Miracle-dz/UniFAR-FTUs huggingface dataset:
Invasive alien plant species in riparian areas of Japan : the contribution of agricultural weeds, revegetation species and aquacultural species

In Japanese riparian habitats, invasion by alien plants constitutes one of the most serious threats to biodiversity through the displacement of native plants. This paper presents a list of “invasive alien plant species” in Japanese riparian habitats. Invasive alien species, defined here as dominant or codominant species in certain plant communities, can be categorized as “agricultural weeds,” “revegetation species,” or “aquacultural species. ” In riparian areas, the category of agricultural weeds contains significantly more invasive alien species than would be expected by chance. The same result was found for introduced revegetation species. To prevent the introduction of new invaders into riparian areas, appropriate alien plant management is needed for both natural habitats and agricultural areas of the catchments. The use of alien pasture species for revegetation or control of soil erosion should be avoided in order to conserve biodiversity and native ecosystems.
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers

We propose a simple, practical, and intuitive approach for domain adaptation in reinforcement learning. Our approach stems from the idea that the agent's experience in the source domain should look similar to its experience in the target domain. Building off of a probabilistic view of RL, we formally show that we can achieve this goal by compensating for the difference in dynamics by modifying the reward function. This modified reward function is simple to estimate by learning auxiliary classifiers that distinguish source-domain transitions from target-domain transitions. Intuitively, the modified reward function penalizes the agent for visiting states and taking actions in the source domain which are not possible in the target domain. Said another way, the agent is penalized for transitions that would indicate that the agent is interacting with the source domain, rather than the target domain. Our approach is applicable to domains with continuous states and actions and does not require learning an explicit model of the dynamics. On discrete and continuous control tasks, we illustrate the mechanics of our approach and demonstrate its scalability to high-dimensional tasks.
Gamma Boltzmann Machine for Simultaneously Modeling Linear- and Log-amplitude Spectra

In audio applications, one of the most important representations of audio signals is the amplitude spectrogram. It is utilized in many machine-learning-based information processing methods including the ones using the restricted Boltzmann machines (RBM). However, the ordinary Gaussian-Bernoulli RBM (the most popular RBM among its variations) cannot directly handle amplitude spectra because the Gaussian distribution is a symmetric model allowing negative values which never appear in the amplitude. In this paper, after proposing a general gamma Boltzmann machine, we propose a practical model called the gamma-Bernoulli RBM that simultaneously handles both linearand log-amplitude spectrograms. Its conditional distribution of the observable data is given by the gamma distribution, and thus the proposed RBM can naturally handle the data represented by positive numbers as the amplitude spectra. It can also treat amplitude in the logarithmic scale which is important for audio signals from the perceptual point of view. The advantage of the proposed model compared to the ordinary Gaussian-Bernoulli RBM was confirmed by PESQ and MSE in the experiment of representing the amplitude spectrograms of speech signals.
Movie Box office Prediction via Joint Actor Representations and Social Media Sentiment

In recent years, driven by the Asian film industry, such as China and India, the global box office has maintained a steady growth trend. Previous studies have rarely used long-term, full-sample film data in analysis, lack of research on actors' social networks. Existing film box office prediction algorithms only use film meta-data, lack of using social network characteristics and the model is less interpretable. I propose a FC-GRU-CNN binary classification model in of box office prediction task, combining five characteristics, including the film meta-data, Sina Weibo text sentiment, actors' social network measurement, all pairs shortest path and actors' art contribution. Exploiting long-term memory ability of GRU layer in long sequences and the mapping ability of CNN layer in retrieving all pairs shortest path matrix features, proposed model is 14% higher in accuracy than the current best C-LSTM model.
One Model to Pronounce Them All: Multilingual Grapheme-to-Phoneme Conversion With a Transformer Ensemble

The task of grapheme-to-phoneme (G2P) conversion is important for both speech recognition and synthesis. Similar to other speech and language processing tasks, in a scenario where only small-sized training data are available, learning G2P models is challenging. We describe a simple approach of exploiting model ensembles, based on multilingual Transformers and self-training, to develop a highly effective G2P solution for 15 languages. Our models are developed as part of our participation in the SIGMORPHON 2020 Shared Task 1 focused at G2P. Our best models achieve 14.99 word error rate (WER) and 3.30 phoneme error rate (PER), a sizeable improvement over the shared task competitive baselines.
NOTES: Leuconostoc mesenteroides subsp. cremoris (Knudsen and Sørensen) comb. nov. and Leuconostoc mesenteroides subsp. dextranicum (Beijerinck) comb. nov.

Leuconostoc mesenteroides (Tsenkovskii) van Tieghem 1878, Leuconostoc dextranicum (Beijerinck) Hucker and Pederson 1930, and Leuconostoc cremoris (Knudsen and Sorensen) Garvie 1960 belong to a single deoxyribonucleic acid homology group. These three organisms have similar lactate dehydrogenases and glucose-6-phosphate dehydrogenases. Because of these common properties, these organisms are here regarded as subspecies within a single species, Leuconostoc mesenteroides. The names of the subspecies are Leuconostoc mesenteroides subsp. mesenteroides, Leuconostoc mesenteroides subsp. dextranicum (Beijerinck) comb. nov., and Leuconostoc mesenteroides subsp. cremoris (Knudsen and Sorensen) comb. nov. The type strains of these subspecies are ATCC 8293, NCDO 529, and NCDO 543, respectively.
Optimum Design of Printable Tunable Stiffness Metamaterial for Bone Healing

A tunable stiffness bone rod was designed, optimized, and 3D printed to address the common shortcomings of existing bone rods in the healing of long fractured bones. The common deficiencies of existing bone fixations are high stiffness, thereby negligible flexibility in deformation for best bone growth results, and stress-shielding effect. Our novel design framework provides the surgeons with ready-for-3D-printing patient-specific designs, optimized to have desired force-displacement response with a stopping mechanism for preventing further deformation under higher than usual loads such as falling. The framework is a design optimization based on the multi-objective genetic algorithm (GA) optimization to quantify the objectives, tunning the varied stiffness while minimizing the maximum Mises stress of the model to avoid plastic and permanent deformation of the bone rod. The optimum design computational framework of tunable stiffness material presented in this paper is not specific for a tibia bone rod. It can be used for any application where bilinear stiffness is desirable.
The Keck Baryonic Structure Survey: using foreground/background galaxy pairs to trace the structure and kinematics of circumgalactic neutral hydrogen at z ∼ 2

We present new measurements of the spatial distribution and kinematics of neutral hydrogen in the circumgalactic and intergalactic medium surrounding star-forming galaxies at z ∼ 2. Using the spectra of ≃3000 galaxies with redshifts 〈z〉 = 2.3 ± 0.4 from the Keck Baryonic Structure Survey, we assemble a sample of more than 200 000 distinct foreground-background pairs with projected angular separations of 3–500 arcsec and spectroscopic redshifts, with 〈zfg〉 = 2.23 and 〈zbg〉 = 2.57 (foreground, background redshifts, respectively.) The ensemble of sightlines and foreground galaxies is used to construct a 2D map of the mean excess $\rm{H\,{\small I}}$$\rm Ly\,\alpha$ optical depth relative to the intergalactic mean as a function of projected galactocentric distance (20 ≲ Dtran/pkpc ≲ 4000) and line-of-sight velocity. We obtain accurate galaxy systemic redshifts, providing significant information on the line-of-sight kinematics of $\rm{H\,{\small I}}$ gas as a function of projected distance Dtran. We compare the map with cosmological zoom-in simulation, finding qualitative agreement between them. A simple two-component (accretion, outflow) analytical model generally reproduces the observed line-of-sight kinematics and projected spatial distribution of $\rm{H\,{\small I}}$. The best-fitting model suggests that galaxy-scale outflows with initial velocity vout ≃ 600 km s$^{-1}\,$ dominate the kinematics of circumgalactic $\rm{H\,{\small I}}$ out to Dtran ≃ 50 kpc, while $\rm{H\,{\small I}}$ at Dtran ≳ 100 kpc is dominated by infall with characteristic vin ≲ circular velocity. Over the impact parameter range 80 ≲ Dtran/pkpc ≲ 200, the $\rm{H\,{\small I}}$ line-of-sight velocity range reaches a minimum, with a corresponding flattening in the rest-frame $\rm Ly\,\alpha$ equivalent width. These observations can be naturally explained as the transition between outflow-dominated and accretion-dominated flows. Beyond Dtran ≃ 300 pkpc (∼1 cMpc), the line-of-sight kinematics are dominated by Hubble expansion.
The simplicial coalgebra of chains determines homotopy types rationally and one prime at a time

We prove that the simplicial cocommutative coalgebra of singular chains on a connected topological space determines the homotopy type rationally and one prime at a time, without imposing any restriction on the fundamental group. In particular, the fundamental group and the homology groups with coefficients in arbitrary local systems of vector spaces are completely determined by the natural algebraic structure of the chains. The algebraic structure is presented as the class of the simplicial cocommutative coalgebra of chains under a notion of weak equivalence induced by a functor from coalgebras to algebras coined by Adams as the cobar construction. The fundamental group is determined by a quadratic equation on the zeroth homology of the cobar construction of the normalized chains which involves Steenrod's chain homotopies for cocommutativity of the coproduct. The homology groups with local coefficients are modeled by an algebraic analog of the universal cover which is invariant under our notion of weak equivalence. We conjecture that the integral homotopy type is also determined by the simplicial coalgebra of integral chains, which we prove when the universal cover is of finite type.
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads

Filtering data based on predicates is one of the most fundamental operations for any modern data warehouse. Techniques to accelerate the execution of filter expressions include clustered indexes, specialized sort orders (e.g., Z-order), multi-dimensional indexes, and, for high selectivity queries, secondary indexes. However, these schemes are hard to tune and their performance is inconsistent. Recent work on learned multi-dimensional indexes has introduced the idea of automatically optimizing an index for a particular dataset and workload. However, the performance of that work suffers in the presence of correlated data and skewed query workloads, both of which are common in real applications. In this paper, we introduce Tsunami, which addresses these limitations to achieve up to 6X faster query performance and up to 8X smaller index size than existing learned multi-dimensional indexes, in addition to up to 11X faster query performance and 170X smaller index size than optimally-tuned traditional indexes.
Evidence for radially independent size growth of early-type galaxies in clusters

It is not well understood whether the growth of early-type cluster galaxies proceeds inside-out, outside-in, or at the same pace at all radii. In this work we measured the galaxy size, defined by the radius including 80% of the galaxy light, non-parametrically. We also determined a non-parametric estimate of galaxy light concentration, which measures the curvature of the surface brightness profile in the galaxy outskirts. We used an almost random sampling of a mass-limited sample formed by 128 morphologically early-type galaxies in clusters with log M/M⊙ ≳ 10.7 spanning the wide range 0.17 < z < 1.81. From these data we derived the size-mass and concentration-mass relations, as well as their evolution. At 80% light radius, early-type galaxies in clusters are about 2.7 times larger than at 50% radius at all redshifts, and close to de Vaucouleurs profiles in the last 10 Gyr. While between z = 2 and z = 0 both half-light and 80% light sizes increase by a factor of 1.7, concentration stays constant within 2%, that is to say the size growth of early-type galaxies in cluster environments proceeds at the same pace at both radii. Existing physical explanations proposed in the literature are inconsistent with our results, demonstrating the need for dedicated numerical simulations to identify the physical mechanism affecting the galaxy structure.
Effect of integrated forage rotation and manure management on yield, nutrient balance and soil organic matter

This paper reports results from a field experiment established in 1995 and still on going. It is located in Lodi, in the irrigated lowlands of Lombardy, Northern Italy. The experiment compares two rotations: the annual double cropping system, Italian ryegrass + silage maize (R1); and the 6-year rotation, in which three years of double crop Italian ryegrass + silage maize are followed by three years of alfalfa harvested for hay (R6) Each rotation have received two types of dairy manure: i) farmyard manure (FYM); ii) semi-liquid manure (SLM). The intent was to apply to each unit land area the excreta produced by the number of adult dairy cows sustained, in terms of net energy, by the forage produced in each rotation, corresponding to about 6 adult cows ha-1 for R1 and 4 adult cows ha-1 for R6. Manure was applied with (N1) or without (N0) an extra supply of mineral N in the form of urea. The objectives of this study were: i) to assess whether the recycling of two types of manure in two forage rotation systems can sustain crop yields in the medium and long term without additional N fertilization; ii) to evaluate the nutrient balance of these integrated forage rotations and manure management systems; iii) to compare the effects of farmyard manure and semi-liquid manure on soil organic matter. The application of FYM, compared to SLM, increased yield of silage maize by 19% and alfalfa by 23%, while Italian ryegrass was not influenced by the manure treatment. Yet, silage maize produced 6% more in rotation R6 compared to rotation R1. The mineral nitrogen fertilization increased yield of Italian ryegrass by 11% and of silage maize by 10%. Alfalfa, not directly fertilized with mineral nitrogen, was not influenced by the nitrogen applied to the other crops in rotation. The application of FYM, compared to SLM, increased soil organic matter (SOM) by +37 % for the rotation R1, and by +20% for the rotation R6. Conversely, no significant difference on SOM was observed between R1 and R6 with the application of SLM. However, the maize stover used for composting FYM was produced by crops not included in the rotations R1 and R6, consequently the increase of soil carbon was counterbalanced by a deprivation of carbon in other land areas.
Spin parity of spiral galaxies II: a catalogue of 80 k spiral galaxies using big data from the Subaru Hyper Suprime-Cam survey and deep learning

We report an automated morphological classification of galaxies into S-wise spirals, Z-wise spirals, and non-spirals using big image data taken from Subaru/Hyper Suprime-Cam (HSC) Survey and a convolutional neural network(CNN)-based deep learning technique. The HSC i-band images are about 25 times deeper than those from the Sloan Digital Sky Survey (SDSS) and have a two times higher spatial resolution, allowing us to identify substructures such as spiral arms and bars in galaxies at z>0.1. We train CNN classifiers by using HSC images of 1447 S-spirals, 1382 Z-spirals, and 51,650 non-spirals. As the number of images in each class is unbalanced, we augment the data of spiral galaxies by horizontal flipping, rotation, and rescaling of images to make the numbers of three classes similar. The trained CNN models correctly classify 97.5% of the validation data, which is not used for training. We apply the CNNs to HSC images of a half million galaxies with an i-band magnitude of i 0.2, where we are hardly able to identify spiral arms in the SDSS images. Our attempt demonstrates that a combination of the HSC big data and CNNs has a large potential to classify various types of morphology such as bars, mergers and strongly-lensed objects.
Inheritance Patterns of Parthenocarpic Fruit Development in Highbush Blueberry (Vaccinium corymbosum L.)

Forty-one half-sib families (>3000 individuals) segregating for parthenocarpic fruit production were evaluated under field conditions. Within these families, '280 parthenocarpic individuals were identified. In general, three categories of segregants were observed: normal-seeded types, small/low-seeded types, and parthenocarpic types. Inheritance patterns suggested that the trait is recessive, but did not fit simple tetrasomic recessive or incomplete dominance models. Lack of fit may be attributable to environ- mental interactions or incomplete penetrance of this trait. Further evaluations are underway with small/low-seeded types with the expectation that offspring of these plants may express the trait at higher frequencies than in F1 · F1 crosses. The reduced vigor in many of the parthenocarpic segregants suggests that this germplasm will be most useful, initially, in improving fruit quality in the intermediate expression types, which have better fruit set and reduced seed development.
Sensing-Throughput analysis in NOMA-based CR Network

The Cognitive Radio (CR) network provides the solution to the spectrum deficiency problem by enhancing the spectrum utilization. In recent years, Non-orthogonal multiple access (NOMA) has also gained significant interest in improving the spectrum efficiency of 5G networks. The simultaneous wireless information and power transfer (SWIPT) is a technique for the transfer of wireless information and power simultaneously for the power-limited wireless networks. In this paper, we are doing a comparative analysis between the obtainable throughput and standard throughput with perfect cancellation in the SIC receiver and also developed the golden selection search algorithm to acquire the optimal sensing time for maximizing the throughput in NOMA based CR network by using the SWIPT technique. In addition to the sensing time optimization has also simulated the throughput with the interference probability in perfect and imperfect sensing case.
Clustering and Power Optimization for NOMA Multi-Objective Problems

This paper considers uplink multiple access (MA) transmissions, where the MA technique is adaptively selected between Non Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA). Two types of users, namely Internet of Things (IoT) and enhanced mobile broadband (eMBB) coexist with different metrics to be optimized, energy efficiency (EE) for IoT and spectral efficiency (SE) for eMBB. The corresponding multi-objective power allocation problems aiming at maximizing a weighted sum of EE and SE are solved for both NOMA and OMA. Based on the identification of the best MA strategy, a clustering algorithm is then proposed to maximize the multi-objective metric per cluster as well as NOMA use. The proposed clustering, power allocation and MA selection algorithm is shown to outperform other clustering solutions and non-adaptive MA techniques.
Vegetation–atmosphere interactions and their role in global warming during the latest Cretaceous

Forest vegetation has the ability to warm Recent climate by its effects on albedo and atmospheric water vapour, but the role of vegetation in warming climates of the geologic past is poorly understood. This study evaluates the role of forest vegetation in maintaining warm climates of the Late Cretaceous by (1) reconstructing global palaeovegetation for the latest Cretaceous (Maastrichtian); (2) modelling latest Cretaceous climate under unvegetated conditions and different distributions of palaeovegetation; and (3) comparing model output with a global database of palaeoclimatic indicators. Simulation of Maastrichtian climate with the land surface coded as bare soil produces high–latitude temperatures that are too cold to explain the documented palaeogeographic distribution of forest and woodland vegetation. In contrast, simulations that include forest vegetation at high latitudes show significantly warmer temperatures that are sufficient to explain the widespread geographic distribution of high–latitude deciduous forests. These warmer temperatures result from decreased albedo and feedbacks between the land surface and adjacent oceans. Prescribing a realistic distribution of palaeovegetation in model simulations produces the best agreement between simulated climate and the geologic record of palaeoclimatic indicators. Positive feedbacks between high–latitude forests, the atmosphere, and ocean contributed significantly to high–latitude warming during the latest Cretaceous, and imply that high–latitude forest vegetation was an important source of polar warmth during other warm periods of geologic history.
Filament intersections and cold dense cores in Orion A North

We studied the filament structures and dense cores in OMC-2,3 region in Orion A North molecular cloud using the high-resolution N2H+ (1-0) spectral cube observed with the Atacama Large Millimeter/Submillimeter Array (ALMA). The filament network over a total length of 2 pc is found to contain 170 intersections and 128 candidate dense cores. The dense cores are all displaced from the infrared point sources (possible young stars), and the major fraction of cores (103) are located around the intersections. Towards the intersections, there is also an increasing trend for the total column density Ntot as well as the the power-law index of the column-density Probability Distribution Function (N-PDF), suggesting that the intersections would in general have more significant gas assembly than the other part of the filament paths. The virial analysis shows that the dense cores mostly have virial mass ratio of alpha_vir=M_vir/M_gas<1.0, suggesting them to be bounded by the self gravity. In the mean time, only about 23 percent of the cores have critical mass ratio of alpha_crit=M_crit/M_gas<1.0, suggesting them to be unstable against core collapse. Combining these results, it shows that the major fraction of the cold starless and possible prestellar cores in OMC-2,3 are being assembled around the intersections, and currently in a gravitationally bound state. But more extensive core collapse and star formation may still require continuous core-mass growth or other perturbatio