Miracle-dz/UniFAR-FTUs huggingface dataset:
Earballs: Neural Transmodal Translation

As is expressed in the adage "a picture is worth a thousand words", when using spoken language to communicate visual information, brevity can be a challenge. This work describes a novel technique for leveraging machine learned feature embeddings to translate visual (and other types of) information into a perceptual audio domain, allowing users to perceive this information using only their aural faculty. The system uses a pretrained image embedding network to extract visual features and embed them in a compact subset of Euclidean space -- this converts the images into feature vectors whose $L^2$ distances can be used as a meaningful measure of similarity. A generative adversarial network (GAN) is then used to find a distance preserving map from this metric space of feature vectors into the metric space defined by a target audio dataset equipped with either the Euclidean metric or a mel-frequency cepstrum-based psychoacoustic distance metric. We demonstrate this technique by translating images of faces into human speech-like audio. For both target audio metrics, the GAN successfully found a metric preserving mapping, and in human subject tests, users were able to accurately classify audio translations of faces.
Effects of Increasing Temperatures on Spikelet Fertility in Different Rice Cultivars based on Temperature Gradient Chamber Experiments

Spikelet sterility in rice (Oryza sativa L.) induced by high temperatures is a major concern given global warming predictions. We studied differences among eight rice cultivars in spikelet fertility at five different temperature levels in temperature gradient chamber (TGC) experiments. Six japonica and two indica cultivars were exposed to high-temperature gradients in TGCs during the 2005 flowering season. Spikelet sterility increased with temperature in TGCs and differed among cultivars because of both variations in temperature tolerance and timing of heading. The correlation between spikelet fertility of individual panicles and both air temperature and panicle temperature during flowering was analyzed to compare tolerances among cultivars. The temperature (T75) at which spikelet fertility was 75 % of maximum ranged from 34 to 39 °C air temperature and differed significantly among cultivars. Indica varieties had higher T75 values than japonica varieties. The T75 values based on panicle temperature also differed among cultivars, but the difference between indica and japonica varieties were less significant. We concluded that the higher temperature tolerances of indica cultivars in our experiments could be attributed to lower spikelet temperatures, and cultivars with similar spikelet temperatures still had different heat tolerances due to differences in pollination ability.
Erratum: Connecting the structure of dark matter haloes to the primordial power spectrum

A large body of work based on collisionless cosmological N-body simulations going back over two decades has advanced the idea that collapsed dark matter (DM) haloes have simple and approximately universal forms for their mass density and pseudo-phase-space density (PPSD) distributions. However, a general consensus on the physical origin of these results has not yet been reached. In the present study, we explore to what extent the apparent universality of these forms holds when we vary the initial conditions (i.e. the primordial power spectrum of density fluctuations) away from the standard CMB-normalized case, but still within the context of lambda cold dark matter with a fixed expansion history. Using simulations that vary the initial amplitude and shape, we show that the structure of DM haloes retains a clear memory of the initial conditions. Specifically, increasing (lowering) the amplitude of fluctuations increases (decreases) the concentration of haloes and, if pushed far enough, the density profiles deviate strongly from the NFW form that is a good approximation for the CMB-normalized case. Although, an Einasto form works well. Rather than being universal, the slope of the PPSD (or pseudo-entropy) profile steepens (flattens) with increasing (decreasing) power spectrum amplitude and can exhibit a strong halo mass dependence. Our results therefore indicate that the previously identified universality of the structure of DM haloes is mostly a consequence of adopting a narrow range of (CMB-normalized) initial conditions for the simulations. Our new suite provides a useful test-bench against which physical models for the origin of halo structure can be validated.
2-2) Behavioral Teratogenicity of Methamphetamine

Pregnant Wistar rats were given daily subcutaneous administrations of methamphetamine (MAPT; varying doses ranging from 1.0 to 4.5 mg/kg) from days 7 to 20 of gestation and teratogenic effects have been determined. The teratogenic effects inducible with orally administered caffeine (90 mg/kg/day) for the same durations were used as the positive controls. MAPT doses greater than 2.0 mg/kg have suppressed the rate of maternal Weight gain. Some of the offsprings (F1) of the prenatal MAPT treated groups had decreased growth rate and delayed development of physical characters and functional reflexes. The male offsprings of the MAPT treated groups had significant decreases in their spontaneous motor activity but had enhanced conditioned avoidance responses. However, the mating performances of these offsprings were not affected. These results indicated that prenatal exposure of MAPT may induce some behavioral teratogenicity in rats.
Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows

We compare the discretize-optimize (Disc-Opt) and optimize-discretize (Opt-Disc) approaches for time-series regression and continuous normalizing flows (CNFs) using neural ODEs. Neural ODEs are ordinary differential equations (ODEs) with neural network components. Training a neural ODE is an optimal control problem where the weights are the controls and the hidden features are the states. Every training iteration involves solving an ODE forward and another backward in time, which can require large amounts of computation, time, and memory. Comparing the Opt-Disc and Disc-Opt approaches in image classification tasks, Gholami et al. (2019) suggest that Disc-Opt is preferable due to the guaranteed accuracy of gradients. In this paper, we extend the comparison to neural ODEs for time-series regression and CNFs. Unlike in classification, meaningful models in these tasks must also satisfy additional requirements beyond accurate final-time output, e.g., the invertibility of the CNF. Through our numerical experiments, we demonstrate that with careful numerical treatment, Disc-Opt methods can achieve similar performance as Opt-Disc at inference with drastically reduced training costs. Disc-Opt reduced costs in six out of seven separate problems with training time reduction ranging from 39% to 97%, and in one case, Disc-Opt reduced training from nine days to less than one day.
Contrastive Learning for Debiased Candidate Generation at Scale

Deep candidate generation (DCG) that narrows down the collection of relevant items from billions to hundreds via representation learning is essential to large-scale recommender systems. Standard approaches approximate maximum likelihood estimation (MLE) through sampling for better scalability and address the problem of DCG in a way similar to language modeling. However, live recommender systems face severe unfairness of exposure with a vocabulary several orders of magnitude larger than that of natural language, implying that (1) MLE will preserve and even exacerbate the exposure bias in the long run in order to faithfully fit the observed samples, and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the unfairly ignored items. In this paper, we introduce CLRec, a Contrastive Learning paradigm that has been successfully deployed in a real-world massive recommender system, to alleviate exposure bias in DCG. We theoretically prove that a popular choice of contrastive loss is equivalently reducing the exposure bias via inverse propensity scoring, which provides a new perspective on the effectiveness of contrastive learning. We further employ a fixed-size queue to store the items' representations computed in previously processed batches, and use the queue to serve as an effective sampler of negative examples. This queue-based design provides great efficiency in incorporating rich features of the thousand negative items per batch thanks to computation reuse. Extensive offline analyses and four-month online A/B tests in Mobile Taobao demonstrate substantial improvement, including a dramatic reduction in the Matthew effect.
Structure dynamics of evolving scientific networks

Co-authorship networks have been extensively studied in network science as they pose as a perfect example of how single elements of a system give rise to collective phenomena on an intricate, non-trivial structure of interactions. However, co-authorship networks are, in fact, one-mode projections of original bipartite networks, which we call here scientific networks, where authors are agents connected to artifacts - the papers they have published. Nonetheless, few studies take into account the structure of the original bipartite network to understand and explain the topological properties of the projected network. Here, we create bipartite networks using extensive datasets from the American Physical Society (APS) dating back to 1893 up to 2015 inclusive, and from arXiv (1986-2015). We look at the time evolution of publications and at the dynamic structure of scientific networks considering four major features of bipartite networks, namely degree distributions, density, redundancy and cycles. We show how such features shape the formation of co-authorship networks and their observed structural properties. While the structure of the networks of most disciplines does not show significant changes over time, the appearance of large collaborations, in physics, generate largely skewed degree distributions of top nodes. The latter, in turn, induces massive cliques in the projection, triggering considerable densification of the co-authorship network, while the density of the original bipartite network remains at the same levels.
Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments

One of the most significant challenges in statistical signal processing and machine learning is how to obtain a generative model that can produce samples of large-scale data distribution, such as images and speeches. Generative Adversarial Network (GAN) is an effective method to address this problem. The GANs provide an appropriate way to learn deep representations without widespread use of labeled training data. This approach has attracted the attention of many researchers in computer vision since it can generate a large amount of data without precise modeling of the probability density function (PDF). In GANs, the generative model is estimated via a competitive process where the generator and discriminator networks are trained simultaneously. The generator learns to generate plausible data, and the discriminator learns to distinguish fake data created by the generator from real data samples. Given the rapid growth of GANs over the last few years and their application in various fields, it is necessary to investigate these networks accurately. In this paper, after introducing the main concepts and the theory of GAN, two new deep generative models are compared, the evaluation metrics utilized in the literature and challenges of GANs are also explained. Moreover, the most remarkable GAN architectures are categorized and discussed. Finally, the essential applications in computer vision are examined.
Robustness of soil ecosystems under different regimes of management

In this study three soil ecosystems, that differ in the type of management, have been compared in the attempt to understand if and how anthropogenic action affects them. The structure of the corresponding food webs was analyzed and their robustness was calculated through the use of a dynamic model. With regard to the topology, it has been found that the structure of all three networks is small world. Furthermore, all three networks have a disassortative nature as expected for foodwebs. The values of the clustering coefficient, of the connectance and of the complexity, together with the calculation of the robustness suggest that the ecosystem related to a fallowed pastures with low pressure management is more robust than two ecosystems related to organic farms subject to middle intensity management. If the evidence suggested by this study was confirmed by further studies, the robustness shown by the networks could be useful for evaluating, from an ecological point of view, the sustainability of the agricultural practices to which the ecosystem is subject.
Vortex-antivortex physics in shell-shaped Bose-Einstein condensates

Shell-shaped hollow Bose-Einstein condensates (BECs) exhibit behavior distinct from their filled counterparts and have recently attracted attention due to their potential realization in microgravity settings. Here we study distinct features of these hollow structures stemming from vortex physics and the presence of rotation. We focus on a vortex-antivortex pair as the simplest configuration allowed by the constraints on superfluid flow imposed by the closed-surface topology. In the two-dimensional limit of an infinitesimally thin shell BEC, we characterize the long-range attraction between the vortex-antivortex pair and find the critical rotation speed that stabilizes the pair against self-annihilation. In the three-dimensional case, we contrast the bounds on vortex stability with those in the two-dimensional limit and the filled sphere BECs, and evaluate the critical rotation speed as a function of shell thickness. We thus demonstrate that analyzing vortex nucleation provides a non-destructive means of characterizing a hollow sphere BEC and distinguishing it from its filled counterpart.
Distribution of twelve moist forest canopy tree species in Liberia and Côte d'Ivoire: response curves to a climatic gradient

The occurrence and abundance of 12 canopy tree species from the moist tropical forests of West Africa have been studied in relation to a climatic gradient. We focused on environmental factors related to water availability: annual amount of rainfall, the length of the dry season, and cumulative water deficit. Species occurrence and abundance data are used for 39 forest sites in Liberia and southwest Cote d'Ivoire. Species responses are modelled using a set of five increasingly complex models, ranging from a no-trend model to a skewed bell-shaped response curve. The study species show different distribution patterns. Most of them suggest a close relationship to climatic conditions. Fitting of species occurrence data to each of the three climatic factors results in most cases in simple models. In only one out of 36 cases a bell-shaped response curve is needed to describe the data. Four of the 12 species show no response to the climatic factors when only occurrence is evaluated. When abundance data are used, in 33 of the 36 cases significant response models are found. In general these are much more complex than in the cases of species occurrence data: in 10 of the 36 cases a bell-shaped response model is found to describe the data best. This is in contrast with the widespread belief that species response curves are bell-shaped: within the forest zone in the area studied this is not generally the case. The importance of the three climatic factors for the distribution of the species is evaluated: for four species mean annual rainfall is the most important variable, for four species the length of the dry period, and for one species cumulative water deficit. Consequently, the assumption that mean annual rainfall is the most important factor determining tree species distribution in West African forests is not correct. Species response models to climatic factors show where species have their geographical optima. Implications for forest management are briefly discussed.
InteractionNet: Modeling and Explaining of Noncovalent Protein-Ligand Interactions with Noncovalent Graph Neural Network and Layer-Wise Relevance Propagation

Expanding the scope of graph-based, deep-learning models to noncovalent protein-ligand interactions has earned increasing attention in structure-based drug design. Modeling the protein-ligand interactions with graph neural networks (GNNs) has experienced difficulties in the conversion of protein-ligand complex structures into the graph representation and left questions regarding whether the trained models properly learn the appropriate noncovalent interactions. Here, we proposed a GNN architecture, denoted as InteractionNet, which learns two separated molecular graphs, being covalent and noncovalent, through distinct convolution layers. We also analyzed the InteractionNet model with an explainability technique, i.e., layer-wise relevance propagation, for examination of the chemical relevance of the model's predictions. Separation of the covalent and noncovalent convolutional steps made it possible to evaluate the contribution of each step independently and analyze the graph-building strategy for noncovalent interactions. We applied InteractionNet to the prediction of protein-ligand binding affinity and showed that our model successfully predicted the noncovalent interactions in both performance and relevance in chemical interpretation.
AlFoCS + Fornax3D: resolved star formation in the Fornax cluster with ALMA and MUSE

We combine data from ALMA and MUSE to study the resolved (∼300 pc scale) star formation relation (star formation rate, SFR, versus molecular gas surface density) in cluster galaxies. Our sample consists of nine Fornax cluster galaxies, including spirals, ellipticals, and dwarfs, covering a stellar mass range of ∼108.8–1011 M⊙. CO(1-0) and extinction corrected Hα were used as tracers for the molecular gas mass and SFR, respectively. We compare our results with Kennicutt and Bigiel et al. Furthermore, we create depletion time maps to reveal small-scale variations in individual galaxies. We explore these further in FCC290, using the ‘uncertainty principle for star formation’ (Kruijssen & Longmore) to estimate molecular cloud lifetimes, which we find to be short (<10 Myr) in this galaxy. Galaxy-averaged depletion times are compared with other parameters such as stellar mass and cluster-centric distance. We find that the star formation relation in the Fornax cluster is close to those from Kennicutt and Bigiel et al., but overlaps mostly with the shortest depletion times predicted by Bigiel et al. This slight decrease in depletion time is mostly driven by dwarf galaxies with disturbed molecular gas reservoirs close to the virial radius. In FCC90, a dwarf galaxy with a molecular gas tail, we find that depletion times are a factor ≳10 higher in its tail than in its stellar body.
A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews

Despite the recent advances in opinion mining for written reviews, few works have tackled the problem on other sources of reviews. In light of this issue, we propose a multi-modal approach for mining fine-grained opinions from video reviews that is able to determine the aspects of the item under review that are being discussed and the sentiment orientation towards them. Our approach works at the sentence level without the need for time annotations and uses features derived from the audio, video and language transcriptions of its contents.We evaluate our approach on two datasets and show that leveraging the video and audio modalities consistently provides increased performance over text-only baselines, providing evidence these extra modalities are key in better understanding video reviews.
A comparison of broadcast and drill methods for no-till seeding winter wheat

No-till seeding into standing stubble of a previous crop is required to provide a trap for snow that protects overwintering wheat from low temperature extremes experienced in most of western Canada. In this production system, the optimum winter wheat seeding date often conflicts with spring crop harvest. Broadcast (aerial) seeding into an immature, standing spring crop would provide an opportunity to eliminate this conflict for limited resources during harvest. The objectives of this study were to determine the effect of broadcast seeding date and rate on winter wheat establishment, development, and productivity and to compare broadcast and conventional no-till winter wheat seeding methods in east-central Saskatchewan. Eight field trials were conducted over 2 yr. Seedlings from broadcast seeding had more prostrate growth habit and lower establishment levels than those produced with conventional seeding methods. Unlike conventional drill seeding, pre-seeding soil moisture was not an important factor in bro.
Xylem phenology of Fagus sylvatica in Rarău Mountains (Eastern Carpathians, Romania).

The cambium activity and the tree ring formation of European beech ( Fagus sylvatica L.) from the RarAƒu Mountains was monitored during 2009, 2010 and 2011 in a beech - coniferous stand, representative for Eastern Carpathian mixed forests. Wood microcores were collected weekly from five trees and prepared in order to describe the different phases of wood formation. Four phases of tree ring development were quantified, in number of cells and phase duration: cambial phase, cell enlargement, cell wall thickening and cell maturation. The onset of the cambial activity took place in the first week of May 2009, one week later in 2010 and in the last week of April 2011. The beech tree ring development period varies between 127 days in 2009 and 137 days in 2011.
Ergodicity and steady state analysis for interference queueing networks

We analyze an interacting queueing network on Z d \mathbb {Z}^d that was introduced in Sankararaman, Baccelli and Foss (2019) as a model for wireless networks. We show that the marginals of the minimal stationary distribution have exponential tails. This is used to furnish asymptotics for the maximum steady state queue length in growing boxes around the origin. We also establish a decay of correlations which shows that the minimal stationary distribution is strongly mixing, and hence, ergodic with respect to translations on Z d \mathbb {Z}^d .
A Literature Review of Machine Learning and Software Development Life cycle Stages

The software engineering community is rapidly adopting machine learning for transitioning modern-day software towards highly intelligent and self-learning systems. However, the software engineering community is still discovering new ways how machine learning can offer help for various software development life cycle stages. In this article, we present a study on the use of machine learning across various software development life cycle stages. The overall aim of this article is to investigate the relationship between software development life cycle stages, and machine learning tools, techniques, and types. We attempt a holistic investigation in part to answer the question of whether machine learning favors certain stages and/or certain techniques.