving the key targets of an infancy-onset 20-year dietary intervention was associated with better insulin sensitivity and serum lipid profile throughout the early life course.
Compact Circular Reconfigurable Antenna for High Directivity and 360° Beam Scanning

A compact reconfigurable antenna with high directivity and 360° beam steering is proposed for 5.8 GHz band application. The concept incorporates via-grounding technique around a circular patch that is shorted with a matching patch, surrounded by a combination of edge-grounded sector patches and arc strips. The matching patch is utilized to improve the impedance matching and lower the resonance frequency. Six beam-steerable states are achieved using 12 p-i-n diodes. Compared to other reconfigurable antennas for similar applications, the presented design has a more compact size (diameter of 1.95<italic>λ<sub>g</sub></italic>) and profile of 0.17<italic>λ<sub>g</sub> </italic>, where <italic>λ<sub>g</sub></italic> is the wavelength at resonance, while maintaining a high gain (8.1 dBi), front-to-back ratio (18 dB), and efficiency (84%).
RPC-Based Orthorectification for Satellite Images Using FPGA

Conventional rational polynomial coefficients (RPC)-based orthorectification methods are unable to satisfy the demands of timely responses to terrorist attacks and disaster rescue. To accelerate the orthorectification processing speed, we propose an on-board orthorectification method, i.e., a field-programmable gate array (FPGA)-based fixed-point (FP)-RPC orthorectification method. The proposed RPC algorithm is first modified using fixed-point arithmetic. Then, the FP-RPC algorithm is implemented using an FPGA chip. The proposed method is divided into three main modules: a reading parameters module, a coordinate transformation module, and an interpolation module. Two datasets are applied to validate the processing speed and accuracy that are achievable. Compared to the RPC method implemented using Matlab on a personal computer, the throughputs from the proposed method and the Matlab-based RPC method are 675.67 Mpixels/s and 61,070.24 pixels/s, respectively. This means that the proposed method is approximately 11,000 times faster than the Matlab-based RPC method to process the same satellite images. Moreover, the root-mean-square errors (RMSEs) of the row coordinate (ΔI), column coordinate (ΔJ), and the distance ΔS are 0.35 pixels, 0.30 pixels, and 0.46 pixels, respectively, for the first study area; and, for the second study area, they are 0.27 pixels, 0.36 pixels, and 0.44 pixels, respectively, which satisfies the correction accuracy requirements in practice.
Classification of EEG Signal based on non-Gaussian Neutral Vector

In the design of brain-computer interface systems, classification of Electroencephalogram (EEG) signals is the essential part and a challenging task. Recently, as the marginalized discrete wavelet transform (mDWT) representations can reveal features related to the transient nature of the EEG signals, the mDWT coefficients have been frequently used in EEG signal classification. In our previous work, we have proposed a super-Dirichlet distribution-based classifier, which utilized the nonnegative and sum-to-one properties of the mDWT coefficients. The proposed classifier performed better than the state-of-the-art support vector machine-based classifier. In this paper, we further study the neutrality of the mDWT coefficients. Assuming the mDWT vector coefficients to be a neutral vector, we transform them non-linearly into a set of independent scalar coefficients. Feature selection strategy is proposed on the transformed feature domain. Experimental results show that the feature selection strategy helps improving the classification accuracy.
Design of Sharp Cut-Off Digital Tuner Using Firefly Algorithm

Usually signal tuning is required to capture the desired narrowband spectrum from a wideband source and frequently used in most of modern broadcast/communication networks. This manuscript describes design of a precise digital tuner with satisfactory performance employing a robust metaheuristic algorithm called firefly algorithm (FA) for the purpose of different modern digital signal processing and communication networks. Mostly sharp cut-off digital tuners (SCDTs) are preferred due to complete absence of co-channel and multichannel interference in modern signal processing applications. Modern artificial intelligence technique is implemented in order to enhance the overall design plan of the SCDT considering it as a multi-objective, multimodal, complex, and nonlinear optimization problem. The performance of the proposed SCDT utilizing FA has been compared with most popular real-coded genetic algorithm (RGA) and particle swarm optimization (PSO).
Explaining Explanation, Part 4: A Deep Dive on Deep Nets

This is the fourth in a series of essays about explainable AI. Previous essays laid out the theoretical and empirical foundations. This essay focuses on Deep Nets, and con-siders methods for allowing system users to generate self-explanations. This is accomplished by exploring how the Deep Net systems perform when they are operating at their boundary conditions. Inspired by recent research into adversarial examples that demonstrate the weakness-es of Deep Nets, we invert the purpose of these adversar-ial examples and argue that spoofing can be used as a tool to answer contrastive explanation questions via user-driven exploration.
Sparse PLS hyper-parameters optimisation for investigating brain-behaviour relationships

Unsupervised learning approaches, such as Partial Least Squares, can be used to investigate relationships between multiple sources of data, such as neuroimaging and behavioural data. In cases of high-dimensional datasets with limited number of examples (e.g. neuroimaging data) there is a need for regularisation to enable the solution of the ill-posed problem and prevent overfitting. Different approaches have been proposed to optimise the regularisation parameters in unsupervised models, however, so far, there has been no comparison between the different approaches using the same data. In this work, two optimisation frameworks (i.e. a permutation and a train/test framework) were compared using sparse PLS to investigate associations between brain connectivity and behaviour data. Both frameworks were able to identify at least one brain-behaviour associative effect. A second brain-behaviour effect was only found using the train/test framework. More importantly, the results show that the multivariate associative effects found with the train/test framework generalise better to new data, suggesting that results based on the permutation framework should be carefully interpreted.
Stable and Passive High-Power Dual Active Bridge Converters Interfacing MVDC Grids

Dual active bridge (DAB) is a topology that is receiving more and more attention as a potential solution to interface dc grids of different voltage levels. From a system level, the implications of DABs on the stability of complex power systems are addressed in this paper. Dynamics modeling and stability assessment for a DAB implementation aimed to interface low-voltage energy resources with medium-voltage dc (MVdc) collection and distribution grids are presented. The DAB admittance is analytically derived and assessed in order to describe its dynamics and anticipate its behavior when integrated in a complex MVdc grid. The model considers the low-frequency range, mostly dominated by the controller action, and the high-frequency range, described by a nonlinear operation. The theoretical analysis is verified by hardware-in-the-loop emulation, with the controller running on a digital signal processor. The proposed implementation is proved to achieve passivity in the whole spectrum, which undoubtedly is a desired feature for massive power electronics integration in the future MVdc grids.
Psoriasis during angiotensin receptor blocker exposure: an underestimated adverse drug reaction

ABSTRACT Background: Angiotensin receptor blockers (ARBs) can induce or exacerbate psoriasis. Psoriasis is unlisted in the Summary of Product Characteristics (SmPC) of ARBs. We aimed to investigate the association between psoriasis and ARB exposure. Methods: We reviewed spontaneous reports recorded in the French national Pharmacovigilance Database (FPVD). The association between psoriasis and ARB exposure was assessed using the case/non-case method. We also analyzed literature reports. Results: We identified 89 reports of psoriasis during ARB exposure in the FPVD. Time to onset was most often less than 1 year. Outcome was favorable in 67% of reports after ARB discontinuation. Almost all ARBs were concerned. The reporting odds ratio (ROR) for psoriasis with this therapeutic class was 4.86 (95%CI 3.92–6.03). In the literature, we found 14 published reports of psoriasis with ARB exposure. Time to onset ranged from 6 weeks to 9 months. Outcome was also favorable after ARB discontinuation in the literature. Conclusions: This underestimated adverse drug reaction is a class effect, time to onset is most often less than 1 year and outcome seems favorable after ARB discontinuation. The case/non-case approach highlights a potential safety signal. The SmPC of ARBs should be updated, increased awareness among healthcare professionals is warranted.
Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures

We propose an online algorithm for sequential learning in the contextual multiarmed bandit setting. Our approach is to partition the context space and, then, optimally combine all of the possible mappings between the partition regions and the set of bandit arms in a data-driven manner. We show that in our approach, the best mapping is able to approximate the best arm selection policy to any desired degree under mild Lipschitz conditions. Therefore, we design our algorithm based on the optimal adaptive combination and asymptotically achieve the performance of the best mapping as well as the best arm selection policy. This optimality is also guaranteed to hold even in adversarial environments since we do not rely on any statistical assumptions regarding the contexts or the loss of the bandit arms. Moreover, we design an efficient implementation for our algorithm using various hierarchical partitioning structures, such as lexicographical or arbitrary position splitting and binary trees (BTs) (and several other partitioning examples). For instance, in the case of BT partitioning, the computational complexity is only log-linear in the number of regions in the finest partition. In conclusion, we provide significant performance improvements by introducing upper bounds (with respect to the best arm selection policy) that are mathematically proven to vanish in the average loss per round sense at a faster rate compared to the state of the art. Our experimental work extensively covers various scenarios ranging from bandit settings to multiclass classification with real and synthetic data. In these experiments, we show that our algorithm is highly superior to the state-of-the-art techniques while maintaining the introduced mathematical guarantees and a computationally decent scalability.
Decentralized PMU-Assisted Power System State Estimation With Reduced Interarea Communication

This paper presents a decentralized approach to multiarea power system state estimation using a combination of conventional measurement devices and newer phasor measurement units (PMU). We employ a reduced-order approach to state estimation, wherein the PMU observable and unobservable components of the state vector are estimated separately in each control area. The estimation problem is solved by exchanging information through an improved gossip-based protocol, which takes into account the weak measurement coupling between control areas. A detailed analysis of the proposed protocol reveals that the amount of information exchange is always less than the naïve gossip-based scheme. We show through simulations on the IEEE 118-bus test system that, in practice, the gossip iterations converge in one iteration regardless of the number of control areas. Our simulations also show that the marginal information exchange is also considerably lower when using the proposed method.
A Model Reference Adaptive Control Based Speed Controller for a Surface-Mounted Permanent Magnet Synchronous Motor Drive

This paper designs a simple model reference adaptive control based speed controller (MRAC-SC) for a surface-mounted permanent magnet synchronous motor drive. The proposed adaptive scheme is designed to track a reference model that ensures the desired exponential decay of a controlled speed error trajectory. Also, the proposed MRAC method includes an adaptive compensating control term and a stabilizing feedback control term. The former is proposed to compensate for uncertain model parameters (i.e., inertia, friction, and load torque) and the latter is constructed to asymptotically stabilize the error dynamics. The asymptotic stability of the closed loop is guaranteed with both control terms using the Lyapunov approach. The comparative studies between the proposed MRAC-SC, the nonadaptive model reference speed controller, and the conventional proportional–integral SC are performed to justify a fast transient response, a good tracking possibility, and robustness against the parameter uncertainties of the proposed MRAC technique.
OntoSenseNet: A Verb-Centric Ontological Resource for Indian Languages

Following approaches for understanding lexical meaning developed by Yaska, Patanjali and Bhartrihari from Indian linguistic traditions and extending approaches developed by Leibniz and Brentano in the modern times, a framework of formal ontology of language was developed. This framework proposes that meaning of words are in-formed by intrinsic and extrinsic ontological structures. The paper aims to capture such intrinsic and extrinsic meanings of words for two major Indian languages, namely, Hindi and Telugu. Parts-of-speech have been rendered into sense-types and sense-classes. Using them we have developed a gold- standard annotated lexical resource to support semantic understanding of a language. The resource has collection of Hindi and Telugu lexicons, which has been manually annotated by native speakers of the languages following our annotation guidelines. Further, the resource was utilised to derive adverbial sense-class distribution of verbs and karaka-verb sense- type distribution. Different corpora (news, novels) were compared using verb sense-types distribution. Word Embedding was used as an aid for the enrichment of the resource. This is a work in progress that aims at lexical coverage of language extensively.
Genetic Signatures of Lipid Metabolism Evolution in Cetacea

In mammalian evolutionary history, Cetacea (whales, dolphins, and porpoises) achieved astonishing success by adapting to an aquatic environment. One unique characteristic of cetaceans, contributing to this adaptive success, is efficient lipid utilization. Here we report comparative genetic analysis of aquatic and terrestrial Cetartiodactyla using 144 genes associated with lipid metabolism. We analyzed mutation rate, amino acid substitution, and metabolic pathways using genetic data publicly available. Our test detected 18 positively selected genes in Cetacea compared to 13 in Bovidae with little overlap between the lineages. We identified lineage-specific patterns of amino acid substitutions and functional domain that were mutually exclusive between cetaceans and bovids, supporting divergent evolution of lipid metabolism since the divergence of these taxa from a common ancestor. Moreover, a pathway analysis showed that the identified genes in cetaceans were associated with lipid digestion, lipid storage, and energy producing pathways. This study emphasizes the evolutionary context of lipid metabolism modification of cetaceans and provides a foundation for future studies of elucidating the adapted biological mechanisms of cetacean lipid metabolism and a framework for incorporating ecological context into studies aimed at investigating adaptive evolution.
Improving IR-based bug localization with context-aware query reformulation

Recent findings suggest that Information Retrieval (IR)-based bug localization techniques do not perform well if the bug report lacks rich structured information (e.g., relevant program entity names). Conversely, excessive structured information (e.g., stack traces) in the bug report might not always help the automated localization either. In this paper, we propose a novel technique--BLIZZARD-- that automatically localizes buggy entities from project source using appropriate query reformulation and effective information retrieval. In particular, our technique determines whether there are excessive program entities or not in a bug report (query), and then applies appropriate reformulations to the query for bug localization. Experiments using 5,139 bug reports show that our technique can localize the buggy source documents with 7%--56% higher Hit@10, 6%--62% higher MAP@10 and 6%--62% higher MRR@10 than the baseline technique. Comparison with the state-of-the-art techniques and their variants report that our technique can improve 19% in MAP@10 and 20% in MRR@10 over the state-of-the-art, and can improve 59% of the noisy queries and 39% of the poor queries.
Adaptive and Economically-Robust Group Selling of Spectrum Slots for Cognitive Radio-Based Networks

Auction theory has found vital application in cognitive radio to relieve spectrum scarcity by redistributing idle channels to those who value them most. However, countries have been slow to introduce spectrum auctions in the secondary market. This could be in part because a number of substantial conflicts could emerge for leasing the spectrum at the micro level. These representative conflicts include the lack of legislation, interference management, setting a reasonable price, etc. In addition, the heterogeneous nature of the spectrum precludes the true evaluation of non-identical channels. The information abstracted from the initial activity in terms of price paid for specific channels may not be a useful indicator for the valuation of another channel. Therefore, auction mechanisms to efficiently redistribute idle channels in the secondary market are of vital interest. In this paper, we first investigate such leading conflicts and then propose a novel Adaptive and Economically-Robust spectrum slot Group-selling scheme (AERG), for cognitive radio-based networks such as IoT, 5G and LTE-Advanced. This scheme enables group-selling behavior among the primary users to collectively sell their uplink slots that are individually not attractive to the buyers due to the auction overhead. AERG is based on two single-round sealed-bid reverse-auction mechanisms accomplished in three phases. In the first phase, participants adapt asks and bids to fairly evaluate uplink slots considering the dynamics of spectrum trading such as space and time. In the second phase, an inner-auction in each primary network is conducted to collect asks on group slots, and then, an outer-auction is held between primary and secondary networks. In the third phase, the winning primary network declares the winners of the inner-auction that can evenly share the revenue of the slots. Simulation results and logical proofs verify that AERG satisfies economic properties such as budget balance, truthfulness and individual rationality and improves the utilities of the participants.