Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship between Coleoptile Elongation and Alcoholic Fermentation in Rice Exposed to Anoxia. II. Cultivar Differences

The relationship between coleoptile elongation and survival vs. alcoholic fermentation of rice under anoxia is examined using eight cultivars differing in submergence tolerance. Anoxia was imposed on either 1 or 4 d aerated seeds either by N2 flushing submerged tissues or by incubating tissues in stagnant deoxygenated agar at 0·1% w/v; the latter simulated the stagnant conditions of waterlogged soil. Two cultivars that were most tolerant to submergence also had the greatest tolerance to anoxia, while a submergence intolerant cultivar was also intolerant to anoxia. Coleoptile growth under anoxia was related to rates of ethanol synthesis (RE), however differences between growth during anoxia and survival after anoxia indicated that post-anoxic injury may also be important in rice seeds exposed to anoxia. The correlation between coleoptile growth and RE measured on a tissue basis using intact seeds was r2 = 0·67 among six varieties over 0-3 d anoxia. This correlation improved to about r2 = 0·85 using RE of (embryos plus coleoptiles) over 0-3 d, or coleoptiles at 3 d after anoxia. Coleoptile growth of individual seeds was usually poorly correlated to RE in these cultivars at 2-3 d after anoxia. When coleoptiles of similar lengths were obtained from different cultivars using 4 d aerated seeds, there were differences in RE and coleoptile growth which were related to coleoptile growth during 3 or 5 d anoxia, either on a tissue (r2 = 0·85) or a fresh weight basis (r2 = 0·70-0·97 respectively). Results are discussed in relation to factors which may limit ethanol synthesis in rice exposed to anoxia and the importance of growth to the survival of seeds and mature plants during submergence in the natural environment.
Network Massive MIMO Transmission Over Millimeter-Wave and Terahertz Bands: Mobility Enhancement and Blockage Mitigation

Mobility and blockage are two critical challenges in wireless transmission over millimeter-wave (mmWave) and Terahertz (THz) bands. In this paper, we investigate network massive multiple-input multiple-output (MIMO) transmission for mmWave/THz downlink in the presence of mobility and blockage. Considering the mmWave/THz propagation characteristics, we first propose to apply per-beam synchronization for network massive MIMO to mitigate the channel Doppler and delay dispersion effects. Accordingly, we establish a transmission model. We then investigate network massive MIMO downlink transmission strategies with only the statistical channel state information (CSI) available at the base stations (BSs), formulating the strategy design as an optimization problem to maximize the network sum-rate. We show that the beam domain is favorable to perform transmission, and demonstrate that BSs can work individually when sending signals to user terminals. Based on these insights, the network massive MIMO precoding design is reduced to a network sum-rate maximization problem with respect to beam domain power allocation. By exploiting the sequential optimization method and random matrix theory, an iterative algorithm with guaranteed convergence performance is further proposed for beam domain power allocation. Numerical results reveal that the proposed network massive MIMO transmission approach with the statistical CSI can effectively alleviate the blockage effects and provide mobility enhancement over mmWave and THz bands.
Token Manipulation Generative Adversarial Network for Text Generation

MaskGAN opens the query for the conditional language model by filling in the blanks between the given tokens. In this paper, we focus on addressing the limitations caused by having to specify blanks to be filled. We decompose conditional text generation problem into two tasks, make-a-blank and fill-in-the-blank, and extend the former to handle more complex manipulations on the given tokens. We cast these tasks as a hierarchical multi agent RL problem and introduce a conditional adversarial learning that allows the agents to reach a goal, producing realistic texts, in cooperative setting. We show that the proposed model not only addresses the limitations but also provides good results without compromising the performance in terms of quality and diversity.
INVITED ARTICLE: Building Journal Impact Factor Quartile into the Assessment of Academic Performance: A Case Study

This study aims to provide information about the Q Concept defined as the division of journal impact factors into quartiles based on given field categories so that the disadvantages resulting from the direct use of journal impact factors can be eliminated. While the number of "Original articles published in the Web of Science (WoS) database-indexed journals like SCI, SSCI and A&HCI" is an important indicator for research assessment in Turkey, neither the journal impact factors nor the Q Concept of these papers have been taken into account. Present study analyzes the scientific production of the Amasya University researchers in journals indexed in WoS database in the period 2014-2018 using the Q concept. The share of publications by Q category journals as well as the average citations received by the works from Amasya University were compared to the average situation in Turkey and other different countries in the world. Results indicate that the articles published by Amasya University researchers were mostly published in low impact factor journals (Q4 journals) (36.49%), in fact, only a small share of papers were published in high impact journals (14.32% in Q1 journals). The share of papers published in low impact journals by researchers from Amasya University is higher than the Turkish average and much higher than the scientific leading countries. The average citations received by papers published in Q1 journals was around six times higher than papers published in Q4 journals (8.92 vs. 1.56), thus papers published in Q1 journals received 30.02% citations despite only 14.32% of the papers was published in these journals. The share of papers published which were never cited in WoS was 27.48%, increasing from 9.68% in Q1 to almost half (48.10%) in Q4. The study concludes with some suggestions on how and where the Q Concept can be used.
A Bernoulli Mixture Model to Understand and Predict Children Longitudinal Wheezing Patterns

In this research, we estimate that around $27.99(\pm2.15)\%$ of the population has experienced wheezing before turning 1 in the United Kingdom. Furthermore, the Bernoulli Mixture Model classification is found to work best with $K=4$ clusters in order to better balance the separability of the clusters with their explanatory nature, based on a cohort of $N=1184$. The probability of the group of parents in the $j$th cluster to say that their children have wheezed during the $i$th age is assumed $P_{ij} \sim \text{Beta}(1/2, 1/2)$, the probabilities of assignment to each cluster is $R \sim \text{Dirichlet}_K(\alpha)$, the assignment of the $n$th patient to each cluster is $Z_n\ |\ R \sim \text{Categorical}(R)$, and the $n$th patient wheezed during the $i$th age is $X_{in}\ |\ P_{ij}, Z_n \sim \text{Bernoulli}(P_{i,Z_n})$; where $i\in\{1,\dots,6\}$, $j\in\{1,\dots,K\}$, and $n\in\{1,\dots, N\}$. The classification is then performed through the E-M optimization algorithm. We found that this clustering method groups efficiently the patients with late-childhood wheezing, persistent wheezing, early-childhood wheezing, and none or sporadic wheezing. Furthermore, we found that this method is not dependent on the data-set, and can include data-sets with missing entries.
Mimicry: Towards the Reproducibility of GAN Research

Advancing the state of Generative Adversarial Networks (GANs) research requires one to make careful and accurate comparisons with existing works. Yet, this is often difficult to achieve in practice when models are often implemented differently using varying frameworks, and evaluated using different procedures even when the same metric is used. To mitigate these issues, we introduce Mimicry, a lightweight PyTorch library that provides implementations of popular state-of-the-art GANs and evaluation metrics to closely reproduce reported scores in the literature. We provide comprehensive baseline performances of different GANs on seven widely-used datasets by training these GANs under the same conditions, and evaluating them across three popular GAN metrics using the same procedures. The library can be found at this https URL.
Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia

The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With this performance, the proposed algorithm could potentially aid radiologists with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19 outbreak.
Cytological Studies of Sterility in Hybrids between Distantly Related Varieties of Rice, Oryza stativa, L

With a number of rice varieties and their F1 hybrids, the behavior of chromosomes in pollen mother cells was investigated. The occasional occurrence of univalent chromosomes, “stretched chromosomes”, and anaphasic bridges was pointed out. All of these abnormalities were found both in F1 plants and in parental pure lines, and in the frequency of occurrence of these phenomena, the F1 and parental plants did not differ significantly. Thus the anaphasic bridge cannot be attributed to inversion. An explanation may be that they come from an occasional reunion of sister chromatids after a breakage at the diplotene stage in meiosis.
Generating PBHs and small-scale GWs in two-field models of inflation

Primordial black holes (PBHs) generated by gravitational collapse of large primordial overdensities can be a fraction of the observed dark matter. In this paper, we introduce a mechanism to produce a large peak in the primordial power spectrum (PPS) in two-field inflationary models characterized by two stages of inflation based on a large non-canonical kinetic coupling. This mechanism is generic to several two-field inflationary models, due to a temporary tachyonic instability of the isocurvature perturbations at the transition between the two stages of inflation. We numerically compute the primordial perturbations from largest scales to the small scales corresponding to that of PBHs using an extension of BINGO (BI-spectra and Non-Gaussianity Operator). Moreover we numerically compute the stochastic background of gravitational waves (SBGW) produced by second order scalar perturbations within frequencies ranging from nano-Hz to KHz that covers the observational scales corresponding to Pulsar Timing Arrays, Square Kilometer Array to that of Einstein telescope. We discuss the prospect of its detection by these proposed and upcoming gravitational waves experiments.
Spin and orbital angular momentum dynamics in counterpropagating vectorially structured light

It is well-known that electric spin angular momentum and electric orbital angular momentum are conserved under paraxial propagation of travelling waves in free-space. Here we study the electric and magnetic angular momentum in counter-propagating waves and show both theoretically and experimentally that neither component alone is conserved except in special cases. We attribute this non-conservation to spin-spin and orbit-orbit coupling between the electric and magnetic fields. This work generalises previous findings based on travelling waves, explains the apparent spin-orbit coupling in counter-propagating paraxial light, and broadens our understanding of angular momentum conservation in arbitrary structured light waves.
Learning to Understand Child-directed and Adult-directed Speech

Speech directed to children differs from adult-directed speech in linguistic aspects such as repetition, word choice, and sentence length, as well as in aspects of the speech signal itself, such as prosodic and phonemic variation. Human language acquisition research indicates that child-directed speech helps language learners. This study explores the effect of child-directed speech when learning to extract semantic information from speech directly. We compare the task performance of models trained on adult-directed speech (ADS) and child-directed speech (CDS). We find indications that CDS helps in the initial stages of learning, but eventually, models trained on ADS reach comparable task performance, and generalize better. The results suggest that this is at least partially due to linguistic rather than acoustic properties of the two registers, as we see the same pattern when looking at models trained on acoustically comparable synthetic speech.
Salinity Resistance in Rice (Oryza sativa L.) And a Pyramiding Approach to Breeding Varieties for Saline Soils

Varieties and breeding lines of rice (Oryza sativa L.) differ in their resistance to NaCl salinity and in their Na uptake. These varieties (which are not single homozygous lines) also show large variation between individuals. The gross uptake of Na+ is, however, poorly correlated with growth and survival because net transport interacts with vigour and degree of dwarfing and because NaCl is compartmentalized within the plant confounding any simple relationship between tissue concentration, growth reduction and toxicity symptoms. It is argued that resistance is conferred by no single factor, but is the sum of a number of contributory traits which include variation in (1) NaCl entry via membrane leakage or apoplastic pathways; (2) preferential accumulation of Na+ in the older leaves; and (3) tolerance of NaCl within the leaf tissue which may reflect differences in apoplastic salt load. It is suggested that the salt resistance of rice can be increased by selecting separately for such physiological traits and then pyramiding them together.
Producing and storing spin-squeezed states and Greenberger-Horne-Zeilinger states in a one-dimensional optical lattice

We study the dynamical generation and storage of spin squeezed states, as well as more entangled states up to macroscopic superpositions, in a system composed by a few ultra-cold atoms trapped in a one-dimensional optical lattice. The system, initially in the superfluid phase with each atom in a superposition of two internal states, is first dynamically entangled by atom-atom interactions then adiabatically brought to the Mott-insulator phase with one atom per site where the quantum correlations are stored. Exact numerical diagonalization allows us to explore the structure of the stored states by looking at various correlation functions, on site and between different sites, both at zero temperature and at finite temperature, as it could be done in an experiment with a quantum-gas microscope.
Impact-induced hardening in dense frictional suspensions

By employing the lattice Boltzmann method, we perform simulations of dense suspensions under impacts, which incorporate the contact between suspended particles as well as the free surface of the suspension. Our simulation for a free falling impactor on a dense suspension semi-quantitatively reproduces experimental results, where we observe rebounds of the impactor by the suspension containing frictional particles for high speed impact and high volume fraction shortly after the impact before subsequently sinking. We observe that the response depends on the radius of the impactor, which leads to fitting our simulation data to a phenomenological model based on the Hertzian contact theory. When the rebound takes place, percolated force chains are formed by the frictional contacts between suspended particles. Furthermore, persistent homology analysis is used to elucidate the significance of the topological structure of the force chains, where the total persistence of connected components correlates with the force supporting the impactor.
Distance, magnetic field, and kinematics of the filamentary cloud LDN 1157

LDN 1157, is one of the several clouds situated in the cloud complex, LDN 1147/1158, represents a coma-shaped morphology with a well-collimated bipolar outflow emanating from a Class 0 protostar, LDN 1157-mm. The main goals of this work are (a) to map the inter-cloud magnetic field (ICMF) geometry of the region surrounding LDN 1157 to investigate its relationship with the cloud morphology, with the outflow direction and with the core magnetic field (CMF) geometry inferred from the mm- and sub-mm polarization results from the literature, and (b) to investigate the kinematic structure of the cloud. We carried out R-band polarization observations of the stars projected on the cloud to map the pc-scale magnetic field geometry and made spectroscopic observations of the entire cloud in 12CO, C18O and N2H+ (J=1-0) lines to investigate its kinematic structure. We obtained a distance of 340$\pm$3 pc to the LDN 1147/1158, complex based on the Gaia DR2 parallaxes and proper motion values of the three YSOs associated with the complex. A single filament of $\sim1.2$ pc in length and $\sim0.09$ pc in width is found to run all along the coma-shaped cloud. Based on the relationships between the ICMF, CMF, filament orientations, outflow direction, and the presence of an hour-glass morphology of the magnetic field, it is likely that the magnetic field had played an important role in the star formation process in LDN 1157. Combining the proper motions of the YSOs and the radial velocity of LDN 1147/1158 and another complex LDN 1172/1174 which is situated $\sim2$\dgr~east of it, we found that both the complexes are moving collectively toward the Galactic plane. The filamentary morphology of the east-west segment of LDN 1157 may have formed as a result of mass lost by ablation due to the interaction of the moving cloud with the ambient interstellar medium.
A Robust Bayesian Copas Selection Model for Quantifying and Correcting Publication Bias

The validity of conclusions from meta-analysis is potentially threatened by publication bias. Most existing procedures for correcting publication bias assume normality of the between-study heterogeneity. However, this assumption may not be valid, and the performance of these procedures may be highly sensitive to departures from normality. Further, there exist few measures to quantify the magnitude of publication bias based on selection models. In this paper, we address both of these issues. First, we introduce the robust Bayesian Copas (RBC) selection model. This model serves as a default prior that requires minimal problem-specific tuning, offers robustness to strong assumptions about the distribution of heterogeneity, and facilitates automatic inference of the unknown parameters. Second, we develop a new measure to quantify the magnitude of publication bias. Our measure is easy to interpret and takes advantage of the natural estimation uncertainty afforded by the posterior distribution. We illustrate our proposed approach through simulation studies and analysis of real data sets. Our methods are implemented in the publicly available R package RobustBayesianCopas.
Density-independent and density-dependent factors affecting temporal changes in spatial distributions of eastern Bering Sea flatfish

The general warming of the eastern Bering Sea (EBS) and the wide range of abundance exhibited by several eastern Bering Sea flatfish motivated an examination of how density-dependent and density-independent factors may influence the spatial distributions of EBS flatfish. In this study, EBS trawl survey data from 1982 to 2006 were used to examine how temporal changes in the distributions of six flatfish species groups [yellowfin sole (Limanda aspera), rock sole (Lepidopsetta sp.), flathead sole (Hippoglossoides sp.), Alaska plaice (Pleuronectes quadrituberculatus), arrowtooth flounder (Atheresthes sp.), and Greenland turbot (Reinhardtius hippoglossoides)] are related to temporal changes in the location of the ‘cold pool’ (bottom water < 2� C), and how the area occupied by flatfish are related to the cold pool and population abundance. Rock sole and flathead sole distributions have generally moved northwest since 1982 and are significantly correlated with the movement of the cold pool, whereas arrowtooth flounder avoid the cold pool and their area occupied is inversely related to the size of the cold pool. The area occupied by arrowtooth flounder and rock sole are also significantly related to stock abundance. Multivariate statistical models indicate that the location of rock sole is more strongly related to stock abundance than to the cold pool, whereas the area occupied by arrowtooth flounder is more strongly related to the area of the cold pool rather than abundance. The temperatures occupied by several flatfish stocks indicate a substantial variability in suitable temperatures. These results suggest that a complex suite of density-dependent and density-independent factors may determine the response of EBS flatfish spatial distributions to increasing temperatures.
Dependency Aware Filter Pruning

Convolutional neural networks (CNNs) are typically over-parameterized, bringing considerable computational overhead and memory footprint in inference. Pruning a proportion of unimportant filters is an efficient way to mitigate the inference cost. For this purpose, identifying unimportant convolutional filters is the key to effective filter pruning. Previous work prunes filters according to either their weight norms or the corresponding batch-norm scaling factors, while neglecting the sequential dependency between adjacent layers. In this paper, we further develop the norm-based importance estimation by taking the dependency between the adjacent layers into consideration. Besides, we propose a novel mechanism to dynamically control the sparsity-inducing regularization so as to achieve the desired sparsity. In this way, we can identify unimportant filters and search for the optimal network architecture within certain resource budgets in a more principled manner. Comprehensive experimental results demonstrate the proposed method performs favorably against the existing strong baseline on the CIFAR, SVHN, and ImageNet datasets. The training sources will be publicly available after the review process.