Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of RAPD markers to characterize commercially grown rust resistant cultivars of sugarcane.

The brown leaf rust caused by Puccinia melanaocephala (Syd. & P. Syd.) is a major cause of reduction in sugar production and withdrawal of sugarcane cultivars from growing fields. In The present study, the eight commercially grown sugarcane cultivar was characterized under natural inoculation and divided into two discrete groups i.e. four rust resistant and four rust susceptible. The extracted genomic DNA was subjected against RAPD (Random Amplified Polymorphic DNA) markers. After screening of decamers, 7 were picked for further analysis on the basis of polymorphism, which is the most important application of DNA markers. These decamers generated 21 trackable loci with the range of 400-2500 bp and 100% polymorphic loci were recorded genomic DNA of eight sugarcane cultivars. From 7 primers, 3 generated 5 specific loci in 4 sugarcane cultivars, which is a potential use of RAPD-PCR for identifying Saccharum spp. hybrids and clones. Primer G-06 generated only one locus 1200 bp in the rust susceptible cultivar NSG-59 while J-05 had produced two loci i.e. 400 bp and 1100 bp in rust resistant cultivars NSG-555 and CP-77-400 respectively. Primer L-18 generated 700 bp and 1400 bp loci in rust susceptible cultivar CoJS-84. The homology tree was constructed using DNAMAN software based on binary data set. During cluster analysis, we found two major groups of cultivars with different minor groups. This study will be useful to characterize rust resistance in sugarcane and could be extended for the development of SCAR (Sequence Characterized Amplified Region) marker, which is specific, reliable and reproducible marker.
Symmetry, Transactions, and the Mechanism of Wave Function Collapse

The Transactional Interpretation of quantum mechanics exploits the intrinsic time-symmetry of wave mechanics to interpret the ψ and ψ* wave functions present in all wave mechanics calculations as representing retarded and advanced waves moving in opposite time directions that form a quantum “handshake” or transaction. This handshake is a 4D standing-wave that builds up across space-time to transfer the conserved quantities of energy, momentum, and angular momentum in an interaction. Here, we derive a two-atom quantum formalism describing a transaction. We show that the bi-directional electromagnetic coupling between atoms can be factored into a matched pair of vector potential Green’s functions: one retarded and one advanced, and that this combination uniquely enforces the conservation of energy in a transaction. Thus factored, the single-electron wave functions of electromagnetically-coupled atoms can be analyzed using Schrödinger’s original wave mechanics. The technique generalizes to any number of electromagnetically coupled single-electron states—no higher-dimensional space is needed. Using this technique, we show a worked example of the transfer of energy from a hydrogen atom in an excited state to a nearby hydrogen atom in its ground state. It is seen that the initial exchange creates a dynamically unstable situation that avalanches to the completed transaction, demonstrating that wave function collapse, considered mysterious in the literature, can be implemented with solutions of Schrödinger’s original wave mechanics, coupled by this unique combination of retarded/advanced vector potentials, without the introduction of any additional mechanism or formalism. We also analyze a simplified version of the photon-splitting and Freedman–Clauser three-electron experiments and show that their results can be predicted by this formalism.
Slimming Neural Networks Using Adaptive Connectivity Scores

In general, deep neural network (DNN) pruning methods fall into two categories: 1) weight-based deterministic constraints and 2) probabilistic frameworks. While each approach has its merits and limitations, there are a set of common practical issues such as trial-and-error to analyze sensitivity and hyper-parameters to prune DNNs, which plague them both. In this work, we propose a new single-shot, fully automated pruning algorithm called slimming neural networks using adaptive connectivity scores (SNACS). Our proposed approach combines a probabilistic pruning framework with constraints on the underlying weight matrices, via a novel connectivity measure, at multiple levels to capitalize on the strengths of both approaches while solving their deficiencies. In SNACS, we propose a fast hash-based estimator of adaptive conditional mutual information (ACMI), that uses a weight-based scaling criterion, to evaluate the connectivity between filters and prune unimportant ones. To automatically determine the limit up to which a layer can be pruned, we propose a set of operating constraints that jointly define the upper pruning percentage limits across all the layers in a deep network. Finally, we define a novel sensitivity criterion for filters that measures the strength of their contributions to the succeeding layer and highlights critical filters that need to be completely protected from pruning. Through our experimental validation, we show that SNACS is faster by over $17\times $ the nearest comparable method and is the state-of-the-art single-shot pruning method across four standard Dataset-DNN pruning benchmarks: CIFAR10-VGG16, CIFAR10-ResNet56, CIFAR10-MobileNetv2, and ILSVRC2012-ResNet50.
Set-Invariant Constrained Reinforcement Learning with a Meta-Optimizer

This paper investigates reinforcement learning with constraints, which is indispensable in safety-critical environments. To drive the constraint violation monotonically decrease, the constraints are taken as Lyapunov functions, and new linear constraints are imposed on the updating dynamics of the policy parameters such that the original safety set is forward-invariant in expectation. As the new guaranteed-feasible constraints are imposed on the updating dynamics instead of the original policy parameters, classic optimization algorithms are no longer applicable. To address this, we propose to learn a neural network-based meta-optimizer to optimize the objective while satisfying such linear constraints. The constraint-satisfaction is achieved via projection onto a polytope formulated by multiple linear inequality constraints, which can be solved analytically with our newly designed metric. Ultimately, the meta-optimizer trains the policy network to monotonically decrease the constraint violation and maximize the cumulative reward. Numerical results validate the theoretical findings.
GABAA Receptor α1 and β2 Subunit Null Mutant Mice: Behavioral Responses to Ethanol

Mice lacking either the α1 or β2 subunit of the GABAA receptor were tested for ethanol, saccharin, or quinine consumption, ethanol-conditioned place preference, ethanol-conditioned taste aversion, ethanol-simulated motor activity, and handling-induced seizures following chronic consumption of an ethanol liquid diet. The α1 null mutants showed decreased ethanol and saccharin consumption, increased aversion to ethanol, and a marked stimulation of motor activity after injection of ethanol. The β2 null mutants showed decreased consumption of saccharin and quinine, but not ethanol. Surprisingly, neither mutant showed marked changes in handling induced seizures before or after withdrawal of ethanol. The unique effects of deletion of these two GABAA receptor subunits on ethanol responses are discussed in terms of the distinct changes in different populations of GABAA receptors.
Hipparion phlegrae, sp. nov. (Mammalia, Perissodactyla): A New Species from the Turolian Locality of Kryopigi (Kassandra, Chalkidiki, Greece)

ABSTRACT A new hipparion species, Hipparion phlegrae, sp. nov., is described on the basis of both morphological characters and metrical data as a member of Hipparion s.s. It is compared with H. dietrichi, H. prostylum, and similar forms of the Greco-Iranian province. The age of the Kryopigi fauna is discussed as Turolian on the basis of the hipparion assemblage, whereas the coexistence of a small Cremohipparion referred here as C. matthewi/nikosi indicates that H. phlegrae, sp. nov., is probably a derived species in respect to H. prostylum. The decreased size of the H. phlegrae and the short and wide muzzle are discussed for Hipparion s.s. during the Turolian period. Phylogenetic relationships of the new species and related taxa are reconstructed and also considered.
Effects of planting date and plant density on crop growth of cut chrysanthemum

Summary The effects of planting date (season) and plant density (32, 48 or 64 plants m–2) on growth of cut chrysanthemum (Chrysanthemum (Indicum group)) were investigated in six greenhouse experiments, applying the expolinear growth equation. Final plant fresh and dry mass and number of flowers per plant decreased with plant density and this decrease was larger in summer than in winter. Stem length hardly responded to plant density and total dry mass production per m2 linearly increased with plant density. Plant dry-matter content was not influenced by plant density. Plant dry-matter content, plant fresh and dry mass, number of flowers per plant, stem length and biomass production per m2 were all higher in summer than in winter. Final plant fresh mass (y; g) showed a linear relationship with cumulative incident photosynthetic active radiation (PAR) per plant (x; MJ plant–1) (y = 16.7x 1 28.0, R2 = 0.97). This relationship implies proportionality between incident light per plant and plant density (at the same final plant fresh mass), showing that plant density effects were primarily mediated through competition for light. Dry-mass production in time could be described accurately by the expolinear growth equation with three regression parameters: maximum relative growth rate (rm; assumed to be independent of plant density), maximum absolute growth rate (cm) and lost time (tb). rm was 2.4 times higher and cm was 4.1 times higher in summer than in winter and no effect of plant density on cm was observed, whereas tb decreased linearly with increased plant density. Using these parameters and measured maximum leaf area index (LAI) for calculating dynamic growth patterns of LAI, resulted in large over- or under-estimations, except for summer-grown crops. When an extended expolinear growth function was fitted simultaneously on dry-mass production and LAI in time, accurate time curves for LAI were obtained, whereas dry-mass production was accurately described only for the summer crops. Only under rather constant radiation levels during a cultivation (summer), could accurate descriptions for both dry mass production and LAI increase in time be obtained. Light use efficiency (LUE), the slope of the linear relationship between crop growth and cumulative intercepted PAR, varied between 3.4 g MJ–1 in summer and 5.3 g MJ–1 in winter and LUE slightly increased with plant density.
Deep Network Approximation with Discrepancy Being Reciprocal of Width to Power of Depth

A new network with super approximation power is introduced. This network is built with Floor ($\lfloor x\rfloor$) and ReLU ($\max\{0,x\}$) activation functions and hence we call such networks as Floor-ReLU networks. It is shown by construction that Floor-ReLU networks with width $\max\{d,\, 5N+13\}$ and depth $64dL+3$ can pointwise approximate a Lipschitz continuous function $f$ on $[0,1]^d$ with an exponential approximation rate $3\mu\sqrt{d}\,N^{-\sqrt{L}}$, where $\mu$ is the Lipschitz constant of $f$. More generally for an arbitrary continuous function $f$ on $[0,1]^d$ with a modulus of continuity $\omega_f(\cdot)$, the constructive approximation rate is $\omega_f(\sqrt{d}\,N^{-\sqrt{L}})+2\omega_f(\sqrt{d}){N^{-\sqrt{L}}}$. As a consequence, this new network overcomes the curse of dimensionality in approximation power since this approximation order is essentially $\sqrt{d}$ times a function of $N$ and $L$ independent of $d$.
Decoupling Shrinkage and Selection in Gaussian Linear Factor Analysis

Factor Analysis is a popular method for modeling dependence in multivariate data. However, determining the number of factors and obtaining a sparse orientation of the loadings are still major challenges. In this paper, we propose a decision-theoretic approach that brings to light the relation between a sparse representation of the loadings and factor dimension. This relation is done through a summary from information contained in the multivariate posterior. To construct such summary, we introduce a three-step approach. In the first step, the model is fitted with a conservative factor dimension. In the second step, a series of sparse point-estimates, with a decreasing number of factors, is obtained by minimizing an expected predictive loss function. In step three, the degradation in utility in relation to the sparse loadings and factor dimensions is displayed in the posterior summary. The findings are illustrated with applications in classical data from the Factor Analysis literature. We used different prior choices and factor dimensions to demonstrate the flexibility of the proposed method.
Few-shot 3D Point Cloud Semantic Segmentation

Many existing approaches for 3D point cloud semantic segmentation are fully supervised. These fully supervised approaches heavily rely on large amounts of labeled training data that are difficult to obtain and cannot segment new classes after training. To mitigate these limitations, we propose a novel attention-aware multi-prototype transductive few-shot point cloud semantic segmentation method to segment new classes given a few labeled examples. Specifically, each class is represented by multiple prototypes to model the complex data distribution of labeled points. Subsequently, we employ a transductive label propagation method to exploit the affinities between labeled multi-prototypes and unlabeled points, and among the unlabeled points. Furthermore, we design an attention-aware multi-level feature learning network to learn the discriminative features that capture the geometric dependencies and semantic correlations between points. Our proposed method shows significant and consistent improvements compared to baselines in different few-shot point cloud semantic segmentation settings (i.e. 2/3-way 1/5-shot) on two benchmark datasets. Our code is available at https://github.com/Na-Z/attMPTI.
The response of bacterial populations to sapropels in deep sediments of the Eastern Mediterranean (Site 969)

Sediment samples were obtained from 36 depths between the near-surface to almost 100 mbsf (meters below seafloor) at Site 969, south of Crete. Bacterial populations were determined by the acrinine orange direct count technique. The bacterial profile agreed with a general relationship between sediment depth and bacterial concentration previously obtained from a range of different marine sites. Bacterial populations decreased from a near-surface value of 7.76 ´ 108 cells/cm3 to 1.0 ´ 106 cells/ cm3 by 97.79 mbsf. Dividing and divided cell numbers roughly paralleled total bacterial numbers representing ~9.9% of the total population. At two depths, sapropels were specifically sampled in duplicate and enumeration suggested that bacterial populations within sapropels were homogeneous. Two additional sapropels were also encountered by chance. The data strongly indicated an active and probably growing bacterial population within the sapropels because: 1. Bacterial populations were considerably, and significantly, greater than those in adjacent nonsapropel sediment layers. 2. There were locally high TOC concentrations and abundant electron acceptors (sulfate). 3. Evidence of postburial bacterial sulfate reduction activity was supported by independent sulfur isotope data. 4. Dividing bacterial cells were present. These results demonstrate the surprising ability of 4.7 Ma organic matter to continue to provide energy for bacterial populations during burial, and this supports the global presence of a deep bacterial biosphere in marine sediments.
COVID‐19 and Finance: Market Developments So Far and Potential Impacts on the Financial Sector and Centres

Abstract This paper offers an informed commentary on the actual and potential impacts of the pandemic on financial markets, sector and centres, grounded in literature on financial centres, the state‐finance nexus, and trends affecting the landscape of finance since the global financial crisis. We expect a slowdown in new financial regulation, continued firm‐level consolidation, and a continued rise of business services related to finance. The application of new financial technologies is likely to accelerate, affecting retail banking in particular, but will not necessarily be led by FinTech firms. Local and regional financial centres are likely to face larger challenges than leading international centres. As the panic and partial recovery in financial markets in March and April 2020 highlighted the significance of the international monetary hierarchy, with the US$ in the lead, a radical shift of financial power to Asia seems unlikely.
EFFECT OF BAP AND IAA ON SHOOT REGENERATION IN COTYLEDONARY EXPLANTS OF COSTA RICAN MELON GENOTYPES

Cultured cotyledon explants of OSO1, OSO-2, OSO-3, PQRG-1, PQRG-2, PQRG3, and EM-1 “criollo” melon (Cucumis melo L) genotypes were evaluated with regard to their morphogenic response to combinations of N 6 -benzylaminopurine (BAP) (0.1, 0.5 and 1.0 mg.l -1 ) with indolacetic acid (IAA) (0, 0.05 and 0.5 mg.l -1 ). Regardless of BAP and IAA concentration in the shoot induction medium, the highest shoot formation percentages were obtained using EM-1>OSO-1>PQRG-3>OSO2>PQRG-2>PQRG-1>OSO-3. On the other hand, independently of the genotype, the shoot induction medium supplemented with 0.5 mg.l -1 BAP and 0.05 mg.l -1 IAA or 1 mg.l -1 BAP and 0 IAA mg.l -1 resulted in the highest average of shoots. Culture of cotyledons of the genotypes evaluated on induction medium supplemented with different BAP and IAA resulted in a different response. The in vitro culture protocol developed in this study will be useful in micropropagation of “criollo” melon genotypes.
θ
-dependence of light nuclei and nucleosynthesis

We investigate the impact of the QCD vacuum at nonzero $\theta$ on the properties of light nuclei, Big Bang nucleosynthesis, and stellar nucleosynthesis. Our analysis starts with a calculation of the $\theta$-dependence of the neutron-proton mass difference and neutron decay using chiral perturbation theory. We then discuss the $\theta$-dependence of the nucleon-nucleon interaction using a one-boson-exchange model and compute the properties of the two-nucleon system. Using the universal properties of four-component fermions at large scattering length, we then deduce the binding energies of the three-nucleon and four-nucleon systems. Based on these results, we discuss the implications for primordial abundances of light nuclei, the production of nuclei in stellar environments, and implications for an anthropic view of the universe.
Neural Cellular Automata Manifold

Very recently, the Neural Cellular Automata (NCA) has been proposed to simulate the morphogenesis process with deep networks. NCA learns to grow an image starting from a fixed single pixel. In this work, we show that the neural network (NN) architecture of the NCA can be encapsulated in a larger NN. This allows us to propose a new model that encodes a manifold of NCA, each of them capable of generating a distinct image. Therefore, we are effectively learning an embedding space of CA, which shows generalization capabilities. We accomplish this by introducing dynamic convolutions inside an Auto-Encoder architecture, for the first time used to join two different sources of information, the encoding and cell’s environment information. In biological terms, our approach would play the role of the transcription factors, modulating the mapping of genes into specific proteins that drive cellular differentiation, which occurs right before the morphogenesis. We thoroughly evaluate our approach in a dataset of synthetic emojis and also in real images of CIFAR-10. Our model introduces a general-purpose network, which can be used in a broad range of problems beyond image generation.
A Codominant Role of FcγRI/III and C5aR in the Reverse Arthus Reaction1

Recent attempts to specify the relative contribution of FcR and complement in various experimental systems of immune complex disease have led to opposing conclusions. As concluded in IgG FcRγ−/− mice, manifestation of disease is almost exclusively determined by FcγR on effector cells, arguing for a minor role of complement. In contrast, data obtained with C5aR−/− mice suggested that, dependent on the tissue site, complement is more important than FcγR. In this paper, we demonstrate that, in response to IgG immune complex formation, FcγRI/III- and C5aR-mediated pathways are both necessary and only together are they sufficient to trigger the full expression of inflammation in skin and lung. Moreover, both effector systems are not entirely independent, suggesting an interaction between FcγR and C5aR. Therefore, FcγR-mediated responses can be integrated through C5aR activation, which may explain why these two receptor pathways have previously been considered to dominate each other.
Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions

We present our view of what is necessary to build an engaging open-domain conversational agent: covering the qualities of such an agent, the pieces of the puzzle that have been built so far, and the gaping holes we have not filled yet. We present a biased view, focusing on work done by our own group, while citing related work in each area. In particular, we discuss in detail the properties of continual learning, providing engaging content, and being well-behaved -- and how to measure success in providing them. We end with a discussion of our experience and learnings, and our recommendations to the community.
Biochemical indices for instantaneous growth estimation in young cephalopod Sepia officinalis L.

Aspartate transcarbamylase (ATCase) activity was used to estimate instantaneous growth in young cuttlefish (Sepia officinalis) reared under different diet conditions, and compared with estimation obtained by RNA concentration. During the first month of life, the changes in ATCase activity and RNA content of muscle are related to growth. ATCase appears to be a good index of growth during the first stage of intense cell multiplication. ATCase activity is correlated to food intake up to a maximum ration, but decreases when animals are more than 40 days old. The approach of using ATCase activity as a biochemical index for estimating short-term change in growth rates of young cuttlefish in experimental rearing could be extended to young cephalopods collected in the field, and used to predict the effect of biotic factors in recruitment. 2000 International Council for the Exploration of the Sea