Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of salt and drought on growth, physiological and biochemical responses of two Tamarix species

Abstract: Tamarix trees are considered of particular interest for afforestation and reforestation of degraded areas prone to salinity and drought. In this study, a comparison of the performance and physiological responses of two species of Tamarix grown in saline and dried soils was carried out. Stem cuttings of T. aphylla and T. jordanis were collected from a location in the Negev desert and the plantlets obtained were subjected to four different soil treatments under semi-controlled conditions for 14 days. The treatments were: fresh water (C); salt (S: 150 mM of NaCl); drought (D: 50% field capacity); and a combined stress (DS: 150 mM of NaCl + 50% FC). Results showed a higher tolerance to salt stress of T. jordanis as compared with T. aphylla. The maintenance of high amount of cell carbohydrates, the high capacity of carbon assimilation and the active growth were considered as markers of salt tolerance in Tamarix spp. T. aphylla showed better performances in terms of growth and biomass production than T. jordanis in dry conditions. The high accumulation of sugars found in the leaves of T. aphylla under mild drought is considered a mechanism of acclimatization. The combined stress (salt+drought) lowered the performance of plants as compared to salt and drought stress applied alone. The possible role of the accumulation of proline observed in the leaves of both species under stressful conditions is discussed.
Lifted Regression/Reconstruction Networks

In this work we propose lifted regression/reconstruction networks (LRRNs), which combine lifted neural networks with a guaranteed Lipschitz continuity property for the output layer. Lifted neural networks explicitly optimize an energy model to infer the unit activations and therefore---in contrast to standard feed-forward neural networks---allow bidirectional feedback between layers. So far lifted neural networks have been modelled around standard feed-forward architectures. We propose to take further advantage of the feedback property by letting the layers simultaneously perform regression and reconstruction. The resulting lifted network architecture allows to control the desired amount of Lipschitz continuity, which is an important feature to obtain adversarially robust regression and classification methods. We analyse and numerically demonstrate applications for unsupervised and supervised learning.
GOODS-ALMA: The slow downfall of star formation in z = 2–3 massive galaxies

We investigate the properties of a sample of 35 galaxies, detected with the Atacama Large Millimeter/Submillimeter Array (ALMA) at 1.1 mm in the GOODS-ALMA field (area of 69 arcmin2, resolution = 0.60″, rms ≃ 0.18 mJy beam−1). Using the ultraviolet-to-radio deep multiwavelength coverage of the GOODS–South field, we fit the spectral energy distributions of these galaxies to derive their key physical properties. The galaxies detected by ALMA are among the most massive at z = 2−4 (M⋆, med = 8.5 × 1010 M⊙) and they are either starburst or located in the upper part of the galaxy star-forming main sequence. A significant portion of our galaxy population (∼40%), located at z ∼ 2.5 − 3, exhibits abnormally low gas fractions. The sizes of these galaxies, measured with ALMA, are compatible with the trend between the rest-frame 5000 Å size and stellar mass observed for z ∼ 2 elliptical galaxies, suggesting that they are building compact bulges. We show that there is a strong link between star formation surface density (at 1.1 mm) and gas depletion time: The more compact a galaxy’s star-forming region is, the shorter its lifetime will be (without gas replenishment). The identified compact sources associated with relatively short depletion timescales (∼100 Myr) are the ideal candidates to be the progenitors of compact elliptical galaxies at z ∼ 2.
A large amount of diffuse molecular gases in the bar of the strongly barred galaxy NGC 1300: cause of the low star formation efficiency

In many barred galaxies, star formation efficiency (SFE) in the bar is lower than those in the arm and bar-end, and its cause has still not been clear. Focusing on the strongly barred galaxy NGC 1300, we investigate the possibility that the presence of a large amount of diffuse molecular gas, which would not contribute to the SF, makes the SFE low in appearance. We examine the relation between the SFE and the diffuse molecular gas fraction ($f_{\rm dif}$), which is derived using the $^{12}$CO($1-0$) flux obtained from the interferometer of ALMA 12-m array, which has no sensitivity on diffuse (extended; FWHM $\gtrapprox 700$ pc) molecular gases due to the lack of ACA, and the total $^{12}$CO($1-0$) flux obtained from Nobeyama 45-m single-dish telescope. We find that the SFE decreases with increasing $f_{\rm dif}$. The $f_{\rm dif}$ and ${\rm SFE}$ are $0.74 - 0.91$ and $(0.06 - 0.16) ~\rm Gyr^{-1}$ in the bar regions, and $0.28 - 0.65$ and $(0.23 - 0.96) ~\rm Gyr^{-1}$ in the arm and bar-end regions. This result supports the idea that the presence of a large amount of diffuse molecular gas makes the SFE low. The suppression of the SFE in the bar has also been seen even when we exclude the diffuse molecular gas components. This suggests that the low SFE appears to be caused not only by a large amount of diffuse molecular gases but also by other mechanisms such as fast cloud-cloud collisions.
Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization

Text summarization refers to the process that generates a shorter form of text from the source document preserving salient information. Many existing works for text summarization are generally evaluated by using recall-oriented understudy for gisting evaluation (ROUGE) scores. However, as ROUGE scores are computed based on n-gram overlap, they do not reflect semantic meaning correspondences between generated and reference summaries. Because Korean is an agglutinative language that combines various morphemes into a word that express several meanings, ROUGE is not suitable for Korean summarization. In this paper, we propose evaluation metrics that reflect semantic meanings of a reference summary and the original document, Reference and Document Aware Semantic Score (RDASS). We then propose a method for improving the correlation of the metrics with human judgment. Evaluation results show that the correlation with human judgment is significantly higher for our evaluation metrics than for ROUGE scores.
SDSS-IV MaNGA: Excavating the fossil record of stellar populations in spiral galaxies

We perform a ‘fossil record’ analysis for ≈800 low-redshift spiral galaxies, using starlight applied to integral field spectroscopic observations from the SDSS-IV MaNGA survey to obtain fully spatially resolved high-resolution star formation histories (SFHs). From the SFHs, we are able to build maps indicating the present-day distribution of stellar populations of different ages in each galaxy. We find small negative mean age gradients in most spiral galaxies, especially at high stellar mass, which reflects the formation times of stellar populations at different galactocentric radii. We show that the youngest (<108.5 yr) populations exhibit significantly more extended distributions than the oldest (>109.5 yr), again with a strong dependence on stellar mass. By interpreting the radial profiles of ‘time slices’ as indicative of the size of the galaxy at the time those populations had formed, we are able to trace the simultaneous growth in mass and size of the spiral galaxies over the last 10 Gyr. Despite finding that the evolution of the measured light-weighted radius is consistent with inside-out growth in the majority of spiral galaxies, the evolution of an equivalent mass-weighted radius has changed little over the same time period. Since radial migration effects are likely to be small, we conclude that the growth of discs in spiral galaxies has occurred predominantly through an inside-out mode (with the effect greatest in high-mass galaxies), but this has not had anywhere near as much impact on the distribution of mass within spiral galaxies.
MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis

Multimodal Sentiment Analysis is an active area of research that leverages multimodal signals for affective understanding of user-generated videos. The predominant approach, addressing this task, has been to develop sophisticated fusion techniques. However, the heterogeneous nature of the signals creates distributional modality gaps that pose significant challenges. In this paper, we aim to learn effective modality representations to aid the process of fusion. We propose a novel framework, MISA, which projects each modality to two distinct subspaces. The first subspace is modality-invariant, where the representations across modalities learn their commonalities and reduce the modality gap. The second subspace is modality-specific, which is private to each modality and captures their characteristic features. These representations provide a holistic view of the multimodal data, which is used for fusion that leads to task predictions. Our experiments on popular sentiment analysis benchmarks, MOSI and MOSEI, demonstrate significant gains over state-of-the-art models. We also consider the task of Multimodal Humor Detection and experiment on the recently proposed UR_FUNNY dataset. Here too, our model fares better than strong baselines, establishing MISA as a useful multimodal framework.
Hierarchical Predictive Coding Models in a Deep-Learning Framework

Bayesian predictive coding is a putative neuromorphic method for acquiring higher-level neural representations to account for sensory input. Although originating in the neuroscience community, there are also efforts in the machine learning community to study these models. This paper reviews some of the more well known models. Our review analyzes module connectivity and patterns of information transfer, seeking to find general principles used across the models. We also survey some recent attempts to cast these models within a deep learning framework. A defining feature of Bayesian predictive coding is that it uses top-down, reconstructive mechanisms to predict incoming sensory inputs or their lower-level representations. Discrepancies between the predicted and the actual inputs, known as prediction errors, then give rise to future learning that refines and improves the predictive accuracy of learned higher-level representations. Predictive coding models intended to describe computations in the neocortex emerged prior to the development of deep learning and used a communication structure between modules that we name the Rao-Ballard protocol. This protocol was derived from a Bayesian generative model with some rather strong statistical assumptions. The RB protocol provides a rubric to assess the fidelity of deep learning models that claim to implement predictive coding.
Structural and magnetic behavior of Cr2Co(1−x)CrxAl inverse Heusler alloys

We report the structural and magnetic behavior of single phase inverse Heusler alloys Cr2Co(1−x)CrxAl (x = 0, 0.2, and 0.4) by using x-ray diffraction (XRD), Raman spectroscopy, isothermal magnetization, and magnetic susceptibility measurements. Interestingly, the Rietveld refinement of XRD data with space group I4¯m2 reveal a tetragonal distortion with a c/a ratio around 1.38 in these inverse Heusler structures. The bulk compositions have been confirmed by energy dispersive x-ray spectroscopy measurements. The active Raman mode F2g is observed at 320 cm−1, which confirms the X-type Heusler structure, as the A2 and B2 type structures are known to be not Raman active. The area of F2g mode decreases with an increase in the Cr concentration, which indicates that the origin of this mode is due to Co vibrations. The isothermal magnetization data confirm the magnetic moment to be close to zero (≤0.02 μB/f.u.) at ≈70 kOe and the negligible coercive field suggests the fully compensated ferrimagnetic nature of these samples. The susceptibility behavior indicates irreversibility between zero-field and field-cooled curves and complex magnetic interactions at low temperatures.
Interpreting Deep Models through the Lens of Data

Identification of input data points relevant for the classifier (i.e. serve as the support vector) has recently spurred the interest of researchers for both interpretability as well as dataset debugging. This paper presents an in-depth analysis of the methods which attempt to identify the influence of these data points on the resulting classifier. To quantify the quality of the influence, we curated a set of experiments where we debugged and pruned the dataset based on the influence information obtained from different methods. To do so, we provided the classifier with mislabeled examples that hampered the overall performance. Since the classifier is a combination of both the data and the model, therefore, it is essential to also analyze these influences for the interpretability of deep learning models. Analysis of the results shows that some interpretability methods can detect mislabels better than using a random approach, however, contrary to the claim of these methods, the sample selection based on the training loss showed a superior performance.
Radial Variation in Microfibril Angle and Compression Properties of Paraserianthes Falcataria Planted in Indonesia

Paraserianthes falcataria (L.) Nielsen is an important plantation species in Indonesia. The objective of this study was to understand the radial variation of microfibril angle in the S2 layer of the wood fiber wall (MFA) in P. falcataria, and its relationship to compression properties parallel to the grain, including modulus of elasticity (E) and compression strength (σ). MFA gradually decreased from pith to bark while both E and σ increased from pith to bark. Significant differences in MFA, E, and σ were found among the five sample trees. Single-regression analysis of wood and compression properties revealed that the compression properties of core wood were affected mainly by density, whereas those in outer wood were affected by both MFA and air-dry density (AD).
Cell morphometry and osmolarity as early indicators of the onset of embryogenesis from cell suspension cultures of grain legumes and model systems

Abstract Whatever the in vitro regeneration pathway, it would be of interest to be able to distinguish regeneration-competent from non-regenerating cells and tissues, and as early in culture as possible, as this would allow a dramatic improvement of biotechnology breeding, particularly for the so-called recalcitrant species. With this aim, we examined a range of genotypes of pea (Pisum sativum) and grass pea (Lathyrus sativus), and of the two model species Medicago truncatula, another legume, and Arabidopsis thaliana. This was done by comparing cell suspension cultures of different ages (young [<6 transfers-old]vs. mature [>6 transfers-old]), densities (dense [>107 cells/ml] or sparse [<106 cells/ml]) and regeneration abilities (non-embryogenic vs. embryogenic), in order to identify early indicators of competence for somatic embryogenesis. All such cell suspensions were subcultured every 14 days and several parameters were assessed every 3–4 days during each 14-day cycle. These included the time course pH and osmolarity of the culture medium, the internal osmolarity of cells, the cell surface and the cell wall thickness (by examining cellulose accumulation in Calcofluor White-stained cells under UV light). As cells underwent embryogenesis they enlarged. Cellulose accumulated in the walls of non-embryogenic cells, but walls became thinner with the onset of embryogenesis, and diminished further as embryos matured. Although medium osmolarity decreased at the onset of embryogenesis, this was never observed for non-embryogenic cell suspensions. Conversely, there was a concomitant increase in intracellular osmolarity for embryogenic cells. Medium pH (analysed with the model species only) was not significantly correlated with regeneration competence of cells. For all genotypes and species, the kinetics of cell wall thickness and cell surface, and that of medium and cell osmolarity were reliable early indicators of the competence of cells to undergo somatic embryogenesis. The implication of these results for biotechnological breeding of grain legumes and for plant regeneration competence in general are discussed.
Flower‐heads, herbivores, and their parasitoids: food web structure along a fertility gradient

Abstract. 1. The ways in which a soil fertility gradient affects three trophic level food webs defined by plants of the family Asteraceae, flower‐head herbivores, and their parasitoids was investigated. It was tested how the fertility gradient alters: (i) the abundance and richness of plants, herbivores, and their parasitoids, (ii) the herbivore–plant ratio, and (iii) the connectance of the plant–herbivore community matrix.
Quantifying the Economic Impact of COVID-19 in Mainland China Using Human Mobility Data

To contain the pandemic of coronavirus (COVID-19) in Mainland China, the authorities have put in place a series of measures, including quarantines, social distancing, and travel restrictions. While these strategies have effectively dealt with the critical situations of outbreaks, the combination of the pandemic and mobility controls has slowed China's economic growth, resulting in the first quarterly decline of Gross Domestic Product (GDP) since GDP began to be calculated, in 1992. To characterize the potential shrinkage of the domestic economy, from the perspective of mobility, we propose two new economic indicators: the New Venues Created (NVC) and the Volumes of Visits to Venue (V^3), as the complementary measures to domestic investments and consumption activities, using the data of Baidu Maps. The historical records of these two indicators demonstrated strong correlations with the past figures of Chinese GDP, while the status quo has dramatically changed this year, due to the pandemic. We hereby presented a quantitative analysis to project the impact of the pandemic on economies, using the recent trends of NVC and V^3. We found that the most affected sectors would be travel-dependent businesses, such as hotels, educational institutes, and public transportation, while the sectors that are mandatory to human life, such as workplaces, residential areas, restaurants, and shopping sites, have been recovering rapidly. Analysis at the provincial level showed that the self-sufficient and self-sustainable economic regions, with internal supplies, production, and consumption, have recovered faster than those regions relying on global supply chains.
No significant correlation between radial velocity planet presence and debris disc properties

We investigate whether the tentative correlation between planets and debris discs which has been previously identified can be confirmed at high significance. We compile a sample of 201 stars with known planets and existing far infrared observations. The sample is larger than those studied previously since we include targets from an unpublished Herschel survey of planet hosts. We use spectral energy distribution modelling to characterise Kuiper belt analogue debris discs within the sample, then compare the properties of the discs against a control sample of 294 stars without known planets. Survival analysis suggests that there is a significant ($p\sim 0.002$) difference between the disc fractional luminosity distributions of the two samples. However, this is largely a result of the fact that the control sample contains a higher proportion of close binaries and of later-type stars; both of these factors are known to reduce disc detection rates. Considering only Sun-like stars without close binary companions in each sample greatly reduces the significance of the difference ($p\sim 0.3$). We also find no evidence for a difference in the disc fractional luminosities of stars hosting planets more or less massive than Saturn ($p\sim 0.9$). Finally, we find that the planet hosts have cooler discs than the control stars, but this is likely a detection bias, since the warmest discs in the control sample are also the faintest, and would thus be undetectable around the more distant planet hosts. Considering only discs in each sample that could have been detected around a typical planet host, we find $p\sim 0.07$ for the temperatures.
GOODS-ALMA: Using IRAC and VLA to probe fainter millimeter galaxies

In this paper, we extend the source detection in the GOODS-ALMA field (69 arcmin2, 1σ ≃ 0.18 mJy beam−1) to deeper levels than presented in our previous work. Using positional information at 3.6 and 4.5 μm (from Spitzer-IRAC) as well as the Very Large Array (VLA) at 3 GHz, we explore the presence of galaxies detected at 1.1 mm with ALMA below our original blind detection limit of 4.8-σ, at which the number of spurious sources starts to dominate over that of real sources. In order to ensure the most reliable counterpart association possible, we have investigated the astrometry differences between different instruments in the GOODS–South field. In addition to a global offset between the Atacama Large Millimeter/submillimeter Array (ALMA) and the Hubble Space Telescope (HST) already discussed in previous studies, we have highlighted a local offset between ALMA and the HST that was artificially introduced in the process of building the mosaic of the GOODS–South image. We created a distortion map that can be used to correct for these astrometric issues. In this Supplementary Catalog, we find a total of 16 galaxies, including two galaxies with no counterpart in HST images (also known as optically dark galaxies), down to a 5σ limiting depth of H = 28.2 AB (HST/WFC3 F160W). This brings the total sample of GOODS-ALMA 1.1 mm sources to 35 galaxies. Galaxies in the new sample cover a wider dynamic range in redshift (z = 0.65−4.73), are on average twice as large (1.3 vs 0.65 kpc), and have lower stellar masses (M⋆SC = 7.6 × 1010 M⊙ vs M⋆MC = 1.2 × 1011 M⊙). Although exhibiting larger physical sizes, these galaxies still have far-infrared sizes that are significantly more compact than inferred from their optical emission.
Detecting Latent Communities in Network Formation Models

This paper proposes a logistic undirected network formation model which allows for assortative matching on observed individual characteristics and the presence of edge-wise ﬁxed eﬀects. We model the coeﬃcients of observed characteristics to have a latent community structure and the edge-wise ﬁxed eﬀects to be of low rank. We propose a multi-step estimation procedure involving nuclear norm regularization, sample splitting, iterative logistic regression and spectral clustering to detect the latent communities. We show that the latent communities can be exactly recovered when the expected degree of the network is of order log n or higher, where n is the number of nodes in the network. The ﬁnite sample performance of the new estimation and inference methods is illustrated through both simulated and real datasets.