Miracle-dz/UniFAR-FTUs huggingface dataset:
Physics-aware deep neural networks for surrogate modeling of turbulent natural convection

Recent works have explored the potential of machine learning as data-driven turbulence closures for RANS and LES techniques. Beyond these advances, the high expressivity and agility of physics-informed neural networks (PINNs) make them promising candidates for full fluid flow PDE modeling. An important question is whether this new paradigm, exempt from the traditional notion of discretization of the underlying operators very much connected to the flow scales resolution, is capable of sustaining high levels of turbulence characterized by multi-scale features? We investigate the use of PINNs surrogate modeling for turbulent Rayleigh-B{\'e}nard (RB) convection flows in rough and smooth rectangular cavities, mainly relying on DNS temperature data from the fluid bulk. We carefully quantify the computational requirements under which the formulation is capable of accurately recovering the flow hidden quantities. We then propose a new padding technique to distribute some of the scattered coordinates-at which PDE residuals are minimized-around the region of labeled data acquisition. We show how it comes to play as a regularization close to the training boundaries which are zones of poor accuracy for standard PINNs and results in a noticeable global accuracy improvement at iso-budget. Finally, we propose for the first time to relax the incompressibility condition in such a way that it drastically benefits the optimization search and results in a much improved convergence of the composite loss function. The RB results obtained at high Rayleigh number Ra = 2 $\bullet$ 10 9 are particularly impressive: the predictive accuracy of the surrogate over the entire half a billion DNS coordinates yields errors for all flow variables ranging between [0.3% -- 4%] in the relative L 2 norm, with a training relying only on 1.6% of the DNS data points.
Time-dependent stochastic basis adaptation for uncertainty quantification

We extend stochastic basis adaptation and spatial domain decomposition methods to solve time varying stochastic partial differential equations (SPDEs) with a large number of input random parameters. Stochastic basis adaptation allows the determination of a low dimensional stochastic basis representation of a quantity of interest (QoI). Extending basis adaptation to time-dependent problems is challenging because small errors introduced in the previous time steps of the low dimensional approximate solution accumulate over time and cause divergence from the true solution. To address this issue we have introduced an approach where the basis adaptation varies at every time step so that the low dimensional basis is adapted to the QoI at that time step. We have coupled the time-dependent basis adaptation with domain decomposition to further increase the accuracy in the representation of the QoI. To illustrate the construction, we present numerical results for one-dimensional time varying linear and nonlinear diffusion equations with random space-dependent diffusion coefficients. Stochastic dimension reducEmail address: ramakrishna.tipireddy@pnnl.gov (Ramakrishna Tipireddy) Preprint submitted to Elsevier ar X iv :2 10 3. 03 31 6v 1 [ m at h. N A ] 4 M ar 2 02 1 tion techniques proposed in the literature have mainly focused on quantifying the uncertainty in time independent and scalar QoI. To the best of our knowledge, this is the first time-dependent dimension reduction approach for time-dependent stochastic PDEs.
The role of CP-conserving annihilations in generating cosmological particle-antiparticle asymmetries

The role of CP-violating decay and annihilation processes have been extensively studied in the context of generating cosmological particle-antiparticle asymmetries, both as sources of the asymmetry and its subsequent wash-out. In the scenarios for which the lowest order source of CP-violation is scattering processes, we highlight the role of additional CP-conserving annihilations in indirectly affecting the asymmetry generation. This stems from the strong dependence of the relevant out-of-equilibrium number densities on the rate of CP-conserving reactions. The net asymmetry generated, in turn, is proportional to the out-of-equilibrium number densities and the rate of CP-violation. Such CP-conserving scatterings occur naturally in scenarios of baryogenesis, leptogenesis and asymmetric dark matter production through scattering, as we illustrate through several examples. We find that the asymmetric yields for relevant particle-antiparticle systems can vary by orders of magnitude depending upon the relative size of the CP-conserving and violating reaction rates. ar X iv :2 10 3. 03 65 0v 1 [ he pph ] 5 M ar 2 02 1
Anwendung des Tocopherolmusters zur Erkennung von Fett- und Ôlvermischungen

Bei vielen Olen und Fetten stellt die Bestimmung des naturlichen Tocopherolgehaltes und der Art der enthaltenen Tocopherole mit eine Moglichkeit zur Reinheitskontrolle dar. Es wird auf die Problematik der Bestimmung mittels HPLC und die sichere Identifizierung eingegangen. Uber eine Klassifizierung der Ole und Fette nach ihrem Tocopherolmuster wird gezeigt, wo die Moglichkeiten und Grenzen zur Erkennung von Vermischungen liegen. Utilization of the Tocopherol pattern for Recognition of Fat and Oil Adulterations The determination of the tocopherols is one of the possibilities for purity control because many oils and fats have a characteristic natural tocopherol-content with a typical tocopherolpattern. It is shown, that there are some problems in the determination by HPLC (preparing of the samples, identification). A classification of the oils and fats is given, which demonstrates not only the possibilities but also the limits for purity control.
Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture

Graph Convolutional Networks (GCNs) are increasingly adopted in large-scale graph-based recommender systems. Training GCN requires the minibatch generator traversing graphs and sampling the sparsely located neighboring nodes to obtain their features. Since real-world graphs often exceed the capacity of GPU memory, current GCN training systems keep the feature table in host memory and rely on the CPU to collect sparse features before sending them to the GPUs. This approach, however, puts tremendous pressure on host memory bandwidth and the CPU. This is because the CPU needs to (1) read sparse features from memory, (2) write features into memory as a dense format, and (3) transfer the features from memory to the GPUs. In this work, we propose a novel GPU-oriented data communication approach for GCN training, where GPU threads directly access sparse features in host memory through zero-copy accesses without much CPU help. By removing the CPU gathering stage, our method significantly reduces the consumption of the host resources and data access latency. We further present two important techniques to achieve high host memory access efficiency by the GPU: (1) automatic data access address alignment to maximize PCIe packet efficiency, and (2) asynchronous zero-copy access and kernel execution to fully overlap data transfer with training. We incorporate our method into PyTorch and evaluate its effectiveness using several graphs with sizes up to 111 million nodes and 1.6 billion edges. In a multi-GPU training setup, our method is 65--92% faster than the conventional data transfer method, and can even match the performance of all-in-GPU-memory training for some graphs that fit in GPU memory.
Entropy Production Due to Electroweak Phase Transition in the Framework of Two Higgs Doublet Model

We revisit the possibility of first order electroweak phase transition (EWPT) in one of the simplest extensions of the Standard Model scalar sector, namely the two-Higgs-doublet model (2HDM). We take into account the ensuing constraints from the electroweak precision tests, Higgs signal strengths and the recent LHC bounds from direct scalar searches. By studying the vacuum transition in 2HDM, we discuss in detail the entropy released in the first order EWPT in various parameter planes of a 2HDM.
Hybrid Car-Following Strategy Based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Control

Deep deterministic policy gradient (DDPG)-based car-following strategy can break through the constraints of the differential equation model due to the ability of exploration on complex environments. However, the car-following performance of DDPG is usually degraded by unreasonable reward function design, insufficient training, and low sampling efficiency. In order to solve this kind of problem, a hybrid car-following strategy based on DDPG and cooperative adaptive cruise control (CACC) is proposed. First, the car-following process is modeled as the Markov decision process to calculate CACC and DDPG simultaneously at each frame. Given a current state, two actions are obtained from CACC and DDPG, respectively. Then, an optimal action, corresponding to the one offering a larger reward, is chosen as the output of the hybrid strategy. Meanwhile, a rule is designed to ensure that the change rate of acceleration is smaller than the desired value. Therefore, the proposed strategy not only guarantees the basic performance of car-following through CACC but also makes full use of the advantages of exploration on complex environments via DDPG. Finally, simulation results show that the car-following performance of the proposed strategy is improved compared with that of DDPG and CACC. Note to Practitioners—This article presents a new car-following strategy, which avoids the impact of deep deterministic policy gradient (DDPG) performance degradation on the system. In the proposed strategy, DDPG is replaced with cooperative adaptive cruise control (CACC) when the performance of DDPG is worse than that of CACC. Meanwhile, a switching rule is designed to guarantee that the change rate of acceleration is smaller than the threshold. Simulation results show that the performance of hybrid car-following strategy has been improved compared with that of only using CACC or DDPG. Moreover, the proposed strategy has the advantages of low computational burden, high real-time performance, and good scalability.
Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food

Understanding the nutritional content of food from visual data is a challenging computer vision problem, with the potential to have a positive and widespread impact on public health. Studies in this area are limited to existing datasets in the field that lack sufficient diversity or labels required for training models with nutritional understanding capability. We introduce Nutrition5k, a novel dataset of 5k diverse, real world food dishes with corresponding video streams, depth images, component weights, and high accuracy nutritional content annotation. We demonstrate the potential of this dataset by training a computer vision algorithm capable of predicting the caloric and macronutrient values of a complex, real world dish at an accuracy that outperforms professional nutritionists. Further we present a baseline for incorporating depth sensor data to improve nutrition predictions. We release Nutrition5k in the hope that it will accelerate innovation in the space of nutritional understanding. The dataset is available at https://github.com/google-research-datasets/Nutrition5k.
Target Localization using Bistatic and Multistatic Radar with 5G NR Waveform

Joint communication and sensing allows the utilization of common spectral resources for communication and localization, reducing the cost of deployment. By using fifth generation (5G) New Radio (NR) (i.e., the 3rd Generation Partnership Project Radio Access Network for 5G) reference signals, conventionally used for communication, this paper shows sub-meter precision localization is possible at millimeter wave frequencies. We derive the geometric dilution of precision of a bistatic radar configuration, a theoretical metric that characterizes how the target location estimation error varies as a function of the bistatic geometry and measurement errors. We develop a 5G NR compliant software test bench to characterize the measurement errors when estimating the time difference of arrival and angle of arrival with 5G NR waveforms. The test bench is further utilized to demonstrate the accuracy of target localization and velocity estimation in several indoor and outdoor bistatic and multistatic configurations and to show that on average, the bistatic configuration can achieve a location accuracy of 10.0 cm over a bistatic range of 25 m, which can be further improved by deploying a multistatic radar configuration.
High bandwidth temporal RF photonic signal processing with Kerr micro-combs: integration, fractional differentiation and Hilbert transforms

Integrated Kerr micro-combs, a powerful source of many wavelengths for photonic RF and microwave signal processing, are particularly useful for transversal filter systems. They have many advantages including a compact footprint, high versatility, large numbers of wavelengths, and wide bandwidths. We review recent progress on photonic RF and microwave high bandwidth temporal signal processing based on Kerr micro-combs with spacings from 49-200GHz. We cover integral and fractional Hilbert transforms, differentiators as well as integrators. The potential of optical micro-combs for RF photonic applications in functionality and ability to realize integrated solutions is also discussed.
Predictive Coding Can Do Exact Backpropagation on Convolutional and Recurrent Neural Networks

Predictive coding networks (PCNs) are an influential model for information processing in the brain. They have appealing theoretical interpretations and offer a single mechanism that accounts for diverse perceptual phenomena of the brain. On the other hand, backpropagation (BP) is commonly regarded to be the most successful learning method in modern machine learning. Thus, it is exciting that recent work formulates inference learning (IL) that trains PCNs to approximate BP. However, there are several remaining critical issues: (i) IL is an approximation to BP with unrealistic/non-trivial requirements, (ii) IL approximates BP in single-step weight updates; whether it leads to the same point as BP after the weight updates are conducted for more steps is unknown, and (iii) IL is computationally significantly more costly than BP. To solve these issues, a variant of IL that is strictly equivalent to BP in fully connected networks has been proposed. In this work, we build on this result by showing that it also holds for more complex architectures, namely, convolutional neural networks and (many-to-one) recurrent neural networks. To our knowledge, we are the first to show that a biologically plausible algorithm is able to exactly replicate the accuracy of BP on such complex architectures, bridging the existing gap between IL and BP, and setting an unprecedented performance for PCNs, which can now be considered as efficient alternatives to BP.
A Machine Learning Approach for Predicting Human Preference for Graph Layouts*

Understanding what graph layout human prefer and why they prefer such graph layout is significant and challenging due to the highly complex visual perception and cognition system in human brain. In this paper, we present the first machine learning approach for predicting human preference for graph layouts.In general, the data sets with human preference labels are limited and insufficient for training deep networks. To address this, we train our deep learning model by employing the transfer learning method, e.g., exploiting the quality metrics, such as shape-based metrics, edge crossing and stress, which are shown to be correlated to human preference on graph layouts. Experimental results using the ground truth human preference data sets show that our model can successfully predict human preference for graph layouts. To our best knowledge, this is the first approach for predicting qualitative evaluation of graph layouts using human preference experiment data.
The hippocampal‐entorhinal system represents nested hierarchical relations between words during concept learning

A fundamental skill of an intelligent mind is that of being able to rapidly discover the structural organization underlying the relations across the objects or the events in the world. Humans, thanks to language, master this skill. For example, a child learning that dolphins and cats can also be referred to as mammals, not only will infer the presence of a hierarchical organization for which dolphins and cats are subordinate exemplars of the category mammals, but will also derive that dolphins are, at least at one conceptual level, more similar to cats than to sharks, despite their indisputable higher perceptual similarity to the latter. The hippocampal‐entorhinal system, classically known for its involvement in relational and inferential memory, is a likely candidate to construct and hold these complex relational structures between concepts. To test this hypothesis, we trained healthy human adults to organize a novel audio‐visual object space into categories labeled with novel words. Crucially, a hierarchical taxonomy existed between the object categories, and participants discovered it via inference during a simple associative object‐to‐word training. Using functional MRI after learning, and a combination of ROI‐based multivariate analyses, we found that both the mid‐anterior hippocampus and the entorhinal cortex represented the inferred hierarchical structure between words: subordinate‐level words were represented more similarly to their related superordinate than to unrelated ones. This was paired, in the entorhinal cortex, by an additional signature of internalized structural representation of nested hierarchy: words referring to subordinate concepts belonging to the same superordinate category were represented more similarly compared with those not belonging to the same superordinate level: interestingly, this similarity was never directly taught to subjects nor it was made explicit during the task, but only indirectly derived through a logical inferential process and, crucially, contrasted the evidence coming from the definitional perceptual properties of the concepts. None of these results were observed before learning, when the same words were not yet semantically organized. A whole‐brain searchlight revealed that the effect in the entorhinal cortex extends to a wider network of areas, encompassing the prefrontal, temporal, and parietal cortices, partially overlapping with the semantic network.
Efficacy and safety of EMA401 in peripheral neuropathic pain: results of two randomised, double-blind, phase 2 studies in patients with postherpetic neuralgia and painful diabetic neuropathy.

ABSTRACT The analgesic efficacy and safety of two Phase 2b studies of EMA401 (a highly selective angiotensin II type 2 receptor antagonist), in patients with postherpetic neuralgia (PHN; EMPHENE) and painful diabetic neuropathy (EMPADINE) were reported. These were multicentre, randomised, double-blind treatment studies conducted in participants with PHN or type I/II diabetes mellitus with painful distal symmetrical sensorimotor neuropathy. Participants were randomised 1:1:1 to either placebo, EMA401 25 mg, or 100 mg twice daily (b.i.d) in the EMPHENE and 1:1 to placebo or EMA401 100 mg b.i. d. in the EMPADINE. The primary outcome for both the studies was change in weekly mean of the 24-hour average pain score, using a numeric rating scale (NRS), from baseline to Week 12. Both the studies were prematurely terminated due to preclinical hepatotoxicity on long-term dosing, although not observed in these studies. Out of the planned participants, a total of 129/360 (EMPHENE) and 137/400 (EMPADINE) participants were enrolled. The least square mean reduction in NRS pain score was numerically in favour of EMA401 100 mg arm in both EMPHENE (TD: -0.5 [95%CI: -1.6, 0.6; p value: 0.35]) and EMPADINE (treatment difference [TD]: -0.6 [95%CI: -1.4, 0.1; p value: 0.10]) at the end of Week 12. However, as the studies were terminated prematurely, no firm conclusion could be drawn but the consistent clinical improvement in pain intensity reduction across these two studies in two different populations is worth noting.
Hydrogen storage in novel carbon materials

This thesis examines the potential of C 60 intercalated graphite and titanium oxide 'decorated' graphite as sorbing hydrogen storage media. Initially, various types of natural flake graphite were examined and characterised with X-ray diffraction and electron microscopy techniques. Each type was assessed for suitability for modification by considering impurities, crystal structure and internal strain. Samples of increased-surface-area exfoliated graphite were then produced as an initial template for surface modification techniques, and a sample against which to compare gas-sorption characteristics. C60 intercalated graphite was produced in order to assess whether fullerenes might be used to create spacing within graphitic structures to maximise surface area and increase energies of adsorption at sites around the C60 molecule. Samples were made via a sublimation method, depositing C60 molecules onto surfaces where it was subsequently inaccessible to its solvents. Titanium oxide coated materials were created via a wet chemistry route followed by thermal decomposition, in order to examine whether this material plays a role in hydrogen uptake via 'spillover' catalysis reported to be caused by palladium/titanium oxide deposits upon surfaces. Each sample created was characterised using X-ray diffraction, electron microscopy and gas-sorption techniques; its apparent density determined by argon displacement, and then assessed for specific nitrogen BET surface area and hydrogen uptake at cryogenic temperatures. Although the methods of creating the C 60 intercalated samples proved detrimental to the material's surface area, it was found that the hydrogen surface sorption capability (uptake per unit surface area) was almost doubled. However, hydrogen uptake was effectively negated by titanium oxide surface depositions even though its surface area was increased. Future work is planned to further investigate the C 60 intercalated material.
Approach to the Patient Treated with Steroidogenesis Inhibitors.

Steroidogenesis inhibitors can be given to control the hypercortisolism of Cushing's syndrome in various situations: when surgery has been unsuccessful or not possible; in metastatic ACTH or cortisol-secreting tumors; when waiting for the maximal efficacy of radiation techniques; for rapid treatment of severe hypercortisolism in patients with occult ACTH-producing tumors; or as a presurgical treatment in patients with severe comorbidities. Whilst biochemical 'control' can be achieved in more than 50% cases, daily management of such drugs can be challenging. Indeed, with a 'dose-titration' or a 'block and replace' approach, defining eucortisolism is usually difficult, requiring measurement of several biological markers. Moreover, each drug has its own side effects, which must be monitored closely. The aim of this "Approach to the patient" is to shed light on the management of hypercortisolism with four steroidogenesis inhibitors (ketoconazole, levoketoconazole, metyrapone, osilodrostat) to help the endocrinologists dealing with patients with Cushing's syndrome. Various points will be discussed such as initial dose of treatment, dose schedule, monitoring of efficacy and side effects of monotherapy. The combination of steroidogenesis inhibitors will also be discussed.