Miracle-dz/UniFAR-FTUs huggingface dataset:
EARLY ENVIRONMENT AND RECRUITMENT OF BLACK BRANT (BRANTA BERNICLA NIGRICANS) INTO THE BREEDING POPULATION

Abstract In geese, growth regulates survival in the first year. We examined whether early growth, which is primarily governed by environmental conditions, also affects the probability that individuals that survive their first year enter the breeding population. We used logistic regression on a sample of Black Brant (Branta bernicla nigricans) that were weighed at a known age in their first summer and observed during winter (indicating that they had survived the principal mortality period in their first year) to study whether early growth influenced the probability that those individuals would be recruited into the breeding population. We also examined the effects of cohort (1986–1996), sex, age when measured, and area where individuals were reared. The model with the lowest Akaike's Information Criterion score contained body mass, age (days) at measurement, cohort, sex, and brood-rearing area. Models that included variable mass had 85% of the cumulative model weight of the models we considered, indicating that gosling mass had a substantial effect on probability of them entering the breeding population. Females were more likely to be detected breeding than males, which is consistent with the differential fidelity of the sexes. Of individuals that survived the first year, larger goslings were more likely to become breeders. More recent cohorts were less likely to have been detected as breeders. Our findings indicate that environment during the growth period affects the ability of individuals to enter the breeding population, even after accounting for the effects of growth on survival.
Safe Learning under Uncertain Objectives and Constraints

In this paper, we consider non-convex optimization problems under \textit{unknown} yet safety-critical constraints. Such problems naturally arise in a variety of domains including robotics, manufacturing, and medical procedures, where it is infeasible to know or identify all the constraints. Therefore, the parameter space should be explored in a conservative way to ensure that none of the constraints are violated during the optimization process once we start from a safe initialization point. To this end, we develop an algorithm called Reliable Frank-Wolfe (Reliable-FW). Given a general non-convex function and an unknown polytope constraint, Reliable-FW simultaneously learns the landscape of the objective function and the boundary of the safety polytope. More precisely, by assuming that Reliable-FW has access to a (stochastic) gradient oracle of the objective function and a noisy feasibility oracle of the safety polytope, it finds an $\epsilon$-approximate first-order stationary point with the optimal ${\mathcal{O}}({1}/{\epsilon^2})$ gradient oracle complexity (resp. $\tilde{\mathcal{O}}({1}/{\epsilon^3})$ (also optimal) in the stochastic gradient setting), while ensuring the safety of all the iterates. Rather surprisingly, Reliable-FW only makes $\tilde{\mathcal{O}}(({d^2}/{\epsilon^2})\log 1/\delta)$ queries to the noisy feasibility oracle (resp. $\tilde{\mathcal{O}}(({d^2}/{\epsilon^4})\log 1/\delta)$ in the stochastic gradient setting) where $d$ is the dimension and $\delta$ is the reliability parameter, tightening the existing bounds even for safe minimization of convex functions. We further specialize our results to the case that the objective function is convex. A crucial component of our analysis is to introduce and apply a technique called geometric shrinkage in the context of safe optimization.
COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19

Background and Objective:Currently, the whole world is facing a pandemic disease, novel Coronavirus also known as COVID-19, which spread in more than 200 countries with around 3.3 million active cases and 4.4 lakh deaths approximately. Due to rapid increase in number of cases and limited supply of testing kits, availability of alternative diagnostic method is necessary for containing the spread of COVID-19 cases at an early stage and reducing the death count. For making available an alternative diagnostic method, we proposed a deep neural network based diagnostic method which can be easily integrated with mobile devices for detection of COVID-19 and viral pneumonia using Chest X-rays (CXR) images. Methods:In this study, we have proposed a method named COVIDLite, which is a combination of white balance followed by Contrast Limited Adaptive Histogram Equalization (CLAHE) and depth-wise separable convolutional neural network (DSCNN). In this method, white balance followed by CLAHE is used as an image preprocessing step for enhancing the visibility of CXR images and DSCNN trained using sparse cross entropy is used for image classification with lesser parameters and significantly lighter in size, i.e., 8.4 MB without quantization. Results:The proposed COVIDLite method resulted in improved performance in comparison to vanilla DSCNN with no pre-processing. The proposed method achieved higher accuracy of 99.58% for binary classification, whereas 96.43% for multiclass classification and out-performed various state-of-the-art methods. Conclusion:Our proposed method, COVIDLite achieved exceptional results on various performance metrics. With detailed model interpretations, COVIDLite can assist radiologists in detecting COVID-19 patients from CXR images and can reduce the diagnosis time significantly.
The challenge of truly cryptic diversity: diagnosis and description of a new madtom catfish (Ictaluridae: Noturus)

The Ozark madtom, Noturus albater, is distributed in upland streams in the upper White, Black, and St. Francis rivers of Missouri and Arkansas, USA. Fixed chromosomal and biochemical differences between populations in the upper White River and the Black and St. Francis rivers led earlier researchers to suggest that the Ozark madtom comprised two distinct species. The current study reviewed previous data, and examined osteology, pigmentation, morphometrics and DNA sequences from the mitochondrial cytochrome b gene and the nuclear Recombination Activating Gene 2. Sequence data were analysed using parsimony and Bayesian methods to construct a phylogenetic hypothesis of relationships within Noturus and examine relationships of populations of Ozark madtoms. Results of the molecular analyses were consistent with earlier chromosomal and biochemical studies. Morphology could not distinguish between populations from the upper White River and populations from the Black and St. Francis rivers. However, karyotypes, allozyme variation, and DNA sequences all provide diagnostic characters demonstrating the presence of two species consistent with the phylogenetic species concept. We describe the populations from the Black and St. Francis rivers as Noturus maydeni, a new cryptic species of madtom catfish.
Stochastic Transceiver Optimization in Multi-Tags Symbiotic Radio Systems

Symbiotic radio (SR) is emerging as a spectrum-and energy-efficient communication paradigm for future passive Internet of Things (IoT), where some single-antenna backscatter devices, referred to as Tags, are parasitic in an active primary transmission. The primary transceiver is designed to assist both direct-link (DL) and backscatter-link (BL) communication. In multi-Tags SR systems, the transceiver designs become much more complicated due to the presence of DL and inter-Tag interference, which further poses new challenges to the availability and reliability of DL and BL transmission. To overcome these challenges, we formulate the stochastic optimization of transceiver design as the general network utility maximization problem (GUMP). The resultant problem is a stochastic multiple-ratio fractional nonconvex problem, and consequently challenging to solve. By leveraging some fractional programming techniques, we tailor a surrogate function with the specific structure and subsequently develop a batch stochastic parallel decomposition (BSPD) algorithm, which is shown to converge to stationary solutions of the GNUMP. The simulation results verify the effectiveness of the proposed algorithm by numerical examples in terms of the achieved system throughput.
Daily responses of mouflon (Ovis gmelini musimon × Ovis sp.) activity to summer climatic conditions

Investigating the factors shaping the activity of large herbivores is of prime importance because changes in their activity patterns may indicate physiological stress, which can affect reproduction and survival of individuals, and thereby population growth. Although climatic conditions are known to impact the activity patterns of large herbivores, few studies have investigated this relationship at a fine temporal scale. From a continuous monitoring of activity, we assessed the influence of temperature and wind on the summer activity of female mouflon (Ovis gmelini musimon Pallas, 1811 × Ovis sp.) facing summer droughts. Females showed a marked bimodal activity pattern, with activity peaks occurring after dawn and before dusk. When temperature increased and wind speed decreased, the morning activity peak occurred earlier, while the evening activity peak was delayed. Hence, under stressing climatic conditions, female mouflon decreased their diurnal activity while increasing their nocturnal activity. However.
Efficient Constituency Parsing by Pointing

We propose a novel constituency parsing model that casts the parsing problem into a series of pointing tasks. Specifically, our model estimates the likelihood of a span being a legitimate tree constituent via the pointing score corresponding to the boundary words of the span. Our parsing model supports efficient top-down decoding and our learning objective is able to enforce structural consistency without resorting to the expensive CKY inference. The experiments on the standard English Penn Treebank parsing task show that our method achieves 92.78 F1 without using pre-trained models, which is higher than all the existing methods with similar time complexity. Using pre-trained BERT, our model achieves 95.48 F1, which is competitive with the state-of-the-art while being faster. Our approach also establishes new state-of-the-art in Basque and Swedish in the SPMRL shared tasks on multilingual constituency parsing.
3D Pose Detection in Videos: Focusing on Occlusion

In this work, we build upon existing methods for occlusion-aware 3D pose detection in videos. We implement a two stage architecture that consists of the stacked hourglass network to produce 2D pose predictions, which are then inputted into a temporal convolutional network to produce 3D pose predictions. To facilitate prediction on poses with occluded joints, we introduce an intuitive generalization of the cylinder man model used to generate occlusion labels. We find that the occlusion-aware network is able to achieve a mean-per-joint-position error 5 mm less than our linear baseline model on the Human3.6M dataset. Compared to our temporal convolutional network baseline, we achieve a comparable mean-per-joint-position error of 0.1 mm less at reduced computational cost.
Unsupervised Discovery, Control, and Disentanglement of Semantic Attributes With Applications to Anomaly Detection

Our work focuses on unsupervised and generative methods that address the following goals: (1) learning unsupervised generative representations that discover latent factors controlling image semantic attributes, (2) studying how this ability to control attributes formally relates to the issue of latent factor disentanglement, clarifying related but dissimilar concepts that had been confounded in the past, and (3) developing anomaly detection methods that leverage representations learned in the first goal. For goal 1, we propose a network architecture that exploits the combination of multiscale generative models with mutual information (MI) maximization. For goal 2, we derive an analytical result, lemma 1, that brings clarity to two related but distinct concepts: the ability of generative networks to control semantic attributes of images they generate, resulting from MI maximization, and the ability to disentangle latent space representations, obtained via total correlation minimization. More specifically, we demonstrate that maximizing semantic attribute control encourages disentanglement of latent factors. Using lemma 1 and adopting MI in our loss function, we then show empirically that for image generation tasks, the proposed approach exhibits superior performance as measured in the quality and disentanglement of the generated images when compared to other state-of-the-art methods, with quality assessed via the Fréchet inception distance (FID) and disentanglement via mutual information gap. For goal 3, we design several systems for anomaly detection exploiting representations learned in goal 1 and demonstrate their performance benefits when compared to state-of-the-art generative and discriminative algorithms. Our contributions in representation learning have potential applications in addressing other important problems in computer vision, such as bias and privacy in AI.
Imbalanced Gradients: A New Cause of Overestimated Adversarial Robustness

Evaluating the robustness of a defense model is a challenging task in adversarial robustness research. Obfuscated gradients, a type of gradient masking, have previously been found to exist in many defense methods and cause a false signal of robustness. In this paper, we identify a more subtle situation called \emph{Imbalanced Gradients} that can also cause overestimated adversarial robustness. The phenomenon of imbalanced gradients occurs when the gradient of one term of the margin loss dominates and pushes the attack towards to a suboptimal direction. To exploit imbalanced gradients, we formulate a \emph{Margin Decomposition (MD)} attack that decomposes a margin loss into individual terms and then explores the attackability of these terms separately via a two-stage process. We examine 12 state-of-the-art defense models, and find that models exploiting label smoothing easily cause imbalanced gradients, and on which our MD attacks can decrease their PGD robustness (evaluated by PGD attack) by over 23%. For 6 out of the 12 defenses, our attack can reduce their PGD robustness by at least 9%. The results suggest that imbalanced gradients need to be carefully addressed for more reliable adversarial robustness.
Occurrence and Control of Triazine-Resistant Common Waterhemp (Amaranthus rudis) in Field Corn (Zea mays)

Triazine-resistant (TR) common waterhemp was reported and confirmed in Fillmore County, NE in 1990. A survey of 81 fields was conducted to characterize the occurrence of common waterhemp and grower practices. Sampled fields fell into three categories: suspected by growers to contain resistant plants (26 fields), randomly selected (28 fields), and adjacent to fields containing TR common waterhemp (27 fields). Resistant plants were found in 64% of all fields. Resistance was confirmed in 92% of the fields suspected by growers to contain resistant plants. Adjacent fields were no more likely to contain resistant plants than randomly chosen fields. Crop rotation did not significantly affect occurrence of resistance. Resistance was associated with the grower, indicating movement of resistant seed between fields via equipment. Atrazine, bromoxynil plus atrazine, and bentazon plus atrazine provided less than 75% postemergence control of TR common waterhemp, while primisulfuron, dicamba plus atrazine, primisulfuron plus dicamba, dicamba, 2,4-D ester, and metribuzin plus bentazon gave over 85% control.
Relative Entropy as a Measure of Difference between Hermitian and Non-Hermitian Systems

We employ the relative entropy as a measure to quantify the difference of eigenmodes between Hermitian and non-Hermitian systems in elliptic optical microcavities. We have found that the average value of the relative entropy in the range of the collective Lamb shift is large, while that in the range of self-energy is small. Furthermore, the weak and strong interactions in the non-Hermitian system exhibit rather different behaviors in terms of the relative entropy, and thus it displays an obvious exchange of eigenmodes in the elliptic microcavity.
Document Classification for COVID-19 Literature

The global pandemic has made it more important than ever to quickly and accurately retrieve relevant scientific literature for effective consumption by researchers in a wide range of fields. We provide an analysis of several multi-label document classification models on the LitCovid dataset. We find that pre-trained language models outperform other models in both low and high data regimes, achieving a maximum F1 score of around 86%. We note that even the highest performing models still struggle with label correlation, distraction from introductory text and CORD-19 generalization. Both data and code are available on GitHub.
The Benefits of Acting Locally: Reconstruction Algorithms for Sparse in Levels Signals With Stable and Robust Recovery Guarantees

The sparsity in levels model recently inspired a new generation of effective acquisition and reconstruction modalities for compressive imaging. Moreover, it naturally arises in various areas of signal processing such as parallel acquisition, radar, and the sparse corruptions problem. Reconstruction strategies for sparse in levels signals usually rely on a suitable convex optimization program. Notably, although iterative and greedy algorithms can outperform convex optimization in terms of computational efficiency and have been studied extensively in the case of standard sparsity, little is known about their generalizations to the sparse in levels setting. In this paper, we bridge this gap by showing new stable and robust uniform recovery guarantees for sparse in level variants of the iterative hard thresholding and the CoSaMP algorithms. Our theoretical analysis generalizes recovery guarantees currently available in the case of standard sparsity and favorably compare to sparse in levels guarantees for weighted $\ell ^1$ minimization. In addition, we also propose and numerically test an extension of the orthogonal matching pursuit algorithm for sparse in levels signals.
OBSERVATIONS ON THE POPULATION BIOLOGY OF THE GRAPE POWDERY MILDEW FUNGUS UNCINULA NECATOR

Random Amplified Polymorphic DNA (RAPD) analysis and mating type distribution were used to investigate the population biology of the phytopathogenic fungus Uncinula necator (Schw.) Burr. , the causal agent of powdery mildew of grapevine. It is known that the fungus has two overwintering strategies, mycelium and conidia in dormant buds or cleistothecia, but their relative importance in disease epidemiology is still undetermined. Recently, the existence has been hypothesized of two genetically separated biotypes in U. necator that would be related with its overwintering modes: a biotype overwintering as conidia and mycelium in buds would infect shoots and leaves early in the season; the other biotype would overwinter as cleistothecia and infect bunches. RAPD analysis was carried out on 374 isolates of U. necator collected in Southern Italy. Statistical analysis of variation clustered the isolates into two major groups according to the time in the season when they were sampled in vineyards, confirming the existence of two different biotypes into the fungal species. Similar proportions of the two mating types were found in the pathogen’s populations, as well as into each biotype, even in single vineyards. Therefore, the two mating types of the fungus are not separated either in space or time. Sexual crosses between isolates belonging to the two biotypes were fertile and yielded viable ascospore progeny. These results suggest that meiotic recombination can be an important source of genetic variation in U. necator and cleistothecia can play an important role in its overwintering.
Effects of dietary protein and energy levels on productive and reproductive performance of lactating buffaloes.

Twenty eight lactating buffaloes were used in a completely randomized design with 2×2 factorial arrangement of four experimental diets including low protein-low energy (LP-LE), low protein-high energy (LPHE), high protein-low energy (HP-LE) and high protein-high energy (HP-HE). Results showed that the HPHE diet recorded the highest digestibility coefficients of CP, EE, NFE, nutritive values, TDN and DCP intake, while HP-LE diet had the highest CF digestibility (P<0.05). The HP-HE diet had the highest (P<0.05) actual milk and 7% FCM yield and the contents of protein, lactose, SNF and TS in milk, HP-LE diet had the highest fat content (P<0.05). The HP-HE diet showed the lowest amounts of DM and TDN per kg, 7% FCM, while LP-HE diet had the lowest amount of DCP per kg 7% FCM (P<0.05). The LP-HE diet recorded the lowest average daily feed cost, while HP-HE diet showed the lowest feed cost/kg 7% FCM and the highest total revenue and economic efficiency (P<0.05). Buffaloes fed HP-HE diet showed short periods from parturition to first estrus and first service, service period, days open, the lowest number of services per conception and the highest conception rate (P<0.05).
FMix: Enhancing Mixed Sample Data Augmentation

Mixed Sample Data Augmentation (MSDA) has received increasing attention in recent years, with many successful variants such as MixUp and CutMix. From insight on the efficacy of CutMix in particular, we propose FMix, an MSDA that uses binary masks obtained by applying a threshold to low frequency images sampled from Fourier space. FMix improves performance over MixUp and CutMix for a number of models across a range of data sets and problem settings, obtaining new state-of-the-art results on CIFAR-10 and Fashion-MNIST. We go on to analyse MixUp, CutMix, and FMix from an information theoretic perspective, characterising learned models in terms of how they progressively compress the input with depth. Ultimately, our analyses allow us to decouple two complementary properties of augmentations that are useful for reasoning about MSDA. Code for all experiments is available at this https URL.
Properties of the multiphase outflows in local (ultra)luminous infrared galaxies

Galactic outflows are known to consist of several gas phases, however, so far the connection between these multiple phases has been investigated little and only in a few objects. In this paper, we analyse MUSE/VLT data of 26 local (U)LIRGs and study their ionised and neutral atomic phases. We also include objects from the literature to obtain a total sample of 31 galaxies with spatially resolved multi-phase outflow information. We find that the ionized phase of the outflows has on average an electron density three times higher than the disc ($n_{\rm e, disc}$ $\sim$ 150 cm$^{-3}$ vs $n_{\rm e, outflow}$ $\sim$ 500 cm$^{-3}$), suggesting that cloud compression in the outflow is more important that cloud dissipation. We find that the difference in extinction between outflow and disc correlates with the outflow gas mass. Together with the analysis of the outflow velocities, this suggests that at least some of the outflows are associated with the ejection of dusty clouds from the disc. The presence of dust in outflows is relevant for potential formation of molecules inside them. We combine our data with millimetre data to investigate the molecular phase. We find that the molecular phase accounts for more than 60 $\%$ of the total mass outflow rate in most objects and this fraction is higher in AGN-dominated systems. The neutral atomic phase contributes of the order of 10 $\%$, while the ionized phase is negligible. The ionized-to-molecular mass outflow rate declines slightly with AGN luminosity, although with a large scatter.