Miracle-dz/UniFAR-FTUs huggingface dataset:
Occurrence and Risk Factors of Chronic Pain After Critical Illness

Supplemental Digital Content is available in the text. Objectives: Occurrence, risk factors, and impact on daily life of chronic pain after critical illness have not been systematically studied. Design: Cohort study. Setting: A tertiary ICU in The Netherlands. Patients: We surveyed patients who had been discharged from our ICU between 2013 and 2016. Three cohorts were defined as follows: 1) ICU survivors; 2) one-year survivors reporting newly-acquired chronic pain; and (3) one-year survivors with pain who lived within 50 km from the study hospital. In cohort 1, we estimated the prevalence of new chronic pain 1 year after ICU discharge and constructed a prediction model for its occurrence incorporating three outcomes: death during follow-up, surviving without new pain, and surviving with newly-acquired pain. In cohort 2, we determined clinical features of pain and its impact on daily life. In cohort 3, we assessed the presence of neuropathic characteristics of pain. Interventions: None. Measurements and Main Results: The three cohorts contained 1,842, 160, and 42 patients, respectively. Estimated occurrence of new chronic pain was 17.7% (95% CI, 15.8–19.8%; n = 242) in 1-year survivors (n = 1,368). Median pain intensity on the numeric rating scale was 4 (interquartile range, 2–6) in the week before survey response, with impact being most evident on activities of daily living, social activities, and mobility. Neuropathic pain features were present in 50% (95% CI, 37–68%) of affected subjects. Among nine predictor variables included in a multinomial model, only female gender and days in ICU with hyperinflammation were associated with pain. Conclusions: Newly-acquired chronic pain is a frequent consequence of critical illness, and its impact on daily life of affected patients is substantial.
Neural speech synthesis for resource-scarce languages

Recent work in sequence-to-sequence neural networks with attention mechanisms, such as the Tacotron 2 and DCTTS architectures, have brought on substantial naturalness improvements in synthesised speech. These architectures require at least an order of magnitude more data than is generally available in resource-scarce language environments. In this paper we propose an efficient feed-forward deep neural network (DNN)-based acoustic model, using stacked bottleneck features, that together with the recently introduced LPCNet vocoder can be used in resource-scarce language environments, with corpora less than 1 hour in size, to build text-to-speech systems of high perceived naturalness. We compare traditional hidden Markov model (HMM)-based acoustic modelling for speech synthesis with the proposed architecture using the World and LPCNet vocoders, giving both objective and MUSHRA based subjective results, showing that the DNN LPCNet combination leads to more natural synthesised speech that can be confused with natural speech. The proposed acoustic model provides for an efficient implementation, with faster than real time synthesis.
Defeasible disjunctive datalog

Datalog is a declarative logic programming language that uses classical logical reasoning as its basic form of reasoning. Defeasible reasoning is a form of non-classical reasoning that is able to deal with exceptions to general assertions in a formal manner. The KLM approach to defeasible reasoning is an axiomatic approach based on the concept of plausible inference. Since Datalog uses classical reasoning, it is currently not able to handle defeasible implications and exceptions. We aim to extend the expressivity of Datalog by incorporating KLM-style defeasible reasoning into classical Datalog. We present a systematic approach to extending the KLM properties and a well-known form of defeasible entailment: Rational Closure. We conclude by exploring Datalog extensions of less conservative forms of defeasible entailment: Relevant and Lexicographic Closure.
Insights regarding overfitting on noise in deep learning

The understanding of generalization in machine learning is in a state of flux. This is partly due to the relatively recent revelation that deep learning models are able to completely memorize training data and still perform appropriately on out-of-sample data, thereby contradicting long-held intuitions about generalization. The phenomenon was brought to light and discussed in a seminal paper by Zhang et al. [24]. We expand upon this work by discussing local attributes of neural network training within the context of a relatively simple and generalizable framework. We describe how various types of noise can be compensated for within the proposed framework in order to allow the global deep learning model to generalize in spite of interpolating spurious function descriptors. Empirically, we support our postulates with experiments involving overparameterized multilayer perceptrons and controlled noise in the training data. The main insights are that deep learning models are optimized for training data modularly, with different regions in the function space dedicated to fitting distinct kinds of sample information. Detrimental overfitting is largely prevented by the fact that different regions in the function space are used for prediction based on the similarity between new input data and that which has been optimized for.
Rational preferential reasoning for datalog

Datalog is a powerful language that can be used to represent explicit knowledge and compute inferences in knowledge bases. Datalog cannot represent or reason about contradictory rules, though. This is a limitation as contradictions are often present in domains that contain exceptions. In this paper, we extend datalog to represent contradictory and defeasible information. We deﬁne an approach to eﬃciently reason about contradictory information in datalog and show that it satisﬁes the KLM requirements for a rational consequence relation. Finally, we introduce an implementation of this approach in the form of a defeasible datalog reasoning tool and evaluate the performance of this tool.
Image processing and analysis

Digital Data In a most generalized way, a digital image is an array of numbers depicting spatial distribution of a certain field parameters (such as reflectivity of EM radiation, emissivity, temperature or some geophysical or topographical elevation. Digital image consists of discrete picture elements called pixels. Associated with each pixel is a number represented as DN (Digital Number), that depicts the average radiance of relatively small area within a scene. The range of DN values being normally 0 to 255. The size of this area effects the reproduction of details within the scene. As the pixel size is reduced more scene detail is preserved in digital representation.
Adolescent substance abuse : perspectives on club drugs

This article contends that in drug abuse prevention is still better than cure. Substance abuse trends in South Africa vary from region to region and from population group to population group. Dagga (cannabis or marijuana) and dagga and Mandrax smoked together in a pipe remain the drugs most favoured by school-going adolescents. The adolescent rave scene contributed largely to the extensive increase in the use of the designer club drug Ecstasy, while other club drugs are also becoming more popular. Many adolescent substance abusers start with alcohol abuse and progress rapidly to the abuse of other drugs. The qualitative research in this article examines the issues in the adolescent substance abuser's lifeworld experiences and adolescent peer group clusters' increased use of club drugs.
Human immune system variation during one year

The human immune system varies extensively between individuals, but variation within individuals over time has not been well characterized. Systems-level analyses allow for simultaneous quantification of many interacting immune system components, and the inference of global regulatory principles. Here we present a longitudinal, systems-level analysis in 99 healthy adults, 50 to 65 years of age and sampled every 3rd month during one year. We describe the structure of inter-individual variation and characterize extreme phenotypes along a principal curve. From coordinated measurement fluctuations, we infer relationships between 115 immune cell populations and 750 plasma proteins constituting the blood immune system. While most individuals have stable immune systems, the degree of longitudinal variability is an individual feature. The most variable individuals, in the absence of overt infections, exhibited markers of poor metabolic health suggestive of a functional link between metabolic and immunologic homeostatic regulation. HIGHLIGHTS Longitudinal variation in immune cell composition during one year Inter-individual variation can be described along a principal curve Immune cell and protein relationships are inferred Variability over time is an individual feature correlating with markers of poor metabolic health
A Versatile Multi-Robot Monte Carlo Tree Search Planner for On-Line Coverage Path Planning

Mobile robots hold great promise in reducing the need for humans to perform jobs such as vacuuming, seeding,harvesting, painting, search and rescue, and inspection. In practice, these tasks must often be done without an exact map of the area and could be completed more quickly through the use of multiple robots working together. The task of simultaneously covering and mapping an area with multiple robots is known as multi-robot on-line coverage and is a growing area of research. Many multi-robot on-line coverage path planning algorithms have been developed as extensions of well established off-line coverage algorithms. In this work we introduce a novel approach to multi-robot on-line coverage path planning based on a method borrowed from game theory and machine learning- Monte Carlo Tree Search. We implement a Monte Carlo Tree Search planner and compare completion times against a Boustrophedon-based on-line multi-robot planner. The MCTS planner is shown to perform on par with the conventional Boustrophedon algorithm in simulations varying the number of robots and the density of obstacles in the map. The versatility of the MCTS planner is demonstrated by incorporating secondary objectives such as turn minimization while performing the same coverage task. The versatility of the MCTS planner suggests it is well suited to many multi-objective tasks that arise in mobile robotics.
A Survey On 3D Inner Structure Prediction from its Outer Shape

The analysis of the internal structure of trees is highly important for both forest experts, biological scientists, and the wood industry. Traditionally, CT-scanners are considered as the most efficient way to get an accurate inner representation of the tree. However, this method requires an important investment and reduces the cost-effectiveness of this operation. Our goal is to design neural-network-based methods to predict the internal density of the tree from its external bark shape. This paper compares different image-to-image(2D), volume-to-volume(3D) and Convolutional Long Short Term Memory based neural network architectures in the context of the prediction of the defect distribution inside trees from their external bark shape. Those models are trained on a synthetic dataset of 1800 CT-scanned look-like volumetric structures of the internal density of the trees and their corresponding external surface.
From SN 2010da to NGC 300 ULX-1: Ten Years of Observations of an Unusual High Mass X-Ray Binary in NGC 300

In May 2010, an intermediate luminosity optical transient was discovered in the nearby galaxy NGC 300 by a South African amateur astronomer. In the decade since its discovery, multi-wavelength observations of the misnamed “SN 2010da” have continually reshaped our understanding of this high mass X-ray binary system. In this review, we present an overview of the multi-wavelength observations and attempt to understand the 2010 transient event, and later, the reclassification of this system as NGC 300 ULX-1: a red supergiant + neutron star ultraluminous X-ray source.
Capturing the Practices, Challenges, and Needs of Transportation Decision-Makers

Transportation decision-makers from government agencies play an important role in addressing the traffic network conditions, which in turn, have a major impact on the well-being of citizens. The practices, challenges, and needs of this group of practitioners are less represented in the HCI literature. We address this gap through an interview study with 19 practitioners from Transports Québec, a government agency responsible for transportation infrastructures in Québec, Canada. We found that this group of decision-makers can most benefit from research about data analysis tools and platforms that (1) provide information to support data quality awareness, (2) are interoperable with other tools in the complex workflow of the practitioners, and (3) support intuitive and customizable visual analytics. These implications can also be informative to the design of tools supporting other decision-making tasks and domains.
Practical Accelerated Optimization on Riemannian Manifolds

We develop a new Riemannian descent algorithm with an accelerated rate of convergence. We focus on functions that are geodesically convex or weakly-quasi-convex, which are weaker function classes compared to prior work that has considered geodesically strongly convex functions. Our proof of convergence relies on a novel estimate sequence which allows to demonstrate the dependency of the convergence rate on the curvature of the manifold. We validate our theoretical results empirically on several optimization problems defined on a sphere and on the manifold of positive definite matrices.
Hrinet: Alternative Supervision Network For High-Resolution Ct Image Interpolation

Image interpolation in the medical area is of high importance as most 3D biomedical volume images are sampled where the distance between consecutive slices is significantly greater than the in-plane pixel size due to radiation dose or scanning time. Image interpolation creates a number of new slices between known slices in order to obtain an isotropic volume image. The results can be used for the higher quality of 3D reconstruction and visualization of human body structures. Semantic interpolation on the manifold has been proved to be very useful for smoothing image interpolation. Nevertheless, all previous methods focused on low-resolution image interpolation, and most of them work poorly on high-resolution images. We propose a novel network, High Resolution Interpolation Network (HRINet), aiming at producing high quality and realistic CT image interpolations. We combine the idea of ACAI and GANs, and propose a novel idea of alternative supervision method by applying supervised and unsupervised training alternatively to raise the accuracy of human organ structures in CT while keeping high quality. We compare an MSE based and a perceptual based loss optimizing methods for high quality interpolation, and show the tradeoff between the structural correctness and sharpness. Our experiments show the great improvement on 25 6 and 5122 images quantitatively and qualitatively.
Full electromagnetic Green's dyadic of spherically symmetric open optical systems and elimination of static modes from the resonant-state expansion

A general analytic form of the full 6x6 dyadic Green's function of a spherically symmetric open optical system is presented, with an explicit solution provided for a homogeneous sphere in vacuum. Different spectral representations of the Green's function are derived using the Mittag-Leffler theorem, and their convergence to the exact solution is analyzed, allowing us to select optimal representations. Based on them, more efficient versions of the resonant-state expansion (RSE) are formulated, with a particular focus on the static mode contribution, including versions of the RSE with a complete elimination of static modes. These general versions of the RSE, applicable to non-spherical optical systems, are verified and illustrated on exactly solvable examples of a dielectric sphere in vacuum with perturbations of its size and refractive index, demonstrating the same level of convergence to the exact solution for both transverse electric and transverse magnetic polarizations.
Pairwise Neural Networks (PairNets) with Low Memory for Fast On-Device Applications

A traditional artificial neural network (ANN) is normally trained slowly by a gradient descent algorithm, such as the backpropagation algorithm, since a large number of hyperparameters of the ANN need to be fine-tuned with many training epochs. Since a large number of hyperparameters of a deep neural network, such as a convolutional neural network, occupy much memory, a memory-inefficient deep learning model is not ideal for real-time Internet of Things (IoT) applications on various devices, such as mobile phones. Thus, it is necessary to develop fast and memory-efficient Artificial Intelligence of Things (AIoT) systems for real-time on-device applications. We created a novel wide and shallow 4-layer ANN called "Pairwise Neural Network" ("PairNet") with high-speed non-gradient-descent hyperparameter optimization. The PairNet is trained quickly with only one epoch since its hyperparameters are directly optimized one-time via simply solving a system of linear equations by using the multivariate least squares fitting method. In addition, an n-input space is partitioned into many n-input data subspaces, and a local PairNet is built in a local n-input subspace. This divide-and-conquer approach can train the local PairNet using specific local features to improve model performance. Simulation results indicate that the three PairNets with incremental learning have smaller average prediction mean squared errors, and achieve much higher speeds than traditional ANNs. An important future work is to develop better and faster non-gradient-descent hyperparameter optimization algorithms to generate effective, fast, and memory-efficient PairNets with incremental learning on optimal subspaces for real-time AIoT on-device applications.
Reproducibility Report for the Paper: Modeling of Request Cloning in Cloud Server Systems using Processor Sharing

The authors have uploaded their artifact on Zenodo, which ensures a long-term retention of the artifact. The code is suitably documented, and some examples are given. A minimalistic overall description of the engine is provided. The artifact allows to setup the environment quite quickly, and the dependencies are well documented. The process to regenerate data for the figures in the paper completes, and all results are reproducible. This paper can thus receive the Artifacts Available badge and the Artifacts Evaluated-Functional. Given the high quality of the artifact, also the Artifacts Evaluated-Reusable badge can be assigned.
Single scattering of polarized light by correlated surface and volume disorder

We study light scattering by systems combining randomly rough surface and volume dielectric fluctuations. We introduce a general model including correlations between surface and volume disorders, and we study the scattering properties within a single scattering approach. We identify different regimes of surface and volume dominated scattering depending on length scales characterizing the surface and volume disorders. For uncorrelated disorders, we discuss the polarization response of each source of disorder, and show how polarimetric measurements can be used to separate the surface and volume contributions in the total measured diffusely scattered intensity. For correlated systems, we identify two configurations of volume disorder which, respectively, couple weakly or strongly to surface scattering via surface-volume cross-correlations. We illustrate these effects on different configurations exhibiting interference patterns in the diffusely scattered intensity, which may be of interest for the characterization of complex systems or for the design of optical components by engineering the degree of surface-volume correlations.
2.75D Convolutional Neural Network for Pulmonary Nodule Classification in Chest CT

Early detection and classification of pulmonary nodules in Chest Computed tomography (CT) images is an essential step for effective treatment of lung cancer. However, due to the large volume of CT data, finding nodules in chest CT is a time consuming thus error prone task for radiologists. Benefited from the recent advances in Convolutional Neural Networks (ConvNets), many algorithms based on ConvNets for automatic nodule detection have been proposed. According to the data representation in their input, these algorithms can be further categorized into: 2D, 3D and 2.5D which uses a combination of 2D images to approximate 3D information. Leveraging 3D spatial and contextual information, the method using 3D input generally outperform that based on 2D or 2.5D input, whereas its large memory footprints becomes the bottleneck for many applications. In this paper, we propose a novel 2D data representation of a 3D CT volume, which is constructed by spiral scanning a set of radials originated from the 3D volume center, referred to as the 2.75D. Comparing to the 2.5D, the 2.75D representation captures omni-directional spatial information of a 3D volume. Based on 2.75D representation of 3D nodule candidates in Chest CT, we train a convolutional neural network to perform the false positive reduction in the nodule detection pipeline. We evaluate the nodule false positive reduction system on the LUNA16 data set which contains 1186 nodules out of 551,065 candidates. By comparing 2.75D with 2D, 2.5D and 3D, we show that our system using 2.75D input outperforms 2D and 2.5D, yet slightly inferior to the systems using 3D input. The proposed strategy dramatically reduces the memory consumption thus allow fast inference and training by enabling larger number of batches comparing to the methods using 3D input.
Searching for a Light (pseudo)Scalar via the Yukawa Process at the ILC

Yukawa production of a light scalar can be explored at a linear collider. Light (pseudo)scalar can exist in extended Higgs models and an interesting example is the light pseudoscalar in Type-X two Higgs doublet model. The model can explain the anomalous magnetic moment of muon at large $\tan \beta$. We show that the available parameter space in this model can be examined by the Yukawa process at 5$\sigma$ at the ILC.