julilili42/papers huggingface dataset:
MIRACLES: atmospheric characterization of directly imaged planets and substellar companions at 4-5 $μ$m. II. Constraints on the mass and radius of the enshrouded planet PDS 70 b
2022-02-24

The circumstellar disk of PDS 70 hosts two forming planets, which are actively accreting gas from their environment. In this work, we report the first detection of PDS 70 b in the Br and filters with VLT/NACO, a tentative detection of PDS 70 c in Br , and a reanalysis of archival NACO and SPHERE and imaging data. The near side of the disk is also resolved with the Br and filters, indicating that scattered light is non-negligible at these wavelengths. The spectral energy distribution of PDS 70 b is well described by blackbody emission, for which we constrain the photospheric temperature and photospheric radius to K and . The relatively low bolometric luminosity, , in combination with the large radius, is not compatible with standard structure models of fully convective objects. With predictions from such models, and adopting a recent estimate of the accretion rate, we derive a planetary mass and radius in the range of and , independently of the age and post-formation entropy of the planet. The blackbody emission, large photospheric radius, and the discrepancy between the photospheric and planetary radius suggests that infrared observations probe an extended, dusty environment around the planet, which obscures the view on its molecular composition. Finally, we derive a rough upper limit on the temperature and radius of potential excess emission from a circumplanetary disk, K and , but we do find weak evidence that the current data favors a model with a single blackbody component.
AI virtues -- The missing link in putting AI ethics into practice
2022-02-24

Several seminal ethics initiatives have stipulated sets of principles and standards for good technology development in the AI sector. However, widespread criticism has pointed out a lack of practical realization of these principles. Following that, AI ethics underwent a practical turn, but without deviating from the principled approach and the many shortcomings associated with it. This paper proposes a different approach. It defines four basic AI virtues, namely justice, honesty, responsibility and care, all of which represent specific motivational settings that constitute the very precondition for ethical decision making in the AI field. Moreover, it defines two second-order AI virtues, prudence and fortitude, that bolster achieving the basic virtues by helping with overcoming bounded ethicality or the many hidden psychological forces that impair ethical decision making and that are hitherto disregarded in AI ethics. Lastly, the paper describes measures for successfully cultivating the mentioned virtues in organizations dealing with AI research and development.
Prolonged Exposure to Oxaliplatin during HIPEC Improves Effectiveness in a Preclinical Micrometastasis Model
2022-02-24

Cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy (HIPEC) was considered a promising treatment for patients with peritoneal metastasis from colorectal cancer. However, the recently published randomized controlled PRODIGE 7 trial failed to demonstrate survival benefits through the addition of short-term oxaliplatin-based HIPEC. Constituting a complex multifactorial treatment, we investigated HIPEC in a preclinical model concerning the elimination of minimal tumor residues, thereby aiming to better understand the size of effects and respective clinical trial results. Patient samples of peritoneal perfusates obtained during HIPEC treatments and oxaliplatin-containing solutions at clinically relevant dosages, conforming with established HIPEC protocols, were assessed regarding their ability to eliminate modelled ~100 μm thickness cancer cell layers. Impedance-based real-time cell analysis and classical end-point assays were used. Flow cytometry was employed to determine the effect of different HIPEC drug solvents on tumor cell properties. Effectiveness of peritoneal perfusate patient samples and defined oxaliplatin-containing solutions proved limited but reproducible. HIPEC simulations for 30 min reduced the normalized cell index below 50% with peritoneal perfusates from merely 3 out of 9 patients within 72 h, indicating full-thickness cytotoxic effects. Instead, prolonging HIPEC to 1 h enhanced these effects and comprised 7 patients’ samples, while continuous drug exposure invariably resulted in complete cell death. Further, frequently used drug diluents caused approximately 25% cell size reduction within 30 min. Prolonging oxaliplatin exposure improved effectiveness of HIPEC to eliminate micrometastases in our preclinical model. Accordingly, insufficient penetration depth, short exposure time, and the physicochemical impact of drug solvents may constitute critical factors.
Unique Problems Require Unique Solutions—Models and Problems of Linking School Effectiveness and School Improvement
2022-02-24

The article aims to outline the proposals for linking school effectiveness research and school improvement that are currently relevant in international discussion and to ask how they deal with the ‘technology deficit’ at the level of model improvement. We will first show the difference between school improvement and school effectiveness. Then, we outline areas showing basic problems associated with linking school effectiveness and school improvement. After that, we present a summary of two relevant models for linking school effectiveness research and school improvement—Comprehensive Framework of Effective School Improvement and Dynamic Model of Educational Effectiveness—and subject them to critical analysis. The result of the critical analysis is that the question arises whether a link between the two traditions leads to a complementation of their respective problems of observation rather than to a compensation of them. This, in particular, concerns the problem of causality, which gives rise to theoretical and methodological considerations in both fields of research.
Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring
2022-02-24

Numerous car accidents are caused by improper driving maneuvers. Serious injuries are however avoidable if such driving maneuvers are detected beforehand and the driver is assisted accordingly. In fact, various recent research has focused on the automated prediction of driving maneuver based on hand-crafted features extracted mainly from in-cabin driver videos. Since the outside view from the traffic scene may also contain informative features for driving maneuver prediction, we present a framework for the detection of the drivers' intention based on both in-cabin and traffic scene videos. More specifically, we (1) propose a Convolutional-LSTM (ConvLSTM)-based auto-encoder to extract motion features from the out-cabin traffic, (2) train a classifier which considers motions from both in- and outside of the cabin jointly for maneuver intention anticipation, (3) experimentally prove that the in- and outside image features have complementary information. Our evaluation based on the publicly available dataset Brain4cars shows that our framework achieves a prediction with the accuracy of 83.98% and F1-score of 84.3%.
Patients With Super Obesity Do Not Perceive Themselves as Being at Higher Risk for a More Severe Course of COVID-19 Infection
2022-02-24

Background The coronavirus disease 2019 (COVID-19) pandemic has led to major health-related concerns in the population. Several risk factors for a severe course of COVID-19 disease have been identified, with obesity taking an important role. However, it is unclear whether this association is only known in the expert world or whether individuals also experience themselves as risk patients due to their obesity and whether the desire for weight reduction may also be associated with a hoped-for risk reduction. These questions were addressed in a cross-sectional study of patients who have presented to an obesity center in order to lose body weight. Methods Patients ( n = 155) of the obesity center were asked to complete an ad hoc questionnaire to assess whether the desire to lose weight is also associated with a hoped-for risk reduction with respect to COVID-19 disease during the middle of the pandemic in the period between October 2020 and April 2021. We additionally assessed their perceived general stress using the Perceived Stress Questionnaire (PSQ). Results In our explorative study, overall worries correlated significantly with worries about contracting COVID-19 ( r = 0.483, p &amp;lt; 0.001). There has been an association with concerns about severe COVID-19 progression and psychological distress from the COVID-19 pandemic ( r = 0.543, p &amp;lt; 0.001). In addition, a correlation was found between persons who worry about contracting COVID-19 and feeling like an at-risk patient ( r = 0.530, p &amp;lt; 0.001). Interestingly, the higher the BMI (&amp;gt;50 kg/m 2 ), the lower were the worries in PSQ (ANOVA p = 0.046). However, COVID-19-related worry was nonetheless present in the higher BMI subgroups. The most intense worries were experienced by individuals with a BMI between 35 and 39 (PSQ worries 50.44), immediately followed by individuals with a BMI between 40 and 49 (PSQ worries 49.36). Discussion and Conclusion An increased risk for a more severe course of COVID-19 infection is not generally perceived by obese individuals. In particular, individuals with very high BMI (&amp;gt;50)—although being at very high risk for a severe course of the COVID-19 disease—do not display increased worries, which might point toward heightened denial.
Impaired phase synchronization of motor‐evoked potentials reflects the degree of motor dysfunction in the lesioned human brain
2022-02-24

Abstract The functional corticospinal integrity (CSI) can be indexed by motor‐evoked potentials (MEP) following transcranial magnetic stimulation of the motor cortex. Glial brain tumors in motor‐eloquent areas are frequently disturbing CSI resulting in different degrees of motor dysfunction. However, this is unreliably mirrored by MEP characteristics. In 59 consecutive patients with diffuse glial tumors and 21 healthy controls (CTRL), we investigated the conventional MEP features, that is, resting motor threshold (RMT), amplitudes and latencies. In addition, frequency‐domain MEP features were analyzed to estimate the event‐related spectral perturbation (ERSP), and the induced phase synchronization by intertrial coherence (ITC). The clinical motor status was captured including the Medical Research Council Scale (MRCS), the Grooved Pegboard Test (GPT), and the intake of antiepileptic drugs (AED). Motor function was classified according to MRCS and GPT as no motor deficit (NMD), fine motor deficits (FMD) and gross motor deficits (GMD). CSI was assessed by diffusion‐tensor imaging (DTI). Motor competent subjects (CTRL and NMD) had similar ERSP and ITC values. The presence of a motor deficit (FMD and GMD) was associated with an impairment of high‐frequency ITC (150–300 Hz). GMD and damage to the CSI demonstrated an additional reduction of high‐frequency ERSP (150–300 Hz). GABAergic AED increased ERSP but not ITC. Notably, groups were indistinguishable based on conventional MEP features. Estimating MEP phase synchronization provides information about the corticospinal transmission after transcranial magnetic stimulation and reflects the degree of motor impairment that is not captured by conventional measures.
Molecular and Physiological Aspects of SARS-CoV-2 Infection in Women and Pregnancy
2022-02-24

Whilst scientific knowledge about SARS-CoV-2 and COVID-19 is rapidly increasing, much of the effects on pregnant women is still unknown. To accommodate pregnancy, the human endometrium must undergo a physiological transformation called decidualization. These changes encompass the remodeling of endometrial immune cells leading to immunotolerance of the semi-allogenic conceptus as well as defense against pathogens. The angiotensin converting enzyme 2 (ACE2) plays an important regulatory role in the renin-angiotensin-system (RAS) and has been shown to be protective against comorbidities known to worsen COVID-19 outcomes. Furthermore, ACE2 is also crucial for decidualization and thus for early gestation. An astounding gender difference has been found in COVID-19 with male patients presenting with more severe cases and higher mortality rates. This could be attributed to differences in sex chromosomes, hormone levels and behavior patterns. Despite profound changes in the female body during pregnancy, expectant mothers do not face worse outcomes compared with non-pregnant women. Whereas mother-to-child transmission through respiratory droplets during labor or in the postnatal period is known, another question of in utero transmission remains unanswered. Evidence of placental SARS-CoV-2 infection and expression of viral entry receptors at the maternal-fetal interface suggests the possibility of in utero transmission. SARS-CoV-2 can cause further harm through placental damage, maternal systemic inflammation, and hindered access to health care during the pandemic. More research on the effects of COVID-19 during early pregnancy as well as vaccination and treatment options for gravid patients is urgently needed.
First Clinical Experience With [89Zr]Zr-Df-IAB22M2C PET/MRI in Patients With Metastatic Cancer
2022-02-24

Abstract Aim/Introduction: Despite the spectacular success of immune checkpoint inhibitor therapy (ICT) in patients with metastatic cancer, only a limited proportion of patients benefit from ICT. An important gatekeeper for the therapeutic response to ICT are CD8 + cytotoxic T cells which are able to recognize MHC class I-dependent tumor antigens and destroy tumor cells. The radiolabeled minibody [ 89 Zr]Zr-Df-IAB22M2C has a high affinity for human CD8 + T cells and was already successfully tested in a phase I study. Here, we aimed to gain first clinical PET/MRI experience with the non-invasive assessment of the CD8 + T cell distribution in cancer patients by in vivo [ 89 Zr]Zr-Df-IAB22M2C.Material and Methods: We investigated 8 patients with metastasized cancers undergoing ICT. Radiolabeling of Df-IAB22M2C with Zr-89 was performed according to Good Manufacturing Practice. Multiparametric PET/MRI was acquired 24 h after injection of 74.2±17.9 MBq [ 89 Zr]Zr-Df-IAB22M2C. We analyzed the [ 89 Zr]Zr-Df-IAB22M2C uptake within the metastases and primary and secondary lymphatic organs. Results: [ 89 Zr]Zr-Df-IAB22M2C injection was tolerated well without noticeable side effects. The CD8 PET/MRI data acquisitions 24 hours post administration of [ 89 Zr]Zr-Df-IAB22M2C revealed a good image quality with a relatively low background signal due to only low unspecific tissue uptake and marginal blood pool retention. Only two metastatic lesions showed a markedly increased tracer uptake in our cohort of patients. Furthermore, we observed a high interpatient variability in the [ 89 Zr]Zr-Df-IAB22M2C uptake within the primary and secondary lymphoid organs. Four out of five ICT patients exhibited a rather high [ 89 Zr]Zr-Df-IAB22M2C uptake in the bone marrow. Two of these four patients as well as two other patients yielded a pronounced [ 89 Zr]Zr-Df-IAB22M2C uptake within non metastatic lymph nodes. Interestingly, cancer progression in ICT patients was associated with a relatively low [ 89 Zr]Zr-Df-IAB22M2C uptake in the spleen compared to the liver in 4 out of the 6 patients.Conclusion: Our first clinical experiences revealed the feasibility of [ 89 Zr]Zr-Df-IAB22M2C PET/MRI to assess potential immune-related changes in the metastases and the primary and secondary lymphatic organs. According to our results, we hypothesize that alteration in the [ 89 Zr]Zr-Df-IAB22M2C uptake in primary and secondary lymphoid organs might be associated with the response to ICT.
Counterfactual Generative Networks
2022-02-24

Neural networks are prone to learning shortcuts -- they often model simple correlations, ignoring more complex ones that potentially generalize better. Prior works on image classification show that instead of learning a connection to object shape, deep classifiers tend to exploit spurious correlations with low-level texture or the background for solving the classification task. In this work, we take a step towards more robust and interpretable classifiers that explicitly expose the task's causal structure. Building on current advances in deep generative modeling, we propose to decompose the image generation process into independent causal mechanisms that we train without direct supervision. By exploiting appropriate inductive biases, these mechanisms disentangle object shape, object texture, and background; hence, they allow for generating counterfactual images. We demonstrate the ability of our model to generate such images on MNIST and ImageNet. Further, we show that the counterfactual images can improve out-of-distribution robustness with a marginal drop in performance on the original classification task, despite being synthetic. Lastly, our generative model can be trained efficiently on a single GPU, exploiting common pre-trained models as inductive biases.
On the surprising similarities between supervised and self-supervised models
2022-02-24

How do humans learn to acquire a powerful, flexible and robust representation of objects? While much of this process remains unknown, it is clear that humans do not require millions of object labels. Excitingly, recent algorithmic advancements in self-supervised learning now enable convolutional neural networks (CNNs) to learn useful visual object representations without supervised labels, too. In the light of this recent breakthrough, we here compare self-supervised networks to supervised models and human behaviour. We tested models on 15 generalisation datasets for which large-scale human behavioural data is available (130K highly controlled psychophysical trials). Surprisingly, current self-supervised CNNs share four key characteristics of their supervised counterparts: (1.) relatively poor noise robustness (with the notable exception of SimCLR), (2.) non-human category-level error patterns, (3.) non-human image-level error patterns (yet high similarity to supervised model errors) and (4.) a bias towards texture. Taken together, these results suggest that the strategies learned through today's supervised and self-supervised training objectives end up being surprisingly similar, but distant from human-like behaviour. That being said, we are clearly just at the beginning of what could be called a self-supervised revolution of machine vision, and we are hopeful that future self-supervised models behave differently from supervised ones, and---perhaps---more similar to robust human object recognition.
Stable Implementation of Probabilistic ODE Solvers
2022-02-24

Probabilistic solvers for ordinary differential equations (ODEs) provide efficient quantification of numerical uncertainty associated with simulation of dynamical systems. Their convergence rates have been established by a growing body of theoretical analysis. However, these algorithms suffer from numerical instability when run at high order or with small step-sizes -- that is, exactly in the regime in which they achieve the highest accuracy. The present work proposes and examines a solution to this problem. It involves three components: accurate initialisation, a coordinate change preconditioner that makes numerical stability concerns step-size-independent, and square-root implementation. Using all three techniques enables numerical computation of probabilistic solutions of ODEs with algorithms of order up to 11, as demonstrated on a set of challenging test problems. The resulting rapid convergence is shown to be competitive to high-order, state-of-the-art, classical methods. As a consequence, a barrier between analysing probabilistic ODE solvers and applying them to interesting machine learning problems is effectively removed.
Differentiable Trust Region Layers for Deep Reinforcement Learning
2022-02-24

Trust region methods are a popular tool in reinforcement learning as they yield robust policy updates in continuous and discrete action spaces. However, enforcing such trust regions in deep reinforcement learning is difficult. Hence, many approaches, such as Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), are based on approximations. Due to those approximations, they violate the constraints or fail to find the optimal solution within the trust region. Moreover, they are difficult to implement, often lack sufficient exploration, and have been shown to depend on seemingly unrelated implementation choices. In this work, we propose differentiable neural network layers to enforce trust regions for deep Gaussian policies via closed-form projections. Unlike existing methods, those layers formalize trust regions for each state individually and can complement existing reinforcement learning algorithms. We derive trust region projections based on the Kullback-Leibler divergence, the Wasserstein L2 distance, and the Frobenius norm for Gaussian distributions. We empirically demonstrate that those projection layers achieve similar or better results than existing methods while being almost agnostic to specific implementation choices. The code is available at https://git.io/Jthb0.
Learning to Adapt Multi-View Stereo by Self-Supervision
2022-02-24

3D scene reconstruction from multiple views is an important classical problem in computer vision. Deep learning based approaches have recently demonstrated impressive reconstruction results. When training such models, self-supervised methods are favourable since they do not rely on ground truth data which would be needed for supervised training and is often difficult to obtain. Moreover, learned multi-view stereo reconstruction is prone to environment changes and should robustly generalise to different domains. We propose an adaptive learning approach for multi-view stereo which trains a deep neural network for improved adaptability to new target domains. We use model-agnostic meta-learning (MAML) to train base parameters which, in turn, are adapted for multi-view stereo on new domains through self-supervised training. Our evaluations demonstrate that the proposed adaptation method is effective in learning self-supervised multi-view stereo reconstruction in new domains.