julilili42/papers huggingface dataset:
Interferon-β Suppresses Transcriptionally Active Parvovirus B19 Infection in Viral Cardiomyopathy: A Subgroup Analysis of the BICC-Trial
2022-02-21

Human parvovirus B19 (B19V) is the predominant virus currently detected in endomyocardial biopsies (EMBs). Recent findings indicate that, specifically, transcriptionally active B19V with detectable viral RNA is of prognostic relevance in inflammatory viral cardiomyopathy. We aimed to evaluate B19V replicative status (viral RNA) and beneficial effects in a sub-collective of the prospective randomized placebo-controlled phase II multi-center BICC-Trial (Betaferon In Chronic Viral Cardiomyopathy) after interferon beta-1b (IFN-β) treatment. EMBs of n = 64 patients with B19V mono-infected tissue were retrospectively analyzed. Viral RNA could be detected in n = 18/64 (28.1%) of B19V DNA positive samples (mean age 51.7 years, 12 male), of whom n = 13 had been treated with IFN-ß. Five patients had received placebo. PCR analysis confirmed in follow-up that EMBs significantly reduced viral RNA loads in n = 11/13 (84.6%) of IFN-ß treated patients (p = 0.001), independently from the IFN-ß dose, in contrast to the placebo group, where viral RNA load was not affected or even increased. Consequently, a significant improvement of left ventricular ejection fraction (LVEF) after treatment with IFN-ß was observed (LVEF mean baseline 51.6 ± 14.1% vs. follow-up 61.0 ± 17.5%, p = 0.03). In contrast, in the placebo group, worsening of LVEF was evaluated in n = 4/5 (80.0%) of patients. We could show for the first-time the beneficial effects from treatment with IFN-ß, suppressing B19V viral RNA and improving the hemodynamic course. Our results need further verification in a larger prospective randomized controlled trial.
X-ray activity of the young solar-like star Kepler-63 and the structure of its corona
2022-02-21

The X-ray satellite XMM-Newton has so far revealed coronal cycles in seven solar-like stars. Of these, the youngest stars ε Eridani (~400 Myr) and ι Horologii (~600 Myr) display the shortest X-ray cycles and the smallest amplitudes, defined as the variation of the X-ray luminosity between the maximum and minimum of the cycle. The X-ray cycle of ε Eridani was characterised by applying a novel technique that allowed us to model the corona of a solar-like star in terms of magnetic structures, such as those observed on the Sun (active regions, cores of active regions, and flares), at various filling factors. The high surface coverage of the magnetic structures on ε Eridani (65%–95%) that emerged from that study was suggested to be responsible for the low cycle amplitude in the X-ray band. It was also hypothesised that the basal surface coverage with magnetic structures may be higher on the corona of solar-like stars while they are young. To investigate this hypothesis, we started the X-ray monitoring campaign of Kepler-63 in 2019. The star had been observed once before, in 2014, with XMM-Newton. With an age of 210 ± 45 Myr and a photospheric cycle of 1.27 yr, Kepler-63 is the youngest star so far to be observed in X-rays in order to reveal its coronal cycle. Our campaign comprised four X-ray observations of Kepler-63 spanning 10 months (i.e. three-fifths of its photospheric cycle). The long-term X-ray light curve did not reveal a periodic variation of the X-ray luminosity, but a factor two change would be compatible with the considerable uncertainties in the low signal-to-noise data for this relatively distant star. In the case of ε Eridani, we describe the coronal emission measure distribution (EMD) of Kepler-63 with magnetic structures such as those observed on the Sun. The best match with the observations is found for an EMD composed of cores and flares of GOES Class C and M following the canonical flare frequency distribution. More energetic flares are occasionally present but they do not contribute significantly to the quasi-stationary high-energy component of the emission measure probed with our modelling. This model yields a coronal filling factor of 100%. This complete coverage of the corona with X-ray-emitting magnetic structures is consistent with the absence of an X-ray cycle, confirming the analogous results derived earlier for ε Eridani. Finally, combining our results with the literature on stellar X-ray cycles, we establish an empirical relation between the cycle amplitude L X,max / L X,min and the X-ray surface flux, F X,surf . From the absence of a coronal cycle in Kepler-63, we infer that stars with higher X-ray flux than Kepler-63 must host an EMD that comprises a significant fraction of higher energy flares than those necessary to model the corona of Kepler-63, that is, flares of Class X or higher. Our study opens a new path for studies of the solar-stellar analogy and the joint exploration of resolved and unresolved variability in stellar X-ray light curves.
Modeling the effects of environmental and perceptual uncertainty using deterministic reinforcement learning dynamics with partial observability
2022-02-21

Assessing the systemic effects of uncertainty that arises from agents' partial observation of the true states of the world is critical for understanding a wide range of scenarios. Yet, previous modeling work on agent learning and decision-making either lacks a systematic way to describe this source of uncertainty or puts the focus on obtaining optimal policies using complex models of the world that would impose an unrealistically high cognitive demand on real agents. In this work we aim to efficiently describe the emergent behavior of biologically plausible and parsimonious learning agents faced with partially observable worlds. Therefore we derive and present deterministic reinforcement learning dynamics where the agents observe the true state of the environment only partially. We showcase the broad applicability of our dynamics across different classes of partially observable agent-environment systems. We find that partial observability creates unintuitive benefits in a number of specific contexts, pointing the way to further research on a general understanding of such effects. For instance, partially observant agents can learn better outcomes faster, in a more stable way and even overcome social dilemmas. Furthermore, our method allows the application of dynamical systems theory to partially observable multiagent leaning. In this regard we find the emergence of catastrophic limit cycles, a critical slowing down of the learning processes between reward regimes and the separation of the learning dynamics into fast and slow directions, all caused by partial observability. Therefore, the presented dynamics have the potential to become a formal, yet practical, lightweight and robust tool for researchers in biology, social science and machine learning to systematically investigate the effects of interacting partially observant agents.
Impact of COVID-19 on the Neurosurgical Resident Training Program: An Early Experience
2022-02-21

Abstract Background The present study evaluates the impact of COVID-19 pandemic restrictions during the first lockdown period in spring 2020 on the neurosurgical resident training program, and provides constructive approaches to deal with such situations. Methods A concurrent embedded mixed methods design was used. Qualitative data were collected through in-depth interviews from all neurosurgical residents at three university hospitals in Germany. Concurrently, quantitative data of the number of performed surgeries, outpatient visits, and the usage of telemedicine in the period from October 2019 to July 2020 were collected and analyzed accordingly. Results During the period of COVID-19 pandemic restrictions in spring 2020, there was a marked reduction in the number of surgeries performed by neurosurgical residents, from an average of 41.26 (median 41) surgeries per month to 25.66 (median 24) per month, representing a decrease of 37.80%. The decrease in the operations was concerning mainly spinal and functional surgery. Outpatient visits were reduced significantly, with a concurrent fivefold increase in the usage of telemedicine. General and pediatric neurosurgery outpatient clinics were the most affected. However, although surgical exposure was reduced during the lockdown phase, neurosurgical residents focused on conducting research and improving theoretical knowledge. Nevertheless, the global uncertainties caused by COVID-19 generated notable psychological stress among some residents. Conclusions The COVID-19 pandemic restrictions significantly affected the neurosurgical training program. Innovative solutions need to be developed to increase teaching and research capacities of neurosurgical residents as well as to improve surgical skills by installing surgical skill laboratories or similar constructs.
To Those Who Have, More Will Be Given? Effects of an Instructional Time Reform on Gender Disparities in STEM Subjects, Stress, and Health
2022-02-21

Educational reformers all around the globe are continuously searching for ways to make schools more effective and efficient. In Germany, this movement has led to reforms that reduced overall school time of high track secondary schools from 9 to 8 years, which was compensated for by increasing average instruction time per week in lower secondary school (Grades 5–10). Based on prior research, we assumed that this reform might increase gender disparities in STEM-related outcomes, stress, and health because it required students to learn similar content in less amount of time. Therefore, we investigated how the school time reform affected gender disparities at the end of upper secondary school between 2011 and 2013. Specifically, we considered representative data of the last two cohorts who completed lower secondary school before the reform ( N = 2,405) and the first two cohorts after the reform ( N = 2,413) from the National Educational Panel Study. Potential differences in gender disparities were investigated for upper secondary school outcomes of subject-specific standardized test performance, self-concept, and interest in mathematics, biology and physics, as well as outcomes of school-related stress and health. Overall, we found substantial disparities between girls and boys, which seemed to change little after the reform. Exceptions were the statistically significant gender × reform interactions for one stress dimension (Overload) and two health dimensions (Overburdening and Achievement-related fear) which increased for both boys and girls, but more strongly for girls.
Quantum and classical temporal correlations in $(1 + 1)D$ Quantum Cellular Automata
2022-02-21

We employ -dimensional quantum cellular automata to study the evolution of entanglement and coherence near criticality in quantum systems that display non-equilibrium steady-state phase transitions. This construction permits direct access to the entire space-time structure of the underlying non-equilibrium dynamics. It contains the full ensemble of classical trajectories and also allows for the analysis of unconventional correlations, such as entanglement in the time direction between the "present" and the "past". Close to criticality, the dynamics of these correlations - which we quantify through the second-order Renyi entropy - displays power-law behavior on its approach to stationarity. Our analysis is based on quantum generalizations of classical non-equilibrium systems: the Domany-Kinzel cellular automaton and the Bagnoli-Boccara-Rechtman model, for which we provide estimates for the critical exponents related to the classical and quantum components of the entropy. Our study shows that -dimensional quantum cellular automata permit an intriguing perspective on the nature of classical and quantum correlations in out-of-equilibrium systems.
Molecular mapping of the PDS70 system: No molecular absorption signatures from the forming planet PDS70 b
2022-02-21

Aims. We aim to detect molecules in the atmosphere of the young forming companion PDS70 b by searching for atmospheric absorption features typical of substellar objects. Methods. We obtained medium-resolution (R 5075) spectra of the PDS70 planetary system with the SINFONI integral field spectrograph at the Very Large Telescope. We applied molecular mapping, based on cross-correlation with synthetic spectra, to identify signatures of molecular species in the atmosphere of the planet. Results. Although the planet emission is clearly detected when resampling the data to lower resolution, no molecular species could be identified with the cross-correlation technique. We estimated upper limits on the abundances of H O, CO and CH ( , and , respectively) assuming a clear atmosphere, and we explored the impact of clouds, which increase the upper limits by a factor up to 0.7 dex. Assuming that the observations directly probe the planet's atmosphere, we found a lack of molecular species compared to other directly imaged companions or field objects. Under the assumption that the planet atmosphere presents similar characteristics to other directly imaged planets, we conclude that a dusty environment surrounds the planet, effectively obscuring any feature generated in its atmosphere. We quantify the extinction necessary to impede the detection ( mag), pointing to the possibility of higher optical thickness than previously estimated from other studies. Finally, the non-detection of molecular species conflicts with atmospheric models previously proposed to describe the forming planet. Conclusions. To unveil how giant planets form, a comprehensive approach that includes constraints from multiple techniques needs to be undertaken. Molecular mapping emerges as an alternative to more classical techniques like SED fitting.
Emulation of physical processes with Emukit
2022-02-21

Decision making in uncertain scenarios is an ubiquitous challenge in real world systems. Tools to deal with this challenge include simulations to gather information and statistical emulation to quantify uncertainty. The machine learning community has developed a number of methods to facilitate decision making, but so far they are scattered in multiple different toolkits, and generally rely on a fixed backend. In this paper, we present Emukit, a highly adaptable Python toolkit for enriching decision making under uncertainty. Emukit allows users to: (i) use state of the art methods including Bayesian optimization, multi-fidelity emulation, experimental design, Bayesian quadrature and sensitivity analysis; (ii) easily prototype new decision making methods for new problems. Emukit is agnostic to the underlying modeling framework and enables users to use their own custom models. We show how Emukit can be used on three exemplary case studies.
Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition
2022-02-21

Decomposing a scene into its shape, reflectance and illumination is a fundamental problem in computer vision and graphics. Neural approaches such as NeRF have achieved remarkable success in view synthesis, but do not explicitly perform decomposition and instead operate exclusively on radiance (the product of reflectance and illumination). Extensions to NeRF, such as NeRD, can perform decomposition but struggle to accurately recover detailed illumination, thereby significantly limiting realism. We propose a novel reflectance decomposition network that can estimate shape, BRDF, and per-image illumination given a set of object images captured under varying illumination. Our key technique is a novel illumination integration network called Neural-PIL that replaces a costly illumination integral operation in the rendering with a simple network query. In addition, we also learn deep low-dimensional priors on BRDF and illumination representations using novel smooth manifold auto-encoders. Our decompositions can result in considerably better BRDF and light estimates enabling more accurate novel view-synthesis and relighting compared to prior art. Project page: https://markboss.me/publication/2021-neural-pil/
Towards robust vision by multi-task learning on monkey visual cortex
2022-02-21

Deep neural networks set the state-of-the-art across many tasks in computer vision, but their generalization ability to image distortions is surprisingly fragile. In contrast, the mammalian visual system is robust to a wide range of perturbations. Recent work suggests that this generalization ability can be explained by useful inductive biases encoded in the representations of visual stimuli throughout the visual cortex. Here, we successfully leveraged these inductive biases with a multi-task learning approach: we jointly trained a deep network to perform image classification and to predict neural activity in macaque primary visual cortex (V1). We measured the out-of-distribution generalization abilities of our network by testing its robustness to image distortions. We found that co-training on monkey V1 data leads to increased robustness despite the absence of those distortions during training. Additionally, we showed that our network's robustness is very close to that of an Oracle network where parts of the architecture are directly trained on noisy images. Our results also demonstrated that the network's representations become more brain-like as their robustness improves. Using a novel constrained reconstruction analysis, we investigated what makes our brain-regularized network more robust. We found that our co-trained network is more sensitive to content than noise when compared to a Baseline network that we trained for image classification alone. Using DeepGaze-predicted saliency maps for ImageNet images, we found that our monkey co-trained network tends to be more sensitive to salient regions in a scene, reminiscent of existing theories on the role of V1 in the detection of object borders and bottom-up saliency. Overall, our work expands the promising research avenue of transferring inductive biases from the brain, and provides a novel analysis of the effects of our transfer.
Score-Based Generative Classifiers
2022-02-21

The tremendous success of generative models in recent years raises the question whether they can also be used to perform classification. Generative models have been used as adversarially robust classifiers on simple datasets such as MNIST, but this robustness has not been observed on more complex datasets like CIFAR-10. Additionally, on natural image datasets, previous results have suggested a trade-off between the likelihood of the data and classification accuracy. In this work, we investigate score-based generative models as classifiers for natural images. We show that these models not only obtain competitive likelihood values but simultaneously achieve state-of-the-art classification accuracy for generative classifiers on CIFAR-10. Nevertheless, we find that these models are only slightly, if at all, more robust than discriminative baseline models on out-of-distribution tasks based on common image corruptions. Similarly and contrary to prior results, we find that score-based are prone to worst-case distribution shifts in the form of adversarial perturbations. Our work highlights that score-based generative models are closing the gap in classification accuracy compared to standard discriminative models. While they do not yet deliver on the promise of adversarial and out-of-domain robustness, they provide a different approach to classification that warrants further research.
Where and When: Space-Time Attention for Audio-Visual Explanations
2022-02-21

Explaining the decision of a multi-modal decision-maker requires to determine the evidence from both modalities. Recent advances in XAI provide explanations for models trained on still images. However, when it comes to modeling multiple sensory modalities in a dynamic world, it remains underexplored how to demystify the mysterious dynamics of a complex multi-modal model. In this work, we take a crucial step forward and explore learnable explanations for audio-visual recognition. Specifically, we propose a novel space-time attention network that uncovers the synergistic dynamics of audio and visual data over both space and time. Our model is capable of predicting the audio-visual video events, while justifying its decision by localizing where the relevant visual cues appear, and when the predicted sounds occur in videos. We benchmark our model on three audio-visual video event datasets, comparing extensively to multiple recent multi-modal representation learners and intrinsic explanation models. Experimental results demonstrate the clear superior performance of our model over the existing methods on audio-visual video event recognition. Moreover, we conduct an in-depth study to analyze the explainability of our model based on robustness analysis via perturbation tests and pointing games using human annotations.
Early Recognition of Ball Catching Success in Clinical Trials with RNN-Based Predictive Classification
2022-02-21

Motor disturbances can affect the interaction with dynamic objects, such as catching a ball. A classification of clinical catching trials might give insight into the existence of pathological alterations in the relation of arm and ball movements. Accurate, but also early decisions are required to classify a catching attempt before the catcher's first ball contact. To obtain clinically valuable results, a significant decision confidence of at least 75% is required. Hence, three competing objectives have to be optimized at the same time: accuracy, earliness and decision-making confidence. Here we propose a coupled classification and prediction approach for early time series classification: a predictive, generative recurrent neural network (RNN) forecasts the next data points of ball trajectories based on already available observations; a discriminative RNN continuously generates classification guesses based on the available data points and the unrolled sequence predictions. We compare our approach, which we refer to as predictive sequential classification (PSC), to state-of-the-art sequence learners, including various RNN and temporal convolutional network (TCN) architectures. On this hard real-world task we can consistently demonstrate the superiority of PSC over all other models in terms of accuracy and confidence with respect to earliness of recognition. Specifically, PSC is able to confidently classify the success of catching trials as early as 123 milliseconds before the first ball contact. We conclude that PSC is a promising approach for early time series classification, when accurate and confident decisions are required.
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning
2022-02-21

Deep Metric Learning (DML) aims to find representations suitable for zero-shot transfer to a priori unknown test distributions. However, common evaluation protocols only test a single, fixed data split in which train and test classes are assigned randomly. More realistic evaluations should consider a broad spectrum of distribution shifts with potentially varying degree and difficulty. In this work, we systematically construct train-test splits of increasing difficulty and present the ooDML benchmark to characterize generalization under out-of-distribution shifts in DML. ooDML is designed to probe the generalization performance on much more challenging, diverse train-to-test distribution shifts. Based on our new benchmark, we conduct a thorough empirical analysis of state-of-the-art DML methods. We find that while generalization tends to consistently degrade with difficulty, some methods are better at retaining performance as the distribution shift increases. Finally, we propose few-shot DML as an efficient way to consistently improve generalization in response to unknown test shifts presented in ooDML. Code available here: https://github.com/CompVis/Characterizing_Generalization_in_DML.