Miracle-dz/UniFAR-FTUs huggingface dataset:
Sunspot penumbral filaments intruding into a light bridge and the resultant reconnection jets

Context. Penumbral filaments and light bridges are prominent structures inside sunspots and are important for understanding the nature of sunspot magnetic fields and magneto-convection underneath. Aims. We investigate an interesting event where several penumbral filaments intrude into a sunspot light bridge. In doing so we aim to gain further insight into the magnetic fields of the sunspot penumbral filament and light bridge, as well as their interaction. Methods. Combining data from the New Vacuum Solar Telescope, Solar Dynamics Observatory, and Interface Region Imaging Spectrograph, we study the emission, kinematic, and magnetic topology characteristics of the penumbral filaments intruding into the light bridge and the resultant jets. Results. At the west part of the light bridge, the intruding penumbral filaments penetrate into the umbrae on both sides of the light bridge, and two groups of jets are also detected. The jets share the same projected morphology with the intruding filaments and are accompanied by intermittent footpoint brightenings. Simultaneous spectral imaging observations provide convincing evidence for the presences of magnetic-reconnection-related heating and bidirectional flows near the jet bases and contribute to measuring the vector velocities of the jets. Additionally, nonlinear force-free field extrapolation results reveal strong and highly inclined magnetic fields along the intruding penumbral filaments, highly consistent with the results deduced from the vector velocities of the jets. Therefore, we propose that the jets could be caused by magnetic reconnections between emerging fields within the light bridge and the nearly horizontal fields of intruding filaments. The jets are then ejected outward along the stronger filament fields. Conclusions. Our study indicates that magnetic reconnection could occur between the penumbral filament fields and emerging fields within the light bridge and produce jets along the stronger filament fields. These results further complement the study of magnetic reconnection and dynamic activities within the sunspot.
Context Encoding for Video Retrieval with Contrastive Learning

Content-based video retrieval plays an important role in areas such as video recommendation, copyright protection, etc. Existing video retrieval methods mainly extract frame-level features independently, therefore lack of efficient aggregation of features between frames, and it is difficult to effectively deal with poor quality frames, such as frames with motion blur, out of focus, etc. In this paper, we propose CECL (Context Encoding for video retrieval with Contrastive Learning), a video representation learning framework that aggregates the context information of frame-level descriptors, and a supervised contrastive learning method that performs automatic hard negative mining, and utilizes the memory bank mechanism to increase the capacity of negative samples. Extensive experiments are conducted on multi video retrieval tasks, such as FIVR, CC_WEB_VIDEO and EVVE. The proposed method shows a significant performance advantage (~17% mAP on FIVR-200K) over state-of-the-art methods with video-level features, and deliver competitive results with much lower computational cost when compared with frame-level features.
Seasonal and Spatial Patterns of Growth of Rainbow Trout in the Colorado River in Grand Canyon, AZ

Rainbow trout (Oncorhynchus mykiss) have been purposely introduced in many regulated rivers, with inadvertent consequences on native fishes. We describe how trout growth rates and condition could be influencing trout population dynamics in a 130 km section of the Colorado River below Glen Canyon Dam based on a large-scale mark–recapture program where ∼8000 rainbow trout were recaptured over a 3-year period (2012–2014). There were strong temporal and spatial variations in growth in both length and weight as predicted from von Bertalanffy and bioenergetic models, respectively. There was more evidence for seasonal variation in the growth coefficient and annual variation in the asymptotic length. Bioenergetic models showed more variability for growth in weight across seasons and years than across reaches. These patterns were consistent with strong seasonal variation in invertebrate drift and effects of turbidity on foraging efficiency. Highest growth rates and relative condition occurred in downstream reaches.
On the Generalizability of Neural Program Analyzers with respect to Semantic-Preserving Program Transformations

With the prevalence of publicly available source code repositories to train deep neural network models, neural program analyzers can do well in source code analysis tasks such as predicting method names in given programs that cannot be easily done by traditional program analyzers. Although such analyzers have been tested on various existing datasets, the extent in which they generalize to unforeseen source code is largely unknown. Since it is impossible to test neural program analyzers on all unforeseen programs, in this paper, we propose to evaluate the generalizability of neural program analyzers with respect to semantic-preserving transformations: a generalizable neural program analyzer should perform equally well on programs that are of the same semantics but of different lexical appearances and syntactical structures. More specifically, we compare the results of various neural program analyzers for the method name prediction task on programs before and after automated semantic-preserving transformations. We use three Java datasets of different sizes and three state-of-the-art neural network models for code, namely code2vec, code2seq, and Gated Graph Neural Networks (GGNN), to build nine such neural program analyzers for evaluation. Our results show that even with small semantically preserving changes to the programs, these neural program analyzers often fail to generalize their performance. Our results also suggest that neural program analyzers based on data and control dependencies in programs generalize better than neural program analyzers based only on abstract syntax trees. On the positive side, we observe that as the size of training dataset grows and diversifies the generalizability of correct predictions produced by the analyzers can be improved too.
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap

Neural architecture search (NAS) has attracted increasing attention. In recent years, individual search methods have been replaced by weight-sharing search methods for higher search efficiency, but the latter methods often suffer lower instability. This article provides a literature review on these methods and owes this issue to the optimization gap. From this perspective, we summarize existing approaches into several categories according to their efforts in bridging the gap, and we analyze both advantages and disadvantages of these methodologies. Finally, we share our opinions on the future directions of NAS and AutoML. Due to the expertise of the authors, this article mainly focuses on the application of NAS to computer vision problems.
Testing the Weak Equivalence Principle with the Binary Neutron Star Merger GW 170817: The Gravitational Contribution of the Host Galaxy

The successful detection of the binary neutron star merger GW 170817 and its electromagnetic counterparts has provided an opportunity to explore the joint effect of the host galaxy and the Milky Way (MW) on the weak equivalence principle (WEP) test. In this paper, using the Navarro–Frenk–White profile and the Hernquist profile, we present an analytic model to calculate the galactic potential, in which the possible locations of the source from the observed angle offset and the second supernova kick are accounted for. We show that the upper limit of Δγ is 10−9 for the comparison between GW 170817 and a gamma-ray burst (GRB 170817A), and it is 10−4 for the comparison between GW 170817 and a bright optical transient (SSS 17a, now with the IAU identification of AT 2017gfo). These limits are more stringent by one to two orders of magnitude than those determined solely using the measured MW potential in the literature. We demonstrate that the WEP test is strengthened by the contribution from the host galaxy to the Shapiro time delay. Meanwhile, we also find that large natal kicks produce a maximum deviation of about 20% from the results with a typical kick velocity of 400 to ∼500 km s−1. Finally, we analyze the impact from the halo mass of NGC 4993 with a typical 0.2 dex uncertainty and find that the upper limit of Δγ, with a maximum mass , is nearly two times more stringent than that of the minimum mass .
To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection

Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly affects the content and acoustics of spontaneous speech, natural language processing and machine learning provide promising techniques for reliably detecting AD. We compare and contrast the performance of two such approaches for AD detection on the recent ADReSS challenge dataset: 1) using domain knowledge-based hand-crafted features that capture linguistic and acoustic phenomena, and 2) fine-tuning Bidirectional Encoder Representations from Transformer (BERT)-based sequence classification models. We also compare multiple feature-based regression models for a neuropsychological score task in the challenge. We observe that fine-tuned BERT models, given the relative importance of linguistics in cognitive impairment detection, outperform feature-based approaches on the AD detection task.
Prior Guided Feature Enrichment Network for Few-Shot Segmentation

State-of-the-art semantic segmentation methods require sufficient labeled data to achieve good results and hardly work on unseen classes without fine-tuning. Few-shot segmentation is thus proposed to tackle this problem by learning a model that quickly adapts to new classes with a few labeled support samples. Theses frameworks still face the challenge of generalization ability reduction on unseen classes due to inappropriate use of high-level semantic information of training classes and spatial inconsistency between query and support targets. To alleviate these issues, we propose the Prior Guided Feature Enrichment Network (PFENet). It consists of novel designs of (1) a training-free prior mask generation method that not only retains generalization power but also improves model performance and (2) Feature Enrichment Module (FEM) that overcomes spatial inconsistency by adaptively enriching query features with support features and prior masks. Extensive experiments on PASCAL-5<inline-formula><tex-math notation="LaTeX">$^i$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mi>i</mml:mi></mml:msup></mml:math><inline-graphic xlink:href="tian-ieq1-3013717.gif"/></alternatives></inline-formula> and COCO prove that the proposed prior generation method and FEM both improve the baseline method significantly. Our PFENet also outperforms state-of-the-art methods by a large margin without efficiency loss. It is surprising that our model even generalizes to cases without labeled support samples.
Germplasm conservation of multipurpose trees and their role in agroforestry for sustainable agricultural production in Pakistan.

With increase in human and livestock population, worldwide increase in deforestation and land degradation has become the matter of global importance. Particularly in developing countries, productive forests and fertile lands are shrinking at an alarming rate making demand and supply relationship imbalanced. Another concern is the depletion of genetic resources of potentially important species either as a result of indiscriminate vegetation or insufficient efforts to preserve and maintain germplasm collections. Agroforestry (AF), as a complete farming system, has recently received attention recently and MPTS (multipurpose trees & shrubs) are considered as its integral component. They occupy prime importance for having potential and vital role in sustainable agricultural development. The importance of MPTS in meeting human needs for wood and its products and sustaining soil productivity is becoming increasingly recognized. Hundreds of species have been considered to have multiple uses. MPTS extremely vary for their natural distribution and variation, methods of exploration, collection and evaluation of their germplasm. In this paper, an attempt has been made to explore the role of MPTS in sustainable agricultural development and the strategies for germplasm acquisition, evaluation and preservation.
Privacy-preserving release of mobility data: a clean-slate approach

The quantity of mobility data is overwhelming nowadays providing tremendous potential for various value-added services. While the benefits of these mobility datasets are apparent, they also provide significant threat to location privacy. Although a multitude of anonymization schemes have been proposed to release location data, they all suffer from the inherent sparseness and high-dimensionality of location trajectories which render most techniques inapplicable in practice. In this paper, we revisit the problem of releasing location trajectories with strong privacy guarantees. We propose a general approach to synthesize location trajectories meanwhile providing differential privacy. We model the generator distribution of the dataset by first constructing a model to generate the source and destination location of trajectories along with time information, and then compute all transition probabilities between close locations given the destination of the synthetic trajectory. Finally, an optimization algorithm is used to find the most probable trajectory between the given source and destination at a given time using the computed transition probabilities. We exploit several inherent properties of location data to boost the performance of our model, and demonstrate its usability on a public location dataset. We also develop a novel composite of generative neural network to synthesize location trajectories which might be of independent interest.
Queueing Network Controls via Deep Reinforcement Learning

Novel advanced policy gradient (APG) methods, such as trust region policy optimization and proximal policy optimization (PPO), have become the dominant reinforcement learning algorithms because of their ease of implementation and good practical performance. A conventional setup for notoriously difficult queueing network control problems is a Markov decision problem (MDP) that has three features: infinite state space, unbounded costs, and long-run average cost objective. We extend the theoretical framework of these APG methods for such MDP problems. The resulting PPO algorithm is tested on a parallel-server system and large-size multiclass queueing networks. The algorithm consistently generates control policies that outperform state-of-art heuristics in literature in a variety of load conditions from light to heavy traffic. These policies are demonstrated to be near optimal when the optimal policy can be computed. A key to the successes of our PPO algorithm is the use of three variance reduction techniques in estimating the relative value function via sampling. First, we use a discounted relative value function as an approximation of the relative value function. Second, we propose regenerative simulation to estimate the discounted relative value function. Finally, we incorporate the approximating martingale-process method into the regenerative estimator.
Domain Adaptive Medical Image Segmentation via Adversarial Learning of Disease-Specific Spatial Patterns

In medical imaging, the heterogeneity of multi-centre data impedes the applicability of deep learning-based methods and results in significant performance degradation when applying models in an unseen data domain, e.g. a new centreor a new scanner. In this paper, we propose an unsupervised domain adaptation framework for boosting image segmentation performance across multiple domains without using any manual annotations from the new target domains, but by re-calibrating the networks on few images from the target domain. To achieve this, we enforce architectures to be adaptive to new data by rejecting improbable segmentation patterns and implicitly learning through semantic and boundary information, thus to capture disease-specific spatial patterns in an adversarial optimization. The adaptation process needs continuous monitoring, however, as we cannot assume the presence of ground-truth masks for the target domain, we propose two new metrics to monitor the adaptation process, and strategies to train the segmentation algorithm in a stable fashion. We build upon well-established 2D and 3D architectures and perform extensive experiments on three cross-centre brain lesion segmentation tasks, involving multicentre public and in-house datasets. We demonstrate that recalibrating the deep networks on a few unlabeled images from the target domain improves the segmentation accuracy significantly.
Action Sequencing Using Visual Permutations

Humans can easily reason about the sequence of high level actions needed to complete tasks, but it is particularly difficult to instill this ability in robots trained from relatively few examples. This work considers the task of neural action sequencing conditioned on a single reference visual state. This task is extremely challenging as it is not only subject to the significant combinatorial complexity that arises from large action sets, but also requires a model that can perform some form of symbol grounding, mapping high dimensional input data to actions, while reasoning about action relationships. This letter takes a permutation perspective and argues that action sequencing benefits from the ability to reason about both permutations and ordering concepts. Empirical analysis shows that neural models trained with latent permutations outperform standard neural architectures in constrained action sequencing tasks. Results also show that action sequencing using visual permutations is an effective mechanism to initialise and speed up traditional planning techniques and successfully scales to far greater action set sizes than models considered previously.
Transmission Map and Atmospheric Light Guided Iterative Updater Network for Single Image Dehazing

Hazy images obscure content visibility and hinder several subsequent computer vision tasks. For dehazing in a wide variety of hazy conditions, an end-to-end deep network jointly estimating the dehazed image along with suitable transmission map and atmospheric light for guidance could prove effective. To this end, we propose an Iterative Prior Updated Dehazing Network (IPUDN) based on a novel iterative update framework. We present a novel convolutional architecture to estimate channel-wise atmospheric light, which along with an estimated transmission map are used as priors for the dehazing network. Use of channel-wise atmospheric light allows our network to handle color casts in hazy images. In our IPUDN, the transmission map and atmospheric light estimates are updated iteratively using corresponding novel updater networks. The iterative mechanism is leveraged to gradually modify the estimates toward those appropriately representing the hazy condition. These updates occur jointly with the iterative estimation of the dehazed image using a convolutional neural network with LSTM driven recurrence, which introduces inter-iteration dependencies. Our approach is qualitatively and quantitatively found effective for synthetic and real-world hazy images depicting varied hazy conditions, and it outperforms the state-of-the-art. Thorough analyses of IPUDN through additional experiments and detailed ablation studies are also presented.
Modifying the network-based stochastic SEIR model to account for quarantine: an application to COVID-19

Abstract Objectives: Diseases such as SARS-CoV-2 have novel features that require modifications to the standard network-based stochastic SEIR model. In particular, we introduce modifications to this model to account for the potential changes in behavior patterns of individuals upon becoming symptomatic, as well as the tendency of a substantial proportion of those infected to remain asymptomatic. Methods: Using a generic network model where every potential contact exists with the same common probability, we conduct a simulation study in which we vary four key model parameters (transmission rate, probability of remaining asymptomatic, and the mean lengths of time spent in the exposed and infectious disease states) and examine the resulting impacts on various metrics of epidemic severity, including the effective reproduction number. We then consider the effects of a more complex network model. Results: We find that the mean length of time spent in the infectious state and the transmission rate are the most important model parameters, while the mean length of time spent in the exposed state and the probability of remaining asymptomatic are less important. We also find that the network structure has a significant impact on the dynamics of the disease spread. Conclusions: In this article, we present a modification to the network-based stochastic SEIR epidemic model which allows for modifications to the underlying contact network to account for the effects of quarantine. We also discuss the changes needed to the model to incorporate situations where some proportion of the individuals who are infected remain asymptomatic throughout the course of the disease.
Rate-Splitting Multiple Access for Overloaded Cellular Internet of Things

In the near future, it is envisioned that cellular networks will have to cope with extensive internet of things (IoT) devices. Therefore, a required feature of cellular IoT will be the capability to serve simultaneously a large number of devices with heterogeneous demands and qualities of channel state information at the transmitter (CSIT). In this paper, we focus on an overloaded multiple-input single-output (MISO) broadcast channel (BC) with two groups of CSIT qualities, namely, one group of users (representative of high-end devices) for which the transmitter has partial knowledge of the CSI, the other group of users (representative of IoT devices) for which the transmitter only has knowledge of the statistical CSI (i.e., the distribution information of the user channels). We introduce rate-splitting multiple access (RSMA), a new multiple access based on multi-antenna rate-splitting (RS) technique for cellular IoT. Two strategies are proposed, namely, time partitioning–RSMA (TP–RSMA) and power partitioning–RSMA (PP–RSMA). The former independently serves the two groups of users over orthogonal time slots while the latter jointly serves the two groups of users within the same time slot in a non-orthogonal manner. We first show at high signal-to-noise ratio (SNR) that PP–RSMA achieves the optimal degrees-of-freedom (DoF) in an overloaded MISO BC with heterogeneous CSIT qualities. We then show at finite SNR that PP–RSMA achieves explicit sum rate gain over TP–RSMA and all baseline schemes by marrying the benefits of PP and RSMA. Furthermore, PP–RSMA is robust to CSIT inaccuracy and flexible to cope with quality of service (QoS) rate constraints of all users. The DoF and rate analysis helps us in drawing the conclusion that PP–RSMA is a powerful framework for cellular IoT with a large number of devices.