Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-invasive prenatal testing of fetal aneuploidies using a new method based on digital droplet PCR and cell free fetal DNA

ABSTRACT Background: Current next generation sequencing (NGS) and microarray based Non-Invasive Prenatal Tests (NIPT), used for the detection of common fetal trisomies, are still expensive, time consuming and need to be performed in centralized laboratories. To improve NIPT in clinical routine practice as universal prenatal screening, we have developed a digital droplet PCR (ddPCR) based assay called iSAFE NIPT using cell free fetal DNA (cffDNA) for detection of fetal trisomies 13, 18 and 21 in a single reaction with advantage of high diagnostic accuracy and reduced cost. Materials and Methods: We first used artificial DNA samples to evaluate analytical sensitivity and specificity of the iSAFE NIPT. Next, we analysed 269 plasma samples for the clinical validation of iSAFE NIPT. Fifty-eight of these, including five trisomies 21, two trisomies 18 and one trisomy 13 were utilised to establish the assay cut-off values based on ratios between chromosome counts. The remaining 211 plasma samples, including 10 trisomies 21, were analysed to evaluate iSAFE NIPT clinical performance. Results: iSAFE NIPT achieved a 100% analytical sensitivity (95% CI 94.9-100% trisomy 21; 79.4-100% trisomy 18; 73.5-100% trisomy 13) and 100% specificity (95% CI 96.3-100% trisomy 21; 97.6-100% trisomy 18; 97.6-100% trisomy 13). It also achieved a 100% clinical sensitivity and specificity for trisomy 21 detection in the 211 clinical samples (95% CI for sensitivity is 69.1-100%, and 95% CI for specificity is 98.2-100%). Conclusions: The iSAFE NIPT is a highly multiplexed ddPCR based assay for detection of fetal trisomies from maternal blood. Based on clinical validation, the iSAFE NIPT has high diagnostic sensitivity and specificity. It can be decentralized in routine clinical laboratories, is fast, easy to use and economical comparing to current NIPT.
Occupational exposures associated with being a COVID-19 case; evidence from three case-control studies

Background The evidence on risk factors for transmission of SARS-CoV-2 in community settings is sparse, yet this information is key to inform public health action. We investigated factors associated with being a COVID-19 case using data collected through contact tracing. Methods We conducted three retrospective, frequency-matched case-control studies between August 2020 and October 2020 using case data from the NHS Test and Trace programme. Controls were obtained through Market Research Panels. Multivariable analyses provided adjusted odds ratios (aORs) for multiple community exposure settings. We analysed the results in meta-analyses using random effects models to obtain pooled odds ratios (pORs). Results Across all study periods, there was strong statistical evidence that working in healthcare (pOR 2.87, aOR range 2.72-3.08), social care (pOR 4.15, aOR range 2.46-5.41) or hospitality (pOR 2.36, aOR range 2.01-2.63) were associated with increased odds of being a COVID-19 case. There was also evidence that working in warehouse setting was associated with increased odds (pOR 3.86, aOR range 1.06-14.19), with a substantial increase in odds observed over the study periods. A similar pattern was also observed in education and construction. Conclusions The studies indicate that some workplace settings are associated with increased odds of being a case. However, it is not possible to determine how much of the transmission of SARS-CoV-2 took place within the workplace, and how much was associated with social, household or transport exposures.
Learning To Predict Vulnerabilities From Vulnerability-Fixes: A Machine Translation Approach

—Vulnerability prediction refers to the problem of identifying the system components that are most likely to be vulnerable based on the information gained from historical data. Typically, vulnerability prediction is performed using manually identiﬁed features that are potentially linked with vulnerable code. Unfortunately, recent studies have shown that existing approaches are ineffective when evaluated in realistic settings due to some unavoidable noise included in the historical data. To deal with this issue, we develop a prediction method using the encoder-decoder framework of machine translation that automatically learns the latent features (context, patterns, etc.) of code that are linked with vulnerabilities. The key idea of our approach is to learn from things we know, the past vulnerability ﬁxes and their context. We evaluate our approach by comparing it with existing techniques on available releases of the three security-critical open source systems (Linux Kernel, OpenSSL, and Wireshark) with historical vulnerabilities that have been reported in the National Vulnerability Database (NVD). Our evaluation demonstrates that the prediction capability of our approach signiﬁcantly outperforms the state-of-the-art vulnerability prediction techniques (Software Metrics, Imports, Function Calls, and Text Mining) in both recall and precision values (yielding 4.7 times higher MCC values) under realistic training setting.
The delay time distribution of supernovae from integral-field spectroscopy of nearby galaxies

Constraining the delay-time distribution (DTD) of different supernova (SN) types can shed light on the timescales of galaxy chemical enrichment and feedback processes affecting galaxy dynamics, and SN progenitor properties. Here, we present an approach to recover SN DTDs based on integral field spectroscopy (IFS) of their host galaxies. Using a statistical analysis of a sample of 116 supernovae in 102 galaxies, we evaluate different DTDmodels for SN types Ia (73), II (28) and Ib/c (15). We find the best SN Ia DTD fit to be a power law with an exponent α = −1.1 ± 0.3 (50% confidence interval), and a time delay (between star formation and the first SNe) Δ = 50+100 −35 Myr (50% C.I.). For core collapse (CC) SNe, both of the Zapartas et al. (2017) DTD models for single and binary stellar evolution are consistent with our results. For SNe II and Ib/c, we find a correlation with a Gaussian DTD model with σ = 82+129 −23 Myr and σ = 56+141 −9 Myr (50% C.I.) respectively. This analysis demonstrates that integral field spectroscopy opens a new way of studying SN DTD models in the local universe.
Labels are Not Perfect: Inferring Spatial Uncertainty in Object Detection

The availability of many real-world driving datasets is a key reason behind the recent progress of object detection algorithms in autonomous driving. However, there exist ambiguity or even failures in object labels due to error-prone annotation process or sensor observation noise. Current public object detection datasets only provide deterministic object labels without considering their inherent uncertainty, as does the common training process or evaluation metrics for object detectors. As a result, an in-depth evaluation among different object detection methods remains challenging, and the training process of object detectors is sub-optimal, especially in probabilistic object detection. In this work, we infer the uncertainty in bounding box labels from LiDAR point clouds based on a generative model, and define a new representation of the probabilistic bounding box through a spatial uncertainty distribution. Comprehensive experiments show that the proposed model reflects complex environmental noises in LiDAR perception and the label quality. Furthermore, we propose Jaccard IoU (JIoU) as a new evaluation metric that extends IoU by incorporating label uncertainty. We conduct an in-depth comparison among several LiDAR-based object detectors using the JIoU metric. Finally, we incorporate the proposed label uncertainty in a loss function to train a probabilistic object detector and to improve its detection accuracy. We verify our proposed methods on two public datasets (KITTI, Waymo), as well as on simulation data. Code is released at https://github.com/ ZiningWang/Inferring-Spatial-Uncertainty-in-Object-Detection.
A Hybrid VDV Model for Automatic Diagnosis of Pneumothorax using Class-Imbalanced Chest X-rays Dataset

Pneumothorax, a life-threatening disease, needs to be diagnosed immediately and efficiently. The prognosis, in this case, is not only time-consuming but also prone to human errors. So, an automatic way of accurate diagnosis using chest X-rays is the utmost requirement. To date, most of the available medical image datasets have a class-imbalance (CI) issue. The main theme of this study is to solve this problem along with proposing an automated way of detecting pneumothorax. To find the optimal approach for CI problem, we first compare the existing approaches and find that under-bagging method (referred as data-level-ensemble formed by creating subsets of majority class and then combining each subset with all samples of minority class) outperforms other existing approaches. After selection of best approach for CI problem, we propose a novel framework, named as VDV model, for pneumothorax detection from highly imbalance dataset. The proposed VDV model is a complex model-level ensemble of data-level-ensembles and uses three convolutional neural networks (CNN) including VGG16, VGG-19, and DenseNet-121 as fixed feature extractors. In each data-level-ensemble, features extracted from one of the pre-defined CNN architectures are fed to support vector machine (SVM) classifier, and output is calculated using the voting method. Once outputs from the three data-level-ensembles (corresponding to three different CNN architectures as feature extractor) are obtained, then, again, the voting method is used to calculate the final prediction. Our proposed framework is tested on the SIIM ACR Pneumothorax dataset and Random Sample of NIH Chest X-ray dataset (RS-NIH). For the first dataset, 85.17% Recall with 86.0% Area under the Receiver Operating Characteristic curve (AUC) is attained. For the second dataset, 90.9% Recall with 95.0% AUC is achieved with a random split of data while 85.45% recall with 77.06% AUC is obtained with a patient-wise split of data. The comparison of our results for both the datasets with related work proves the effectiveness of proposed VDV model for pneumothorax detection.
Empirical Bayes PCA in high dimensions

When the dimension of data is comparable to or larger than the number of data samples, principal components analysis (PCA) may exhibit problematic high‐dimensional noise. In this work, we propose an empirical Bayes PCA method that reduces this noise by estimating a joint prior distribution for the principal components. EB‐PCA is based on the classical Kiefer–Wolfowitz non‐parametric maximum likelihood estimator for empirical Bayes estimation, distributional results derived from random matrix theory for the sample PCs and iterative refinement using an approximate message passing (AMP) algorithm. In theoretical ‘spiked’ models, EB‐PCA achieves Bayes‐optimal estimation accuracy in the same settings as an oracle Bayes AMP procedure that knows the true priors. Empirically, EB‐PCA significantly improves over PCA when there is strong prior structure, both in simulation and on quantitative benchmarks constructed from the 1000 Genomes Project and the International HapMap Project. An illustration is presented for analysis of gene expression data obtained by single‐cell RNA‐seq.
Time-travel rephotography

Many historical people were only ever captured by old, faded, black and white photos, that are distorted due to the limitations of early cameras and the passage of time. This paper simulates traveling back in time with a modern camera to rephotograph famous subjects. Unlike conventional image restoration filters which apply independent operations like denoising, colorization, and superresolution, we leverage the StyleGAN2 framework to project old photos into the space of modern high-resolution photos, achieving all of these effects in a unified framework. A unique challenge with this approach is retaining the identity and pose of the subject in the original photo, while discarding the many artifacts frequently seen in low-quality antique photos. Our comparisons to current state-of-the-art restoration filters show significant improvements and compelling results for a variety of important historical people. Please go to time-travell-rephotography.github. io for many more results.
Autonomous sPOMDP Environment Modeling With Partial Model Exploitation

A state space representation of an environment is a classic and yet powerful tool used by many autonomous robotic systems for efficient and often optimal solution planning. However, designing these representations with high performance is laborious and costly, necessitating an effective and versatile tool for autonomous generation of state spaces for autonomous robots. We present a novel state space exploration algorithm by extending the original surprise-based partially-observable Markov Decision Processes (sPOMDP), and demonstrate its effective long-term exploration planning performance in various environments. Through extensive simulation experiments, we show the proposed model significantly increases efficiency and scalability of the original sPOMDP learning techniques with a range of 31-63% gain in training speed while improving robustness in environments with less deterministic transitions. Our results pave the way for extending sPOMDP solutions to a broader set of environments.
Power-SLIC: Diagram-based superpixel generation

Superpixel algorithms, which group pixels similar in color and other low-level properties, are increasingly used for pre-processing in image segmentation. Commonly important criteria for the computation of superpixels are boundary adherence, speed, and regularity. Boundary adherence and regularity are typically contra-dictory goals. Most recent algorithms have focused on improving boundary adherence. Motivated by improving superpixel regularity, we propose a diagram-based superpixel generation method called Power-SLIC. On the BSDS500 data set, Power-SLIC outperforms other state-of-the-art algorithms in terms of compactness and boundary precision, and its boundary adherence is the most robust against varying levels of Gaussian noise. In terms of speed, Power-SLIC is competitive with SLIC.
Early kinetic decoupling effect on the forbidden dark matter annihilations into standard model particles

The early kinetic decoupling (eKD) effect is an inevitable ingredient in calculating the relic density of dark matter (DM) for various well-motivated scenarios. It appears naturally in forbidden dark matter annihilation, the main focus of this work, which contains fermionic DM and a light singlet scalar that connects the DM and standard model (SM) leptons. The strong suppression of the scattering between DM and SM particles happens quite early in the DM depletion history, where the DM temperature drops away from the thermal equilibrium, $T_\chi<T_{\rm SM}$, leading to the decreased kinetic energy of DM. The forbidden annihilation thus becomes inefficient since small kinetic energy cannot help exceed the annihilation threshold, naturally leading to a larger abundance. To show the eKD discrepancy, we numerically solve the coupled Boltzmann equations that govern the evolution of DM number density and temperature. It is found that eKD significantly affects the DM abundance, resulting in almost an order of magnitude higher than that by the traditional calculation. We also discuss the constraints from experimental searches on the model parameters, where the viable parameter space shrinks when considering the eKD effect.
Explainable Abstract Trains Dataset

The Explainable Abstract Trains Dataset is an image dataset containing simplified representations of trains. It aims to provide a platform for the application and research of algorithms for justification and explanation extraction. The dataset is accompanied by an ontology that conceptualizes and classifies the depicted trains based on their visual characteristics, allowing for a precise understanding of how each train was labeled. Each image in the dataset is annotated with multiple attributes describing the trains' features and with bounding boxes for the train elements.
Disentangling images with Lie group transformations and sparse coding

Discrete spatial patterns and their continuous transformations are two important regularities contained in natural signals. Lie groups and representation theory are mathematical tools that have been used in previous works to model continuous image transformations. On the other hand, sparse coding is an important tool for learning dictionaries of patterns in natural signals. In this paper, we combine these ideas in a Bayesian generative model that learns to disentangle spatial patterns and their continuous transformations in a completely unsupervised manner. Images are modeled as a sparse superposition of shape components followed by a transformation that is parameterized by n continuous variables. The shape components and transformations are not predefined, but are instead adapted to learn the symmetries in the data, with the constraint that the transformations form a representation of an n-dimensional torus. Training the model on a dataset consisting of controlled geometric transformations of specific MNIST digits shows that it can recover these transformations along with the digits. Training on the full MNIST dataset shows that it can learn both the basic digit shapes and the natural transformations such as shearing and stretching that are contained in this data.
Mesh Denoising and Inpainting using the Total Variation of the Normal

In this paper we present a novel approach to solve surfacemesh denoising and inpainting problems. The purpose is not only to remove noise while preserving important features such as sharp edges, but also to ll in missing parts of the geometry. A discrete variant of the total variation of the unit normal vector eld serves as a regularizing functional to achieve this goal. In order to solve the resulting problem, we present a novel variant of the split Bregman (ADMM) iteration. Numerical examples are included demonstrating the performance of the method with some complex 3D geometries.
Can I Still Trust You?: Understanding the Impact of Distribution Shifts on Algorithmic Recourses

As predictive models are being increasingly deployed to make a variety of consequential decisions ranging from hiring decisions to loan approvals, there is growing emphasis on designing algorithms that can provide reliable recourses to affected individuals. To this end, several recourse generation algorithms have been proposed in recent literature. However, there is little to no work on systematically assessing if these algorithms are actually generating recourses that are reliable . In this work, we assess the reliability of algorithmic recourses through the lens of distribution shifts i.e., we study if the recourses generated by state-of-the-art algorithms are robust to distribution shifts. We also theoretically and empirically analyze what kinds of recourses get invalidated as a result of distribution shifts. To the best of our knowledge, this work makes the first attempt at addressing this critical question. We experiment with multiple synthetic and real world datasets capturing different kinds of distribution shifts including temporal shifts, geospatial shifts, and shifts due to data corrections. Our results demonstrate that all the aforementioned distribution shifts could potentially invalidate the recourses generated by state-of-the-art algorithms. In addition, we also find that recourse interventions themselves may cause distribution shifts which in turn invalidate previously prescribed recourses. Our theoretical results establish that the recourses (counterfactuals) that are close to the model decision boundary are more likely to be invalidated upon model updation. However, state-of-the-art algorithms tend to prefer exactly these recourses because their cost functions penalize recourses (counterfactuals) that require large modifications to the original instance. Our findings not only expose fundamental flaws in recourse finding strategies but also pave new way for rethinking the design and development of recourse generation algorithms.
Generalized group-theoretic coherent states

Tommaso Guaita, 2, ∗ Lucas Hackl, 4, † Tao Shi, 6 Eugene Demler, and J. Ignacio Cirac 2 Max-Planck-Institut für Quantenoptik, Hans-Kopfermann-Str. 1, 85748 Garching, Germany Munich Center for Quantum Science and Technology, Schellingstr. 4, 80799 München, Germany School of Physics, The University of Melbourne, Parkville, VIC 3010, Australia QMATH, Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen, Denmark CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China CAS Center for Excellence in Topological Quantum Computation, University of Chinese Academy of Sciences, Beijing 100049, China Lyman Laboratory, Department of Physics, Harvard University, 17 Oxford St., Cambridge, MA 02138, USA
Multiple Instance Segmentation in Brachial Plexus Ultrasound Image Using BPMSegNet

The identification of nerve is difficult as structures of nerves are challenging to image and to detect in ultrasound images. Nevertheless, the nerve identification in ultrasound images is a crucial step to improve performance of regional anesthesia. In this paper, a network called Brachial Plexus Multi-instance Segmentation Network (BPMSegNet) is proposed to identify different tissues (nerves, arteries, veins, muscles) in ultrasound images. The BPMSegNet has three novel modules. The first is the spatial local contrast feature, which computes contrast features at different scales. The second one is the self-attention gate, which reweighs the channels in feature maps by their importance. The third is the addition of a skip concatenation with transposed convolution within a feature pyramid network. The proposed BPMSegNet is evaluated by conducting experiments on our constructed Ultrasound Brachial Plexus Dataset (UBPD). Quantitative experimental results show the proposed network can segment multiple tissues from the ultrasound images with a good performance.
A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data

Ramp metering that uses traffic signals to regulate vehicle flows from the on-ramps has been widely implemented to improve vehicle mobility of the freeway. Previous studies generally update signal timings in real-time based on predefined traffic measurements collected by point detectors, such as traffic volumes and occupancies. Comparing with point detectors, traffic cameras—which have been increasingly deployed on road networks—could cover larger areas and provide more detailed traffic information. In this work, we propose a deep reinforcement learning (DRL) method to explore the potential of traffic video data in improving the efficiency of ramp metering. Vehicle locations are extracted from the traffic video frames and are reformed as position matrices. The proposed method takes the preprocessed video data as inputs and learns the optimal control strategies directly from the high-dimensional inputs. A series of simulation experiments based on real-world traffic data are conducted to evaluate the proposed approach. The results demonstrate that, in comparison with a state-of-the-practice method, the proposed DRL method results in (1) lower travel times in the mainline, (2) shorter vehicle queues at the on-ramp, and (3) higher traffic flows downstream of the merging area. The results suggest that the proposed method is able to extract useful information from the video data for better ramp metering controls.