Miracle-dz/UniFAR-FTUs huggingface dataset:
Incorporating a Full-Physics Meteorological Model into an Applied Atmospheric Dispersion Modeling System

A new modeling system has been developed to provide a non-meteorologist with tools to predict air pollution transport in regions of complex terrain. This system couples the Penn State/NCAR Mesoscale Model 5 (MM5) with Earth Tech’s CALMET-CALPUFF system using a unique Graphical User Interface (GUI) developed at Pacific Northwest National Laboratory. This system is most useful in data-sparse regions, where there are limited observations to initialize the CALMET model. The user is able to define the domain of interest, provide details about the source term, and enter a surface weather observation through the GUI. The system then generates initial conditions and time constant boundary conditions for use by MM5. MM5 is run and the results are piped to CALPUFF for the dispersion calculations. Contour plots of pollutant concentration are prepared for the user. The primary advantages of the system are the streamlined application of MM5 and CALMET, limited data requirements, and the ability to run the coupled system on a desktop or laptop computer. In comparison with data collected as part of a field campaign, the new modeling system shows promise that a full-physics mesoscale model can be used in an applied modeling system to effectively simulate locally thermally-driven winds more » with minimal observations as input. An unexpected outcome of this research was how well CALMET represented the locally thermally-driven flows. « less
Describing SGRs/AXPs as fast and magnetized white dwarfs

Over the last decade, observational evidence has mounted that SGRs/AXPs belong to a particular class of pulsars. Furthermore, fast and very magnetic white dwarfs have been observed, and recently two SGRs with low magnetic fields $B\sim(10^{12}-10^{13})$ G, namely SGR 0418+5729 and Swift J1822.3-1606 were discovered with a period of $P\sim9.08$ s and $P\sim8.44$ s, respectively. These new discoveries opens the question of the nature of SGRs/AXPs, emerging alternative scenarios, in particular the white dwarf (WD) pulsar model. These astronomical observations have based an alternative description of the SGRs/AXPs expressed on rotating highly magnetized and very massive WDs. In this new description, several observational properties are easy understood and well explained as a consequence of the large radius of a massive white dwarf that manifests a new scale of mass density, moment of inertia, rotational energy, and magnetic dipole moment in comparison with the case of neutron stars. In this contribution, we will show that these recent discoveries of SGRs with low magnetic field share some properties with the recent detected fast WD pulsar AE Aquarii, and also with RXJ 0648.0-4418, and EUVE J0317-855, supporting the understanding of at least these SGRs with low-B as belonging to a class of very fast and magnetic massive WDs. Furthermore, these recent astronomical observations suggest that we should revisit the real nature of AXP/SGRs: are they really magnetars or very fast and massive white dwarfs?
Large-scale wind power grid modelling and stability evaluation using stochastic approaches

Due to dwindling fossil fuel reserves, there is a global demand to increase the level of low-carbon based renewable energy resources (RERs) for electric power generation. Coupled with concerns that emissions from fossil fuels is leading to climate change with possible disastrous consequences, effort is seriously under way to discover the probable usage of RERs on large-scale without being integrated into an existing power grid. It is envisaged that such a large-scale RER power grid will operate solely on its own and expected to be stable and reliable comparable to a conventional power grid. The impact of wind power generation as part of the electric power grid is no longer negligible. Wind energy generation is one of the most established renewable energy resources to help ensure low carbon-based renewable energy (RE) self-sufficiency, yet it is also one of the most volatile RERs. Despite its disadvantages, wind power generation is expected to continue its strong growth in the coming years as result of high interest in clean energy to curb the global warming. Various studies are looking at the prospects for solely RER power grids for usage on large-scale. However, the issue has been the stability and the reliability of such power grids. Variability of power output, intermittency and load mismatch are wind farms’ unique characteristics potentially harmful to grid voltage stability. In response to this problem, A large-scale wind power system was modelled using stochastic approach and the results analyzed using Lyapunov method, matrix exponential and Hurwitz criterion to ascertain the stability behavior of an entirely 100% RE grid being envisaged for the near future.
Dorsolateral Prefrontal Cortex Metabolic Profiles in Autism Spectrum Disorder Correlate Atypically with Nonverbal IQ and Typically with Attention Switching

Background: The neurometabolic profile of associated with autism spectrum disorder (ASD) has been reported to be abnormal by some studies showing region specific metabolite levels in ASD, while others report no group differences. The neurometabolic profile of the left dorsolateral prefrontal cortex (DLPFC) is of particular interest due to the DLPFC relevance to cognitive and executive function, and to ASD. Method: We used 1H-MRS to investigate neurometabolic profiles in the DLPFC of ASD and sex/IQ-matched typically developing (TD) children (ages 9-13). We focused on levels of Glutamate and Glutamine (Glx) due to many reported Glx abnormalities ASD, and of Choline (Cho) because of its relationship to intelligence quotient (IQ) and to attentional re-orienting difficulties. Results: While no significant group differences were observed in absolute concentrations, metabolite levels were correlated with the behavioral phenotype of ASD children. In the ASD group but not the TD group, nonverbal IQ (NVIQ) was negatively associated with Cho (r=-0.59, p=0.026) and positively associated with Glx/Cr (r=0.66, p=0.011). Furthermore, attentional-switching scores in the ASD group correlated negatively with Cho (r=-0.69, p=0.009), and positively with Glx/Cr for both the ASD (r=0.73, p=0.004) and TD (r=0.54, p=0.040) groups. Conclusions: Cho and Glx/Cr have different neurometabolic roles in modulating NVIQ in ASD compared to TD children, while their role in attentional switching seems preserved in ASD. Elucidating the apparently divergent role of neurometabolites in ASD in the absence of significant group differences in absolute levels is an important step towards understanding and mapping the neural correlates of ASD. These results are also relevant in the context of the significant cognitive function heterogeneity associated with the ASD phenotype, as they suggest possible underlying neural mechanisms that do not overlap which those expected from typical development.
The Nascent Red Sequence at z ∼ 2

We present new constraints on the evolution of the early-type galaxy color-magnitude relation (CMR) based on deep near-infrared imaging of a galaxy protocluster at z = 2.16 obtained using NICMOS on board the Hubble Space Telescope. This field contains a spectroscopically confirmed space overdensity of Lyα- and Hα-emitting galaxies that surrounds the powerful radio galaxy MRC 1138–262. Using these NICMOS data we identify a significant surface overdensity (=6.2 times) of red J110 − H160 galaxies in the color-magnitude diagram (when compared with deep NICMOS imaging from the HDF-N and UDF). The optical-NIR colors of these prospective red-sequence galaxies indicate the presence of ongoing dust-obscured star formation or recently formed (≲1.5 Gyr) stellar populations in a majority of the red galaxies. We measure the slope and intrinsic scatter of the CMR for three different red galaxy samples selected by a wide color cut and using photometric redshifts both with and without restrictions on rest-frame optical morphology. In all three cases both the rest-frame U − B slope and intrinsic color scatter are considerably higher than corresponding values for lower redshift galaxy clusters. These results suggest that while some relatively quiescent galaxies do exist in this protocluster both the majority of the galaxy population and hence the color-magnitude relation are still in the process of forming, as expected.
Spinal cord stimulation restores sensation, improves function, and reduces phantom limb pain after transtibial amputation

In the United States, over 1.5 million people live with lower-limb amputation. Existing prosthetic limbs do not restore somatosensory feedback from the limb, resulting in functional impairments including balance deficits and an increased risk of falls. Further, these prostheses do not alleviate the severe phantom limb pain that often follows amputation. Leveraging clinically available spinal cord stimulation electrodes, we designed a system that restores somatosensation in the missing limb, thereby improving balance and gait and reducing phantom limb pain. We show that spinal cord stimulation can evoke sensations in the missing foot and that we can control the location and intensity of those sensations. Further, by modulating stimulation intensity in real time based on signals from a wireless pressure-sensitive shoe insole, subjects exhibit improvements in functional measures of balance and gait stability. Finally, over the duration of the implant period, subjects experienced a clinically meaningful decrease in phantom limb pain. These combined results demonstrate that, with an electrode technology that is currently in widespread clinical use, our approach has the potential to become an important intervention for lower-limb amputation.
Reinforcement Learning-Based Cooperative P2P Power Trading between DC Nanogrid Clusters with Wind and PV Energy Resources

In replacing fossil fuels with renewable energy resources for carbon neutrality, the unbalanced resource production of intermittent wind and photovoltaic (PV) power is a critical issue for peer-to-peer (P2P) power trading. To address this issue, a reinforcement learning (RL) technique is introduced in this paper. For RL, a graph convolutional network (GCN) and a bi-directional long short-term memory (Bi-LSTM) network are jointly applied to P2P power trading between nanogrid clusters, based on cooperative game theory. The flexible and reliable DC nanogrid is suitable for integrating renewable energy for a distribution system. Each local nanogrid cluster takes the position of prosumer, focusing on power production and consumption simultaneously. For the power management of nanogrid cluster, multi-objective optimization is applied to each local nanogrid cluster with the Internet of Things (IoT) technology. Charging/discharging of an electric vehicle (EV) is executed considering the intermittent characteristics of wind and PV power production. RL algorithms, such as GCN- convolutional neural network (CNN) layers for deep Q-learning network (DQN), GCN-LSTM layers for deep recurrent Q-learning network (DRQN), GCN-Bi-LSTM layers for DRQN, and GCN-Bi-LSTM layers for proximal policy optimization (PPO), are used for simulations. Consequently, the cooperative P2P power trading system maximizes the profit by considering the time of use (ToU) tariff-based electricity cost and the system marginal price (SMP), and minimizes the amount of grid power consumption. Power management of nanogrid clusters with P2P power trading is simulated on a distribution test feeder in real time, and the proposed GCN-Bi-LSTM-PPO technique achieving the lowest electricity cost among the RL algorithms used for comparison reduces the electricity cost by 36.7%, averaging over nanogrid clusters.
Theroretical Insight into Batch Normalization: Data Dependant Auto-Tuning of Regularization Rate

—Batch normalization is widely used in deep learning to normalize intermediate activations. Deep networks suffer from notoriously increased training complexity, mandating careful initialization of weights, requiring lower learning rates, etc. These issues have been addressed by Batch Normalization (BN), by normalizing the inputs of activations to zero mean and unit standard deviation. Making this batch normalization part of the training process dramatically accelerates the training process of very deep networks. A new ﬁeld of research has been going on to examine the exact theoretical explanation behind the success of BN. Most of these theoretical insights attempt to explain the beneﬁts of BN by placing them on its inﬂuence on optimization, weight scale invariance, and regularization. Despite BN undeni- able success in accelerating generalization, the gap of analytically relating the effect of BN to the regularization parameter is still missing. This paper aims to bring out the data-dependent auto-tuning of the regularization parameter by BN with analytical proofs. We have posed BN as a constrained optimization imposed on non-BN weights through which we demonstrate its data statistics dependant auto-tuning of regularization parameter. We have also given analytical proof for its behavior under a noisy input scenario, which reveals the signal vs. noise tuning of the regularization parameter. We have also substantiated our claim with empirical results from the MNIST dataset experiments. side classiﬁcation By introducing , BN generalization The tendency to memorize the data decreases with increased the tuning
Model Predictive Robustness of Signal Temporal Logic Predicates

The robustness of signal temporal logic not only assesses whether a signal adheres to a specification but also provides a measure of how much a formula is fulfilled or violated. The calculation of robustness is based on evaluating the robustness of underlying predicates. However, the robustness of predicates is usually defined in a model-free way, i.e., without including the system dynamics. Moreover, it is often nontrivial to define the robustness of complicated predicates precisely. To address these issues, we propose a notion of model predictive robustness, which provides a more systematic way of evaluating robustness compared to previous approaches by considering model-based predictions. In particular, we use Gaussian process regression to learn the robustness based on precomputed predictions so that robustness values can be efficiently computed online. We evaluate our approach for the use case of autonomous driving with predicates used in formalized traffic rules on a recorded dataset, which highlights the advantage of our approach compared to traditional approaches in terms of precision. By incorporating our robustness definitions into a trajectory planner, autonomous vehicles obey traffic rules more robustly than human drivers in the dataset.
Not As Easy As You Think - Experiences and Lessons Learnt from Trying to Create a Bottom-Up Visualization Image Typology

We present and discuss the results of a two-year qualitative analysis of images published in IEEE Visualization (VIS) papers. Specifically, we derive a typology of 13 visualization image types, coded to distinguish visualizations and several image characteristics. The categorization process required much more time and was more difficult than we initially thought. The resulting typology and image analysis may serve a number of purposes: to study the evolution of the community and its research output over time, to facilitate the categorization of visualization images for the purpose of teaching, to identify visual designs for evaluation purposes, or to enable progress towards standardization in visualization. In addition to the typology and image characterization, we provide a dataset of 6,833 tagged images and an online tool that can be used to explore and analyze the large set of tagged images. We thus facilitate a discussion of the diverse visualizations used and how they are published and communicated in our community.
Supporting capabilities for underground facilities

The 2021 particle physics community study, known as"Snowmass 2021", has brought together particle physicists around the world to create a unified vision for the field over the next decade. One of the areas of focus is the Underground Facilities (UF) frontier, which addresses underground infrastructure and the scientific programs and goals of underground-based experiments. To this effect, the UF Supporting Capabilities topical group created two surveys for the community to identify potential gaps between the supporting capabilities of facilities and those needed by current and future experiments. Capabilities surveyed are discussed in this report and include underground cleanroom space size and specifications, radon-reduced space needs and availability, the assay need and other underground space needs as well timeline for future experiments. Results indicate that future, larger experiments will increasingly require underground assembly in larger, cleaner cleanrooms, often with better radon-reduction systems and increased monitoring capability for ambient contaminants. Most assay needs may be met by existing worldwide capabilities with organized cooperation between facilities and experiments. Improved assay sensitivity is needed for assays of bulk and surface radioactivity for some materials for some experiments, and would be highly beneficial for radon emanation.
Less Is Better: Recovering Intended-Feature Subspace to Robustify NLU Models

Datasets with significant proportions of bias present threats for training a trustworthy model on NLU tasks. Despite yielding great progress, current debiasing methods impose excessive reliance on the knowledge of bias attributes. Definition of the attributes, however, is elusive and varies across different datasets. In addition, leveraging these attributes at input level to bias mitigation may leave a gap between intrinsic properties and the underlying decision rule. To narrow down this gap and liberate the supervision on bias, we suggest extending bias mitigation into feature space. Therefore, a novel model, Recovering Intended-Feature Subspace with Knowledge-Free (RISK) is developed. Assuming that shortcut features caused by various biases are unintended for prediction, RISK views them as redundant features. When delving into a lower manifold to remove redundancies, RISK reveals that an extremely low-dimensional subspace with intended features can robustly represent the highly biased dataset. Empirical results demonstrate our model can consistently improve model generalization to out-of-distribution set, and achieves a new state-of-the-art performance.
Rejuvenation engineering in metallic glasses by stress and structure modulation

Residual stress engineering is very widely used in the design of new advanced lightweight materials. For metallic glasses the attention has been on structural changes and rejuvenation processes. High energy scanning X-ray diﬀraction strain mapping reveals large elastic ﬂuctuations in metallic glasses after deformation under triaxial compression. Transmission electron microscopy proves that structural rejuvenation under room temperature deformation relates to the shear band formation that closely correlates to the underlying distribution of elastic heterogeneities. Microindentation hardness mapping hints at an unsymmetrical hardening/softening after compression and further reveals the competing eﬀects of elastic ﬂuctuations and structure modulation. Molecular dynamics simulations provide an atomistic understanding of the correlation between shear banding and ﬂuctuations in the local strain/stress heterogeneity. Thus, future focus should be given to stress engineering and elastic heterogeneity that together with structure modulation may allow to design metallic glasses with enhanced ductility and strain hardening ability.
Filling an empty lattice by local injection of quantum particles

We study the quantum dynamics of filling an empty lattice of size $L$, by connecting it locally with an equilibrium thermal bath that injects non-interacting bosons or fermions. We adopt four different approaches, namely (i) direct exact numerics, (ii) Redfield equation, (iii) Lindblad equation, and (iv) quantum Langevin equation -- which are unique in their ways for solving the time dynamics and the steady-state. Our setup offers a simplistic platform to understand fundamental aspects of dynamics and approach to thermalization. The quantities of interest that we consider are the spatial density profile and the total number of bosons/fermions in the lattice. The spatial spread is ballistic in nature and the local occupation eventually settles down owing to equilibration. The ballistic spread of local density admits a universal scaling form. We show that this universality is only seen when the condition of detailed balance is satisfied by the baths. The difference between bosons and fermions shows up in the early time growth rate and the saturation values of the profile. The techniques developed here are applicable to systems in arbitrary dimensions and for arbitrary geometries.
PA-Boot: A Formally Verified Authentication Protocol for Multiprocessor Secure Boot

Hardware supply-chain attacks are raising significant security threats to the boot process of multiprocessor systems. This paper identifies a new, prevalent hardware supply-chain attack surface that can bypass multiprocessor secure boot due to the absence of processor-authentication mechanisms. To defend against such attacks, we present PA-Boot, the first formally verified processor-authentication protocol for secure boot in multiprocessor systems. PA-Boot is proved functionally correct and is guaranteed to detect multiple adversarial behaviors, e.g., processor replacements, man-in-the-middle attacks, and tampering with certificates. The fine-grained formalization of PA-Boot and its fully mechanized security proofs are carried out in the Isabelle/HOL theorem prover with 306 lemmas/theorems and ~7,100 LoC. Experiments on a proof-of-concept implementation indicate that PA-Boot can effectively identify boot-process attacks with a considerably minor overhead and thereby improve the security of multiprocessor systems.
Enhanced hyperalignment via spatial prior information

Functional alignment between subjects is an important assumption of functional magnetic resonance imaging (fMRI) group‐level analysis. However, it is often violated in practice, even after alignment to a standard anatomical template. Hyperalignment, based on sequential Procrustes orthogonal transformations, has been proposed as a method of aligning shared functional information into a common high‐dimensional space and thereby improving inter‐subject analysis. Though successful, current hyperalignment algorithms have a number of shortcomings, including difficulties interpreting the transformations, a lack of uniqueness of the procedure, and difficulties performing whole‐brain analysis. To resolve these issues, we propose the ProMises (Procrustes von Mises–Fisher) model. We reformulate functional alignment as a statistical model and impose a prior distribution on the orthogonal parameters (the von Mises–Fisher distribution). This allows for the embedding of anatomical information into the estimation procedure by penalizing the contribution of spatially distant voxels when creating the shared functional high‐dimensional space. Importantly, the transformations, aligned images, and related results are all unique. In addition, the proposed method allows for efficient whole‐brain functional alignment. In simulations and application to data from four fMRI studies we find that ProMises improves inter‐subject classification in terms of between‐subject accuracy and interpretability compared to standard hyperalignment algorithms.