Driving dynamic colloidal assembly using eccentric self-propelled colloids

Designing protocols to dynamically direct the self-assembly of colloidal particles has become an important direction in soft matter physics because of the promising applications in fabrication of dynamic responsive functional materials. Here using computer simulations, we found that in the mixture of passive colloids and eccentric self-propelled active particles, when the eccentricity and self-propulsion of active particles are high enough, the eccentric active particles can push passive colloids to form a large dense dynamic cluster, and the system undergoes a novel dynamic demixing transition. Our simulations show that the dynamic demixing occurs when the eccentric active particles move much faster than the passive particles such that the dynamic trajectories of different active particles can overlap with each other while passive particles are depleted from the dynamic trajectories of active particles. Our results suggest that this is in analogy to the entropy driven demixing in colloid-polymer mixtures, in which polymer random coils can overlap with each other while deplete the colloids. More interestingly, we find that by fixing the passive colloid composition at certain value, with increasing the density, the system undergoes an intriguing re-entrant mixing, and the demixing only occurs within certain intermediate density range. This suggests a new way of designing active matter to drive the self-assembly of passive colloids and fabricate dynamic responsive materials.Comment: Accepted in Soft Matter. Supplementary information can found at https://www.dropbox.com/sh/xb3u5iaoucc2ild/AABFUyqjXips7ewaie2rFbj_a?dl=

Self-Assembled Chiral Photonic Crystals From Colloidal Helices Racemate

Chiral crystals consisting of micro-helices have many optical properties while presently available fabrication processes limit their large-scale applications in photonic devices. Here, by using a simplified simulation method, we investigate a bottom-up self-assembly route to build up helical crystals from the smectic monolayer of colloidal helices racemate. With increasing the density, the system undergoes an entropy-driven co-crystallization by forming crystals of various symmetries with different helical shapes. In particular, we identify two crystals of helices arranged in the binary honeycomb and square lattices, which are essentially composed by two sets of opposite-handed chiral crystal. Photonic calculations show that these chiral structures can have large complete photonic bandgaps. In addition, in the self-assembled chiral square crystal, we also find dual polarization bandgaps that selectively forbid the propagation of circularly polarized lights of a specific handedness along the helical axis direction. The self-assembly process in our proposed system is robust, suggesting possibilities of using chiral colloids to assemble photonic metamaterials.Comment: Accepted in ACS Nan

Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks

Fetal mid-pregnancy scans are typically carried out according to fixed protocols. Accurate detection of abnormalities and correct biometric measurements hinge on the correct acquisition of clearly defined standard scan planes. Locating these standard planes requires a high level of expertise. However, there is a worldwide shortage of expert sonographers. In this paper, we consider a fully automated system based on convolutional neural networks which can detect twelve standard scan planes as defined by the UK fetal abnormality screening programme. The network design allows real-time inference and can be naturally extended to provide an approximate localisation of the fetal anatomy in the image. Such a framework can be used to automate or assist with scan plane selection, or for the retrospective retrieval of scan planes from recorded videos. The method is evaluated on a large database of 1003 volunteer mid-pregnancy scans. We show that standard planes acquired in a clinical scenario are robustly detected with a precision and recall of 69 % and 80 %, which is superior to the current state-of-the-art. Furthermore, we show that it can retrospectively retrieve correct scan planes with an accuracy of 71 % for cardiac views and 81 % for non-cardiac views

Clostridium botulinum Type E Toxins Bind to Caco-2 Cells by a Different Mechanism from That of Type A Toxins

Cultured Clostridium botulinum strains produce progenitor toxins designated as 12S, 16S, and 19S toxins. The 12S toxin consists of a neurotoxin (NTX, 7S) and a non-toxic non-hemagglutinin (NTNH). The 16S and 19S toxins are formed by conjugation of the 12S toxin with hemagglutinin (HA), and the 19S toxin is a dimer of the 16S toxin. Type A cultures produce all 3 of these progenitor toxins, while type E produces only the 12S toxin. The 7S toxin is cleaved into heavy (H) and light (L) chains by a protease(s) in some strains, and the H chain has 2 domains, the N-terminus (Hn) and C-terminus (Hc). It has been reported that type A toxins bind to the intestinal cells or cultured cells via either HA or Hc. In this study, we investigated the binding of type A and E toxins to Caco-2 cells using Western blot analysis. Both the type E 7S and 12S toxins bound to the cells, with the 7S toxin binding more strongly, whereas, in the type A strain, only the 16S/19S toxins showed obvious binding. Pre-incubation of the type E 7S toxin with IgG against recombinant type E Hc significantly inhibited the 7S toxin binding, indicating that Hc might be a main binding domain of the type E toxin

Constrained Output Embeddings for End-to-End Code-Switching Speech Recognition with Only Monolingual Data

The lack of code-switch training data is one of the major concerns in the development of end-to-end code-switching automatic speech recognition (ASR) models. In this work, we propose a method to train an improved end-to-end code-switching ASR using only monolingual data. Our method encourages the distributions of output token embeddings of monolingual languages to be similar, and hence, promotes the ASR model to easily code-switch between languages. Specifically, we propose to use Jensen-Shannon divergence and cosine distance based constraints. The former will enforce output embeddings of monolingual languages to possess similar distributions, while the later simply brings the centroids of two distributions to be close to each other. Experimental results demonstrate high effectiveness of the proposed method, yielding up to 4.5% absolute mixed error rate improvement on Mandarin-English code-switching ASR task.Comment: 5 pages, 3 figures, accepted to INTERSPEECH 201

Reversible Embedding to Covers Full of Boundaries

In reversible data embedding, to avoid overflow and underflow problem, before data embedding, boundary pixels are recorded as side information, which may be losslessly compressed. The existing algorithms often assume that a natural image has little boundary pixels so that the size of side information is small. Accordingly, a relatively high pure payload could be achieved. However, there actually may exist a lot of boundary pixels in a natural image, implying that, the size of side information could be very large. Therefore, when to directly use the existing algorithms, the pure embedding capacity may be not sufficient. In order to address this problem, in this paper, we present a new and efficient framework to reversible data embedding in images that have lots of boundary pixels. The core idea is to losslessly preprocess boundary pixels so that it can significantly reduce the side information. Experimental results have shown the superiority and applicability of our work

Optimal Bidding Strategies for Generation Companies in Electricity Markets with Transmission Capacity Constraints Taken into Account

In the electricity market environment, how to build optimal bidding strategies has become a major concern for generation companies. The deficiency of transmission capacity could lead to congestion, and as a result, the whole electricity market can then be actually divided into two or more submarkets. A direct consequence of transmission congestion is the change of competitive positions of generation companies concerned in the electricity market, and the optimal bidding strategies of them should accordingly be changed. In this paper, the problem of developing optimal bidding strategies for generation companies is systematically investigated with transmission capacity constraints taken into account. A stochastic optimization model is first formulated under the presumption that the bidding behaviors of rival generation companies could be modeled as normal probability distributions. An approach is next presented for solving the optimization problem using the well-known Monte Carlo simulation method and the genetic algorithm. Finally, a simple sample example and the modified IEEE 14-bus system are employed to illustrate the essential features of the proposed model and method.published_or_final_versio

A Descriptive Study of Male and Female Teachers' Preferences of Strategies Used for Teaching Speaking in SMPN 1 Singaraja,

Penelitian ini bertujuan untuk mendeskripsikan strategi yang dipilih oleh guru pria dan wanita dalam mengajar berbicara di SMPN 1 Singaraja. Penelitian ini menganalisis strategi yang dipilih oleh lima guru bahasa Inggris yang mengajar kelas tujuh dan delapan di SMPN 1 Singaraja. Penelitian ini dirancang sebagai sebuah penelitian deskriptif yang menerapkan teknik analisis isi (content analysis) untuk menunjukkan dan memverifikasi data yang diperlukan. Pengambilan data dilakukan dengan observasi, kuesioner, dan wawancara dengan guru pria dan wanita. Hasil penelitian ini menunjukkan bahwa guru pria dan wanita menggunakan empat dari tujuh strategi mengajar dalam mengajar berbicara pada siswa mereka. Strategi tersebut adalah instruksi langsung, diskusi, bekerja berkelompok, dan aktivitas kinerja. Alasan dari guru pria dan wanita menggunakan strategi tersebut karena strategi tersebut dapat membuat siswa aktif berpartisipasi di kelas. Siswa dapat bertukar pengetahuan, ide atau opini dalam diskusi atau bekerja berkelompok dengan siswa lainnya. Siswa juga dapat belajar berbicara lebih banyak dalam aktivitas kinerja yang dapat membuat siswa percaya diri ketika siswa harus berbicara dalam bahasa Inggris dengan orang lain. Kata Kunci : strategi mengajar, guru pria dan wanita, mengajar berbicara This study was carried out to describe male and female teachers' preferences of strategies used for teaching speaking and the reasons why male and female teachers used certain strategies for teaching speaking in SMPN 1 Singaraja. This study analyzed strategy preferences of five English teachers who taught in grade seven and eight in SMPN 1 Singaraja. This study was designed as a descriptive study by applying seven Killen's teaching strategies that adapted content analysis technique to reveal and verify the needed data. The data collection was done through observation, questionnaire, and interviewing male and female English teachers. The results show that male English teachers used four of seven Killen's teaching strategies in teaching speaking to their students. The strategies were direct instruction, discussion, group work, and performance activity. While female English teachers also preferred four of seven Killen's teaching strategies used for teaching speaking in the classroom. The reasons of male and female teachers used certain strategies because those strategies made students actively participate in the class. They could share their knowledge, ideas or opinion in discussion session or in group work with their partner or their group. They also learned to speak English more in the performance activity that could make them confident when they should speak English with other people