327 research outputs found
Memory-Augmented LLM Personalization with Short- and Long-Term Memory Coordination
Large Language Models (LLMs), such as GPT3.5, have exhibited remarkable
proficiency in comprehending and generating natural language. However, their
unpersonalized generation paradigm may result in suboptimal user-specific
outcomes. Typically, users converse differently based on their knowledge and
preferences. This necessitates the task of enhancing user-oriented LLM which
remains unexplored. While one can fully train an LLM for this objective, the
resource consumption is unaffordable. Prior research has explored memory-based
methods to store and retrieve knowledge to enhance generation without
retraining for new queries. However, we contend that a mere memory module is
inadequate to comprehend a user's preference, and fully training an LLM can be
excessively costly. In this study, we propose a novel computational bionic
memory mechanism, equipped with a parameter-efficient fine-tuning schema, to
personalize LLMs. Our extensive experimental results demonstrate the
effectiveness and superiority of the proposed approach. To encourage further
research into this area, we are releasing a new conversation dataset generated
entirely by LLM based on an open-source medical corpus, as well as our
implementation code
Study on the Reasonable Smoke Exhaust Rate of the Crossrange Exhaust Duct in Double-layer Shield Tunnel
AbstractThe research on the concentrated smoke extraction system of crossrange exhaust duct in double-layer shield tunnel is still very lack in the world. This paper is on the smoke extraction system of double-layer shield tunnel. It will provide the supports and references for the smoke control of tunnel fire and the determination of related technical parameters in the design of tunnel fire ventilation and smoke extraction, so it has important scientific value, practical significance and application prospects. This paper bases on the tunnel project of Slender West Lake in Yangzhou. By using the method of combining theory and numerical simulation, a conclusion can be drawn that the reasonable smoke exhaust rate of the upper tunnel is 140 m3/s
H2CGL: Modeling Dynamics of Citation Network for Impact Prediction
The potential impact of a paper is often quantified by how many citations it
will receive. However, most commonly used models may underestimate the
influence of newly published papers over time, and fail to encapsulate this
dynamics of citation network into the graph. In this study, we construct
hierarchical and heterogeneous graphs for target papers with an annual
perspective. The constructed graphs can record the annual dynamics of target
papers' scientific context information. Then, a novel graph neural network,
Hierarchical and Heterogeneous Contrastive Graph Learning Model (H2CGL), is
proposed to incorporate heterogeneity and dynamics of the citation network.
H2CGL separately aggregates the heterogeneous information for each year and
prioritizes the highly-cited papers and relationships among references,
citations, and the target paper. It then employs a weighted GIN to capture
dynamics between heterogeneous subgraphs over years. Moreover, it leverages
contrastive learning to make the graph representations more sensitive to
potential citations. Particularly, co-cited or co-citing papers of the target
paper with large citation gap are taken as hard negative samples, while
randomly dropping low-cited papers could generate positive samples. Extensive
experimental results on two scholarly datasets demonstrate that the proposed
H2CGL significantly outperforms a series of baseline approaches for both
previously and freshly published papers. Additional analyses highlight the
significance of the proposed modules. Our codes and settings have been released
on Github (https://github.com/ECNU-Text-Computing/H2CGL)Comment: Accepted by IP&
Simultaneous Detection of Chlamydia Trachomatis, Neisseria Gonorrhoeae, Ureaplasma Urealyticum by Multiplex PCR-Running
Chlamydia trachomatis (CT), Ureaplasma urealyticum (UU) and Neisseria gonorrhoeae (NG) are the most common pathogens of sexually transmitted infections (STIs), frequently founded in urogenital infections, and showed a criminal role in increasing the risk of potential adverse outcomes. In this study a multiplex PCR assay for the simultaneous detection and accurate identification of 3 clinically relevant pathogens of STIs, i.e., CT, NG and UU in a single tube was developed and evaluated. The limits of detection for the multiplex PCR assay were ~10 copies of DNAs per reaction. This assay has comparable clinical sensitivity to the conventional monoplex real-time PCR assay and considerable potential to be routine molecular diagnostic tool for simultaneous identification of STIs at relatively low cost due to multiplexing
ICNMM2008-62315 A PIV STUDY OF AC-ELECTROOSMOTIC FLOW IN MICROCHANNELS
ABSTRACT Microscale mixing is difficult because the small channeldimensions lead to low Reynolds number and the mixing is due to the diffusion only. This study focus on mixing in a microchannel based on AC electroosmosis by using a PIV technique
RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction
Universal Information Extraction (UIE) is an area of interest due to the
challenges posed by varying targets, heterogeneous structures, and
demand-specific schemas. However, previous works have only achieved limited
success by unifying a few tasks, such as Named Entity Recognition (NER) and
Relation Extraction (RE), which fall short of being authentic UIE models
particularly when extracting other general schemas such as quadruples and
quintuples. Additionally, these models used an implicit structural schema
instructor, which could lead to incorrect links between types, hindering the
model's generalization and performance in low-resource scenarios. In this
paper, we redefine the authentic UIE with a formal formulation that encompasses
almost all extraction schemas. To the best of our knowledge, we are the first
to introduce UIE for any kind of schemas. In addition, we propose RexUIE, which
is a Recursive Method with Explicit Schema Instructor for UIE. To avoid
interference between different types, we reset the position ids and attention
mask matrices. RexUIE shows strong performance under both full-shot and
few-shot settings and achieves State-of-the-Art results on the tasks of
extracting complex schemas
Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models
Neural text ranking models have witnessed significant advancement and are
increasingly being deployed in practice. Unfortunately, they also inherit
adversarial vulnerabilities of general neural models, which have been detected
but remain underexplored by prior studies. Moreover, the inherit adversarial
vulnerabilities might be leveraged by blackhat SEO to defeat better-protected
search engines. In this study, we propose an imitation adversarial attack on
black-box neural passage ranking models. We first show that the target passage
ranking model can be transparentized and imitated by enumerating critical
queries/candidates and then train a ranking imitation model. Leveraging the
ranking imitation model, we can elaborately manipulate the ranking results and
transfer the manipulation attack to the target ranking model. For this purpose,
we propose an innovative gradient-based attack method, empowered by the
pairwise objective function, to generate adversarial triggers, which causes
premeditated disorderliness with very few tokens. To equip the trigger
camouflages, we add the next sentence prediction loss and the language model
fluency constraint to the objective function. Experimental results on passage
ranking demonstrate the effectiveness of the ranking imitation attack model and
adversarial triggers against various SOTA neural ranking models. Furthermore,
various mitigation analyses and human evaluation show the effectiveness of
camouflages when facing potential mitigation approaches. To motivate other
scholars to further investigate this novel and important problem, we make the
experiment data and code publicly available.Comment: 15 pages, 4 figures, accepted by ACM CCS 2022, Best Paper Nominatio
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