119 research outputs found
Impact of Inorganically Bound Sulfur on Late Shale Gas Generation
Nonisothermal,
confined pyrolysis was applied to a mature shale
sample from the Ordovician Salgan Formation in Tarim Basin, northwest
China. Experiments were conducted using gold-tubes with added water
at a very slow heating rate (2 °C/h) and end temperatures between
336 and 600 °C. To investigate the influence of inorganically
bound sulfur on the generation of gases and to consider the geological
occurrence of sulfur-containing minerals, such as prevalent pyrite
in shales, the experiments were carried out with and without admixtures
of MgSO<sub>4</sub>, CaSO<sub>4</sub>, and pyrite. High amounts of
methane along with lower amounts of wet gases were formed from highly
mature shale without minerals added, demonstrating a huge late gas
generation potential at post peak-oil window maturities. In the experiments
with added sulfates and pyrite, all organic gases were consumed in
varying proportions, resulting in different chemical and stable carbon
isotopic compositions. Pyrite treatment affects wet gas (C<sub>2</sub>–C<sub>5</sub>) evolution directly, but it affects methane
(C<sub>1</sub>) evolution indirectly. In contrast, sulfate treatments
affect C<sub>1</sub>–C<sub>5</sub> evolution directly. The
cumulative yield ratio of CO<sub>2</sub>/H<sub>2</sub>S indicates
that pyrite impacts on the hydrocarbon gas generation through low
valence sulfur such as S<sup>0</sup> or others, which are associated
with H<sub>2</sub>S generation. In the pyrite series, the smooth increase
in ethane yield at temperatures exceeding 504 °C, together with
a concomitant stable carbon isotope reversal, demonstrates a new origin
for ethane at high temperatures. The isotopic reversal may come from
reactions between water and solid kerogen/coke/pyrobitumen. Isotopic
reversal of ethane occurs only in the control and pyrite series but
not in the sulfate treatments. This provides evidence that anoxic
conditions are required. Thus, one can expect to encounter isotopic
reversals in high maturity, unconventional gas shale environments
in the presence of pyrite
Gen2Det: Generate to Detect
Recently diffusion models have shown improvement in synthetic image quality
as well as better control in generation. We motivate and present Gen2Det, a
simple modular pipeline to create synthetic training data for object detection
for free by leveraging state-of-the-art grounded image generation methods.
Unlike existing works which generate individual object instances, require
identifying foreground followed by pasting on other images, we simplify to
directly generating scene-centric images. In addition to the synthetic data,
Gen2Det also proposes a suite of techniques to best utilize the generated data,
including image-level filtering, instance-level filtering, and better training
recipe to account for imperfections in the generation. Using Gen2Det, we show
healthy improvements on object detection and segmentation tasks under various
settings and agnostic to detection methods. In the long-tailed detection
setting on LVIS, Gen2Det improves the performance on rare categories by a large
margin while also significantly improving the performance on other categories,
e.g. we see an improvement of 2.13 Box AP and 1.84 Mask AP over just training
on real data on LVIS with Mask R-CNN. In the low-data regime setting on COCO,
Gen2Det consistently improves both Box and Mask AP by 2.27 and 1.85 points. In
the most general detection setting, Gen2Det still demonstrates robust
performance gains, e.g. it improves the Box and Mask AP on COCO by 0.45 and
0.32 points
Exploring Open-Vocabulary Semantic Segmentation without Human Labels
Semantic segmentation is a crucial task in computer vision that involves
segmenting images into semantically meaningful regions at the pixel level.
However, existing approaches often rely on expensive human annotations as
supervision for model training, limiting their scalability to large, unlabeled
datasets. To address this challenge, we present ZeroSeg, a novel method that
leverages the existing pretrained vision-language (VL) model (e.g. CLIP) to
train open-vocabulary zero-shot semantic segmentation models. Although acquired
extensive knowledge of visual concepts, it is non-trivial to exploit knowledge
from these VL models to the task of semantic segmentation, as they are usually
trained at an image level. ZeroSeg overcomes this by distilling the visual
concepts learned by VL models into a set of segment tokens, each summarizing a
localized region of the target image. We evaluate ZeroSeg on multiple popular
segmentation benchmarks, including PASCAL VOC 2012, PASCAL Context, and COCO,
in a zero-shot manner (i.e., no training or adaption on target segmentation
datasets). Our approach achieves state-of-the-art performance when compared to
other zero-shot segmentation methods under the same training data, while also
performing competitively compared to strongly supervised methods. Finally, we
also demonstrated the effectiveness of ZeroSeg on open-vocabulary segmentation,
through both human studies and qualitative visualizations
EgoObjects: A Large-Scale Egocentric Dataset for Fine-Grained Object Understanding
Object understanding in egocentric visual data is arguably a fundamental
research topic in egocentric vision. However, existing object datasets are
either non-egocentric or have limitations in object categories, visual content,
and annotation granularities. In this work, we introduce EgoObjects, a
large-scale egocentric dataset for fine-grained object understanding. Its Pilot
version contains over 9K videos collected by 250 participants from 50+
countries using 4 wearable devices, and over 650K object annotations from 368
object categories. Unlike prior datasets containing only object category
labels, EgoObjects also annotates each object with an instance-level
identifier, and includes over 14K unique object instances. EgoObjects was
designed to capture the same object under diverse background complexities,
surrounding objects, distance, lighting and camera motion. In parallel to the
data collection, we conducted data annotation by developing a multi-stage
federated annotation process to accommodate the growing nature of the dataset.
To bootstrap the research on EgoObjects, we present a suite of 4 benchmark
tasks around the egocentric object understanding, including a novel instance
level- and the classical category level object detection. Moreover, we also
introduce 2 novel continual learning object detection tasks. The dataset and
API are available at https://github.com/facebookresearch/EgoObjects.Comment: ICCV 2023 final version and supplement. See more details in project
page: https://github.com/facebookresearch/EgoObject
Optimal response to tislelizumab plus chemotherapy in metastatic triple-negative breast cancer: a case report and literature review
Metastatic triple-negative breast cancer (mTNBC) has the worst prognosis among breast cancer subtypes. Immune checkpoint inhibitors (ICIs) plus chemotherapy have promising survival benefits. Herein, we report a 51-year-old woman whose metastatic lesions were diagnosed as triple-negative subtype and who received tislelizumab plus eribulin treatment and achieved excellent efficacy. To our knowledge, this study is the first attempt to present tislelizumab in combination with eribulin for mTNBC treatment. New treatments resulting in prolonged survival and durable clinical responses would benefit mTNBC patients. Then, we summarize the possible influencing factors of the interaction between tislelizumab and eribulin
Influence of paste thickness on the coated aggregates on properties of high-density sulphoaluminate cement concrete
An improved method for the densified mixture design algorithm and Fuller curve were used to design high-density sulphoaluminate cement concrete (HDSC). The performance of HDSC is significantly influenced by the paste thickness on the coated aggregates. Sulphoaluminate cement concrete mixtures containing aggregates coated with 3 different paste thickness of t=10μm, 20μm, and 30μm and water-binder ratios (W/B) of 0.25, 0.30 and 0.35 were prepared. The results of experiments show that paste thickness on the coated aggregates significantly influences the mechanical properties and durability of HDSC. With the increase of paste thickness, the compressive strength is increased, but the electrical resistivity is decreased, particularly at the early ages of 1 and 3 days. The sulfate corrosion resistance coefficients of HDSC are larger than 1.0, the total porosity can be less than 7%, and the micropore (i.e. with pore size less than 20nm) can be larger than 70%
A Radiomics Approach Based on Follow-Up CT for Pathological Subtypes Classification of Pulmonary Ground Glass Nodules
Preoperative, non-invasive, and accurate identification of the pathological subtypes of pulmonary ground glass nodules (GGNs) play an important role in the precise selection of clinical surgical operations and individualized treatment plans. Efforts have been made for the classification of pathological subtypes of GGNs, but most existing methods focus on benign or malignant diagnosis of GGNs by means of a one-time computed tomography image (CTI), which fails to capture the nodule development based on follow-up CTI. In this paper, a novel method for subtype classification based on follow-up CTIs is presented as a viable option to the existing one-time CTI-based approach. A total of 383 follow-up CTIs with GGNs from 146 patients was collected and retrospectively labeled via posterior surgical pathology. Feature extraction is performed individually to the follow-up CTIs. The extracted feature differences were represented as a vector, which was then used to construct a set of vectors for all the patients. Finally, a subspace K-nearest neighbor classifier was built to predict the pathological subtypes of GGNs. Experimental validation confirmed the efficacy of the new method over the existing method. Results showed that the accuracy of the new method could reach 72.5%, while the existing methods had an upper bound of 67.5% accuracy. Subsequent three-category comparison experiments were also performed to demonstrate that the new method could increase the accuracy up to 21.33% compared to the existing methods that use one-time CTI
A novel subsurface adjustable dam for preventing active seawater intrusion in coastal aquifers
Subsurface physical barriers are widely used to prevent seawater intrusion in the world. After the construction of physical barriers, the residual saltwater is trapped upstream the barriers. Traditional physical barriers, including cutoff walls and subsurface dams, are fixed in structure and fail in prohibiting active seawater intrusion. In this work, a novel subsurface adjustable dam, composed of dam bodies and sluice gates, was designed to prevent active seawater intrusion and store groundwater flexibly according to seasonal variations in precipitation. We set three-dimensional field-scale numerical simulations to compare the control effects of adjustable dams, cutoff walls, and subsurface dams. The results revealed that the traditional subsurface physical barriers could mitigate the velocity of active seawater intrusion but were inadequate in completely preventing the intrusion process. Furthermore, although the traditional physical barriers temporarily alleviate the residual saltwater during the wet periods, the saltwater wedge would subsequently invade during next dry periods. Thus, the salt mass in the aquifer of traditional physical barriers scenarios exhibited a gradual annual increase. In contrast, the novel subsurface adjustable dam demonstrated the ability to prevent active seawater intrusion and remove the residual saltwater. During the dry periods, characterized by low precipitation recharge, the sluice gates were closed to obstruct the path of active seawater intrusion. Conversely, during the wet periods with abundant precipitation, the sluice gates were opened, facilitating the gradual removal of the residual saltwater. The flexible adjustment mechanism of subsurface adjustable dams resulted in a annual decrease in both the seawater intrusion length and the salt mass in the entire aquifer. These findings underscore the efficacy of the subsurface adjustable dam as a measure for preventing active seawater intrusion and simultaneously eliminating the residual saltwater
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