Miracle-dz/UniFAR-FTUs huggingface dataset:
GNNVis: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks

Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and have achieved significant progress in graph analysis tasks (e.g., node classification) in recent years. However, similar to other deep neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), GNNs behave like a black box with their details hidden from model developers and users. It is therefore difficult to diagnose possible errors of GNNs. Despite many visual analytics studies being done on CNNs and RNNs, little research has addressed the challenges for GNNs. This paper fills the research gap with an interactive visual analysis tool, GNNVis, to assist model developers and users in understanding and analyzing GNNs. Specifically, Parallel Sets View and Projection View enable users to quickly identify and validate error patterns in the set of wrong predictions; Graph View and Feature Matrix View offer a detailed analysis of individual nodes to assist users in forming hypotheses about the error patterns. Since GNNs jointly model the graph structure and the node features, we reveal the relative influences of the two types of information by comparing the predictions of three models: GNN, Multi-Layer Perceptron (MLP), and GNN Without Using Features (GNNWUF). Two case studies and interviews with domain experts demonstrate the effectiveness of GNNVis in facilitating the understanding of GNN models and their errors.
Human exposure to radiofrequency energy above 6 GHz: review of computational dosimetry studies

International guidelines/standards for human protection from electromagnetic fields have been revised recently, especially for frequencies above 6 GHz where new wireless communication systems have been deployed. Above this frequency a new physical quantity ‘absorbed/epithelial power density’ has been adopted as a dose metric. Then, the permissible level of external field strength/power density is derived for practical assessment. In addition, a new physical quantity, fluence or absorbed energy density, is introduced for protection from brief pulses (especially for shorter than 10 s). These limits were explicitly designed to avoid excessive increases in tissue temperature, based on electromagnetic and thermal modeling studies but supported by experimental data where available. This paper reviews the studies on the computational modeling/dosimetry which are related to the revision of the guidelines/standards. The comparisons with experimental data as well as an analytic solution are also been presented. Future research needs and additional comments on the revision will also be mentioned.
Semantic-Based VPS for Smartphone Localization in Challenging Urban Environments

Accurate smartphone-based outdoor localization system in deep urban canyons are increasingly needed for various IoT applications such as augmented reality, intelligent transportation, etc. The recently developed feature-based visual positioning system (VPS) by Google detects edges from smartphone images to match with pre-surveyed edges in their map database. As smart cities develop, the building information modeling (BIM) becomes widely available, which provides an opportunity for a new semantic-based VPS. This article proposes a novel 3D city model and semantic-based VPS for accurate and robust pose estimation in urban canyons where global navigation satellite system (GNSS) tends to fail. In the offline stage, a material segmented city model is used to generate segmented images. In the online stage, an image is taken with a smartphone camera that provides textual information about the surrounding environment. The approach utilizes computer vision algorithms to rectify and hand segment between the different types of material identified in the smartphone image. A semantic-based VPS method is then proposed to match the segmented generated images with the segmented smartphone image. Each generated image holds a pose that contains the latitude, longitude, altitude, yaw, pitch, and roll. The candidate with the maximum likelihood is regarded as the precise pose of the user. The positioning results achieves 2.0m level accuracy in common high rise along street, 5.5m in foliage dense environment and 15.7m in alleyway. A 45% positioning improvement to current state-of-the-art method. The estimation of yaw achieves 2.3{\deg} level accuracy, 8 times the improvement to smartphone IMU.
Linearly Implicit Multistep Methods for Time Integration

Time integration methods for solving initial value problems are an important component of many scientific and engineering simulations. Implicit time integrators are desirable for their stability properties, significantly relaxing restrictions on timestep size. However, implicit methods require solutions to one or more systems of nonlinear equations at each timestep, which for large simulations can be prohibitively expensive. This paper introduces a new family of linearly implicit multistep methods (LIMM), which only requires the solution of one linear system per timestep. Order conditions and stability theory for these methods are presented, as well as design and implementation considerations. Practical methods of order up to five are developed that have similar error coefficients, but improved stability regions, when compared to the widely used BDF methods. Numerical testing of a self-starting variable stepsize and variable order implementation of the new LIMM methods shows measurable performance improvement over a similar BDF implementation.
Maximum performance of strange-jet tagging at hadron colliders

The maximum achievable performance of strange-jet tagging at hadron colliders and the loss in performance in different detector designs is estimated based on simulated truth jets from strange-quark and down-quark hadronisation. Both jet types are classified with a recurrent neural network using long short-term memory units, at first using all available truth particles and then applying selections to study the impacts of ideal tracking detectors, Cherenkov detectors, and calorimeters. Additionally, a manual reconstruction of strange hadron decays such as KS → π+π- from charged tracks is considered.
Deep learning model trained on mobile phone-acquired frozen section images effectively detects basal cell carcinoma

Background: Margin assessment of basal cell carcinoma using the frozen section is a common task of pathology intraoperative consultation. Although frequently straight-forward, the determination of the presence or absence of basal cell carcinoma on the tissue sections can sometimes be challenging. We explore if a deep learning model trained on mobile phone-acquired frozen section images can have adequate performance for future deployment. Materials and Methods: One thousand two hundred and forty-one (1241) images of frozen sections performed for basal cell carcinoma margin status were acquired using mobile phones. The photos were taken at 100x magnification (10x objective). The images were downscaled from a 4032 x 3024 pixel resolution to 576 x 432 pixel resolution. Semantic segmentation algorithm Deeplab V3 with Xception backbone was used for model training. Results: The model uses an image as input and produces a 2-dimensional black and white output of prediction of the same dimension; the areas determined to be basal cell carcinoma were displayed with white color, in a black background. Any output with the number of white pixels exceeding 0.5% of the total number of pixels is deemed positive for basal cell carcinoma. On the test set, the model achieves area under curve of 0.99 for receiver operator curve and 0.97 for precision-recall curve at the pixel level. The accuracy of classification at the slide level is 96%. Conclusions: The deep learning model trained with mobile phone images shows satisfactory performance characteristics, and thus demonstrates the potential for deploying as a mobile phone app to assist in frozen section interpretation in real time.
Locally Linear Embedding and its Variants: Tutorial and Survey

This is a tutorial and survey paper for Locally Linear Embedding (LLE) and its variants. The idea of LLE is fitting the local structure of manifold in the embedding space. In this paper, we first cover LLE, kernel LLE, inverse LLE, and feature fusion with LLE. Then, we cover out-of-sample embedding using linear reconstruction, eigenfunctions, and kernel mapping. Incremental LLE is explained for embedding streaming data. Landmark LLE methods using the Nystrom approximation and locally linear landmarks are explained for big data embedding. We introduce the methods for parameter selection of number of neighbors using residual variance, Procrustes statistics, preservation neighborhood error, and local neighborhood selection. Afterwards, Supervised LLE (SLLE), enhanced SLLE, SLLE projection, probabilistic SLLE, supervised guided LLE (using Hilbert-Schmidt independence criterion), and semi-supervised LLE are explained for supervised and semi-supervised embedding. Robust LLE methods using least squares problem and penalty functions are also introduced for embedding in the presence of outliers and noise. Then, we introduce fusion of LLE with other manifold learning methods including Isomap (i.e., ISOLLE), principal component analysis, Fisher discriminant analysis, discriminant LLE, and Isotop. Finally, we explain weighted LLE in which the distances, reconstruction weights, or the embeddings are adjusted for better embedding; we cover weighted LLE for deformed distributed data, weighted LLE using probability of occurrence, SLLE by adjusting weights, modified LLE, and iterative LLE.
Analysis and Evaluation of Baseline Manipulation in Demand Response Programs

The customer baseline is required to assign rebates to participants in baseline-based demand response (DR) programs. The average baseline method has been widely accepted in practice due to its simplicity and reliability. However, the customer's baseline manipulation is little-known in the literature. We start from a customer's perspective and establish a Markov decision process to model the customer's payoff-maximizing problem. The behavior of a rational customer's underconsumption on DR days and overconsumption on non-DR days are revealed. Furthermore, we propose an approximated baseline method and show how the consumption distribution and program parameters affect the results. Due to the curse of dimensionality, a linear policy-based rollout algorithm is introduced to obtain a practical approximate solution. Finally, a case study is carried out to illustrate the baseline manipulation, where the simulation results confirm the effectiveness of the proposed methods and shed light on how to properly design baseline methods.
Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games

This paper introduces an information-theoretic constraint on learned policy complexity in the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithm. Previous research with a related approach in continuous control experiments suggests that this method favors learning policies that are more robust to changing environment dynamics. The multi-agent game setting naturally requires this type of robustness, as other agents' policies change throughout learning, introducing a nonstationary environment. For this reason, recent methods in continual learning are compared to our approach, termed Capacity-Limited MADDPG. Results from experimentation in multi-agent cooperative and competitive tasks demonstrate that the capacity-limited approach is a good candidate for improving learning performance in these environments.
Near-Optimal Data Source Selection for Bayesian Learning

We study a fundamental problem in Bayesian learning, where the goal is to select a set of data sources with minimum cost while achieving a certain learning performance based on the data streams provided by the selected data sources. First, we show that the data source selection problem for Bayesian learning is NP-hard. We then show that the data source selection problem can be transformed into an instance of the submodular set covering problem studied in the literature, and provide a standard greedy algorithm to solve the data source selection problem with provable performance guarantees. Next, we propose a fast greedy algorithm that improves the running times of the standard greedy algorithm, while achieving performance guarantees that are comparable to those of the standard greedy algorithm. We provide insights into the performance guarantees of the greedy algorithms by analyzing special classes of the problem. Finally, we validate the theoretical results using numerical examples, and show that the greedy algorithms work well in practice.
Percolative charge transport in binary nanocrystal solids

We simulated electron transport across a binary nanocrystal solid (BNS) of PbSe NCs with diameters of 6.5nm and 5.1nm. We used our Hierarchical Nanoparticle Transport Simulator HINTS to model the transport in these BNSs. The mobility exhibits a minimum at a Large-NC-fraction f_LNC=0.25. The mobility minimum is deep at T=80K and partially smoothed at T=300K. We explain this minimum as follows. As the LNC fraction f_LNC starts growing from zero, the few LNCs act as traps for the electrons traversing the BNS because their relevant energy level is lower. Therefore, increasing the f_LNC concentration of these traps decreases the mobility. As increasing f_LNC reaches the percolation threshold f_LNC=f_p, the LNCs form sample-spanning networks that enable electrons to traverse the entire BNS via these percolating LNC networks. Transport through the growing percolating LNC networks drives the rapid growth of the mobility as f_LNC grows past f_p. Therefore, the electron mobility exhibits a pronounced minimum as a function of f_LNC, centered at f_LNC=f_p. The position of the mobility minimum shifts to larger LNC fractions as the electron density increases. We have studied the trends of this mobility minimum with temperature, electron density, charging energy, ligand length, and disorder. We account for the trends by a "renormalized trap model", in which capturing an electron renormalizes a deep LNC trap into a shallow trap or a kinetic obstacle, depending on the charging energy. We verified this physical picture by constructing and analyzing heat maps of the mobile electrons in the BNS.
Possible ∼0.4 h X-ray quasi-periodicity from an ultrasoft active galactic nucleus

RX J1301.9+2747 is an ultrasoft active galactic nucleus (AGN) with unusual X-ray variability that is characterized by a long quiescent state and a short-lived flare state. The X-ray flares are found to recur quasi-periodically on a timescale of 13−20 ks. Here, we report the analysis of the light curve in the quiescent state from two XMM-Newton observations spanning 18.5 years, along with the discovery of a possible quasi-periodic X-ray oscillation (QPO) with a period of ∼1500 s. The QPO is detected at the same frequency in the two independent observations, with a combined significance of > 99.89%. The QPO is in agreement with the relation between frequency and black hole mass (MBH) that has been reported in previous works for AGNs and Galactic black hole X-ray binaries (XRBs). The QPO frequency is stable over almost two decades, suggesting that it may correspond to the high-frequency type found in XRBs and originates, perhaps, from a certain disk resonance mode. In the 3:2 twin-frequency resonance model, our best estimate on the MBH range implies that a maximal black hole spin can be ruled out. We find that all ultrasoft AGNs reported so far display quasi-periodicities in the X-ray emission, suggesting a possible link on the part of the extreme variability phenomenon to the ultrasoft X-ray component. This indicates that ultrasoft AGNs could be the most promising candidates in future searches for X-ray periodicities.
On-Chip Error-Triggered Learning of Multi-Layer Memristive Spiking Neural Networks

Recent breakthroughs in neuromorphic computing show that local forms of gradient descent learning are compatible with Spiking Neural Networks (SNNs) and synaptic plasticity. Although SNNs can be scalably implemented using neuromorphic VLSI, an architecture that can learn using gradient-descent in situ is still missing. In this paper, we propose a local, gradient-based, error-triggered learning algorithm with online ternary weight updates. The proposed algorithm enables online training of multi-layer SNNs with memristive neuromorphic hardware showing a small loss in the performance compared with the state-of-the-art. We also propose a hardware architecture based on memristive crossbar arrays to perform the required vector-matrix multiplications. The necessary peripheral circuitry including presynaptic, post-synaptic and write circuits required for online training, have been designed in the subthreshold regime for power saving with a standard 180 nm CMOS process.
Dexmedetomidine inhibits the PSD95-NMDA receptor interaction to promote functional recovery following traumatic brain injury

The present study examined the effects of dexmedetomidine (Dex) on cognitive and motor recovery in mice following traumatic brain injury (TBI). TBI induces synaptic damage, which leads to motor dysfunction and cognitive decline. Although Dex is known to induce neuroprotection, its role following TBI remains unknown. In the present study, male C57BL/6 mice (8 weeks old; n=72) were subjected to cortical impact injury to generate a TBI mice model. Mice were divided into four groups: TBI, sham, TBI + vehicle, and TBI + Dex. Mice in the TBI + vehicle and TBI + Dex groups received intraperitoneal injections of saline (n=18) and 100 µg/kg Dex (n=18), respectively, at 1 and 12 h following surgery. At 24 h post-injury, 10 animals from each group were sacrificed, and brain tissue was isolated for Fluoro-Jade B staining and RNA and protein extraction. At 72 h post-TBI, motor function was evaluated. Furthermore, cognitive impairment was assessed between day 14 and 19 using the Morris water maze. The results demonstrated that the mRNA and protein expression of post-synaptic density 95 (PSD95) was reduced post-TBI. In addition, neuronal degeneration was evaluated using FJB staining, where PSD95 formed a complex with the N-methyl-D-aspartic acid (NMDA) receptor subunit (NR2B) and neuronal nitric oxide synthase (nNOS) inducing neuronal death post-TBI. Treatment with Dex efficiently decreased the PSD95-NR2B-nNOS interaction, which reduced the TBI-induced neuronal death. Furthermore, Dex treatment contributed to the enhanced cognitive and motor recovery following TBI. The results from the present study reported a potential mechanistic action of Dex treatment post-TBI, which may be associated with the inhibition of PSD95-NMDA interaction.
Multitask Learning for Human Settlement Extent Regression and Local Climate Zone Classification

Human settlement extent (HSE) and local climate zone (LCZ) maps are both essential sources, e.g., for sustainable urban development and Urban Heat Island (UHI) studies. Remote sensing (RS)- and deep learning (DL)-based classification approaches play a significant role by providing the potential for global mapping. However, most of the efforts only focus on one of the two schemes, usually on a specific scale. This leads to unnecessary redundancies since the learned features could be leveraged for both of these related tasks. In this letter, the concept of multitask learning (MTL) is introduced to HSE regression and LCZ classification for the first time. We propose an MTL framework and develop an end-to-end convolutional neural network (CNN), which consists of a backbone network for shared feature learning, attention modules for task-specific feature learning, and a weighting strategy for balancing the two tasks. We additionally propose to exploit HSE predictions as a prior for LCZ classification to enhance the accuracy. The MTL approach was extensively tested with Sentinel-2 data of 13 cities across the world. The results demonstrate that the framework is able to provide a competitive solution for both tasks.
Substructures in the core of Abell 2319

We analysed the deep archival Chandra observations of the high-temperature galaxy cluster Abell~2319 to investigate the prominent cold front in its core. The main sharp arc of the front shows wiggles, or variations of the radius of the density jump along the arc. At the southern end of the arc is a feature that resembles a Kelvin-Helmholtz (KH) eddy, beyond which the sharp front dissolves. These features suggest that KH instabilities develop at the front. Under this assumption, we can place an upper limit on the ICM viscosity that is several times below the isotropic Spitzer value. Other features include a split of the cold front at its northern edge, which may be another KH eddy. There is a small pocket of hot, less-dense gas inside the cold front, which may indicate a `hole' in the front's magnetic insulation layer that lets the heat from the outer gas to penetrate inside the front. Finally, a large concave brightness feature southwest of the cluster core can be caused by the gasdynamic instabilities. We speculate that it can also be an inner boundary of a giant AGN bubble, similar to that in Ophiuchus. If the latter interpretation is supported by better radio data, this could be a remnant of another extremely powerful AGN outburst.
Magnetization of the intergalactic medium in the IllustrisTNG simulations: the importance of extended, outflow-driven bubbles

We study the effects of galaxy formation physics on the magnetization of the intergalactic medium (IGM) using the IllustrisTNG simulations. We demonstrate that large-scale regions affected by the outflows from galaxies and clusters contain magnetic fields that are several orders of magnitude stronger than in unaffected regions with the same electron density. Moreover, like magnetic fields amplified inside galaxies, these magnetic fields do not depend on the primordial seed, i.e. the adopted initial conditions for magnetic field strength. We study the volume filling fraction of these strong field regions and their occurrence in random lines of sight. As a first application, we use these results to put bounds on the photon–axion conversion from spectral distortion of the CMB. As photon–axion coupling grows with energy, stronger constraints could potentially be obtained using data on the propagation of gamma-ray photons through the IGM. Finally, we also briefly discuss potential applications of our results to the Faraday Rotation measurements.
Nonconformality, subregion complexity, and meson binding

We study holographically the zero and finite temperature behavior of the potential energy and holographic subregion complexity corresponding to a probe meson in a non-conformal model. Interestingly, in the specific regime of the model parameters, at zero and low temperatures, we find a nicely linear relation between dimensionless meson potential energy and dimensionless volume implying that the less bounded meson state needs less information to be specified and vice versa. But this behavior does not confirmed in the high temperature limit. We also observe that the non-conformality corrections increases holographic subregion complexity in both zero and finite temperature. However, non-conformality has a decreasing effect on the dimensionless meson potential energy. We finally find that in the vicinity of the phase transition, the zero temperature meson state is more favourable than the finite temperature state, from the holographic subregion complexity point of view.