Improved Loss Function for Image Classification

An improved loss function free of sampling procedures is proposed to improve the ill-performed classification by sample shortage. Adjustable parameters are used to expand the loss scope, minimize the weight of easily classified samples, and further substitute the sampling function, which are added to the cross-entropy loss and the SoftMax loss. Experiment results indicate that improvements in all classification performance of our loss function are shown in various network architectures and on different datasets. To summarize, compared with traditional loss functions, our improved version not only elevates classification performance but also lowers the difficulty of network training.

Lattice-Based Linearly Homomorphic Signature Scheme over F 2

In this paper, we design a new lattice-based linearly homomorphic signature scheme over F 2 . The existing schemes are all constructed based on hash-and-sign lattice-based signature framework, where the implementation of preimage sampling function is Gaussian sampling, and the use of trapdoor basis needs a larger dimension m ≥ 5 n log q . Hence, they cannot resist potential side-channel attacks and have larger sizes of public key and signature. Under Fiat–Shamir with aborting signature framework and general SIS problem restricted condition m ≥ n log q , we use uniform sampling of filtering technology to design the scheme, and then, our scheme has a smaller public key size and signature size than the existing schemes and it can resist side-channel attacks.

Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

The Markov chain Monte Carlo (MCMC) method based on Metropolis–Hastings (MH) sampling is a popular approach in solving seismic acoustic impedance (AI) inversion problem, as it can improve the inversion resolution by statistical prior information. However, the sampling function of the traditional MH sampling is a fixed parameter distribution. The parameter ignores the statistical information of AI that expands sampling range and reduces the inversion efficiency and resolution. To reduce the sampling range and improve the efficiency, we apply the statistical information of AI to the sampling function and build a Gaussian MH sampling with data driving (GMHDD) approach to the sampling function. Moreover, combining GMHDD and MCMC, we propose a novel Bayesian AI inversion method based on GMHDD. Finally, we use the Marmousi2 data and field data to test the proposed method based on GMHDD and other methods based on traditional MH. The results reveal that the proposed method can improve the efficiency and resolution of impedance inversion than other methods.

ID-Based Strong Designated Verifier Signature over R-SIS Assumption

In this paper, we propose an ID-based strong designated verifier signature (SDVS) over R-SIS assumption in the random model. We remove pre-image sampling function and Bonsai trees such complex structures used in previous lattice-based SDVS schemes. We only utilize simple rejection sampling to protect the security of our scheme. Hence, we will show our design has the shortest signature size comparing with existing lattice-based ID-based SDVS schemes. In addition, our scheme satisfies anonymity (privacy of signer’s identity) proved in existing schemes rarely, and it can resist side-channel attacks with uniform sampling.

Vision System for an Autonomous Underwater Vehicle with a Benthos Sampling Function

We developed a vision system for an autonomous underwater robot with a benthos sampling function, specifically sampling-autonomous underwater vehicle (AUV). The sampling-AUV includes the following five modes: preparation mode (PM), observation mode (OM), return mode (RM), tracking mode (TM), and sampling mode (SM). To accomplish the mission objective, the proposed vision system comprises software modules for image acquisition, image enhancement, object detection, image selection, and object tracking. The camera in the proposed system acquires images in intervals of five seconds during OM and RM, and in intervals of one second during TM. The system completes all processing stages in the time required for image acquisition by employing high-speed algorithms. We verified the effective operation of the proposed system in a pool.

Continuous spectrum for a class of smooth mixing Schrödinger operators

We give the first example of a smooth volume preserving mixing dynamical system such that the discrete Schrödinger operators on the line defined with a potential generated by this system and a Hölder sampling function have almost surely a continuous spectrum.