Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining 5-Aminolevulinic Acid Fluorescence and Intraoperative Magnetic Resonance Imaging in Glioblastoma Surgery: A Histology-Based Evaluation.

BACKGROUND Glioblastoma resection guided by 5-aminolevulinic acid (5-ALA) fluorescence and intraoperative magnetic resonance imaging (iMRI) may improve surgical results and prolong survival. OBJECTIVE To evaluate 5-ALA fluorescence combined with subsequent low-field iMRI for resection control in glioblastoma surgery. METHODS Fourteen patients with suspected glioblastoma suitable for complete resection of contrast-enhancing portions were enrolled. The surgery was carried out using 5-ALA-induced fluorescence and frameless navigation. Areas suspicious for tumor underwent biopsy. After complete resection of fluorescent tissue, low-field iMRI was performed. Areas suspicious for tumor remnant underwent biopsy under navigation guidance and were resected. The histological analysis was blinded. RESULTS In 13 of 14 cases, the diagnosis was glioblastoma multiforme. One lymphoma and 1 case without fluorescence were excluded. In 11 of 12 operations, residual contrast enhancement on iMRI was found after complete resection of 5-ALA fluorescent tissue. In 1 case, the iMRI enhancement was in an eloquent area and did not undergo a biopsy. The 28 biopsies of areas suspicious for tumor on iMRI in the remaining 10 cases showed tumor in 39.3%, infiltration zone in 25%, reactive central nervous system tissue in 32.1%, and normal brain in 3.6%. Ninety-three fluorescent and 24 non-fluorescent tissue samples collected before iMRI contained tumor in 95.7% and 87.5%, respectively. CONCLUSION 5-ALA fluorescence-guided resection may leave some glioblastoma tissue undetected. MRI might detect areas suspicious for tumor even after complete resection of all fluorescent tissue; however, due to the limited accuracy of iMRI in predicting tumor remnant (64.3%), resection of this tissue has to be considered with caution in eloquent regions.
Probing Neural Language Models for Human Tacit Assumptions

Humans carry stereotypic tacit assumptions (STAs) (Prince, 1978), or propositional beliefs about generic concepts. Such associations are crucial for understanding natural language. We construct a diagnostic set of word prediction prompts to evaluate whether recent neural contextualized language models trained on large text corpora capture STAs. Our prompts are based on human responses in a psychological study of conceptual associations. We find models to be profoundly effective at retrieving concepts given associated properties. Our results demonstrate empirical evidence that stereotypic conceptual representations are captured in neural models derived from semi-supervised linguistic exposure.
Catch & Carry: Reusable Neural Controllers for Vision-Guided Whole-Body Tasks

We address the longstanding challenge of producing flexible, realistic humanoid character controllers that can perform diverse whole-body tasks involving object interactions. This challenge is central to a variety of fields, from graphics and animation to robotics and motor neuroscience. Our physics-based environment uses realistic actuation and first-person perception -- including touch sensors and egocentric vision -- with a view to producing active-sensing behaviors (e.g. gaze direction), transferability to real robots, and comparisons to the biology. We develop an integrated neural-network based approach consisting of a motor primitive module, human demonstrations, and an instructed reinforcement learning regime with curricula and task variations. We demonstrate the utility of our approach for several tasks, including goal-conditioned box carrying and ball catching, and we characterize its behavioral robustness. The resulting controllers can be deployed in real-time on a standard PC. See overview video, this https URL .
Metrizing Weak Convergence with Maximum Mean Discrepancies

Theorem 12 of Simon-Gabriel & Scholkopf (JMLR, 2018) seemed to close a 40-year-old quest to characterize maximum mean discrepancies (MMD) that metrize the weak convergence of probability measures. We prove, however, that the theorem is incorrect and provide a correction. We show that, on a locally compact, non-compact, Hausdorff space, the MMD of a bounded continuous Borel measurable kernel k, whose RKHS-functions vanish at infinity, metrizes the weak convergence of probability measures if and only if k is continuous and integrally strictly positive definite (ISPD) over all signed, finite, regular Borel measures. We also show that, contrary to the claim of the aforementioned Theorem 12, there exist both bounded continuous ISPD kernels that do not metrize weak convergence and bounded continuous non-ISPD kernels that do metrize it.
Quasinormal modes of Kerr–de Sitter black holes via the Heun function

This note addresses quasinormal mode (QNM) frequencies of four-dimensional asymptotically de Sitter rotating black holes. The main motivation is that Mathematica 12.1 has implemented a new family of special functions: Heun functions. Using the fact that Teukolsky’s equations for Kerr–de Sitter black holes are mapped to Heun’s equations, we are able to compute their QNM frequencies by the Heun function. In this approach, Mathematica normally evaluates these frequencies to arbitrary numerical precision in a few seconds. We further discuss an application to asymptotically flat rotating black holes.
Hot gas heating via magnetic arms in spiral galaxies

Context. Reconnection heating has been considered as a potential source of the heating of the interstellar medium. In some galaxies, significant polarised radio emission has been found between the spiral arms. This emission has a form of “magnetic arms” that resembles the spiral structure of the galaxy. Reconnection effects could convert some of the energy of the turbulent magnetic field into the thermal energy of the surrounding medium, leaving more ordered magnetic fields, as is observed in the magnetic arms. Aims. Sensitive radio and X-ray data for the grand-design spiral galaxy M 83 are used for a detailed analysis of the possible interactions of magnetic fields with hot gas, including a search for signatures of gas heating by magnetic reconnection effects. Methods. Magnetic field strengths and energies derived from the radio emission are compared with the parameters of the hot gas calculated from the model fits to sensitive X-ray spectra of the hot gas emission. Results. The available X-ray data allowed us to distinguish two thermal components in the halo of M 83. We found slightly higher average temperatures of the hot gas in the interarm regions, which results in higher energies per particle and is accompanied by a decrease in the energy density of the magnetic fields. Conclusions. The observed differences in the energy budget between the spiral arms and the interarm regions suggest that, similar to the case of another spiral galaxy NGC 6946, we may be observing hints for gas heating by magnetic reconnection effects in the interarm regions. These effects, which act more efficiently on the turbulent component of the magnetic field, are expected to be stronger in the spiral arms. However, with the present data it is only possible to trace them in the interarm regions, where the star formation and the resulting turbulence is low.
The dust and cold gas content of local star-forming galaxies

We use dust masses (Mdust) derived from far-infrared data and molecular gas masses (Mmol) based on CO luminosity to calibrate proxies based on a combination of the galaxy Balmer decrement, disc inclination, and gas metallicity. We use such proxies to estimate Mdust and Mmol in the local SDSS sample of star-forming galaxies (SFGs). We study the distribution of Mdust and Mmol along and across the main sequence (MS) of SFGs. We find that Mdust and Mmol increase rapidly along the MS with increasing stellar mass (M*), and more marginally across the MS with increasing SFR (or distance from the relation). The dependence on M* is sub-linear for both Mdust and Mmol. Thus, the fraction of dust (fdust) and molecular gas mass (fmol) decreases monotonically towards large M*. The star formation efficiency (SFE, inverse of the molecular gas depletion time) depends strongly on the distance from the MS and it is constant along the MS. As nearly all galaxies in the sample are central galaxies, we estimate the dependence of fdust and fgas on the host halo mass and find a tight anticorrelation. As the region where the MS is bending is numerically dominated by massive haloes, we conclude that the bending of the MS is due to a lower availability of molecular gas mass in massive haloes rather than a lower efficiency in forming stars.
Simultaneous multi-telescope observations of FRB 121102

We present 11 detections of FRB 121102 in ∼3 h of observations during its ‘active’ period on the 10th of 2019 September. The detections were made using the newly deployed MeerTRAP system and single pulse detection pipeline at the MeerKAT radio telescope in South Africa. Fortuitously, the Nançay radio telescope observations on this day overlapped with the last hour of MeerKAT observations and resulted in four simultaneous detections. The observations with MeerKAT’s wide band receiver, which extends down to relatively low frequencies (900–1670 MHz usable L-band range), have allowed us to get a detailed look at the complex frequency structure, intensity variations, and frequency-dependent sub-pulse drifting. The drift rates we measure for the full-band and sub-banded data are consistent with those published between 600 and 6500 MHz with a slope of −0.147 ± 0.014 ms−1. Two of the detected bursts exhibit fainter ‘precursors’ separated from the brighter main pulse by ∼28 and ∼34 ms. A follow-up multi-telescope campaign on the 6th and 8th of 2019 October to better understand these frequency drifts and structures over a wide and continuous band was undertaken. No detections resulted, indicating that the source was ‘inactive’ over a broad frequency range during this time.
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning

Model-based reinforcement learning algorithms with probabilistic dynamical models are amongst the most data-efficient learning methods. This is often attributed to their ability to distinguish between epistemic and aleatoric uncertainty. However, while most algorithms distinguish these two uncertainties for {\em learning} the model, they ignore it when {\em optimizing} the policy. In this paper, we show that ignoring the epistemic uncertainty leads to greedy algorithms that do not explore sufficiently. In turn, we propose a {\em practical optimistic-exploration algorithm} (\alg), which enlarges the input space with {\em hallucinated} inputs that can exert as much control as the {\em epistemic} uncertainty in the model affords. We analyze this setting and construct a general regret bound for well-calibrated models, which is provably sublinear in the case of Gaussian Process models. Based on this theoretical foundation, we show how optimistic exploration can be easily combined with state-of-the-art reinforcement learning algorithms and different probabilistic models. Our experiments demonstrate that optimistic exploration significantly speeds up learning when there are penalties on actions, a setting that is notoriously difficult for existing model-based reinforcement learning algorithms.
Model Embedding Model-Based Reinforcement Learning

Model-based reinforcement learning (MBRL) has shown its advantages in sample-efficiency over model-free reinforcement learning (MFRL). Despite the impressive results it achieves, it still faces a trade-off between the ease of data generation and model bias. In this paper, we propose a simple and elegant model-embedding model-based reinforcement learning (MEMB) algorithm in the framework of the probabilistic reinforcement learning. To balance the sample-efficiency and model bias, we exploit both real and imaginary data in the training. In particular, we embed the model in the policy update and learn $Q$ and $V$ functions from the real data set. We provide the theoretical analysis of MEMB with the Lipschitz continuity assumption on the model and policy. At last, we evaluate MEMB on several benchmarks and demonstrate our algorithm can achieve state-of-the-art performance.
Ergoregion instabilities in rotating two-dimensional Bose-Einstein condensates: Perspectives on the stability of quantized vortices

We investigate the stability of vortices in two-dimensional Bose--Einstein condensates. In analogy with rotating spacetimes and with a careful account of boundary conditions, we show that the dynamical instability of multiply quantized vortices in trapped condensates persists in untrapped, spatially homogeneous geometries and has an ergoregion nature with some modification due to the peculiar dispersion of Bogoliubov sound. Our results open new perspectives to the physics of vortices in trapped condensates, where multiply quantized vortices can be stabilized by interference effects and singly charged vortices can become unstable in suitably designed trap potentials. We show how superradiant scattering can be observed also in the short-time dynamics of dynamically unstable systems, providing an alternative point of view on dynamical (in)stability phenomena in spatially finite systems.
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts

Accurate estimation of aleatoric and epistemic uncertainty is crucial to build safe and reliable systems. Traditional approaches, such as dropout and ensemble methods, estimate uncertainty by sampling probability predictions from different submodels, which leads to slow uncertainty estimation at inference time. Recent works address this drawback by directly predicting parameters of prior distributions over the probability predictions with a neural network. While this approach has demonstrated accurate uncertainty estimation, it requires defining arbitrary target parameters for in-distribution data and makes the unrealistic assumption that out-of-distribution (OOD) data is known at training time. In this work we propose the Posterior Network (PostNet), which uses Normalizing Flows to predict an individual closed-form posterior distribution over predicted probabilites for any input sample. The posterior distributions learned by PostNet accurately reflect uncertainty for in- and out-of-distribution data -- without requiring access to OOD data at training time. PostNet achieves state-of-the art results in OOD detection and in uncertainty calibration under dataset shifts.
Progressive Skeletonization: Trimming more fat from a network at initialization

Recent studies have shown that skeletonization (pruning parameters) of networks at initialization provides all the practical benefits of sparsity both at inference and training time, while only marginally degrading their performance. However, we observe that beyond a certain level of sparsity (approx 95%), these approaches fail to preserve the network performance, and to our surprise, in many cases perform even worse than trivial random pruning. To this end, we propose to find a skeletonized network with maximum foresight connection sensitivity (FORCE). Intuitively, out of all possible sub-networks, we propose to find the one whose connections would have a maximum impact on the loss when perturbed. Our approximate solution to maximize the FORCE, progressively prunes connections of a given network at initialization. This allows parameters that were unimportant at earlier stages of skeletonization to become important at later stages. In many cases, our approach enables us to remove up to 99.9% parameters, while keeping networks trainable and providing significantly better performance than recent approaches. We demonstrate the effectiveness of our approach at various levels of sparsity (from medium to extreme) through extensive experiments and analysis. Code can be found in this https URL.
The dominance of big teams in China’s scientific output

Modern science is dominated by scientific productions from teams. A recent finding shows that teams of both large and small sizes are essential in research, prompting us to analyze the extent to which a country’s scientific work is carried out by big or small teams. Here, using over 26 million publications from Web of Science, we find that China’s research output is more dominated by big teams than the rest of the world, which is particularly the case in fields of natural science. Despite the global trend that more papers are written by big teams, China’s drop in small team output is much steeper. As teams in China shift from small to large size, the team diversity that is essential for innovative work does not increase as much as that in other countries. Using the national average as the baseline, we find that the National Natural Science Foundation of China (NSFC) supports fewer small teams than the National Science Foundation (NSF) of the United States does, implying that big teams are preferred by grant agencies in China. Our finding provides new insights into the concern of originality and innovation in China, which indicates a need to balance small and big teams.
The Wasserstein Proximal Gradient Algorithm

Wasserstein gradient flows are continuous time dynamics that define curves of steepest descent to minimize an objective function over the space of probability measures (i.e., the Wasserstein space). This objective is typically a divergence w.r. t. a fixed target distribution. In recent years, these continuous time dynamics have been used to study the convergence of machine learning algorithms aiming at approximating a probability distribution. However, the discrete-time behavior of these algorithms might differ from the continuous time dynamics. Besides, although discretized gradient flows have been proposed in the literature, little is known about their minimization power. In this work, we propose a Forward Backward (FB) discretization scheme that can tackle the case where the objective function is the sum of a smooth and a nonsmooth geodesically convex terms. Using techniques from convex optimization and optimal transport, we analyze the FB scheme as a minimization algorithm on the Wasserstein space. More precisely, we show under mild assumptions that the FB scheme has convergence guarantees similar to the proximal gradient algorithm in Euclidean spaces (resp. similar to the associated Wasserstein gradient flow).
Effectiveness of Kinesio Taping on the Management of Knee Osteoarthritis: A Systematic Review of Randomized Controlled Trials

Abstract The purpose of this review was to summarize the current best evidence for the effectiveness of Kinesio Taping in reducing pain and increasing knee function for patients with knee osteoarthritis. A comprehensive search of literature published between 2014 and 2019 was conducted using the following electronic databases: PubMed, Google Scholar, Physiotherapy Evidence Database (PEDro), Science Direct, and Scopus. Only randomized controlled trials evaluating the effect of Kinesio Taping on knee osteoarthritis were included. PEDro was used to assess the risk of bias of included trials. This study was reported according to the guideline of the PRISMA statement. The methodological quality of the studies was done using the PEDro scale and GRADE approach. The overall quality of evidence was rated from moderate to high. Eighteen randomized trials involving 876 patients were included. The present systematic review demonstrated that there were significant differences between Kinesio Taping groups and control groups in terms of visual analog scale (VAS), Western Ontario and MacMaster Universities Osteoarthritis Index (WOMAC) scale and flexion range of motion. Kinesio Taping is effective in improving pain and joint function in patients with knee OA.
Multistate Models to Predict Development of Late Complications of Type 2 Diabetes in an Open Cohort Study

Background Increase in the prevalence of type 2 diabetic mellitus (T2DM) as a complex disease, its complications, and spread has become a dominant global health threat in recent decades. Objective The aim of the current study was to investigate the impact of risk factors and transition probability on the development and progression of the late complications of T2DM. Methods This study was an open cohort one which was conducted at Isfahan Endocrine and Metabolism Research Center (IEMRC). The data were collected from 1993 to 2018. The sample size consisted of 2519 adults diagnosed with type 2 diabetes. We applied the homogeneous multistate models including no complication, retinopathy alone, coronary artery disease (CAD), microalbuminuria, retinopathy and CAD, and the final absorbing mortality states. Results Based on our results, time-varying hypertension strongly intensified the hazard of transition to mortality in CAD, no complication, CAD and retinopathy, and retinopathy patients by 4.99, 4.09, 3.42, and 2.65 times, respectively. Hypertension seemed to be a potential factor for the transition of microalbuminuria to no complication in diabetic patients. One-unit increase in LDL increased the hazard ratio of transition from CAD, and retinopathy and CAD to mortality by 1.8% and 2.4%, respectively. Moreover, one level increase in time-varying HbA1c increased the hazard ratio of transition to retinopathy and mortality among no complication diabetic patients by 30% and 67%, respectively. One level increase in time-varying HbA1c also intensified the hazard ratio of transition from retinopathy to mortality by 45%. The same level of increase in time-varying HbA1c also intensified the hazard ratio of transition from CAD alone to CAD and retinopathy, and microalbuminuria to retinopathy by 26% and 50%, respectively. Conclusion In addition to glycemic control, our study indicates that controlling hypertension and hyperlipidemia is more effective in reducing mortality and the diabetic macro- and microvascular complications.