Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Lead Stress on Chlorophyll Content, Malondialdehyde and peroxidase activity in Seedlings of mung bean (Vigna radiata)

The presence of heavy metals in solid and liquid wastes is an important issue that is related to the pollution of the environment. These are one of the most important environmental pollutants and reaching dangerous amount requires more research. Among the heavy metals, lead particularly has become a cosmopolitan enviornmental pollutant. This study was conducted to investigate the effects of Pb stress on different varieties (Virat, SML-668, A1 Gold and K-851) of mung bean (Vigna radiata). V.radiata occupies a very important position in Indian agriculture. Seeds of four different mung bean varieties were grown under laboratory conditions in dark at 0- 1500 μM concentration of lead nitrate (Pb(NO3)2). Distilled water was used as control treatment. To access the effect of lead stress on peroxidase activity, Malondialdehyde (MDA) and chlorophyll content,7 day old seedlings were used. The results revealed that the chlorophyll content of of mung bean seedlings significantly (p 0.9) to the increasing concentration of (Pb(NO3)2). Increased amount of MDA was indicative for the formation of free radicals in plants under heavy metal stress, while increased level of peroxidase activity pointed to the occurrence of a scavenging mechanism. The decrease in chlorophyll content is an indication of reduction in the growth of the plants leading to a decrease in the yield.
DOES BURNING OF HARVESTING RESIDUES INCREASE SOIL CARBON STORAGE

La influencia de 31 anos de quema de residuos sobre el almacenaje de carbono y tambien la composicion de la maieria organica (SOM) fue estudiada en un suelo agricola de un experimento de largo plazo. Ademas el potencial de perdida del carbono condensado (BC) que se produce por la quema de los residuos de cosecha por el trasporte horizontal y vertical fue cuantificado durante un experimento de simulacion de lluvia. Nuestros resultados muestran que 31 anos de quema de residuos, como una practica regular de la agricultura, no cambio el almacenaje de C o la composicion quimica de la SOM. Esto es muy probable que se deba a que una pequena cantidad de carbono altamente aromatico entra al suelo. Una porcion significativa de BC depositado en la superficie del suelo por el fuego puede haber sido exportada desde el sitio como fue demostrado durante el experimento de simulacion de lluvia.
Urchin-like Pd@CuO–Pd yolk–shell nanostructures: synthesis, characterization and electrocatalysis

Novel urchin-like Pd@CuO–Pd yolk–shell nanostructures are synthesized through subsequent oxidation of Pd@Cu2O truncated octahedron core–shell precursors with a PdCl42− aqueous solution. The permeable hierarchical CuO shells are constructed by a radially standing 1D single-crystalline nanothorn with Pd nanoparticles. The Pd nanocube cores are encapsulated and confined in the void space of the urchin-like shells. The urchin-like yolk–shell Pd@CuO–Pd nanostructures demonstrate excellent electrocatalytic activity and selectivity for glucose oxidation. Enzyme-free glucose biosensors based on the urchin-like yolk–shell Pd@CuO–Pd electrocatalysts display a higher sensitivity (665.9 μA cm−2 mM−1) than those of the CuO nanoparticles (455.8 μA cm−2 mM−1), Cu2O nanoparticles (220.4 μA cm−2 mM−1), Pd@Cu2O truncated octahedra (179.1 μA cm−2 mM−1), Pd mixtures (65.5 μA cm−2 mM−1) and Pd nanocubes (1.42 μA cm−2 mM−1). The outstanding electrocatalytic performance of the urchin-like Pd@CuO–Pd yolk–shell nanostructures might be ascribed to two reasons: the unique hierarchical yolk–shell structures provide highly exposed active sites and act as an individualized nanoreactor to enhance the mass diffusion and transport of reactants at the electrode/electrolyte interface. Moreover, the nanocomposite of metal oxide semiconductor CuO and noble metal Pd would result in a synergetic effect to improve the electrocatalysis.
Novel Microelectromechanical System Force Transducer to Quantify Contractile Characteristics from Isolated Cardiac Muscle Cells

A custom-designed microelectromechanical system (MEMS) force transducer, with a volume less than 1 mm 3 , is being fabricated to measure force development in isolated cardiac muscle cells to elucidate the physiology of muscle contraction. A single heart cell is attached to flexible, hinged polysilicon plates submerged in a nutrient saline solution. As the cell contracts, the plates bend, and the contractile force can be measured based on the known spring constant of the plate. The amount of deflection is measured by piezoresistive, ion-implanted strain gauges placed at the base of the plates. Prototype structures have been fabricated and have been mechanically tested using probes. We have demonstrated that living rat heart cells can be attached to polysilicon using a silicone sealant. Polysilicon is an inert material when exposed to cardiac cells and their saline environment and has no effect on the cells themselves
Sugar Mimics: An Artificial Receptor for Cholera Toxin

The paper describes the pseudosugar 2 [Galβ1−3GalNAcβ1−4(NeuAcα2−3)DCCHD], a high affinity binder of cholera toxin (CT). The molecule was designed using molecular modeling techniques to mimic the natural CT membrane receptor, ganglioside GM1. The central residue of GM1, a 3,4-disubstituted galactose unit, was recognized as the ganglioside scaffold element and substituted with a conformationally locked cyclohexanediol (DCCHD). DCCHD was synthesized in enantiopure form using enantioselective Diels Alder methodology and regioselectively α-sialylation at the equatorial position. Glycosylation with a Galβ(1−3)GalNAc donor completed the synthesis of 2. The solution structure of 2 and its binding ability to CT were found to be analogous to those of the GM1 oligosaccharide.
Switchable two-color electroluminescence based on a Si metal-oxide-semiconductor structure doped with Eu

A Si metal-oxide-semiconductor electroluminescent device structure is reported which emits two colors, while being doped with a single rare-earth element. Thermally grown SiO2 oxide layers were implanted with Eu and subseqently annealed. Depending on the electrical excitation current, the luminescence is red or blue, which can be ascribed to electronic transitions in tri- and divalent europium (Eu3+ and Eu2+), respectively.
Group actions on contractible $2$-complexes I

In this series of two articles, we prove that every action of a finite group G on a finite and contractible 2-complex has a fixed point. The proof goes by constructing a nontrivial representation of the fundamental group of each of the acyclic 2-dimensional G-complexes constructed by Oliver and Segev. In the first part we develop the necessary theory and cover the cases where G = PSL2(2), G = PSL2(q) with q ≡ 3 (mod 8) or G = Sz(2). The cases G = PSL2(q) with q ≡ 5 (mod 8) are addressed in the second part.
Axi-Higgs cosmology

If the electroweak Higgs vacuum expectation value v in early universe is ∼ 1 % higher than its present value v0=246 GeV, the 7Li puzzle in BBN and the CMB/ΛCDM tension with late-universe measurements on Hubble parameter are mitigated. We propose a model of an axion coupled to the Higgs field, named “axi-Higgs”, with its mass ma ∼ 10-30 - 10-29 eV and decay constant fa ∼ 1017 - 1018 GeV, to achieve this goal. The axion initial value aini yields an initial Δ vini/v0 ∼ 0.01 throughout the BBN-recombination epoch and a percent level contribution to the total matter density today. Because of its very large de Broglie wavelength, this axion matter density ωa suppresses the matter power spectrum, alleviating the CMB/ΛCDM S8/σ8 tension with the weak-lensing data. It also explains the recently reported isotropic cosmic birefringence by its coupling with photons. Adding the axion (m ∼ 10-22eV) in the fuzzy dark matter model to the axi-Higgs model allows bigger Δ vrec and ωa to address the Hubble and S8/σ8 tensions simultaneously. The model predicts that Δ v may be detected by the spectral measurements of quasars, while its oscillation may be observed in the atomic clock measurements.
Controllable and Diverse Text Generation in E-commerce

In E-commerce, a key challenge in text generation is to find a good trade-off between word diversity and accuracy (relevance) in order to make generated text appear more natural and human-like. In order to improve the relevance of generated results, conditional text generators were developed that use input keywords or attributes to produce the corresponding text. Prior work, however, do not finely control the diversity of automatically generated sentences. For example, it does not control the order of keywords to put more relevant ones first. Moreover, it does not explicitly control the balance between diversity and accuracy. To remedy these problems, we propose a fine-grained controllable generative model, called Apex, that uses an algorithm borrowed from automatic control (namely, a variant of the proportional, integral, and derivative (PID) controller) to precisely manipulate the diversity/accuracy trade-off of generated text. The algorithm is injected into a Conditional Variational Autoencoder (CVAE), allowing Apex to control both (i) the order of keywords in the generated sentences (conditioned on the input keywords and their order), and (ii) the trade-off between diversity and accuracy. Evaluation results on real world datasets 1 show that the proposed method outperforms existing generative models in terms of diversity and relevance. Moreover, it achieves about 97% accuracy in the control of the order of keywords. Apex is currently deployed to generate production descriptions and item recommendation reasons in Taobao2, the largest E-commerce platform in China. The A/B production test results show that our method improves click-through rate (CTR) by 13.17% compared to the existing method for production descriptions. For item recommendation reason, it is able to increase CTR by 6.89% and 1.42% compared to user reviews and top-K item recommendation without reviews, respectively.
Perpendicular magnetic anisotropy in Pd/Co and Pt/Co thin‐film layered structures

rf sputtered Pd/Co and Pt/Co thin‐film layered structures (LS) have perpendicular magnetic anisotropy, when the Co layer is ultrathin (<8 A in Pd/Co and <14 A in Pt/Co). The Co thickness (T) dependence of the anisotropy energy (Ku∼T) and the effective anisotropy field (HK∼1/T) in Pd/Co LS support an interfacial anisotropy as the source of the perpendicular magnetic easy axis. In contrast, the anisotropy is independent of Co thickness for thin Co layers in Pt/Co LS, and thus the mechanism for the perpendicular easy axis is thought to be different. We describe magnetization, resistivity, and x‐ray diffraction data that consistently support sharper interfaces in Pd/Co LS than in Pt/Co LS.
Learning Contact Dynamics using Physically Structured Neural Networks

Learning physically structured representa-tions of dynamical systems that include contact between diﬀerent objects is an important problem for learning-based approaches in robotics. Black-box neural networks can learn to approximately represent discontinuous dynamics, but they typically require large quantities of data and often suﬀer from patho-logical behaviour when forecasting for longer time horizons. In this work, we use connec-tions between deep neural networks and dif-ferential equations to design a family of deep network architectures for representing contact dynamics between objects. We show that these networks can learn discontinuous contact events in a data-eﬃcient manner from noisy observations in settings that are tra-ditionally diﬃcult for black-box approaches and recent physics inspired neural networks. Our results indicate that an idealised form of touch feedback—which is heavily relied upon by biological systems—is a key component of making this learning problem tractable. Together with the inductive biases introduced through the network architectures, our techniques enable accurate learning of contact dynamics from observations.
Paraphrases do not explain word analogies

Many types of distributional word embeddings (weakly) encode linguistic regularities as directions (the difference between jump and jumped will be in a similar direction to that of walk and walked, and so on). Several attempts have been made to explain this fact. We respond to Allen and Hospedales’ recent (ICML, 2019) theoretical explanation, which claims that word2vec and GloVe will encode linguistic regularities whenever a specific relation of paraphrase holds between the four words involved in the regularity. We demonstrate that the explanation does not go through: the paraphrase relations needed under this explanation do not hold empirically
Quasi-Distributed Antenna Selection for Spectral Efficiency Maximization in Subarray Switching XL-MIMO Systems

In this paper, we consider the downlink (DL) of a zero-forcing (ZF) precoded extra-large scale massive MIMO (XL-MIMO) system. The base-station (BS) operates with limited number of radio-frequency (RF) transceivers due to high cost, power consumption and interconnection bandwidth associated to the fully digital implementation. The BS, which is implemented with a subarray switching architecture, selects groups of active antennas inside each subarray to transmit the DL signal. This work proposes efficient resource allocation (RA) procedures to perform joint antenna selection (AS) and power allocation (PA) to maximize the DL spectral efficiency (SE) of an XL-MIMO system operating under different loading settings. Two metaheuristic RA procedures based on the genetic algorithm (GA) are assessed and compared in terms of performance, coordination data size and computational complexity. One algorithm is based on a quasi-distributed methodology while the other is based on the conventional centralized processing. Numerical results demonstrate that the quasi-distributed GA-based procedure results in a suitable trade-off between performance, complexity and exchanged coordination data. At the same time, it outperforms the centralized procedures with appropriate system operation settings.
Greedy Multi-step Off-Policy Reinforcement Learning

Multi-step off-policy reinforcement learning has achieved great success. However, existing multi-step methods usually impose a ﬁxed prior on the bootstrap steps, while the off-policy methods often require additional correction, suffering from certain undesired effects. In this paper, we pro-pose a novel bootstrapping method, which greed-ily takes the maximum value among the bootstrapping values with varying steps. The new method has two desired properties:1) it can ﬂexibly adjust the bootstrap step based on the quality of the data and the learned value function; 2) it can safely and robustly utilize data from arbitrary behavior policy without additional correction, whatever its quality or “off-policyness”. We analyze the theoretical properties of the related operator, showing that it is able to converge to the global optimal value function, with a ratio faster than the traditional Bellman Optimality Operator. Furthermore, based on this new operator, we derive new model-free RL algorithms named Greedy Multi-Step Q Learning (and Greedy Multi-step DQN). Experiments reveal that the proposed methods are reliable, easy to implement, and achieves state-of-the-art performance on a series of standard benchmark datasets.
Differentiable Logic Machines

The integration of reasoning, learning, and decision-making is key to build more general artificial intelligence systems. As a step in this direction, we propose a novel neural-logic architecture, called differentiable logic machine (DLM), that can solve both inductive logic programming (ILP) and reinforcement learning (RL) problems, where the solution can be interpreted as a first-order logic program. Our proposition includes several innovations. Firstly, our architecture defines a restricted but expressive continuous relaxation of the space of first-order logic programs by assigning weights to predicates instead of rules, in contrast to most previous neural-logic approaches. Secondly, with this differentiable architecture, we propose several (supervised and RL) training procedures, based on gradient descent, which can recover a fully-interpretable solution (i.e., logic formula). Thirdly, to accelerate RL training, we also design a novel critic architecture that enables actor-critic algorithms. Fourthly, to solve hard problems, we propose an incremental training procedure that can learn a logic program progressively. Compared to state-of-the-art (SOTA) differentiable ILP methods, DLM successfully solves all the considered ILP problems with a higher percentage of successful seeds (up to 3.5$\times$). On RL problems, without requiring an interpretable solution, DLM outperforms other non-interpretable neural-logic RL approaches in terms of rewards (up to 3.9%). When enforcing interpretability, DLM can solve harder RL problems (e.g., Sorting, Path) Moreover, we show that deep logic programs can be learned via incremental supervised training. In addition to this excellent performance, DLM can scale well in terms of memory and computational time, especially during the testing phase where it can deal with much more constants ($>$2$\times$) than SOTA.
Comptonization as an Origin of the Continuum in Intermediate Polars

In this paper we test if the ∼0.3–15 keV XMM-Newton EPIC pn spectral continuum of IPs can be described by the thermal Comptonization compTT model. We used publicly observations of 12 IPs (AE Aqr, EX Hya, V1025 Cen, V2731 Oph, RX J2133.7+5107, PQ Gem, NY Lup, V2400 Oph, IGR J00234+6141, IGR J17195-4100, V1223 Sgr, and XY Ari). We find that our modeling is capable of fitting well the average spectral continuum of these sources. In this framework, UV/soft X-ray seed photons (with 〈kT s〉 of 0.096 ± 0.013 keV) coming presumably from the star surface are scattered off by electrons present in an optically thick plasma (with 〈kT e〉 of 3.05 ± 0.16 keV and optical depth 〈τ〉 of 9.5 ± 0.6 for plane geometry) located nearby (on top) to the more central seed photon emission regions. A soft blackbody (bbody) component is observed in 5 out of the 13 observations analyzed, with a mean temperature 〈kT bb 〉 of 0.095 ± 0.004 keV. We observed that the spectra of IPs show in general two photon indices Γ, which are driven by the source luminosity and optical depth. Low luminosity IPs show 〈Γ〉 of 1.83 ± 0.19, whereas high luminosity IPs show lower 〈Γ〉 of 1.34 ± 0.02. Moreover, the good spectral fits of PQ Gem and V2400 Oph indicate that the polar subclass of CVs may be successfully described by the thermal Comptonization as well.
Strategic Classification in the Dark

Strategic classification studies the interaction between a classification rule and the strategic agents it governs. Under the assumption that the classifier is known, rational agents respond to it by manipulating their features. However, in many real-life scenarios of high-stake classification (e.g., credit scoring), the classifier is not revealed to the agents, which leads agents to attempt to learn the classifier and game it too. In this paper we generalize the strategic classification model to such scenarios. We define the price of opacity as the difference in prediction error between opaque and transparent strategy-robust classifiers, characterize it, and give a sufficient condition for this price to be strictly positive, in which case transparency is the recommended policy. Our experiments show how Hardt et al.'s robust classifier is affected by keeping agents in the dark.
Histo-fetch – on-the-fly processing of gigapixel whole slide images simplifies and speeds neural network training

Background: Training convolutional neural networks using pathology whole slide images (WSIs) is traditionally prefaced by the extraction of a training dataset of image patches. While effective, for large datasets of WSIs, this dataset preparation is inefficient. Methods: We created a custom pipeline (histo-fetch) to efficiently extract random patches and labels from pathology WSIs for input to a neural network on-the-fly. We prefetch these patches as needed during network training, avoiding the need for WSI preparation such as chopping/tiling. Results & Conclusions: We demonstrate the utility of this pipeline to perform artificial stain transfer and image generation using the popular networks CycleGAN and ProGAN, respectively. For a large WSI dataset, histo-fetch is 98.6% faster to start training and used 7535x less disk space.
Contributions from populations and coherences in non-equilibrium entropy production

The entropy produced when a quantum system is driven away from equilibrium can be decomposed in two parts, one related with populations and the other with quantum coherences. The latter is usually based on the so-called relative entropy of coherence, a widely used quantifier in quantum resource theories. In this paper we argue that, despite satisfying fluctuation theorems and having a clear resource-theoretic interpretation, this splitting has shortcomings. First, it predicts that at low temperatures the entropy production will always be dominated by the classical term, irrespective of the quantum nature of the process. Second, for infinitesimal quenches, the radius of convergence diverges exponentially as the temperature decreases, rendering the functions non-analytic. Motivated by this, we provide here a complementary approach, where the entropy production is split in a way such that the contributions from populations and coherences are written in terms of a thermal state of a specially dephased Hamiltonian. The physical interpretation of our proposal is discussed in detail. We also contrast the two approaches by studying work protocols in a transverse field Ising chain, and a macrospin of varying dimension.
Assigning confidence to molecular property prediction

ABSTRACT Introduction: Computational modeling has rapidly advanced over the last decades. Recently, machine learning has emerged as a powerful and cost-effective strategy to learn from existing datasets and perform predictions on unseen molecules. Accordingly, the explosive rise of data-driven techniques raises an important question: What confidence can be assigned to molecular property predictions and what techniques can be used? Areas covered: The authors discuss popular strategies for predicting molecular properties, their corresponding uncertainty sources and methods to quantify uncertainty. First, the authors’ considerations for assessing confidence begin with dataset bias and size, data-driven property prediction and feature design. Next, the authors discuss property simulation via computations of binding affinity in detail. Lastly, they investigate how these uncertainties propagate to generative models, as they are usually coupled with property predictors. Expert opinion: Computational techniques are paramount to reduce the prohibitive cost of brute-force experimentation during exploration. The authors believe that assessing uncertainty in property prediction models is essential whenever closed-loop drug design campaigns relying on high-throughput virtual screening are deployed. Accordingly, considering sources of uncertainty leads to better-informed validations, more reliable predictions and more realistic expectations of the entire workflow. Overall, this increases confidence in the predictions and, ultimately, accelerates drug design.
Improving Deep Learning Sound Events Classifiers Using Gram Matrix Feature-Wise Correlations

In this paper, we propose a new Sound Event Classification (SEC) method which is inspired in recent works for out-of-distribution detection. In our method, we analyse all the activations of a generic CNN in order to produce feature representations using Gram Matrices. The similarity metrics are evaluated considering all possible classes, and the final prediction is defined as the class that minimizes the deviation with respect to the features seeing during training. The proposed approach can be applied to any CNN and our experimental evaluation of four different architectures on two datasets demonstrated that our method consistently improves the baseline models.