Miracle-dz/UniFAR-FTUs huggingface dataset:
Variation in hypothallus width and the growth of the lichen Rhizocarpon geographicum (L.) DC.

Variations in hypothallus width were studied in relation to radial growth in the lichen Rhizocarpon geographicum (L.) DC. in South Gwynedd, Wales, UK. Variations were present both within and between thalli and in successive three-month growth periods, but there was no significant variation associated with thallus size. In individual thalli, there were increases and reductions in hypothallus width in successive three-month growth periods attributable to hypothallus growth and changes at the margin of the areolae. Total radial growth over 18 months was positively correlated with initial hypothallus width. These results suggest: 1) individual thalli of similar size vary considerably in hypothallus width, 2) fluctuations in the location of the margin of the areolae in successive three month periods is an important factor determining this variability, 3) hypothallus width predicts subsequent radial growth over 18 months, and 4) variation in hypothallus; width is a factor determining between thallus variability in radial growth rates in yellow-green species of Rhizocarpon.
The pineal gland and anxiogenic-like action of £uoxetine in mice

Fluoxetine produces initial paradoxical anxiogenic e¡ect in some patients. In an elevatedplus-maze (EPM), £uoxetine triggers an anxiogenic-like e¡ect in rodents. Behavioral responses to psychoactive drugs can be modi¢ed by the pineal gland.We assessed the actions of £uoxetine in the EPM inmelatonin-pro¢cient C3Hmice, melatonin-de¢cient C57BL6 mice, and in sham-operated and pinealectomized mice. Mice were assayed 30min after the ¢rst injection and on day 14. Protracted £uoxetine treatment reduced the time on the anxiogenic open arms and increased the entries into the safe closed arms in sham-operated C3H mice. Fluoxetine was ine¡ective in pinealectomized C3H or C57BL6 mice. It is possible that the pineal system contributes to the previously observed anxiogenic action of £uoxetine in humans. NeuroReport15:691^694 c 2004 LippincottWilliams &Wilkins.
Research Note: Establishing a Common Method to Compare Soil Systems Designed for Both Tree Growth and Pavement Support

Abstract Establishing and managing trees under urban conditions provide a lesson in opposing needs within limited spaces. In the same soil volume, there are competing requirements for pavement support versus biological carrying capacity. Media to support both pavements and tree growth have been used for decades. However, with several media designs being advanced and discussed, there is limited research testing to inform mix design when new component sources are considered, or when comparing different components and mixtures. Designed media need to perform an engineering function immediately, but trees require time for growth to reach the designed service size. Therefore, there is a need to understand fundamental trends prior to waiting decades for tree performance evaluation. A common evaluation rubric needs to be developed in order to compare successful and unsuccessful systems and to better target mix designs of locally available materials. The media discussed in this article is a group that establishes an aggregate skeletal matrix for load bearing with an added horticultural component for tree root growth within the matrix. To understand both matrix development for load bearing and the critical soil component for tree biotic capacity, one convenient media evaluation method would be a voids analysis based on a four-component system of air, water, soil, and aggregate. A further adaptation from a similar method to define the critical amount of bitumen in asphalt concrete design seems contextually appropriate. This approach would use a voids analysis on a four-component system, akin to historic Marshall Testing Protocols. The term “percentage of apparent voids filled” is used as one component in a voids analysis approach. An initial voids analysis output series is suggested using peak compacted density, California bearing ratio, hydraulic conductivity, plant-available moisture/percent air voids at field capacity, percent voids in compacted mineral matrix, and percentage of apparent voids filled as measured across increasing soil-stone ratios within defined aggregate and soil pairings. Using archived data from a series of past experiments, a voids analysis example is used to illustrate a common format to reconcile the varied soil design systems into a common matrix for better comparison, understanding, and refinement.
Efficient adjustment sets in causal graphical models with hidden variables

We study the selection of covariate adjustment sets for estimating the value of point exposure dynamic policies, also known as dynamic treatment regimes, assuming a non-parametric causal graphical model with hidden variables, in which at least one adjustment set is fully observable. We show that recently developed criteria, for graphs without hidden variables, to compare the asymptotic variance of non-parametric estimators of static policy values that control for certain adjustment sets, are also valid under dynamic policies and graphs with hidden variables. We show that there exist adjustment sets that are optimal minimal (minimum), in the sense of yielding estimators with the smallest variance among those that control for adjustment sets that are minimal (of minimum cardinality). Moreover, we show that if either no variables are hidden or if all the observable variables are ancestors of either treatment, outcome, or the variables that are used to decide treatment, a globally optimal adjustment set exists. We provide polynomial time algorithms to compute the globally optimal (when it exists), optimal minimal, and optimal minimum adjustment sets. Our results are based on the construction of an undirected graph in which vertex cuts between the treatment and outcome variables correspond to adjustment sets. In this undirected graph, a partial order between minimal vertex cuts can be defined that makes the set of minimal cuts a lattice. This partial order corresponds directly to the ordering of the asymptotic variances of the corresponding non-parametrically adjusted estimators.
Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI

Compressed sensing (CS) MRI relies on adequate under-sampling of the k-space to accelerate the acquisition without compromising image quality. Consequently, the design of optimal sampling patterns for these k-space coefficients has received significant attention, with many CS MRI methods exploiting variable-density probability distributions. Realizing that an optimal sampling pattern may depend on the downstream task (e.g. image reconstruction, segmentation, or classification), we here propose joint learning of both task-adaptive k-space sampling and a subsequent model-based proximal-gradient recovery network. The former is enabled through a probabilistic generative model that leverages the Gumbel-softmax relaxation to sample across trainable beliefs while maintaining differentiability. The proposed combination of a highly flexible sampling model and a model-based (sampling-adaptive) image reconstruction network facilitates exploration and efficient training, yielding improved MR image quality compared to other sampling baselines.
COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts

The COVID-19 pandemic requires a fast response from researchers to help address biological, medical, and public health issues to minimize its impact. In this rapidly evolving context, scholars, professionals, and the public may need to identify important new studies quickly. In response, this paper assesses the coverage of scholarly databases and impact indicators during March 21, 2020 to April 18, 2020. The rapidly increasing volume of research is particularly accessible through Dimensions, and less through Scopus, the Web of Science, and PubMed. Google Scholar’s results included many false matches. A few COVID-19 papers from the 21,395 in Dimensions were already highly cited, with substantial news and social media attention. For this topic, in contrast to previous studies, there seems to be a high degree of convergence between articles shared in the social web and citation counts, at least in the short term. In particular, articles that are extensively tweeted on the day first indexed are likely to be highly read and relatively highly cited 3 weeks later. Researchers needing wide scope literature searches (rather than health-focused PubMed or medRxiv searches) should start with Dimensions (or Google Scholar) and can use tweet and Mendeley reader counts as indicators of likely importance.
Policy Gradient From Demonstration and Curiosity

With reinforcement learning, an agent can learn complex behaviors from high-level abstractions of the task. However, exploration and reward shaping remain challenging for existing methods, especially in scenarios where extrinsic feedback is sparse. Expert demonstrations have been investigated to solve these difficulties, but a tremendous number of high-quality demonstrations are usually required. In this work, an integrated policy gradient algorithm is proposed to boost exploration and facilitate intrinsic reward learning from only a limited number of demonstrations. We achieved this by reformulating the original reward function with two additional terms, where the first term measured the Jensen–Shannon divergence between current policy and the expert’s demonstrations, and the second term estimated the agent’s uncertainty about the environment. The presented algorithm was evaluated by a range of simulated tasks with sparse extrinsic reward signals, where only limited demonstrated trajectories were provided to each task. Superior exploration efficiency and high average return were demonstrated in all tasks. Furthermore, it was found that the agent could imitate the expert’s behavior and meanwhile sustain high return.
Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi

The primary obstacle to developing technologies for low-resource languages is the lack of usable data. In this paper, we report the adaption and deployment of 4 technology-driven methods of data collection for Gondi, a low-resource vulnerable language spoken by around 2.3 million tribal people in south and central India. In the process of data collection, we also help in its revival by expanding access to information in Gondi through the creation of linguistic resources that can be used by the community, such as a dictionary, children’s stories, an app with Gondi content from multiple sources and an Interactive Voice Response (IVR) based mass awareness platform. At the end of these interventions, we collected a little less than 12,000 translated words and/or sentences and identified more than 650 community members whose help can be solicited for future translation efforts. The larger goal of the project is collecting enough data in Gondi to build and deploy viable language technologies like machine translation and speech to text systems that can help take the language onto the internet.
M-LVC: Multiple Frames Prediction for Learned Video Compression

We propose an end-to-end learned video compression scheme for low-latency scenarios. Previous methods are limited in using the previous one frame as reference. Our method introduces the usage of the previous multiple frames as references. In our scheme, the motion vector (MV) field is calculated between the current frame and the previous one. With multiple reference frames and associated multiple MV fields, our designed network can generate more accurate prediction of the current frame, yielding less residual. Multiple reference frames also help generate MV prediction, which reduces the coding cost of MV field. We use two deep auto-encoders to compress the residual and the MV, respectively. To compensate for the compression error of the auto-encoders, we further design a MV refinement network and a residual refinement network, taking use of the multiple reference frames as well. All the modules in our scheme are jointly optimized through a single rate-distortion loss function. We use a step-by-step training strategy to optimize the entire scheme. Experimental results show that the proposed method outperforms the existing learned video compression methods for low-latency mode. Our method also performs better than H.265 in both PSNR and MS-SSIM. Our code and models are publicly available.
Yoga-82: A New Dataset for Fine-grained Classification of Human Poses

Human pose estimation is a well-known problem in computer vision to locate joint positions. Existing datasets for learning of poses are observed to be not challenging enough in terms of pose diversity, object occlusion and view points. This makes the pose annotation process relatively simple and restricts the application of the models that have been trained on them. To handle more variety in human poses, we propose the concept of fine-grained hierarchical pose classification, in which we formulate the pose estimation as a classification task, and propose a dataset, Yoga-82§, for large-scale yoga pose recognition with 82 classes. Yoga-82 consists of complex poses where fine annotations may not be possible. To resolve this, we provide hierarchical labels for yoga poses based on the body configuration of the pose. The dataset contains a three-level hierarchy including body positions, variations in body positions, and the actual pose names. We present the classification accuracy of the state-of-the-art convolutional neural network architectures on Yoga-82. We also present several hierarchical variants of DenseNet in order to utilize the hierarchical labels.
ParaCNN: Visual Paragraph Generation via Adversarial Twin Contextual CNNs

Image description generation plays an important role in many real-world applications, such as image retrieval, automatic navigation, and disabled people support. A well-developed task of image description generation is image captioning, which usually generates a short captioning sentence and thus neglects many of fine-grained properties, e.g., the information of subtle objects and their relationships. In this paper, we study the visual paragraph generation, which can describe the image with a long paragraph containing rich details. Previous research often generates the paragraph via a hierarchical Recurrent Neural Network (RNN)-like model, which has complex memorising, forgetting and coupling mechanism. Instead, we propose a novel pure CNN model, ParaCNN, to generate visual paragraph using hierarchical CNN architecture with contextual information between sentences within one paragraph. The ParaCNN can generate an arbitrary length of a paragraph, which is more applicable in many real-world applications. Furthermore, to enable the ParaCNN to model paragraph comprehensively, we also propose an adversarial twin net training scheme. During training, we force the forwarding network's hidden features to be close to that of the backwards network by using adversarial training. During testing, we only use the forwarding network, which already includes the knowledge of the backwards network, to generate a paragraph. We conduct extensive experiments on the Stanford Visual Paragraph dataset and achieve state-of-the-art performance.
Hierarchically Fair Federated Learning

When the federated learning is adopted among competitive agents with siloed datasets, agents are self-interested and participate only if they are fairly rewarded. To encourage the application of federated learning, this paper employs a management strategy, i.e., more contributions should lead to more rewards. We propose a novel hierarchically fair federated learning (HFFL) framework. Under this framework, agents are rewarded in proportion to their pre-negotiated contribution levels. HFFL+ extends this to incorporate heterogeneous models. Theoretical analysis and empirical evaluation on several datasets confirm the efficacy of our frameworks in upholding fairness and thus facilitating federated learning in the competitive settings.
SIRNet: Understanding Social Distancing Measures with Hybrid Neural Network Model for COVID-19 Infectious Spread

The SARS-CoV-2 infectious outbreak has rapidly spread across the globe and precipitated varying policies to effectuate physical distancing to ameliorate its impact. In this study, we propose a new hybrid machine learning model, SIRNet, for forecasting the spread of the COVID-19 pandemic that couples with the epidemiological models. We use categorized spatiotemporally explicit cellphone mobility data as surrogate markers for physical distancing, along with population weighted density and other local data points. We demonstrate at varying geographical granularity that the spectrum of physical distancing options currently being discussed among policy leaders have epidemiologically significant differences in consequences, ranging from viral extinction to near complete population prevalence. The current mobility inflection points vary across geographical regions. Experimental results from SIRNet establish preliminary bounds on such localized mobility that asymptotically induce containment. The model can support in studying non-pharmacological interventions and approaches that minimize societal collateral damage and control mechanisms for an extended period of time.
Towards Universal Cosmological Emulators with Generative Adversarial Networks

Generative adversarial networks (GANs) have been recently applied as a novel emulation technique for large scale structure simulations. Recent results show that GANs can be used as a fast, efficient and computationally cheap emulator for producing novel weak lensing convergence maps as well as cosmic web data in 2-D and 3-D. However, like any algorithm, the GAN approach comes with a set of limitations, such as an unstable training procedure and the inherent randomness of the produced outputs. In this work we employ a number of techniques commonly used in the machine learning literature to address the mentioned limitations. In particular, we train a GAN to produce both weak lensing convergence maps and dark matter overdensity field data for multiple redshifts, cosmological parameters and modified gravity models. In addition, we train a GAN using the newest Illustris data to emulate dark matter, gas and internal energy distribution data simultaneously. Finally, we apply the technique of latent space interpolation to control which outputs the algorithm produces. Our results indicate a 1-20% difference between the power spectra of the GAN-produced and the training data samples depending on the dataset used and whether Gaussian smoothing was applied. Finally, recent research on generative models suggests that such algorithms can be treated as mappings from a lower-dimensional input (latent) space to a higher dimensional (data) manifold. We explore such a theoretical description as a tool for better understanding the latent space interpolation procedure.
When and Why is Unsupervised Neural Machine Translation Useless?

This paper studies the practicality of the current state-of-the-art unsupervised methods in neural machine translation (NMT). In ten translation tasks with various data settings, we analyze the conditions under which the unsupervised methods fail to produce reasonable translations. We show that their performance is severely affected by linguistic dissimilarity and domain mismatch between source and target monolingual data. Such conditions are common for low-resource language pairs, where unsupervised learning works poorly. In all of our experiments, supervised and semi-supervised baselines with 50k-sentence bilingual data outperform the best unsupervised results. Our analyses pinpoint the limits of the current unsupervised NMT and also suggest immediate research directions.
Scaling through abstractions - high-performance vectorial wave simulations for seismic inversion with Devito

[Devito] is an open-source Python project based on domain-specific language and compiler technology. Driven by the requirements of rapid HPC applications development in exploration seismology, the language and compiler have evolved significantly since inception. Sophisticated boundary conditions, tensor contractions, sparse operations and features such as staggered grids and sub-domains are all supported; operators of essentially arbitrary complexity can be generated. To accommodate this flexibility whilst ensuring performance, data dependency analysis is utilized to schedule loops and detect computational-properties such as parallelism. In this article, the generation and simulation of MPI-parallel propagators (along with their adjoints) for the pseudo-acoustic wave-equation in tilted transverse isotropic media and the elastic wave-equation are presented. Simulations are carried out on industry scale synthetic models in a HPC Cloud system and reach a performance of 28TFLOP/s, hence demonstrating Devito's suitability for production-grade seismic inversion problems.
Making Science Personal: Inclusivity-Driven Design for General Education Courses

General-education college astronomy courses offer instructors both a unique audience and a unique challenge. For many students, such a course may be their first time encountering a standalone astronomy class, and it is also likely one of the last science courses they will take. Thus, in a single semester, primary course goals often include both imparting knowledge about the Universe and giving students some familiarity with the processes of science. In traditional course environments, students often compartmentalize information into separate "life files" and "course files" rather than integrating information into a coherent framework. The astronomy course created through this project, taught at the University of Arizona in Spring 2019, was designed around inclusivity-driven guiding principles that help students engage with course content in ways that are meaningful, relevant, and accessible. Our course bridges the gap between students' "life" and "course files", encourages and respects diverse points of view, and empowers students to connect course content with their personal lives and identities. In this paper, we provide insight into the guiding principles that informed our course design and share research results on the effectiveness of the instructional strategies and assessment techniques implemented in the course.
Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation

Reinforcement learning (RL) can be used to create a tactical decision-making agent for autonomous driving. However, previous approaches only output decisions and do not provide information about the agent's confidence in the recommended actions. This paper investigates how a Bayesian RL technique, based on an ensemble of neural networks with additional randomized prior functions (RPF), can be used to estimate the uncertainty of decisions in autonomous driving. A method for classifying whether or not an action should be considered safe is also introduced. The performance of the ensemble RPF method is evaluated by training an agent on a highway driving scenario. It is shown that the trained agent can estimate the uncertainty of its decisions and indicate an unacceptable level when the agent faces a situation that is far from the training distribution. Furthermore, within the training distribution, the ensemble RPF agent outperforms a standard Deep Q-Network agent. In this study, the estimated uncertainty is used to choose safe actions in unknown situations. However, the uncertainty information could also be used to identify situations that should be added to the training process.