Solubilization of M2 Transmembrane Peptide of Influenza A in Pure Water: Implications for Emergence of Proteins and Protein-embedded Primeval Membranes in Unsalted Oceans

We demonstrated that M2 transmembrane peptide, one of the most hydrophobic sequences in nature, can be solublized to at least ~100 µM in unsalted water without any lipid molecules. Strikingly, the M2 peptide also forms a highly-helical conformation in water which remains almost unchanged even at 95 ºC, as characterized by CD spectroscopy. Our result has critical implications in understanding emergence of proteins and protein-embedded primeval membranes in unsalted oceans

Random graph models for wireless communication networks

PhDThis thesis concerns mathematical models of wireless communication networks, in particular ad-hoc networks and 802:11 WLANs. In ad-hoc mode each of these devices may function as a sender, a relay or a receiver. Each device may only communicate with other devices within its transmission range. We use graph models for the relationship between any two devices: a node stands for a device, and an edge for a communication link, or sometimes an interference relationship. The number of edges incident on a node is the degree of this node. When considering geometric graphs, the coordinates of a node give the geographical position of a node. One of the important properties of a communication graph is its connectedness | whether all nodes can reach all other nodes. We use the term connectivity, the probability of graphs being connected given the number of nodes and the transmission range to measure the connectedness of a wireless network. Connectedness is an important prerequisite for all communication networks which communication between nodes. This is especially true for wireless ad-hoc networks, where communication relies on the contact among nodes and their neighbours. Another important property of an interference graph is its chromatic number | the minimum number of colours needed so that no adjacent nodes are assigned the same colour. Here adjacent nodes share an edge; adjacent edges share at least one node; and colours are used to identify di erent frequencies. This gives the minimum number of frequencies a network needs in order to attain zero interference. This problem can be solved as an optimization problem deterministically, but is algorithmically NP-hard. Hence, nding good asymptotic approximations for this value becomes important. Random geometric graphs describe an ensemble of graphs which share common features. In this thesis, node positions follow a Poisson point process or a binomial point process. We use probability theory to study the connectedness of random graphs and random geometric graphs, which is the fraction of connected graphs among many graph samples. This probability is closely related to the property of minimum node degree being at least unity. The chromatic number is closely related to the maximum degree as n ! 1; the chromatic number converges to maximum degree when graph is sparse. We test existing theorems and improve the existing ones when possible. These motivated me to study the degree of random (geometric) graph models. We study using deterministic methods some degree-related problems for Erda}os-R enyi random graphs G(n; p) and random geometric graphs G(n; r). I provide both theoretical analysis and accurate simulation results. The results lead to a study of dependence or non-dependence in the joint distribution of the degrees of neighbouring nodes. We study the probability of no node being isolated in G(n; p), that is, minimum node degree being at least unity. By making the assumption of non-dependence of node degree, we derive two asymptotics for this probability. The probability of no node being isolated is an approximation to the probability of the graph being connected. By making an analogy to G(n; p), we study this problem for G(n; r), which is a more realistic model for wireless networks. Experiment shows that this asymptotic result also works well for small graphs. We wish to nd the relationship between these basic features the above two important problems of wireless networks: the probability of a network being connected and the minimum number of channels a network needs in order to minimize interference. Inspired by the problem of maximum degree in random graphs, we study the problem of the maximum of a set of Poisson random variables and binomial random variables, which leads to two accurate formulae for the mode of the maximum for general random geometric graphs and for sparse random graphs. To our knowledge, these are the best results for sparse random geometric graphs in the literature so far. By approximating the node degrees as independent Poisson or binomial variables, we apply the result to the problem of maximum degree in general and sparse G(n; r), and derived much more accurate results than in the existing literature. Combining the limit theorem from Penrose and our work, we provide good approximations for the mode of the clique number and chromatic number in sparse G(n; r). Again these results are much more accurate than existing ones. This has implications for the interference minimization of WLANs. Finally, we apply our asymptotic result based on Poisson distribution for the chromatic number of random geometric graph to the interference minimization problem in IEEE 802:11b/g WLAN. Experiments based on the real planned position of the APs in WLANs show that our asymptotic results estimate the minimum number of channels needed accurately. This also means that sparse random geometric graphs are good models for interference minimization problem of WLANs. We discuss the interference minimization problem in single radio and multi-radio wireless networking scenarios. We study branchand- bound algorithms for these scenarios by selecting di erent constraint functions and objective functions

Development Status of Digital Economy in Northeast Asian Countries and China’s Opportunities

With the vigorous development of digital technology, the development of the digital economy has become an important component of the global economy. Against the background of negative growth of major global economies hit hard by the COVID-19 epidemic, the steadily rising digital economy has become a key force to boost the global economy and an important engine to promote global economic development. Northeast Asian countries should use the dividends released by the digital economy to promote the coordinated development of digital technology innovation within the region, respond to technological revolution and industrial transformation, and build new international competitive advantages. This article provides a detailed overview of the current development status of digital economy in various countries in Northeast Asia, analyzes the challenges faced by countries in the region in developing cross-border digital economic and trade cooperation from three aspects: political mutual trust, digital divide, and network security. It further proposes to build a cross-border digital service trade platform in Northeast Asia, establish a China North Russia Far East digital free trade zone, so as to promote the deepening of cooperation and common development in digital trade within the region

Strategic priorities of cooperation between Heilongjiang province and Russia

During the five-year period of implementation of the Belt and Road Initiative, Heilongjiang Province, which is one of the nine Chinese border provinces, has actively responded to the national development policy and achieved some impressive results in its strategic cooperation with the Russian Far East. The article characterizes the current state of Heilongjiang Province’s relationship with Russia and describes its strategic plans for findings new paths of cooperation as a result of the province’s integration into the Belt and Road Initiative and participation in China-Mongolia-Russia Economic Corridor construction. The key projects crucial for the province’s development are the Eastern Land-Sea Silk Road Economic Belt (hereinafter referred to as the Eastern Silk Road Belt) and the Heilongjiang Land-Sea Silk Road Economic Belt (hereinafter referred to as Longjiang Silk Road Belt). Both projects are aimed at increasing the interconnectedness between regions and countries, promoting international trade and fostering understanding and tolerance. The article describes the background, objectives, results and problems associated with these projects in Heilongjiang Province and their role in ensuring further socio-economic development of the territory. Finally, recommendations are given concerning the main areas of cooperation between the province and Russia: these include modernization of trade (e-commerce), fostering cooperation in the industrial sphere and agriculture, and opening a free cross-border trade zone

Human behaviour-based automatic depression analysis using hand-crafted statistics and deep learned spectral features

Depression is a serious mental disorder that affects millions of people all over the world. Traditional clinical diagnosis methods are subjective, complicated and need extensive participation of experts. Audio-visual automatic depression analysis systems predominantly base their predictions on very brief sequential segments, sometimes as little as one frame. Such data contains much redundant information, causes a high computational load, and negatively affects the detection accuracy. Final decision making at the sequence level is then based on the fusion of frame or segment level predictions. However, this approach loses longer term behavioural correlations, as the behaviours themselves are abstracted away by the frame-level predictions. We propose to on the one hand use automatically detected human behaviour primitives such as Gaze directions, Facial action units (AU), etc. as low-dimensional multi-channel time series data, which can then be used to create two sequence descriptors. The first calculates the sequence-level statistics of the behaviour primitives and the second casts the problem as a Convolutional Neural Network problem operating on a spectral representation of the multichannel behaviour signals. The results of depression detection (binary classification) and severity estimation (regression) experiments conducted on the AVEC 2016 DAIC-WOZ database show that both methods achieved significant improvement compared to the previous state of the art in terms of the depression severity estimation

Noise invariant frame selection: a simple method to address the background noise problem for text-independent speaker verification

The performance of speaker-related systems usually degrades heavily in practical applications largely due to the background noise. To improve the robustness of such systems in unknown noisy environments, this paper proposes a simple pre-processing method called Noise Invariant Frame Selection (NIFS). Based on several noisy constraints, it selects noise invariant frames from utterances to represent speakers. Experiments conducted on the TIMIT database showed that the NIFS can significantly improve the performance of Vector Quantization (VQ), Gaussian Mixture Model-Universal Background Model (GMM-UBM) and i-vector-based speaker verification systems in different unknown noisy environments with different SNRs, in comparison to their baselines. Meanwhile, the proposed NIFS-based speaker systems has achieves similar performance when we change the constraints (hyper-parameters) or features, which indicates that it is easy to reproduce. Since NIFS is designed as a general algorithm, it could be further applied to other similar tasks