150 research outputs found
WordSup: Exploiting Word Annotations for Character based Text Detection
Imagery texts are usually organized as a hierarchy of several visual
elements, i.e. characters, words, text lines and text blocks. Among these
elements, character is the most basic one for various languages such as
Western, Chinese, Japanese, mathematical expression and etc. It is natural and
convenient to construct a common text detection engine based on character
detectors. However, training character detectors requires a vast of location
annotated characters, which are expensive to obtain. Actually, the existing
real text datasets are mostly annotated in word or line level. To remedy this
dilemma, we propose a weakly supervised framework that can utilize word
annotations, either in tight quadrangles or the more loose bounding boxes, for
character detector training. When applied in scene text detection, we are thus
able to train a robust character detector by exploiting word annotations in the
rich large-scale real scene text datasets, e.g. ICDAR15 and COCO-text. The
character detector acts as a key role in the pipeline of our text detection
engine. It achieves the state-of-the-art performance on several challenging
scene text detection benchmarks. We also demonstrate the flexibility of our
pipeline by various scenarios, including deformed text detection and math
expression recognition.Comment: 2017 International Conference on Computer Visio
Optimization Study of Combination Energy-Saving Measure for Mechanical Oil Production Well
In this paper, Fibonacci optimization-searching method and Golden section method are applied for optimization of combination energy-saving measure. And technology evaluating for several main energy-saving equipment and combination installations is made. The optimal energy-saving installation is determined by using the method established
Automatic Recognition and Classification of Future Work Sentences from Academic Articles in a Specific Domain
Future work sentences (FWS) are the particular sentences in academic papers
that contain the author's description of their proposed follow-up research
direction. This paper presents methods to automatically extract FWS from
academic papers and classify them according to the different future directions
embodied in the paper's content. FWS recognition methods will enable subsequent
researchers to locate future work sentences more accurately and quickly and
reduce the time and cost of acquiring the corpus. The current work on automatic
identification of future work sentences is relatively small, and the existing
research cannot accurately identify FWS from academic papers, and thus cannot
conduct data mining on a large scale. Furthermore, there are many aspects to
the content of future work, and the subdivision of the content is conducive to
the analysis of specific development directions. In this paper, Nature Language
Processing (NLP) is used as a case study, and FWS are extracted from academic
papers and classified into different types. We manually build an annotated
corpus with six different types of FWS. Then, automatic recognition and
classification of FWS are implemented using machine learning models, and the
performance of these models is compared based on the evaluation metrics. The
results show that the Bernoulli Bayesian model has the best performance in the
automatic recognition task, with the Macro F1 reaching 90.73%, and the SCIBERT
model has the best performance in the automatic classification task, with the
weighted average F1 reaching 72.63%. Finally, we extract keywords from FWS and
gain a deep understanding of the key content described in FWS, and we also
demonstrate that content determination in FWS will be reflected in the
subsequent research work by measuring the similarity between future work
sentences and the abstracts
Absolute frequency measurements with a robust, transportable ^{40}Ca^{+} optical clock
We constructed a transportable 40Ca+ optical clock (with an estimated minimum
systematic shift uncertainty of 1.3*10^(-17) and a stability of
5*10^(-15)/sqrt{tau} ) that can operate outside the laboratory. We transported
it from the Innovation Academy for Precision Measurement Science and
Technology, Chinese Academy of Sciences, Wuhan to the National Institute of
Metrology, Beijing. The absolute frequency of the 729 nm clock transition was
measured for up to 35 days by tracing its frequency to the second of
International System of Units. Some improvements were implemented in the
measurement process, such as the increased effective up-time of 91.3 % of the
40Ca+ optical clock over a 35-day-period, the reduced statistical uncertainty
of the comparison between the optical clock and hydrogen maser, and the use of
longer measurement times to reduce the uncertainty of the frequency
traceability link. The absolute frequency measurement of the 40Ca+ optical
clock yielded a value of 411042129776400.26 (13) Hz with an uncertainty of
3.2*10^(-16), which is reduced by a factor of 1.7 compared with our previous
results. As a result of the increase in the operating rate of the optical
clock, the accuracy of 35 days of absolute frequency measurement can be
comparable to the best results of different institutions in the world based on
different optical frequency measurements.Comment: 15 pages, 5 figure
Recognize Anything: A Strong Image Tagging Model
We present the Recognize Anything Model (RAM): a strong foundation model for
image tagging. RAM can recognize any common category with high accuracy. RAM
introduces a new paradigm for image tagging, leveraging large-scale image-text
pairs for training instead of manual annotations. The development of RAM
comprises four key steps. Firstly, annotation-free image tags are obtained at
scale through automatic text semantic parsing. Subsequently, a preliminary
model is trained for automatic annotation by unifying the caption and tagging
tasks, supervised by the original texts and parsed tags, respectively. Thirdly,
a data engine is employed to generate additional annotations and clean
incorrect ones. Lastly, the model is retrained with the processed data and
fine-tuned using a smaller but higher-quality dataset. We evaluate the tagging
capabilities of RAM on numerous benchmarks and observe impressive zero-shot
performance, significantly outperforming CLIP and BLIP. Remarkably, RAM even
surpasses the fully supervised manners and exhibits competitive performance
with the Google API. We are releasing the RAM at
\url{https://recognize-anything.github.io/} to foster the advancements of large
models in computer vision
Willing to pay more for high-quality schools?: A hedonic pricing and propensity score matching approach
Principally, the enrolment of elementary school in China is solely based on residential location. Due to the scarcity of prestigious schools, the household registration (or hukou in Chinese) and the territorial-based school admission policy, a feasible approach for parents to provide the children with good education is to purchase the house in the attendance zone of a high-quality school (or xuequfang in Chinese). The supply-demand imbalance gives rise to the xuequfang phenomenon (much higher prices of xuequfang relative to non xuequfang). Based on 1250 housing samples in 286 multi- or high-storey residential districts, this paper firstly develops two basic and four Box-Cox transformed hedonic price models to estimate the effect of high-quality schools on residential property prices. The consistency of six models greatly enhances the credibility of this study. Moreover, complementary, the propensity score matching technique is used to estimate the treatment effect. The two methods consistently suggest that residential property values are affected by top-tier schools. They reveal that xuequfang exhibit values that are between 9.3% and 12.1% higher than non-xuequfang, ceteris paribus. The negative influences of the xuequfang phenomenon and several countermeasures (gradually reforming household registration system, optimizing resource distribution to balance the quality of education, highlighting family education and children’s all-round development) are discussed
Abnormal expression of an ADAR2 alternative splicing variant in gliomas downregulates adenosine-to-inosine RNA editing
BACKGROUND: RNA editing is catalyzed by adenosine deaminases acting on RNA (ADARs). ADAR2 is the main enzyme responsible for recoding editing in humans. Adenosine-to-inosine (A-to-I) editing at the Q/R site is reported to be decreased in gliomas; however, the expression of ADAR2 mRNA was not greatly affected. METHODS: We determined ADAR2 mRNA expression in human glioblastoma cell lines and in normal human glial cells by real-time RT-PCR. We also determined ADAR2 mRNA expression in 44 glioma tissues and normal white matter. After identifying an alternative splicing variant (ASV) of ADAR2 in gliomas, we performed sequencing. We then classified glioblastomas based on the presence (+) or absence (–) of the ASV to determine the correlations between ASV + and malignant features of glioblastomas, such as invasion, peritumoral brain edema, and survival time. RESULTS: There were no significant differences in ADAR2 mRNA expression among human glioblastoma cell lines or in gliomas compared with normal white matter (all p > 0.05). The ASV, which contained a 47-nucleotide insertion in the ADAR2 mRNA transcript, was detected in the U251 and BT325 cell lines, and in some glioma tissues. The expression rate of ASV differed among gliomas of different grades. ASV + glioblastomas were more malignant than ASV – glioblastomas. CONCLUSIONS: ADAR2 is a family of enzymes in which ASVs result in differences in enzymatic activity. The ADAR2 ASV may be correlated with the invasiveness of gliomas. Identification of the mechanistic characterization of ADAR2 ASV may have future potential for individualized molecular targeted-therapy for glioma
Prognostic relevance of a T-type calcium channels gene signature in solid tumours: A correlation ready for clinical validation
BackgroundT-type calcium channels (TTCCs) mediate calcium influx across the cell membrane. TTCCs regulate numerous physiological processes including cardiac pacemaking and neuronal activity. In addition, they have been implicated in the proliferation, migration and differentiation of tumour tissues. Although the signalling events downstream of TTCC-mediated calcium influx are not fully elucidated, it is clear that variations in the expression of TTCCs promote tumour formation and hinder response to treatment.MethodsWe examined the expression of TTCC genes (all three subtypes; CACNA-1G, CACNA-1H and CACNA-1I) and their prognostic value in three major solid tumours (i.e. gastric, lung and ovarian cancers) via a publicly accessible database.ResultsIn gastric cancer, expression of all the CACNA genes was associated with overall survival (OS) among stage I-IV patients (all pConclusionsAlterations in CACNA gene expression are linked to tumour prognosis. Gastric cancer represents the most promising setting for further evaluation
Heterochromatin protein 1α mediates development and aggressiveness of neuroendocrine prostate cancer
Neuroendocrine prostate cancer (NEPC) is a lethal subtype of prostate cancer (PCa) arising mostly from adenocarcinoma via NE transdifferentiation following androgen deprivation therapy. Mechanisms contributing to both NEPC development and its aggressiveness remain elusive. In light of the fact that hyperchromatic nuclei are a distinguishing histopathological feature of NEPC, we utilized transcriptomic analyses of our patient-derived xenograft (PDX) models, multiple clinical cohorts, and genetically engineered mouse models to identify 36 heterochromatin-related genes that are significantly enriched in NEPC. Longitudinal analysis using our unique, first-in-field PDX model of adenocarcinoma-to-NEPC transdifferentiation revealed that, among those 36 heterochromatin-related genes, heterochromatin protein 1α (HP1α) expression increased early and steadily during NEPC development and remained elevated in the developed NEPC tumor. Its elevated expression was further confirmed in multiple PDX and clinical NEPC samples. HP1α knockdown in the NCI-H660 NEPC cell line inhibited proliferation, ablated colony formation, and induced apoptotic cell death, ultimately leading to tumor growth arrest. Its ectopic expression significantly promoted NE transdifferentiation in adenocarcinoma cells subjected to androgen deprivation treatment. Mechanistically, HP1α reduced expression of androgen receptor (AR) and RE1 silencing transcription factor (REST) and enriched the repressive trimethylated histone H3 at Lys9 (H3K9me3) mark on their respective gene promoters. These observations indicate a novel mechanism underlying NEPC development mediated by abnormally expressed heterochromatin genes, with HP1α as an early functional mediator and a potential therapeutic target for NEPC prevention and management
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