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When applying artificial intelligence technology to quantitative trading, high noise and unpredictability of market environment are the first practical problems to be considered. Therefore, how to select the learning features of the market based on rapidly changing financial data is particularly important. In this paper, the real time financial data are first processed by K-line theory, which uses candlesticks as a generalization of price movements over a period of time, so this process can play the role of de-noising. Then, the candlesticks are decomposed into different subparts by mean of a specified spatio-temporal relationship, based on which cluster analysis of the subparts to get the learning features. Further, the learning features that are clustered by the above K-lines are put into the model, and the online adaptive control of the parameters in the unknown environment is realized by the deep reinforcement learning method, so as to realize the high frequency transaction strategy. In order to verify the performance of the model, the data on different financial derivatives transactions such as stocks, financial futures and commodity futures are used. The proposal approach is compared with other methods which are based on price, fuzzified price and K-lines for features learning. In order to verify the accuracy of the proposal approach, prediction-based methods such as recurrent neural network and fuzzy neural network are used for comparison. Experimental results show that the proposed method has higher robustness and prediction accuracy.

Since the existence of fuzziness in the real world, uncertainty could be always present in the reasoning results. Therefore, how to tackle with the hesitation existing in the process of reasoning is a meaningful issue for the construction of the inference system. In this paper, a novel interval type-2 intuitionistic fuzzy neural network based on long-short term mechanism is proposed (LSTM-IT2IFNN). By means of interval type-2 intuitionistic set, the hesitation of reasoning is described and involved to determine fuzzy rules in inference system, by which the chosen of fuzzy rules not only considers the membership value but also non-membership degree. In order to handle the regression problems with long-term time dependency, long-short term mechanism is introduced into fuzzy neural networks, which can effectively improve the memory ability of long-term knowledge. Furthermore, by means of intuitionistic sets, an extended metacognitive learning mechanism is proposed for the adaptively online learning of network structure, where it achieves when, what, and how to learn from the sequential data. The performance of the LSTM-IT2IFNN is evaluated on different regression problems, including synthetic and real-world data. By comparing to existing algorithms, the experimental results show the efficiency and accuracy of the proposed scheme.

To apply the real-time automatic mode-locking technique to mode-locked lasers with high repetition rates, we propose a pre-stretch technique, where a slice of dispersion is used to stretch the output pulses thereby reducing the required sampling rate and the bandwidth of the acquisition system. To illustrate, we demonstrate a real-time automatic mode-locked fibre laser with a repetition rate of 48 MHz, where a real-time feedback circuit with a 400-MSa/s analog-to-digital converter is used as the hardware backend. Given a fixed acquisition system, the correlation between the repetition rate and the optimum amount of dispersion is well discussed through comparing the countable threshold range for mode-locking discrimination. Furthermore, a conclusion based on experiments is drawn that, to apply automatic mode-locking technique, the minimum acquisition bandwidth should be above twice of the fundamental repetition rate, and the minimum sampling rate follows the Nyquist’s law, which is four times of the fundamental repetition rate. [ABSTRACT FROM AUTHOR]

Wide attention has been focused on cryogenic CMOS (cryo-CMOS) operation because of its promising improvement of devices’ and circuits’ performance and wide application prospects. However, hot carrier degradation (HCD) limits the long-term reliability of cryo-CMOS. This article investigates HCD in 0.18 $\mu \text{m}$ bulk CMOS at cryogenic temperature down to 4.2 K. Particularly, the relationship between HCD and the current overshoot phenomenon and the influence of substrate bias on HCD are discussed. Besides, we predict the lifetime of the device at 77 K and 4.2 K. It is concluded that cryogenic NMOS cannot reach the ten years’ commercial standard lifetime at standard $V_{DD}$. And it is predicted that the reliability requirements can be reached when $V_{DD} \leq 1.768$ V and 1.734 V at 77 K and 4.2 K, respectively. Differently, the lifetime of PMOS is long enough even at low temperatures. [ABSTRACT FROM AUTHOR]

The extremely low threshold voltage (${V}_{\text {TH}}$) of native MOSFETs (${V}_{\text {TH}}~\approx $ 0 V@300 K) is conducive to the design of cryogenic circuits. Previous research on cryogenic MOSFETs mainly focused on the standard threshold voltage (SVT) and low threshold voltage (LVT) MOSFETs. In this article, we characterize native MOSFETs within the temperature range from 300 to 4.2 K. The cryogenic ${V}_{\text {TH}}$ increases up to ~0.25 V (W/L = 10/ $10~\mu \text{m}$) and the improved subthreshold swing (SS) ≈ 14.30 mV/dec@4.2 K. The OFF-state current (${I}_{ \mathrm{\scriptscriptstyle OFF}}$) and the gate-induced drain leakage (GIDL) effect are ameliorated greatly. The step-up effect caused by the substrate charge and the transconductance peak effect caused by the energy quantization in different sub-bands are also discussed. Based on the EKV model, we modified the mobility calculation equations and proposed a compact model of large-size native MOSFETs suitable for the range of 300-4.2 K. The mobility-related parameters are extracted via a machine learning (ML) approach, and the temperature dependences of the scattering mechanisms are analyzed. This work is beneficial to both the research on cryogenic MOSFETs’ modeling and the design of cryogenic CMOS circuits for quantum chips. [ABSTRACT FROM AUTHOR]

The Wave Controlled Aliasing In Parallel Imaging (Wave-CAIPI) technique manifests great potential to highly accelerate three-dimensional (3D) balanced steady-state free precession (bSSFP) through substantially reducing the geometric factor (g-factor) and aliasing artifacts of image reconstruction. However, severe banding artifacts appear in bSSFP imaging due to unbalanced gradients with nonzero 0th moment applied by the conventional Wave-CAIPI technique. In this study, we propose a 3D Wave-bSSFP scheme that adopts truncated wave gradients with zero 0th moment to avoid introducing additional banding artifacts and to maintain the advantages of wave encoding. The simulation results indicate that the number of wave cycles that are truncated and different options of applying wave gradients affect both the g-factor reduction and image quality, but the influence is limited. In phantom experiments, the proposed technique shows similar acceleration performance as the conventional Wave-CAIPI technique and effectively eliminates its introduced banding artifacts. Additionally, Wave-bSSFP obtains up to $12\times $ retrospective acceleration at 0.8 mm isotropic resolution in in vivo 3D brain experiments and is superior to the state-of-the-art Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration (CAIPIRINHA) technique, according to both visual validation and quantitative analysis. Moreover, in vivo 3D spine and abdomen imaging demonstrate the potential clinical applications of Wave-bSSFP with fast acquisition speed, improved isotropic resolution and fine image quality. [ABSTRACT FROM AUTHOR]

Fuzzy cognitive maps (FCMs) as a kind of knowledge-based tools are widely applied to model complex dynamical systems using causal relations. Besides the representation and reasoning of systems behaviors, how to control the given systems into a desirable target by causal objects established by FCMs is also an open problem. Although, so far, there are some existing works about the applications of FCMs on the control-related problems, it is still a lack of the theoretical analysis in this domain. In this paper, the controllability of k-valued FCMs is studied. To improve the universality of models, a temporal extension of generalized FCMs is implemented. By means of semitensor product, the algebraic representation of k-valued FCMs with controls is established and a generalized formula of control-depending network transition matrices is achieved. A necessary and sufficient condition is proved to determine the control-depending fixed points of k-valued FCMs with temporalization. By utilizing three kinds of controls, the controllability of the discrete FCMs is discussed, respectively. The reachability condition of a specific target state from a given initial state at time s is studied, and the reachable set along with the corresponding reachable probability are also provided by analytic formula. Results provide a way to make FCMs evolving into the designed states by controls, which can further conduct the behaviors of the modeled systems in reality. Examples are shown to demonstrate the effectiveness and feasibility of the proposed scheme. [ABSTRACT FROM AUTHOR]

Conventional homojunction bipolar junction transistors (BJTs) are not suitable for cryogenic operation due to heavy doping-induced emitter band-gap narrowing and strong degradation in current gain (β) at low temperature. In this letter, we show that, on lateral version of the BJTs (LBJTs) fabricated on silicon-on-insulator (SOI) substrate, such β degradation can be mitigated by applying a substrate bias (Vsub), and a β over unity is achieved in a base current (IB) range over 5 orders of magnitudes at 4.2 K, with a peak β ~ 100 demonstrated. The β improvement is explained by the enhanced electron tunneling through base region as a result of base barrier lowering and thinning by a positive Vsub, which leads to dramatic increase of collector current (IC) while IB is negligibly affected. [ABSTRACT FROM AUTHOR]

The coupling characteristics between adjacent components can affect the performance of coils, and the efficiency of the RF coil is highly dependent on the decoupling effect. Minimizing the electromagnetic (EM) coupling between the coil elements is critical in the RF coil array design. With the more uniqueness and complexity of coil components in RF systems, traditional decoupling methods are less able to overcome the technical challenges in reducing EM coupling of RF coils arrays. In this article, the feasibility of a decoupling method based on induced current elimination (ICE) with non-overlapping decoupled elements, between which there are no physical connections, is investigated for the two-channel 1H/19F RF dual-tuned coil arrays. The isolation between the adjacent coils at both 1H and 19F Larmor frequencies allows for more than −20 dB using the ICE-decoupled method. The co-simulation approach, which can be used in strongly coil arrays per our previous study, was employed to calculate the values of the circuit decoupling components. To validate the efficiency of the ICE-decoupled method, the comparison studies among the proposed method, a two-channel capacitively decoupled, and the overlapping method were conducted. The results, including the S-parameters and the B1 – field distribution, indicated that the two-channel dual-tuned 1H/19F RF coils designed with ICE-decoupled method outperformed those using conventional capacitively decoupled and overlapping decoupled at 3 T. [ABSTRACT FROM AUTHOR]

XTS-advanced encryption standard (AES) is an advanced mode of AES for data protection of sector-based devices. It features two secret keys instead of one, and an additional tweak for each data block. These characteristics make the mode not only resistant against cryptoanalysis attacks, but also more challenging for side-channel attack. In this paper, we comprehensively analyze the side-channel power leakage of various XTS-AES implementations and invent effective attacks. We first run a simple power analysis of a software implementation. For a hardware implementation on field-programmable gate array (FPGA), we analyze side-channel leakage of the particular modular multiplication in XTS-AES mode. In addition, we utilize the relationship between two consecutive block tweaks and propose a method to work around the masking of ciphertext by the tweak. These attacks are verified on an FPGA implementation of XTS-AES. The results show that XTS-AES is susceptible to side-channel power analysis attacks, and therefore dedicated protections are required for security of XTS-AES in storage devices. [ABSTRACT FROM AUTHOR]