Retrieval Mechanism Research Articles

Memory-guided perception is shaped by dynamic two-stage theta- and alpha-mediated retrieval.

How does memory influence auditory perception, and what are the underlying mechanisms that drive these interactions? Most empirical studies on the neural correlates of memory-guided perception have used static visual tasks, resulting in a bias in the literature that contrasts with recent research highlighting the dynamic nature of memory retrieval. Here, we used electroencephalography to track the retrieval of auditory associative memories in a cue-target paradigm. Participants (N = 64) listened to real-world soundscapes that were either predictive of an upcoming target tone or nonpredictive. Three key results emerged. First, targets were detected faster when embedded in predictive than in nonpredictive soundscapes (memory-guided perceptual benefit). Second, changes in theta and alpha power differentiated soundscape contexts that were predictive from nonpredictive contexts at two distinct temporal intervals from soundscape onset (early-950ms peak for theta and alpha, and late-1650ms peak for alpha only). Third, early theta activity in the left anterior temporal lobe was correlated with memory-guided perceptual benefits. Together, these findings underscore the role of distinct neural processes at different time points during associative retrieval. By emphasizing temporal sensitivity and by isolating cue-related activity, we reveal a two-stage retrieval mechanism that advances our understanding of how memory influences auditory perception.

AI-Driven Network Supervision in 6G Enhancing Self-Association, Optimization, and Self-governing Maintenance

The advent of 6G networks is expected to transfigure communication by providing ultra-high speeds, low latency, and omnipresent connectivity. One of the central challenges in 6G is well-organized network management, specifically as the scale and complexity of the network grow. This paper explores the role of Artificial Intelligence (AI) and Machine Learning (ML) in 6G network design, concentrating on their application in network self-organization, optimization, and analytical maintenance. By utilizing AI-driven algorithms, 6G networks can separately manage resources, optimize presentation, and predictively address faults before they interrupt services. The paper discusses the implementation of AI technologies in network resource allocation, real-time traffic management, fault detection, and automated retrieval mechanisms, prominence both the opportunities and challenges associated with these innovations. The proposed approach aims to establish a more resilient, adaptive, and effectual network capable of meeting the demands of upcoming generations.

Microwave catalytic treatment using magnetically separable CoFe2O4 spinel catalyst for high-rate degradation of malachite green dye.

The release of toxic chemical dyes from the industrial effluent poses huge challenges for the environmental engineers to treat it. Azo dyes encompass the huge part of textile discharges which are difficult to degrade due to their complex chemical aromatic structures and due to the presence of strong bonds (-N=N-). Thus, the removal of a carcinogenic azo dye (i.e., malachite green (MG)), via microwave (MW) - assisted technology in the presence of spinel cobalt ferrite (CoFe2O4) catalyst was investigated. The synthesized CoFe2O4 nanoparticles were characterized via XRD, FTIR, TGA, VSM, and SEM-EDAX analytical techniques. The nanoparticles were found to be spherical (17.42nm), and crystalline in nature. The impact of MW power (300-700W), MW temperature (60°- 90°C), CoFe2O4 dosage (0-1.2g/L), and initial MG dye concentrations (15-100mg/L) on MG removal were studied. The maximum MG decolorization (>90%) was observed within 2min, and the remaining decolorization was completed within next 3min under the optimized condition. The reaction rates were significantly boosted by MW irradiation, resulting in faster MG degradation with pseudo second-order kinetics rate (R2 ∼ 0.99). The MW irradiation would induce the localised hotspot zones over the catalytic surface, thus promoting the reactive radical species generation, which targeted the organic pollutant achieving the higher degree of mineralization (TOC∼90%). The toxicity reduction after the MW treatment suggests that the bulky toxic aromatic chains of the dye compound might have fragmented into simpler, smaller, and less toxic compounds. The ⦁OH played a major role in the degradation of MG dye through demethylation, elimination of benzene rings, and subsequent mineralization to CO2, and H2O. No detectable leaching of cobalt (Co) metal was observed from the catalyst, which ensured the stability of Co in the catalyst. Moreover, CoFe2O4 was recovered easily after the MW treatment via external magnetic separation, due to its high saturation magnetization value (i.e., 59.5 emu/g). Additionally, cost incurred for dye removal via MW irradiation was compared ($ 308 per kg of the dye pollutant removed) with several other processes, and found on economic side in comparison. According to the findings, the microwave irradiation process assisted by CoFe2O4 treatment offers a practical, and potential approach for treating dye laden wastewater, along with the easier catalyst retrieval mechanism using magnetism.

Enhancing Retrieval-Augmented Generation Accuracy with Dynamic Chunking and Optimized Vector Search

Retrieval-Augmented Generation (RAG) architectures depend on the integration of efficient retrieval and ranking mechanisms to enhance response accuracy and relevance. This study investigates a novel approach to improving the response performance of RAG systems, leveraging dynamic chunking for contextual coherence, Sentence-Transformers (all-mpnet-base-v2) for high-quality embeddings, and cross-encoder-based re-ranking for retrieval refinement. Our evaluation utilizes RAGAS metrics to assess key performance metrics, including faithfulness, relevancy, correctness, and context precision. Empirical evaluations highlighted the significant impact of index choice on the performance. Our proposed approach integrates the FAISS HNSW index with re-ranking, resulting in a balanced architecture that improves response fidelity without compromising efficiency. These insights underscore the importance of advanced indexing and retrieval techniques in bridging the gap between large-scale language models and domain-specific information needs. The findings provide a robust framework for future research in optimizing RAG systems, particularly in scenarios requiring high-context preservation and precision.

Deep deterministic policy gradients with a self-adaptive reward mechanism for image retrieval

Traditional image retrieval methods often face challenges in adapting to varying user preferences and dynamic datasets. To address these limitations, this research introduces a novel image retrieval framework utilizing deep deterministic policy gradients (DDPG) augmented with a self-adaptive reward mechanism (SARM). The DDPG-SARM framework dynamically adjusts rewards based on user feedback and retrieval context, enhancing the learning efficiency and retrieval accuracy of the agent. Key innovations include dynamic reward adjustment based on user feedback, context-aware reward structuring that considers the specific characteristics of each retrieval task, and an adaptive learning rate strategy to ensure robust and efficient model convergence. Extensive experimentation with the three distinct datasets demonstrates that the proposed framework significantly outperforms traditional methods, achieving the highest retrieval accuracy having 3.38%, 5.26%, and 0.21% improvement overall as compared to the mainstream models over DermaMNIST, PneumoniaMNIST, and OrganMNIST datasets, respectively. The findings contribute to the advancement of reinforcement learning applications in image retrieval, providing a user-centric solution adaptable to various dynamic environments. The proposed method also offers a promising direction for future developments in intelligent image retrieval systems.

Chronology versus centrality: uncovering age-related differences in order effects during the retrieval of autobiographical memories.

Despite the crucial role that the recall of autobiographical memories (AMs) plays for identity, the process of how we recall AMs, and whether retrieval processes undergo changes across the lifespan, has received little attention. The present study thus examined the order of AMs during recall, with a specific focus on time and centrality as guiding dimensions. A total of 364 participants (aged 18-89 years) recalled up to ten positive and negative AMs. They provided their age at the time of the event and rated AMs according to their centrality to identity and life story. Based on linear growth models, we found emotional AMs to be recalled chronologically. Additionally, people ordered their emotional AMs along their relative importance. Notably, both order effects vary across age: Whereas the chronological order effect became more pronounced with increasing age, the reversed pattern was found regarding the centrality order effect for negative AMs. Positive AMs were also ordered according to their centrality, but the strength of this effect was independent of participants' age. Findings are discussed in terms of potential motivational factors underlying the retrieval mechanisms associated with the recall of positive and negative AMs and age-related differences therein.

Improving citizen-government interactions with generative artificial intelligence: Novel human-computer interaction strategies for policy understanding through large language models.

Effective communication of government policies to citizens is crucial for transparency and engagement, yet challenges such as accessibility, complexity, and resource constraints obstruct this process. In the digital transformation and Generative AI era, integrating Generative AI and artificial intelligence technologies into public administration has significantly enhanced government governance, promoting dynamic interaction between public authorities and citizens. This paper proposes a system leveraging the Retrieval-Augmented Generation (RAG) technology combined with Large Language Models (LLMs) to improve policy communication. Addressing challenges of accessibility, complexity, and engagement in traditional dissemination methods, our system uses LLMs and a sophisticated retrieval mechanism to generate accurate, comprehensible responses to citizen queries about policies. This novel integration of RAG and LLMs for policy communication represents a significant advancement over traditional methods, offering unprecedented accuracy and accessibility. We experimented with our system with a diverse dataset of policy documents from both Chinese and US regional governments, comprising over 200 documents across various policy topics. Our system demonstrated high accuracy, averaging 85.58% for Chinese and 90.67% for US policies. Evaluation metrics included accuracy, comprehensibility, and public engagement, measured against expert human responses and baseline comparisons. The system effectively boosted public engagement, with case studies highlighting its impact on transparency and citizen interaction. These results indicate the system's efficacy in making policy information more accessible and understandable, thus enhancing public engagement. This innovative approach aims to build a more informed and participatory democratic process by improving communication between governments and citizens.

Dental Loop Chatbot: A Prototype Large Language Model Framework for Dentistry

The Dental Loop Chatbot was developed as a real-time, evidence-based guidance system for dental practitioners using a fine-tuned large language model (LLM) and Retrieval-Augmented Generation (RAG). This paper outlines the development and preliminary evaluation of the chatbot as a scalable clinical decision-support tool designed for resource-limited settings. The system’s architecture incorporates Quantized Low-Rank Adaptation (QLoRA) for efficient fine-tuning, while dynamic retrieval mechanisms ensure contextually accurate and relevant responses. This prototype lays the groundwork for future triaging and diagnostic support systems tailored specifically to the field of dentistry.

Ciphertext Fuzzy Retrieval Mechanism With Bidirectional Verification and Privacy Protection

Ciphertext Fuzzy Retrieval Mechanism With Bidirectional Verification and Privacy Protection

Survey on Speech Recognition and Retrieval-Augmented Generation

Automatic speech recognition (ASR) and retrieval-augmented generation (RAG) systems have seen remarkable progress in handling multilingualism, noise robustness, real-time transcription, and knowledge-intensive tasks. The survey reviews 12 key papers that contribute to advancements in ASR and RAG, covering approaches like end-to-end multilingual models, noise-reduction techniques, and real-time speech processing. It also examines RAG systems that enhance generative models by integrating retrieval mechanisms for improved accuracy in tasks like question answering and summarization. By categorizing the papers into themes, this survey highlights key methodologies, compares their performance, and identifies future directions for improving ASR and RAG technologies in handling real-world challenges.

Revisiting novel word semantic priming: The role of strategic priming mechanisms.

Although it has been proposed that new words are encoded in a qualitatively different way from established words-in episodic rather than semantic memory-such accounts are challenged by the finding that newly learnt words influence the processing of well-known words in semantic priming tasks. In this article, we explore whether this apparent contradiction is due to differences in task design. Specifically, we hypothesised that a large stimulus onset asynchrony (SOA) would allow the participant to engage strategic retrieval and priming mechanisms to facilitate the recognition of a semantically related word, compared with a shorter SOA, which promotes more automatic processing. In Experiment 1, 60 participants learned 34 novel words and their meanings that later served as primes for related/unrelated existing word targets in a primed lexical decision task, with a 450 ms SOA. There was no significant priming effect. In Experiment 2, we increased the SOA to 1,000 ms, and found a significant priming effect with novel words. Finally, there was no significant priming effect with novel words in Experiment 3 that used a 200 ms SOA. A semantic priming effect with familiar words was found in Experiments 1 and 3, but not Experiment 2 (the longest SOA). We interpret these results as providing evidence for the idea that new and existing words are represented differently, with the former encoded outside of conventional language networks as they appear to rely predominantly on slow (strategic) mechanisms to prime related, existing words.

A P2P Approach to Routing in Hierarchical MANETs

Abstract: We present an effective routing solution for the backbone of hierarchical MANETs. Our solution leverages the storage and retrieval mechanisms of a Distributed Hash Table (DHT) common to many (structured) P2P overlays. The DHT provides routing information in a decentralized fashion, while supporting different forms of node and network mobility. We split a flat network into clusters, each having a gateway who participates in a DHT overlay. These gateways interconnect the clusters in a backbone network. Two routing approaches for the backbone are explored: flooding and a new solution exploiting the storage and retrieval capabilities of a P2P overlay based on a DHT. We implement both approaches in a network simulator and thoroughly evaluate the performance of the proposed scheme using a range of static and mobile scenarios. We also compare our solution against flooding. The simulation results show that our solution, even in the presence of mobility, achieved well above 90% success rates and maintained very low and constant round trip times, unlike the flooding approach. In fact, the performance of the proposed inter-cluster routing solution, in many cases, is comparable to the performance of the intra-cluster routing case. The advantage of our proposed approach compared to flooding increases as the number of clusters increases, demonstrating the superior scalability of our proposed approach.

Content-Based Image Retrieval and Image Classification System for Early Prediction of Bladder Cancer.

Bladder cancer is a type of cancer that begins in the cells lining the inner surface of the bladder. Although it usually begins in the bladder, it can spread to surrounding tissues, lymph nodes, and other organs in later stages. Early detection of bladder cancer is, therefore, of great importance. Therefore, this study developed two systems based on classification and Content-Based Image Retrieval (CBIR). The primary purpose of CBIR systems is to compare the visual similarities of a user-provided image with the images in the database and return the most similar ones. CBIR systems offer an effective search and retrieval mechanism by directly using the content of the image data. In the proposed CBIR system, five different CNNs, two different textural-based feature extraction methods, and seven different similarity measurement metrics were tested for feature selection and similarity measurement. Successful feature extraction methods and similarity measurement metrics formed the infrastructure of the developed system. Densenet201 was preferred for feature extraction in the developed system. The cosine metric was used in the proposed CBIR system as a similarity measurement metric, the most successful among seven different metrics. As a result, it was seen that the proposed CBIR model showed the highest success using the Densenet201 model for feature extraction and the Cosine similarity measurement method.

Health Diagnostic Assistant using LLMs

The Health Diagnostic Assistant leverages advanced Large Language Models (LLMs) and Natural Language Processing (NLP) techniques to enhance patient diagnosis and healthcare decision-making. This innovative system employs Retrieval-Augmented Generation (RAG) to combine the strengths of pre-trained language models with a dynamic retrieval mechanism, allowing it to access and synthesize real-time medical knowledge from a wide array of databases. By analyzing patient symptoms, medical histories, and contextual data, the assistant generates accurate, context-aware recommendations and insights. The project aims to streamline the diagnostic process, reduce the burden on healthcare professionals, and improve patient outcomes by providing evidence-based suggestions tailored to individual cases. Through continuous learning and integration of user feedback, the Health Diagnostic Assistant aspires to evolve into a reliable tool for both patients and clinicians, fostering informed decision-making in the healthcare landscape.

Cholinergic regulation of dendritic Ca2+ spikes controls firing mode of hippocampal CA3 pyramidal neurons

Active dendritic integrative mechanisms such as regenerative dendritic spikes enrich the information processing abilities of neurons and fundamentally contribute to behaviorally relevant computations. Dendritic Ca2+ spikes are generally thought to produce plateau-like dendritic depolarization and somatic complex spike burst (CSB) firing, which can initiate rapid changes in spatial coding properties of hippocampal pyramidal cells (PCs). However, here we reveal that a morpho-topographically distinguishable subpopulation of rat and mouse hippocampal CA3PCs exhibits compound apical dendritic Ca2+ spikes with unusually short duration that do not support the firing of sustained CSBs. These Ca2+ spikes are mediated by L-type Ca2+ channels and their time course is restricted by A- and M-type K+ channels. Cholinergic activation powerfully converts short Ca2+ spikes to long-duration forms, and facilitates and prolongs CSB firing. We propose that cholinergic neuromodulation controls the ability of a CA3PC subtype to generate sustained plateau potentials, providing a state-dependent dendritic mechanism for memory encoding and retrieval.

A novel blockchain-based system for improving information integrity in building projects from the perspective of building energy performance

Global population growth has long been intertwined with environmental concerns and sustainable development. The building sector, a significant energy consumer worldwide, contributes substantially to total energy consumption and greenhouse gas emissions. With the projected global population projected to reach 9.7 billion by 2050, building energy consumption is expected to rise continuously, emphasizing the need to optimize energy performance for environmental sustainability. Despite efforts by many countries to formulate energy conservation objectives and strategies, empirical evidence reveals a substantial disparity between designed and actual building energy consumption, known as the building energy performance gap (BEPG). This gap poses a significant challenge to energy conservation efforts, and the lack of information integrity is a significant contributor to this gap. To address the challenge of inadequate information integrity in building energy projects, this research proposes an innovative solution based on blockchain technology. By utilizing blockchain's decentralized, transparent, and tamper-resistant nature, the proposed model aims to enhance information transmission mechanisms among stakeholders. Ten key energy-related stakeholders and various information flows within building projects are identified. Based on this, an exploratory architectural information management model incorporating blockchain technology is developed. The model features functionalities such as data recording, retrieval, transmission, and incentive mechanisms to ensure data immutability and reliable access security. Through a case study, the model's performance is evaluated in terms of storage cost, latency, and privacy, demonstrating significant time savings in uploading and transferring files. The proposed blockchain-based model offers a feasible solution to the challenges of inadequate information integrity in building energy projects, promoting collaboration and accurate information transmission. This model contributes to improving project outcomes and advancing sustainable development in the construction industry.

Closed-loop modulation of remote hippocampal representations with neurofeedback.

Humans can remember specific remote events without acting on them and influence which memories are retrieved based on internal goals. However, animal models typically present sensory cues to trigger memory retrieval and then assess retrieval based on action. Thus, it is difficult to determine whether measured neural activity patterns relate to the cue(s), the memory, or the behavior. We therefore asked whether retrieval-related neural activity could be generated in animals without cues or a behavioral report. We focused on hippocampal "place cells" which primarily represent the animal's current location (local representations) but can also represent locations away from the animal (remote representations). We developed a neurofeedback system to reward expression of remote representations and found that rats could learn to generate specific spatial representations that often jumped directly to the experimenter-defined target location. Thus, animals can deliberately engage remote representations, enabling direct study of retrieval-related activity in the brain.

Executive control can query hidden human memories.

When we try to retrieve a representation from visual long-term memory there is a chance that we will fail to recall seeing it even though the memory is stored in our brain. Here we show that although mechanisms of explicit memory retrieval are sometimes unable to retrieve stored memories, that mechanisms of executive control can quickly query memory and determine if a representation is stored therein. Our findings suggest that the representations stored in human memory that cannot be accessed explicitly at that moment are nonetheless directly accessible by the brain's higher level control mechanisms.

Design and Test of Seedling-Picking Mechanism of Fully Automatic Transplanting Machine

The seedling retrieval mechanism is a crucial component of fully automatic transplanting machines, significantly influencing the quality, reliability, and efficiency of the transplanting process. Nonetheless, the existing seedling retrieval mechanisms in current transplanting machines exhibit several deficiencies, including substantial damage to seedlings and inadequate retrieval accuracy. To overcome these challenges, we propose an integrated approach combining pneumatic and mechanical techniques to further improve performance. By employing a lower thimble elevation and clamping mechanism, alongside a mathematical model based on the seedling removal process, this method ensures precise seedling extraction and minimizes damage to the root system and substrate. The novelty of this study lies in its ability to reduce the adhesion between seedlings and the holes of the plug plate, thereby minimizing non-destructive extraction of the seedlings and preserving the integrity of the matrix, which is essential for ensuring healthy seedling growth. Moreover, the optimization of the seedling retrieval trajectory enhances the accuracy of the seedling retrieval mechanism while also meeting the requisite speed requirements. Experimental results indicate that at a rate of 72 seedlings per minute, the extraction success rate reached 94.90%, and the casting success rate was 98.53%. The seedling injury rate was only 1.95%, resulting in an overall success rate of 91.69%. These findings confirm that the device meets operational efficiency requirements and delivers effective performance.

Application of Multimedia Information Retrieval Technology in Japanese Text Content Information Query Platform

The research and development of Japanese operating platform has become a new industry. At the same time, it is necessary to strengthen the research on query and feedback technology in MIR(Multimedia Information Retrieval). Based on this, this paper introduces the research status and progress of query and feedback technology in multimedia retrieval, and introduces the overall framework and technical links of MIR. This paper discusses the application of MIR technology in Japanese text content information query platform. A new multimodal retrieval mechanism is proposed, which combines cross-reference index and underlying features in retrieval and related feedback to realize unified processing of multimodal data. The time consumption of the algorithm is lower than that of the two comparative algorithms, which also verifies the superiority. It is feasible to apply the MIR technology proposed in this paper to the Japanese text content information query platform. It provides a new path for Japanese text content information retrieval.