Miracle-dz/UniFAR-FTUs huggingface dataset:
Host-Pathongen Co-evolution Inspired Algorithm Enables Robust GAN Training

Generative adversarial networks (GANs) are pairs of artificial neural networks that are trained one against each other. The outputs from a generator are mixed with the real-world inputs to the discriminator and both networks are trained until an equilibrium is reached, where the discriminator cannot distinguish generated inputs from real ones. Since their introduction, GANs have allowed for the generation of impressive imitations of real-life films, images and texts, whose fakeness is barely noticeable to humans. Despite their impressive performance, training GANs remains to this day more of an art than a reliable procedure, in a large part due to training process stability. Generators are susceptible to mode dropping and convergence to random patterns, which have to be mitigated by computationally expensive multiple restarts. Curiously, GANs bear an uncanny similarity to a co-evolution of a pathogen and its host's immune system in biology. In a biological context, the majority of potential pathogens indeed never make it and are kept at bay by the hots' immune system. Yet some are efficient enough to present a risk of a serious condition and recurrent infections. Here, we explore that similarity to propose a more robust algorithm for GANs training. We empirically show the increased stability and a better ability to generate high-quality images while using less computational power.
Invariant Adversarial Learning for Distributional Robustness

Machine learning algorithms with empirical risk minimization are vulnerable to distributional shifts due to the greedy adoption of all the correlations found in training data. Recently, there are robust learning methods aiming at this problem by minimizing the worst-case risk over an uncertainty set. However, they equally treat all covariates to form the uncertainty sets regardless of the stability of their correlations with the target, resulting in the overwhelmingly large set and low confidence of the learner. In this paper, we propose the Invariant Adversarial Learning (IAL) algorithm that leverages heterogeneous data sources to construct a more practical uncertainty set and conduct robustness optimization, where covariates are differentiated according to the stability of their correlations with the target. We theoretically show that our method is tractable for stochastic gradient-based optimization and provide the performance guarantees for our method. Empirical studies on both simulation and real datasets validate the effectiveness of our method in terms of robust performance across unknown distributional shifts.
AutoHAS: Differentiable Hyper-parameter and Architecture Search

Neural Architecture Search (NAS) has achieved significant progress in pushing state-of-the-art performance. While previous NAS methods search for different network architectures with the same hyper-parameters, we argue that such search would lead to sub-optimal results. We empirically observe that different architectures tend to favor their own hyper-parameters. In this work, we extend NAS to a broader and more practical space by combining hyper-parameter and architecture search. As architecture choices are often categorical whereas hyper-parameter choices are often continuous, a critical challenge here is how to handle these two types of values in a joint search space. To tackle this challenge, we propose AutoHAS, a differentiable hyper-parameter and architecture search approach, with the idea of discretizing the continuous space into a linear combination of multiple categorical basis. A key element of AutoHAS is the use of weight sharing across all architectures and hyper-parameters which enables efficient search over the large joint search space. Experimental results on MobileNet/ResNet/EfficientNet/BERT show that AutoHAS significantly improves accuracy up to 2% on ImageNet and F1 score up to 0.4 on SQuAD 1.1, with search cost comparable to training a single model. Compared to other AutoML methods, such as random search or Bayesian methods, AutoHAS can achieve better accuracy with 10x less compute cost.
Regulation of vascular endothelial cells and vascular smooth muscle cells by multiple P2Y receptor subtypes

Receptors for nucleotides regulate all aspects of vascular function. Here, the role and function of P2Y receptors in vascular endothelium and smooth muscle cell proliferation are explored. In both cell types the presence of multiple P2Y receptors is commonplace; however, the identity and role of these receptors is dependent on the vessel of origin. This is clearly seen with endothelial cells. The commonest observation is coexisting P2Y1 and P2Y2 (or P2Y4) receptors coupled through inositol 1,4,5‐trisphosphate production to Ca2+ mobilisation. However, in some endothelial cells other P2Y receptors are present. Brain microvascular endothelial cells, for example, exhibit a variety of responses with profiles which cannot be fully explained by cloned P2Y receptors, and which are coupled to diverse receptor–effector mechanisms. These include p42/p44 mitogen‐activated protein kinase activation, enhanced cyclic AMP synthesis and raised cytosolic Ca2+ in the absence of detectable rises in inositol 1,4,5‐trisphosphate. These responses can be expected to influence endothelial prostacyclin and nitric oxide production, permeability, adhesion factor expression, and leukocyte‐endothelial interaction. P2Y receptors also regulate proliferative responses. Previously, data has been presented indicating that nucleotides acting on P2Y receptors can enhance cell proliferation in response to other agents. Here, we discuss evidence that UTP is an antiproliferative regulator in human vascular smooth muscle cells and that P2Y4 receptors may be involved in this response. Drug Dev. Res. 53:180–185, 2001. © 2001 Wiley‐Liss, Inc.
Unique properties of adversarially trained linear classifiers on Gaussian data

Machine learning models are vulnerable to adversarial perturbations, that when added to an input, can cause high confidence misclassifications. The adversarial learning research community has made remarkable progress in the understanding of the root causes of adversarial perturbations. However, most problems that one may consider important to solve for the deployment of machine learning in safety critical tasks involve high dimensional complex manifolds that are difficult to characterize and study. It is common to develop adversarially robust learning theory on simple problems, in the hope that insights will transfer to `real world datasets'. In this work, we discuss a setting where this approach fails. In particular, we show with a linear classifier, it is always possible to solve a binary classification problem on Gaussian data under arbitrary levels of adversarial corruption during training, and that this property is not observed with non-linear classifiers on the CIFAR-10 dataset.
New species of Hypocopra (Xylariaceae)

Hypocopra (Fr.) Kickx is used for those representatives of the Xylariaceae typically possessing sessile, clypeoid, or reduced stromata; asci with a complex apical apparatus staining blue or brick-red in Melzer's reagent; either unicellular dark-walled ascospores or bicellular ascospores with one large dark-walled cell and a very small hyaline cell; and a single germ slit of variable length in the ascospores of the different species. A synoptic key to all currently recognized species is provided. One new combination, H. hypocoproides (Sordaria hypocoproides Speg.), is described and illustrated along with fourteen new species: H. annulata, found on deer and moose dung from Sweden and U.S.A. ; H. anomala and H. keniensis, both on burro and cow dung from Kenya and Mexico; H. bicolor and H. cataphracta, both on horse dung from U.S.A. ; H. dolichopoda, on horse, cow, deer, and rabbit dung from Canada and U.S.A. ; H. elachyglossina, on horse, cow, and goat dung from Bulgaria, Canada, Spain, Sweden, and Yugoslavia; .
Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation

In order to improve the performance of Bayesian optimisation, we develop a modified Gaussian process upper confidence bound (GP-UCB) acquisition function. This is done by sampling the exploration-exploitation trade-off parameter from a distribution. We prove that this allows the expected trade-off parameter to be altered to better suit the problem without compromising a bound on the function's Bayesian regret. We also provide results showing that our method achieves better performance than GP-UCB in a range of real-world and synthetic problems.
Polycyclic aromatic hydrocarbon excitation in nearby spiral galaxies

We have examined polycyclic aromatic hydrocarbon (PAH) excitation in a sample of 25 nearby face-on spiral galaxies using the ratio of mid-infrared PAH emission to dust mass. Within 11 of the galaxies, we found that the PAH excitation was straightforwardly linked to ultraviolet (UV) or mid-infrared star formation tracers, which, along with other results studying the relation of PAH emission to star formation, indicates that the PAHs are most strongly excited in dusty shells around the star-forming (SF) regions. Within another five galaxies, the PAH emission is enhanced around SF regions only at specific galactocentric radii. In six more galaxies, PAH excitation is more strongly correlated with the evolved stellar populations as traced by 3.6 μm emission. The results for the remaining three galaxies were ambiguous. The radial gradients of the PAH/dust ratios were generally not linked to log(O/H) gradients except when the log(O/H) gradients were relatively steep. Galaxies in which PAHs were excited by evolved stars had relatively high far-UV to mid-infrared ratios, implying that variations in the link between PAH excitation and different stellar populations are connected to changes in dust attenuation within galaxies. Alternately, differences in morphology could make it more likely that PAHs are excited by evolved stars, as five of the six galaxies where this occurs are late-type flocculent spiral galaxies. These heterogeneous results demonstrate the complexity of describing PAH excitation and have broad implications for using PAH emission as a star formation tracer as well as for modelling dust emission and radiative transfer.
ColdGANs: Taming Language GANs with Cautious Sampling Strategies

Training regimes based on Maximum Likelihood Estimation (MLE) suffer from known limitations, often leading to poorly generated text sequences. At the root of these limitations is the mismatch between training and inference, i.e. the so-called exposure bias, exacerbated by considering only the reference texts as correct, while in practice several alternative formulations could be as good. Generative Adversarial Networks (GANs) can mitigate those limitations but the discrete nature of text has hindered their application to language generation: the approaches proposed so far, based on Reinforcement Learning, have been shown to underperform MLE. Departing from previous works, we analyze the exploration step in GANs applied to text generation, and show how classical sampling results in unstable training. We propose to consider alternative exploration strategies in a GAN framework that we name ColdGANs, where we force the sampling to be close to the distribution modes to get smoother learning dynamics. For the first time, to the best of our knowledge, the proposed language GANs compare favorably to MLE, and obtain improvements over the state-of-the-art on three generative tasks, namely unconditional text generation, question generation, and abstractive summarization.
A Comparison of Self-Play Algorithms Under a Generalized Framework

The notion of self-play, albeit often cited in multiagent reinforcement learning as a process by which to train agent policies from scratch, has received little efforts to be taxonomized within a formal model. We present a formalized framework, with clearly defined assumptions, which encapsulates the meaning of self-play as abstracted from various existing self-play algorithms. This framework is framed as an approximation to a theoretical solution concept for multiagent training. Through a novel qualitative visualization metric, on a simple environment, we show that different self-play algorithms generate different distributions of episode trajectories, leading to different explorations of the policy space by the learning agents. Quantitatively, on two environments, we analyze the learning dynamics of policies trained under different self-play algorithms captured under our framework and perform cross self-play performance comparisons. Our results indicate that, throughout training, various widely used self-play algorithms exhibit cyclic policy evolutions and that the choice of self-play algorithm significantly affects the final performance of trained agents.
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations

Sentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various downstream tasks, such as clustering and retrieval. Unlike word embeddings, the highest performing solutions for learning sentence embeddings require labelled data, limiting their usefulness to languages and domains where labelled data is abundant. In this paper, we present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations. Inspired by recent advances in deep metric learning (DML), we carefully design a self-supervised objective for learning universal sentence embeddings that does not require labelled training data. When used to extend the pretraining of transformer-based language models, our approach closes the performance gap between unsupervised and supervised pretraining for universal sentence encoders. Importantly, our experiments suggest that the quality of the learned embeddings scale with both the number of trainable parameters and the amount of unlabelled training data. Our code and pretrained models are publicly available and can be easily adapted to new domains or used to embed unseen text.
Study of the Ground-State Energies of Some Nuclei Using Hybrid Model

The quark-quark (QQ) interaction as a perturbed term to the nucleon-nucleon interaction (NN) without any coupling between them is studied in a hybrid model. This model is used to calculate the ground-state energies of 2H1 and 4He2 nuclei. In a semirelativistic framework, this model is encouraged for light nuclei and the instanton-induced interaction by using the QQ potential and the NN interaction for a small scale around the hadron boundaries. This hybrid model depends on two theories, the one-boson exchange potential (OBEP) and the Cornell-dressed potential (CDP) for QQ. A small effect of quark-quark interaction is obtained on the values of the ground-state energies, around 6.7 and 1.2 percentage for 2H1 and 4He2, respectively nuclei.
Realistic Text Replacement With Non-Uniform Style Conditioning

In this work, we study the possibility of realistic text replacement. The goal of realistic text replacement is to replace text present in the image with user-supplied text. The replacement should be performed in a way that will not allow distinguishing the resulting image from the original one. We achieve this goal by developing a novel non-uniform style conditioning layer and apply it to an encoder-decoder ResNet based architecture. The resulting model is a single-stage model, with no post-processing. We train the model with a combination of adversarial, style, content and $L_{1}$ losses. Qualitative and quantitative evaluations show that the model achieves realistic text replacement and outperforms existing approaches in multilingual and challenging scenarios. Quantitative evaluation is performed with direct metrics, like SSIM and PSNR, and proxy metrics based on the performance of a text recognition model. The proposed model has several potential applications in augmented reality.
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity

In this paper we consider the problem of learning an $\epsilon$-optimal policy for a discounted Markov Decision Process (MDP). Given an MDP with $S$ states, $A$ actions, the discount factor $\gamma \in (0,1)$, and an approximation threshold $\epsilon > 0$, we provide a model-free algorithm to learn an $\epsilon$-optimal policy with sample complexity $\tilde{O}(\frac{SA\ln(1/p)}{\epsilon^2(1-\gamma)^{5.5}})$ (where the notation $\tilde{O}(\cdot)$ hides poly-logarithmic factors of $S,A,1/(1-\gamma)$, and $1/\epsilon$) and success probability $(1-p)$. For small enough $\epsilon$, we show an improved algorithm with sample complexity $\tilde{O}(\frac{SA\ln(1/p)}{\epsilon^2(1-\gamma)^{3}})$. While the first bound improves upon all known model-free algorithms and model-based ones with tight dependence on $S$, our second algorithm beats all known sample complexity bounds and matches the information theoretic lower bound up to logarithmic factors.
Influence of source, timing and placement of nitrogen on grain yield and nitrogen removal of durum wheat under reduced- and conventional-tillage management

Effective fertilizer management is critical to maintain economic production and protect long-term environmental quality. Field studies were conducted over 4 yr at two locations in southwestern Manitoba to determine the effect of source, timing and placement of N on grain yield and N recovery of durum wheat (Triticum durum L. ‘Sceptre’) under reduced-tillage (RT) and conventional-tillage (CT) management. The effect of N management on durum grain yield and N recovery differed with soil type and tillage system. On the clay loam (CL) soil, lower yields with fall- as compared with spring-banded N were more frequent under RT than CT. Lower yields occurred more frequently with fall-applied as compared with spring-applied urea ammonium nitrate (UAN) than when urea or NH3 was the N source. On the drier fine sandy loam (FSL) soil, fall applications of N generally produced similar to higher grain yield than did spring applications. Differences among fertilizer sources and tillage systems were much less frequent with.
Neural Architecture Search without Training

The time and effort involved in hand-designing deep neural networks is immense. This has prompted the development of Neural Architecture Search (NAS) techniques to automate this design. However, NAS algorithms tend to be extremely slow and expensive; they need to train vast numbers of candidate networks to inform the search process. This could be remedied if we could infer a network's trained accuracy from its initial state. In this work, we examine how the linear maps induced by data points correlate for untrained network architectures in the NAS-Bench-201 search space, and motivate how this can be used to give a measure of modelling flexibility which is highly indicative of a network's trained performance. We incorporate this measure into a simple algorithm that allows us to search for powerful networks without any training in a matter of seconds on a single GPU. Code to reproduce our experiments is available at this https URL.
Why are the Levels of Control (so) Different in German and UK Companies? Evidence from Initial Public Offerings

We analyse why the control of listed German and UK companies is so different. As shareholders in Germany are less protected and control is less expensive, German investors prefer controlling stakes. We also focus on economic factors such as profitability, risk and growth to predict the probability of occurrence of different states of control six years after the flotation. Large UK companies become widely held whereas in large German firms new shareholders control significantly larger stakes. Wealth constraints become binding for UK shareholders whereas German shareholders can avoid this by using pyramids. We find substantial differences between a takeover by a concentrated shareholder and one by a widely-held company. For the UK, the probability of the former increases when the company is risky, small and poorly performing. Conversely, the latter is more likely when the target is large, fast growing and profitable. Poor performance and high risk require control and monitoring by a concentrated shareholder. Conversely, high growth and profitability attract widely-held companies. Founders are less inclined to dilute their stake to retain private benefits of control. When German firms are profitable and risky, control is likely to go to a concentrated shareholder, but growth and low profitability increase the probability of a control acquisition by a widely-held firm.
Accelerating Natural Language Understanding in Task-Oriented Dialog

Task-oriented dialog models typically leverage complex neural architectures and large-scale, pre-trained Transformers to achieve state-of-the-art performance on popular natural language understanding benchmarks. However, these models frequently have in excess of tens of millions of parameters, making them impossible to deploy on-device where resource-efficiency is a major concern. In this work, we show that a simple convolutional model compressed with structured pruning achieves largely comparable results to BERT on ATIS and Snips, with under 100K parameters. Moreover, we perform acceleration experiments on CPUs, where we observe our multi-task model predicts intents and slots nearly 63x faster than even DistilBERT.
Antioxidant and neuroprotective effect of organic and conventional white grape juices on oxidative stress induced by sodium azide in cerebral cortex of rats.

Aims: Diet plays an important role in the prevention of some diseases related to oxidative stress. Although the beneficial effects of a moderate intake of wine are well known, information about the antioxidant properties of grape juice is still limited. Therefore, the objective of this study was to investigate the Invitroneuroprotective effect of conventional and organic white grape juices (
A computer simulation protocol to assess the accuracy of a Radio Stereometric Analysis (RSA) image processor according to the ISO-5725

Radio-Stereometric-Analysis and x-ray fluoroscopy are radiological techniques that require dedicated software to process data. The accurate calibration of these software is therefore critical. The aim of this work is to produce a protocol for evaluating the softwares' accuracy according to the ISO-5725. A series of computer simulations of the radiological setup and images were employed. The noise level of the images was also changed to evaluate the accuracy with different image qualities. The protocol was tested on a custom software developed by the authors. Radiological scene reconstruction accuracy was of (0.092 +- 0.14) mm for tube position, and (0.38 +- 0.31) mm / (2.09 +- 1.39) deg for detectors oriented in a direction other than the source-detector direction. In the source-detector direction the accuracy was of (2.68 +- 3.08) mm for tube position, and of (0.16 +- 0.27) mm / (0.075 +- 1.16) deg for the detectors. These disparate results are widely discussed in the literature. Model positioning and orientation was also highly accurate: (0.22 +- 0.46) mm / (0.26 +- 0.22) deg. Accuracy was not affected by the noise level. The protocol was able to assess the accuracy of the RSA system. It was also useful to detect and fix hidden bugs. It was also useful to detect and resolve hidden bugs in the software, and in optimizing the algorithms.