Quantum singular value transformation (QSVT) is a powerful quantum algorithm that can effectively perform a polynomial transformation of the singular values of a linear operator embedded in a unitary matrix. This article extends QSVT to perform a polynomial transformation of the amplitudes of a quantum state. This article presents an algorithm that can convert quantum state$\sum_{i=0}^{N-1}x_{i}\vert i\rangle$to quantum state$\frac{1}{\sqrt{\sum_{i=0}^{N-1}\vert f(x_{i})\vert^{2}}}\sum_{i=0}^{N-1}f(x_{i})\vert\rangle$and demonstrates the application of the algorithm in the quantum state small amplitude extraction.

Recognizing driver speed intention such as acceleration and deceleration is of great significance for intelligent assisted driving systems, drive energy management, and gear decision of automatic transmissions, among other applications. However, existing studies have mainly focused on recognizing only a few typical speed intentions. They have not adequately considered the effects of various factors of the driving environment, including road slopes, curves, as well as other critical factors like lane changes and vehicle gears, on intention recognition. To address this gap, this study comprehensively categorizes speed intentions and establishes a speed intention recognition model that considers the influence of these factors. First, naturalistic driving data is collected to ensure the robustness and practicality of the model. To integrate the effects of the driving environment into speed intention recognition, the road slope and turning/lane-changing operations of the driver are extracted from driving data. Furthermore, the speed intention is comprehensively categorized. The effects of road slope, vehicle gear, and turning/lane changing on the intention recognition are analyzed separately, and the Toeplitz inverse covariance-based clustering algorithm is used to label the driving data while considering these effects. Finally, a supervised feature selection algorithm is used to select intention recognition features, and a deep-learning-based hierarchical recognition model is established for speed intentions. Validation results indicate that the constructed intention recognition model exhibits excellent recognition performance and satisfies the requirements for real-time recognition.

We describe a subfield attack for NTRU problem by using the symmetric function map Sk, which is a generalization of results presented by Albrecht, Bai and Ducas [1] and Cheon, Jeong and [2]. At first, we prove that Skwith an appropriate k can fits for more cases compared with previous related works while maintaining the same or better efficiency. Then we show the subfield attack has its advantage compared with the subring attack described in [3]. At last we present a method to make the subfield attack algorithm more efficient.

We obtain new non-existence results of perfect p-ary sequences with period n (called type [p, n]). The first case contains a class with type [p m 5 (mod 8), paqn']. The second case contains five types [p m 3 (mod 4), paqln'] for certain p, q, and l. Moreover, we also obtain similar nonexistence results of perfect almost p-ary sequences.

We obtain new non-existence results of generalized bent functions from Znqto Zq(called type [n, q]). The first case is a class with types, where q = 2pr11pr22. The second case contains two types [1e] and [1e].