Towards Quantifying the Impact of Triaxiality on Optical Signatures of Galaxy Clusters: Weak Lensing and Galaxy Distributions

We present observational evidence of the impact of triaxiality on radial profiles that extend to 40~Mpc from galaxy cluster centres in optical measurements. We perform a stacked profile analysis from a sample of thousands of nearly relaxed galaxy clusters from public data releases of the Dark Energy Survey (DES) and the Dark Energy Camera Legacy Survey (DECaLS). Using the central galaxy elliptical orientation angle as a proxy for galaxy cluster orientation, we measure cluster weak lensing and excess galaxy density axis-aligned profiles, extracted along the central galaxy's major or minor axes on the plane-of-the-sky. Our measurements show a $\gtrsim2-3\sigma$ difference per radial bin between the normalized axis-aligned profiles. The profile difference between each axis-aligned profile and the azimuthally averaged profile ($\sim\pm10-20\%$ along major/minor axis) appears inside the clusters ($\sim0.4$ Mpc) and extends to the large-scale structure regime ($\sim10-20$ Mpc). The magnitude of the difference appears to be relatively insensitive to cluster richness and redshift, and extends further out in the weak lensing surface mass density than in the galaxy overdensity. Looking forward, this measurement can easily be applied to other observational or simulation datasets and can inform the systematics in cluster mass modeling related to triaxiality. We expect imminent upcoming wide-area deep surveys, such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), to improve our quantification of optical signatures of cluster triaxiality.Comment: Submitted to MNRAS, minor differences because of recent comments, comments are welcome and appreciate

Structured electrode additive manufacturing for lithium-ion batteries

A thick electrode with high areal capacity has been developed as a strategy for high-energy-density lithium-ion batteries, but thick electrodes have difficulties in manufacturing and limitations in ion transport. Here, we reported a new manufacturing approach for ultra-thick electrode with aligned structure, called structure electrode additive manufacturing or SEAM, which aligns active materials to the through-thicknesses direction of electrodes using shear flow and a designed printing path. The ultra-thick electrodes with high loading of active materials, low tortuous structure, and good structure stability resulting from a simple and scalable SEAM lead to rapid ion transport and fast electrolyte infusion, delivering a higher areal capacity than slurry-casted thick electrodes. SEAM shows strengths in design flexibility and scalability, which allows the production of practical high energy/power density structure electrodes

An Empirical Study on the Language Modal in Visual Question Answering

Generalization beyond in-domain experience to out-of-distribution data is of paramount significance in the AI domain. Of late, state-of-the-art Visual Question Answering (VQA) models have shown impressive performance on in-domain data, partially due to the language priors bias which, however, hinders the generalization ability in practice. This paper attempts to provide new insights into the influence of language modality on VQA performance from an empirical study perspective. To achieve this, we conducted a series of experiments on six models. The results of these experiments revealed that, 1) apart from prior bias caused by question types, there is a notable influence of postfix-related bias in inducing biases, and 2) training VQA models with word-sequence-related variant questions demonstrated improved performance on the out-of-distribution benchmark, and the LXMERT even achieved a 10-point gain without adopting any debiasing methods. We delved into the underlying reasons behind these experimental results and put forward some simple proposals to reduce the models' dependency on language priors. The experimental results demonstrated the effectiveness of our proposed method in improving performance on the out-of-distribution benchmark, VQA-CPv2. We hope this study can inspire novel insights for future research on designing bias-reduction approaches.Comment: Accepted by IJCAI202

Seismic anisotropy and shear wave splitting associated with mantle plume-plate interaction

Geodynamic simulations of the development of lattice preferred orientation in the flowing mantle are used to characterize the seismic anisotropy and shear wave splitting (SWS) patterns expected for the interaction of mantle plumes and lithospheric plates. Models predict that in the deeper part of the plume layer ponding beneath the plate, olivine a axes tend to align perpendicular to the radially directed plume flow, forming a circular pattern reflecting circumferential stretching. In the shallower part of the plume layer, plate shear is more important and the a axes tend toward the direction of plate motion. Predicted SWS over intraplate plumes reflects the asymmetric influence of plate shear with fast S wave polarization directions forming a pattern of nested U shapes that open in the direction opposing both plate motion and the parabolic shape often used to describe the flow lines of the plume. Predictions explain SWS observations around the Eifel hot spot with an eastward, not westward, moving Eurasian plate, consistent with global studies that require relatively slow net (westward) rotation of all of the plates. SWS at the Hawaiian hot spot can be explained by the effects of plume-plate interaction, combined with fossil anisotropy in the Pacific lithosphere. In ridge-centered plume models, the fast polarization directions angle diagonally toward the ridge axis when the plume is simulated as having low viscosity beneath the thermal lithosphere. Such a model better explains SWS observations in northeast Iceland than a model that incorporates a high-viscosity layer due to dehydration of the shallow-most upper mantle

Governance Policy Evaluation in the United States during the Pandemic: Nonpharmaceutical Interventions or Else?

Scientific evidence suggests that nonpharmaceutical interventions (NPIs) effectively curb the spread of COVID-19 before a pharmaceutical solution. Implementing these interventions also significantly affects regular socioeconomic activities and practices of social, racial, and political justice. Local governments often face conflicting goals during policymaking. Striking a balance among competing goals during a global pandemic is a fine science of governance. How well state governments consume the scientific evidence and maintain such a balance remains less understood. This study employs a set of Bayesian hierarchical models to evaluate how state governments in the United States use scientific evidence to balance the fighting against the spread of COVID-19 disease and socioeconomic, racial, social justice, and other demands. We modeled the relationships between five NPI strategies and COVID-19 caseload information and used the modeled result to perform a balanced governance evaluation. The results suggest that governmental attitude and guidance effectively guide the public to fight back against a global pandemic. The more detailed spatiotemporally varying coefficient process model produces 612,000 spatiotemporally varying coefficients, suggesting all measures sometimes work somewhere. Summarized results indicate that states emphasizing NPIs fared well in curbing the spread of COVID-19. With over 1 million deaths due to COVID-19 in the United States, we feel the balance scale likely needs to tip toward preserving human lives. Our evaluation of governance policies is hence based on such an argument. This study aims to provide decision support for policymaking during a national emergency

STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in sentiment analysis research, aiming to extract triplets of the aspect term, its corresponding opinion term, and its associated sentiment polarity from a given sentence. Recently, many neural networks based models with different tagging schemes have been proposed, but almost all of them have their limitations: heavily relying on 1) prior assumption that each word is only associated with a single role (e.g., aspect term, or opinion term, etc. ) and 2) word-level interactions and treating each opinion/aspect as a set of independent words. Hence, they perform poorly on the complex ASTE task, such as a word associated with multiple roles or an aspect/opinion term with multiple words. Hence, we propose a novel approach, Span TAgging and Greedy infErence (STAGE), to extract sentiment triplets in span-level, where each span may consist of multiple words and play different roles simultaneously. To this end, this paper formulates the ASTE task as a multi-class span classification problem. Specifically, STAGE generates more accurate aspect sentiment triplet extractions via exploring span-level information and constraints, which consists of two components, namely, span tagging scheme and greedy inference strategy. The former tag all possible candidate spans based on a newly-defined tagging set. The latter retrieves the aspect/opinion term with the maximum length from the candidate sentiment snippet to output sentiment triplets. Furthermore, we propose a simple but effective model based on the STAGE, which outperforms the state-of-the-arts by a large margin on four widely-used datasets. Moreover, our STAGE can be easily generalized to other pair/triplet extraction tasks, which also demonstrates the superiority of the proposed scheme STAGE.Comment: Accepted by AAAI 202

Equivalence of Discrete Fracture Network and Porous Media Models by Hydraulic Tomography

Hydraulic tomography (HT) has emerged as a potentially viable method for mapping fractures in geologic media as demonstrated by recent studies. However, most of the studies adopted equivalent porous media (EPM) models to generate and invert hydraulic interference test data for HT. While these models assign significant different hydraulic properties to fractures and matrix, they may not fully capture the discrete nature of the fractures in the rocks. As a result, HT performance may have been overrated. To explore this issue, this study employed a discrete fracture network (DFN) model to simulate hydraulic interference tests. HT with the EPM model was then applied to estimate the distributions of hydraulic conductivity (K) and specific storage (S-s) of the DFN. Afterward, the estimated fields were used to predict the observed heads from DFN models, not used in the HT analysis (i.e., validation). Additionally, this study defined the spatial representative elementary volume (REV) of the fracture connectivity probability for the entire DFN dominant. The study showed that if this spatial REV exists, the DFN is deemed equivalent to EPM and vice versa. The hydraulic properties estimated by HT with an EPM model can then predict head fields satisfactorily over the entire DFN domain with limited monitoring wells. For a sparse DFN without this spatial REV, a dense observation network is needed. Nevertheless, HT is able to capture the dominant fractures.National Science and Technology Major Project of China [2017ZX05008-003-021]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDB10030601]; Youth Innovation Promotion Association of the Chinese Academy of Sciences [2016063]; US Civilain Research and Development Foundation (CRDF) under the award: Hydraulic tomography in shallow alluvial sediments: Nile River Valley, Egypt [DAA2-15-61224-1]; Global Expert award through Tianjin Normal University from the Thousand Talents Plan of Tianjin City6 month embargo; published online: 23 April 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]