Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMs

In the field of document understanding, significant advances have been made in the fine-tuning of Multimodal Large Language Models (MLLMs) with instruction-following data. Nevertheless, the potential of text-grounding capability within text-rich scenarios remains underexplored. In this paper, we present a text-grounding document understanding model, termed TGDoc, which addresses this deficiency by enhancing MLLMs with the ability to discern the spatial positioning of text within images. Empirical evidence suggests that text-grounding improves the model's interpretation of textual content, thereby elevating its proficiency in comprehending text-rich images. Specifically, we compile a dataset containing 99K PowerPoint presentations sourced from the internet. We formulate instruction tuning tasks including text detection, recognition, and spotting to facilitate the cohesive alignment between the visual encoder and large language model. Moreover, we curate a collection of text-rich images and prompt the text-only GPT-4 to generate 12K high-quality conversations, featuring textual locations within text-rich scenarios. By integrating text location data into the instructions, TGDoc is adept at discerning text locations during the visual question process. Extensive experiments demonstrate that our method achieves state-of-the-art performance across multiple text-rich benchmarks, validating the effectiveness of our method

Cuproptosis-related MTF1 inhibits kidney renal clear cell carcinoma progression by suppressing proliferation and regulating immune cell infiltration

Cuproptosis is a newly identified specific form of programmed cell death. Our study aimed to identify cuproptosis-related genes (CRGs) in patients with kidney renal clear cell carcinoma (KIRC) from the The Cancer Genome Atlas database and to evaluate CRG biological functions. Using lasso regression, we identified four KIRC prognosis-associated CRGs and constructed an associated prognostic risk signature. Kaplan-Meier curves showed that patients with high-risk scores had significantly lower survival times than patients with low-risk scores. Multivariate Cox analysis identified MTF1 and FDX1 as two independent overall survival CRGs. Moreover, qRT-PCR showed that MTF1 and FDX1 expression was downregulated in KIRC and knockdown of MTF1 and FDX1 significantly promoted KIRC cell proliferation and migration ability. In addition, the MTF1 level was positively correlated with immune cell infiltration and knockdown of MTF1 promoted tumor growth in vivo. We developed a signature of prognostic risk-associated CRGs that accurately predicted the prognostic status of KIRC patients. MTF1 and FDX1 were shown to be key CRGs. MTF1 acts as a tumor suppressor, and may be involved in the progression of KIRC by inhibiting proliferation and regulating immune cell infiltration

BEKG: A Built Environment Knowledge Graph

Practices in the built environment have become more digitalized with the rapid development of modern design and construction technologies. However, the requirement of practitioners or scholars to gather complicated professional knowledge in the built environment has not been satisfied yet. In this paper, more than 80,000 paper abstracts in the built environment field were obtained to build a knowledge graph, a knowledge base storing entities and their connective relations in a graph-structured data model. To ensure the retrieval accuracy of the entities and relations in the knowledge graph, two well-annotated datasets have been created, containing 2,000 instances and 1,450 instances each in 29 relations for the named entity recognition task and relation extraction task respectively. These two tasks were solved by two BERT-based models trained on the proposed dataset. Both models attained an accuracy above 85% on these two tasks. More than 200,000 high-quality relations and entities were obtained using these models to extract all abstract data. Finally, this knowledge graph is presented as a self-developed visualization system to reveal relations between various entities in the domain. Both the source code and the annotated dataset can be found here: https://github.com/HKUST-KnowComp/BEKG

Whether the AGV/AMR can be used in e-commerce

At a time when labor is becoming more and more expensive in Europe, replacing workers with robots will reduce amount of money that is spend by companies on employees. Meanwhile, the number of people of working age is declining in some Asian countries. Automated warehouses can ease the burden of finding staff. To do so, choosing the proper AGV/ AMR in warehouse is not only optimizes the use of warehouse land space, but also increase efficiency and decreases the rate of damaged cargo. The purposes of research was to analysis, highlight and illustrate how picking robots were changing the status quo in e-commerce warehouses. It was also examining what were benefits and risks of using robots for warehousing. Equipment and technology for automated warehouses required high investment upfront. Consequently, uncertainty of recouping capital was deterred many small and medium-sized e-commerce companies from taking the risk. My goal was to help more hesitant online sellers by analyzing the performance, pros and cons of the picking robot's equipment available on the market. Qualitative and desk research methods were gathered in this paper for accomplishing the goals, because it is less common today for articles on AGV to be analyzed as a whole, usually experimented with a particular technology or algorithm. Massive data were used in article which involved scholarly journals, peer-reviewed papers, white paper, and expert statements. As a result, the data within the automated warehouse was traceable. Data could be used to optimize the path of picking robots or to adjust the layout of the shelves in the warehouse. The increased speed of dispatch made it possible to improve the competitiveness of e-commerce platforms. Furthermore, the data collected can also contributed to other areas such as goods sorting and customer management

The impact of guardian's usage of the Internet on children's cognitive skills

Abstract In the digital age, the internet has become an essential source of information gathering and maintains a network of social contact. In this paper, we use microdata from the China Family Panel Studies to examine the effects of guardian's internet use on teenager's cognitive skills. Using mobile internet users and base station density as instruments, we found that guardian's internet use had a significant impact on teenager's cognitive abilities as measured by math and verbal tests. Heterogeneity analysis shows that the impact is more substantial for guardians in rural areas and less‐educated guardians. Further exploration of the mechanism shows that internet usage affects cognitive skills through monetary investment, time investment, and parental environment. The result suggests that promoting internet usage for less educated families in rural areas can potentially improve children's academic performance and decrease inequality across regions and generations

Intimate Partner Violence Screening Instruments: A Protocol for a COSMIN-Based Systematic Review

Intimate partner violence (IPV) is a major public health problem resulting in a significant impediment to equal participation, quality of life, and personal, social, and economic development. At present, a variety of screening instruments for IPV have emerged in developed countries, and some of them have been adapted to the language and culture of different countries, such as Hurt, Insult, Threaten, Scream (HITS) and the Abuse Assessment Screen (AAS). The selection of the most appropriate IPV screening instrument for the target population and context from among those instruments has become difficult for researchers when intending to start screening. Therefore, a systemic review of IPV screening instruments is needed. This protocol describes a COSMIN-based systematic review of the measurement properties of these instruments. The aims of the systematic review are to (1) evaluate the methodological quality of studies on the measurement properties including the validity, reliability, and internal consistency of these IPV screening instruments, and (2) provide suggestions for relevant researchers in their local context for using the IPV screening instruments

Long-Term Tibetan Alpine Vegetation Responses to Elevation-Dependent Changes in Temperature and Precipitation in an Altered Regional Climate: A Case Study for the Three Rivers Headwaters Region, China

Recent studies offer more evidence that the rate of warming is amplified with elevation, indicating thereby that high-elevation ecosystems tend to be exposed to more accelerated changes in temperature than ecosystems at lower elevations. The phenomenon of elevation-dependent warming (EDW), as one of the regional climate-change impacts, has been observed across the Tibetan Plateau. Studies have often found large-scale greening trends, but the drivers of vegetation dynamics are still not fully understood in this region, such that the local implications of vegetation change have been infrequently discussed. This study was designed to quantify and characterize the seasonal changes in vegetation across the Three Rivers Headwaters Region (TRHR), where the land cradles the headwaters of the Yangtze, the Yellow, and the Lancang (Mekong). By mapping the normalized difference vegetation index (NDVI) over the growing season from 1982 to 2015, we were able to evaluate seasonal changes in vegetation cover over time. The results show a slightly increased tendency in green vegetation cover, which could possibly be attributed to sustained warming in this region over the past three decades, whereas a decline in the green-up rate with elevation was found, indicating an inconsistent trend of vegetation greening with EDW. The cause of the green-up rate decline at high elevations could be linked to the reduced soil water availability induced by the fast increase in warming rates associated with EDW. The findings of this study have important implications for devising adaptation strategies for alpine ecosystems in a changing climate

TERA: A FPGA-based trace-driven emulation framework for designing on-chip communication architectures

An efficient and accurate performance analysis technique is indispensable for developing on-chip communication architectures. However, traditional approaches cannot achieve both efficiency and accuracy. To address this problem, we present a FPGA-based trace-driven emulation framework named TERA. TERA uses FPGA to accelerate both trace extraction and trace execution. To avoid the potential influence of inaccurate models on trace extraction, TERA captures traces based on the hardware platform for the target SoC. In addition, TERA uses classified traffic generators as well as a cycle-accurate memory controller model to improve the accuracy of trace execution. Experimental results demonstrate that TERA is over three orders of magnitude faster than cycle-accurate software simulation, and the variation of TERA from the real chip is less than 4.15%. ? 2010 IEEE.EI