Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Molecular Analysis of Freshwater Centric Diatoms with Particular Emphasis on the Nuclear Ribosomal DNA of Stephanodiscus (Bacillariophyceae)

DNA-based discrimination of species is a powerful way for morphologically otherwise similar species, like centric diatoms. Here, the author sequenced long-range nuclear ribosomal DNAs, spanning from the 18S to the D5 region of the 28S rDNA, of Stephanodiscus, particularly including a Korean isolate. By comparisons, high DNA similarities were detected from the rDNAs of nine Stephanodiscus (>99.4% in 18S rDNA, >98.0% in 28S rDNA). Their genetic distances, however, were significantly different (Kruskal-Wallis test, p
Growth rate and tissue magnesium concentration in adult freshwater tilapia, Oreochvomis mossambicus (Peters), fed diets differing in magnesium content

The growth rate and magnesium concentration in scales, bone and muscle of freshwater tilapia, Oreochronus mossambicus (Peters), initially weighing between 70 and 300 g, were followed during low-magnesium feeding. The growth rate decreased in fish on low-magnesium diets, but no changes were observed in the magnesium concentration in the scales, bone or muscle. No changes were observed in calcium or sodium concentrations in these tissues. We conclude that adult tilapia fed a low-magnesium diet manage, in contrast to other fish species, to maintain their magnesium balance and must therefore obtain magnesium from the water.
LOCALIZED ECOTYPES OF THUJA OCCIDENTALIS L. IN WISCONSIN

A bstract. Northern white cedar (Thuja occidentalis L.) sometimes occurs in wet lowlands, and adjacent, relatively dry upland sites suggesting potential localized ecotypic differentiation as a response to selection for moisture stress tolerance. This study examined morphological and physiological responses to root-zone water potential (RWP) of adjacent upland and lowland northern white cedar populations, and related the responses to soil moisture of the habitats as a basis for ecotypic differentiation. In common environment experiments, seedlings from one pair of upland and lowland populations separated by less than 0.7 km illustrated separate ecotypes presumably resulting from natural selection for response to moisture stress. Variation between two study areas in degree of ecotypic differentiation suggested a polytopic origin of the upland and lowland ecotypes. Lowland soils had higher moisture-holding capacity than did upland soils. A study of seedling growth in different soil media showed that as moisture-holding capacity increased in the soil, seedling tops increased in size and root form changed from a long taproot with few laterals to shorter, thicker roots with many laterals. In tests of photosynthetic response to temperature, irradiance, and RWP, 6-mo-old northern white cedar seedlings maintained high rates of net photosynthesis under a wide range of environmental conditions.
Signal Transducer and Activator of Transcription-1 and Heat Shock Factor-1 Interact and Activate the Transcription of the Hsp-70 and Hsp-90β Gene Promoters*

We have previously demonstrated that interleukin-6 (IL-6) increases the levels of the heat shock protein 90 (Hsp-90) and activates the Hsp-90β promoter via the IL-6-activated transcription factors NF-IL6 and signal transducer and activator of transcription-3 (STAT-3). Here, we show that interferon-γ (IFN-γ) treatment increases the levels of Hsp-70 and Hsp-90 and also enhances the activity of the Hsp-70 and Hsp-90β promoters with these effects being dependent on activation of the STAT-1 transcription factor by IFN-γ. These effects were not seen in a STAT-1-deficient cell line, indicating that IFN-γ modulates Hsp induction via a STAT-1-dependent pathway. The effect of IFN-γ/STAT-1 was mediated via a short region of the Hsp-70/Hsp-90 promoters, which also mediates the effects of NF-IL6 and STAT-3 and can bind STAT-1. This region also contains a binding site for the stress-activated transcription factor HSF-1. We show that STAT-1 and HSF-1 interact with one another via a protein-protein interaction and produce a strong activation of transcription, which is in contrast to our previous finding that STAT-3 and HSF-1 antagonize one another. To our knowledge this is the first report of HSF-1 interacting directly via a protein-protein interaction with another transcription factor. Such protein-protein interactions and the binding of a number of different stress and cytokine-activated transcription factors to a short region of the Hsp-90 and Hsp-70 gene promoters are likely to play a very important role in Hsp gene activation by non-stressful stimuli and the integration of these responses with the stress response of these genes.
Simpler Proofs of Quantumness

A proof of quantumness is a method for provably demonstrating (to a classical verifier) that a quantum device can perform computational tasks that a classical device with comparable resources cannot. Providing a proof of quantumness is the first step towards constructing a useful quantum computer. There are currently three approaches for exhibiting proofs of quantumness: (i) Inverting a classically-hard one-way function (e.g. using Shor's algorithm). This seems technologically out of reach. (ii) Sampling from a classically-hard-to-sample distribution (e.g. BosonSampling). This may be within reach of near-term experiments, but for all such tasks known verification requires exponential time. (iii) Interactive protocols based on cryptographic assumptions. The use of a trapdoor scheme allows for efficient verification, and implementation seems to require much less resources than (i), yet still more than (ii). In this work we propose a significant simplification to approach (iii) by employing the random oracle heuristic. (We note that we do not apply the Fiat-Shamir paradigm.) We give a two-message (challenge-response) proof of quantumness based on any trapdoor claw-free function. In contrast to earlier proposals we do not need an adaptive hard-core bit property. This allows the use of smaller security parameters and more diverse computational assumptions (such as Ring Learning with Errors), significantly reducing the quantum computational effort required for a successful demonstration.
Finding Universal Grammatical Relations in Multilingual BERT

Recent work has found evidence that Multilingual BERT (mBERT), a transformer-based multilingual masked language model, is capable of zero-shot cross-lingual transfer, suggesting that some aspects of its representations are shared cross-lingually. To better understand this overlap, we extend recent work on finding syntactic trees in neural networks’ internal representations to the multilingual setting. We show that subspaces of mBERT representations recover syntactic tree distances in languages other than English, and that these subspaces are approximately shared across languages. Motivated by these results, we present an unsupervised analysis method that provides evidence mBERT learns representations of syntactic dependency labels, in the form of clusters which largely agree with the Universal Dependencies taxonomy. This evidence suggests that even without explicit supervision, multilingual masked language models learn certain linguistic universals.
Growth Rate Inheritance in Japanese Quail 2. Early Responses to Selection under Different Nutritional Environments

Abstract IN 1962, Siegel summarized 176 published heritability estimates of body weight obtained for chickens 6 to 12 weeks of age. The median heritability of these estimates, which were obtained by various methods of computation, was 0.41 and the interquartile range was 0.29 to 0.54. Siegel (1962) reported a realized heritability of 0.30 for 8-week body weight, while Maloney et al. (1963) obtained an estimate of 0.34 for body weight at 12 weeks of age. Recently, Maloney et al. (1967) observed a realized heritability of 0.42 in a population selected for high 12-week body weight. Although the magnitude of these reported heritability estimates indicate that responses to selection for body weight are expected to be large, it has generally been observed that response in early generations of selection is greater than that observed in later generations. The utilization of environmental restrictions as selection aids has not been fully investigated. Falconer and .
The SAMI galaxy survey: gas velocity dispersions in low-z star-forming galaxies and the drivers of turbulence

We infer the intrinsic ionized gas kinematics for 383 star-forming galaxies across a range of integrated star formation rates (SFR ∈ [10−3, 102] M⊙ yr−1) at z ≲ 0.1 using a consistent 3D forward-modelling technique. The total sample is a combination of galaxies from the Sydney-AAO Multiobject Integral field Spectrograph (SAMI) Galaxy survey and DYnamics of Newly Assembled Massive Objects survey. For typical low-z galaxies taken from the SAMI Galaxy Survey, we find the vertical velocity dispersion (σv,z) to be positively correlated with measures of SFR, stellar mass, H i gas mass, and rotational velocity. The greatest correlation is with SFR surface density (ΣSFR). Using the total sample, we find σv,z increases slowly as a function of integrated SFR in the range SFR ∈ [10−3, 1] M⊙ yr−1 from 17 ± 3 to 24 ± 5 km s−1 followed by a steeper increase up to σv,z ∼80 km s−1 for SFR ≳ 1 M⊙ yr−1. This is consistent with recent theoretical models that suggest a σv,z floor driven by star formation feedback processes with an upturn in σv,z at higher SFR driven by gravitational transport of gas through the disc.
Nonresonant Density of States Enhancement at Low Energies for Three or Four Neutrons.

The low energy systems of three or four neutrons are treated within the adiabatic hyperspherical framework, yielding an understanding of the low energy quantum states in terms of an adiabatic potential energy curve. The dominant low energy potential curve for each system, computed here using widely accepted nucleon-nucleon interactions with and without the inclusion of a three-nucleon force, shows no sign of a low energy resonance. However, both systems exhibit a low energy enhancement of the density of states, or of the Wigner-Smith time delay, which derives from long-range universal physics analogous to the Efimov effect. That enhancement could be relevant to understanding the low energy excess of correlated four-neutron ejection events observed experimentally in a nuclear reaction by Kisamori et al. [Phys. Rev. Lett. 116, 052501 (2016)PRLTAO0031-900710.1103/PhysRevLett.116. 052501].
Gaussian fluctuations and a law of the iterated logarithm for Nerman’s martingale in the supercritical general branching process

In his, by now, classical work from 1981, Nerman made extensive use of a crucial martingale $(W_t)_{t \geq 0}$ to prove convergence in probability, in mean and almost surely, of supercritical general branching processes (a.k.a. Crump-Mode-Jagers branching processes) counted with a general characteristic. The martingale terminal value $W$ figures in the limits of his results. We investigate the rate at which the martingale, now called Nerman's martingale, converges to its limit $W$. More precisely, assuming the existence of a Malthusian parameter $\alpha > 0$ and $W_0\in L^2$, we prove a functional central limit theorem for $(W-W_{t+s})_{s\in\mathbb{R}}$, properly normalized, as $t\to\infty$. The weak limit is a randomly scaled time-changed Brownian motion. Under an additional technical assumption, we prove a law of the iterated logarithm for $W-W_t$.
Local well-posedness for the Klein-Gordon-Zakharov system in 3D

We study the Cauchy problem for the Klein-Gordon-Zakharov system in 3D with low regularity data. We lower down the regularity to the critical value with respect to scaling up to the endpoint. The decisive bilinear estimates are proved by means of methods developed by Bejenaru-Herr for the Zakharov system and already applied by Kinoshita to the Klein-Gordon-Zakharov system in 2D.
Electron transport properties of graphene nanoribbons with Gaussian deformation

Gaussian deformation in graphene structures exhibits an interesting effect in which flower-shaped confinement states are observed in the deformed region [Carrillo-Bastos et al., Phys. Rev. B 90 041411 (2014)]. To exploit such a deformation for various applications, tunable electronic features including a bandgap opening for semi-metallic structures are expected. Besides, the effects of disorders and external excitations also need to be considered. In this work, we present a systematic study on quantum transport of graphene ribbons with Gaussian deformation. Different levels of deformation are explored to find a universal behavior of the electron transmission. Using a tight-binding model in combination with Non-Equilibrium Green Functions formalism, we show that Gaussian deformation influences strongly the electronic properties of ribbons in which the electron transmission decreases remarkably in high energy regions even if small deformations are considered. Interestingly, it unveils that the first plateau of the transmission of semi-metallic armchair ribbons is just weakly affected in the case of small deformations. However, significant large Gaussian bumps can induce a strong drop of this plateau and a transport gap is formed. The transmission at the zero energy is found to decrease exponentially with increasing the size of the Gaussian bump. Moreover, the gap of semi-conducting ribbons is enlarged with large deformations. The opening or the widening of the transport gap in large deformed armchair structures is interpreted by a formation of a three-zone behavior along the transport direction of the hopping profile. On the other hand, a transport gap is not observed in zigzag ribbons regardless of the size of Gaussian bumps. This behavior is due to the strong localization of edge states at the energy point E = 0.
Continual Learning Using Task Conditional Neural Networks

Conventional deep learning models have limited capacity in learning multiple tasks sequentially. The issue of forgetting the previously learned tasks in continual learning is known as catastrophic forgetting or interference. When the input data or the goal of learning change, a continual model will learn and adapt to the new status. However, the model will not remember or recognise any revisits to the previous states. This causes performance reduction and re-training curves in dealing with periodic or irregularly reoccurring changes in the data or goals. The changes in goals or data are referred to as new tasks in a continual learning model. Most of the continual learning methods have a task-known setup in which the task identities are known in advance to the learning model. We propose Task Conditional Neural Networks (TCNN) that does not require to known the reoccurring tasks in advance. We evaluate our model on standard datasets using MNIST and CIFAR10, and also a real-world dataset that we have collected in a remote healthcare monitoring study (i.e. TIHM dataset). The proposed model outperforms the state-of-the-art solutions in continual learning and adapting to new tasks that are not defined in advance.
Dynamics of an Intra-host Diffusive Pathogen Infection Model

In this paper, we first propose a diffusive pathogen infection model with general incidence rate which incorporates cell-to-cell transmission. By applying the theory of monotone dynamical systems, we prove that the model admits the global threshold dynamics in terms of the basic reproduction number ($\mathcal{R}_{0}$), which is defined by the spectral radius of the next generation operator. Then, we derive a discrete counterpart of the continuous model by nonstandard finite difference scheme. The results show that the discrete model preserves the positivity and boundedness of solutions in order to ensure the well-posedness of the problem. Moreover, this method preserves all equilibria of the original continuous model. By constructing appropriate Lyapunov functionals for both models, we show that the global threshold dynamics is completely determined by the basic reproduction number. Further, with the help of sensitivity analysis we also have identified the most sensitive parameters which effectively contribute to change the disease dynamics. Finally, we conclude the paper by an example and numerical simulations to improve and generalize some known results.
Synaptic Learning With Augmented Spikes

Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements in efficiency and computational capability. They extend the computation of traditional neurons with an additional dimension of time carried by all-or-nothing spikes. Could one benefit from both the accuracy of analog values and the time-processing capability of spikes? In this article, we introduce a concept of augmented spikes to carry complementary information with spike coefficients in addition to spike latencies. New augmented spiking neuron model and synaptic learning rules are proposed to process and learn patterns of augmented spikes. We provide systematic insights into the properties and characteristics of our methods, including classification of augmented spike patterns, learning capacity, construction of causality, feature detection, robustness, and applicability to practical tasks, such as acoustic and visual pattern recognition. Our augmented approaches show several advanced learning properties and reliably outperform the baseline ones that use typical all-or-nothing spikes. Our approaches significantly improve the accuracies of a temporal-based approach on sound and MNIST recognition tasks to 99.38% and 97.90%, respectively, highlighting the effectiveness and potential merits of our methods. More importantly, our augmented approaches are versatile and can be easily generalized to other spike-based systems, contributing to a potential development for them, including neuromorphic computing.
Fast and accurate detection of Covid-19-related pneumonia from chest X-ray images with novel deep learning model

Background: Novel coronavirus disease has spread rapidly worldwide. As recent radiological literatures on Covid-19 related pneumonia is primarily focused on CT findings, the American College of Radiology (ACR) recommends using portable chest X-radiograph (CXR). A tool to assist for detection and monitoring of Covid-19 cases from CXR is highly required. Purpose: To develop a fully automatic framework to detect Covid-19 related pneumonia using CXR images and evaluate its performance. Materials and Methods: In this study, a novel deep learning model, named CovIDNet (Covid-19 Indonesia Neural-Network), was developed to extract visual features from chest x-ray images for the detection of Covid-19 related pneumonia. The model was trained and validated by chest x-rays datasets collected from several open source provided by GitHub and Kaggle. Results and Discussion: In the validation stage using open-source data, the accuracy to recognize Covid-19 and others classes reaches 98.44%, that is, 100% Covid-19 precision and 97% others precision. Discussion: The use of the model to classify Covid-19 and other pathologies might slightly decrease the accuracy. Although SoftMax was used to handle classification bias, this indicates the benefit of additional training upon the introduction of new set of data. Conclusion: The model has been tested and get 98.4% accuracy for open source datasets, the sensitivity and specificity are 100% and 96.97%, respectively.
Non-Autoregressive Image Captioning with Counterfactuals-Critical Multi-Agent Learning

Most image captioning models are autoregressive, i.e. they generate each word by conditioning on previously generated words, which leads to heavy latency during inference. Recently, non-autoregressive decoding has been proposed in machine translation to speed up the inference time by generating all words in parallel. Typically, these models use the word-level cross-entropy loss to optimize each word independently. However, such a learning process fails to consider the sentence-level consistency, thus resulting in inferior generation quality of these non-autoregressive models. In this paper, we propose a Non-Autoregressive Image Captioning (NAIC) model with a novel training paradigm: Counterfactuals-critical Multi-Agent Learning (CMAL). CMAL formulates NAIC as a multi-agent reinforcement learning system where positions in the target sequence are viewed as agents that learn to cooperatively maximize a sentence-level reward. Besides, we propose to utilize massive unlabeled images to boost captioning performance. Extensive experiments on MSCOCO image captioning benchmark show that our NAIC model achieves a performance comparable to state-of-the-art autoregressive models, while brings 13.9x decoding speedup.
Frequently cocited publications: Features and kinetics

Cocitation measurements can reveal the extent to which a concept representing a novel combination of existing ideas evolves towards a specialty. The strength of cocitation is represented by its frequency, which accumulates over time. Of interest is whether underlying features associated with the strength of cocitation can be identified. We use the proximal citation network for a given pair of articles (x, y) to compute θ, an a priori estimate of the probability of cocitation between x and y, prior to their first cocitation. Thus, low values for θ reflect pairs of articles for which cocitation is presumed less likely. We observe that cocitation frequencies are a composite of power-law and lognormal distributions, and that very high cocitation frequencies are more likely to be composed of pairs with low values of θ, reflecting the impact of a novel combination of ideas. Furthermore, we note that the occurrence of a direct citation between two members of a cocited pair increases with cocitation frequency. Finally, we identify cases of frequently cocited publications that accumulate cocitations after an extended period of dormancy.
Asymptotics of maxima of (2,1) and (1,2) random walks with asymptotically zero drifts and product of nonnegative 2-by-2 matrices

Consider (2,1) and (1,2) random walks with asymptotically zero drifts. Let $M$ be the maximum of an excursion starting from $2$ and ending at some point below $2. $ We study the distribution of $M$ and characterize its asymptotics, which are quite different from the ones of simple random walks. Our methods are based on the asymptotics of product of 2-by-2 nonnegative matrices and continued fractions. So, we also get a limit theorem of the product of nonnegative 2-by-2 matrices which has its own interest. Let $A_kA_{k-1}\cdots A_1$ be the product of some nonnegative 2-by-2 matrices. Under certain conditions, we show that $\forall i,j= 1,2,$ $(A_kA_{k-1}\cdots A_1)_{i,j}\sim c\varrho(A_k)\varrho(A_{k-1})\cdots \varrho(A_1)$ as $k\rightarrow\infty,$ where $\varrho(A_n)$ is the spectral radius of $A_n$ and $c\in(0,\infty)$ is some constant.
Discards and discarding practices in German fisheries in the North Sea and Northeast Atlantic during 2002–2008

Summary Since 2002, Germany is obliged to collect fishery data to support the European Common Fisheries Policy. Sampling of the commercial fishery under German flag is one duty within the EU fisheries data collection programme. Between 2002 and 2008, 191 fishing trips were sampled by scientific observers onboard commercial fishing vessels to collect data on catch compositions with special emphasis on the proportions of landed and discarded fish. The results show that the proportion of discard is depending on the type of fishing activity (fishing metier). Highest discard rates were observed on beam trawl fishing trips. At the level of individual fishing trips, discard rates varied extremely within a fishing metier. Highest variations were found in the beam and bottom otter trawl fishery. In contrast, the discard rates in high-seas fisheries were low. Reasons for discarding are discussed in the current legislation and policy context.