Miracle-dz/UniFAR-FTUs huggingface dataset:
The Star Formation in Radio Survey: 3–33 GHz Imaging of Nearby Galaxy Nuclei and Extranuclear Star-forming Regions

We present 3, 15, and 33 GHz imaging toward galaxy nuclei and extranuclear star-forming regions using the Karl G. Jansky Very Large Array as part of the Star Formation in Radio Survey. With 3–33 GHz radio spectra, we measured the spectral indices and corresponding thermal (free–free) emission fractions for a sample of 335 discrete regions having significant detections in at least two radio bands. After removing 14 likely background galaxies, we find that the median thermal fraction at 33 GHz is 92% ± 0.8% with a median absolute deviation of 11%, when a two-component power-law model is adopted to fit the radio spectrum. Limiting the sample to 238 sources that are confidently identified as star-forming regions and not affected by potential AGN contamination (i.e., having galactocentric radii rG ≥ 250 pc) results in a median thermal fraction of 93% ± 0.8% with a median absolute deviation of 10%. We further measure the thermal fraction at 33 GHz for 163 regions identified at 7″ resolution to be 94% ± 0.8% with a median absolute deviation of 8%. Together, these results confirm that free–free emission dominates the radio spectra of star-forming regions on scales up to ∼500 pc in normal star-forming galaxies. We additionally find a factor of ∼1.6 increase in the scatter of the measured spectral index and thermal fraction distributions as a function of decreasing galactocentric radius. This trend is likely reflective of the continuous star formation activity occurring in the galaxy centers, resulting in a larger contribution of diffuse nonthermal emission relative to star-forming regions in the disk.
Combining Deep Learning Classifiers for 3D Action Recognition

The popular task of 3D human action recognition is almost exclusively solved by training deep-learning classifiers. To achieve a high recognition accuracy, the input 3D actions are often pre-processed by various normalization or augmentation techniques. However, it is not computationally feasible to train a classifier for each possible variant of training data in order to select the best-performing subset of pre-processing techniques for a given dataset. In this paper, we propose to train an independent classifier for each available pre-processing technique and fuse the classification results based on a strict majority vote rule. Together with a proposed evaluation procedure, we can very efficiently determine the best combination of normalization and augmentation techniques for a specific dataset. For the best-performing combination, we can retrospectively apply the normalized/augmented variants of input data to train only a single classifier. This also allows us to decide whether it is better to train a single model, or rather a set of independent classifiers.
Normative theory of patch foraging decisions

Foraging is a fundamental behavior as animals’ search for food is crucial for their survival. Patch leaving is a canonical foraging behavior, but classic theoretical conceptions of patch leaving decisions lack some key naturalistic details. Optimal foraging theory provides general rules for when an animal should leave a patch, but does not provide mechanistic insights about how those rules change with the structure of the environment. Such a mechanistic framework would aid in designing quantitative experiments to unravel behavioral and neural underpinnings of foraging. To address these shortcomings, we develop a normative theory of patch foraging decisions. Using a Bayesian approach, we treat patch leaving behavior as a statistical inference problem. We derive the animals’ optimal decision strategies in both non-depleting and depleting environments. A majority of these cases can be analyzed explicitly using methods from stochastic processes. Our behavioral predictions are expressed in terms of the optimal patch residence time and the decision rule by which an animal departs a patch. We also extend our theory to a hierarchical model in which the forager learns the environmental food resource distribution. The quantitative framework we develop will therefore help experimenters move from analyzing trial based behavior to continuous behavior without the loss of quantitative rigor. Our theoretical framework both extends optimal foraging theory and motivates a variety of behavioral and neuroscientific experiments investigating patch foraging behavior.
Late time cosmological evolution in DHOST models

Hamza Boumaza,1 David Langlois,2 and Karim Noui3, 2 Laboratory of Theoretical Physics and Department of Physics, Faculty of Exact and Computer Sciences, University Mohamed Seddik Ben Yahia, BP 98, Ouled Aissa, Jijel 18000, Algeria Astroparticule et Cosmologie (APC), CNRS, Université de Paris, F-75013 Paris, France Institut Denis Poisson, CNRS, Université de Tours, 37200 Tours, France (Dated: April 23, 2020)
Focal Cerebral Ischemia

We investigated the temporal threshold for focal cerebral infarction in the spontaneously hypertensive rat The right middle cerebral artery and common carotid artery were occluded for 0,1 ,2 ,3 ,4 , or 24 hours, and all the animals were sacrificed 24 hours after the onset of ischemia. Cortical infarct volumes and edema volumes were quantified in serial frozen sections of hematoxylin and eosin-stained tissue using image analysis. Upon occlusion, blood flow in the core of the ischemic zone, measured with laser-Doppler flowmetry, fell to a mean ± standard deviation of 21±7% of the preocclusion baseline value (n=26). During the first hour of ischemia, blood flow in the densely ischemic zone rose to 27±8% of baseline (n=25). Release of the middle cerebral artery and common carotid artery occlusions rapidly restored cortical blood flow to 213±83% of baseline (n=21). Focal ischemia of 1 hour's duration caused little or no infarction, while ischemic intervals of 2 and 3 hours produced successively larger volumes of infarcted cortex. Ischemic intervals of 3 -4 hours' duration followed by approximately 20 hours of recirculation yielded infarct volumes that were not significantly different from those after 24 hours of permanent focal ischemia. The results indicate that 3 -4 hours of focal cerebral ischemia in this rat model is sufficient to attain maximal infarction and suggest that recirculation or pharmacological interventions after this time will provide little benefit (Stroke 1991^2:1032-1039)
Taylor dispersion in osmotically driven laminar flows in phloem

Abstract Sucrose is among the main products of photosynthesis that are deemed necessary for plant growth and survival. It is produced in the mesophyll cells of leaves and translocated to different parts of the plant through the phloem. Progress in understanding this transport process remains fraught with experimental difficulties, thereby prompting interest in theoretical approaches and laboratory studies. The Münch pressure and mass flow model is one of the accepted hypotheses describing the physics of sucrose transport in the phloem. It is based on osmosis creating an energy potential difference between the source and the sink. The flow responding to this energy potential is assumed laminar and described by the Hagen–Poiseuille equation. This study revisits such osmotically driven flows in tubes with membrane walls by including the effects of Taylor dispersion on mass transport. This effect has been overlooked in phloem flow studies. Taylor dispersion can increase the effective transport of solutes by increasing the apparent diffusion coefficient. It is shown that, in addition to the conventional diffusive correction derived for impermeable tube walls, a new adjustment to the mean advective terms arises because of osmotic effects. Because the molecular Schmidt number is very large for sucrose in water, the sucrose front speed and travel times have a direct dependence on the Péclet number for different ranges of the Münch number. This study establishes upper limits on expected Taylor dispersion enhancement of sucrose transport.
On-the-Fly Joint Feature Selection and Classification

Joint feature selection and classification in an online setting is essential for time-sensitive decision making. However, most existing methods treat this coupled problem independently. Specifically, online feature selection methods can handle either streaming features or data instances offline to produce a fixed set of features for classification, while online classification methods classify incoming instances using full knowledge about the feature space. Nevertheless, all existing methods utilize a set of features, common for all data instances, for classification. Instead, we propose a framework to perform joint feature selection and classification on-the-fly, so as to minimize the number of features evaluated for every data instance and maximize classification accuracy. We derive the optimum solution of the associated optimization problem and analyze its structure. Two algorithms are proposed, ETANA and F-ETANA, which are based on the optimum solution and its properties. We evaluate the performance of the proposed algorithms on several public datasets, demonstrating (i) the dominance of the proposed algorithms over the state-of-the-art, and (ii) its applicability to broad range of application domains including clinical research and natural language processing.
A Bayesian perspective on classical control

The connections between optimal control and Bayesian inference have long been recognised, with the field of stochastic (optimal) control combining these frameworks for the solution of partially observable control problems. In particular, for the linear case with quadratic functions and Gaussian noise, stochastic control has shown remarkable results in different fields, including robotics, reinforcement learning and neuroscience, especially thanks to the established duality of estimation and control processes. Following this idea we recently introduced a formulation of PID control, one of the most popular methods from classical control, based on active inference, a theory with roots in variational Bayesian methods, and applications in the biological and neural sciences. In this work, we highlight the advantages of our previous formulation and introduce new and more general ways to tackle some existing problems in current controller design procedures. In particular, we consider 1) a gradient-based tuning rule for the parameters (or gains) of a PID controller, 2) an implementation of multiple degrees of freedom for independent responses to different types of signals (e.g., two-degree-of-freedom PID), and 3) a novel time-domain formalisation of the performance-robustness trade-off in terms of tunable constraints (i.e., priors in a Bayesian model) of a single cost functional, variational free energy.
Stabilizing Training of Generative Adversarial Nets via Langevin Stein Variational Gradient Descent

Generative adversarial networks (GANs), which are famous for the capability of learning complex underlying data distribution, are, however, known to be tricky in the training process, which would probably result in mode collapse or performance deterioration. Current approaches of dealing with GANs’ issues almost utilize some practical training techniques for the purpose of regularization, which, on the other hand, undermines the convergence and theoretical soundness of GAN. In this article, we propose to stabilize GAN training via a novel particle-based variational inference—Langevin Stein variational gradient descent (LSVGD), which not only inherits the flexibility and efficiency of original SVGD but also aims to address its instability issues by incorporating an extra disturbance into the update dynamics. We further demonstrate that, by properly adjusting the noise variance, LSVGD simulates a Langevin process whose stationary distribution is exactly the target distribution. We also show that LSVGD dynamics has an implicit regularization, which is able to enhance particles’ spread-out and diversity. Finally, we present an efficient way of applying particle-based variational inference on a general GAN training procedure no matter what loss function is adopted. Experimental results on one synthetic data set and three popular benchmark data sets—Cifar-10, Tiny-ImageNet, and CelebA—validate that LSVGD can remarkably improve the performance and stability of various GAN models.
EMAP: A Cloud-Edge Hybrid Framework for EEG Monitoring and Cross-Correlation Based Real-time Anomaly Prediction

State-of-the-art techniques for detecting, or predicting, neurological disorders (1) focus on predicting each disorder individually, and are (2) computationally expensive, leading to a delay that can potentially render the prediction useless, especially in critical events. Towards this, we present a real-time two-tiered framework called EMAP, which cross-correlates the input with all the EEG signals in our mega-database (a combination of multiple EEG datasets) at the cloud, while tracking the signal in real-time at the edge, to predict the occurrence of a neurological anomaly. Using the proposed framework, we have demonstrated a prediction accuracy of up to 94% for the three different anomalies that we have tested.
Twins: White-Glove Approach for BFT Testing

Byzantine Fault Tolerant (BFT) systems have seen extensive study for more than two decades, yet we lack a principled strategy for testing BFT implementations. This paper presents Twins, a new approach for testing BFT systems. The main idea of Twins is that we can emulate Byzantine behavior by running two (or generally up to $k$) instances of a node with the same identity. Each of the two instances (or Twins) runs unmodified, correct code. The Twins approach requires only a thin network wrapper that delivers messages to/from both Twins. To the rest of the system, the Twins appear indistinguishable from a single node behaving in a `questionable' manner. Twins generates `interesting' Byzantine behaviors, including equivocation, double voting, and losing internal state, while forgoing `uninteresting' behaviors that are trivially rejected by honest nodes, such as producing semantically invalid messages. Building on this idea, Twins can systematically generate Byzantine attack scenarios at scale, execute them in a controlled manner, and check for desired protocol properties. The paper demonstrates that Twins successfully reinstates several famous attacks on BFT protocols. In all cases, protocols break within fewer than a dozen protocol steps, hence it is realistic for the Twins approach to expose the problems. In two of these attacks, it took the community more than a decade to discover protocol flaws that Twins would have surfaced within minutes. Additionally, Twins testing was successfully incorporated into a production setting in which Twins executed 3M Twins-generated scenarios, and exposed (self-injected) subtle safety bugs within minutes of testing.
Where is the context? - A critique of recent dialogue datasets

Recent dialogue datasets like MultiWOZ 2.1 and Taskmaster-1 constitute some of the most challenging tasks for present-day dialogue models and, therefore, are widely used for system evaluation. We identify several issues with the above-mentioned datasets, such as history independence, strong knowledge base dependence, and ambiguous system responses. Finally, we outline key desiderata for future datasets that we believe would be more suitable for the construction of conversational artificial intelligence.
ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots

We investigate the problem of predicting driver behavior in parking lots, an environment which is less structured than typical road networks and features complex, interactive maneuvers in a compact space. Using the CARLA simulator, we develop a parking lot environment and collect a dataset of human parking maneuvers. We then study the impact of model complexity and feature information by comparing a multi-modal Long Short-Term Memory (LSTM) prediction model and a Convolution Neural Network LSTM (CNN-LSTM) to a physics-based Extended Kalman Filter (EKF) baseline. Our results show that 1) intent can be estimated well (roughly 85% top-1 accuracy and nearly 100% top-3 accuracy with the LSTM and CNN-LSTM model); 2) knowledge of the human driver's intended parking spot has a major impact on predicting parking trajectory; and 3) the semantic representation of the environment improves long term predictions.
Learning an Adaptive Model for Extreme Low-light Raw Image Processing

Low-light images suffer from severe noise and low illumination. Current deep learning models that are trained with real-world images have excellent noise reduction, but a ratio parameter must be chosen manually to complete the enhancement pipeline. In this work, we propose an adaptive low-light raw image enhancement network to avoid parameter-handcrafting and to improve image quality. The proposed method can be divided into two sub-models: Brightness Prediction (BP) and Exposure Shifting (ES). The former is designed to control the brightness of the resulting image by estimating a guideline exposure time $t_1$. The latter learns to approximate an exposure-shifting operator $ES$, converting a low-light image with real exposure time $t_0$ to a noise-free image with guideline exposure time $t_1$. Additionally, structural similarity (SSIM) loss and Image Enhancement Vector (IEV) are introduced to promote image quality, and a new Campus Image Dataset (CID) is proposed to overcome the limitations of the existing datasets and to supervise the training of the proposed model. Using the proposed model, we can achieve high-quality low-light image enhancement from a single raw image. In quantitative tests, it is shown that the proposed method has the lowest Noise Level Estimation (NLE) score compared with the state-of-the-art low-light algorithms, suggesting a superior denoising performance. Furthermore, those tests illustrate that the proposed method is able to adaptively control the global image brightness according to the content of the image scene. Lastly, the potential application in video processing is briefly discussed.
VO2max DURING INTERMITTENT SUPRAMAXIMAL EXERCISE

Wakefield, BR and Glaister, M. Influence of work-interval intensity and duration on time spent at a high percentage of _ VO2max during intermittent supramaximal exercise. J Strength Cond Res 23(9): 2548–2554, 2009—The purpose of this study was to examine the effect of work-interval duration (WID) and intensity on the time spent at, or above, 95% _ VO2max (T95 _ VO2max) during intermittent bouts of supramaximal exercise. Over a 5-week period, 7 physically active men with a mean (6SD) age, height, body mass, and _ VO2max of 22 6 5 years, 181.56 5.6 cm, 86.46 11.4 kg, and 51.56 1.5 ml kg min, respectively, attended 7 testing sessions. After completing a submaximal incremental test on a treadmill to identify individual oxygen uptake/running velocity relationships, subjects completed a maximal incremental test to exhaustion to establish _ VO2max and subsequently (from the aforementioned relationship) the minimum velocity required to elicit _ VO2max (v _ VO2max). In a random order, subjects then carried out 3 intermittent runs to exhaustion at both 105% and 115% v _ VO2max. Each test used a different WID (20 s, 25 s, or 30 s) interspersed with 20-second passive recovery periods. Results revealed no significant difference in T95 v _ VO2max for intermittent runs at 105% versus 115% v _ VO2max (p = 0.142). There was, however, a significant effect (p , 0.001) of WID on T95 _ VO2max, with WIDs of 30 seconds enabling more time relative to WIDs of 20 seconds (p = 0.018) and 25 seconds (p = 0.009). Moreover, there was an interaction between intensity and duration such that the effect of WID was magnified at the lower exercise intensity (p = 0.046). In conclusion, despite a number of limitations, the results of this investigation suggest that exercise intensities of approximately 105% v _ VO2max combined with WIDs greater than 25 seconds provide the best way of optimizing T95 _ VO2max when using fixed 20-second stationary rest periods.
Effect of Aspirin and Ticiopidine on Platelet Deposition in Carotid Atherosclerosis: Assessment by Indium-Ill Platelet Scintigraphy

The antiplatelet effects of aspirin and ticiopidine were studied by a dual-tracer method, using indium-Ill labeled platelets and technetium-99m human serum albumin, in a group of 12 patients with suspected ischemic cerebrovascular disease. The magnitude of platelet accumulation at the carotid bifurcation was expressed as the ratio of radioactivity of indium-Ill platelets deposited on the vascular wall to those circulating in the blood-pool (PAI, platelet accumulation index), 48 hr after injection of labeled platelets. PA I values were measured before (baseline studies) and after the antithrombotic therapies (aspirin studies: 325 mg bid for 22.3 ± 1 . 3 days, ticiopidine studies: 100 mg tid for 21.8 ± 2 . 1 days). At the baseline, the mean PAI value at 24 carotid bifurcations in the patient group was 15.7 ± 15.3% (mean ± S.D.) compared to 4 . 3 ± 9.1 at 24 carotid bifurcations in 12 normal subjects (p < 0.01). We defined the upper limit for a normal PAI (%) value to be +13.9, namely the mean PAI plus 2 SD for the carotid bifurcation in normal subjects and used this value for semiquantitative analysis. At the baseline, significant elevation of PAI (more than 13.9%; positive scintigram) was observed at 12 of 24 vessels, while 12 other regions were negative (less than 13.9%). In the lesions with positive scintigraphic results at the baseline, the mean PAI (%) value from the baseline, aspirin and ticiopidine studies was 29.5 ± 7.0,11.2 ± 8.5 (p < 0.01 versus baseline) and 21.4 ± 21.3 (not significant from baseline), respectively. The regions with negative scintigraphic findings (PAI < 13.9%), accumulated few, if any, labeled platelets and antithrombotic therapy had no significant influence on PAI (baseline 1.9 ± 5.0; aspirin 1.0 ± 8.3; ticiopidine 0.1 ± 7.6). Our results suggest that a moderate dose of aspirin (325 rag bid) inhibits platelet deposition on carotid atheromatous lesions in vivo in patients with recent cerebral ischemia while the ticiopidine therapy (100 mg tid) was less conclusive than that of aspirin. The procedures described in this study may be useful for evaluation of antithrombotic drugs on platelet deposition at carotid atherosclerotic lesions.
Extent of recent polewards range margin shifts in Finnish birds depends on their body mass and feeding ecology

Global warming would predict that, all else being equal, species should shift their range margins polewards, and that a failure to do so could be detrimental for a population. A polewards range margin shift has been documented previously in the Finnish avifauna. Here, I showed that the polewards range margin shift of 116 Southern Finnish bird species was larger for small-bodied species and differed according to their feeding ecology, but not to their migration ecology. Species foraging in wet habitats had experienced strong range margin shifts as compared with other feeding ecologies. For 53 species, population trends for 1983–2005 were available. I found no evidence that those feeding ecological groups that showed a relatively small shift in range margin had experienced low population growth or a population decline. Instead, I found some evidence that the long-term trend in population numbers varied across migration ecologies after correcting for changes in their Finnish breeding range. The results suggest that those processes that cause a shift in the range margin of birds are not involved in the recent changes in bird abundances.