Miracle-dz/UniFAR-FTUs huggingface dataset:
Individual and combined toxicity of imidacloprid and two seed dressing insecticides on collembolans Folsomia candida

ABSTRACT The aim of this study was to examine the chronic toxicity of imidacloprid (IMI), clothianidin (CLO) and fipronil (FIP) as a single exposure, as well as binary mixtures of IMI with CLO or FIP toward collembolans Folsomia candida, which are fauna present in the soil. Chronic toxicity assays were performed following an ISO guideline in a Tropical Artificial Soil (TAS), and the influence on the number and growth of the juveniles produced were determined. The range of nominal concentrations used in the tests with the individual compounds was 0.08–1.28 mg/kg (IMI), 0.079–1.264 mg/kg (FIP) and 0.007–0.112 mg/kg (CLO), whereas the mixture assays were performed with half the value used in the tests with individual compounds. Based upon single exposures, IMI produced a similar impact of reducing reproduction by 50% (EC50 ranging from 0.74 to 0.85 mg/kg) compared to FIP (EC50 = 0.78 mg/kg), whereas CLO was the most toxic to F. candida (EC50 = 0.08 mg/kg). Their mixtures generally resulted in a diminished effect on reproduction, as evidenced by the higher EC50 values. In contrast, in the case of the IMI+FIP combination at high concentrations at the EC50 level, a synergistic effect on toxicity was observed. The single exposure to the three insecticides and the mixture of IMI-FIP also decreased the size of generated juveniles, which was evidenced by the reduction in the proportion of large juveniles and increased proportion of small juveniles. However, both binary mixtures (IMI-FIP and IMI-CLO) presented antagonistic effects as evidenced by less than expected reductions in growth. Data on the toxic effects of IMI in a mixture with other seed dressing insecticides to collembolans provides useful information to environmental risk assessors by diminishing the uncertainties on the ecological risk of exposure to pesticides, enabling soil management degradation by utilizing multiple insecticides.
Novel Cytochrome P450 2C94 Functionally Metabolizes Diclofenac and Omeprazole in Dogs

Cytochromes P450 (P450s or CYPs) are important drug-metabolizing enzymes. Because dogs are frequently used in drug metabolism studies, knowledge of dog CYP2C enzymes is essential because in humans these enzymes are abundant and play major roles in liver and intestine. The present study identified and characterized novel dog CYP2C94 along with previously identified dog CYP2C21 and CYP2C41. Dog CYP2C21, CYP2C41, and CYP2C94 cDNAs, respectively, contained open reading frames of 490, 489, and 496 amino acids and shared high-sequence identities (70%, 75%, and 58%) with human CYP2Cs. Dog CYP2C94 mRNA was preferentially expressed in liver, just as dog CYP2C21 and CYP2C41 mRNAs were. In dog liver, CYP2C21 mRNA was the most abundant, followed by CYP2C94 and CYP2C41 mRNAs. Moreover, the hepatic expressions of all three dog CYP2C mRNAs varied in four individual dogs, two of which did not express CYP2C41 mRNA. The three dog CYP2C genes had similar gene structures, and CYP2C94, although located on the same chromosome, was in a genomic region far from the gene cluster containing CYP2C21 and CYP2C41. Metabolic assays with recombinant proteins showed that dog CYP2C94, along with CYP2C21 and CYP2C41, efficiently catalyzed oxidations of diclofenac, warfarin, and/or omeprazole, indicating that dog CYP2C94 is a functional enzyme. Novel dog CYP2C94 is expressed abundantly in liver and encodes a functional enzyme that metabolizes human CYP2C substrates; it is, therefore, likely responsible for drug clearances in dogs. SIGNIFICANCE STATEMENT Novel dog cytochrome P450 2C94 (CYP2C94) was identified and characterized along with dog CYP2C21 and CYP2C41. Dog CYP2C94, isolated from liver, had 58% sequence identity and a close phylogenetic relationship with its human homologs and was expressed in liver at the mRNA level. Dog CYP2C94 (and CYP2C21 and CYP2C41) catalyzed oxidations of diclofenac and omeprazole, human CYP2C9 and CYP2C19 substrates, respectively, but CYP2C41 also hydroxylated warfarin. CYP2C94 is therefore a functional drug-metabolizing enzyme likely responsible for drug clearances in dogs.
On the plastic flow of metals under conditions of axial symmetry

This paper is concerned with the axially symmetric plastic flow of a rigid-plastic nonhardening material which obeys the Tresca yield criterion of constant maximum shearing stress and the associated flow rule. A general discussion of the basic equations is given. The discussion shows that the hypothesis of Haar and von Kármán is likely to be of great importance in the solution of axially symmetric problems. This conclusion is substantiated by the remainder of the work which considers problems in which the hypothesis is satisfied, i.e. problems in which the circumferential stress is equal to one of the principal stresses in the meridional planes. Possible plastic velocity fields in a circular cylindrical bar stressed to yielding in compression or tension are obtained in §3. Section 4 examines plastic stress fields in the neighbourhood of stress-free conical surfaces. In the final sections of the paper, the plastic stress field and a permissible deformation mode for the problem of the indentation of the plane surface of a semi-infinite body by a circular flat-ended rigid punch are obtained. It is shown that the plastic stress field near the punch can be extended into the rigid region without violating the yield criterion.
Revisiting Collisional Dust Growth in Class 0/I Protostellar Disks: Sweep-up Can Convert a Few 10 M ⊕ of Dust into Kilogram Pebbles in 0.1 Myr

Recent observations suggest that the first stages of planet formation likely take place in the Class 0/I phase of young stellar object evolution, when the star and the disk are still embedded in an infalling envelope. In this study we perform grain coagulation calculations to investigate the very first stage of planet formation, the collisional growth of dust grains, in Class 0/I disks. We find that the slow increase in grain mass by high-velocity collision with much smaller grains (“sweep-up”) allows ∼50 M ⊕ of grains to grow well beyond the fragmentation barrier into ∼kilogram pebbles by the end of Class 0/I (0.1 Myr). We analyze the linear growth and saturation of sweep-up to understand our results quantitatively, and test whether the sweep-up outcome is sensitive to disk parameters and details of the grain coagulation model. The sweep-up pebble population could be important for planet formation, because they are less well-coupled to the gas (compared to the main population below the fragmentation barrier) and therefore more favorable to known mechanisms of dust clump formation (which initiate planetesimal formation). It also contains enough mass to form all planet cores, based on observational estimates of the planet mass budget. Our findings motivate future studies of grain growth and planetesimal formation in Class 0/I disks, including the subsequent evolution of this sweep-up population.
Light and Accurate: Neural Architecture Search via Two Constant Shared Weights Initialisations

In recent years, zero-cost proxies are gaining ground in neural architecture search (NAS). These methods allow finding the optimal neural network for a given task faster and with a lesser computational load than conventional NAS methods. Equally important is the fact that they also shed some light on the internal workings of neural architectures. This paper presents a zero-cost metric that highly correlates with the train set accuracy across the NAS-Bench-101, NAS-Bench-201 and NAS-Bench-NLP benchmark datasets. Architectures are initialised with two distinct constant shared weights, one at a time. Then, a fixed random mini-batch of data is passed forward through each initialisation. We observe that the dispersion of the outputs between two initialisations positively correlates with trained accuracy. The correlation further improves when we normalise dispersion by average output magnitude. Our metric, epsilon, does not require gradients computation or labels. It thus unbinds the NAS procedure from training hyperparameters, loss metrics and human-labelled data. Our method is easy to integrate within existing NAS algorithms and takes a fraction of a second to evaluate a single network.
Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery

Generalized Zero-Shot Learning (GZSL) and Open-Set Recognition (OSR) are two mainstream settings that greatly extend conventional visual object recognition. However, the limitations of their problem settings are not negligible. The novel categories in GZSL require pre-defined semantic labels, making the problem setting less realistic; the oversimplified unknown class in OSR fails to explore the innate fine-grained and mixed structures of novel categories. In light of this, we are motivated to consider a new problem setting named Zero-Knowledge Zero-Shot Learning (ZK-ZSL) that assumes no prior knowledge of novel classes and aims to classify seen and unseen samples and recover semantic attributes of the fine-grained novel categories for further interpretation. To achieve this, we propose a novel framework that recovers the clustering structures of both seen and unseen categories where the seen class structures are guided by source labels. In addition, a structural alignment loss is designed to aid the semantic learning of unseen categories with their recovered structures. Experimental results demonstrate our method's superior performance in classification and semantic recovery on four benchmark datasets.
Mixed-order self-paced curriculum learning for universal lesion detection

Self-paced curriculum learning (SCL) has demonstrated its great potential in computer vision, natural language processing, etc. During training, it implements easy-to-hard sampling based on online estimation of data difficulty. Most SCL methods commonly adopt a loss-based strategy of estimating data difficulty and deweighting the `hard' samples in the early training stage. While achieving success in a variety of applications, SCL stills confront two challenges in a medical image analysis task, such as universal lesion detection, featuring insufficient and highly class-imbalanced data: (i) the loss-based difficulty measurer is inaccurate; ii) the hard samples are under-utilized from a deweighting mechanism. To overcome these challenges, in this paper we propose a novel mixed-order self-paced curriculum learning (Mo-SCL) method. We integrate both uncertainty and loss to better estimate difficulty online and mix both hard and easy samples in the same mini-batch to appropriately alleviate the problem of under-utilization of hard samples. We provide a theoretical investigation of our method in the context of stochastic gradient descent optimization and extensive experiments based on the DeepLesion benchmark dataset for universal lesion detection (ULD). When applied to two state-of-the-art ULD methods, the proposed mixed-order SCL method can provide a free boost to lesion detection accuracy without extra special network designs.
Robust and Scalable Bayesian Online Changepoint Detection

This paper proposes an online, provably robust, and scalable Bayesian approach for changepoint detection. The resulting algorithm has key advantages over previous work: it provides provable robustness by leveraging the generalised Bayesian perspective, and also addresses the scalability issues of previous attempts. Specifically, the proposed generalised Bayesian formalism leads to conjugate posteriors whose parameters are available in closed form by leveraging diffusion score matching. The resulting algorithm is exact, can be updated through simple algebra, and is more than 10 times faster than its closest competitor.
Gaussian Mask Convolution for Convolutional Neural Networks

Square convolution is a default unit in convolutional neural networks as it ﬁts well on the tensor computation for convolution operation, which usually has a ﬁxed N × N receptive ﬁeld (RF). However, what matters most to the network is the effective receptive ﬁeld (ERF), which indicates the extent each pixel contributes to the output. ERF shows a Gaussian distribution and can not be modeled by simply sampling pixels with offsets. To simulate ERF, we pro-pose a Gaussian Mask convolutional kernel (GMConv) in this work. Speciﬁcally, GMConv utilizes the Gaussian function to generate a concentric symmetry mask and put the mask over the kernel to reﬁne the RF. Our GMConv can directly replace the standard convolutions in existing CNNs and can be easily trained end-to-end by standard back-propagation. Extensive experiments on multiple image clas-siﬁcation benchmark datasets show that our method is comparable to, and outperforms in many cases, the standard convolution. For instance, using GMConv for AlexNet and ResNet-50, the top-1 accuracy on ImageNet classiﬁcation is boosted by 0 . 98% and 0 . 85% , respectively.
Log-Paradox: Necessary and sufficient conditions for confounding statistically significant pattern reversal under the log-transform

The log-transform is a common tool in statistical analysis, reducing the impact of extreme values, compressing the range of reported values for improved visualization, enabling the usage of parametric statistical tests requiring normally distributed data, or enabling linear models on non-linear data. Practitioners are rarely aware that log-transformed results can reverse findings: a hypothesis test without the transform can show a negative trend, while with the log-transform, it can show a positive trend, both statistically significant. We derive necessary and sufficient conditions underlying this paradoxical pattern reversal using finite difference notation. We show that biomedical image quantification is very susceptible to these conditions. Using a novel heuristic maximizing the reversal, we show that statistical significance of the paradoxical pattern reversal can be easily induced by changing as little as 5% of a dataset. We illustrate how quantifying the sizes of objects in proportional data, especially where object sizes capture underlying creation and destruction dynamics, satisfies the precondition for the paradox. We discuss recommendations on proper use of the log-transform, discuss methods to explore the underlying patterns robustly, and emphasize that any transformed result should always be accompanied by its non-transformed source equivalent to exclude accidental confounded findings.
A Benchmark on Uncertainty Quantification for Deep Learning Prognostics

Reliable uncertainty quantification on RUL prediction is crucial for informative decision-making in predictive maintenance. In this context, we assess some of the latest developments in the field of uncertainty quantification for prognostics deep learning. This includes the state-of-the-art variational inference algorithms for Bayesian neural networks (BNN) as well as popular alternatives such as Monte Carlo Dropout (MCD), deep ensembles (DE) and heteroscedastic neural networks (HNN). All the inference techniques share the same inception deep learning architecture as a functional model. We performed hyperparameter search to optimize the main variational and learning parameters of the algorithms. The performance of the methods is evaluated on a subset of the large NASA NCMAPSS dataset for aircraft engines. The assessment includes RUL prediction accuracy, the quality of predictive uncertainty, and the possibility to break down the total predictive uncertainty into its aleatoric and epistemic parts. The results show no method clearly outperforms the others in all the situations. Although all methods are close in terms of accuracy, we find differences in the way they estimate uncertainty. Thus, DE and MCD generally provide more conservative predictive uncertainty than BNN. Surprisingly, HNN can achieve strong results without the added training complexity and extra parameters of the BNN. For tasks like active learning where a separation of epistemic and aleatoric uncertainty is required, radial BNN and MCD seem the best options.
Structural changes induced by electric currents in a single crystal of Pr$_2$CuO$_4$

We demonstrate a novel approach to the structural and electronic property modification of perovskites, focusing on Pr$_2$CuO$_4$, an undoped parent compound of a class of electron-doped copper-oxide superconductors. Currents were passed parallel or perpendicular to the copper-oxygen layers with the voltage ramped up until a rapid drop in the resistivity was achieved, a process referred to as"flash". The current was then further increased tenfold in current-control mode. This state was quenched by immersion into liquid nitrogen. Flash can drive many compounds into different atomic structures with new properties, whereas the quench freezes them into a long-lived state. Single-crystal neutron diffraction of as-grown and modified Pr$_2$CuO$_4$ revealed a $\sqrt{10}$x$\sqrt{10}$ superlattice due to oxygen-vacancy order. The diffraction peak intensities of the superlattice of the modified sample were significantly enhanced relative to the pristine sample. Raman-active phonons in the modified sample were considerably sharper. Measurements of electrical resistivity, magnetization and two-magnon Raman scattering indicate that the modification affected only the Pr-O layers, but not the Cu-O planes. These results point to enhanced oxygen-vacancy order in the modified samples well beyond what can be achieved without passing electrical current. Our work opens a new avenue toward electric field/quench control of structure and properties of layered perovskite oxides.
The Edge of Orthogonality: A Simple View of What Makes BYOL Tick

Self-predictive unsupervised learning methods such as BYOL or SimSiam have shown impressive results, and counter-intuitively, do not collapse to trivial representations. In this work, we aim at exploring the simplest possible mathematical arguments towards explaining the underlying mechanisms behind self-predictive unsupervised learning. We start with the observation that those methods crucially rely on the presence of a predictor network (and stop-gradient). With simple linear algebra, we show that when using a linear predictor, the optimal predictor is close to an orthogonal projection, and propose a general framework based on orthonormalization that enables to interpret and give intuition on why BYOL works. In addition, this framework demonstrates the crucial role of the exponential moving average and stop-gradient operator in BYOL as an efficient orthonormalization mechanism. We use these insights to propose four new \emph{closed-form predictor} variants of BYOL to support our analysis. Our closed-form predictors outperform standard linear trainable predictor BYOL at $100$ and $300$ epochs (top-$1$ linear accuracy on ImageNet).
Adaptive State-Dependent Diffusion for Derivative-Free Optimization

This paper develops and analyzes a stochastic derivative-free optimization strategy. A key feature is the state-dependent adaptive variance. We prove global convergence in probability with algebraic rate and give the quantitative results in numerical examples. A striking fact is that convergence is achieved without explicit information of the gradient and even without comparing different objective function values as in established methods such as the simplex method and simulated annealing. It can otherwise be compared to annealing with state-dependent temperature.
On the Permanence of Backdoors in Evolving Models

Existing research on training-time attacks for deep neural networks (DNNs), such as backdoors, largely assume that models are static once trained, and hidden backdoors trained into models remain active indefinitely. In practice, models are rarely static but evolve continuously to address distribution drifts in the underlying data. This paper explores the behavior of backdoor attacks in time-varying models, whose model weights are continually updated via fine-tuning to adapt to data drifts. Our theoretical analysis shows how fine-tuning with fresh data progressively"erases"the injected backdoors, and our empirical study illustrates how quickly a time-varying model"forgets"backdoors under a variety of training and attack settings. We also show that novel fine-tuning strategies using smart learning rates can significantly accelerate backdoor forgetting. Finally, we discuss the need for new backdoor defenses that target time-varying models specifically.
NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors

Many real-world data are naturally represented as a sparse reorderable matrix, whose rows and columns can be arbitrarily ordered (e.g., the adjacency matrix of a bipartite graph). Storing a sparse matrix in conventional ways requires an amount of space linear in the number of non-zeros, and lossy compression of sparse matrices (e.g., Truncated SVD) typically requires an amount of space linear in the number of rows and columns. In this work, we propose NeuKron for compressing a sparse reorderable matrix into a constant-size space. NeuKron generalizes Kronecker products using a recurrent neural network with a constant number of parameters. NeuKron updates the parameters so that a given matrix is approximated by the product and reorders the rows and columns of the matrix to facilitate the approximation. The updates take time linear in the number of non-zeros in the input matrix, and the approximation of each entry can be retrieved in logarithmic time. We also extend NeuKron to compress sparse reorderable tensors (e.g. multi-layer graphs), which generalize matrices. Through experiments on ten real-world datasets, we show that NeuKron is (a) Compact: requiring up to five orders of magnitude less space than its best competitor with similar approximation errors, (b) Accurate: giving up to 10 × smaller approximation error than its best competitors with similar size outputs, and (c) Scalable: successfully compressing a matrix with over 230 million non-zero entries.
Feature Likelihood Score: Evaluating Generalization of Generative Models Using Samples

Deep generative models have demonstrated the ability to generate complex, high-dimensional, and photo-realistic data. However, a uniﬁed framework for evaluating different generative modeling families remains a challenge. Indeed, likelihood-based metrics do not apply in many cases while pure sample-based metrics such as FID fail to capture known failure modes such as overﬁtting on training data. In this work, we introduce the Feature Likelihood Score (FLS), a parametric sample-based score that uses density estimation to quantitatively measure the quality/diversity of generated samples while taking into account overﬁtting. We empirically demonstrate the ability of FLS to identify speciﬁc overﬁtting problem cases, even when previously proposed metrics fail. We further perform an extensive experimental evaluation on various image datasets and model classes. Our results indicate that FLS matches intuitions of previous metrics, such as FID, while providing a more holistic evaluation of generative models that highlights models whose generalization abilities are under or overappreciated. Code for computing FLS is provided at https://github.com/marcojira/ﬂs.