Valence-programmable nanoparticle architectures.

Nanoparticle-based clusters permit the harvesting of collective and emergent properties, with applications ranging from optics and sensing to information processing and catalysis. However, existing approaches to create such architectures are typically system-specific, which limits designability and fabrication. Our work addresses this challenge by demonstrating that cluster architectures can be rationally formed using components with programmable valence. We realize cluster assemblies by employing a three-dimensional (3D) DNA meshframe with high spatial symmetry as a site-programmable scaffold, which can be prescribed with desired valence modes and affinity types. Thus, this meshframe serves as a versatile platform for coordination of nanoparticles into desired cluster architectures. Using the same underlying frame, we show the realization of a variety of preprogrammed designed valence modes, which allows for assembling 3D clusters with complex architectures. The structures of assembled 3D clusters are verified by electron microcopy imaging, cryo-EM tomography and in-situ X-ray scattering methods. We also find a close agreement between structural and optical properties of designed chiral architectures

A New Species of the Asian Toad Genus Megophrys sensu lato(Amphibia: Anura: Megophryidae) from Guizhou Province, China

We describe a new species of the genus Megophrys sensu lato from Guizhou Province, China. Molecular phylogenetic analyses based on mitochondrial DNA and nuclear DNA sequences all strongly supported the new species as an independent lineage in Megophrys (Panophrys) clade. The new species is distinguished from its congeners by a combination of the following morphological characteristics: (1) small body size with SVL < 38.8 mm in male and SVL < 42.3 mm in female; (2) vomerine teeth absent; (3) tongue not notched behind; (4) a small horn-like tubercle at the edge of each upper eyelid; (5) tympanum distinctly visible, rounded; (6) two metacarpal tubercles in hand; (7) relative finger lengths: II < I < V < III; (8) toes with rudimentary webbing at bases; (9) heels overlapping when thighs are positioned at right angles to the body; (10) tibiotarsal articulation reaching the level between tympanum to eye when leg stretched forward; (11) an internal single subgular vocal sac in male; (12) in breeding male, the nuptial pads with black nuptial spines on the dorsal bases of the first and second fingers

Deep Learning with Convolutional Neural Networks for Motor Brain-Computer Interfaces based on Stereo-electroencephalography (SEEG)

Objective: Deep learning based on convolutional neural networks (CNN) has achieved success in brain-computer interfaces (BCIs) using scalp electroencephalography (EEG). However, the interpretation of the so-called 'black box' method and its application in stereo-electroencephalography (SEEG)-based BCIs remain largely unknown. Therefore, in this paper, an evaluation is performed on the decoding performance of deep learning methods on SEEG signals. Methods: Thirty epilepsy patients were recruited, and a paradigm including five hand and forearm motion types was designed. Six methods, including filter bank common spatial pattern (FBCSP) and five deep learning methods (EEGNet, shallow and deep CNN, ResNet, and a deep CNN variant named STSCNN), were used to classify the SEEG data. Various experiments were conducted to investigate the effect of windowing, model structure, and the decoding process of ResNet and STSCNN. Results: The average classification accuracy for EEGNet, FBCSP, shallow CNN, deep CNN, STSCNN, and ResNet were 35 ± 6.1%, 38 ± 4.9%, 60 ± 3.9%, 60 ± 3.3%, 61 ± 3.2%, and 63 ± 3.1% respectively. Further analysis of the proposed method demonstrated clear separability between different classes in the spectral domain. Conclusion: ResNet and STSCNN achieved the first- and second-highest decoding accuracy, respectively. The STSCNN demonstrated that an extra spatial convolution layer was beneficial, and the decoding process can be partially interpreted from spatial and spectral perspectives. Significance: This study is the first to investigate the performance of deep learning on SEEG signals. In addition, this paper demonstrated that the so-called 'black-box' method can be partially interpreted.</p

A DNS Tunnel Sliding Window Differential Detection Method Based on Normal Distribution Reasonable Range Filtering

A covert attack method often used by APT organizations is the DNS tunnel, which is used to pass information by constructing C2 networks. And they often use the method of frequently changing domain names and server IP addresses to evade monitoring, which makes it extremely difficult to detect them. However, they carry DNS tunnel information traffic in normal DNS communication, which inevitably brings anomalies in some statistical characteristics of DNS traffic, so that it would provide security personnel with the opportunity to find them. Based on the above considerations, this paper studies the statistical discovery methodology of typical DNS tunnel high-frequency query behavior. Firstly, we analyze the distribution of the DNS domain name length and times and finds that the DNS domain name length and times follow the normal distribution law. Secondly, based on this distribution law, we propose a method for detecting and discovering high-frequency DNS query behaviors of non-single domain names based on the statistical rules of domain name length and frequency and we also give three theorems as theoretical support. Thirdly, we design a sliding window difference scheme based on the above method. Experimental results show that our method has a higher detection rate. At the same time, since our method does not need to construct a data set, it has better practicability in detecting unknown DNS tunnels. This also shows that our detection method based on mathematical models can effectively avoid the dilemma for machine learning methods that must have useful training data sets, and has strong practical significance

Deep Learning with Convolutional Neural Networks for Motor Brain-Computer Interfaces based on Stereo-electroencephalography (SEEG)

Objective: Deep learning based on convolutional neural networks (CNN) has achieved success in brain-computer interfaces (BCIs) using scalp electroencephalography (EEG). However, the interpretation of the so-called 'black box' method and its application in stereo-electroencephalography (SEEG)-based BCIs remain largely unknown. Therefore, in this paper, an evaluation is performed on the decoding performance of deep learning methods on SEEG signals. Methods: Thirty epilepsy patients were recruited, and a paradigm including five hand and forearm motion types was designed. Six methods, including filter bank common spatial pattern (FBCSP) and five deep learning methods (EEGNet, shallow and deep CNN, ResNet, and a deep CNN variant named STSCNN), were used to classify the SEEG data. Various experiments were conducted to investigate the effect of windowing, model structure, and the decoding process of ResNet and STSCNN. Results: The average classification accuracy for EEGNet, FBCSP, shallow CNN, deep CNN, STSCNN, and ResNet were 35 ± 6.1%, 38 ± 4.9%, 60 ± 3.9%, 60 ± 3.3%, 61 ± 3.2%, and 63 ± 3.1% respectively. Further analysis of the proposed method demonstrated clear separability between different classes in the spectral domain. Conclusion: ResNet and STSCNN achieved the first- and second-highest decoding accuracy, respectively. The STSCNN demonstrated that an extra spatial convolution layer was beneficial, and the decoding process can be partially interpreted from spatial and spectral perspectives. Significance: This study is the first to investigate the performance of deep learning on SEEG signals. In addition, this paper demonstrated that the so-called 'black-box' method can be partially interpreted.</p

SDF-Pack: Towards Compact Bin Packing with Signed-Distance-Field Minimization

Robotic bin packing is very challenging, especially when considering practical needs such as object variety and packing compactness. This paper presents SDF-Pack, a new approach based on signed distance field (SDF) to model the geometric condition of objects in a container and compute the object placement locations and packing orders for achieving a more compact bin packing. Our method adopts a truncated SDF representation to localize the computation, and based on it, we formulate the SDF minimization heuristic to find optimized placements to compactly pack objects with the existing ones. To further improve space utilization, if the packing sequence is controllable, our method can suggest which object to be packed next. Experimental results on a large variety of everyday objects show that our method can consistently achieve higher packing compactness over 1,000 packing cases, enabling us to pack more objects into the container, compared with the existing heuristics under various packing settings

Advertisement calls of Leptobrachella suiyangensis and Leptobrachella bashaensis (Anura, Megophryidae)

In this study, the advertisement calls of Leptobrachella suiyangensis and Leptobrachella bashaensis are described. The advertisement call of L. suiyangensis includes simple and complex calls, with four different call types and a dominant frequency ranging 4.13–4.82 kHz. The advertisement call of L. bashaensis consists of a single note, with a dominant frequency 6.03–6.46 kHz. We compare the advertisement calls with other species in the genus Leptobrachella, and discuss the definitions of primary advertisement calls and secondary advertisement calls. Our results provide basic data for further acoustic, taxonomic and ecological studies in the genus Leptobrachella

Ambipolar ferromagnetism by electrostatic doping of a manganite

Complex-oxide materials exhibit physical properties that involve the interplay of charge and spin degrees of freedom. However, an ambipolar oxide that is able to exhibit both electron-doped and hole-doped ferromagnetism in the same material has proved elusive. Here we report ambipolar ferromagnetism in LaMnO3, with electron–hole asymmetry of the ferromagnetic order. Starting from an undoped atomically thin LaMnO3 film, we electrostatically dope the material with electrons or holes according to the polarity of a voltage applied across an ionic liquid gate. Magnetotransport characterization reveals that an increase of either electron-doping or hole-doping induced ferromagnetic order in this antiferromagnetic compound, and leads to an insulator-to-metal transition with colossal magnetoresistance showing electron–hole asymmetry. These findings are supported by density functional theory calculations, showing that strengthening of the inter-plane ferromagnetic exchange interaction is the origin of the ambipolar ferromagnetism. The result raises the prospect of exploiting ambipolar magnetic functionality in strongly correlated electron systems

Bargaining-Based Profit Allocation Model for Fixed Return Investment Water-Saving Management Contract

Fixed Return Investment (FRI) is one of the main operating modes of a Water-Saving Management Contract (WSMC). Aiming at the critical profit allocation of FRI WSMC projects, a new profit allocation model based on bargaining theory is proposed. First, the net present value is adopted to determine the profit interval to be allocated. Second, the bargaining process is divided into two levels. The first-level bargaining process is between a water user and an alliance, which consists of a Water Service Company (WSCO) and a financial institution. The second-level bargaining process is between the WSCO and the financial institution. Given the imbalance caused by offering first, the number of bargaining stages and sunk cost are introduced, and the equilibrium offers of the two parties in different bargaining stages are determined by using backward induction and mathematical induction. According to the feature that the number of bargaining stages is an integer in practice, the deterrence discount factors are introduced to redistribute the remaining part, and sixteen situations of profit allocation among participants are given. Third, the model analysis shows that the profit allocation of participants is closely related to the minimum profit requirements, deterrence discount factors, the number of bargaining stages, and sunk cost. Finally, the effectiveness of the model and the influence of various factors on profit allocation are verified by an example. The example shows that in the early stage of FRI WSMC, the water users enjoy more profits

Fabrication of Conductive Polypyrrole Nanofibers by Electrospinning

Electrospinning is employed to prepare conductive polypyrrole nanofibers with uniform morphology and good mechanical strength. Soluble PPy was synthesized with NaDEHS as dopant and then applied to electrospinning with or without PEO as carrier. The PEO contents had great influence on the morphology and conductivity of the electrospun material. The results of these experiments will allow us to have a better understanding of PPy electrospun nanofibers and will permit the design of effective electrodes in the BMIs fields