retrieval phase
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: The medical diagnostic process works very similarly to the Case Based Reasoning (CBR) cycle scheme. CBR is a problem solving approach based on the reuse of past experiences called cases. To improve the performance of the retrieval phase, a Random Forest (RF) model is proposed, in this respect we used this algorithm in three different ways (three different algorithms): Classic Random Forest (CRF) algorithm, Random Forest with Feature Selection (RF_FS) algorithm where we selected the most important attributes and deleted the less important ones and Weighted Random Forest (WRF) algorithm where we weighted the most important attributes by giving them more weight. We did this by multiplying the entropy with the weight corresponding to each attribute.We tested our three algorithms CRF, RF_FS and WRF with CBR on data from 11 medical databases and compared the results they produced. We found that WRF and RF_FS give better results than CRF. The experiemental results show the performance and robustess of the proposed approach.

Neuroimaging, neuropsychological, and brain stimulation studies have led to contrasting findings regarding the potential roles of the lateral parietal lobe in episodic memory. Studies using brain stimulation methods reported in the literature do not offer unequivocal findings on the interactions with stimulation location (left vs. right hemisphere) or timing of the stimulation (encoding vs. retrieval). To address these issues, active and sham 1 Hz repetitive transcranial magnetic stimulation (rTMS) trains of 600 stimuli were applied over the right or left posterior parietal cortex (PPC) before the encoding or before the retrieval phase of a recognition memory task of unknown faces in a group of 40 healthy subjects. Active rTMS over the right but not the left PPC significantly improved non-verbal recognition memory performance without any significant modulation of speed of response when applied before the retrieval phase. In contrast, rTMS over the right or the left PPC before the encoding phase did not modulate memory performance. Our results support the hypothesis that the PPC plays a role in episodic memory retrieval that appears to be dependent on both the hemispheric lateralization and the timing of the stimulation (encoding vs. retrieval).

AbstractWe propose an efficient algorithm to solve inverse problems in the presence of binary clustered datasets. We consider the paradigmatic Hopfield model in a teacher student scenario, where this situation is found in the retrieval phase. This problem has been widely analyzed through various methods such as mean-field approaches or the pseudo-likelihood optimization. Our approach is based on the estimation of the posterior using the Thouless–Anderson–Palmer (TAP) equations in a parallel updating scheme. Unlike other methods, it allows to retrieve the original patterns of the teacher dataset and thanks to the parallel update it can be applied to large system sizes. We tackle the same problem using a restricted Boltzmann machine (RBM) and discuss analogies and differences between our algorithm and RBM learning.

Abstract Question answering(Q&A) system is important for accelerating the landing of artificial intelligence. This paper makes an improvement on the Q&A system which uses the method of retrieval-machine reading comprehension (MRC). In the retrieval phase, we use BM25 to recall some documents and split these documents into paragraphs, then we reorder the paragraphs according to the correlation with the question, so as to reduce the number of recalled paragraphs and improve the speed of MRC. In the MRC stage, we design a multi-task MRC structure, which can judge whether the paragraph contains answer and locate answer accurately. Besides, we modify the loss function to fit the sparse labels during the training. The experiments are carried out on multiple data sets to verify the effectiveness of the improved system.

Objective: Amnesia in Alzheimer's disease (AD) appears early and could be caused by encoding deficiency, consolidation dysfunction, and/or impairment in the retrieval of stored memory information. The relationship between AD pathology biomarker β-amyloid and memory dysfunction is unclear.Method: The memory task functional MRI and amyloid PET were simultaneously performed to investigate the relationship between memory performance, memory phase-related functional connectivity, and cortical β-amyloid deposition. We clustered functional networks during memory maintenance and compared network connectivity between groups in each memory phase. Mediation analysis was performed to investigate the mediator between β-amyloid and related cognitive performance.Results: Alzheimer's disease was primarily characterized by decreased functional connectivity in a data-driven network composed of an a priori default mode network, limbic network, and frontoparietal network during the memory maintenance (0.205 vs. 0.236, p = 0.04) and retrieval phase (0.159 vs. 0.183, p = 0.017). Within the network, AD had more regions with reduced connectivity during the retrieval than the maintenance and encoding phases (chi-square p = 0.01 and < 0.001). Furthermore, the global cortical β-amyloid negatively correlated with network connectivity during the memory retrieval phase (R = – 0.247, p = 0.032), with this relationship mediating the effect of cortical β-amyloid on memory performance (average causal mediation effect = – 0.05, p = 0.035).Conclusion: We demonstrated that AD had decreased connectivity in specific networks during the memory retrieval phase. Impaired functional connectivity during memory retrieval mediated the adverse effect of β-amyloid on memory. These findings help to elucidate the involvement of cortical β-amyloid (Aβ) in the memory performance in the early stages of AD.

Music’s power to trigger memories has rarely been tested; in particular, it is not clear what mechanisms govern memory retrieval elicited by musical cues. Previous research has suggested that memory retrieval is underpinned by two mechanisms: (1) distinctiveness—the probability that a cue will retrieve a memory declines with the number of stimuli previously encoded with that same cue and (2) incongruence—a cue encoded with emotionally incongruent targets triggers more memories of the stimuli associated with it than a cue encoded with emotionally congruent stimuli. Our participants experienced an implicit encoding phase where they were presented with auditory-visual pairs of stimuli (pieces of music and images of facial expressions). In the retrieval phase, participants were asked to remember encoded stimuli triggered by music. As expected, musical cues encoded with emotionally incongruent facial expressions triggered more memories than cues encoded with congruent facial expressions. Contrary to our prediction and to previous findings, music from distinctive pairs of stimuli triggered fewer memories than cue pairs displayed multiple times in the encoding phase. Our finding suggests that the manipulation of stimuli at encoding is crucial when using musical cues to trigger memories.

One of the influential models in the artificial neural network (ANN) research field for addressing the issue of knowledge in the non-systematic logical rule is Random k Satisfiability. In this context, knowledge structure representation is also the potential application of Random k Satisfiability. Despite many attempts to represent logical rules in a non-systematic structure, previous studies have failed to consider higher-order logical rules. As the amount of information in the logical rule increases, the proposed network is unable to proceed to the retrieval phase, where the behavior of the Random Satisfiability can be observed. This study approaches these issues by proposing higher-order Random k Satisfiability for k ≤ 3 in the Hopfield Neural Network (HNN). In this regard, introducing the 3 Satisfiability logical rule to the existing network increases the synaptic weight dimensions in Lyapunov’s energy function and local field. In this study, we proposed an Election Algorithm (EA) to optimize the learning phase of HNN to compensate for the high computational complexity during the learning phase. This research extensively evaluates the proposed model using various performance metrics. The main findings of this research indicated the compatibility and performance of Random 3 Satisfiability logical representation during the learning and retrieval phase via EA with HNN in terms of error evaluations, energy analysis, similarity indices, and variability measures. The results also emphasized that the proposed Random 3 Satisfiability representation incorporates with EA in HNN is capable to optimize the learning and retrieval phase as compared to the conventional model, which deployed Exhaustive Search (ES).

The self-reference effect (SRE) refers to better memory for self-relevant than for other-relevant information. Generally, the SRE is found in conditions in which links between the stimuli and the self are forged in the encoding phase. To investigate the possibility that such conditions are not prerequisites for the SRE, this research developed two conditions by using two recognition tasks involving abstract geometric shapes (AGSs). One was the cue-in-encoding condition in which self- and other-cues were presented to construct links with AGSs during the encoding phase, and the other was the cue-in-retrieval condition in which self- and other-cues were presented to construct links with AGSs during the retrieval phase. The SRE was found in both conditions. The findings reveal that self-cues merely presented during the retrieval phase are sufficient to induce the SRE. Links between the stimuli and the self constructed during the encoding phase may not be necessary prerequisites for the SRE.