Episodic memory enhancement versus impairment is determined by contextual similarity across events

For over a century, stability of spatial context across related episodes has been considered a source of memory interference, impairing memory retrieval. However, contemporary memory integration theory generates a diametrically opposite prediction. Here, we aimed to resolve this discrepancy by manipulating local context similarity across temporally disparate but related episodes and testing the direction and underlying mechanisms of memory change. A series of experiments show that contextual stability produces memory integration and marked reciprocal strengthening. Variable context, conversely, seemed to result in competition such that new memories become enhanced at the expense of original memories. Interestingly, these patterns were virtually inverted in an additional experiment where context was reinstated during recall. These observations 1) identify contextual similarity across original and new memories as an important determinant in the volatility of memory, 2) present a challenge to classic and modern theories on episodic memory change, and 3) indicate that the sensitivity of context-induced memory changes to retrieval conditions may reconcile paradoxical predictions of interference and integration theory.

Neural temporal context reinstatement of event structure during memory recall

Although life unfolds continuously, experiences are generally perceived and remembered as discrete events. Accumulating evidence suggests that event boundaries disrupt temporal representations and weaken memory associations. However, less is known about the consequences of event boundaries on temporal representations during retrieval, especially when temporal information is not tested explicitly. Using a neural measure of temporal context extracted from scalp electroencephalography, we found reduced temporal context similarity between studied items separated by an event boundary when compared to items from the same event. Further, while participants free recalled list items, neural activity reflected reinstatement of temporal context representations from study, including temporal disruption. A computational model of episodic memory, the Context Maintenance and Retrieval model (CMR; Polyn, Norman & Kahana, 2009), predicted these results, and made novel predictions regarding the influence of temporal disruption on recall order. These findings implicate the impact of event structure on memory organization via temporal representations.

Representation Learning with LDA Models for Entity Disambiguation in Specific Domains

Entity disambiguation is extremely important in knowledge construction. The word representation model ignores the influence of the ordering between words on the sentence or text information. Thus, we propose a domain entity disambiguation method that fuses the doc2vec and LDA topic models. In this study, the doc2vec document is used to indicate that the model obtains the vector form of the entity reference item and the candidate entity from the domain corpus and knowledge base, respectively. Moreover, the context similarity and category referential similarity calculations are performed based on the knowledge base of the upper and lower relation domains that are constructed. The LDA topic model and doc2vec model are used to obtain word expressions with different meanings of polysemic words. We use the k-means algorithm to cluster the word vectors under different topics to obtain the topic domain keywords of the text, and perform the similarity calculations under the domain keywords of the different topics. Finally, the similarities of the three feature types are merged and the candidate entity with the highest similarity degree is used as the final target entity. The experimental results demonstrate that the proposed method outperforms the existing model, which proves its feasibility and effectiveness.

Reconstruction Method for Missing Measurement Data Based on Wasserstein Generative Adversarial Network

Focusing on the issue of missing measurement data caused by complex and changeable working conditions during the operation of high-speed trains, in this paper, a framework for the reconstruction of missing measurement data based on a generative adversarial network is proposed. Suitable parameters were set for each frame. Discrete measurement data are taken as the input of the frame for preprocessing the data dimensionality. The convolutional neural network then learns the correlation between different characteristic values of each device in an unsupervised pattern and constrains and improves the reconstruction accuracy by taking advantage of the context similarity of authenticity. It was determined experimentally that when there are different extents of missing measurement data, the model described in the present paper can still maintain a high reconstruction accuracy. In addition, the reconstruction data also conform well to the distribution law of the measurement data.

Episodic memory enhancement versus impairment is determined by contextual similarity across events

For over a century, stability of environmental context across related episodes has been considered a source of memory interference. However, contemporary memory integration theory generates a diametrically opposite prediction. Here, we aimed to resolve this discrepancy by manipulating local context similarity across temporally disparate but related episodes, and testing the direction and underlying mechanisms of memory change. A series of experiments show that contextual stability produces memory integration and marked reciprocal strengthening, whereas variable context results in one memory to dominate at a related memory’s expense. Intriguingly, however, retrieval patterns reversed when the original encoding contexts were reintroduced during memory recall. These observations (i) identify environmental context during new learning and subsequent recall as opposing determinants in the volatility of memory, (ii) present a challenge to several classic and modern theories on episodic memory change, and (iii) reconcile paradoxical predictions of memory interference and integration.

A citation recommendation method based on context correlation

Researchers need to formulate their achievements as research papers. Representative references are essential to high-quality papers. Academic citation recommendation refers to providing the recommendation of citations for the author of papers when they write. With the help of citation recommendation, researchers can improve the efficiency of writing academic papers and reduce the omission of important related literature. To achieve this goal, some methods were proposed. Many of them used citation networks to learn the representation of papers and chose references, they tended to ignore the content properties of papers. There are also some methods used partial properties to recommend citation. But their performance can be further improved. In this paper, we propose a citation recommendation method based on context correlation. We use two neural network models to learn the representations of papers and their references, then calculate the context similarity of them. Besides, we also introduce the publishing time and authority of papers, two key properties of papers for citation evaluation. In the experiment section, we compare our method with other methods and evaluate the performance of different properties choice in our method, it shows that our method outperforms some baselines and the combination of the dimensions including time, authority and context performs better.

Building Change Detection Using a Shape Context Similarity Model for LiDAR Data

In this paper, a novel building change detection approach is proposed using statistical region merging (SRM) and a shape context similarity model for Light Detection and Ranging (LiDAR) data. First, digital surface models (DSMs) are generated from LiDAR acquired at two different epochs, and the difference data D-DSM is created by difference processing. Second, to reduce the noise and registration error of the pixel-based method, the SRM algorithm is applied to segment the D-DSM, and multi-scale segmentation results are obtained under different scale values. Then, the shape context similarity model is used to calculate the shape similarity between the segmented objects and the buildings. Finally, the refined building change map is produced by the k-means clustering method based on shape context similarity and area-to-length ratio. The experimental results indicated that the proposed method could effectively improve the accuracy of building change detection compared with some popular change detection methods.