Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing the Environmental Kuznets Curve with Identifying its Influencing Factors in the Context of Low-carbon Economy: A Case of Heilongjiang Province, China

Heilongjiang Province of China is an energy-intensive area that is experiencing the low-carbon economy transition to implement the new policy for revitalizing the Northeastern China. Following the hypothesis of Environmental Kuznets Curve with collecting data from 1985 to 2015, this paper examines the relation between per capita emissions of main pollutants as well as CO2 and per capita GDP deflator in Heilongjiang Province. Empirical results present that different from the traditional inverted U-shaped curve that was proposed by Environmental Kuznets Curve hypothesis, the curves between each type of emission and per capita GDP deflator have different shapes. Specifically, the shape of curves between per capita emissions of waste water, waste gas, solid waste, CO2 and per cap GDP deflator are the upright N-shape, inverted N-shape, upright U-shape, and inverted N-shape, respectively. With respect to the influencing factors for the disparity of their shapes, per capita annual total trade, regional energy intensity, industrial structure, and technology progress significantly affect the relation between each per capita emission and per capita GDP deflator, but government corruption can concurrently hinder and promote the environmental quality. This case reflects that in terms of regional environmental governance, the actual administrative efficiency in Heilongjiang Province is still low, and thus it needs to invest the extra opportunity cost to reverse this unfavorable state.
Transfer Learning for Collision Localization in Collaborative Robotics

In this work, the transfer learning paradigm is used to improve the performance of neural networks. These networks estimate the collision point between the robot and an external object, using two different approaches. One of them uses a neural network to predict collision point on the internal robot axis and the second uses classification in order to find a collision in points which are sampled on the robot surface. The neural networks are trained on two datasets, one dataset is generated in simulation, the other one captured from the real robot. Obtained results show, that using a pre-trained network allows to greatly increase the overall accuracy of collision localization.
A Real-time Monitoring and Control System for Crop

The current production of agricultural products has some problems, such as poor customized service, difficulty in quality control and high wastage in production process. Based on the technology of Internet of Things (IoT), artificial intelligence and blockchains, this paper designs an smart precision agricultural system called Agricultural Computer, which can realize the holographic, automatic and intelligent production of agricultural products. The system can solve the separation of information collection, processing and application in agricultural production, so it can supplement corresponding nutrients according to the physiological needs of plants. The system realizes the standardization, precision and intelligence of agricultural production, improves the quality of agricultural products, promotes the greening and high quality of agricultural production, and meets the needs of consumers for personalized and healthy consumption.
Can Earables Support Effective User Engagement during Weight-Based Gym Exercises?

We explore the use of personal 'earable' devices (widely used by gym-goers) in providing personalized, quantified insights and feedback to users performing gym exercises. As in-ear sensing by itself is often too weak to pick up exercise-driven motion dynamics, we propose a novel, low-cost system that can monitor multiple concurrent users by fusing data from (a) wireless earphones, equipped with inertial and physiological sensors and (b) inertial sensors attached to exercise equipment. We share preliminary findings from a small-scale study to demonstrate the promise of this approach, as well as identify open challenges.
Efficient Enumeration Algorithms for Regular Document Spanners

Regular expressions and automata models with capture variables are core tools in rule-based information extraction. These formalisms, also called regular document spanners, use regular languages to locate the data that a user wants to extract from a text document and then store this data into variables. Since document spanners can easily generate large outputs, it is important to have efficient evaluation algorithms that can generate the extracted data in a quick succession, and with relatively little precomputation time. Toward this goal, we present a practical evaluation algorithm that allows output-linear delay enumeration of a spanner’s result after a precomputation phase that is linear in the document. Although the algorithm assumes that the spanner is specified in a syntactic variant of variable-set automata, we also study how it can be applied when the spanner is specified by general variable-set automata, regex formulas, or spanner algebras. Finally, we study the related problem of counting the number of outputs of a document spanner and provide a fine-grained analysis of the classes of document spanners that support efficient enumeration of their results.
Single-Cell Transcriptomes Reveal Diverse Regulatory Strategies for Olfactory Receptor Expression and Axon Targeting

The regulatory mechanisms by which neurons coordinate their physiology and connectivity are not well understood. The Drosophila olfactory receptor neurons (ORNs) provide an excellent system to investigate this question. Each ORN type expresses a unique olfactory receptor, or a combination thereof, and sends their axons to a stereotyped glomerulus. Using single-cell RNA sequencing, we identified 33 transcriptomic clusters for ORNs and mapped 20 to their glomerular types, demonstrating that transcriptomic clusters correspond well with anatomically and physiologically defined ORN types. Each ORN type expresses hundreds of transcription factors. Transcriptome-instructed genetic analyses revealed that (1) one broadly expressed transcription factor (Acj6) only regulates olfactory receptor expression in one ORN type and only wiring specificity in another type, (2) one type-restricted transcription factor (Forkhead) only regulates receptor expression, and (3) another type-restricted transcription factor (Unplugged) regulates both events. Thus, ORNs utilize diverse strategies and complex regulatory networks to coordinate their physiology and connectivity.
Reduced Bipolar Conduction in Bandgap-Engineered n-Type Cu0.008Bi2(Te,Se)3 by Sulfur Doping

Significant bipolar conduction of the carriers in Bi2Te3-based alloys occurs at high temperatures due to their narrow bandgaps. Therefore, at high temperatures, their Seebeck coefficients decrease, the bipolar thermal conductivities rapidly increase, and the thermoelectric figure of merit, zT, rapidly decreases. In this study, band modification of n-type Cu0.008Bi2(Te,Se)3 alloys by sulfur (S) doping, which could widen the bandgap, is investigated regarding carrier transport properties and bipolar thermal conductivity. The increase in bandgap by S doping is demonstrated by the Goldsmid–Sharp estimation. The bipolar conduction reduction is shown in the carrier transport characteristics and thermal conductivity. In addition, S doping induces an additional point-defect scattering of phonons, which decreases the lattice thermal conductivity. Thus, the total thermal conductivity of the S-doped sample is reduced. Despite the reduced power factor due to the unfavorable change in the conduction band, zT at high temperatures is increased by S doping with simultaneous reductions in bipolar and lattice thermal conductivity.
A Further Look at the Propagation Mechanism of Monetary Policy Shocks in HANK

We provide quantitative guidance on the relative importance of Heterogeneous Agent New Keynesian (HANK) model elements for amplification and dampening of the response of aggregate consumption to a monetary shock. We emphasize four findings. First, the presence of capital adjustment costs does not affect the aggregate response, but does change the transmission mechanism so that a larger share of indirect effects come from equity prices rather than labor income. Second, incorporating estimated unequal incidence functions for aggregate labor income fluctuations leads to a small degree of either amplification or dampening, depending on the data and estimation methods. Third, distribution rules for monopoly profits that allocate a larger share to liquid assets lead to greater amplification. Fourth, assumptions about the fiscal reaction to a monetary policy shock have a larger effect on the aggregate consumption response than any of these three elements. JEL Codes: D14, D31, E21, E52.
Bananas as underutilized fruit having huge potential as raw materials for food and non-food processing industries: A brief review

Banana is among the world’s leading fruit crops. Banana serves as ideal food among low cost foods and serves nutrients to 4-5 billion population residing in developing country. Banana normally has a short shelf life and start deteriorating just after plucking. The quality of the bananas get further lowered during transhipment to the markets. Over and above over ripening is also a big problem. In order to overcome these problems, the processing of banana must be encouraged. Very small portion of total bananas production undergo industrial processing. Plantain and unripe banana are cooked as vegetable, chips, snacks, powder etc., whereas, mature dessert banana is eaten raw. Characterizing bananas, their processed products and processed consumption forms is necessary to enhance its processing. This will enable niche markets for this major crop, undifferentiated product flows of which are in competition on the world- wide market, to be structured on an objective qualitative basis. This will improve the market efficiency and income to the farmers on one hand and generate employment on the other hand. This review discusses usefulness of banana fruits, peel, leaves, and prospects of using these materials in industry.
Robust Predictive Uncertainty for Neural Networks via Confidence Densities

Neural networks are known to make incorrect predictions with high confidence when given inputs that are unlike their training set. This creates problems when a trained network is deployed on real-world data, since covariate shift away from the training set is usually inevitable. We propose confidence densities for explicitly controlling the predictions made by a network on out-ofsample inputs. Intuitively, our method makes confidence a finite resource for our network, which encourages making uniform predictions in regions away from the training data. Confidence densities may be used in conjunction with any offthe-shelf network architecture and can be trained using stochastic gradient descent. We show empirically that our method significantly improves outof-sample robustness while maintaining the same classification accuracy across several datasets. 1. Problem setting We consider the task of learning a predictive distribution p(y|x) given n i.i. d. data points (xi, yi) ∼ p(x, y), where the xi ∈ X are data and the yi ∈ {1, . ,K} are discrete labels. Here, given some x, p(y|x) denotes the full distribution over labels. We will denote the probability that y specifically takes label k by p(y = k|x). At present, neural networks provide state-of-the-art classification performance for a wide variety of different p(x, y). Standard methods use a given network architecture to parameterise a family of predictive distributions q(y|x) (often by attaching a softmax output layer), and then try to choose q(y|x) as close as possible to p(y|x) as measured by some loss function. A widely used choice of loss function is the Department of Engineering Science, University of Oxford, Oxford, UK Department of Statistics, University of Oxford, Oxford, UK. Correspondence to: Rob Cornish <rcornish@robots.ox. ac.uk>. Presented at the ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning. Copyright 2019 by the author(s). Standard neural network D ec is io n re gi on s Monte Carlo dropout Our method En tro py o f p re di ct io ns
Improvements to MCTS Simulation Policies in Go

Since its introduction in 2006, Monte-Carlo Tree Search has been a major breakthrough in computer Go. Performance of an MCTS engine is highly dependent on the quality of its simulations, though despite this, simulations remain one of the most poorly understand aspects of MCTS. In this paper, we explore in-depth the simulations policy of Pachi, an open-source computer Go agent. This research attempts to better understand how simulation policies affect the overall performance of MCTS, building on prior work in the field by doing so. Through this research we develop a deeper understanding of the underlying components in Pachi’s simulation policy, which are common to many modern MCTS Go engines, and evaluate the metrics used to measure them.
Hyperspectral Classification With Noisy Label Detection via Superpixel-to-Pixel Weighting Distance

Classification is an important technique for remotely sensed hyperspectral image (HSI) exploitation. Often, the presence of wrong (noisy) labels presents a drawback for accurate supervised classification. In this article, we introduce a new framework for noisy label detection that combines a superpixel-to-pixel weighting distance (SPWD) and density peak clustering. The proposed method is able to accurately detect and remove noisy labels in the training set before HSI classification. It considers two weak assumptions when exploiting the spectral–spatial information contained in the HSI: 1) all the pixels in a superpixel belong to the same class and 2) close pixels in spectral space have the same label. The proposed method consists of the following steps. First, a superpixel segmentation step is used to obtain self-adaptive spatial information for each training sample. Then, a metric is utilized to measure the spectral distance information between each superpixel and pixel. Meanwhile, in order to overcome the first weak assumption, we use $K$ nearest neighbors to obtain the closest neighborhoods of pixels around each superpixel, and a Gaussian weight is employed to mitigate the second weak assumption by adapting the original distance information. Next, the noisy labels in the original training set are removed by a density threshold-based decision function. Finally, the support vector machine (SVM) classifier is employed to evaluate the effectiveness of the proposed SPWD detection method in terms of classification accuracy. Experiments performed on several real HSI data sets demonstrate that the method can effectively improve the performance of classifiers trained with noisy training sets in terms of classification accuracy.
Energy Storage Sizing Optimization and Sensitivity Analysis Based on Wind Power Forecast Error Compensation

To better track the planned output (forecast output), energy storage systems (ESS) are used by wind farms to compensate the forecast error of wind power and reduce the uncertainty of wind power output. When the error compensation degree is the same, the compensation interval is not unique, different compensation intervals need different ESS sizing. This paper focused on finding the optimal compensation interval not only satisfied the error compensation degree but also obtained the max profit of the wind farm. First, a mathematical model was proposed as well as a corresponding optimization method aiming at maximizing the profit of the wind farm. Second, the effect of the influencing factors (compensation degree, electricity price, ESS cost, and wind penalty cost) on the optimal result was fully analyzed and deeply discussed. Through the analysis, the complex relationship between the factors and the optimal results was found. Finally, the comparison between the proposed and traditional method was given, and the simulation results showed that the proposed method can provide a powerful decision-making basis for ESS planning in current and future market.
Methicillin and vancomycin resistance in coagulase-negative Staphylococci isolated from the nostrils of hospitalized patients.

INTRODUCTION Nasal colonization by coagulase-negative Staphylococci (CoNS) play an important role in nosocomial infections. This study aims to determine antibiotics susceptibility pattern and molecular screening of methicillin- and vancomycin-resistant nasal CoNS among hospitalized patients. METHODOLOGY Nasal swabs were collected from 202 inpatients at Prince Hamzah Hospital, Jordan. Swabs were processed according to standard microbiological procedures to isolate Staphylococci. Antibiotic susceptibility testing was performed using disk diffusion, E-test, microdilution, and Vitek 2. Molecular analysis was performed using PCR for the detection of mecA, vanA, and vanB genes. RESULTS Nasal Staphylococci was isolated in 64/202 (31.7%) samples. Thirty isolates (14.8%) were CoNS, including S. haemolyticus (n = 17, 8.4%), S. sciuri (n = 6, 3%), S. epidermidis (n = 2, 1%), S. warneri (n = 2, 1%), S. hominis (n = 2, 1%), and S. lentus (n = 1, 0.5%). Twenty-two (10.9%) isolates were MR-CoNS harboring mecA gene. CoNS and MR-CoNS isolates were highly resistant to benzylpenicillin, erythromycin, fosfomycin, and imipenem. All isolates were sensitive to vancomycin by E-test and microdilution test and were negative for vanA and vanB genes. Nasal CoNS colonization was associated with an increased number of family members living with the participant (P = 0.04) and with admission to the orthopedic department (P = 0.03), while MR-CoNS colonization was associated with smoking (P = 0.03). CONCLUSIONS Nasal colonization by unusual CoNS species and mecA-positive MR-CoNS are common among hospitalized patients. Absence of vanA and vanB genes suggests little contribution of nasal CoNS to vancomycin resistance transmission.
Constituents of essential oils from leaves and seeds of Laurus nobilis L.: A chemotaxonomic approach

In the present study, chemical compositions of essential oils from seeds and leaves of laurel (Laurus nobilis L.) were evaluated using GC-GC/MS system. Sixty nine different compounds were identified constituting 86.7% of the total oil from the seed, while 76 compounds were determined, constituting 95.8% of the total oil extracted from the leaves. The major compounds of essential oil from laurel seeds included eucalyptol (17.2%), α-terpinyl acetate (9.0%), caryophyllene oxide (6.1%), spathulenol (5.0%) and methyl eugenol (4.2%), constituting 41.5% of the total oil. However, eucalyptol (18.0%), α-terpinyl acetate (13.1%), sabinene (7.8%), α-pinene (4.5%), 2 (4-methoxyphenyl)-N,N,2-trimethyl-1-pyrroline (4.4%) were identified as the major compounds in the oil from laurel leaves, constituting 47.8% of the total oil. Eucalyptol and α- terpinyl acetate, belonging to monoterpenoids, were determined in the highest concentrations within both oils. However, the other principle compounds differ between the two volatile oils.
Optimization of nylon nanowires/graphene composite process by surface response methodology approach

This work shows electrospinning and electrochemical exfoliation techniques to produce nylon nanowires/graphene composite seeking an optimization process. SEM, AFM, XRD, and UV blocking analyses and techniques were performed for raw and composite materials. Experimental design optimization parameters were carried out to obtain graphene and nylon nanowires: some of those parameters were polarization potential around 11.45 V using (NH4)2SO4 0.68 M, electric field of 2.39 kV/cm and pumping rate of 2.30 mL/h to produce nylon-based nanofibers by electrospinning process. The final composite exhibit significantly UV radiation blocking properties to be consider in the use of smart textile and IoT applications.
Monte-Carlo Search Techniques in the Modern Board Game Thurn and Taxis

Modern board games present a new and challenging field when researching search techniques in the field of Artificial Intelligence. These games differ to classic board games, such as chess, in that they can be non-deterministic, have imperfect information or more than two players. While tree-search approaches, such as alpha-beta pruning, have been quite successful in playing classic board games, by for instance defeating the then reigning world champion Gary Kasparov in Chess, these techniques are not as effective when applied to modern board games. This thesis investigates the effectiveness of Monte-Carlo Tree Search when applied to a modern board game, for which the board game Thurn and Taxis was used. This is a non-deterministic modern board game with imperfect information that can be played with more than 2 players, and is hence suitable for research. First, the state-space and game-tree complexities of this game are computed, from which the conclusion can be drawn that the two-player version of the game has a complexity similar to the game Shogi. Several techniques are investigated in order to improve the sampling process, for instance by adding domain knowledge. Given the results of the experiments, one can conclude that Monte-Carlo Tree Search gives a slight performance increase over standard Monte-Carlo search. In addition, the most effective improvements appeared to be the application of pseudo-random simulations and limiting simulation lengths, while other techniques have been shown to be less effective or even ineffective. Overall, when applying the best performing techniques, an AI with advanced playing strength has been created, such that further research is likely to push this performance to a strength of expert level.
Meat and Muscle BiologyTM Introduction

Consumer interest in the source of their food, its environmental footprint, and the impact of diet on health has supported the growth of the grass-finished beef (GFB) industry. Studies have concluded that GFB has distinct nutritional differences from conventionally-finished beef. As the GFB industry continues to expand, it is vital to continue to explore the nutritional complexities and variation in the product. To achieve this, a survey of grass-finishing production systems throughout the USA was conducted, and beef finished on the participating farms was analyzed for its nutritional composition, including fatty acid (FA), mineral and fat-soluble vitamin contents. Samples were analyzed from 12 producers and annual production capacity of farms ranged from 25 to 5,000 cattle, with a mean age of cattle at harvest of 26.8 ± 2.30 mo. An array of finishing diets included grazing exclusively in perennial pasture, incorporating annual forage crops, and feeding a variety of harvested forages with supplementation of non-starch feed byproducts. Beef muscle tissue FA content was analyzed by gas chromatography-mass spectrometry (GC–MS). The mean ratio of omega-6 (n-6) to omega-3 (n-3) FA in samples varied significantly by producer, ranging from 1.80 to 28.3 (P < 0.0001), with an overall sample set median of 4.10. A selection of minerals including iron, magnesium, and potassium were analyzed by ICP emission spectroscopy and mineral content significantly differed by producer for all minerals (P < 0.001). Mean α-tocopherol and β-carotene content was 610.6 μg/100 g beef and 32.2 μg/100 g, respectively. The amount of these antioxidants also varied between producers (P < 0.0001), but tended to be greater in beef finished solely on fresh forages. This survey indicates that commercially available GFB can vary in nutritional composition due to the diverse practices used to grass-finish cattle.