Congregational Music-Making and Community in a Mediated Age

A book review is presented for Congregational Music-Making and Community in a Mediated Age, edited by Anna E. Nekola and Tom Wagner. Congregational Music Studies Series. London: Routledge, 2017. xiv + 263 pp. ISBN 978-1-4724-5919-0 (hardcover first published 2015 by Ashgate), ISBN 978-1-138-56901-0 (paperback)

Mediating Gospel Singing: Audiovisual Recording and the Transformation of Voice among the Christian Lisu in Post-2000 Nujiang, China

The contemporary gospel singing of the Nujiang Lisu in China’s southwestern Yunnan province seems to have been predominated by new media technologies and recorded popular mutgguat ssat music. The prevalence of Christian audiovisual recordings reflects more than a shift in the materiality of Lisu religious practices. Moreover, it speaks to the transformative ways that the Christian Lisu have engaged with technologies for their gospel singing as a practice of religious mediation. New musical styles and expressive forms have been disseminated through recordings and further institutionalized in the worship service and other religious settings. Drawing on a material approach from the recent studies of religion as mediation, rather than perceiving modern mass media as destructive to the traditional hymn singing and authentic religious experience, I examine how recording-mediated mutgguat ssat singing celebrates Lisu religious, social and cultural identities in contemporary Nujiang society. I contend that the adoption of audiovisual recording has enriched Lisu singing to operate as a resilient medium through the use of which elements of traditional cultural forms are incorporated to encourage religious participation and further produce a more genuine Lisu expression of Christianity on a daily basis

GOSPEL SINGING IN THE VALLEY: AN INVESTIGATION INTO THE HYMNODY AND CHORAL SINGING OF THE LISU ON THE CHINA-BURMA/MYANMAR BORDER

This dissertation is an ethnomusicological study of contemporary musical practices of the Christian Lisu in Nujiang Prefecture in northwest Yunnan on the China-Myanmar border. Among all the changes that the Nujiang Lisu have experienced since the twentieth century, the spread of Protestant Christianity throughout Nujiang’s mountainous villages has existed for the longest time and had one of the greatest effects. Combining historical investigation and ethnographic description, this study uses the lens of music to examine the impact of this social change on the Lisu living in this impoverished frontier region. The Lisu characteristics have never been vital in the music written by the Christian Lisu in Nujiang. Compared with the practices described in other ethnomusicological writings on Christian music around the world that I have read, this absence of incorporation of indigenous musical elements is unusual. There are probably many other cases similar to that of the Lisu, but few ethnomusicologists have paid attention to them. I aim to elucidate this particular scenario of Lisu Christian music in relation to three social and cultural forces: the missionary legacy of conventions; the government’s identification of the Lisu as a minority nationality and its national policies toward them since the 1950s; and the transnational religious exchange between the Christian Lisu in China and Myanmar since the late 1980s. My examination focuses on two genres which the Lisu use to express their Christian beliefs today: ddoqmuq mutgguat, derived from American northern urban gospel songs, the basis of the Lisu choral singing; and mutgguat ssat, influenced by the Christian pop of the Burmese Lisu, with instrumental accompaniment and daibbit dance and preferred by the young people. Besides studying these two genres in the religious context, I also juxtapose them with other musical traditions in the overall Nujiang music soundscape and look at their role in local social interactions such as those between sacred and secular, and majority and minority. This dissertation demonstrates that the collective performances of shared repertoires have not only created a sense of affinity for the Nujiang Christian Lisu but also have reinforced the formation of Lisu transnational religious networks

In vitro micro-propagation of Longiflorum-Asiatic (LA) hybrids lily (Lilium) cultivar ‘eyeliner’

Bulblets propagation by tissue culture was one of the key techniques in the production of lily (Lilium) bulbs. Therefore, in vitro micro propagation of lily bulblets was studied in detail in this paper. L A hybrids lily cultivar ‘eyeliner’ was selected as the materials. By using the method of orthogonal design, the following were concluded from the research: the optimum treatment and disinfection methods of ‘eyeliner’ bulb scales was soaking in 1:500 carbendazim solution for 30 min, disinfection in 75% alcohol for 10 to 60 s, disinfection in 2% NaClO solution for 15 min; the optimum medium for bud induction of ‘eyeliner’ was MS + 0.5 mg·L-1 6-benzyl aminopurine (6-BA) + 0.1 mg·L-1 naphlene acetic acid (NAA) + 90 g·L-1 sucrose, and 25°C and in darkness; the optimum medium for bulblets induction of ‘eyeliner’ was 2MS + 1.0 mg·L-1 6-BA + 0.5 mg·L-1 NAA + sucrose 90 g·L-1 + Paclobutrazol (PP333) 2 mg·L-1; the optimum culture condition for bulblets induction of ‘eyeliner’ was 20°C, 14 h·day-1 lightness + 10 h·day-1 darkness. The optimum medium for rooting culture of ‘eyeliner’ was ½ MS + 0.8 mg·L-1 NAA + 3 g·L-1 activated charcoal, 20°C, 14 h·day-1 lightness + 10 h·day-1 darkness.Keywords: Lily bulb, orthogonal experiment, in vitro micro propagatio

Similarity Reasoning and Filtration for Image-Text Matching

Image-text matching plays a critical role in bridging the vision and language, and great progress has been made by exploiting the global alignment between image and sentence, or local alignments between regions and words. However, how to make the most of these alignments to infer more accurate matching scores is still underexplored. In this paper, we propose a novel Similarity Graph Reasoning and Attention Filtration (SGRAF) network for image-text matching. Specifically, the vector-based similarity representations are firstly learned to characterize the local and global alignments in a more comprehensive manner, and then the Similarity Graph Reasoning (SGR) module relying on one graph convolutional neural network is introduced to infer relation-aware similarities with both the local and global alignments. The Similarity Attention Filtration (SAF) module is further developed to integrate these alignments effectively by selectively attending on the significant and representative alignments and meanwhile casting aside the interferences of non-meaningful alignments. We demonstrate the superiority of the proposed method with achieving state-of-the-art performances on the Flickr30K and MSCOCO datasets, and the good interpretability of SGR and SAF modules with extensive qualitative experiments and analyses.Comment: 14 pages, 8 figures, Accepted by AAAI202

Embryogenesis and plant regeneration from unpollinated ovaries of Amorphophallus konjac

The system of somatic embryogenesis of Amorphophallus konjac had been built through unpollinated ovaries. The embryogenic calli were induced on Murashige and Skoog (MS) basal medium supplemented with 9.0 μM 6- benzylaminopurine (BA), 0.4 μM 2,4dichlorophenoxyacetic acid (D), 1.0 μM naphthaleneacetic acid (NAA), and the induction rate was 34.0%. The differentiation rate was 35.5% on the medium of MS basal medium supplemented with 6.7 μM 6-BA and 2.2 μM NAA. The obtained plantlets were transferred into rooting medium which was 1/2MS supplementing with 2.7 μM NAA, and the rooting rate was above 95%. All of the media were added 3% (w/v) sucrose and 0.3% (w/v) phytagel, the experimental materials for each step were cultured at 25 ± 2°C with a photoperiod of 12 h and light intensity of 50 μmol m-2 s-1.Keywords: Amorphophallus konjac, unpollinated ovary, embryogenic calli, plant regeneratio

Plug-and-Play Regulators for Image-Text Matching

Exploiting fine-grained correspondence and visual-semantic alignments has shown great potential in image-text matching. Generally, recent approaches first employ a cross-modal attention unit to capture latent region-word interactions, and then integrate all the alignments to obtain the final similarity. However, most of them adopt one-time forward association or aggregation strategies with complex architectures or additional information, while ignoring the regulation ability of network feedback. In this paper, we develop two simple but quite effective regulators which efficiently encode the message output to automatically contextualize and aggregate cross-modal representations. Specifically, we propose (i) a Recurrent Correspondence Regulator (RCR) which facilitates the cross-modal attention unit progressively with adaptive attention factors to capture more flexible correspondence, and (ii) a Recurrent Aggregation Regulator (RAR) which adjusts the aggregation weights repeatedly to increasingly emphasize important alignments and dilute unimportant ones. Besides, it is interesting that RCR and RAR are plug-and-play: both of them can be incorporated into many frameworks based on cross-modal interaction to obtain significant benefits, and their cooperation achieves further improvements. Extensive experiments on MSCOCO and Flickr30K datasets validate that they can bring an impressive and consistent R@1 gain on multiple models, confirming the general effectiveness and generalization ability of the proposed methods. Code and pre-trained models are available at: https://github.com/Paranioar/RCAR.Comment: 13 pages, 9 figures, Accepted by TIP202

On Fast-Converged Deep Reinforcement Learning for Optimal Dispatch of Large-Scale Power Systems under Transient Security Constraints

Power system optimal dispatch with transient security constraints is commonly represented as Transient Security-Constrained Optimal Power Flow (TSC-OPF). Deep Reinforcement Learning (DRL)-based TSC-OPF trains efficient decision-making agents that are adaptable to various scenarios and provide solution results quickly. However, due to the high dimensionality of the state space and action spaces, as well as the non-smoothness of dynamic constraints, existing DRL-based TSC-OPF solution methods face a significant challenge of the sparse reward problem. To address this issue, a fast-converged DRL method for TSC-OPF is proposed in this paper. The Markov Decision Process (MDP) modeling of TSC-OPF is improved by reducing the observation space and smoothing the reward design, thus facilitating agent training. An improved Deep Deterministic Policy Gradient algorithm with Curriculum learning, Parallel exploration, and Ensemble decision-making (DDPG-CPEn) is introduced to drastically enhance the efficiency of agent training and the accuracy of decision-making. The effectiveness, efficiency, and accuracy of the proposed method are demonstrated through experiments in the IEEE 39-bus system and a practical 710-bus regional power grid. The source code of the proposed method is made public on GitHub.Comment: 10 pages, 11 figure

UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory

Parameter-efficient transfer learning (PETL), i.e., fine-tuning a small portion of parameters, is an effective strategy for adapting pre-trained models to downstream domains. To further reduce the memory demand, recent PETL works focus on the more valuable memory-efficient characteristic. In this paper, we argue that the scalability, adaptability, and generalizability of state-of-the-art methods are hindered by structural dependency and pertinency on specific pre-trained backbones. To this end, we propose a new memory-efficient PETL strategy, Universal Parallel Tuning (UniPT), to mitigate these weaknesses. Specifically, we facilitate the transfer process via a lightweight and learnable parallel network, which consists of: 1) A parallel interaction module that decouples the sequential connections and processes the intermediate activations detachedly from the pre-trained network. 2) A confidence aggregation module that learns optimal strategies adaptively for integrating cross-layer features. We evaluate UniPT with different backbones (e.g., T5, VSE$\infty$, CLIP4Clip, Clip-ViL, and MDETR) on various vision-and-language and pure NLP tasks. Extensive ablations on 18 datasets have validated that UniPT can not only dramatically reduce memory consumption and outperform the best competitor, but also achieve competitive performance over other plain PETL methods with lower training memory overhead. Our code is publicly available at: https://github.com/Paranioar/UniPT.Comment: 15 pages, 11 figures, Accepted by CVPR202

1,2-Bis(2,4,6-trinitro­phen­yl)ethane

The title compound, C14H8N6O12, is centrosymmetric, the mid-point of the central C—C bond being located on an inversion centre. Two of the three independent nitro groups are disordered over two sites, with a site-occupancy ratio of 0.513 (3):0.487 (3). Weak inter­molecular C—H⋯O hydrogen bonding is present in the crystal structure