Miracle-dz/UniFAR-FTUs huggingface dataset:
Electron fluxes and pitch‐angle distributions at dipolarization fronts: THEMIS multipoint observations

Taking advantage of multipoint observations from a Cluster‐like Time History of Events and Macroscale Interactions during Substorms (THEMIS) probe configuration repeated in three events, we study pitch‐angle distributions (PAD) of lower energy (0.2–keV) electrons and omnidirectional energy‐time spectrograms of higher energy (30–500 keV) electrons observed at and near dipolarization fronts in the plasma sheet. Recent observations have shown that dipolarization fronts in the plasma sheet provide an impulsive electric field suggested to cause electron energization and dispersionless injections. Increase and decrease in energetic electron flux are equally probable at the fronts, however. Our case studies demonstrate increased energetic electron flux in the front's central region but decreased flux on its dusk side, where diverted plasma flow forms a vortex. An electric field associated with this vortex causes the electron flux decrease. We also find that shorter‐term energetic flux decreases, often observed before injections, coincide with a dip in the northward magnetic field ahead of the front. We attribute these decreases to particle energy loss via the inverse betatron effect. Our case studies reveal that pancake‐type (maximum at 90° pitch angle) and cigar‐type (maxima at 0 and 180°) PADs coexist at the same front. Our data analysis suggests that energetic electron PADs are mainly pancake type near the neutral sheet (|Bx| < 5 nt) and mainly cigar type at |Bx| > 10 nt. These results, to be confirmed in statistical studies, provide important constraints for further modeling of electron energization and transport toward the inner magnetosphere.
Effects of aged garlic and ginkgo biloba extracts on the pharmacokinetics of sofosbuvir in rats

Sofosbuvir is a direct acting antiviral (DAA) approved for the treatment of hepatitis C virus (HCV). Sofosbuvir is a substrate of P‐glycoprotein (P‐gp). For this reason, inhibitors, or inducers of intestinal P‐gp may alter the plasma concentration of sofosbuvir and increase or decrease its efficacy causing a significant change in its pharmacokinetic parameters. The purpose of the study was to evaluate the pharmacokinetic interaction between either aged garlic or ginkgo biloba extracts with sofosbuvir through targeting P‐gp as well as possible toxicities in rats. Rats were divided into four groups and treated for 14 days with saline, verapamil (15 mg/kg, PO), aged garlic extract (120 mg/kg, PO), or ginkgo biloba extract (25 mg/kg, PO) followed by a single oral dose of sofosbuvir (40 mg/kg). Validated LC‐MS/MS was used to determine sofosbuvir and its metabolite GS‐331007 in rat plasma. Aged garlic extract caused a significant decrease of sofosbuvir AUC(0−t) by 36%, while ginkgo biloba extract caused a significant increase of sofosbuvir AUC(0−t) by 11%. Ginkgo biloba extract exhibited a significant increase of the sofosbuvir t1/2 by 60%, while aged garlic extract significantly increased the clearance of sofosbuvir by 63%. The pharmacokinetic parameters of GS‐331007 were not affected. The inhibitory action of ginkgo biloba on P‐gp and the subsequent increase in the sofosbuvir plasma concentration did not show a significant risk of renal or hepatic toxicity. Conversely, although aged garlic extracts increased intestinal P‐gp expression, they did not alter the Cmax and Tmax of sofosbuvir and did not induce significant hepatic or renal toxicities.
Responses of Wild Finescale Dace (Phoxinus neogaeus) to Experimental Spills of Cold Lake Blend Diluted Bitumen at the International Institute for Sustainable Development‐Experimental Lakes Area, Northwestern Ontario

Pipelines carrying diluted bitumen (dilbit) traverse North America and may result in dilbit release into sensitive freshwater ecosystems. To better understand the potential effects of a freshwater oil release, the Boreal‐lake Oil Release Experiment by Additions to Limnocorrals project at the International Institute for Sustainable Development‐Experimental Lakes Area (Ontario, Canada) modeled seven dilbit spills contained within a 10‐m diameter of littoral limnocorrals in a boreal lake. Wild finescale dace (Phoxinus neogaeus) were released in the limnocorrals 21 days after oil addition and remained there for 70 days. Dilbit volumes covered a large range representing a regression of real spill sizes and total polycyclic aromatic compounds (TPAC) between 167 ng L−1 day−1 and 1989 ng L−1 day−1. We report the effects of chronic exposure on reproductive potential as well as physiological responses in the gallbladder and liver. In exposures >1000 ng L−1 day−1, there was a significant decrease in fish retrieval, culminating in zero recapture from the three highest treatments. Among the fish from the limnocorrals with lower levels of TPAC (<500 ng L−1 day−1), effects were inconsistent. Gallbladder bile fluorescence for a naphthalene metabolite was significantly different in fish from the oil‐exposed limnocorrals when compared to the lake and reference corral, indicating that fish in these lower exposures were interacting with dilbit‐derived polycyclic aromatic compounds. There were no significant differences in condition factor, somatic indices, or hepatocyte volume indices. There were also no significant changes in the development of testes or ovaries of exposed dace. The results from the present study may serve to orient policymakers and emergency responders to the range of TPAC exposures that may not significantly affect wild fish. Environ Toxicol Chem 2022;41:2745–2757. © 2022 SETAC
Fenretinide combines perturbation of signaling kinases, cell–extracellular matrix interactions and matrix metalloproteinase activation to inhibit invasion in oral squamous cell carcinoma cells

Abstract Basement membrane invasion defines malignant transformation of surface premalignancy. Treatment of oral squamous cell carcinoma (OSCC) cells with the synthetic vitamin A derivative, fenretinide (4HPR), induces numerous cancer-preventive effects including suppression of basement membrane invasion, elimination of anchorage-independent growth, disruption of actin cytoskeletal components and inhibition of the invasion-enabling focal adhesive kinase. The purpose of this study was to elucidate 4HPR’s effects on additional invasion-relevant mechanisms including matrix metalloproteinase (MMP) activation and function, cell–extracellular matrix (ECM) attachments and interaction with a kinase that is essential for the epithelial–myoepithelial transformation i.e. c-Jun NH2-terminal kinase (JNK). Our data revealed that 4HPR binds with high affinity to the ATP-binding site of all three JNK isoforms with concurrent suppression of kinase function. Additional studies showed 4HPR treatment inhibited both OSCC cell–ECM adhesion and MMP activation and function. JNK downregulation and induced expression studies confirmed that the JNK3 isoform conveyed that largest impact on OSCC migration and invasion. Biodegradable polymeric implants formulated to preserve 4HPR’s function and bioavailability were employed to assess 4HPR’s chemopreventive impact on an OSCC tumor induction model. These studies revealed 4HPR local delivery significantly inhibited OSCC tumor size, mitotic indices and expression of the endothelial marker, erythroblast transformation-specific-related gene with concurrent increases in tumor apoptosis (cleaved caspase-3). Collectively, these data show that 4HPR suppresses invasion at multiple sites including ‘outside-in’ signaling, cell–ECM interactions and suppression of MMPs. These functions are also essential for physiologic function. Regulation is therefore essential and reinforces the pharmacologic advantage of local delivery chemopreventive formulations.
Bayesian Optimization of Hyperparameters from Noisy Marginal Likelihood Estimates

Bayesian models often involve a small set of hyperparameters determined by maximizing the marginal likelihood. Bayesian optimization is a popular iterative method where a Gaussian process posterior of the underlying function is sequentially updated by new function evaluations. An acquisition strategy uses this posterior distribution to decide where to place the next function evaluation. We propose a novel Bayesian optimization framework for situations where the user controls the computational effort, and therefore the precision of the function evaluations. This is a common situation in econometrics where the marginal likelihood is often computed by Markov chain Monte Carlo (MCMC) or importance sampling methods, with the precision of the marginal likelihood estimator determined by the number of samples. The new acquisition strategy gives the optimizer the option to explore the function with cheap noisy evaluations and therefore find the optimum faster. The method is applied to estimating the prior hyperparameters in two popular models on US macroeconomic time series data: the steady-state Bayesian vector autoregressive (BVAR) and the time-varying parameter BVAR with stochastic volatility. The proposed method is shown to find the optimum much quicker than traditional Bayesian optimization or grid search.
Intersecting psi-classes on tropical Hassett spaces

We study the intersection of tropical psi-classes on tropical heavy/light Hassett spaces, generalising a result of Kerber--Markwig for tropical moduli spaces of rational stable curves with distinct marked points. Our computation reveals that the weight of a maximal cone in an intersection has a combinatorial intepretation in terms of the underlying tropical curve and it is always nonnegative. In particular, our result specialises to that, in top dimension, the tropical intersection product coincides with its classical counterpart.
Multimodal foundation models are better simulators of the human brain

Multimodal learning, especially large-scale multimodal pre-training, has developed rapidly over the past few years and led to the greatest advances in artificial intelligence (AI). Despite its effectiveness, understanding the underlying mechanism of multimodal pre-training models still remains a grand challenge. Revealing the explainability of such models is likely to enable breakthroughs of novel learning paradigms in the AI field. To this end, given the multimodal nature of the human brain, we propose to explore the explainability of multimodal learning models with the aid of non-invasive brain imaging technologies such as functional magnetic resonance imaging (fMRI). Concretely, we first present a newly-designed multimodal foundation model pre-trained on 15 million image-text pairs, which has shown strong multimodal understanding and generalization abilities in a variety of cognitive downstream tasks. Further, from the perspective of neural encoding (based on our foundation model), we find that both visual and lingual encoders trained multimodally are more brain-like compared with unimodal ones. Particularly, we identify a number of brain regions where multimodally-trained encoders demonstrate better neural encoding performance. This is consistent with the findings in existing studies on exploring brain multi-sensory integration. Therefore, we believe that multimodal foundation models are more suitable tools for neuroscientists to study the multimodal signal processing mechanisms in the human brain. Our findings also demonstrate the potential of multimodal foundation models as ideal computational simulators to promote both AI-for-brain and brain-for-AI research.
AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets

This paper studies the Binary Neural Networks (BNNs) in which weights and activations are both binarized into 1-bit values, thus greatly reducing the memory usage and computational complexity. Since the modern deep neural networks are of sophisticated design with complex architecture for the accuracy reason, the diversity on distributions of weights and activations is very high. Therefore, the conventional sign function cannot be well used for effectively binarizing full-precision values in BNNs. To this end, we present a simple yet effective approach called AdaBin to adaptively obtain the optimal binary sets $\{b_1, b_2\}$ ($b_1, b_2\in \mathbb{R}$) of weights and activations for each layer instead of a fixed set (\textit{i.e.}, $\{-1, +1\}$). In this way, the proposed method can better fit different distributions and increase the representation ability of binarized features. In practice, we use the center position and distance of 1-bit values to define a new binary quantization function. For the weights, we propose an equalization method to align the symmetrical center of binary distribution to real-valued distribution, and minimize the Kullback-Leibler divergence of them. Meanwhile, we introduce a gradient-based optimization method to get these two parameters for activations, which are jointly trained in an end-to-end manner. Experimental results on benchmark models and datasets demonstrate that the proposed AdaBin is able to achieve state-of-the-art performance. For instance, we obtain a 66.4% Top-1 accuracy on the ImageNet using ResNet-18 architecture, and a 69.4 mAP on PASCAL VOC using SSD300. The PyTorch code is available at \url{https://github.com/huawei-noah/Efficient-Computing/tree/master/BinaryNetworks/AdaBin} and the MindSpore code is available at \url{https://gitee.com/mindspore/models/tree/master/research/cv/AdaBin}.
Impact of convective activity in the western tropical Pacific on the East Asian summer circulation

A statistical study is made of the relationship between the Northern Hemisphere atmospheric circulation and tropical convective activity during summer on intraseasonal time scales. An emphasis is placed on differences in the tropical-midlatitude interaction over East Asia between two 50-day periods: May 26-July 14 and July 15-September 2. Each period corresponds to the East Asian rainy season or the East Asian mid-summer season, respectively. Barotropic instability of climatological monthly mean 300mb stream functions is also investigated to interpret the observational results. It is found from correlative analysis that the tropical convection is not strongly related to the midlatitude flow over East Asia during the rainy season, and there are no definite indications that the tropical convection forces the midlatitude flow. On the other hand, during the mid-summer season convective activity around the Philippines and the Indo-China Peninsula has a large impact on the East Asian atmospheric circulation. In particular, geopotential height over the Yellow Sea is most strongly affected by the tropical convection. Active convection in the western tropical Pacific is accompanied by a wave train with zonal wavenumber 6 over the North Pacific, which originates from the western tropical Pacific. The location of the wave train does not depend strongly on the location of the convective activity. One of the major barotropic unstable modes for the climatological 300mb flow for August resembles the wave train observed over the North Pacific during the mid-summer season. It behaves like a standing wave, and has a large amplitude around the Yellow Sea. The e-folding time of this mode is 11 days when dissipation is ignored. Barotropic unstable modes for June do not have large amplitudes over East Asia. These results are consistent with the above observational results and suggest that barotropic instability plays an important role in the tropical-midlatitude interaction over East Asia during the mid-summer season.
LAMA-Net: Unsupervised Domain Adaptation via Latent Alignment and Manifold Learning for RUL Prediction

Prognostics and Health Management (PHM) is an emerging field which has received much attention from the manufacturing industry because of the benefits and efficiencies it brings to the table. And Remaining Useful Life (RUL) prediction is at the heart of any PHM system. Most recent data-driven research demand substantial volumes of labelled training data before a performant model can be trained under the supervised learning paradigm. This is where Transfer Learning (TL) and Domain Adaptation (DA) methods step in and make it possible for us to generalize a supervised model to other domains with different data distributions with no labelled data. In this paper, we propose \textit{LAMA-Net}, an encoder-decoder based model (Transformer) with an induced bottleneck, Latent Alignment using Maximum Mean Discrepancy (MMD) and manifold learning is proposed to tackle the problem of Unsupervised Homogeneous Domain Adaptation for RUL prediction. \textit{LAMA-Net} is validated using the C-MAPSS Turbofan Engine dataset by NASA and compared against other state-of-the-art techniques for DA. The results suggest that the proposed method offers a promising approach to perform domain adaptation in RUL prediction. Code will be made available once the paper comes out of review.
Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning

—Exploration of unknown environments is a fundamental problem in robotics and an essential component in numerous applications of autonomous systems. A major challenge in exploring unknown environments is that the robot has to plan with the limited information available at each time step. While most current approaches rely on heuristics and assumption to plan paths based on these partial observations, we instead propose a novel way to integrate deep learning into exploration by leveraging 3D scene completion for informed, safe, and interpretable exploration mapping and planning. Our approach, SC-Explorer , combines scene completion using a novel incremental fusion mechanism and a newly proposed hierarchical multi-layer mapping approach, to guarantee safety and efﬁciency of the robot. We further present an informative path planning method, leveraging the capabilities of our mapping approach and a novel scene-completion-aware information gain. While our method is generally applicable, we evaluate it in the use case of a Micro Aerial Vehicle (MAV). We thoroughly study each component in high-ﬁdelity simulation experiments using only mobile hardware, and show that our method can speed up coverage of an environment by 73% compared to the baselines with only minimal reduction in map accuracy. Even if scene completions are not included in the ﬁnal map, we show that they can be used to guide the robot to choose more informative paths, speeding up the measurement of the scene with the robot’s sensors by 35%. We make our methods available as open-source.
Brain Topography Adaptive Network for Satisfaction Modeling in Interactive Information Access System

With the growth of information on the Web, most users heavily rely on information access systems (e.g., search engines, recommender systems, etc.) in their daily lives. During this procedure, modeling users' satisfaction status plays an essential part in improving their experiences with the systems. In this paper, we aim to explore the benefits of using Electroencephalography (EEG) signals for satisfaction modeling in interactive information access system design. Different from existing EEG classification tasks, the arisen of satisfaction involves multiple brain functions, such as arousal, prototypicality, and appraisals, which are related to different brain topographical areas. Thus modeling user satisfaction raises great challenges to existing solutions. To address this challenge, we propose BTA, a Brain Topography Adaptive network with a multi-centrality encoding module and a spatial attention mechanism module to capture cognitive connectives in different spatial distances. We explore the effectiveness of BTA for satisfaction modeling in two popular information access scenarios, i.e., search and recommendation. Extensive experiments on two real-world datasets verify the effectiveness of introducing brain topography adaptive strategy in satisfaction modeling. Furthermore, we also conduct search result re-ranking task and video rating prediction task based on the satisfaction inferred from brain signals on search and recommendation scenarios, respectively. Experimental results show that brain signals extracted with BTA help improve the performance of interactive information access systems significantly.
Interference Cancellation GAN Framework for Dynamic Channels

Symbol detection is a fundamental and challenging problem in modern communication systems, e.g., multiuser multiple-input multiple-output (MIMO) setting. Iterative Soft Interference Cancellation (SIC) is a state-of-the-art method for this task and recently motivated data-driven neural network models, e.g. DeepSIC, that can deal with unknown non-linear channels. However, these neural network models require thorough timeconsuming training of the networks before applying, and is thus not readily suitable for highly dynamic channels in practice. We introduce an online training framework that can swiftly adapt to any changes in the channel. Our proposed framework unifies the recent deep unfolding approaches with the emerging generative adversarial networks (GANs) to capture any changes in the channel and quickly adjust the networks to maintain the top performance of the model. We demonstrate that our framework significantly outperforms recent neural network models on highly dynamic channels and even surpasses those on the static channel in our experiments.
A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search

Textual adversarial attacks expose the vulnerabilities of text classifiers and can be used to improve their robustness. Existing context-aware methods solely consider the gold label probability and use the greedy search when searching an attack path, often limiting the attack efficiency. To tackle these issues, we propose PDBS, a context-aware textual adversarial attack model using Probability Difference guided Beam Search. The probability difference is an overall consideration of all class label probabilities, and PDBS uses it to guide the selection of attack paths. In addition, PDBS uses the beam search to find a successful attack path, thus avoiding suffering from limited search space. Extensive experiments and human evaluation demonstrate that PDBS outperforms previous best models in a series of evaluation metrics, especially bringing up to a +19.5% attack success rate. Ablation studies and qualitative analyses further confirm the efficiency of PDBS.
Rigidity results for Riemann solitons

In this paper we take a look at conditions that make a Riemann soliton trivial, compacity being one of them. We also show that the behaviour at inﬁnity of the gradient ﬁeld of a non-compact gradient Riemann soliton might cause the soliton to be an Einstein manifold. Finally, we obtain scalar curvature estimates for complete shrinking or steady gradient Riemann solitons whose scalar curvature is bounded from below at inﬁnity.
A Mid-infrared Flare in the Seyfert Galaxy NGC 3786: A Changing-look Event Triggered by an Obscured Tidal Disruption Event?

We report an exceptional mid-infrared flare in the Seyfert 1.8 NGC 3786. In the multiepoch data from the Wide-field Infrared Survey Explorer, the nuclear mid-infrared brightness of NGC 3786 appears to vary substantially up to 0.5–0.8 mag around mid-2020. However, there is no evidence of significant variation in the corresponding light curve of the optical band from the Zwicky Transient Facility. This implies that the flare may have been heavily obscured by nuclear dust. Through follow-up spectroscopic observations with Gemini-North after the flare, we find that broad emission lines in Paα and Paβ newly appear, while the broad Hβ emission is marginally detected in the postflare spectrum. In addition, their central wavelengths are systematically redshifted up to 900 km s−1 with respect to the narrow emission lines. This reveals that the flare is associated with the changing-look phenomenon from type 1.8 to type 1. Based on these findings, we argue that the flare is likely to originate from an obscured tidal disruption event, although extreme variation in the accretion rate may not be ruled out completely.