Distinct roles of delta- and theta-band neural tracking for sharpening and predictive coding of multi-level speech features during spoken language processing

The brain tracks and encodes multi‐level speech features during spoken language processing. It is evident that this speech tracking is dominant at low frequencies (<8 Hz) including delta and theta bands. Recent research has demonstrated distinctions between delta‐ and theta‐band tracking but has not elucidated how they differentially encode speech across linguistic levels. Here, we hypothesised that delta‐band tracking encodes prediction errors (enhanced processing of unexpected features) while theta‐band tracking encodes neural sharpening (enhanced processing of expected features) when people perceive speech with different linguistic contents. EEG responses were recorded when normal‐hearing participants attended to continuous auditory stimuli that contained different phonological/morphological and semantic contents: (1) real‐words, (2) pseudo‐words and (3) time‐reversed speech. We employed multivariate temporal response functions to measure EEG reconstruction accuracies in response to acoustic (spectrogram), phonetic and phonemic features with the partialling procedure that singles out unique contributions of individual features. We found higher delta‐band accuracies for pseudo‐words than real‐words and time‐reversed speech, especially during encoding of phonetic features. Notably, individual time‐lag analyses showed that significantly higher accuracies for pseudo‐words than real‐words started at early processing stages for phonetic encoding (<100 ms post‐feature) and later stages for acoustic and phonemic encoding (>200 and 400 ms post‐feature, respectively). Theta‐band accuracies, on the other hand, were higher when stimuli had richer linguistic content (real‐words > pseudo‐words > time‐reversed speech). Such effects also started at early stages (<100 ms post‐feature) during encoding of all individual features or when all features were combined. We argue these results indicate that delta‐band tracking may play a role in predictive coding leading to greater tracking of pseudo‐words due to the presence of unexpected/unpredicted semantic information, while theta‐band tracking encodes sharpened signals caused by more expected phonological/morphological and semantic contents. Early presence of these effects reflects rapid computations of sharpening and prediction errors. Moreover, by measuring changes in EEG alpha power, we did not find evidence that the observed effects can be solitarily explained by attentional demands or listening efforts. Finally, we used directed information analyses to illustrate feedforward and feedback information transfers between prediction errors and sharpening across linguistic levels, showcasing how our results fit with the hierarchical Predictive Coding framework. Together, we suggest the distinct roles of delta and theta neural tracking for sharpening and predictive coding of multi‐level speech features during spoken language processing

Gate-Tunable Critical Current of the Three-Dimensional Niobium Nano-Bridge Josephson Junction

Recent studies have shown that the critical currents of several metallic superconducting nanowires and Dayem bridges can be locally tuned using a gate voltage {V_g}. Here, we report a gate-tunable Josephson junction structure constructed from a three-dimensional (3D) niobium nano-bridge junction (NBJ) with a voltage gate on top. Measurements up to 6 K showed that the critical current of this structure can be tuned to zero by increasing {V_g}. The critical gate voltage Vgc was reduced to 16 V and may possibly be reduced further by reducing the thickness of the insulation layer between the gate and the NBJ. Furthermore, the flux modulation generated by Josephson interference of two parallel 3D NBJs can also be tuned using {V_g} in a similar manner. Therefore, we believe that this gate-tunable Josephson junction structure is promising for superconducting circuit fabrication at high integration levels.Comment: 15 pages, 5 figure

Geometric Scaling of the Current-Phase Relation of Niobium Nano-Bridge Junctions

The nano-bridge junction (NBJ) is a type of Josephson junction that is advantageous for the miniaturization of superconducting circuits. However, the current-phase relation (CPR) of the NBJ usually deviates from a sinusoidal function which has been explained by a simplified model with correlation only to its effective length. Here, we investigated both measured and calculated CPRs of niobium NBJs of a cuboidal shape with a three-dimensional bank structure. From a sine-wave to a saw-tooth-like form, we showed that deviated CPRs of NBJs can be described quantitatively by its skewness {\Delta}{\theta}. Furthermore, the measured dependency of {\Delta}{\theta} on the critical current {I_0} from 108 NBJs turned out to be consistent with the calculated ones derived from the change in geometric dimensions. It suggested that the CPRs of NBJs can be tuned by their geometric dimensions. In addition, the calculated scaling behavior of {\Delta}{\theta} versus {I_0} in three-dimensional space was provided for the future design of superconducting circuits of a high integration level by using niobium NBJs.Comment: 20 pages, 10 figure

The Ninth Visual Object Tracking VOT2021 Challenge Results

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On the 2-MRS Problem in a Tree with Unreliable Edges

This paper extends the well-known most reliable source (1-MRS) problem in unreliable graphs to the 2-most reliable source (2-MRS) problem. Two kinds of reachable probability models of node pair in unreliable graphs are considered, that is, the superior probability and united probability. The 2-MRS problem aims to find a node pair in the graph from which the expected number of reachable nodes or the minimum reachability is maximized. It has many important applications in large-scale unreliable computer or communication networks. The #P-hardness of the 2-MRS problem in general graphs follows directly from that of the 1-MRS problem. This paper deals with four models of the 2-MRS problem in unreliable trees where every edge has an independent working probability and devises a cubic-time and quadratic-space dynamic programming algorithm, respectively, for each model

Ag NPs-Assisted Synthesis of Stable Cu NPs on PET Fabrics for Antibacterial and Electromagnetic Shielding Performance

In this study, Cu/Ag/polydopamine (PDA)/polyester (PET) fabrics were fabricated for multi-functional textiles. The PET fabrics were firstly modified by dopamine to form a polydopamine (PDA) layer on the fiber surface, then Ag nanoparticles (Ag NPs) were anchored on fiber surface through chelation between PDA and Ag+ ions, and the Ag NPs were further used as catalytic seeds for in situ reduction of Cu nanoparticles (Cu NPs). The surface morphology, chemistry, and crystalline structure of the prepared PET fabrics were characterized by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD). As expected, Cu NPs were evenly dispersed on the surface of fibers. The Cu/Ag/PDA/PET fabrics showed good antibacterial property against Escherichia coli and exhibited excellent electromagnetic interference (EMI) shielding ability. The Cu/Ag/PDA/PET fabrics with high performance antibacterial and EMI shielding properties can be applied as functional protective textiles

The Influence of Fiber Length Distribution on Yarn Properties Based on Fiber Random Arrangement in the Yarn

This study discussed the influence of fiber length distribution on yarn qualities (yarn irregularity and strength) based on simulation on fiber random arrangement. Yarn limit irregularity is expressed as the variation of total fiber length that could be found in each yarn subsection. Yarn strength is expressed as the total contributions that breaking and slipping fibers make to yarn strength. Fiber slippage or breakage depends on critical slipping length. Ramie yarns with different stretch-broken fiber lengths were introduced to verify the calculation. Results show that yarn limit irregularity is only dependent on average fiber length and is irrelevant with the distribution type of fiber length. Yarn limit irregularity rises slowly with the increase of average fiber length. Nevertheless, fiber length distribution has significant effect on yarn strength. When average fiber length and its variation are identical, the left deviated distribution may lead to a lower yarn strength, while the right deviated distribution may obtain a higher yarn strength. Therefore, understanding the influence of fiber length distribution on yarn properties will be helpful for selection of fiber length to produce high-quality yarns