Miracle-dz/UniFAR-FTUs huggingface dataset:
Bridging the Gap Between Training and Inference for Spatio-Temporal Forecasting

Spatio-temporal sequence forecasting is one of the fundamental tasks in spatio-temporal data mining. It facilitates many real world applications such as precipitation nowcasting, citywide crowd flow prediction and air pollution forecasting. Recently, a few Seq2Seq based approaches have been proposed, but one of the drawbacks of Seq2Seq models is that, small errors can accumulate quickly along the generated sequence at the inference stage due to the different distributions of training and inference phase. That is because Seq2Seq models minimise single step errors only during training, however the entire sequence has to be generated during the inference phase which generates a discrepancy between training and inference. In this work, we propose a novel curriculum learning based strategy named Temporal Progressive Growing Sampling to effectively bridge the gap between training and inference for spatio-temporal sequence forecasting, by transforming the training process from a fully-supervised manner which utilises all available previous ground-truth values to a less-supervised manner which replaces some of the ground-truth context with generated predictions. To do that we sample the target sequence from midway outputs from intermediate models trained with bigger timescales through a carefully designed decaying strategy. Experimental results demonstrate that our proposed method better models long term dependencies and outperforms baseline approaches on two competitive datasets.
The POlarised GLEAM Survey (POGS) II: Results from an all-sky rotation measure synthesis survey at long wavelengths

Abstract The low-frequency linearly polarised radio source population is largely unexplored. However, a renaissance in low-frequency polarimetry has been enabled by pathfinder and precursor instruments for the Square Kilometre Array. In this second paper from the POlarised GaLactic and Extragalactic All-Sky MWA Survey-the POlarised GLEAM Survey, or POGS-we present the results from our all-sky MWA Phase I Faraday Rotation Measure survey. Our survey covers nearly the entire Southern sky in the Declination range $-82^\circ$ to $+30^\circ$ at a resolution between around three and seven arcminutes (depending on Declination) using data in the frequency range 169−231 MHz. We have performed two targeted searches: the first covering 25 489 square degrees of sky, searching for extragalactic polarised sources; the second covering the entire sky South of Declination $+30^\circ$, searching for known pulsars. We detect a total of 517 sources with 200 MHz linearly polarised flux densities between 9.9 mJy and 1.7 Jy, of which 33 are known radio pulsars. All sources in our catalogues have Faraday rotation measures in the range $-328.07$ to $+279.62$ rad m−2. The Faraday rotation measures are broadly consistent with results from higher-frequency surveys, but with typically more than an order of magnitude improvement in the precision, highlighting the power of low-frequency polarisation surveys to accurately study Galactic and extragalactic magnetic fields. We discuss the properties of our extragalactic and known-pulsar source population, how the sky distribution relates to Galactic features, and identify a handful of new pulsar candidates among our nominally extragalactic source population.
UvA-DARE (Digital Academic Repository) Policy-Aware Unbiased Learning to Rank for Top-k Rankings

Counterfactual Learning to Rank (LTR) methods optimize ranking systems using logged user interactions that contain interaction biases. Existing methods are only unbiased if users are presented with all relevant items in every ranking. There is currently no existing counterfactual unbiased LTR method for top- 𝑘 rankings. We introduce a novel policy-aware counterfactual estimator for LTR metrics that can account for the effect of a stochastic logging policy. We prove that the policy-aware estimator is unbiased if every relevant item has a non-zero probability to appear in the top- 𝑘 ranking. Our experimental results show that the performance of our estimator is not affected by the size of 𝑘 : for any 𝑘 , the policy-aware estimator reaches the same retrieval performance while learning from top- 𝑘 feedback as when learning from feedback on the full ranking. Lastly, we introduce novel extensions of traditional LTR methods to perform counterfactual LTR and to optimize top- 𝑘 metrics. Together, our contributions introduce the first policy-aware unbiased LTR approach that learns from top- 𝑘 feedback and optimizes top- 𝑘 metrics. As a result, counterfactual LTR is now applicable to the very prevalent top- 𝑘 ranking setting in search and recommendation.
The Power of Regeneration: Lessons from a Degraded Grassland

To gain insight into mechanisms controlling ecosystem regeneration, we investigated a grassland that was heavily polluted by a phosphate fertilizer factory between 1960 and 1990. Abiotic and biotic filters that influenced species assemblage and succession were monitored and restoration approaches assessed. Studies included the investigation of soil parameters, impact of abiotic stress on microbes and plants, plant succession, recolonization mechanisms, functional group interactions, mycorrhizal diversity and function, and food web analysis. Results indicate that after cessation of pollution, the system had high potential for self‐recovery. About 15 years of research are now summarized by interrelating the results of all projects and fitting them into one conceptual model.
Robust Beam Search for Encoder-Decoder Attention Based Speech Recognition Without Length Bias

As one popular modeling approach for end-to-end speech recognition, attention-based encoder-decoder models are known to suffer the length bias and corresponding beam problem. Different approaches have been applied in simple beam search to ease the problem, most of which are heuristic-based and require considerable tuning. We show that heuristics are not proper modeling refinement, which results in severe performance degradation with largely increased beam sizes. We propose a novel beam search derived from reinterpreting the sequence posterior with an explicit length modeling. By applying the reinterpreted probability together with beam pruning, the obtained final probability leads to a robust model modification, which allows reliable comparison among output sequences of different lengths. Experimental verification on the LibriSpeech corpus shows that the proposed approach solves the length bias problem without heuristics or additional tuning effort. It provides robust decision making and consistently good performance under both small and very large beam sizes. Compared with the best results of the heuristic baseline, the proposed approach achieves the same WER on the 'clean' sets and 4% relative improvement on the 'other' sets. We also show that it is more efficient with the additional derived early stopping criterion.
GPT-too: A Language-Model-First Approach for AMR-to-Text Generation

Abstract Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs. Existing approaches to generating text from AMR have focused on training sequence-to-sequence or graph-to-sequence models on AMR annotated data only. In this paper, we propose an alternative approach that combines a strong pre-trained language model with cycle consistency-based re-scoring. Despite the simplicity of the approach, our experimental results show these models outperform all previous techniques on the English LDC2017T10 dataset, including the recent use of transformer architectures. In addition to the standard evaluation metrics, we provide human evaluation experiments that further substantiate the strength of our approach.
SVAG: Stochastic Variance Adjusted Gradient Descent and Biased Stochastic Gradients

We examine biased gradient updates in variance reduced stochastic gradient methods. For this purpose we introduce SVAG, a SAG/SAGA-like method with adjustable bias. SVAG is analyzed under smoothness assumptions and we provide step-size conditions for convergence that match or improve on previously known conditions for SAG and SAGA. The analysis highlights a step-size requirement difference between when SVAG is applied to cocoercive operators and when applied to gradients of smooth functions, a difference not present in ordinary gradient descent. This difference is verified with numerical experiments. A variant of SVAG that adaptively selects the bias is presented and compared numerically to SVAG on a set of classification problems. The adaptive SVAG frequently performs among the best and always improves on the worst-case performance of the non-adaptive variant.
Quantum-Classical Machine learning by Hybrid Tensor Networks

Tensor networks (TN) have found a wide use in machine learning, and in particular, TN and deep learning bear striking similarities. In this work, we propose the quantum-classical hybrid tensor networks (HTN) which combine tensor networks with classical neural networks in a uniform deep learning framework to overcome the limitations of regular tensor networks in machine learning. We first analyze the limitations of regular tensor networks in the applications of machine learning involving the representation power and architecture scalability. We conclude that in fact the regular tensor networks are not competent to be the basic building blocks of deep learning. Then, we discuss the performance of HTN which overcome all the deficiency of regular tensor networks for machine learning. In this sense, we are able to train HTN in the deep learning way which is the standard combination of algorithms such as Back Propagation and Stochastic Gradient Descent. We finally provide two applicable cases to show the potential applications of HTN, including quantum states classification and quantum-classical autoencoder. These cases also demonstrate the great potentiality to design various HTN in deep learning way.
Deep Learning improves Radio Frequency Interference Classification

Flagging of Radio Frequency Interference (RFI) is an increasingly important challenge in radio astronomy. We present R-Net, a deep convolutional ResNet architecture that significantly outperforms existing algorithms -- including the default MeerKAT RFI flagger, and deep U-Net architectures -- across all metrics including AUC, F1-score and MCC. We demonstrate the robustness of this improvement on both single dish and interferometric simulations and, using transfer learning, on real data. Our R-Net model's precision is approximately $90\%$ better than the current MeerKAT flagger at $80\%$ recall and has a 35\% higher F1-score with no additional performance cost. We further highlight the effectiveness of transfer learning from a model initially trained on simulated MeerKAT data and fine-tuned on real, human-flagged, KAT-7 data. Despite the wide differences in the nature of the two telescope arrays, the model achieves an AUC of 0.91, while the best model without transfer learning only reaches an AUC of 0.67. We consider the use of phase information in our models but find that without calibration the phase adds almost no extra information relative to amplitude data only. Our results strongly suggest that deep learning on simulations, boosted by transfer learning on real data, will likely play a key role in the future of RFI flagging of radio astronomy data.
Pinus monophylla establishment in an expanding Pinus-Juniperus woodland: Environmental conditions, facilitation and interacting factors

The tree species comprising Pinus-Juniperus woodlands are rapidly expanding into shrub-grasslands throughout their range. Observational studies indicate that establishment is facilitated by nurse plants, but little information exists on the mechanisms involved. I examined both abiotic and biotic factors influencing Pinus monophylla establishment in Artemisia tridentata steppe with expanding populations of P. monophylla and Juniperus osteosperma. I determined soil water contents, temperatures, and nutrient characteristics for the primary establishment microhabitats, i.e. under Pinus, under Juniperus, under Artemisia, tree interspace and sage interspace, and evaluated the emergence and survival response of two seedling cohorts over a 3-yr period for the different microhabitats. I also examined the effects of seed burial and predation on seedling establishment. Microhabitats under trees and shrubs had higher extractable P and K, higher organic matter, total nitrogen and cation exchange capacity than interspace microhabitats. Soil water contents (0–15 cm) were lower in interspaces than under shrubs or trees due to dry surface (0–5 cm) soils. Soil temperatures (at 1 and 15 cm) were lowest under trees, intermediate under shrubs, and highest in interspaces. Timing and rate of seedling emergence were temperature dependent with the order of emergence paralleling mean growing season temperatures: tree interspace = shrub interspace > under shrub > under Juniperus under Pinus. Seed burial was required for rooting and the highest emergence occurred from depths of 1 and 3 cm indicating that caching by birds and rodents is essential and that animals bury seeds at adequate if not optimal depths for emergence. Seedlings required microenvironmental modification for survival; all seedlings, including those that emerged from seeds and transplants, died within the first year in interspace microhabitats. Survival in under-tree or under-shrub microhabitats depended on soil water availability and corresponded closely to soil water contents over the 3-yr study. Under-shrub microhabitats had more favourable soil and micro-environmental characteristics than under-tree microhabitats and had the highest seedling life spans for the first-year seedling cohort. Predation of Pinus seedlings by rodents was a significant cause of mortality with caged transplants exhibiting life spans that were 74% longer overall than uncaged transplants. Emergence and survival of P. monophylla within the expanding woodland were dependent upon a complex set of interacting factors including growing season conditions, microhabitat characteristics, and animal species.
Phenyldiazenyl benzothiazole derivatives as probes for in vivo imaging of neurofibrillary tangles in Alzheimer's disease brains

This paper describes the synthesis and biological evaluation of novel phenyldiazenyl benzothiazole (PDB) derivatives as probes for imaging neurofibrillary tangles (NFTs) in patients with Alzheimer's disease (AD). We successfully synthesized three PDB derivatives using a diazo coupling reaction. In binding experiments in vitro, the compounds displayed higher affinity for tau aggregates than for Aβ aggregates. In fluorescent staining experiments using AD brain sections, 9 visualized NFTs clearly. No-carrier-added radioiodinated PDB derivatives were successfully prepared through an iododestannylation reaction from the corresponding tributyltin derivatives. [125I]9 labeled NFTs in sections of brain tissue from a patient with AD, but not a control. In biodistribution experiments using normal mice, the PDB derivatives displayed an uptake into the brain, sufficient for imaging NFTs, ranging from 0.94 to 3.2% ID g−1, but a relatively slow washout. Although further modifications are necessary to improve the pharmacokinetics in the brain, PDB with high affinity for tau aggregates may be useful as a backbone structure to develop agents for imaging NFTs in AD brains.
Employing drug delivery strategies to create safe and effective pharmaceuticals for COVID‐19

The outbreak of the novel SARS-CoV-2 pathogen and corresponding coronavirus disease 2019 (COVID-19) have had an enormous impact on both global health and the daily lives of billions of people worldwide. With a proven vaccine at least a year from being fully tested for safety and efficacy, there may be an opportunity to rapidly repurpose existing drugs in order to prevent SARS-CoV-2 infections and improve outcomes for patients already infected with COVID-19. At present, more than 40 different drugs are being explored for efficacy against COVID-19, including antivirals and immune modulating compounds. Unfortunately, many of these drugs are associated with side effects that limit their use to the most severe cases and thereby prevent their use as prophylactics. This commentary describes drug formulation strategies that can be used to maintain the efficacy of these drugs through controlled release, targeted delivery, and non-viral nucleic acid delivery. If successful, these approaches could enable the expanded use of drugs to reduce the mortality of this devastating disease and equip healthcare providers with the tools to accelerate our recovery from this pandemic and improve our response to the next outbreak of a novel pathogenic virus. The author has no conflicts of interest to declare. This article is protected by copyright. All rights reserved.
Culturally Relevant Pedagogy: A Model To Guide Cultural Transformation in STEM Departments

Despite recent interest and pressing need, we lack a clear model of culturally relevant, responsive, sensitive teaching in university STEM departments. Most culturally relevant efforts within STEM education address actions individual professors can take within their own classrooms and mentoring, rather than describing how to go about enacting cultural transformation at the departmental level. In this article, we propose the application of the Ladson-Billings model of culturally relevant pedagogy to promote an inclusive culture within undergraduate STEM departments. The model consists of three components: academic success, cultural competence and integrity, and critical consciousness. We define each component and describe what it looks like and how it can be used to guide departmental transformation, including examples in biology, physics, mathematics, and computer science departments at our own institution. This model can help guide faculty committed to creating departments where all kinds of STEM students can thrive, provided they are willing to work hard.
Intestinal methicillin-resistant Staphylococcus aureus causes prosthetic infection via ‘Trojan Horse’ mechanism: Evidence from a rat model

Aims Methicillin-resistant Staphylococcus aureus (MRSA) can cause wound infections via a ‘Trojan Horse’ mechanism, in which neutrophils engulf intestinal MRSA and travel to the wound, releasing MRSA after apoptosis. The possible role of intestinal MRSA in prosthetic joint infection (PJI) is unknown. Methods Rats underwent intestinal colonization with green fluorescent protein (GFP)-tagged MRSA by gavage and an intra-articular wire was then surgically implanted. After ten days, the presence of PJI was determined by bacterial cultures of the distal femur, joint capsule, and implant. We excluded several other possibilities for PJI development. Intraoperative contamination was excluded by culturing the specimen obtained from surgical site. Extracellular bacteraemia-associated PJI was excluded by comparing with the infection rate after intravenous injection of MRSA or MRSA-carrying neutrophils. To further support this theory, we tested the efficacy of prophylactic membrane-permeable and non-membrane-permeable antibiotics in this model. Results After undergoing knee surgery eight or 72 hours after colonization, five out of 20 rats (25.0%) and two out of 20 rats (10.0%) developed PJI, respectively. Strikingly, 11 out of 20 rats (55.0%) developed PJI after intravenous injection of MRSA-carrying neutrophils that were isolated from rats with intestinal MRSA colonization. None of the rats receiving intravenous injections of MRSA developed PJI. These results suggest that intestinal MRSA carried by neutrophils could cause PJI in our rat model. Ten out of 20 (50.0%) rats treated with non-membrane-permeable gentamicin developed PJI, whereas only one out of 20 (5.0%) rats treated with membrane-permeable linezolid developed PJI. Conclusion Neutrophils as carriers of intestinal MRSA may play an important role in PJI development. Cite this article: Bone Joint Res. 2020;9(4):152–161.
An Improved Method for Determining Cellulolytic Activity in Anaerobic Bacteria

SUMMARY An improved method for determining cellulolytic activity in anaerobic bacteria is described in which the disintegration of a filter paper strip denotes activity. By inoculating the paper-containing media from dilutions of material such as rumen contents estimates of the numbers of cellulolytic bacteria in the original material may be made. The method can also be used for determining cellulolytic activity of pure cultures. Morphology and other properties of the bacteria may be determined.
Effects of agricultural change on nesting Lapwings Vanellus vanellus in England and Wales

The impact of agricultural change on the nesting of Lapwings in England and Wales between 1962 and 1985 was analysed using nest-record cards of the BTO. Habitat use changed over the period with a general increase in nesting in upland grass. There was no change in the timing of nesting in grass, although apparently it was earlier than between 1940–61; more later nests were recorded in tillage. Overall clutch size showed no change between 1962–85, but clutches were consistently larger in tillage because of fewer partial losses. Larger clutches were found in emerged tillage crops than newly tilled land, and unimproved compared with improved grassland. There were also regional variations in clutch size in grassland. Brood size at hatching showed a progressive decline through the period because of steadily rising losses to farming. Nests in cereal and bare tilth were by far the most successful, and least successful in bare plough and upland grass. Agriculture (farmwork and the presence of farm animals) caused .
Isolating the sources of heterogeneity in nano-engineered particle–cell interactions

Nano-engineered particles have the potential to enhance therapeutic success and reduce toxicity-based treatment side effects via the targeted delivery of drugs to cells. This delivery relies on complex interactions between numerous biological, chemical and physical processes. The intertwined nature of these processes has thus far hindered attempts to understand their individual impact. Variation in experimental data, such as the number of particles inside each cell, further inhibits understanding. Here, we present a mathematical framework that is capable of examining the impact of individual processes during particle delivery. We demonstrate that variation in experimental particle uptake data can be explained by three factors: random particle motion; variation in particle–cell interactions; and variation in the maximum particle uptake per cell. Without all three factors, the experimental data cannot be explained. This work provides insight into biological mechanisms that cause heterogeneous responses to treatment, and enables precise identification of treatment-resistant cell subpopulations.