Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Synthetic Soil Aggregates as a Soil Ameliorant to Enhance Properties of Problematic Gray (“Jahgaru”) Soils in Okinawa, Japan

Synthetic aggregates (SA) were developed using low productive red soil, paper, and starch waste and then utilized as a soil ameliorant to improve poor physical and chemical properties of a gray soil (“Jahgaru”). The gray soil was amended with SA at 10, 20, 30, 40, and 50% and then used for the cultivation of French marigold (Tagetes patula). The SA addition enhanced bulk density, porosity, water‐holding capacity, hydraulic conductivity, pH, organic matter, and carbon (C) content of original gray soil. Plant height, number of flowers per plant, shoot fresh weight, shoot dry weight, root length, root fresh weight, and root dry weight in 30% of SA addition were the greatest and were increased by 1.5, 2.9, 3.5, 4.7, 3.4, 4.3, and 9 times, respectively, compared to gray soil. It can be concluded that developed SA can be utilized as a soil ameliorant to improve poor physical and chemical properties of gray soil.
Induction principles for type theories, internally to presheaf categories

We present new induction principles for the syntax of dependent type theories. These induction principles are expressed in the internal language of presheaf categories. This ensures for free that any construction is stable under substitution. In order to combine the internal languages of multiple presheaf categories, we use Dependent Right Adjoints and Multimodal Type Theory. Categorical gluing is used to prove these induction principles, but it not visible in their statements, which involve a notion of model without context extensions. We illustrate the derivation of these induction principles by the example of type theory with a hierarchy of universes closed under function space and natural numbers, but we expect that our method can be applied to any syntax with bindings. As example applications of these induction principles, we give short and boilerplate-free proofs of canonicity and normalization for our example type theory.
Correlated interface electron gas in infinite-layer nickelate versus cuprate films on 
SrTiO3(001)

Based on first-principles calculations including a Coulomb repulsion term, we identify trends in the electronic reconstruction of ANiO2/SrTiO3(001) (A = Pr, La) and ACuO2/SrTiO3(001) (A = Ca, Sr). Common to all cases is the emergence of a quasi-two-dimensional electron gas (q2DEG) in SrTiO3(001), albeit the higher polarity mismatch at the interface of nickelates vs. cuprates to the nonpolar SrTiO3(001) substrate (3+/0 vs. 2+/0) results in an enhanced q2DEG carrier density. The simulations reveal a significant dependence of the interfacial Ti 3dxy band bending on the rare-earth ion in the nickelate films, being 20-30% larger for PrNiO2 and NdNiO2 than for LaNiO2. Contrary to expectations from the formal polarity mismatch, the electrostatic doping in the films is twice as strong in cuprates as in nickelates. We demonstrate that the depletion of the selfdoping rare-earth 5d states enhances the similarity of nickelate and cuprate Fermi surfaces in film geometry, reflecting a single hole in the Ni and Cu 3dx2−y2 orbitals. Finally, we show that NdNiO2 films grown on a polar NdGaO3(001) substrate feature a simultaneous suppression of q2DEG formation as well as Nd 5d self-doping.
Benzoylation of 2(3H)-benzothiazolones in the Presence of 8 Equivalents of Zinc Chloride in N,N-dimethylformamide

The use of the mixture of aluminum chloride-N,N-dimethylformamide 1,2 (AlCl 3 -DMF, 11:1) reagent in the Friedl-Crafts C acylation reaction of 2(3H)-benzothiazolones was previously reported. 3 These acylations reactions were found to proceed with high regioselectivity. The precise position of acylation was uniquivocally assigned by x-ray single-crystal defraction in the case of 6-benzoyl-2(3H)-benzothiazolone. 4 The assignment of the position of acylation was extended to other compounds by the use of high-field 1 H-NMR . 3
GLC-MS Studies on Pine Kraft Black Liquors Part V. Identification of Catechol Compounds

The following catechol compounds were identified in pine kraft black liquor: Catechol, 7 alkylcatechols, 3,4-dihydroxybenzaldehyde, 3,4-dihydroxyacetophenone, 3,4-dihydroxybenzoic acid, 3,4-dihydroxyphenylacetic acid, 3-(3,4-dihydroxyphenyl)-l-propanol, and 3 a-hydroxy-(3,4-dihydroxyphenyl)alkanoic acids.Their total amount corresponded to about 46 g/100 kg wood, of which catechol constituted the major pari. Catechol was mainly formed during the heating-up period, whereas the formation of other compounds was more pronounced during the later stages. Their formation from both lignin and carbohydrates is discussed.
Learning to Fairly Classify the Quality of Wireless Links

Machine learning (ML) has been used to develop increasingly accurate link quality estimators for wireless networks. However, more in depth questions regarding the most suitable class of models, most suitable metrics and model performance on imbalanced datasets remain open. In this paper, we propose a new tree based link quality classifier that meets high performance and fairly classifies the minority class and, at the same time, incurs low training cost. We compare the tree based model, to a multilayer perceptron non-linear model and two linear models, namely logistic regression and support vector machine, on a selected imbalanced dataset and evaluate their results using five different performance metrics. Our study shows that 1) non-linear models perform slightly better than linear models in general, 2) the proposed non linear tree-based model yields the best performance trade-off considering F1, training time and fairness, 3) single metric aggregated evaluations based only on accuracy can hide poor, unfair performance especially on minority classes, and 4) it is possible to improve the performance on minority classes, by over 40% through feature selection and by over 20% through resampling, therefore leading to fairer classification results.
OGLE-2018-BLG-1428Lb: a Jupiter-mass planet beyond the snow line of a dwarf star

We present the analysis of the microlensing event OGLE-2018-BLG-1428, which has a short-duration (∼1 d) caustic-crossing anomaly. The event was caused by a planetary lens system with planet/host mass ratio q = 1.7 × 10−3. Because of the detection of the caustic-crossing anomaly, the finite source effect was well measured, but the microlens parallax was not constrained due to the relatively short time-scale (tE = 24 d). From a Bayesian analysis, we find that the host star is a dwarf star $M_{\rm host}=0.43^{+0.33}_{-0.22} \ \mathrm{M}_{\odot }$ at a distance $D_{\rm L}=6.22^{+1.03}_{-1.51}\ {\rm kpc}$ and the planet is a Jovian-mass planet $M_{\rm p}=0.77^{+0.77}_{-0.53} \ M_{\rm J}$ with a projected separation $a_{\perp }=3.30^{+0.59}_{-0.83}\ {\rm au}$. The planet orbits beyond the snow line of the host star. Considering the relative lens-source proper motion of $\mu _{\rm rel} = 5.58 \pm 0.38\ \rm mas\ yr^{-1}$, the lens can be resolved by adaptive optics with a 30 m telescope in the future.
Convergence rates for gradient descent in the training of overparameterized artificial neural networks with biases

In recent years, artificial neural networks have developed into a powerful tool for dealing with a multitude of problems for which classical solution approaches reach their limits. However, it is still unclear why randomly initialized gradient descent optimization algorithms, such as the well-known batch gradient descent, are able to achieve zero training loss in many situations even though the objective function is non-convex and non-smooth. One of the most promising approaches to solving this problem in the field of supervised learning is the analysis of gradient descent optimization in the so-called overparameterized regime. In this article we provide a further contribution to this area of research by considering overparameterized fully-connected rectified artificial neural networks with biases. Specifically, we show that for a fixed number of training data the mean squared error using batch gradient descent optimization applied to such a randomly initialized artificial neural network converges to zero at a linear convergence rate as long as the width of the artificial neural network is large enough, the learning rate is small enough, and the training input data are pairwise linearly independent.
Resilient Path Planning of UAVs against Covert Attacks on UWB Sensors

In this letter, a resilient path planning scheme is proposed to navigate a UAV to the planned (nominal) destination with minimum energy-consumption in the presence of a smart attacker. The UAV is equipped with two sensors, a GPS sensor, which is vulnerable to the spoofing attacker, and a well-functioning Ultra-Wideband (UWB) sensor, which is possible to be fooled. We show that a covert attacker can significantly deviate the UAV's path by simultaneously corrupting the GPS signals and forging control inputs without being detected by the UWB sensor. The prerequisite for the attack occurrence is first discussed. Based on this prerequisite, the optimal attack scheme is proposed, which maximizes the deviation between the nominal destination and the real one. Correspondingly, an energy-efficient and resilient navigation scheme based on Pontryagin's maximum principle \cite{gelfand2000calculus} is formulated, which depresses the above covert attacker effectively. To sum up, this problem can be seen as a Stackelberg game \cite{bacsar1998dynamic} between a secure path planner (defender) and a covert attacker. The effectiveness and practicality of our theoretical results are illustrated via two series of simulation examples and a UAV experiment.
Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference

Efficient machine learning implementations optimized for inference in hardware have wide-ranging benefits, depending on the application, from lower inference latency to higher data throughput and reduced energy consumption. Two popular techniques for reducing computation in neural networks are pruning, removing insignificant synapses, and quantization, reducing the precision of the calculations. In this work, we explore the interplay between pruning and quantization during the training of neural networks for ultra low latency applications targeting high energy physics use cases. Techniques developed for this study have potential applications across many other domains. We study various configurations of pruning during quantization-aware training, which we term quantization-aware pruning, and the effect of techniques like regularization, batch normalization, and different pruning schemes on performance, computational complexity, and information content metrics. We find that quantization-aware pruning yields more computationally efficient models than either pruning or quantization alone for our task. Further, quantization-aware pruning typically performs similar to or better in terms of computational efficiency compared to other neural architecture search techniques like Bayesian optimization. Surprisingly, while networks with different training configurations can have similar performance for the benchmark application, the information content in the network can vary significantly, affecting its generalizability.
Group actions on contractible $2$-complexes II

In this second part we prove that, if G is one of the groups PSL2(q) with q > 5 and q ≡ 5 (mod 24) or q ≡ 13 (mod 24), then the fundamental group of every acyclic 2-dimensional, fixed point free and finite G-complex admits a nontrivial representation in a unitary group U(m). This completes the proof of the following result: every action of a finite group on a finite and contractible 2-complex has a fixed point.
Subspace-Based Feature Fusion from Hyperspectral and Multispectral Images for Land Cover Classification

In remote sensing, hyperspectral (HS) and multispectral (MS) image fusion have emerged as a synthesis tool to improve the data set resolution. However, conventional image fusion methods typically degrade the performance of the land cover classification. In this paper, a feature fusion method from HS and MS images for pixel-based classification is proposed. More precisely, the proposed method first extracts spatial features from the MS image using morphological profiles. Then, the feature fusion model assumes that both the extracted morphological profiles and the HS image can be described as a feature matrix lying in different subspaces. An algorithm based on combining alternating optimization (AO) and the alternating direction method of multipliers (ADMM) is developed to solve efficiently the feature fusion problem. Finally, extensive simulations were run to evaluate the performance of the proposed feature fusion approach for two data sets. In general, the proposed approach exhibits a competitive performance compared to other feature extraction methods.
Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Models

In this paper, we learn dynamics models for parametrized families of dynamical systems with varying properties. The dynamics models are formulated as stochastic processes conditioned on a latent context variable which is inferred from observed transitions of the respective system. The probabilistic formulation allows us to compute an action sequence which, for a limited number of environment interactions, optimally explores the given system within the parametrized family. This is achieved by steering the system through transitions being most informative for the context variable. We demonstrate the effectiveness of our method for exploration on a non-linear toyproblem and two well-known reinforcement learning environments.
Experimental study of an electrical discharge used in reactive media ignition

In order to understand the influence of electrical discharges on combustion ignition processes, a wire-to-wire spark created in an air/methane mixture has been studied. The electron temperature and density of this spark discharge have been determined. Voltage, current measurements, photography and optical emission spectroscopy have been used to characterize this discharge as a function of time, pressure and equivalence ratio. The size and shape of the phenomenon were observed using photography. Voltage and current measurement were correlated to these photos to determine the time scale of the discharge and the propagating flame. The Boltzmann plot method was applied to the results of time-resolved spectroscopy during the discharge phase and yielded to the electron temperature the first 10 µs. Finally, from Stark broadening of Hα, Hβ and copper lines, the electron density of the plasma was deduced for the first 10 µs, with values up to 1018 cm−3. These data will be used as the initial condition in a numerical simulation.
On Stability and Convergence of Distributed Filters

Recent years have bore witness to the proliferation of distributed filtering techniques, where a collection of agents communicating over an ad-hoc network aim to collaboratively estimate and track the state of a system. These techniques form the enabling technology of modern multi-agent systems and have gained great importance in the engineering community. Although most distributed filtering techniques come with a set of stability and convergence criteria, the conditions imposed are found to be unnecessarily restrictive. The paradigm of stability and convergence in distributed filtering is revised in this manuscript. Accordingly, a general distributed filter is constructed and its estimation error dynamics is formulated. The conducted analysis demonstrates that conditions for achieving stable filtering operations are the same as those required in the centralized filtering setting. Finally, the concepts are demonstrated in a Kalman filtering framework and validated using simulation examples.
Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

Quantum reservoir computing and quantum extreme learning machines are two emerging approaches that have demonstrated their potential both in classical and quantum machine learning tasks. They exploit the quantumness of physical systems combined with an easy training strategy, achieving an excellent performance. The increasing interest in these unconventional computing approaches is fueled by the availability of diverse quantum platforms suitable for implementation and the theoretical progresses in the study of complex quantum systems. In this review article, recent proposals and first experiments displaying a broad range of possibilities are reviewed when quantum inputs, quantum physical substrates and quantum tasks are considered. The main focus is the performance of these approaches, on the advantages with respect to classical counterparts and opportunities.
Development Assay for of a 5-Hydroxytryptamine2A Receptor Binding High Throughput Screening Using 96-Well Microfilter Plates

A high throughput screening method for the analysis of 5-hydroxytryptamine2A (5-HT2A) receptor binding parameters has been developed, using 96-well filter plates of the Millipore MultiScreen system in combination with a MicroBeta PLUS microplate scintillation counter. MAFB filter plates (GF/B filter over a Durapore membrane) were used because of the lower nonspecific binding of the radioligand to GF/B filter material than to GF/C filters. Comparing different scintillation cocktails, highest counting efficiency and shortest equilibration time were detected with Betaplatescint, after drying the plates at 50°C for 2 h. Measuring the plates without the plastic underdrain increased the counting efficiency by about 39% as compared with counting the plate with the underdrain intact. Presoaking the wells with 0.5% polyethyleneimine for 2 h reduced the nonspecific binding to the filter material by about 50%. A linear relationship of protein concentration and radioligand binding was established up to a protein concentration of 165 μg of protein/well. In the assays, 70 μg of protein/well was generally used, which has turned out to be favorable with respect to the number of counts obtained. When a higher concentration of protein was used, the period of time needed to aspirate the plate was too long because of obstruction of the filter material. Receptor-radioligand equilibration was reached after about 20 min at concentrations less than 0.05 nM [3H]ketanserin-HCl; at higher concentrations it was reached after about 10 min. Saturation analysis of [3H]ketanserin-HCI resulted in a mean Bmax of 393 fmol/mg protein and a KD of 2.0 nM using rat frontal cortex as a receptor source. Competition experiments with known 5-HT2A receptor ligands-DOB-HCI (Ki = 59 nM), DOET-HCl (Ki = 137 nM), DOM-HCl (Ki = 533 nM), DMT (K; = 1,985 nM), and TMA-HCl (Ki = 22,340 nM)-were in accordance with literature values.
VisualCheXbert: addressing the discrepancy between radiology report labels and image labels

Automatic extraction of medical conditions from free-text radiology reports is critical for supervising computer vision models to interpret medical images. In this work, we show that radiologists labeling reports significantly disagree with radiologists labeling corresponding chest X-ray images, which reduces the quality of report labels as proxies for image labels. We develop and evaluate methods to produce labels from radiology reports that have better agreement with radiologists labeling images. Our best performing method, called VisualCheXbert, uses a biomedically-pretrained BERT model to directly map from a radiology report to the image labels, with a supervisory signal determined by a computer vision model trained to detect medical conditions from chest X-ray images. We find that VisualCheXbert outperforms an approach using an existing radiology report labeler by an average F1 score of 0.14 (95% CI 0.12, 0.17). We also find that VisualCheXbert better agrees with radiologists labeling chest X-ray images than do radiologists labeling the corresponding radiology reports by an average F1 score across several medical conditions of between 0.12 (95% CI 0.09, 0.15) and 0.21 (95% CI 0.18, 0.24).
CIAO: An Optimization Framework for Client-Assisted Data Loading

Data loading has been one of the most common performance bottlenecks for many big data applications, especially when they are running on inefficient human-readable formats, such as JSON or CSV. Parsing, validating, integrity checking and data structure maintenance are all computationally expensive steps in loading these formats. Regardless of these costs, many records may be filtered later during query evaluation due to highly selective predicates – resulting in wasted computation. Meanwhile, the computing power of client ends is typically not exploited. Here, we explore investing limited cycles of clients on prefiltering to accelerate data loading and enable data skipping for query execution. In this paper, we present CIAO, a tunable system to enable client cooperation with the server to enable efficient partial loading and data skipping for a given workload. We proposed an efficient algorithm that would select a near-optimal predicate set to push down within a given budget. Our experiments show that CIAO can significantly accelerate the data loading processing and query execution.
L0 regularization-based compressed sensing with coherent Ising machines

We propose a quantum-classical hybrid system consisting of coherent Ising machines (CIM) and classical digital processors, which performs alternating minimization for L0 regularization-based compressed sensing (CS). A truncated Wigner stochastic diﬀerential equation (W-SDE) is obtained from the master equation for the density operator of the network of degenerate optical parametric oscillators. The macroscopic equations in statistical mechanics are derived from the W-SDE to evaluate theoretically the performance of CIM L0 regularization-based CS. We show that the system performance in principle approaches the theoretical limit of L0 minimization-based CS derived by Nakanishi et al. (2016) and possibly exceeds that of the least absolute shrinkage and selection operator (LASSO). We also conduct numerical experiments conﬁrming that in practical situations this hybrid system can exceed LASSO’s estimation accuracy.
Automatic Ship Classification Utilizing Bag of Deep Features

Abstract: Detection and classification of ships based on their silhouette profiles in natural imagery is an important undertaking in computer science. This problem can be viewed from a variety of perspectives, including security, traffic control, and even militarism. Therefore, in each of the aforementioned applications, specific processing is required. In this paper, by applying the "bag of words" (BoW), a new method is presented that its words are the features that are obtained using pre-trained models of deep convolutional networks. , Three VGG models are utilized which provide superior accuracy in identifying objects. The regions of the image that are selected as the initial proposals are derived from a greedy algorithm on the key points generated by the Scale Invariant Feature Transform (SIFT) method. Using the deep features in the BOW method provides a good improvement in the recognition and classification of ships. Eventually, we obtained an accuracy of 91.8% in the classification of the ships which shows the improvement of about 5% compared to