Cross-Modal Learning with 3D Deformable Attention for Action Recognition

An important challenge in vision-based action recognition is the embedding of spatiotemporal features with two or more heterogeneous modalities into a single feature. In this study, we propose a new 3D deformable transformer for action recognition with adaptive spatiotemporal receptive fields and a cross-modal learning scheme. The 3D deformable transformer consists of three attention modules: 3D deformability, local joint stride, and temporal stride attention. The two cross-modal tokens are input into the 3D deformable attention module to create a cross-attention token with a reflected spatiotemporal correlation. Local joint stride attention is applied to spatially combine attention and pose tokens. Temporal stride attention temporally reduces the number of input tokens in the attention module and supports temporal expression learning without the simultaneous use of all tokens. The deformable transformer iterates L times and combines the last cross-modal token for classification. The proposed 3D deformable transformer was tested on the NTU60, NTU120, FineGYM, and Penn Action datasets, and showed results better than or similar to pre-trained state-of-the-art methods even without a pre-training process. In addition, by visualizing important joints and correlations during action recognition through spatial joint and temporal stride attention, the possibility of achieving an explainable potential for action recognition is presented.Comment: 10 pages, 8 figure

Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning

Machine learning, particularly classification algorithms, constructs mathematical models from labeled data that can predict labels for new data. Using its capability to identify distinguishing patterns among multi-dimensional data, we investigated the impact of three factors on the observation of architectural scenes: Individuality, education, and image stimuli. An analysis of the eye-tracking data revealed that (1) a velocity histogram was unique to individuals, (2) students of architecture and other disciplines could be distinguished via endogenous parameters, but (3) they were more distinct in terms of seeking structural versus symbolic elements. Because of the reverse nature of the classification algorithms that automatically learn from data, we could identify relevant parameters and distinguishing eye-tracking patterns that have not been reported in previous studies

A SIMPLE METHOD FOR MEASURING SYSTEMIC RISK USING CREDIT DEFAULT SWAP MARKET DATA

This paper proposes a simple method that employs credit default swap (CDS) data for analyzing systemic risk. The proposed method overcomes inconsistency problems in existing methods and can produce various indicators of systemic risk in a consistent manner. In addition, this method can measure systemic risk contributions. In particular, the method measures systemic risk contributions in both directions, that is, the overall effect of systemic risk on individual credit risks and vice versa. Using CDS data, we employ the proposed method to measure systemic risk for a group of large financial institutions in the U.S. In addition, we provide empirical results for systemic risk contributions as well as various measures of the overall level of systemic risk and verify the applicability of the proposed method

Development of a Resource Allocation Model Using Competitive Advantage

In general, during decision making or negotiations, the investor and the investee may often have different opinions which result in conflicts. So, an objective standard to mitigate potential conflicts between investors and investees should be provided since it is highly important that rational decisions must be made when choosing investments from various options. However, the models currently used come with some problems for several reasons, for instance, the arbitrariness of the evaluator, the difficulty in understanding the relationships that exist among the various investment options (that is, alternatives to investments), inconsistency in priorities, and simply providing selection criteria without detailing the proportion of investment in each option or evaluating only a single investment option at a time without considering all options. Thus, in this research, we present a project selection model which can enable reasonable resource allocation or determination of return rates by considering the core competencies for various investment options. Here, core competency is based on both performance and ability to create a competitive advantage. For this, we deduce issue-specific structural power indicators and analyze quantitatively the resource allocation results based on negotiation power. Through this, it is possible to examine whether the proposed project selection model considers core competencies or not by comparing several project selection models currently used. Furthermore, the proposed model can be used on its own, or in combination with other methods. Consequently, the presented model can be used as a quantitative criterion for determining behavioral tactics, and also can be used to mitigate potential conflicts between the investor and the investee who are considering idiosyncratic investments, determined by an interplay between power and core competency

Geochemical Composition, Source and Geothermometry of Thermal Water in the Bugok Area, South Korea

Thermal water from the hot springs around Bugok, South Korea, has the highest discharge temperature (78 °C), and the source of that heat is of primary interest. The key 3He/4He ratio runs along a single air-mixing line between the mantle and the crust, with the latter accounting for 97.0–97.3%. This suggests that the thermal source is radioactive decay in granodiorite, rock that intruded beneath the Cetaceous era sedimentary rock. Thermal water containing Na–HCO3 (SO4) evolved geochemically from stream water and groundwater containing Ca–HCO3. With respect to δ34S, there are two types of thermal water: low temperature with low δ34S (−3.00~+1.00‰), and high temperature with high δ34S (+4.60~+15.0‰), which is enriched by the kinetic fractionation of H2S. The thermal water samples, except for a few, reached partial chemical equilibrium. The thermal reservoir temperatures were estimated as in the range of 90–126 °C by the K–Mg geothermometer of Giggenbach and the thermodynamic equilibrium of quartz and muscovite. This study suggests a conceptual model for the formation of geothermal water, including the thermal reservoir in the Bugok area

Effects of Experimental Parameters on the Extraction of Silica and Carbonation of Blast Furnace Slag at Atmospheric Pressure in Low-Concentration Acetic Acid

Blast furnace slag (BFS), a calcium-rich industrial byproduct, has been utilized since 2005 as a mineral carbonation feedstock for CO2 sequestration, producing calcium carbonate precipitates. In this study, the conditions for the dissolution of Ca and Si in acetic acid, and subsequent carbonation, were elaborated. For this purpose, the retardation of the polymerization of silicon was attempted by varying the concentration of acetic acid, temperature, and leaching time. An inductively coupled plasma (ICP) analysis revealed that both the Ca and Si dissolved completely within 30 min in 5% acetic acid at room temperature. This high dissolution value can be attributed to the fact that Ca was bound to O rather than to Si, as determined by X-ray photoelectron spectroscopy (XPS). The use of CO2-absorbed monoethanolamine enabled the complete carbonation of BFS at ambient conditions without the need for a pH swing. The presence of dissolved silica was found to affect the polymorphs of the precipitated CaCO3. We believe that this process offers a simple method for manipulating the composites of products obtained by mineral carbonation diminishing the leaching residues

Preparation of Silica-Alumina Nanoparticles via Blast-Furnace Slag Dissolution in Low-Concentration Acetic Acid for Carbonation

Blast-furnace slag (BFS) has been used as a feedstock for CO2 sequestration by indirect mineral carbonation to produce calcium carbonate precipitates and solid residues. The most-abundant elements in these residues, Si and Al, are usually considered to be impurities that need to be removed in acid-dissolution processes involving BFS. The co-production of value-added materials from these residues is an attractive option for strengthening the economic competitiveness of mineral carbonation methods. In view of this, we separated the Si and Al, as their hydrated forms, during the dissolution of BFS in acetic acid prior to carbonation. During the sol-gel processing of Si-Al nanoparticles, a catalyst is usually required during the hydrolysis and subsequent condensation processes. In this study, only condensation occurs because the low-concentrations of acetic acid used facilitate in-situ hydrolysis during the dissolution process. Aging was carried out not only to structurally arrange the Si and Al but also to oxidize the marginal Fe(II) to reddish Fe(III). Silica-alumina nanoparticles (78% Si and 22% Al) were prepared by a simple sol-gel route at ambient pressure. These nanoparticles were amorphous and below 20 nm in size. Fourier-transform infrared (FT-IR) studies reveal that the nanoparticles consist of Si–O–Si and Si–O–Al bonds. 27Al nuclear magnetic resonance (NMR) spectroscopy reveals a significant resonance corresponding to tetra-coordinated Al inside the particle framework