Physiologically-Motivated Feature Extraction Methods for Speaker Recognition

Speaker recognition has received a great deal of attention from the speech community, and significant gains in robustness and accuracy have been obtained over the past decade. However, the features used for identification are still primarily representations of overall spectral characteristics, and thus the models are primarily phonetic in nature, differentiating speakers based on overall pronunciation patterns. This creates difficulties in terms of the amount of enrollment data and complexity of the models required to cover the phonetic space, especially in tasks such as identification where enrollment and testing data may not have similar phonetic coverage. This dissertation introduces new features based on vocal source characteristics intended to capture physiological information related to the laryngeal excitation energy of a speaker. These features, including RPCC, GLFCC and TPCC, represent the unique characteristics of speech production not represented in current state-of-the-art speaker identification systems. The proposed features are evaluated through three experimental paradigms including cross-lingual speaker identification, cross song-type avian speaker identification and mono-lingual speaker identification. The experimental results show that the proposed features provide information about speaker characteristics that is significantly different in nature from the phonetically-focused information present in traditional spectral features. The incorporation of the proposed glottal source features offers significant overall improvement to the robustness and accuracy of speaker identification tasks

Weak degeneracy of planar graphs and locally planar graphs

Weak degeneracy is a variation of degeneracy which shares many nice properties of degeneracy. In particular, if a graph $G$ is weakly $d$-degenerate, then for any $(d + 1)$-list assignment $L$ of $G$, one can construct an $L$-coloring of $G$ by a modified greedy coloring algorithm. It is known that planar graphs of girth 5 are 3-choosable and locally planar graphs are 5-choosable. This paper strengthens these results and proves that planar graphs of girth 5 are weakly 2-degenerate and locally planar graphs are weakly 4-degenerate.Comment: 13page

Latent Graph Inference with Limited Supervision

Latent graph inference (LGI) aims to jointly learn the underlying graph structure and node representations from data features. However, existing LGI methods commonly suffer from the issue of supervision starvation, where massive edge weights are learned without semantic supervision and do not contribute to the training loss. Consequently, these supervision-starved weights, which may determine the predictions of testing samples, cannot be semantically optimal, resulting in poor generalization. In this paper, we observe that this issue is actually caused by the graph sparsification operation, which severely destroys the important connections established between pivotal nodes and labeled ones. To address this, we propose to restore the corrupted affinities and replenish the missed supervision for better LGI. The key challenge then lies in identifying the critical nodes and recovering the corrupted affinities. We begin by defining the pivotal nodes as $k$-hop starved nodes, which can be identified based on a given adjacency matrix. Considering the high computational burden, we further present a more efficient alternative inspired by CUR matrix decomposition. Subsequently, we eliminate the starved nodes by reconstructing the destroyed connections. Extensive experiments on representative benchmarks demonstrate that reducing the starved nodes consistently improves the performance of state-of-the-art LGI methods, especially under extremely limited supervision (6.12% improvement on Pubmed with a labeling rate of only 0.3%)

Identifying teleconnections and multidecadal variability of East Asian surface temperature during the last millennium in CMIP5 simulations

We examine the relationships in models and reconstructions between the multidecadal variability of surface temperature in East Asia and two extratropical modes of variability: the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO). We analyse the spatial, temporal and spectral characteristics of the climate modes in the last millennium, historical and pre-industrial control simulations of seven Coupled Model Intercomparison Project phase 5 (CMIP5)/Paleoclimate Model Intercomparison Project phase 3 (PMIP3) global climate models (GCMs) to assess the relative influences of external forcing and unforced variability. These models produce PDO and AMO variability with realistic spatial patterns but widely varying spectral characteristics. AMO internal variability significantly influences East Asian temperature in five models (MPI, HadCM3, MRI, IPSL and CSIRO) but has a weak influence in the other two (BCC and CCSM4). In most models, external forcing greatly strengthens these statistical associations and hence the apparent teleconnection with the AMO. PDO internal variability strongly influences East Asian temperature in two out of seven models, but external forcing makes this apparent teleconnection much weaker. This indicates that the AMO–East Asian temperature relationship is partly driven by external forcing, whereas the PDO–temperature relationship is largely from internal variability within the climate system. Our findings suggest that external forcing confounds attempts to diagnose the teleconnections of internal multidecadal variability. Using AMO and PDO indices that represent internal variability more closely and minimising the influence of external forcing on East Asian temperature can partly ameliorate this confounding effect. Nevertheless, these approaches still yield differences between the forced and control simulations and they cannot always be applied to paleoclimate reconstructions. Thus, we recommend caution when interpreting teleconnections diagnosed from reconstructions that contain both forced and internal variations

Cognitive manipulation of emotional and non-emotional information in working memory of patients with depression: a rigid processing style

IntroductionCognitive psychology is one of the important perspectives to understand depression. Compared with previous studies, recent researchers increasingly focused on the exploration of the comprehensive cognitive process of patients with depression. The cognitive operation ability of working memory is an important comprehensive cognitive process, which reflects how individuals establish representations. This is the basis for the formation of experience and schema. The purpose of this study is to explore whether there are abnormalities in cognitive manipulation in patients with depression, and to analyze its possible role in the pathogenesis and maintenance of depression.MethodIn this cross-sectional study, depressed patients was enrolled in the clinical psychology department of Beijing Chaoyang Hospital as the case group, while healthy individuals were recruited in the hospital and social meetings as the control group. Hamilton Depression Scale (HAMD)-17, Hamilton Anxiety Scale (HAMA) and rumination thinking scale (RRS) were adopted as measurement tools, and working memory operation tasks were adopted to test each subject, so as to measure their cognitive operation ability.ResultA total of 78 depressed patients and 81 healthy individuals completed the study. The results showed that the rumination level of the case group was higher than that of the control group, and the difference was significant first; Second, in the “inconsistent” condition, the case group under different stimulus conditions when the response was significantly higher than the control group; Thirdly, the “cognitive operation consumption” value of the case group was significantly higher than that of the control group under the three stimulus conditions, among which, the operational cost value of sadness—neutral stimulus was significantly higher than that of the other two stimulus conditions.ConclusionPatients with depression had obvious difficulties in cognitive manipulation of information with different values in working memory, which reflected in the fact that it took them longer time to adjust the relationship between information and established new representations. Among them, patients with depression had a higher degree of cognitive manipulation of sad stimuli, indicating that their abnormal cognitive manipulation had certain emotion specificity. Finally, the difficulty of cognitive operation was closely related to the level of rumination

Causes of East Asian temperature multidecadal variability since 850 CE

The drivers of multidecadal to centennial‐scale variability in East Asian temperature, apparent in temperature reconstructions, are poorly understood. Here, we apply a multivariate regression analysis to distinguish the influences of large‐scale modes of internal variability (Atlantic Multidecadal Oscillation, AMO; and Pacific Multidecadal Oscillation, PMO), and external natural (orbital, solar and volcanic) and anthropogenic (greenhouse gas concentrations, aerosols, and land use changes) forcings on East Asian warm‐season temperature over the period 850–1999 AD. We find that ~80% of the temperature change on timescales longer than 30 years can be explained including all drivers over the full‐length period. The PMO was the most important driver of multidecadal temperature variability during the Medieval Climate Anomaly (here, 950–1250), while solar contribution was important during the Little Ice Age (here, 1350–1850). Since 1850, two‐thirds of temperature change can be explained with anthropogenic forcing, whereas one‐third was related mainly to the AMO and volcanic forcing

Group delay dispersion monitoring for computational manufacturing of dispersive mirrors

We present a computational manufacturing program for monitoring group delay dispersion (GDD). Two kinds of dispersive mirrors computational manufactured by GDD, broadband, and time monitoring simulator are compared. The results revealed the particular advantages of GDD monitoring in dispersive mirror deposition simulations. The self-compensation effect of GDD monitoring is discussed. GDD monitoring can improve the precision of layer termination techniques, it may become a possible approach to manufacture other optical coatings

Distributed secure surrounding control for multiple USVs against deception attacks: a Stackelberg game approach with reinforcement learning

This article investigates the Stackelberg game-based distributed secure surrounding control (SSC) problem for multiple unmanned surface vehicles (USVs) with unknown dynamics under deception attacks (DAs). The proposed scheme is rooted in the Stackelberg game with reinforcement learning (RL), in which the attacker and controller respectively play the roles of the follower and leader, making sequential decisions of the DA and the SSC. Specifically, a distributed target estimator is established to access the target position. By utilizing this estimated position to formulate an intermediate control law, the target surrounding scenario is effectively transformed into the Stackelberg game-solving problem. The RL approach with a neural networks-based actor-critic learning structure is deployed to directly derive the distributed optimal SSC and DA policies from the Bellman error, whilst learning the unknown dynamics of USVs. Moreover, a value function decomposition technique is applied optimally for using the designed control parameters, thereby accelerating the acquisition of optimal policies. A rigorous theoretical analysis is employed to ensure the closed-loop stability of multiple USVs. Simulations are provided to validate the effectiveness of the proposed distributed SSC scheme for multiple USVs