Constrained structure of ancient Chinese poetry facilitates speech content grouping

Ancient Chinese poetry is constituted by structured language that deviates from ordinary language usage [1, 2]; its poetic genres impose unique combinatory constraints on linguistic elements [3]. How does the constrained poetic structure facilitate speech segmentation when common linguistic [4, 5, 6, 7, 8] and statistical cues [5, 9] are unreliable to listeners in poems? We generated artificial Jueju, which arguably has the most constrained structure in ancient Chinese poetry, and presented each poem twice as an isochronous sequence of syllables to native Mandarin speakers while conducting magnetoencephalography (MEG) recording. We found that listeners deployed their prior knowledge of Jueju to build the line structure and to establish the conceptual flow of Jueju. Unprecedentedly, we found a phase precession phenomenon indicating predictive processes of speech segmentation—the neural phase advanced faster after listeners acquired knowledge of incoming speech. The statistical co-occurrence of monosyllabic words in Jueju negatively correlated with speech segmentation, which provides an alternative perspective on how statistical cues facilitate speech segmentation. Our findings suggest that constrained poetic structures serve as a temporal map for listeners to group speech contents and to predict incoming speech signals. Listeners can parse speech streams by using not only grammatical and statistical cues but also their prior knowledge of the form of language

Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM

Critical Current Density and Resistivity of MgB2 Films

The high resistivity of many bulk and film samples of MgB2 is most readily explained by the suggestion that only a fraction of the cross-sectional area of the samples is effectively carrying current. Hence the supercurrent (Jc) in such samples will be limited by the same area factor, arising for example from porosity or from insulating oxides present at the grain boundaries. We suggest that a correlation should exist, Jc ~ 1/{Rho(300K) - Rho(50K)}, where Rho(300K) - Rho(50K) is the change in the apparent resistivity from 300 K to 50 K. We report measurements of Rho(T) and Jc for a number of films made by hybrid physical-chemical vapor deposition which demonstrate this correlation, although the "reduced effective area" argument alone is not sufficient. We suggest that this argument can also apply to many polycrystalline bulk and wire samples of MgB2.Comment: 11 pages, 3 figure

An enhanced fluorescent ZIF-8 film by capturing guest molecules for light-emitting applications

Metal organic frameworks (MOFs) constructed from metal ions/clusters and organic linkers have led to new porous luminescent materials and, thus, have attracted much attention. But their photoluminescence quantum yields (PLQYs) are not high and the photoluminescence mechanism is still unclear. In order to solve this problem, in this work, zeolitic imidazolate framework-8 (ZIF-8, a kind of MOF) composite films containing small molecules of acetic acid were successfully prepared on a glass substrate via a sol–gel method, and they exhibited a high PLQY of 54.42%. Compared with pure ZIF-8 film, the obtained ZIF-8 composite film presented a significant enhancement in blue light emission. Moreover, the PLQY of the ZIF-8 composite film can be controlled to a certain extent via adjusting the molar ratio of the ligands to the zinc source. Among them, ZIF-8 film prepared at a molar ratio of 2.5 : 1 showed higher transmittance, and the fluorescence quantum efficiency reached 54.42%, which may be attributed to electron transfer between acetic acid as an electron-donor and the ZIF-8 structure caused by their relatively strong hydrogen bonding interactions. This work may provide new insights into the enhanced fluorescence of MOF materials for light-emitting applications