Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.

Effective interventions to improve domain-specific social, emotional, or academic outcomes for twice-exceptional individuals who are gifted with ADHD as a disability

The demand for effective interventions to improve domain-specific academic outcomes for individuals with special needs at either end of the spectrum has existed for some time.Since the earlier contributions to the literature documenting gifted individuals who were simultaneously exhibiting disabilities,there has been some progress in our understanding.We now know that in individuals with both gifts and disabilities,potentially,either or both of the exceptionalities can obscure the effects of the other,which significantly delays the average time to receive a diagnosis.Such delays in diagnosis detrimentally impact the the form of effective interventions without a diagnosis.The purpose of this paper is t quality of life across various domains because there can be no opportunity to receive help in o determine whether effective interventions exist to improve domain-specific(i.e.,social,emotional,or academic)outcomes for people with both gifts and disabilities.A query was performed using evidence databases TRIP and PDQ for“twice-exceptional,”“Giftedness,”“Disability,”and“intervention.”The four most relevant,freely available studies in English were selected for critique.Despite identifying potential threats to validity among the studies,methodological similarities among them were strong enough to confidently conclude that not only do effective interventions exist for the population of gifted with ADHD,but the outcomes of these interventions may also carry over into other domains resulting in indirect effects.

Isolation and Characterization of Recombinant Variable Domain of Heavy Chain Anti-idiotypic Antibodies Specific to Aflatoxin B_1

Some unique subclasses of Camelidae antibodies are devoid of the light chain, and the antigen binding site is comprised exclusively of the variable domain of the heavy chain （VHH）. The recombinant VHHs have a high potential as alternative reagents for the next generation of immunoassay. In particular, they might be very useful for molecular mimicry. The present study demonstrated an alpaca immunized with the F（ab＇）z fragment of anti-aflatoxin B1 mAb and developed an important anti-idiotypic （anti-ld） responses. Antigen-specific elution method was used for panning private anti-ld VHHs from the constructed alpaca VHH library. The selected VHHs were expressed, renatured, purified, and then identified by a competitive enzyme-linked immunosorbent assay （ELISA）. Our findings indicated that the VHH would be an alternative tool for haptens mimicry studies.

UML-based combat effectiveness simulation system modeling within MDE

To reduce complexity, the combat effectiveness simulation system(CESS) is often decomposed into static structure,physical behavior, and cognitive behavior, and model abstraction is layered onto domain invariant knowledge(DIK) and application variant knowledge(AVK) levels. This study concentrates on the specification of CESS’s physical behaviors at the DIK level of abstraction, and proposes a model driven framework for efficiently developing simulation models within model-driven engineering(MDE). Technically, this framework integrates the four-layer metamodeling architecture and a set of model transformation techniques with the objective of reducing model heterogeneity and enhancing model continuity. As a proof of concept, a torpedo example is illustrated to explain how physical models are developed following the proposed framework. Finally, a combat scenario is constructed to demonstrate the availability, and a further verification is shown by a reasonable agreement between simulation results and field observations.

Model architecture-oriented combat system effectiveness simulation based on MDE

Combat system effectiveness simulation (CSES) is a special type of complex system simulation. Three non-functional requirements (NFRs), i.e. model composability, domain specific modeling, and model evolvability, are gaining higher priority from CSES users when evaluating different modeling methodologies for CSES. Traditional CSES modeling methodologies are either domain-neutral (lack of domain characteristics consideration and limited support for model composability) or domain-oriented (lack of openness and evolvability) and fall short of the three NFRs. Inspired by the concept of architecture in systems engineering and software engineering fields, we extend it into a concept of model architecture for complex simulation systems, and propose a model architecture-oriented modeling methodology in which the model architecture plays a central role in achieving the three NFRs. Various model-driven engineering (MDE) approaches and technologies, including simulation modeling platform (SMP), unified modeling language (UML), domain specific modeling (DSM), eclipse modeling framework (EMF), graphical modeling framework (GMF), and so forth, are applied where possible in representing the CSES model architecture and its components' behaviors from physical and cognitive domain aspects. A prototype CSES system, called weapon effectiveness simulation system (WESS), and a non-trivial air-combat simulation example are presented to demonstrate the methodology.

LACC:a hardware and software co-design accelerator for deep neural networks

With the increasing of data size and model size,deep neural networks(DNNs)show outstanding performance in many artificial intelligence(AI)applications.But the big model size makes it a challenge for high-performance and low-power running DNN on processors,such as central processing unit(CPU),graphics processing unit(GPU),and tensor processing unit(TPU).This paper proposes a LOGNN data representation of 8 bits and a hardware and software co-design deep neural network accelerator LACC to meet the challenge.LOGNN data representation replaces multiply operations to add and shift operations in running DNN.LACC accelerator achieves higher efficiency than the state-of-the-art DNN accelerators by domain specific arithmetic computing units.Finally,LACC speeds up the performance per watt by 1.5 times,compared to the state-of-the-art DNN accelerators on average.