Studies of the functional role of the primate ventral visual stream have traditionally focused on object categorization, often ignoring -- despite much prior evidence -- its role in estimating "spatial" latents such as object position and pose. Most leading ventral stream models are derived by optimizing networks for object categorization, which seems to imply that the ventral stream is also derived under such an objective. Here, we explore an alternative hypothesis: Might the ventral stream be optimized for estimating spatial latents? And a closely related question: How different -- if at all -- are representations learned from spatial latent estimation compared to categorization? To ask these questions, we leveraged synthetic image datasets generated by a 3D graphic engine and trained convolutional neural networks (CNNs) to estimate different combinations of spatial and category latents. We found that models trained to estimate just a few spatial latents achieve neural alignment scores comparable to those trained on hundreds of categories, and the spatial latent performance of models strongly correlates with their neural alignment. Spatial latent and category-trained models have very similar -- but not identical -- internal representations, especially in their early and middle layers. We provide evidence that this convergence is partly driven by non-target latent variability in the training data, which facilitates the implicit learning of representations of those non-target latents. Taken together, these results suggest that many training objectives, such as spatial latents, can lead to similar models aligned neurally with the ventral stream. Thus, one should not assume that the ventral stream is optimized for object categorization only. As a field, we need to continue to sharpen our measures of comparing models to brains to better understand the functional roles of the ventral stream.

The presence of a large number of low-availability nodes in a decentralized environment results in lower overall data availability. Existing systems improve data availability by replicating a data item into multiple replicas across different nodes. Due to disparity in node availability in decentralized environments, replica placement strategies significantly affect overall data availability. Compared to random strategies, greedy strategies effectively improve overall data availability under low system storage utilization. However, greedy strategies fail to achieve the global optimum in the stable storage utilization period. Our modeling analysis reveals that, under stable storage utilization, (1)the overall data availability has a theoretical upper bound; and (2)this bound is achieved if and only if the availability of each data item equals the standard value, which is determined by the geometric mean of node availability and the replication factor. Based on the above analysis, we propose AvailabilityBalanced Store (ABS) to achieve near-optimal overall data availability during the stable period. ABS estimates the standard value based on the preset stable period storage utilization, replication factor and node availability statistics. When a new data item is added, it looks for appropriate nodes to place replicas so that the data item's availability approaches the standard value. In particular, we develop a fast node selection algorithm with$O(N)$complexity. In addition, we employ the combination of static and dynamic replica techniques to reduce the data migration volume during system utilization adjustments. We conduct extensive simulations to validate the effectiveness of ABS.

EEG (Electroencephalography)-based emotion recognition has emerged as a crucial area of research due to its potential applications in mental health, brain-computer interfaces (BCIs), and affective computing. However, the inherent variability in EEG signals across individuals, coupled with limited dataset sizes, significantly hinders the development of robust and generalizable emotion recognition models. To overcome these challenges, we propose the Sparse Bayesian Learning with Enhanced Covariance Alignment (SBLECA) algorithm. SBLECA formulates cross-subject emotion recognition as an end-to-end decoding problem, integrating spatiotemporal filtering and classification within a sparse Bayesian learning (SBL) framework. Crucially, SBLECA incorporates a novel covariance alignment technique to mitigate inter-subject variability in EEG patterns. Rigorous evaluations on two publicly available emotion datasets demonstrate that SBLECA consistently outperforms state-of-the-art methods. Furthermore, SBLECA offers valuable insights into the neural correlates of emotion through interpretable visualizations of learned spatial and temporal filters. SBLECA holds promise as a valuable EEG decoding tool to advance the development and translation of neurotechnologies and biomarkers for brain disorders.

An affective brain-computer interface (aBCI) can detect affective states based on brain signals and might assist people in improving their emotion regulation abilities. However, individual differences in emotional brain patterns make cross-subject emotion identification extremely challenging. Traditional supervised single-subject classification schemes require considerable calibration samples from new individuals to train subject-dependent models. Individuals are easily fatigued with long-term EEG collection processes, which may affect performance in subsequent online experiments. In this study, we propose a real-time aBCI system using domain-fusion-based multisource style transfer mapping (DF-MS-STM) to detect positive, neutral, and negative emotional states without the need for additional training sessions. Sixteen subjects participated in our online experiments to test the performance of our aBCI system and an average online prediction accuracy of 72.17$\pm$12.25% was obtained for three-class emotion recognition tasks in the last three experimental sessions. Our proposed algorithm significantly outperformed numerous baseline methods in terms of cross-subject emotion classification. In addition, we identified distinct brain patterns in response to different emotional stimuli based on the results of event-related spectral perturbation (ERSP) analyses. These neural patterns might provide new insights for emotional brain mechanistic studies and related aBCIs.

Electroencephalography (EEG)-based emotion recognition plays a key role in the development of affective brain-computer interfaces (BCIs). However, emotions are complex and extracting salient EEG features underlying distinct emotional states is inherently limited by low signal-to-noise ratio (SNR) and low spatial resolution of practical EEG data, which is further compounded by the lack of effective spatio-temporal filter optimization approaches for generic EEG features. To address these challenges, this study proposes a set of neural networks termed the Filter-Bank Spatio-Temporal Convolutional Networks (FBSTCNets) for performing end-to-end multi-class emotion recognition via robust extraction of power and/or connectivity features from EEG. First, a filter bank is employed to construct a multiview spectral representation of EEG data. Next, a temporal convolutional layer, followed by a depth-wise spatial convolutional layer, performs spatio-temporal filtering, transforming EEG into latent signals with higher SNR. A feature extraction layer then extracts power and/or connectivity features from the latent signals. Finally, a fully connected layer with a cropped decoding strategy predicts the emotional state. Experimental results on two public emotion EEG datasets, SEED and SEED-IV, demonstrate that FBSTCNets outperform previous benchmark methods in decoding accuracy. Our approach provides a principled emotion decoding framework for designing high-performance spatio-temporal filtering networks tailored to specific EEG feature types. The FBSTCNet source code is available athttps://github.com/TimeSpacerRob/FBSTCNet.

Emotion regulation plays a vital role in human beings daily lives by helping them deal with social problems and protects mental and physical health. However, objective evaluation of the efficacy of emotion regulation and assessment of the improvement in emotion regulation ability at the individual level remain challenging. In this study, we leveraged neurofeedback training to design a real-time EEG-based brain-computer interface (BCI) system for users to effectively regulate their emotions. Twenty healthy subjects performed 10 BCI-based neurofeedback training sessions to regulate their emotion towards a specific emotional state (positive, negative, or neutral), while their EEG signals were analyzed in real time via machine learning to predict their emotional states. The prediction results were presented as feedback on the screen to inform the subjects of their immediate emotional state, based on which the subjects could update their strategies for emotion regulation. The experimental results indicated that the subjects improved their ability to regulate these emotions through our BCI neurofeedback training. Further EEG-based spectrum analysis revealed how each emotional state was related to specific EEG patterns, which were progressively enhanced through long-term training. These results together suggested that long-term EEG-based neurofeedback training could be a promising tool for helping people with emotional or mental disorders.

Data reliability and availability, and serviceability (RAS) of erasure-coded data centers are highly affected by data repair induced by node failures. In a traditional failure identification scheme, all chunks share the same identification time threshold, thus losing opportunities to further improve the RAS. To solve this problem, we propose RAFI, a novel risk-aware failure identification scheme. In RAFI, chunk failures in stripes experiencing different numbers of failed chunks are identified using different time thresholds. For those chunks in a high-risk stripe, a shorter identification time is adopted, thus improving the overall data reliability and availability. For those chunks in a low-risk stripe, a longer identification time is adopted, thus reducing the repair network traffic. Therefore, RAS can be improved simultaneously. We also propose three optimization techniques to reduce the additional overhead that RAFI imposes on management nodes and to ensure that RAFI can work properly under large-scale clusters. We use simulation, emulation, and prototyping implementation to evaluate RAFI from multiple aspects. Simulation and prototype results prove the effectiveness and correctness of RAFI, and the performance improvement of the optimization techniques on RAFI is demonstrated by running the emulator.