Search

Depositional environments in areas located near sea level are vulnerable to even subtle changes in both tectonism, climate and eustatic sea level. Sorting out mechanisms behind depositional changes in inner shelf-margins and epeiric seas is therefore far from straight forward. The epeiric Malay-Tho Chu Basin, located in the shallow Gulf of Thailand offshore Vietnam, contains a thick uppermost Oligocene to Recent post-rift succession. Based on 18000 km of 2D seismic profiles, the uppermost Oligocene-Recent succession is subdivided into six seismic sequences. Seismic facies mapping of each sequence complemented by information from industry completion reports of 14 wells document a gradual increase in marine influence from latest Oligocene through Middle Miocene time. The increased marine influence reflects the propagation and opening of the southwestern spreading arm of the East Vietnam Sea (South China Sea (East Sea)) combined with rapid subsidence in the Malay-Tho Chu Basin resulting from fast thermal relaxation following Oligocene rifting and amplified by mild fault-controlled subsidence in especially the western part of the area. From the latest Middle Miocene, slowed subsidence and regression occurred in association with uplift in the nearby southwestern East Vietnam Sea. From the latest Miocene, and during the Pliocene and Pleistocene, subsidence rates increased promoting marine conditions towards the East Vietnam Sea in the southeast. Meanwhile, Mekong River avulsion combined with hinterland uplift stimulated fluvial and alluvial deposition in the northern and western parts of the basin. The long-term depositional pattern was controlled primarily by tectonics, drainage evolution and climate. Meanwhile, flood- and delta plain deposits intercalated with stacked fluvial incisions, often filled by estuarine deposits, document short-lived environmental changes most likely controlled by eustatic sea level fluctuations. Such intervals developed during latest Middle Mi

Depositional environments in areas located near sea level are vulnerable to even subtle changes in both tectonism, climate and eustatic sea level. Sorting out mechanisms behind depositional changes in inner shelf-margins and epeiric seas is therefore far from straight forward. The epeiric Malay-Tho Chu Basin, located in the shallow Gulf of Thailand offshore Vietnam, contains a thick uppermost Oligocene to Recent post-rift succession. Based on 18000 km of 2D seismic profiles, the uppermost Oligocene-Recent succession is subdivided into six seismic sequences. Seismic facies mapping of each sequence complemented by information from industry completion reports of 14 wells document a gradual increase in marine influence from latest Oligocene through Middle Miocene time. The increased marine influence reflects the propagation and opening of the southwestern spreading arm of the East Vietnam Sea (South China Sea (East Sea)) combined with rapid subsidence in the Malay-Tho Chu Basin resulting from fast thermal relaxation following Oligocene rifting and amplified by mild fault-controlled subsidence in especially the western part of the area. From the latest Middle Miocene, slowed subsidence and regression occurred in association with uplift in the nearby southwestern East Vietnam Sea. From the latest Miocene, and during the Pliocene and Pleistocene, subsidence rates increased promoting marine conditions towards the East Vietnam Sea in the southeast. Meanwhile, Mekong River avulsion combined with hinterland uplift stimulated fluvial and alluvial deposition in the northern and western parts of the basin. The long-term depositional pattern was controlled primarily by tectonics, drainage evolution and climate. Meanwhile, flood- and delta plain deposits intercalated with stacked fluvial incisions, often filled by estuarine deposits, document short-lived environmental changes most likely controlled by eustatic sea level fluctuations. Such intervals developed during latest Middle Mi

Understanding whether a property is priced fairly hinders buyers and sellers since they usually do not have an objective viewpoint of the price distribution for the overall market of their interest. Drawing from data collected of all possible available properties for rent in Manhattan as of September 2023, this paper aims to strengthen our understanding of model residuals; specifically on machine learning models which generalize for a majority of the distribution of a well-proportioned dataset. Most models generally perceive deviations from predicted values as mere inaccuracies, however this paper proposes a different vantage point: when generalizing to at least 75\% of the data-set, the remaining deviations reveal significant insights. To harness these insights, we introduce the Price Anomaly Score (PAS), a metric capable of capturing boundaries between irregularly predicted prices. By combining relative pricing discrepancies with statistical significance, the Price Anomaly Score (PAS) offers a multifaceted view of rental valuations. This metric allows experts to identify overpriced or underpriced properties within a dataset by aggregating PAS values, then fine-tuning upper and lower boundaries to any threshold to set indicators of choice., Comment: 8 pages, 8 figures, dataset is available with DOI

Image augmentation techniques have been widely investigated to improve the performance of deep learning (DL) algorithms on mammography classification tasks. Recent methods have proved the efficiency of image augmentation on data deficiency or data imbalance issues. In this paper, we propose a novel transparency strategy to boost the Breast Imaging Reporting and Data System (BI-RADS) scores of mammogram classifiers. The proposed approach utilizes the Region of Interest (ROI) information to generate more high-risk training examples for breast cancer (BI-RADS 3, 4, 5) from original images. Our extensive experiments on three different datasets show that the proposed approach significantly improves the mammogram classification performance and surpasses a state-of-the-art data augmentation technique called CutMix. This study also highlights that our transparency method is more effective than other augmentation strategies for BI-RADS classification and can be widely applied to other computer vision tasks., Comment: Accepted for presentation at the 22nd IEEE Statistical Signal Processing (SSP) workshop

Advanced deep learning (DL) algorithms may predict the patient's risk of developing breast cancer based on the Breast Imaging Reporting and Data System (BI-RADS) and density standards. Recent studies have suggested that the combination of multi-view analysis improved the overall breast exam classification. In this paper, we propose a novel multi-view DL approach for BI-RADS and density assessment of mammograms. The proposed approach first deploys deep convolutional networks for feature extraction on each view separately. The extracted features are then stacked and fed into a Light Gradient Boosting Machine (LightGBM) classifier to predict BI-RADS and density scores. We conduct extensive experiments on both the internal mammography dataset and the public dataset Digital Database for Screening Mammography (DDSM). The experimental results demonstrate that the proposed approach outperforms the single-view classification approach on two benchmark datasets by huge F1-score margins (+5% on the internal dataset and +10% on the DDSM dataset). These results highlight the vital role of combining multi-view information to improve the performance of breast cancer risk prediction., Comment: This paper has been accepted by the 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2022 IEEE EMBC)

The paper describes the organisation of the "HateSpeech Detection" (HSD) task at the VLSP workshop 2019 on detecting the fine-grained presence of hate speech in Vietnamese textual items (i.e., messages) extracted from Facebook, which is the most popular social network site (SNS) in Vietnam. The task is organised as a multi-class classification task and based on a large-scale dataset containing 25,431 Vietnamese textual items from Facebook. The task participants were challenged to build a classification model that is capable of classifying an item to one of 3 classes, i.e., "HATE", "OFFENSIVE" and "CLEAN". HSD attracted a large number of participants and was a popular task at VLSP 2019. In particular, there were 71 teams signed up for the task, 14 of them submitted results with 380 valid submissions from 20th September 2019 to 4th October 2019.

A dynamic partial order reduction (DPOR) algorithm is optimal when it always explores at most one representative per Mazurkiewicz trace. Existing literature suggests that the reduction obtained by the non-optimal, state-of-the-art Source-DPOR (SDPOR) algorithm is comparable to optimal DPOR. We show the first program with $\mathop{\mathcal{O}}(n)$ Mazurkiewicz traces where SDPOR explores $\mathop{\mathcal{O}}(2^n)$ redundant schedules and identify the cause of the blow-up as an NP-hard problem. Our main contribution is a new approach, called Quasi-Optimal POR, that can arbitrarily approximate an optimal exploration using a provided constant k. We present an implementation of our method in a new tool called Dpu using specialised data structures. Experiments with Dpu, including Debian packages, show that optimality is achieved with low values of k, outperforming state-of-the-art tools., Comment: Minor corrections after review for publication