Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk Factors of Restroke in Patients with Lacunar Cerebral Infarction Using Magnetic Resonance Imaging Image Features under Deep Learning Algorithm

This study was aimed to explore the magnetic resonance imaging (MRI) image features based on the fuzzy local information C-means clustering (FLICM) image segmentation method to analyze the risk factors of restroke in patients with lacunar infarction. In this study, based on the FLICM algorithm, the Canny edge detection algorithm and the Fourier shape descriptor were introduced to optimize the algorithm. The difference of Jaccard coefficient, Dice coefficient, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), running time, and segmentation accuracy of the optimized FLICM algorithm and other algorithms when the brain tissue MRI images were segmented was studied. 36 patients with lacunar infarction were selected as the research objects, and they were divided into a control group (no restroke, 20 cases) and a stroke group (restroke, 16 cases) according to whether the patients had restroke. The differences in MRI imaging characteristics of the two groups of patients were compared, and the risk factors for restroke in lacunar infarction were analyzed by logistic multivariate regression. The results showed that the Jaccard coefficient, Dice coefficient, PSNR value, and SSIM value of the optimized FLICM algorithm for segmenting brain tissue were all higher than those of other algorithms. The shortest running time was 26 s, and the highest accuracy rate was 97.86%. The proportion of patients with a history of hypertension, the proportion of patients with paraventricular white matter lesion (WML) score greater than 2 in the stroke group, the proportion of patients with a deep WML score of 2, and the average age of patients in the stroke group were much higher than those in the control group (P < 0.05). Logistic multivariate regression showed that age and history of hypertension were risk factors for restroke after lacunar infarction (P < 0.05). It showed that the optimized FLICM algorithm can effectively segment brain MRI images, and the risk factors for restroke in patients with lacunar infarction were age and hypertension history. This study could provide a reference for the diagnosis and prognosis of lacunar infarction.
Deep Learning for interpretable end-to-end survival (E-ESurv) prediction in gastrointestinal cancer histopathology

Digitized histopathology slides contain a wealth of information, only a fraction of which is being used in clinical routine. Deep learning can extract subtle visual features from digitized slides and thus can infer clinically relevant endpoints from raw image data. While classiﬁcation and regression methods are well established in this domain, end-to-end prediction of patient survival still remains a comparably novel approach. To account for diﬀerent follow-up times and censored data, previous approaches have largely used discretized survival data. Here, we demonstrate and validate EE-Surv, a powerful yet algorithmically simple method to predict survival directly from whole slide images which we validate in colorectal and gastric cancer, two clinically relevant and markedly diﬀerent tumor types. We experimentally show that this method yields a highly signiﬁcant prediction of survival and enables explainability of predictions. This method is publicly available under an open-source license and can be applied to any type of disease.
When Sally Met Trackers: Web Tracking From the Users' Perspective

Web tracking has evolved to become a norm on the Internet. As a matter of fact, the web tracking market has grown to raise billions of dollars. Privacy cautious web practitioners and researchers extensively studied the phenomenon proving how widespread this practice is, and providing effective solutions to give users the option of feeling private while freely surﬁng the web. However, because all those studies looked at this trend only from the trackers’ perspective, still there are a lot of unknowns regarding what the real impact of tracking is on real users. Our goal with this paper is to ﬁll this gap in the web tracking topic. Thanks to logs of web browsing telemetry, we were able to look at this trend from the users’ eyes. Precisely, we measure how fast a user encounters trackers and research on options to reduce her privacy risk. Moreover, we also estimate the fraction of browsing histories that are known by trackers and discuss two tracking strategies to increase the existing knowledge about users.
The opt out paradigm First steps towards a new experimental method that measures meta-linguistic awareness

A common assumption is that children learn a language implicitly and without conscious awareness of form and grammar, but this assumption has vir-tually never been tested experimentally. We propose a novel experimental method to examine if children’s ability to acquire linguistic regularities relates to awareness of these regularities. Traditional methods investigating awareness often rely on learners’ abilities to verbalize their awareness. For young children, such methods are not adequate because they often cannot reflect explicitly on their acquired knowledge, although they might be aware of it in a way they cannot verbalize. To test this, we adapted a method that is used to investigate awareness in animals, because it does not rely on verbalization for demonstrating awareness. Pilot results with 26 adults and 48 kindergartners show some important procedural prerequisites are met. In future research, this procedure could be used to investigate the development of meta-linguistic awareness in children.
SCARF: A Semantic Constrained Attention Refinement Network for Semantic Segmentation

Semantic segmentation has achieved great progress by exploiting the contextual dependencies. In this paper, we propose an end-to-end Semantic Constrained Attention ReFinement (SCARF) network, based on semantic constrained contextual dependencies, to fully utilize the semantic information across different layers. Our novelties lie in the following aspects: Firstly, we present a general framework for capturing the non-local contextual dependencies. Secondly, within the framework, we introduce an efficient Category Attention (CA) block to capture semantic-related context by using the category constraint from coarse segmentation, which reduces the computational complexity from O(n2) to O(n) for image with n pixels. Thirdly, we overcome the contextual information confusion problem by balancing the non-local contextual dependencies and the local consistency adaptively using a category-wise learning weight. Finally, we fully utilize the multi-scale semantic-related con-textual information by refining the segmentation iteratively across layers with semantic constraint. Extensive evaluations demonstrate that our SCARF network significantly improves the segmentation results and achieves superior performance 85.0% mIoU on PASCAL VOC 2012, 55.0% mIoU on PASCAL Context, and 82.1% mIoU on Cityscapes.
SmartShadow: Artistic Shadow Drawing Tool for Line Drawings

SmartShadow is a deep learning application for digital painting artists to draw shadows on line drawings, with three proposed tools. (1) Shadow brush: artists can draw scribbles to coarsely indicate the areas inside or outside their wanted shadows, and the application will generate the shadows in real-time. (2) Shadow boundary brush: this brush can precisely control the boundary of any specific shadow. (3) Global shadow generator: this tool can estimate the global shadow direction from input brush scribbles, and then consistently propagate local shadows to the entire image. These three tools can not only speed up the shadow drawing process (by 3.1× as experiments validate), but also allow for the flexibility to achieve various shadow effects and facilitate richer artistic creations. To this end, we train Convolutional Neural Networks (CNNs) with a collected large-scale dataset of both real and synthesized data, and especially, we collect 1670 shadow samples drawn by real artists. Both qualitative analysis and user study show that our approach can generate high-quality shadows that are practically usable in the daily works of digital painting artists. We present 30 additional results and 15 visual comparisons in the supplementary materiel.
N ov 2 02 1 Non-local linear-response functions for thermal transport computed with equilibrium molecular-dynamics simulation

We establish an approach to compute linear-response functions to elucidate heat waves and non-local thermal transport. The theory is able to describe the response of a system to external heat sources that are nonuniform in space and time. The response functions are computed using equilibrium molecular-dynamics simulations of an Ar crystal modeled using the standard Lennard-Jones potential. It is shown that for low temperatures and short length scales, transport can be partially or even completely ballistic, with the response primarily limited by the group velocity of lattice waves. By contrast, at longer length scales and higher temperatures, the response functions correspond more closely to diffusive transport characteristic of Fourier’s law. It is also shown how the effective thermal conductivity can be determined in a partially-ballistic regime. The results demonstrate the known reduction in the effective thermal conductivity observed when system dimensions are smaller than the mean-free path for lattice waves. Finally, we show how determination of the relevant response functions can be used to model heating of a crystal without requiring additional atomic-scale simulations. Differences between computed results and predictions from Fourier’s law represent wave-like, partially-ballistic transport.
Positron Emission Tomography in Animal Models of Alzheimer’s Disease Amyloidosis: Translational Implications

Animal models of Alzheimer’s disease amyloidosis that recapitulate cerebral amyloid-beta pathology have been widely used in preclinical research and have greatly enabled the mechanistic understanding of Alzheimer’s disease and the development of therapeutics. Comprehensive deep phenotyping of the pathophysiological and biochemical features in these animal models is essential. Recent advances in positron emission tomography have allowed the non-invasive visualization of the alterations in the brain of animal models and in patients with Alzheimer’s disease. These tools have facilitated our understanding of disease mechanisms and provided longitudinal monitoring of treatment effects in animal models of Alzheimer’s disease amyloidosis. In this review, we focus on recent positron emission tomography studies of cerebral amyloid-beta accumulation, hypoglucose metabolism, synaptic and neurotransmitter receptor deficits (cholinergic and glutamatergic system), blood–brain barrier impairment, and neuroinflammation (microgliosis and astrocytosis) in animal models of Alzheimer’s disease amyloidosis. We further propose the emerging targets and tracers for reflecting the pathophysiological changes and discuss outstanding challenges in disease animal models and future outlook in the on-chip characterization of imaging biomarkers towards clinical translation.
Addicted to Compulsions: A Complex Case Study

Obsessive-Compulsive Disorder (OCD) is characterised by distressing, intrusive obsessive thoughts and/or repetitive compulsive physical or mental acts. OCD negatively impacts patients’ functioning and quality of life. Acceptance and Commitment Therapy (ACT) promotes acceptance of difficult sensations, emotions, and thoughts and when doing so facilitates living a values-based life. This study describes ACT for improving functioning and wellbeing for a young 30-year-old woman with complex OCD. She attended fourteen sessions, using Acceptance and Commitment Therapy (ACT) with Exposure and Response Prevention (ERP). Specific measures were obtained between the sessions (YaleBrown Obsessive-Compulsive Scale; Obsessional Compulsive Inventory and CORE Outcome Measure). Improvements were evident; outcome measures suggest a reduction on the symptoms of OCD and an increase in well-being. Anecdotal comments support these findings and provide additional evidence that ACT and ERP is an effective treatment in Obsessive-Compulsive Disorder.
DeepFake MNIST+: A DeepFake Facial Animation Dataset Supplementary Material

The Figure 1 shows the pipeline to generate and collect our proposed DeepFake MNIST+ dataset. First, we collect the real videos from the VoxCeleb1 [5], extract the frames from these real videos as the source identity images. Then we shot driving videos with ten actions through the volunteers. In order to match the format of VoxCeleb1 videos, which are face-cropped and have the size of 256x256 resolution. We made a face-cropped version of driving videos with MTCNN modules [7] and also resize them into 256x256 resolution. We use Siarohin’s framework [6] for animation video generation to produce face animation videos. It is a SOTA animation framework such that it even could capture the detail of eyeball moving. A single source image and a driving video were passed to the generator each time to produce single animation videos with a specific action. The generated videos were filtered with Liveness detector APIs to collect the challenging videos. Finally, 10,000 passed animation videos with ten actions (1000 videos for each action) and selected 10,000 real videos from VoxCeleb1 from our proposed DeepFake MNIST+ dataset. 2. More Examples of Data
GanoDIP - GAN Anomaly Detection through Intermediate Patches: a PCBA Manufacturing Case

Industry 4.0 and recent deep learning progress make it possible to solve problems that traditional methods could not. This is the case for anomaly detection that received a particular attention from the machine learning community, and resulted in a use of generative adversarial networks (GANs). In this work, we propose to use intermediate patches for the inference step, after a WGAN training procedure suitable for highly imbalanced datasets, to make the anomaly detection possible on full size Printed Circuit Board Assembly (PCBA) images. We therefore show that our technique can be used to support or replace actual industrial image processing algorithms, as well as to avoid a waste of time for industries.
The Effects of College Desegregation on Academic Achievement and Students' Social Interactions: Evidence from Turnstile Data (Poster 28)

How does the desegregation of elite schools impact academic achievement? How does it affect students’ interactions within schools? In this paper, I study a natural experiment at an elite university in Colombia where the number of low–income students unexpectedly tripled. The change was driven by the forgivable loan program Ser Pilo Paga. I exploit the variation in the percentage of low–income students across degree majors and within entry cohorts to measure the effects of exposure to desegregation on students’ achievement. Having 9.5 percentage points more low–income peers had modest to null impacts on wealthy students’ academic performance. To assess the role of social interactions, I use novel data on students’ co–movements across campus as captured by turnstiles located at all entrances. Increasing exposure to desegregation led to increased connections between wealthy and low–income students of 0.5 standard deviations. At least half of the increase in interactions between wealthy and low–income students is explained by interactions of wealthy with low–income high– achieving students. These results are consistent with a theoretical framework where students diversify their interactions by matching by academic achievement, such that adverse peer effects are avoided.
Cloth mechanical parameter estimation and simulation for optimized robotic manipulation

In this article a method for the estimation of cloth simulation parameters is presented. We propose a method, based on already published methods from different fields, that can successfully create the mechanical model of a cloth, based only on a single monocular video source of a cloth been held and moved in the air by two hands. We propose the use of a moving graph generation method using Scale Invariant Feature Transformation (SIFT). Having the moving graph of the real cloth as the goal, a method based on genetic algorithms was designed to produce the mechanical properties of the cloth’s mechanical model. This way a simulated cloth with similar mechanical properties will be created. For our experiments a mechanical model based on Provot’s mass-spring-damper (MSD) cloth model with adjustable springs and dampers was used. However, we present a method that can be easily adjusted to any particle-based cloth model. The method presented was designed to be easily applicable so as to enable the broader use of cloth models in robotized cloth manipulation tasks. The use of a cloth’s digital twin, enables the major part of tuning of a robot controller to be made offline. This will significantly accelerate the tuning process, enabling the broader use of robots in more delicate cloth manipulation tasks. Finally, to prove the validity of our method we provide the results of experiments with cloths of different patterns and physical parameters.
Supplementary File for: “Painting from Part”

Our part encoder consists of six stride-2 partial convolutions [9] and two sibling fully-connected (FC) layers to extract part-features and produce part-noise. Our painting generator is composed of five repainting residual blocks and a normal residual blocks. We adopt the similar structure to DenseNet [4] as our whole discriminator and multi-scale PatchGAN [6] as our painting discriminator. Our network is trained using Adam optimizer [8] with parameters of β1 = 0.0, β2 = 0.9, learning rate α1 = 0.0001 for part encoder and painting generator, and learning rate α2 = 0.0004 for whole and painting discriminators. Figure A shows the detailed architecture of our Repainting Residual Block (RRB). Table A, Table B, Table C, and Table D present the detailed architectures of our part encoder, painting generator, whole discriminator, and painting discriminator, respectively.
Cyber Range for Industrial Control Systems (CR-ICS) for Simulating Attack Scenarios

Cyberattacks targeting industrial control systems (ICS) pose a particularly serious threat due to their potential to cause not only physical damage but also cascading disruptions to the supply of critical services (such as water, electricity, or gas). One way to address these threats is through training in a cyber range. Such training can bolster defensive capabilities by increasing cross-domain knowledge between IT and OT teams about real-world industrial processes and equipment on the one hand and attacker tactics, techniques, and procedures (TTPs) and cyber defense tools on the other hand. To that end, this paper presents the development of a Cyber Range for ICS (CR-ICS) that is based on a real-time attacker-defender gameplay model in conjunction with dynamic simulation models of typical industrial systems. As a proof of concept, we present an industrial gas turbine as one use-case of an archetypal industrial system. In addition to the architecture of the range and the building of the simulation model, this paper also provides a demonstration of a sample training exercise.
Planck 2018 results

This paper presents the High Frequency Instrument (HFI) data processing procedures for the Planck 2018 release. Major improvements in mapmaking have been achieved since the previous Planck 2015 release, many of which were used and described already in an intermediate paper dedicated to the Planck polarized data at low multipoles. These improvements enabled the first significant measurement of the reionization optical depth parameter using Planck-HFI data. This paper presents an extensive analysis of systematic effects, including the use of end-to-end simulations to facilitate their removal and characterize the residuals. The polarized data, which presented a number of known problems in the 2015 Planck release, are very significantly improved, especially the leakage from intensity to polarization. Calibration, based on the cosmic microwave background (CMB) dipole, is now extremely accurate and in the frequency range 100–353 GHz reduces intensity-to-polarization leakage caused by calibration mismatch. The Solar dipole direction has been determined in the three lowest HFI frequency channels to within one arc minute, and its amplitude has an absolute uncertainty smaller than 0.35 μK, an accuracy of order 10−4. This is a major legacy from the Planck HFI for future CMB experiments. The removal of bandpass leakage has been improved for the main high-frequency foregrounds by extracting the bandpass-mismatch coefficients for each detector as part of the mapmaking process; these values in turn improve the intensity maps. This is a major change in the philosophy of “frequency maps”, which are now computed from single detector data, all adjusted to the same average bandpass response for the main foregrounds. End-to-end simulations have been shown to reproduce very well the relative gain calibration of detectors, as well as drifts within a frequency induced by the residuals of the main systematic effect (analogue-to-digital convertor non-linearity residuals). Using these simulations, we have been able to measure and correct the small frequency calibration bias induced by this systematic effect at the 10−4 level. There is no detectable sign of a residual calibration bias between the first and second acoustic peaks in the CMB channels, at the 10−3 level.
Matt Farr-What's So Special About Initial Conditions?

It is often said that the world is explained by laws of nature together with initial conditions. But does that mean initial conditions don’t require further explanation? And does the explanatory role played by initial conditions entail or require that time has a preferred direction? This chapter looks at the use of the ‘initialness defence’ in physics, the idea that initial conditions are intrinsically special in that they don’t require further explanation, unlike the state of the world at other times. Such defences commonly assume a primitive directionality of time to distinguish between initial and final conditions. Using the case study of the time-asymmetry of thermodynamics and the so-called ‘past hypothesis’ — the hypothesis that the early universe was in a state of very low entropy —, I outline and support a deflationary account of the initialness defence that does not presuppose a basic directionality of time, and argue that there is a relevant explanatory asymmetry between initial conditions and the state of systems at other times only if certain causal conditions are satisfied. Hence, the initialness defence is available to those who reject a fundamental direction of