Miracle-dz/UniFAR-FTUs huggingface dataset:
MotioNet

We introduce MotioNet, a deep neural network that directly reconstructs the motion of a 3D human skeleton from a monocular video. While previous methods rely on either rigging or inverse kinematics (IK) to associate a consistent skeleton with temporally coherent joint rotations, our method is the first data-driven approach that directly outputs a kinematic skeleton, which is a complete, commonly used motion representation. At the crux of our approach lies a deep neural network with embedded kinematic priors, which decomposes sequences of 2D joint positions into two separate attributes: a single, symmetric skeleton encoded by bone lengths, and a sequence of 3D joint rotations associated with global root positions and foot contact labels. These attributes are fed into an integrated forward kinematics (FK) layer that outputs 3D positions, which are compared to a ground truth. In addition, an adversarial loss is applied to the velocities of the recovered rotations to ensure that they lie on the manifold of natural joint rotations. The key advantage of our approach is that it learns to infer natural joint rotations directly from the training data rather than assuming an underlying model, or inferring them from joint positions using a data-agnostic IK solver. We show that enforcing a single consistent skeleton along with temporally coherent joint rotations constrains the solution space, leading to a more robust handling of self-occlusions and depth ambiguities.
The physical drivers of the atomic hydrogen–halo mass relation

We use SHARK, a semi-analytic galaxy formation model, to investigate the physical processes involved in dictating the shape, scatter and evolution of the HI-halo mass relation at $0\leq z \leq 2$. We compare SHARK with HI clustering and spectral stacking of the HI-halo mass relation derived from observations finding excellent agreement with the former and a deficiency of HI in SHARK at $M_{\rm vir}\approx 10^{12-13} M_{\odot}$ in the latter, but otherwise great agreement below and above that mass threshold. In SHARK, we find that the HI mass increases with the halo mass up to a critical mass of $\approx 10^{11.8} M_{\odot}$; between $\sim 10^{11.8}-10^{13}M_{\odot}$, the scatter in the relation increases by 0.7 dex and the HI mass decreases with the halo mass on average; at $M_{\rm vir} \geq 10^{13} M_{\odot}$, the HI content continues to increase with halo mass. We find that the critical halo mass of $\approx 10^{12} M_{\odot}$ is largely set by feedback from Active Galactic Nuclei (AGN), and the exact shape and scatter of the HI-halo mass relation around that mass is extremely sensitive to how AGN feedback is modelled, with other physical processes playing a less significant role. We determine the main secondary parameters responsible for the scatter of the HI-halo mass relation, namely the halo spin parameter at $M_{\rm vir}\leq 10^{11.8} M_{\odot}$, and the fractional contribution from substructure to the total halo mass for $M_{\rm vir}\geq 10^{13} M_{\odot}$. The scatter at $10^{11.8}<M_{\rm vir}<10^{13} M_{\odot}$ is best described by the black-hole-to-stellar mass ratio of the central galaxy, reflecting the AGN feedback relevance. We present a numerical model to populate dark matter-only simulations with HI at $0\leq z \leq 2$ based solely on halo parameters that are measurable in such simulations.
Fish Biomass Estimates from the Littoral Zone of an Estuarine Coastal Lake

The fish community of Swartvlei, a southern African coastal lake, was sampled using gill and lift nets. A primary aim of the research was to determine the fish biomass of the lake shelf region as well as the distribution patterns of marine species which utilize this estuarine system as a nursery and/or foraging area. The catch per unit effort of fishes was more than three times higher in the littoral zone when compared to the pelagic, slope, or profundal zones. Both habitat diversity and available food resources were maximal in the shelf region. The biomass of the littoral fish community during 1980 was estimated at 12.4 g m2 wet weight, with detritivorous species contributing 3.2 g m−2, zoobenthos consumers 2.8 gm2, piscivorous species 2.3 g m2, herbivorous/epifaunal consumers 2.7 g m−2, and zooplanktivorous species 1.4 g m2. Published information on fish atanding stock estimates from various estuaries and coastal habitats around the world is collated and compared. It is concluded that estuarine fish biomasses do not excced those of productive freshwater or marine environments and that further research is necessary to determine the size and variability of estuarine fish stocks in relation to other ecosystems.
A pilot survey for Fusarium mycotoxin biomarkers in women from Golestan, northern Iran

Fusarium mycotoxins are frequent contaminants of cereals in many world regions, and are suggested risk factors for various acute and chronic human diseases. To date a lack of exposure tools has restricted epidemiological studies of the potential health effects. Recently established exposure biomarkers for deoxynivalenol (DON) and fumonisins are now available and here a pilot biomarker survey of 110 women (aged 39 to 72 years) from Golestan, northern Iran was conducted on samples collected at one time point during August-September 2007. Urinary DON and DON-glucuronide combined were detected frequently (79/110, 72%), mean 1.3 ng DON/ml urine, range not detected (nd)-6.5 ng/ml; mean creatinine adjusted levels were 1.5 ng DON/mg creatinine, range nd-7.1 ng/mg). Neither urinary de-epoxy DON (DOM-1) and DOM-1 glucuronide combined, nor urinary fumonisin B1 were detected. This study is the first reported biomarker based exposure assessment of DON and fumonisins in this region. Overall DON exposure at this time po.
Latent feature sharing: an adaptive approach to linear decomposition models

Ubiquitous linear Gaussian exploratory tools such as principle component analysis (PCA) and factor analysis (FA) remain widely used as tools for: exploratory analysis, pre-processing, data visualization and related tasks. However, due to their rigid assumptions including crowding of high dimensional data, they have been replaced in many settings by more flexible and still interpretable latent feature models. The Feature allocation is usually modelled using discrete latent variables assumed to follow either parametric Beta-Bernoulli distribution or Bayesian nonparametric prior. In this work we propose a simple and tractable parametric feature allocation model which can address key limitations of current latent feature decomposition techniques. The new framework allows for explicit control over the number of features used to express each point and enables a more flexible set of allocation distributions including feature allocations with different sparsity levels. This approach is used to derive a novel adaptive Factor analysis (aFA), as well as, an adaptive probabilistic principle component analysis (aPPCA) capable of flexible structure discovery and dimensionality reduction in a wide case of scenarios. We derive both standard Gibbs sampler, as well as, an expectation-maximization inference algorithms that converge orders of magnitude faster to a reasonable point estimate solution. The utility of the proposed aPPCA model is demonstrated for standard PCA tasks such as feature learning, data visualization and data whitening. We show that aPPCA and aFA can infer interpretable high level features both when applied on raw MNIST and when applied for interpreting autoencoder features. We also demonstrate an application of the aPPCA to more robust blind source separation for functional magnetic resonance imaging (fMRI).
Effects of Growth History and Exogenous Thyroxine on Size and Age at Metamorphosis in the Toad Bufo americanus

Abstract By using food rations and thyroxine supplements, I manipulated the growth and differentiation of tadpoles of the toad Bufo americanus to test the hypothesis that the plasticity in metamorphic timing can be lost. A significant food effect indicated that tadpoles that grew rapidly during the middle period of the experiment metamorphosed earlier than slow-growing tadpoles. The changes in growth induced early and late in this experiment did not influence metamorphic timing. There was a significant thyroxine effect: all tadpoles treated with thyroxine metamorphosed early. All thyroxine-treated tadpoles metamorphosed at the same time, indicating that differentiation at the time of thyroxine supplementation was independent of growth rate. A food-by-thyroxine interaction provided evidence that the growth rate/differentiation antagonism may have been active at least during the middle of the experiment. This suggests that the growth rate/differentiation antagonism is decoupled or overridden during later stages of larval development. This result implies limits to metamorphic plasticity and is consistent with a fixed-rate model of amphibian metamorphosis.
Neural networks adapting to datasets: learning network size and topology

We introduce a flexible setup allowing for a neural network to learn both its size and topology during the course of a standard gradient-based training. The resulting network has the structure of a graph tailored to the particular learning task and dataset. The obtained networks can also be trained from scratch and achieve virtually identical performance. We explore the properties of the network architectures for a number of datasets of varying difficulty observing systematic regularities. The obtained graphs can be therefore understood as encoding nontrivial characteristics of the particular classification tasks.
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks

We study the problem of PAC learning one-hidden-layer ReLU networks with $k$ hidden units on $\mathbb{R}^d$ under Gaussian marginals in the presence of additive label noise. For the case of positive coefficients, we give the first polynomial-time algorithm for this learning problem for $k$ up to $\tilde{O}(\sqrt{\log d})$. Previously, no polynomial time algorithm was known, even for $k=3$. This answers an open question posed by~\cite{Kliv17}. Importantly, our algorithm does not require any assumptions about the rank of the weight matrix and its complexity is independent of its condition number. On the negative side, for the more general task of PAC learning one-hidden-layer ReLU networks with arbitrary real coefficients, we prove a Statistical Query lower bound of $d^{\Omega(k)}$. Thus, we provide a separation between the two classes in terms of efficient learnability. Our upper and lower bounds are general, extending to broader families of activation functions.
Zooplankton versus phyto- and bacterioplankton in the Maltański Reservoir (Poland) during an extensive biomanipulation experiment

Qualitative and quantitative analyses of plankton were carried out during biomanipulation studies conducted in 1993-1996 in the Maltanski Reservoir (64 ha, mean depth 3.1 m), Poland. The taxonomic composition and size fractions of phytoplankton, as well as number and biomass of phyto-, zoo- and bacterioplankton, were investigated. Despite the removal of all fish before the experiment and extensive stocking with predatory fish, attempts to biomanipulate a top-down control of phytoplankton in the Maltanski Reservoir were not successful except for the first season. Macrozooplankton filtration (including cladocerans and calanoids) was found to control the development of small organisms only, such as pico-, nano- and bacterioplankton. Microplankton were also under the influence of filter feeders, but these were not effectively eliminated. Zooplankton grazing stimulated the development of large phytoplanktonic organisms both by providing inter- mediate disturbances for their growth and by supplying them with nutrients released from grazed small-size organisms. A similar influence on micro- and nanophytoplankton was exerted by microzooplankton (including rotifers and nauplii). The presence of picophytoplankton and bacterio- plankton, however, was positively correlated with microzooplankton number.
Studies ON MUTUALISTIC SYMBIOSIS BETWEEN SYCONIA AND SYCOPHILOUS WASPS IN MONOECIOUS FIGS

Summary The constituents of a normal monoecious fig, namely the male and female flowers, the short-and long-styled female flowers and the male and female wasp galls, are arranged in definite spatial and numerical relations. The normal development of the syconium depends on a balance of these different elements. Occasionally various types of aberrant figs are found in nature, differing from normal figs in development and in final structure. These are purely gall figs, seed figs and male wasp or female wasp figs. Such abnormal figs were obtained experimentally by introducing specially treated female wasps into receptive young syconia at the receptive B phase. Treatments of the wasps involved one of the following: shortening of their ovipositor, preventing contact of wasps with pollen, preventing wasp fertilization or their sterilization by irradiation with gamma rays at various doses. In some of the already inhabited figs, the wasp larvae were killed by insecticidal sprays. Analysis of the development and structure of naturally-found aberrant syconia, taking into account also the conditions under which they may be produced experimentally, provides data for elucidating various aspects of the mutualistic symbiosis in figs.
Enzymes of starch and sugar phosphate metabolism in achlorophyllous ribosome-deficient plastids from high-temperature-grown rye leaves

Several enzymes of non–photosynthetic sugar phosphate and starch metabolism were measured in gradient–purified chloroplasts from normal rye leaves (Secale cereale L. cv. Halo) grown at 22°C and in the non-photosynthetic plastids isolated from 70S ribosome-deficient rye leaves grown at a non–permissive elevated temperature of 32°C. Activities of the enzymes phosphoglycerate kinase (EC 2.7.2.3), hexokinase (EC 2.7.1.1), phosphoglucose isomerase (EC 5.3.1.9), phosphoglucomutase (EC 2.7.5.1), glucose-6-phosphate dehydrogenase (EC 1.1.1.49), 6-phosphogluconate de-hydrogenase (EC 1.1.1.46), ADPglucose pyrophosphorylase (EC 2.7.7.27), starch synthase (EC 2.4.1.21), and phosphorylase (EC 2.4.1.1) were present in ribosome-deficient plastids from 32°C-grown leaves indicating a cytoplasmic origin of the plastid-specific forms of these enzymes. While the photosynthetic marker enzyme NADP+-dependent glyceraldehyde-3-phosphate dehydrogenase (EC 1.2.1.13) was considerably diminished, both the specific activities and the total activities per leaf of the plastid-specific forms of hexokinase, phosphoglucose isomerase and phosphoglucomutase were markedly increased in the ribosome–deficient plastids, relative to normal chloroplasts. The results demonstrate that after elimination of functional protein synthesis in the chloroplasts the supply of chloroplast–specific enzymes by the cytoplasm is not generally suppressed as observed for many enzymes and proteins involved in photosynthesis, but may even be increased in accord with changed metabolic demands.
Revisiting Loss Modelling for Unstructured Pruning

By removing parameters from deep neural networks, unstructured pruning methods aim at cutting down memory footprint and computational cost, while maintaining prediction accuracy. In order to tackle this otherwise intractable problem, many of these methods model the loss landscape using first or second order Taylor expansions to identify which parameters can be discarded. We revisit loss modelling for unstructured pruning: we show the importance of ensuring locality of the pruning steps. We systematically compare first and second order Taylor expansions and empirically show that both can reach similar levels of performance. Finally, we show that better preserving the original network function does not necessarily transfer to better performing networks after fine-tuning, suggesting that only considering the impact of pruning on the loss might not be a sufficient objective to design good pruning criteria.
Output-Weighted Importance Sampling for Bayesian Experimental Design and Uncertainty Quantification

We introduce a class of acquisition functions for sample selection that leads to faster convergence in applications related to Bayesian experimental design and uncertainty quantification. The approach follows the paradigm of active learning, whereby existing samples of a black-box function are utilized to optimize the next most informative sample. The proposed method aims to take advantage of the fact that some input directions of the black-box function have a larger impact on the output than others, which is important especially for systems exhibiting rare and extreme events. The acquisition functions introduced in this work leverage the properties of the likelihood ratio, a quantity that acts as a probabilistic sampling weight and guides the active-learning algorithm towards regions of the input space that are deemed most relevant. We demonstrate superiority of the proposed approach in the uncertainty quantification of a hydrological system as well as the probabilistic quantification of rare events in dynamical systems and the identification of their precursors.
Isolation and characterization of cellulose nanofibres from three common Nigerian grasses

Nanocellulose may be in nanocrystal or nanofibre form which may be extracted from purified cellulose by different methods. These include methods like dual-opposite-spinneret electrospinning, mechanical methods, a combination of chemical and mechanical methods, cryocrushing and enzymatic approaches. Nitrocelluloses have been extracted from various plants but not much has been reported on the yield from grasses. In this study, cellulose nanofibres (CNFs) were extracted from three common grasses; Pennisetum purpureum, Cynodon dactylon and Axonopus compressus by formic acid hydrolysis with the micrograph of CNF for each grass obtained. The CNFs are web-like long fibrous structure with diameter ranging between 3 to 5 nm and yield of over 75%. The crystallinity index averaged 76% and the onset temperature of thermal decomposition was 199 °C. Thus, Pennisetum purpureum, Cynodon dactylon and Axonopus compressus are good eco-friendly sources of CNF for potential application as new source of nanofillers for reinforcement of nanocomposite films.
G2D: Generate to Detect Anomalies

In this paper, we propose a novel method for irregularity detection. Previous researches solve this problem as an One-Class Classiﬁcation (OCC) task where they train a reference model on all of the available samples. Then, they consider a test sample as an anomaly if it has a diversion from the reference model. Generative Adversarial Networks (GANs) have achieved the most promising results for OCC while implementing and training such networks, especially for the OCC task, is a cumbersome and computationally expensive procedure. To cope with the mentioned challenges, we present a simple but effective method to solve the irregularity detection as a binary classiﬁcation task in order to make the implementation easier along with improving the detection performance. We learn two deep neural networks (generator and discriminator) in a GAN-style setting on merely the normal samples. During training, the generator gradually becomes an expert to generate samples which are similar to the normal ones. In the training phase, when the generator fails to produce normal data (in the early stages of learning and also prior to the complete convergence), it can be considered as an irregularity generator. In this way, we simultaneously generate the irregular samples. Afterward, we train a binary classiﬁer on the generated anomalous samples along with the normal instances in order to be capable of detecting irregularities. The proposed framework applies to different related applications of outlier and anomaly detection in images and videos, respectively. The results conﬁrm that our proposed method is superior to the baseline and state-of-the-art solutions.
Negative feedback in the cold: ice retreat produces new carbon sinks in Antarctica

Feedbacks on climate change so far identified are predominantly positive, enhancing the rate of change. Loss of sea‐ice, increase in desert areas, water vapour increase, loss of tropical rain forest and the restriction of significant areas of marine productivity to higher latitude (thus smaller geographical zones) all lead to an enhancement of the rate of change. The other major feedback identified, changes in cloud radiation, will produce either a positive feedback, if high level clouds are produced, or a negative feedback if low level clouds are produced. Few significant negative feedbacks have been identified, let alone quantified. Here, we show that the loss of ice shelves and retreat of coastal glaciers around the Antarctic Peninsula in the last 50 years has exposed at least 2.4 × 104 km2 of new open water. We estimate that these new areas of open water have allowed new phytoplankton blooms containing a total standing stock of ∼5.0 × 105 tonnes of carbon to be produced. New marine zooplankton and seabed communities have also been produced, which we estimate contain ∼4.1 × 105 tonnes of carbon. This previously unquantified carbon sink acts as a negative feedback to climate change. New annual productivity, as opposed to standing stock, amounts to 3.5 × 106 tonnes yr−1 of carbon, of which 6.9 × 105 tonnes yr−1 deposits to the seabed. By comparison the total aboveground biomasses of lowland American tropical rainforest is 160–435 tonnes ha−1. Around 50% of this is carbon. On this basis the carbon held in new biomass described here is roughly equivalent to 6000–17 000 ha of tropical rainforest. As ice loss increases in polar regions this feedback will become stronger, and eventually, over thousands to hundreds of thousands of years, over 50 Mtonnes of new carbon could be fixed annually in new coastal phytoplankton blooms and over 10 Mtonnes yr−1 locked in biological standing stock around Antarctica.
Feeding of the chaetognath Sagitta crassa Tokioka in heavily eutrophicated Tokyo Bay, Japan

Sagitta crassa is the only autochthonous chaetognath in Tokyo Bay, central Japan. We analyzed the gut contents of S. crassa at a fixed station in May and September 1997. The species fed on prey organisms whose body widths were in a range between 1.4-6.0% of the length of S. crassa. Prey with body widths less than 2% of the length of S. crassa were minor components of the gut contents. Although the cyclopoid copepod Oithona davisae was abun- dant in the water column, the prey selectivity of S. crassa on O. davisae was low. Our results suggest that S. crassa con- sumes catchable prey organisms that are larger and easier to detect. As planktonic species diversity in the Tokyo Bay ecosystem is low, it has been assumed that a simple trophic pathway from the most dominant primary consumer O. davisae to the secondary consumer S. crassa exists. However, such a simple assumption is inadequate to understand the prey-predator relationships and food-web dynamics in Tokyo Bay.
DeepTopPush: Simple and Scalable Method for Accuracy at the Top

Accuracy at the top is a special class of binary classification problems where the performance is evaluated only on a small number of relevant (top) samples. Applications include information retrieval systems or processes with manual (expensive) postprocessing. This leads to the minimization of irrelevant samples above a threshold. We consider classifiers in the form of an arbitrary (deep) network and propose a new method DeepTopPush for minimizing the top loss function. Since the threshold depends on all samples, the problem is non-decomposable. We modify the stochastic gradient descent to handle the non-decomposability in an end-to-end training manner and propose a way to estimate the threshold only from values on the current minibatch. We demonstrate the good performance of DeepTopPush on visual recognition datasets and on a real-world application of selecting a small number of molecules for further drug testing.