DeepGAR: Deep Graph Learning for Analogical Reasoning

Analogical reasoning is the process of discovering and mapping correspondences from a target subject to a base subject. As the most well-known computational method of analogical reasoning, Structure-Mapping Theory (SMT) abstracts both target and base subjects into relational graphs and forms the cognitive process of analogical reasoning by finding a corresponding subgraph (i.e., correspondence) in the target graph that is aligned with the base graph. However, incorporating deep learning for SMT is still under-explored due to several obstacles: 1) the combinatorial complexity of searching for the correspondence in the target graph; 2) the correspondence mining is restricted by various cognitive theory-driven constraints. To address both challenges, we propose a novel framework for Analogical Reasoning (DeepGAR) that identifies the correspondence between source and target domains by assuring cognitive theory-driven constraints. Specifically, we design a geometric constraint embedding space to induce subgraph relation from node embeddings for efficient subgraph search. Furthermore, we develop novel learning and optimization strategies that could end-to-end identify correspondences that are strictly consistent with constraints driven by the cognitive theory. Extensive experiments are conducted on synthetic and real-world datasets to demonstrate the effectiveness of the proposed DeepGAR over existing methods.Comment: 22nd IEEE International Conference on Data Mining (ICDM 2022

STAIBT: Blockchain and CP-ABE Empowered Secure and Trusted Agricultural IoT Blockchain Terminal

The integration of agricultural Internet of Things (IoT) and blockchain has become the key technology of precision agriculture. How to protect data privacy and security from data source is one of the difficult issues in agricultural IoT research. This work integrates cryptography, blockchain and Interplanetary File System (IPFS) technologies, and proposes a general IoT blockchain terminal system architecture, which strongly supports the integration of the IoT and blockchain technology. This research innovatively designed a fine-grained and flexible terminal data access control scheme based on the ciphertext-policy attribute-based encryption (CP-ABE) algorithm. Based on CP-ABE and DES algorithms, a hybrid data encryption scheme is designed to realize 1-to-N encrypted data sharing. A "horizontal + vertical" IoT data segmentation scheme under blockchain technology is proposed to realize the classified release of different types of data on the blockchain. The experimental results show that the design scheme can ensure data access control security, privacy data confidentiality, and data high-availability security. This solution significantly reduces the complexity of key management, can realize efficient sharing of encrypted data, flexibly set access control strategies, and has the ability to store large data files in the agricultural IoT

Open-ended Commonsense Reasoning with Unrestricted Answer Scope

Open-ended Commonsense Reasoning is defined as solving a commonsense question without providing 1) a short list of answer candidates and 2) a pre-defined answer scope. Conventional ways of formulating the commonsense question into a question-answering form or utilizing external knowledge to learn retrieval-based methods are less applicable in the open-ended setting due to an inherent challenge. Without pre-defining an answer scope or a few candidates, open-ended commonsense reasoning entails predicting answers by searching over an extremely large searching space. Moreover, most questions require implicit multi-hop reasoning, which presents even more challenges to our problem. In this work, we leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base, which does not require task-specific supervision. The reasoning paths can help to identify the most precise answer to the commonsense question. We conduct experiments on two commonsense benchmark datasets. Compared to other approaches, our proposed method achieves better performance both quantitatively and qualitatively.Comment: Findings of EMNLP 202

Biomass fuel usage for cooking and frailty among older adults in China: a population-based cohort study

BackgroundAlthough outdoor air pollution is reported to have a negative effect on frailty, evidence involving household air pollution is sparse.MethodsA cohort study on older participants aged ≥65 years from the Chinese Longitudinal Healthy Longevity Survey was conducted between 2011/2012 and 2014. Household cooking fuel types were determined by self-reported questionaries, and were dichotomized into clean or biomass fuels. The frailty status was evaluated via a 46-item frailty index (FI) and the FRAIL scale, respectively. Frailty was identified if FI >0.21 or FRAIL score ≥3. Cox proportional hazards models were employed to examine the relationship between cooking fuels and incident frailty. And the effects of swapping cooking fuels on frailty risk were also explored.ResultsAmong 4,643 participants (mean age at baseline 80.9 ± 9.6 years, 53.7% male) totaling 11,340 person-years, 923 (19.9%) incident frailty was identified using FI. Compared to clean fuels, cooking with biomass fuels was intricately linked to a 23% rise in frailty risk (hazard ratio [HR] 1.23, 95% confidence interval [CI] 1.06–1.43). A similar association was detected between biomass cooking fuels and frailty measured by the FRAIL scale (HR 1.24, 95% CI 1.04–1.50). Sensitive analyses supported the independent relationship between biomass fuels and frailty. Stratified analyses revealed that the frailty risk was higher among town residents (HR 1.44, 95% CI 1.13–1.84) and participants not exercising regularly (HR 1.35, 95% CI 1.11–1.64). In comparison with persistent biomass fuels usage, switching to clean fuels had a trend to reduce the frailty risk, and the opposite effect was observed when swapping from clean to biomass fuels.ConclusionCooking with biomass fuels was associated with an increased frailty risk in older adults, especially amongst those living in town and those lacking regular exercise. More studies are needed to confirm our findings and to evaluate the potential benefits of reducing indoor biomass fuel usage

Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey

Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles, caused by the heterogeneity of domain data, the sophistication of domain knowledge, the uniqueness of domain objectives, and the diversity of the constraints (e.g., various social norms, cultural conformity, religious beliefs, and ethical standards in the domain applications). Domain specification techniques are key to make large language models disruptive in many applications. Specifically, to solve these hurdles, there has been a notable increase in research and practices conducted in recent years on the domain specialization of LLMs. This emerging field of study, with its substantial potential for impact, necessitates a comprehensive and systematic review to better summarize and guide ongoing work in this area. In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications. First, we propose a systematic taxonomy that categorizes the LLM domain-specialization techniques based on the accessibility to LLMs and summarizes the framework for all the subcategories as well as their relations and differences to each other. Second, we present an extensive taxonomy of critical application domains that can benefit dramatically from specialized LLMs, discussing their practical significance and open challenges. Last, we offer our insights into the current research status and future trends in this area

Incorporation of a hinge domain improves the expansion of chimeric antigen receptor T cells

© 2017 The Author(s). Background: Multiple iterations of chimeric antigen receptors (CARs) have been developed, mainly focusing on intracellular signaling modules. However, the effect of non-signaling extracellular modules on the expansion and therapeutic efficacy of CARs remains largely undefined. Methods: We generated two versions of CAR vectors, with or without a hinge domain, targeting CD19, mesothelin, PSCA, MUC1, and HER2, respectively. Then, we systematically compared the effect of the hinge domains on the growth kinetics, cytokine production, and cytotoxicity of CAR T cells in vitro and in vivo. Results: During in vitro culture period, the percentages and absolute numbers of T cells expressing the CARs containing a hinge domain continuously increased, mainly through the promotion of CD4+ CAR T cell expansion, regardless of the single-chain variable fragment (scFv). In vitro migration assay showed that the hinges enhanced CAR T cells migratory capacity. The T cells expressing anti-CD19 CARs with or without a hinge had similar antitumor capacities in vivo, whereas the T cells expressing anti-mesothelin CARs containing a hinge domain showed enhanced antitumor activities. Conclusions: Hence, our results demonstrate that a hinge contributes to CAR T cell expansion and is capable of increasing the antitumor efficacy of some specific CAR T cells. Our results suggest potential novel strategies in CAR vector design.Link_to_subscribed_fulltex

Does the democratization level of village governance affect perceptions of security and integrity of land rights? -An analysis from the perspective of social network abundance

Improving the land property rights system is an effective way to enhance the rural economy and resist the effects of rural decay caused by rapid urbanization. However, in many developing countries, farmers' perception of the security and integrity of their allocated land rights, which is the core of farmers' decision making, deviates from the forms stipulated by formal land property rights systems. China is no exception in this regard. Based on two household surveys conducted in Jiangsu, Jiangxi, and Liaoning Provinces in China, which cover 2014 and 2018 information, this paper uses an ologit model to explore the impact of the democratization level on farmers' perception of land rights security and integrity and identifies the moderating effect of social network abundance. The results show that a higher democratization level of village governance enhances farmers' perception of the effectiveness of land certificates and the integrity of the bundle of rights but exerts an insignificant impact on farmers’ perception of land reallocation. We also find that social network abundance has a moderating effect. Social network abundance weakens the effect of democratization level on the perception of certificate effectiveness but enhance its effect on the perception of rights integrity. Furthermore, our findings provide a reference for reforming rural land property rights systems and rural governance systems during the process of developing rural revitalization strategies