Miracle-dz/UniFAR-FTUs huggingface dataset:
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging

Improving the image resolution and acquisition speed of magnetic resonance imaging (MRI) is a challenging problem. There are mainly two strategies dealing with the speed-resolution trade-off: (1) $k$-space undersampling with high-resolution acquisition, and (2) a pipeline of lower resolution image reconstruction and image super-resolution. However, these approaches either have limited performance at certain high acceleration factor or suffer from the error accumulation of two-step structure. In this paper, we combine the idea of MR reconstruction and image super-resolution, and work on recovering HR images from low-resolution under-sampled $k$-space data directly. Particularly, the SR-involved reconstruction can be formulated as a variational problem, and a learnable network unrolled from its solution algorithm is proposed. A discriminator was introduced to enhance the detail refining performance. Experiment results using in-vivo HR multi-coil brain data indicate that the proposed SRR-Net is capable of recovering high-resolution brain images with both good visual quality and perceptual quality.
Injection of Inner Oort Cloud Objects into the Distant Kuiper Belt by Planet Nine

The outer solar system exhibits an anomalous pattern of orbital clustering, characterized by an approximate alignment of the apsidal lines and angular momentum vectors of distant, long-term stable Kuiper Belt objects. One explanation for this dynamical confinement is the existence of a yet-undetected planetary-mass object, “Planet Nine (P9). ” Previous work has shown that trans-Neptunian objects, that originate within the scattered disk population of the Kuiper Belt, can be corralled into orbital alignment by Planet Nine’s gravity over ∼Gyr timescales, and characteristic P9 parameters have been derived by matching the properties of a synthetic Kuiper Belt generated within numerical simulations to the available observational data. In this work, we show that an additional dynamical process is in play within the framework of the Planet Nine hypothesis, and demonstrate that P9-induced dynamical evolution facilitates orbital variations within the otherwise dynamically frozen inner Oort cloud. As a result of this evolution, inner Oort cloud bodies can acquire orbits characteristic of the distant scattered disk, implying that if Planet Nine exists, the observed census of long-period trans-Neptunian objects is comprised of a mixture of Oort cloud and Kuiper Belt objects. Our simulations further show that although inward-injected inner Oort cloud objects exhibit P9-driven orbital confinement, the degree of clustering is weaker than that of objects originating within the Kuiper Belt. Cumulatively, our results suggest that a more eccentric Planet Nine is likely necessary to explain the data than previously thought.
Self-supervised object detection from audio-visual correspondence

We tackle the problem of learning object detectors without supervision. Differently from weakly-supervised object detection, we do not assume image-level class labels. Instead, we extract a supervisory signal from audio-visual data, using the audio component to “teach” the object detector. While this problem is related to sound source localisation, it is considerably harder because the detector must classify the objects by type, enumerate each instance of the object, and do so even when the object is silent. We tackle this problem by first designing a self-supervised framework with a contrastive objective that jointly learns to classify and localise objects. Then, without using any supervision, we simply use these self-supervised labels and boxes to train an image-based object detector. With this, we outperform previous unsupervised and weakly-supervised detectors for the task of object detection and sound source localization. We also show that we can align this detector to ground-truth classes with as little as one label per pseudo-class, and show how our method can learn to detect generic objects that go beyond instruments, such as airplanes and cats.
An acceleration strategy for randomize-then-optimize sampling via deep neural networks

Randomize-then-optimize (RTO) is widely used for sampling from posterior distributions in Bayesian inverse problems. However, RTO may be computationally intensive for complexity problems due to repetitive evaluations of the expensive forward model and its gradient. In this work, we present a novel strategy to substantially reduce the computation burden of RTO by using a goal-oriented deep neural networks (DNN) surrogate approach. In particular, the training points for the DNN-surrogate are drawn from a local approximated posterior distribution, and it is shown that the resulting algorithm can provide a flexible and efficient sampling algorithm, which converges to the direct RTO approach. We present a Bayesian inverse problem governed by a benchmark elliptic PDE to demonstrate the computational accuracy and efficiency of our new algorithm (i.e., DNN-RTO). It is shown that with our algorithm, one can significantly outperform the traditional RTO.
Acoustic Plasmons in Graphene Sandwiched between Two Metallic Slabs

We study the effect of two metallic slabs on the collective dynamics of electrons in graphene positioned between the two slabs. We show that if the slabs are perfect conductors, the plasmons of graphene display a linear dispersion relation. The velocity of these acoustic plasmons crucially depends on the distance between the two metal gates and the graphene sheet. In the case of generic slabs, the dispersion relation of graphene plasmons is much more complicated, but we find that acoustic plasmons can still be obtained under specific conditions.
Bi-level Off-policy Reinforcement Learning for Volt/VAR Control Involving Continuous and Discrete Devices

In Volt/Var control (VVC) of active distribution networks(ADNs), both slow timescale discrete devices (STDDs) and fast timescale continuous devices (FTCDs) are involved. The STDDs such as on-load tap changers (OLTC) and FTCDs such as distributed generators should be coordinated in time sequence. Such VCC is formulated as a two-timescale optimization problem to jointly optimize FTCDs and STDDs in ADNs. Traditional optimization methods are heavily based on accurate models of the system, but sometimes impractical because of their unaffordable effort on modelling. In this paper, a novel bi-level off-policy reinforcement learning (RL) algorithm is proposed to solve this problem in a model-free manner. A Bi-level Markov decision process (BMDP) is defined to describe the two-timescale VVC problem and separate agents are set up for the slow and fast timescale sub-problems. For the fast timescale sub-problem, we adopt an off-policy RL method soft actor-critic with high sample efficiency. For the slow one, we develop an off-policy multi-discrete soft actor-critic (MDSAC) algorithm to address the curse of dimensionality with various STDDs. To mitigate the non-stationary issue existing the two agents' learning processes, we propose a multi-timescale off-policy correction (MTOPC) method by adopting importance sampling technique. Comprehensive numerical studies not only demonstrate that the proposed method can achieve stable and satisfactory optimization of both STDDs and FTCDs without any model information, but also support that the proposed method outperforms existing two-timescale VVC methods.
A new coreset framework for clustering

Given a metric space, the (k,z)-clustering problem consists of finding k centers such that the sum of the of distances raised to the power z of every point to its closest center is minimized. This encapsulates the famous k-median (z=1) and k-means (z=2) clustering problems. Designing small-space sketches of the data that approximately preserves the cost of the solutions, also known as coresets, has been an important research direction over the last 15 years. In this paper, we present a new, simple coreset framework that simultaneously improves upon the best known bounds for a large variety of settings, ranging from Euclidean space, doubling metric, minor-free metric, and the general metric cases.
Aliasing is your Ally: End-to-End Super-Resolution from Raw Image Bursts

This presentation addresses the problem of reconstructing a high-resolution image from multiple lower-resolution snapshots captured from slightly different viewpoints in space and time. Key challenges for solving this super-resolution problem include (i) aligning the input pictures with sub-pixel accuracy, (ii) handling raw (noisy) images for maximal faithfulness to native camera data, and (iii) designing/learning an image prior (regularizer) well suited to the task. We address these three challenges with a hybrid al-gorithm building on the insight from [43] that aliasing is an ally in this setting, with parameters that can be learned end to end, while retaining the interpretability of classical approaches to inverse problems. The effectiveness of our approach is demonstrated on synthetic and real image bursts, setting a new state of the art on several benchmarks and delivering excellent qualitative results on real raw bursts captured by smartphones and prosumer cameras.
Real-time Forecast Models for TBM Load Parameters Based on Machine Learning Methods

Because of the fast advance rate and the improved personnel safety, tunnel boring machines (TBMs) have been widely used in a variety of tunnel construction projects. The dynamic modeling of TBM load parameters (including torque, advance rate and thrust) plays an essential part in the design, safe operation and fault prognostics of this complex engineering system. In this paper, based on in-situ TBM operational data, we use the machine-learning (ML) methods to build the real-time forecast models for TBM load parameters, which can instantaneously provide the future values of the TBM load parameters as long as the current data are collected. To decrease the model complexity and improve the generalization, we also apply the least absolute shrinkage and selection (Lasso) method to extract the essential features of the forecast task. The experimental results show that the forecast models based on deep-learning methods, {\it e.g.}, recurrent neural network and its variants, outperform the ones based on the shallow-learning methods, {\it e.g.}, support vector regression and random forest. Moreover, the Lasso-based feature extraction significantly improves the performance of the resultant models.
RECON: Rapid Exploration for Open-World Navigation with Latent Goal Models

We describe a robotic learning system for autonomous exploration and navigation in diverse, open-world environments. At the core of our method is a learned latent variable model of distances and actions, along with a non-parametric topological memory of images. We use an information bottleneck to regularize the learned policy, giving us (i) a compact visual representation of goals, (ii) improved generalization capabilities, and (iii) a mechanism for sampling feasible goals for exploration. Trained on a large offline dataset of prior experience, the model acquires a representation of visual goals that is robust to task-irrelevant distractors. We demonstrate our method on a mobile ground robot in open-world exploration scenarios. Given an image of a goal that is up to 80 meters away, our method leverages its representation to explore and discover the goal in under 20 minutes, even amidst previously-unseen obstacles and weather conditions. Please check out the project website for videos of our experiments and information about the real-world dataset used at https://sites.google. com/view/recon-robot.
Fighting the COVID-19 Infodemic with a Holistic BERT Ensemble

This paper describes the TOKOFOU system, an ensemble model for misinformation detection tasks based on six different transformer-based pre-trained encoders, implemented in the context of the COVID-19 Infodemic Shared Task for English. We fine tune each model on each of the task’s questions and aggregate their prediction scores using a majority voting approach. TOKOFOU obtains an overall F1 score of 89.7%, ranking first.
Near-perfect fidelity polarization-encoded multilayer optical data storage based on aligned gold nanorods

Encoding information in light polarization is of great importance in facilitating optical data storage (ODS) for information security and data storage capacity escalation. However, despite recent advances in nanophotonic techniques vastly enhancing the feasibility of applying polarization channels, the data fidelity in reconstructed bits has been constrained by severe crosstalks occurring between varied polarization angles during data recording and reading process, which gravely hindered the utilization of this technique in practice. In this paper, we demonstrate an ultra-low crosstalk polarization-encoding multilayer optical data storage technique for high-fidelity data recording and retrieving by utilizing a nanofibre-based nanocomposite film involving highly aligned gold nanorods (GNRs). With parallelizing the gold nanorods in the recording medium, the information carrier configuration minimizes miswriting and misreading possibilities for information input and output, respectively, compared with its randomly self-assembled counterparts. The enhanced data accuracy has significantly improved the bit recall fidelity that is quantified by a correlation coefficient higher than 0.99. It is anticipated that the demonstrated technique can facilitate the development of multiplexing ODS for a greener future.
OCM3D: Object-Centric Monocular 3D Object Detection

Image-only and pseudo-LiDAR representations are commonly used for monocular 3D object detection. However, methods based on them have shortcomings of either not well capturing the spatial relationships in neighbored image pixels or being hard to handle the noisy nature of the monocular pseudo-LiDAR point cloud. To overcome these issues, in this paper we propose a novel object-centric voxel representation tailored for monocular 3D object detection. Specifically, voxels are built on each object proposal, and their sizes are adaptively determined by the 3D spatial distribution of the points, allowing the noisy point cloud to be organized effectively within a voxel grid. This representation is proved to be able to locate the object in 3D space accurately. Furthermore, prior works would like to estimate the orientation via deep features extracted from an entire image or a noisy point cloud. By contrast, we argue that the local RoI information from the object image patch alone with a proper resizing scheme is a better input as it provides complete semantic clues meanwhile excludes irrelevant interferences. Besides, we decompose the confidence mechanism in monocular 3D object detection by considering the relationship between 3D objects and the associated 2D boxes. Evaluated on KITTI, our method outperforms state-of-the-art methods by a large margin. The code will be made publicly available soon.
Fibred categorical theory of obstruction and classification of morphisms

We set up a fibred categorical theory of obstruction and classification of morphisms that specializes to the one of monoidal functors between categorical groups and also to the Schreier-Mac Lane theory of group extensions. Further applications are provided, as for example a classification of unital associative algebra extensions with non-abelian kernel in terms of Hochschild cohomology.
The COVID Connection: Pandemic Anxiety, COVID-19 Comprehension, and Digital Confidence

This article presents logistic models examining how pandemic anxiety and COVID-19 comprehension vary with digital confidence among adults in the United States during the first wave of the pandemic. As we demonstrate statistically with a nationally representative data set, the digitally confident have lower probability of experiencing physical manifestations of pandemic anxiety and higher probability of adequately comprehending critical information on COVID-19. The effects of digital confidence on both pandemic anxiety and COVID-19 comprehension persist, even after a broad range of potentially confounding factors are taken into account, including sociodemographic factors such as age, gender, race/ethnicity, metropolitan status, and partner status. They also remain discernable after the introduction of general anxiety, as well as income and education. These results offer evidence that the digitally disadvantaged experience greater vulnerability to the secondary effects of the pandemic in the form of increased somatized stress and decreased COVID-19 comprehension. Going forward, future research and policy must make an effort to address digital confidence and digital inequality writ large as crucial factors mediating individuals’ responses to the pandemic and future crises.
Food Insecurity and Diabetes: The Role of Federally Qualified Health Centers as Pillars of Community Health

Food insecurity is a complex public health problem affecting millions of people globally. It leads to negative health outcomes in the afflicted population and the society at large. There is a self-perpetuating vicious cycle between food insecurity and chronic health conditions like diabetes. It is important for healthcare professionals to be aware of its existence, to be able to recognize it, and to work with their patients to find solutions for it. Simultaneously, the providers should advocate for their patients and make program administrators, policymakers, and legislatures aware of this crisis. During the current coronavirus disease 2019 (COVID-19) pandemic, when economies have been badly affected and many people have lost their jobs, this subject has arguably assumed much greater importance. In this article, we discuss the magnitude of the problem, its relation to diabetes mellitus, and the role that a Federally Qualified Health Center (FQHC) can play in mitigating this problem.
Human T-Cell Leukemia Virus Type 1 Infection Is a Risk Factor for Atherosclerosis

Background Infection, such as by human immunodeficiency virus (HIV), has been reported to cause atherosclerosis by inducing inflammation. Because human T-cell leukemia virus type 1 (HTLV-1) is a retrovirus, as is HIV, we investigated the possible influence of HTLV-1 on the pathogenesis of atherosclerosis by use of established atherosclerosis parameters. Methods The study was done on Iki Island, Fukuoka, an area endemic for HTLV-1. The data of 1,424 residents who reported to an annual health check were available for analysis. Anti-HTLV-1 antibody status and factors associated with atherosclerosis were examined, including maximum intima-media thickness (Max-IMT) and brachial-ankle pulse wave velocity (PWV). Results HTLV-1 positive participants had significantly higher Max-IMT (1.15 ± 0.55 vs. 1.08 ± 0.61 mm, P = 0.04) and PWV (1,760.6 ± 414.5 vs. 1,657.1 ± 425.5 cm/s, P < 0.01) values than did those negative. Moreover, in multiple regression analysis (odds ratio: 1.39, P < 0.01) of participants with Max-IMT 1.1 mm or over, HTLV-1 was extracted as an independent factor for the development of atherosclerosis. Conclusion Our results indicate that HTLV-1 infection confers a high risk of atherosclerosis, although its opposite relation is also possible. It is important to carefully follow the health status of HTLV-1 carriers.
Long-term storage of feces at −80 °C versus −20 °C is negligible for 16S rRNA amplicon profiling of the equine bacterial microbiome

The development of next-generation sequencing technologies has spurred a surge of research on bacterial microbiome diversity and function. But despite the rapid growth of the field, many uncertainties remain regarding the impact of differing methodologies on downstream results. Sample storage temperature is conventionally thought to be among the most important factors for ensuring reproducibility across marker gene studies, but to date much of the research on this topic has focused on short-term storage in the context of clinical applications. Consequently, it has remained unclear if storage at −80 °C, widely viewed as the gold standard for long-term archival of feces, is truly required for maintaining sample integrity in amplicon-based studies. A better understanding of the impacts of long-term storage conditions is important given the substantial cost and limited availability of ultra-low temperature freezers. To this end, we compared bacterial microbiome profiles inferred from 16S V3–V4 amplicon sequencing for paired fecal samples obtained from a feral horse population from Sable Island, Nova Scotia, Canada, stored at either −80 °C or −20 °C for 4 years. We found that storage temperature did not significantly affect alpha diversity measures, including amplicon sequence variant (ASV) richness and evenness, and abundance of rare sequence variants, nor presence/absence, relative abundances and phylogenetic diversity weighted measures of beta diversity. These results indicate that storage of equine feces at −20 °C for periods ranging from a few months to a few years is equivalent to storage at −80 °C for amplicon-based microbiome studies, adding to accumulating evidence indicating that standard domestic freezers are both economical and effective for microbiome research.
Crosstalk between Bone and Nerves within Bone

For the past two decades, the function of intrabony nerves on bone has been a subject of intense research, while the function of bone on intrabony nerves is still hidden in the corner. In the present review, the possible crosstalk between bone and intrabony peripheral nerves will be comprehensively analyzed. Peripheral nerves participate in bone development and repair via a host of signals generated through the secretion of neurotransmitters, neuropeptides, axon guidance factors and neurotrophins, with additional contribution from nerve‐resident cells. In return, bone contributes to this microenvironmental rendezvous by housing the nerves within its internal milieu to provide mechanical support and a protective shelf. A large ensemble of chemical, mechanical, and electrical cues works in harmony with bone marrow stromal cells in the regulation of intrabony nerves. The crosstalk between bone and nerves is not limited to the physiological state, but also involved in various bone diseases including osteoporosis, osteoarthritis, heterotopic ossification, psychological stress‐related bone abnormalities, and bone related tumors. This crosstalk may be harnessed in the design of tissue engineering scaffolds for repair of bone defects or be targeted for treatment of diseases related to bone and peripheral nerves.