Miracle-dz/UniFAR-FTUs huggingface dataset:
Aerially guided leak detection and repair: A pilot field study for evaluating the potential of methane emission detection and cost-effectiveness

ABSTRACT Novel aerial methane (CH4) detection technologies were used in this study to identify anomalously high-emitting oil and gas (O&G) facilities and to guide ground-based “leak detection and repair” (LDAR) teams. This approach has the potential to enable a rapid and effective inspection of O&G facilities under voluntary or regulatory LDAR programs to identify and mitigate anomalously large CH4 emissions from a disproportionately small number of facilities. This is the first study of which the authors are aware to deploy, evaluate, and compare the CH4 detection volumes and cost-effectiveness of aerially guided and purely ground-based LDAR techniques. Two aerial methods, the Kairos Aerospace infrared CH4 column imaging and the Scientific Aviation in situ aircraft CH4 mole fraction measurements, were tested during a 2-week period in the Fayetteville Shale region contemporaneously with conventional ground-based LDAR. We show that aerially guided LDAR can be at least as cost-effective as ground-based LDAR, but several variable parameters were identified that strongly affect cost-effectiveness and which require field research and improvements beyond this pilot study. These parameters include (i) CH4 minimum dectectable limit of aerial technologies, (ii) emission rate size distributions of sources, (iii) remote distinction of fixable versus nonfixable CH4 sources (“leaks” vs. CH4 emissions occurring by design), and (iv) the fraction of fixable sources to total CH4 emissions. Suggestions for future study design are provided. Implications: Mitigation of methane leaks from existing oil and gas operations currently relies on on-site inspections of all applicable facilities at a prescribed frequency. This approach is labor- and cost-intensive, especially because a majority of oil and gas–related methane emissions originate from a disproportionately small number of facilities and components. We show for the first time in real-world conditions how aerial methane measurements can identify anomalously high-emitting facilities to enable a rapid, focused, and directed ground inspection of these facilities. The aerially guided approach can be more cost-effective than current practices, especially when implementing the aircraft deployment improvements discussed here.
Genome-Wide Analysis Elucidates the Role of CONSTANS-like Genes in Stress Responses of Cotton

The CONSTANS (CO)-like gene family has been well studied for its role in the regulation of plant flowering time. However, their role remains poorly understood in cotton. To better understand the possible roles of CO-like in cotton, we performed a comprehensive genome-wide analysis of CO-like genes in cotton. Phylogenetic tree analysis showed that CO-like genes naturally clustered into three groups. Segmental duplication and whole genome duplication (WGD), which occurred before polyploidy, were important contributors to its expansion within the At (“t” indicates tetraploid) and Dt subgenomes, particularly in Group III. Long-terminal repeat retroelements were identified as the main transposable elements accompanying 18 genes. The genotype of GhCOL12_Dt displayed low diversity; it was a candidate involved in domestication. Selection pressure analyses indicated that relaxed purifying selection might have provided the main impetus during the evolution of CO-like genes in upland cotton. In addition, the high expression in the torus and calycle indicated that CO-like genes might affect flowering. The genes from Group II, and those from Group III involved in segmental duplication or WGD, might play important roles in response to drought and salt stress. Overall, this comprehensive genome-wide study of the CO-like gene family would facilitate further detailed studies in cotton.
Retinal Vessel Detection in Wide-Field Fluorescein Angiography with Deep Neural Networks: A Novel Training Data Generation Approach

Retinal blood vessel detection is a crucial step in automatic retinal image analysis. Recently, deep neural networks have significantly advanced the state of the art for retinal blood vessel detection in color fundus (CF) images. Thus far, similar gains have not been seen in fluorescein angiography (FA) because the FA modality is entirely different from CF and annotated training data has not been available for FA imagery. We address retinal vessel detection in wide-field FA images with generative adversarial networks (GAN) via a novel approach for generating training data. Using a publicly available dataset that contains concurrently acquired pairs of CF and fundus FA images, vessel maps are detected in CF images via a pre-trained neural network and registered with fundus FA images via parametric chamfer matching to a preliminary FA vessel detection map. The co-aligned pairs of vessel maps (detected from CF images) and fundus FA images are used as ground truth labeled data for de novo training of a deep neural network for FA vessel detection. Specifically, we utilize adversarial learning to train a GAN where the generator learns to map FA images to binary vessel maps and the discriminator attempts to distinguish generated vs. ground-truth vessel maps. We highlight several important considerations for the proposed data generation methodology. The proposed method is validated on VAMpIRE dataset that contains high-resolution wide-field FA images and manual annotation of vessel segments. Experimental results demonstrate that the proposed method achieves an estimated ROC AUC of 0.9758.
Temporal Attentive Pooling for Acoustic Event Detection

Deep convolutional neural network (DCNN) based model has been successfully applied to acoustic event detection (AED) due to its efﬁciency to explore temporal-frequency structure for feature representations. In most studies, the ﬁnal representation either uses a temporal average-or max-pooling algorithm to accumulate local temporal features as a global representation for event classiﬁcation. The temporal pooling algorithm in the DCNN is based on the assumption that the target label is assigned to all temporal locations (average pooling) or to only one temporal location with a maximum response (max-pooling). However, the acoustic event labels are holistic descriptions in a semantic level, it is difﬁcult or even impossible to decide features from which temporal locations contribute to the event perception. In this study, we propose a weighted temporal-pooling algorithm to accumulate local temporal features for AED. The pooling algorithm integrates global and local attention modules in a convolutional recurrent neural network to integrate temporal features. Experiments on an AED task were carried out to evaluate the proposed model. Results showed that with the global and local attentions, a large gain was obtained.
Integral Privacy for Sampling from Mollifier Densities with Approximation Guarantees

$\varepsilon$-differential privacy is a leading protection setting, focused by design on individual privacy. Many applications, such as in the medical / pharmaceutical domains, would rather posit privacy at a group level, furthermore of unknown size, a setting in which classical budget scaling tricks typically cannot guarantee non-trivial privacy levels. We call this privacy setting integral privacy. In this paper, we study a major problem of machine learning and statistics with related applications in domains cited above that have recently met with substantial press: sampling. Our formal contribution is twofolds: we provide a general theory for sampling to be integrally private, and we show how to achieve integral privacy with guarantees on the approximation of the true (non-private) density. Our theory introduces $\varepsilon$-mollifiers, subsets of densities whose sampling is guaranteed to be integrally private. Guaranteed approximation bounds of the true density are obtained via the boosting theory as it was originally formulated: we learn sufficient statistics in an $\varepsilon$-mollifier of exponential families using classifiers, which brings guaranteed approximation and convergence rates that degrade gracefully with the privacy budget, under weak assumptions. Approximation guarantees cover the mode capture problem. Experimental results against private kernel density estimation and private GANs displays the quality of our results, in particular for high privacy regimes.
Intrauterine Device Expulsion After Postpartum Placement: A Systematic Review and Meta-Analysis.

We read with interest the article by Jatlaoui et al,1 which estimated postpartum intrauterine device (IUD) expulsion rates through a systematic review and meta-analysis. We agree that it is important to provide women with the IUD they desire immediately or soon after delivery, and, per the evidence, doing so likely outweighs the risk of expulsion. However, we find that both the methods and the analysis of the review overlooked several important clinical and programmatic issues. The authors make a point to emphasize the importance of immediate postpartum contraception, because women often do not attend their postpartum visit, but they do not recognize this in their interval groups. By grouping insertions occurring between 11 minutes after placenta delivery and 4 weeks postpartum, the authors have disregarded any insertions provided beyond the arbitrary first 10 minutes but before the woman leaves the delivery room or birth facility, which is also a safe and convenient time for insertion. We encourage health care providers to prioritize delivery-room postpartum IUD insertions for interested women. We wonder what the results would be if the interval groups were divided as any insertion from placental delivery to 48 hours postpartum, beyond 48 hours until 4 weeks postpartum, and interval placements (6 weeks postpartum and beyond). We hypothesize that this would result in observed expulsion rates that are more consistent with uterine physiology.2 Relatedly, including IUDs previously available in the United States (Copper 7 and TCu200) may have resulted in using old data irrelevant to current practice and that also did not emphasize fundal placement, as is the norm today. All things considered, we also question what led the authors to focus on expulsion as their outcome of interest: continuation is the more important public health outcome, and, especially in settings where IUDs are very inexpensive, only unrecognized expulsions are clinically important. With this in mind and in light of the wealth of data accumulating around postpartum IUDs, we advocate moving forward with more emphasis on uptake and continuation as the outcomes of programmatic value in our attempt to improve maternal and child health through postpartum family planning.
Study protocol for a randomised controlled trial of consenting processes and their effects on patient decision-making when undergoing spinal injections: the Risks In Spinal Consenting for Surgery (RISCS) trial

Introduction There are major differences between legal and medical approaches to informed consent. Medically, consent is obtained prospectively for an intended procedure, to inform the patient of choices, risks and benefits, and to manage expectations. Legally, consent is reviewed retrospectively, usually following unmet expectations and/or the occurrence of complications. Recent legal cases relating to clinical negligence define the establishment of causation and breach of duty related to informed consent. However, there is no prospective evidence to validate the current judicial perspectives on causation and thus clinical negligence. The aim of this randomised controlled trial (RCT) is to investigate whether variations in consenting processes for the same procedure lead to changes in patient decision-making related to consent for that procedure. Methods and analysis The Risks In Spinal Consenting for Surgery trial is a single-centre, non-inferiority RCT, where 220 patients, aged over 18 years, receiving an elective, day case spinal injection, will be randomised to either a ‘legally styled’ consent form with 55 risks identified in the world literature, or a ‘medically styled’ consent form with the 13 serious or most common risks usually quoted by reference to specialist society guidelines. Following explanation of the medical reasons for considering an injection therapy and consent to the trial, participants will be randomly allocated to one of two groups (1:1). The patients are then given the opportunity to discuss any concerns relating to the procedure and/or risks with a single specialist practitioner. The primary outcome will be rates of consent withdrawal due to the risks explained. Secondary outcomes include scores from the State-Trait Anxiety Inventory, Visual Analogue Scale, EuroQol 5-dimension questionnaire and Oswestry Disability Index. Ethics and dissemination Results will be presented in peer-reviewed journals and at international conferences. This study is approved by the Health Research Authority: REC 16/SC/0510. Trial registration number ISRCTN67513618; Pre-results.
A Matrix-Free Reconstruction Method for Compressive Focal Plane Array Imaging

In this study, we propose a novel algorithm for compressive imaging using digital micromirror device (DMD) modulated focal plane array (FPA) data. In this setting, DMD modulates the scene in the image domain by blocking some of the pixels at a higher resolution level. For reconstruction, a regularized optimization problem is solved, whereas reconstruction time is crucial for a practical compressive sensing application. Here, we propose an augmented Lagrangian based method that employs sparsity in both Fourier and gradient spaces, where we apply a separate proximal mapping for each snapshot. We demonstrate the performance of the algorithm via comparison to the state-of-the-art. The proposed method yields higher pSNR at shorter convergence times, especially at higher compression ratios.
Joint Estimation of Local Variance and Local Regularity for Texture Segmentation. Application to Multiphase Flow Characterization

Texture segmentation constitutes a task of utmost importance in statistical image processing. Focusing on the broad class of monofractal textures characterized by piecewise constancy of the statistics of their multiscale representations, recently shown to be versatile enough for real-world texture modeling, the present work renews this recurrent topic by proposing an original approach enrolling jointly scale-free and local variance descriptors into a convex, but non smooth, minimization strategy. The performance of the proposed joint approach are compared against disjoint strategies working independently on scale-free features and on local variance on synthetic piecewise monofractal textures. Performance are also compared for multiphase flow image characterization, a topic of crucial importance in geophysics as well as in industrial processes. Applied to large-size images (above two million pixels), the proposed approach is shown to significantly improve state-of-the-art strategies by permitting the detection of the smallest gas bubbles and by offering a better understanding of multiphase flow structures.
A New Glottal Neural Vocoder for Speech Synthesis

Direct modeling of waveform generation for speech synthesis, e.g. WaveNet, has made signiﬁcant progress on improving the naturalness and clarity of TTS. Such deep neural network-based models can generate highly realistic speech but at high computational and memory costs. We propose here a novel neural glottal vocoder which tends to bridge the gap between the traditional parametric vocoder and end-to-end speech sample generation. In the analysis, speech signals are decomposed into corresponding glottal source signals and vocal tract ﬁlters by the glottal inverse ﬁltering. Glottal pulses are parameterized into energy, DCT coefﬁcients (shape) and phase. The phase trajectory of successive glottal pulses is rendered with a trainable weighting matrix to keep a smooth pitch synchronous phase trajectory. We design a hybrid, i.e., both feed-forward and recurrent, neural network to reconstruct the glottal waveform including the optimized weighting matrix. Speech is then synthesized by ﬁltering the generated glottal waveform with the vocal tract ﬁlter. The new neural glottal vocoder can generate high-quality speech with efﬁcient computations. Subjective tests show that it gets an MOS score of 4.12 and 75% preference over the conventional glottal vocoder with a perceived quality comparable to WaveNet and natural recording in analysis-by-synthesis.
Acoustic-dependent Phonemic Transcription for Text-to-speech Synthesis

Text-to-speech synthesis (TTS) purpose is to produce a speech signal from an input text. This implies the annotation of speech recordings with word and phonemic transcriptions. The over-all quality of TTS highly depends on the accuracy of phone-mic transcriptions. However, they are generally automatically produced by grapheme-to-phoneme conversion systems, which do not deal with speaker variability. In this work, we explore ways to obtain signal-dependent phonemic transcriptions. We investigate forced-alignment with enriched pronunciation lexicon and multimodal phonemic transcription. We then apply our results on error detection of grapheme-to-phoneme conversion hypotheses in order to ﬁnd where the phonemic transcriptions may be erroneous. On a French TTS dataset, we show that we can detect up to 90.5% of errors of a state-of-the-art grapheme-to-phoneme conversion system by annotating less than 15.8% of phonemes as erroneous. This can help a human annotator to correct most of grapheme-to-phoneme conversion errors without checking a lot of data. In other words, our method can sig-niﬁcantly reduce the cost of high quality TTS data creation.
DA-VLAD: Discriminative Action Vector of Locally Aggregated Descriptors for Action Recognition

In this paper, we propose a novel encoding method for the representation of human action videos, that we call Discriminative Action Vector of Locally Aggregated Descriptors (DA-VLAD). DA-VLAD is motivated by the fact that there are many unnecessary and overlapping frames that cause non-discriminative codewords during the training process. DA-VLAD deals with this issue by extracting class-specific clusters and learning the discriminative power of these codewords in the form of informative weights. We use these discriminative action weights with standard VLAD encoding as a contribution of each codeword. DA-VLAD reduces the inter-class similarity efficiently by diminishing the effect of common codewords among multiple action classes during the encoding process. We present the effectiveness of DA-VLAD on two challenging action recognition datasets: UCF101 and HMDB51, improving the state-of-the-art with accuracies of 95.1 % and 80.1 % respectively.
Iterative Learning of Speech Recognition Models for Air Traffic Control

Automatic Speech Recognition (ASR) has recently proved to be a useful tool to reduce the workload of air trafﬁc controllers leading to signiﬁcant gains in operational efﬁciency. Air Trafﬁc Control (ATC) systems in operation rooms around the world generate large amounts of untranscribed speech and radar data each day, which can be utilized to build and improve ASR models. In this paper, we propose an iterative approach that utilizes increasing amounts of untranscribed data to incrementally build the necessary ASR models for an ATC operational area. Our approach uses a semi-supervised learning framework to combine speech and radar data to iteratively update the acoustic model, language model and command prediction model (i.e. prediction of possible commands from radar data for a given air trafﬁc situation) of an ASR system. Starting with seed models built with a limited amount of manually transcribed data, we simulate an operational scenario to adapt and improve the models through semi-supervised learning. Experiments on two inde-pendent ATC areas (Vienna and Prague) demonstrate the utility of our proposed methodology that can scale to operational environments with minimal manual effort for learning and adaptation. We the bi-modal nature of and developed a radar data based data selection method for Our experiments on data from and Vienna ATC show a signiﬁcant improvement over a baseline that does not use any untranscribed data with further improvements in CER in subsequent iterations despite a marginal increase in WER. We built and evaluated systems for and separately. In the future, we wish to explore combining all data and do cross domain adaptation different
Evaluation on the Compactness of Supervoxels

Supervoxels are perceptually meaningful atomic spatiotemporal regions in videos, which has great potential to reduce the computational complexity of downstream video applications. Many methods have been proposed for generating supervoxels. To effectively evaluate these methods, a novel supervoxel library and benchmark called LIBSVX with seven collected metrics was recently established. In this paper, we propose a new compactness metric which measures the shape regularity of supervoxels and is served as a necessary complement to the existing metrics. To demonstrate its necessity, we first explore the relations between the new metric and existing ones. Correlation analysis shows that the new metric has a weak correlation with (i.e., nearly independent of) existing metrics, and so reflects a new characteristic of supervoxel quality. Second, we investigate two real-world video applications. Experimental results show that the new metric can effectively predict some important application performance, while most existing metrics cannot do so.
Association between serum magnesium concentration and metabolic syndrome, diabetes, hypertension and hyperuricaemia in knee osteoarthritis: a cross-sectional study in Hunan Province, China

Objectives To examine the associations between serum magnesium (Mg) concentration with the prevalence of metabolic syndrome (MetS), diabetes mellitus (DM), hypertension (HP) and hyperuricaemia (HU) in patients with radiographic knee osteoarthritis (OA). Methods The present study was conducted at the Health Management Center of Xiangya Hospital. Radiographic OA was evaluated for patients aged over 40 years with basic characteristics and blood biochemical assessment. Serum Mg concentration was measured using the chemiluminescence method. MetS, DM, HP and HU were diagnosed based on standard protocols. The associations between serum Mg concentration with MetS, DM, HP and HU were evaluated by conducting multivariable adjusted logistic regression. Results A total of 962 patients with radiographic knee OA were included. Compared with the lowest quintile, the multivariable adjusted ORs and related 95% CIs of DM were 0.40 (95% CI 0.23 to 0.70, p=0.001), 0.33 (95% CI 0.18 to 0.60, p<0.001), 0.27 (95% CI 0.14 to 0.52, p<0.001) and 0.22 (95% CI 0.11 to 0.44, p<0.001) in the second, third, fourth and highest quintiles of serum Mg, respectively (p for trend <0.001); the multivariable adjusted ORs of HU were 0.33 (95% CI 0.19 to 0.59, p<0.001), 0.52 (95% CI 0.30 to 0.91, p=0.022) and 0.39 (95% CI 0.22 to 0.70, p=0.001) in the third, fourth and highest quintiles of serum Mg, respectively (p for trend <0.001); and the multivariable adjusted ORs of MetS were 0.59 (95% CI 0.36 to 0.94, p=0.027) in the second and 0.56 (95% CI 0.34 to 0.93, p=0.024) in the highest quintiles of serum Mg. However, the inverse association between serum Mg and the prevalence of MetS was non-linear (p for trend=0.067). There was no significant association between serum Mg and HP in patients with OA. Conclusions The serum Mg concentration was inversely associated with the prevalence of MetS, DM and HU in patients with radiographic knee OA. Level of evidence Level III, cross-sectional study.