26Al/10Be Age of Peking Man

The chronological position of Peking Man, or Homo erectus pekinensis, has long been pursued, but has remained problematic due to lack of a suitable dating method^1-7^. Here we report cosmogenic ^26^Al/ ^10^Be burial dating of quartz sediments and artifacts from the lower strata of Zhoukoudian Locality 1 where the remains of early members of the Peking Man family were discovered. This study marks the first radioisotopic dating of any early hominin site in China beyond the range of mass spectrometric U-series dating. The weighted mean of six meaningful measurements, 0.75 +/-; 0.09 (0.11) Ma (million years), provides the best age estimate for lower cultural Layers ^7-10^. Together with previously reported U-series^3^ and paleomagnetic^4^ data, as well as sedimentological considerations^8, 9^ these layers may be further correlated to S6-S7 in Chinese loess stratigraphy or marine isotope stages 17-18, in the range of ~0.68-0.75 Ma. These ages are substantially older than previously supposed and may imply hominin presence in northern China throughout early Middle Pleistocene climate cycles

High-dynamic-range Foveated Near-eye Display System

Wearable near-eye display has found widespread applications in education, gaming, entertainment, engineering, military training, and healthcare, just to name a few. However, the visual experience provided by current near-eye displays still falls short to what we can perceive in the real world. Three major challenges remain to be overcome: 1) limited dynamic range in display brightness and contrast, 2) inadequate angular resolution, and 3) vergence-accommodation conflict (VAC) issue. This dissertation is devoted to addressing these three critical issues from both display panel development and optical system design viewpoints. A high-dynamic-range (HDR) display requires both high peak brightness and excellent dark state. In the second and third chapters, two mainstream display technologies, namely liquid crystal display (LCD) and organic light emitting diode (OLED), are investigated to extend their dynamic range. On one hand, LCD can easily boost its peak brightness to over 1000 nits, but it is challenging to lower the dark state to \u3c 0.01 nits. To achieve HDR, we propose to use a mini-LED local dimming backlight. Based on our simulations and subjective experiments, we establish practical guidelines to correlate the device contrast ratio, viewing distance, and required local dimming zone number. On the other hand, self-emissive OLED display exhibits a true dark state, but boosting its peak brightness would unavoidably cause compromised lifetime. We propose a systematic approach to enhance OLED\u27s optical efficiency while keeping indistinguishable angular color shift. These findings will shed new light to guide future HDR display designs. In Chapter four, in order to improve angular resolution, we demonstrate a multi-resolution foveated display system with two display panels and an optical combiner. The first display panel provides wide field of view for peripheral vision, while the second panel offers ultra-high resolution for the central fovea. By an optical minifying system, both 4x and 5x enhanced resolutions are demonstrated. In addition, a Pancharatnam-Berry phase deflector is applied to actively shift the high-resolution region, in order to enable eye-tracking function. The proposed design effectively reduces the pixelation and screen-door effect in near-eye displays. The VAC issue in stereoscopic displays is believed to be the main cause of visual discomfort and fatigue when wearing VR headsets. In Chapter five, we propose a novel polarization-multiplexing approach to achieve multiplane display. A polarization-sensitive Pancharatnam-Berry phase lens and a spatial polarization modulator are employed to simultaneously create two independent focal planes. This method enables generation of two image planes without the need of temporal multiplexing. Therefore, it can effectively reduce the frame rate by one-half. In Chapter six, we briefly summarize our major accomplishments

Object Discovery via Cohesion Measurement

Color and intensity are two important components in an image. Usually, groups of image pixels, which are similar in color or intensity, are an informative representation for an object. They are therefore particularly suitable for computer vision tasks, such as saliency detection and object proposal generation. However, image pixels, which share a similar real-world color, may be quite different since colors are often distorted by intensity. In this paper, we reinvestigate the affinity matrices originally used in image segmentation methods based on spectral clustering. A new affinity matrix, which is robust to color distortions, is formulated for object discovery. Moreover, a Cohesion Measurement (CM) for object regions is also derived based on the formulated affinity matrix. Based on the new Cohesion Measurement, a novel object discovery method is proposed to discover objects latent in an image by utilizing the eigenvectors of the affinity matrix. Then we apply the proposed method to both saliency detection and object proposal generation. Experimental results on several evaluation benchmarks demonstrate that the proposed CM based method has achieved promising performance for these two tasks.Comment: 14 pages, 14 figure

Model Checking ofWorkflow Nets with Tables and Constraints

Many operations in workflow systems are dependent on database tables. The classical workflow net and its extensions (e.g., worflow net with data) cannot model these operations so that some related errors cannot be found by them. Recently, workflow nets with tables (WFT-nets) were proposed to remedy such a flaw. However, when the reachability graph of a WFT-net is constructed by their method, some pseudo states are possibly generated since it does not consider the guards that constrain the enabling and firing of transitions. Additionally, they only considered the soundness property that just represents a single design requirement, so that many other requirements, especially those related to tables, cannot be analyzed. In this paper, therefore, we re-define the WFT-net by augmenting constraints of guards to it and re-name it as workflow net with tables and constraints (WFTC-net). We propose a new method to generate the state reachability graphs (SRG) of WFTC-nets such that SRG can avoid pseudo states, due to the consideration of the guards in it. To represent design requirements related to database operations, we define database-oriented computation tree logic (DCTL), to represent more design requirements. We design the model checking algorithms of DCTL based on the SRG of WFTC-nets and develop a tool. Experiments on a number of public benchmarks show the usefulness of our methods

Studies on Well-Being of Urban Residents from the Perspective of Green Growth-Based on Empirical Analysis on Residents in Chengdu City of Western China

In this paper, we make empirical analysis on the well-being of urban residents and its influencing factors from the perspective of green growth by using the survey data from the studying team which made the Urban Green Growth and Public Well-Being Investigation on 19 jurisdictional counties (districts and cities) of Chengdu City in August, 2015. The results suggest that residents’ well-being has obvious regional differences, and the main city zone has the lowest green well-being while the third city cycle-layer has the highest green well-being. From the view of internal influencing factors of the resident well-being, the results show that age and income are positively correlated with the residents’ green well-being, and the former correlation is significant, while residents’ well-being of Chengdu is negatively correlated with the educational level. From the view of external influencing factors, green cover percentage, air quality and water quality are positively correlated with the residents’ green well-being, while waste treatment and amount of factory have no significant correlations with residents’ green well-being

Verifying Computation Tree Logic of Knowledge via Knowledge-Oriented Petri Nets and Ordered Binary Decision Diagrams

Computation Tree Logic of Knowledge (CTLK) can specify many requirements of privacy and security of multi-agent systems (MAS). In our previous papers, we defined Knowledge-oriented Petri Net (KPN) to model MAS, proposed similar reachability graph to verify CTLK, gave their model checking algorithms and developed a related tool. In this paper, we use the technique of Ordered Binary Decision Diagrams (OBDD) to encode similar reachability graph in order to alleviate the state explosion problem, and verify more epistemic operators of CTLK. We design the corresponding symbolic model checking algorithms and improve our tool. We compare our model and method with MCMAS that is the state-of-the-art CTLK model checker, and experiments illustrate the advantages of our model and method. We also explain the reasons why our model and method can obtain better performances

Transaction Fraud Detection via Spatial-Temporal-Aware Graph Transformer

How to obtain informative representations of transactions and then perform the identification of fraudulent transactions is a crucial part of ensuring financial security. Recent studies apply Graph Neural Networks (GNNs) to the transaction fraud detection problem. Nevertheless, they encounter challenges in effectively learning spatial-temporal information due to structural limitations. Moreover, few prior GNN-based detectors have recognized the significance of incorporating global information, which encompasses similar behavioral patterns and offers valuable insights for discriminative representation learning. Therefore, we propose a novel heterogeneous graph neural network called Spatial-Temporal-Aware Graph Transformer (STA-GT) for transaction fraud detection problems. Specifically, we design a temporal encoding strategy to capture temporal dependencies and incorporate it into the graph neural network framework, enhancing spatial-temporal information modeling and improving expressive ability. Furthermore, we introduce a transformer module to learn local and global information. Pairwise node-node interactions overcome the limitation of the GNN structure and build up the interactions with the target node and long-distance ones. Experimental results on two financial datasets compared to general GNN models and GNN-based fraud detectors demonstrate that our proposed method STA-GT is effective on the transaction fraud detection task