Model-Based Method for Social Network Clustering

We propose a simple mixed membership model for social network clustering in this note. A flexible function is adopted to measure affinities among a set of entities in a social network. The model not only allows each entity in the network to possess more than one membership, but also provides accurate statistical inference about network structure. We estimate the membership parameters by using an MCMC algorithm. We evaluate the performance of the proposed algorithm by applying our model to two empirical social network data, the Zachary club data and the bottlenose dolphin network data. We also conduct some numerical studies for different types of simulated networks for assessing the effectiveness of our algorithm. In the end, some concluding remarks and future work are addressed briefly

Reconstructing ERP amplitude effects after compensating for trial-to-trial latency jitter: A solution based on a novel application of residue iteration decomposition

© 2016 The Authors Stimulus-locked averaged event-related potentials (ERPs) are among the most frequently used signals in Cognitive Neuroscience. However, the late, cognitive or endogenous ERP components are often variable in latency from trial to trial in a component-specific way, compromising the stability assumption underlying the averaging scheme. Here we show that trial-to-trial latency variability of ERP components not only blurs the average ERP waveforms, but may also attenuate existing or artificially induce condition effects in amplitude. Hitherto this problem has not been well investigated. To tackle this problem, a method to measure and compensate component-specific trial-to-trial latency variability is required. Here we first systematically analyze the problem of single trial latency variability for condition effects based on simulation. Then, we introduce a solution by applying residue iteration decomposition (RIDE) to experimental data. RIDE separates different clusters of ERP components according to their time-locking to stimulus onsets, response times, or neither, based on an algorithm of iterative subtraction. We suggest to reconstruct ERPs by re-aligning the component clusters to their most probable single trial latencies. We demonstrate that RIDE-reconstructed ERPs may recover amplitude effects that are diminished or exaggerated in conventional averages by trial-to-trial latency jitter. Hence, RIDE-corrected ERPs may be a valuable tool in conditions where ERP effects may be compromised by latency variability.Link_to_subscribed_fulltex

Improving Adversarial Robustness by Contrastive Guided Diffusion Process

Synthetic data generation has become an emerging tool to help improve the adversarial robustness in classification tasks since robust learning requires a significantly larger amount of training samples compared with standard classification tasks. Among various deep generative models, the diffusion model has been shown to produce high-quality synthetic images and has achieved good performance in improving the adversarial robustness. However, diffusion-type methods are typically slow in data generation as compared with other generative models. Although different acceleration techniques have been proposed recently, it is also of great importance to study how to improve the sample efficiency of generated data for the downstream task. In this paper, we first analyze the optimality condition of synthetic distribution for achieving non-trivial robust accuracy. We show that enhancing the distinguishability among the generated data is critical for improving adversarial robustness. Thus, we propose the Contrastive-Guided Diffusion Process (Contrastive-DP), which adopts the contrastive loss to guide the diffusion model in data generation. We verify our theoretical results using simulations and demonstrate the good performance of Contrastive-DP on image datasets

Re-examination of chinese semantic processing and syntactic processing: Evidence from conventional ERPs and reconstructed ERPs by residue iteration decomposition (RIDE)

© 2015 Wang et al. A number of studies have explored the time course of Chinese semantic and syntactic processing. However, whether syntactic processing occurs earlier than semantics during Chinese sentence reading is still under debate. To further explore this issue, an event-related potentials (ERPs) experiment was conducted on 21 native Chinese speakers who read individually-presented Chinese simple sentences (NP1+VP+NP2) word-by-word for comprehension and made semantic plausibility judgments. The transitivity of the verbs was manipulated to form three types of stimuli: congruent sentences (CON), sentences with a semantically violated NP2 following a transitive verb (semantic violation, SEM), and sentences with a semantically violated NP2 following an intransitive verb (combined semantic and syntactic violation, SEM+SYN). The ERPs evoked from the target NP2 were analyzed by using the Residue Iteration Decomposition (RIDE) method to reconstruct the ERP waveform blurred by trial-to-trial variability, as well as by using the conventional ERP method based on stimulus-locked averaging. The conventional ERP analysis showed that, compared with the critical words in CON, those in SEM and SEM+SYN elicited an N400-P600 biphasic pattern. The N400 effects in both violation conditions were of similar size and distribution, but the P600 in SEM+SYN was bigger than that in SEM. Compared with the conventional ERP analysis, RIDE analysis revealed a larger N400 effect and an earlier P600 effect (in the time window of 500-800 ms instead of 570-810ms). Overall, the combination of conventional ERP analysis and the RIDE method for compensating for trial-to-trial variability confirmed the non-significant difference between SEM and SEM+SYN in the earlier N400 time window. Converging with previous findings on other Chinese structures, the currentstudy provides further precise evidence that syntactic processing in Chinese does not occur earlier than semantic processing.Link_to_subscribed_fulltex

Compensation of trial-to-trial latency jitter reveals the parietal retrieval success effect to be both variable and thresholded in older adults

JM was supported by the SINAPSE Postdoctoral and Early Career Researcher Exchange (PECRE) grant: SFC project code H11004. GO is partly supported by Seed Fund for Basic Research from The University of Hong Kong (201804159003). DD is a member of the SINAPSE collaboration (www.sinapse.ac.uk), a pooling initiative funded by the Scottish Funding Council and the Chief Scientific Office of the Scottish Executive.Although the neural mechanism supporting episodic recollection has been well characterized in younger adults, exactly how recollection is supported in older adults remains unclear. The electrophysiological correlate of recollection-the parietal retrieval success effect-for example, has been shown to be sensitive to both the amount of information recollected and the accuracy of remembered information in younger adults. To date, there is mixed evidence that parietal effect also scales with the amount of information remembered in older adults whilst there is little evidence that the same mechanism is sensitive to the accuracy of recollected information. Here, we address one potential concern when investigating Event Related Potentials (ERPs) among older adults-namely, the greater potential for single-trial latency variability to smear and reduces the amplitudes of averaged ERPs. We apply a well-established algorithm for correcting single-trial latency variability, Residual Iteration Decomposition Analysis (RIDE), to investigate whether the parietal retrieval success effect among older adults is sensitive to retrieval accuracy. Our results reveal that similar to younger adults, older adult parietal retrieval success effects scale with the accuracy of recollected information-i.e., is greater in magnitude when recollected information is of high accuracy, reduced in magnitude when accuracy is low, and entirely absent when guessing. The results help clarify the functional significance of the neural mechanism supporting recollection in older adults whilst also highlighting the potential issues with interpreting average ERPs in older adult populations.Publisher PDFPeer reviewe

Effect of the Sodium Silicate Modulus and Slag Content on Fresh and Hardened Properties of Alkali-Activated Fly Ash/Slag

This paper presents the results of an experimental study performed to investigate the effect of activator modulus (SiO2/Na2O) and slag addition on the fresh and hardened properties of alkali-activated fly ash/slag (AAFS) pastes. Four activator moduli (SiO2/Na2O), i.e., 0.0, 1.0, 1.5, and 2.0, and five slag-to-binder ratios, i.e., 0, 0.3, 0.5, 0.7, 1.0, were used to prepare AAFS mixtures. The setting time, flowability, heat evolution, compressive strength, microstructure, and reaction products of AAFS pastes were studied. The results showed that the activator modulus and slag content had a combined effect on the setting behavior and workability of AAFS mixtures. Both the activator modulus and slag content affected the types of reaction products formed in AAFS. The coexistence of N-A-S-H gel and C-A-S-H gel was identified in AAFS activated with high pH but low SiO2 content (low modulus). C-A-S-H gel had a higher space-filling ability than N-A-S-H gel. Thus, AAFS with higher slag content had a finer pore structure and higher heat release (degree of reaction), corresponding to a higher compressive strength. The dissolution of slag was more pronounced when NaOH (modulus of 0.0) was applied as the activator. The use of Na2SiO3 as activator significantly refined the pores in AAFS by incorporating soluble Si in the activator, while further increasing the modulus from 1.5 to 2.0 prohibited the reaction process of AAFS, resulting in a lower heat release, coarser pore structure, and reduced compressive strength. Therefore, in view of the strength and microstructure, the optimum modulus is 1.5

Handling EEG artifacts and searching individually optimal experimental parameter in real time: a system development and demonstration

Abstract: Objective. Neuroadaptive paradigms that systematically assess event-related potential (ERP) features across many different experimental parameters have the potential to improve the generalizability of ERP findings and may help to accelerate ERP-based biomarker discovery by identifying the exact experimental conditions for which ERPs differ most for a certain clinical population. Obtaining robust and reliable ERPs online is a prerequisite for ERP-based neuroadaptive research. One of the key steps involved is to correctly isolate electroencephalography artifacts in real time because they contribute a large amount of variance that, if not removed, will greatly distort the ERP obtained. Another key factor of concern is the computational cost of the online artifact handling method. This work aims to develop and validate a cost-efficient system to support ERP-based neuroadaptive research. Approach. We developed a simple online artifact handling method, single trial PCA-based artifact removal (SPA), based on variance distribution dichotomies to distinguish between artifacts and neural activity. We then applied this method in an ERP-based neuroadaptive paradigm in which Bayesian optimization was used to search individually optimal inter-stimulus-interval (ISI) that generates ERP with the highest signal-to-noise ratio. Main results. SPA was compared to other offline and online algorithms. The results showed that SPA exhibited good performance in both computational efficiency and preservation of ERP pattern. Based on SPA, the Bayesian optimization procedure was able to quickly find individually optimal ISI. Significance. The current work presents a simple yet highly cost-efficient method that has been validated in its ability to extract ERP, preserve ERP effects, and better support ERP-based neuroadaptive paradigm

What Does Temporal Brain Signal Complexity Reveal About Verbal Creativity?

Recent empirical evidence reveals that creative idea generation builds upon an interplay of multiple neural networks. Measures of temporal complexity yield important information about the underlying mechanisms of these co-activated neural networks. A few neurophysiological studies investigated brain signal complexity (BSC) during the production of creative verbal associations and resting states, aiming to relate it with creative task performance. However, it is unknown whether the complexity of brain signals can distinguish between productions of typical and original verbal associations. In the present study, we investigated verbal creativity with multiscale entropy (MSE) of electroencephalography (EEG) signals, which quantifies complexity over multiple timescales, capturing unique dynamic features of neural networks. MSE was measured in verbal divergent thinking (DT) states while emphasizing on producing either typical verbal associations or original verbal associations. We hypothesized that MSE differentiates between brain states characterizing the production of typical and original associations and is a sensitive neural marker of individual differences in producing original associations. Results from a sample of N = 92 young adults revealed slightly higher average MSE for original as compared with typical association production in small and medium timescales at frontal electrodes and slightly higher average MSE for typical association production in higher timescales at parietal electrodes. However, measurement models failed to uncover specificity of individual differences as MSE in typical vs. original associations was perfectly correlated. Hence, individuals with higher MSE in original association condition also exhibit higher MSE during the production of typical associations. The difference between typical and original association MSE was not significantly associated with human-rated originality of the verbal associations. In sum, we conclude that MSE is a potential marker of creative verbal association states, but replications and extensions are needed, especially with respect to the brain-behavior relationships.Peer Reviewe