Miracle-dz/UniFAR-FTUs huggingface dataset:
The reproductive strategy of giant pandas ( Ailuropoda melanoleuca ): infant growth and development and mother–infant relationships

Reproductive activities of six giant panda females ( Ailuropoda melanoleuca ) living in the Qinling Mountains of China (Shaanxi Province) were recorded from 1989 into early 1995. Data on mating and birth dates, litter size, cub sex and den use were collected for three of the pandas. At this site, pregnant females migrated to lower elevations in advance of other pandas to seek dens for birthing. One dam was videotaped in the den with her cub from birth to the end of the denning phase at 94 days of age. This cub was repeatedly handled during the dam's absences to obtain data on development and weight. During the early part of the denning phase, the cub was maintained on the dam's body virtually continuously. Post-partum fasting for up to 3 weeks was noted. Dams invariably moved cubs to new dens three or four times during the birth season. By the time cubs left the den at about 3–4 months, they were fully furred and capable of surviving winter temperatures without further shelter. Reproductive parameters for the wild population fell within the range of values for captive pandas, based on information in the literature and in the giant panda studbook. The combined data on length of gestation, intervals between births, litter size and weight, infant development, and reproductive life span reveal a range of life-history variables that strongly resemble the Ursidae.
Production, properties and processing of American bison (Bison bison) wool grown in southern Australia

American bison grow a thick coat of fibres which assists them to withstand severe climatic conditions. Bison fibre was traditionally used in textiles by native North Americans. This study aimed to quantify the production, fibre attributes and dehairing processing of bison fibre produced from bison grazed in north-eastern Victoria. Three age/sex classes were sampled (n = 16) at seven body positions in spring. The fibre growing area was measured. Fibre was tested for diameter distribution, clean washing yield, proportion of fine fibres <36 µm and fine fibre length, and processed by cashmere dehairing. Bison were 1–2 years of age, liveweights 160–450 kg and had mean fibre growing area of 1.4 m2. They produced an average 1184 g (range 530–1640 g) of fine fibre with mean fibre diameter 18.5 µm, clean washing yield 76.5%, wax content 9.8%, suint content 14.5%, clean fine fibre yield 56.4%, fine fibre length 37 mm and fibre curvature was 93°/mm. Mid-side fibre had a crimp frequency of 6.5/cm and mean resistance to compression of 6.6 kPa. Fibre had a tenacity of 8.7 cN/tex and an extension of 39.3%. Restricted maximum likelihood mixed model analysis showed age/sex class and sampling site significantly affected all fibre attributes. Finer and longer fibre was produced in anterior sites and in younger bison. Fibre curvature declined 5.3°/mm for each 1-µm increase in mean fibre diameter. Dehaired fibre had a mean fibre diameter of 17.8 µm and mid-length of 28 mm, suitable for woollen spinning. The production by bison of coats containing significant amounts of fibre indicates that careful harvesting of fibre could form an important source of income in bison enterprises.
GEOM: Energy-annotated molecular conformations for property prediction and molecular generation

Machine learning outperforms traditional approaches in many molecular design tasks. Although molecules are often thought of as 2D graphs, they in fact consist of an ensemble of inter-converting 3D structures called conformers. Molecular properties arise from the contribution of many conformers, and in the case of a drug binding a target, may be due mainly to a few distinct members. Molecular representations in machine learning are typically based on either one single 3D conformer or on a 2D graph that strips geometrical information. No reference datasets exist that connect these graph and point cloud ensemble representations. Here, we use first-principles simulations to annotate over 400,000 molecules with the ensemble of geometries they span. The Geometrical Embedding Of Molecules (GEOM) dataset contains over 33 million molecular conformers labeled with their relative energies and statistical probabilities at room temperature. This dataset will assist benchmarking and transfer learning in two classes of tasks: inferring 3D properties from 2D molecular graphs, and developing generative models to sample 3D conformations.
The 4-dimensional disc embedding theorem and dual spheres

We modify the proof of the disc embedding theorem for 4-manifolds, which appeared as Theorem 5.1A in the book "Topology of 4-manifolds" by Freedman and Quinn, in order to construct geometrically dual spheres. These were claimed in the statement but not constructed in the proof. Fundamental results in 4-manifold topology such as the existence and exactness of the surgery sequence, the s-cobordism theorem, and thence the classification of closed, simply connected topological 4-manifolds up to homeomorphism, rely on a closely related sphere embedding theorem with geometrically dual spheres, which is proven using the disc embedding theorem. We give an example to show that geometrically dual spheres are essential in applications.
Deep Neural Networks for the Sequential Probability Ratio Test on Non-i.i.d. Data Series

Classifying sequential data as early as and as accurately as possible is a challenging yet critical problem, especially when a sampling cost is high. One algorithm that achieves this goal is the sequential probability ratio test (SPRT), which is known as Bayes-optimal: it can keep the expected number of data samples as small as possible, given the desired error upper-bound. The SPRT has recently been found to be the best model that explains the activities of the neurons in the primate parietal cortex that are thought to mediate our complex decision-making processes. However, the original SPRT makes two critical assumptions that limit its application in real-world scenarios: (i) samples are independently and identically distributed, and (ii) the likelihood of the data being derived from each class can be calculated precisely. Here, we propose the SPRT-TANDEM, a deep neural network-based SPRT algorithm that overcomes the above two obstacles. The SPRT-TANDEM estimates the log-likelihood ratio of two alternative hypotheses by leveraging a novel Loss function for Log-Likelihood Ratio estimation (LLLR), while allowing for correlations up to $N (\in \mathbb{N})$ preceding samples. In tests on one original and two public video databases, Nosaic MNIST, UCF101, and SiW, the SPRT-TANDEM achieves statistically significantly better classification accuracy than other baseline classifiers, with a smaller number of data samples. The code and Nosaic MNIST are publicly available at this https URL.
Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions

In this paper, we present an improved analysis for dynamic regret of strongly convex and smooth functions. Specifically, we investigate the Online Multiple Gradient Descent (OMGD) algorithm proposed by Zhang et al. (2017). The original analysis shows that the dynamic regret of OMGD is at most $\mathcal{O}(\min\{\mathcal{P}_T,\mathcal{S}_T\})$, where $\mathcal{P}_T$ and $\mathcal{S}_T$ are path-length and squared path-length that measures the cumulative movement of minimizers of the online functions. We demonstrate that by an improved analysis, the dynamic regret of OMGD can be improved to $\mathcal{O}(\min\{\mathcal{P}_T,\mathcal{S}_T,\mathcal{V}_T\})$, where $\mathcal{V}_T$ is the function variation of the online functions. Note that the quantities of $\mathcal{P}_T, \mathcal{S}_T, \mathcal{V}_T$ essentially reflect different aspects of environmental non-stationarity---they are not comparable in general and are favored in different scenarios. Therefore, the dynamic regret presented in this paper actually achieves a \emph{best-of-three-worlds} guarantee and is strictly tighter than previous results.
THE UNIVERSAL THEORY OF THE HYPERFINITE II1 FACTOR IS NOT COMPUTABLE

We show that the universal theory of the hyperfinite II$_1$ factor is not computable. The proof uses the recent result that MIP*=RE. Combined with an earlier observation of the authors, this yields a proof that the Connes Embedding Problem has a negative solution that avoids the equivalences with Kirchberg's QWEP Conjecture and Tsirelson's Problem. +
A Field Test of Distance Sampling Methods for a Tropical Forest Bird Community

ABSTRACT. Distance sampling is usually preferred over uncorrected point counts for surveys of forest birds, but rarely has its accuracy been assessed against known numbers, particularly in tropical forests. We compared density estimates of eight species of breeding bird—Black-naped Monarch (Hypothymis azurea), Hill Blue Flycatcher (Cyornis banyumas), White-rumped Shama (Copsychus malabaricus), Puff-throated Bulbul (Alophoixus pallidus), Abbott's Babbler (Malacocincla abbotti), Puff-throated Babbler (Pellorneum ruficeps), White-browed Scimitar Babbler (Pomatorhinus schisticeps), and White-bellied Yuhina (Yuhina zantholeuca)—obtained through color-banding, nest finding, and territory mapping with those derived from distance methods in evergreen forest in northeastern Thailand. We also assessed the availability of birds to be detected using a closed-capture model and incorporated this with point-transect distance sampling. Abundance estimates from territory mapping and distance sampling were highly correlated, but biased for two species using line transects and five species using point transects. Six of the seven biased estimates were biased low. Probabilities of detection were not significantly different between lines and points, and there was no significant difference in the overall accuracy between methods. Accounting for observer differences improved density estimates but reduced precision. The variance in accuracy was mostly related to the behavior of the different species. Adjusting for availability did not improve the overall accuracy of the estimates, because of the low singing rates of tropical birds. Nonetheless, distance sampling provided relatively robust estimates despite the near total dependence on aural cues. Violations of distance-sampling assumptions may be frequent in heavily forested habitats, where both availability for detection and probability of detection on the transect line (or point) are likely to be <1.
Edge modes in active systems of subwavelength resonators

Wave scattering structures with amplification and dissipation can be modelled by non-Hermitian systems, opening new ways to control waves at small length scales. In this work, we study the phenomenon of topologically protected edge states in acoustic systems with gain and loss. We demonstrate that localized edge modes appear in a periodic structure of subwavelength resonators with a defect in the gain/loss distribution, and explicitly compute the corresponding frequency and decay length. Similarly to the Hermitian case, these edge modes can be attributed to the winding of the eigenmodes. In the non-Hermitian case, the topological invariants fail to be quantized, but can nevertheless predict the existence of localized edge modes.
A Comparative Study on Early Detection of COVID-19 from Chest X-Ray Images

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent state-of-the-art Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 175 early-stage COVID-19 Pneumonia samples (very limited or no infection signs) labelled by the medical doctors and 1579 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 98.5%) sensitivity with over 96% specificity. Moreover, transfer learning over the deep CheXNet fine-tuned with the augmented data produces the leading performance among other deep networks with 97.14% sensitivity and 99.49% specificity.
Plant traits in relation to the performance and distribution of woody species in wet and dry tropical forest types in Panama

Summary Understanding the factors that limit species distributions along environmental gradients is a central question of ecology. Here, we evaluate the hypothesis that the traits that result in performance trade-offs between habitats contribute to the turnover of woody species along a rainfall gradient in the Isthmus of Panama. We studied 24 plant species with contrasting distributions along this rainfall gradient. We measured 18 morphological and physiological traits, and three performance variables in seedlings planted in common garden experiments in two contrasting sites across the Isthmus. We found evidence for a trade-off suggesting that better survival during the dry season corresponded to a lower growth rate in the forest understorey. This trade-off correlated well with the distribution of the species along the rainfall gradient and was explained mostly by variation in photosynthetic capacity. While not all species fit into this trade-off, most dry-distribution species, which we had previously reported to have higher drought survival, were associated with higher stem hydraulic conductance and higher capacity for CO2 assimilation. Our interpretation is that this combination of traits may be associated mostly with desiccation avoidance (deep roots) or desiccation delay (deciduousness) rather than desiccation tolerance. Despite their higher photosynthetic capacity, these species had lower growth in the low-light understorey, probably because of higher maintenance costs (dark respiration rates). Wet-distribution species, on the other hand, had lower photosynthetic capacity and higher leaf area ratio. This strategy is typical of shade-tolerant species and may explain their higher growth rates in the low-light understorey. In conclusion, our results suggest that habitat associations along the rainfall gradient in the Isthmus of Panama may result in part from a trade-off between traits that are favourable to species that avoid or delay desiccation but that otherwise limit shade tolerance. This trade-off may limit the capacity of some dry-distribution species to colonize wet forests.
Wavelet Networks: Scale Equivariant Learning From Raw Waveforms

Inducing symmetry equivariance in deep neural architectures has resolved into improved data efficiency and generalization. In this work, we utilize the concept of scale and translation equivariance to tackle the problem of learning on time-series from raw waveforms. As a result, we obtain representations that largely resemble those of the wavelet transform at the first layer, but that evolve into much more descriptive ones as a function of depth. Our empirical results support the suitability of our Wavelet Networks which with a simple architecture design perform consistently better than CNNs on raw waveforms and on par with spectrogram-based methods.
A Self-supervised Approach for Adversarial Robustness

Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems e.g., for classification, segmentation and object detection. The vulnerability of DNNs against such attacks can prove a major roadblock towards their real-world deployment. Transferability of adversarial examples demand generalizable defenses that can provide cross-task protection. Adversarial training that enhances robustness by modifying target model's parameters lacks such generalizability. On the other hand, different input processing based defenses fall short in the face of continuously evolving attacks. In this paper, we take the first step to combine the benefits of both approaches and propose a self-supervised adversarial training mechanism in the input space. By design, our defense is a generalizable approach and provides significant robustness against the \textbf{unseen} adversarial attacks (\eg by reducing the success rate of translation-invariant \textbf{ensemble} attack from 82.6\% to 31.9\% in comparison to previous state-of-the-art). It can be deployed as a plug-and-play solution to protect a variety of vision systems, as we demonstrate for the case of classification, segmentation and detection.
Quantum Channel State Masking

Communication over a quantum channel that depends on a quantum state is considered, when the encoder has channel side information (CSI) and is required to mask information on the quantum channel state from the decoder. A full characterization is established for the entanglement-assisted masking equivocation region, and a regularized formula is given for the quantum capacity-leakage function without assistance. For Hadamard channels without assistance, we derive single-letter inner and outer bounds, which coincide in the standard case of a channel that does not depend on a state.
Effect of Isolation Procedure on Molecular Weight Distribution of Wheat Straw Lignins

Neutral detergent fiber (NDF) of wheat straw (WS) war ball-milled for 7, 14, 21, and 28 days in a porcelain rotary ball-mill and hydrolyzed by a cellulare for 4 days, and the residue was used for lignin extraction by either diorane or 1 M NaOH. The effects of ball-milling duration (BMD) and extraction procedure on lignin yield and its high-performance size exclusion chromatography (HPSEC) features were examined in this study. Optimal BMD was 14 days, at which 80% and 100% of the permanganate lignin has been extracted by the dioxane and alkali systems, respectively. At a BMD of 14 days, carbohydrate content war 6% and 15% of the alkali lignin (AL) and diorane lignin (DL), respectively
OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms

Local Interpretable Model-Agnostic Explanations (LIME) is a popular method to perform interpretability of any kind of Machine Learning (ML) model. It explains one ML prediction at a time, by learning a simple linear model around the prediction. The model is trained on randomly generated data points, sampled from the training dataset distribution and weighted according to the distance from the reference point - the one being explained by LIME. Feature selection is applied to keep only the most important variables. LIME is widespread across different domains, although its instability - a single prediction may obtain different explanations - is one of the major shortcomings. This is due to the randomness in the sampling step, as well as to the flexibility in tuning the weights and determines a lack of reliability in the retrieved explanations, making LIME adoption problematic. In Medicine especially, clinical professionals trust is mandatory to determine the acceptance of an explainable algorithm, considering the importance of the decisions at stake and the related legal issues. In this paper, we highlight a trade-off between explanation's stability and adherence, namely how much it resembles the ML model. Exploiting our innovative discovery, we propose a framework to maximise stability, while retaining a predefined level of adherence. OptiLIME provides freedom to choose the best adherence-stability trade-off level and more importantly, it clearly highlights the mathematical properties of the retrieved explanation. As a result, the practitioner is provided with tools to decide whether the explanation is reliable, according to the problem at hand. We extensively test OptiLIME on a toy dataset - to present visually the geometrical findings - and a medical dataset. In the latter, we show how the method comes up with meaningful explanations both from a medical and mathematical standpoint.
A Stochastic Subgradient Method for Distributionally Robust Non-Convex Learning

We consider a distributionally robust formulation of stochastic optimization problems arising in statistical learning, where robustness is with respect to uncertainty in the underlying data distribution. Our formulation builds on risk-averse optimization techniques and the theory of coherent risk measures. It uses semi-deviation risk for quantifying uncertainty, allowing us to compute solutions that are robust against perturbations in the population data distribution. We consider a large family of loss functions that can be non-convex and non-smooth and develop an efficient stochastic subgradient method. We prove that it converges to a point satisfying the optimality conditions. To our knowledge, this is the first method with rigorous convergence guarantees in the context of non-convex non-smooth distributionally robust stochastic optimization. Our method can achieve any desired level of robustness with little extra computational cost compared to population risk minimization. We also illustrate the performance of our algorithm on real datasets arising in convex and non-convex supervised learning problems.
Statistical Estimation of High-Dimensional Vector Autoregressive Models

High-dimensional vector autoregressive (VAR) models are important tools for the analysis of multivariate time series. This paper focuses on high-dimensional time series and on the different regularized estimation procedures proposed for fitting sparse VAR models to such time series. Attention is paid to the different sparsity assumptions imposed on the VAR parameters and how these sparsity assumptions are related to the particular consistency properties of the estimators established. A sparsity scheme for high-dimensional VAR models is proposed which is found to be more appropriate for the time series setting considered. Furthermore, it is shown that, under this sparsity setting, threholding extents the consistency properties of regularized estimators to a wide range of matrix norms. Among other things, this enables application of the VAR parameters estimators to different inference problems, like forecasting or estimating the second-order characteristics of the underlying VAR process. Extensive simulations compare the finite sample behavior of the different regularized estimators proposed using a variety of performance criteria.
Vida-de-prateleira de goiabas minimamente processadas acondicionadas em embalagens sob atmosfera modificada

Goiabas (Psidium guajava L.), variedade vermelha "Paluma", minimamente processadas por desidratacao osmotica branda, foram acondicionadas em embalagens rigidas de polietileno tereftalato (PET) e estocadas a temperatura de 5oC, com o objetivo de avaliar a influencia da embalagem sob atmosfera modificada e do processo de desidratacao osmotica na vida-de-prateleira do produto. Goiabas sem o tratamento osmotico, acondicionadas nas embalagens de PET e em ar atmosferico, foram utilizadas como controle. Durante a estocagem foram avaliadas a perda de peso, a contaminacao microbiologica, as caracteristicas fisico-quimicas do produto e as concentracoes de O2 e CO2 no interior das embalagens. As goiabas foram submetidas tambem a uma analise sensorial de aceitacao, acompanhada da avaliacao da intencao de compra do produto pelo consumidor. Verificou-se que a combinacao da temperatura de estocagem, da embalagem sob atmosfera modificada passiva e do processo de desidratacao osmotica proporcionou as goiabas minimamente processadas uma vida-de-prateleira de 24 dias, resultando num produto mais estavel a contaminacao microbiologica e com caracteristicas sensoriais superiores as amostras controle, apresentando, tambem, melhor aceitacao pelo consumidor.