Private Information Retrieval (PIR) is a mechanism for efficiently downloading messages while keeping the index secret. The information-theoretic upper bound on efficiency has been proved in previous studies; PIR properties and the proofs of capacity were notated in terms of entropy and probability. However, in order to construct a linear PIR, it is necessary to clarify the properties of the query matrix. In this study, we prove the necessary and sufficient conditions for PIR properties, and represent them in matrix form. We also show a PIR construction method that satisfies the conditions.

We propose a conceptually simple and thus fast multi-object tracking (MOT) model that does not require any attached modules, such as the Kalman filter, Hungarian algorithm, transformer blocks, or graph networks. Conventional MOT models are built upon the multi-step modules listed above, and thus the computational cost is high. Our proposed end-toend MOT model, TicrossNet, is composed of a base detector and a cross-attention module only. As a result, the overhead of tracking does not increase significantly even when the number of instances (Nt) increases. We show that TicrossNet runs in real-time; specifically, it achieves 32.6 FPS on MOT17 and 31.0 FPS on MOT20 (Tesla V100), which includes as many as >100 instances per frame. We also demonstrate that TicrossNet is robust to Nt; thus, it does not have to change the size of the base detector, depending on Nt, as is often done by other models for real-time processing.

In recent years there have been great expectations of biometric authentication in view of increasing vicious crimes and terrorist threats. Face recognition is expected to be applied widely not only to security applications but also to image indexing and natural user interfaces. Accuracy of face recognition has been improved steadily in these years, but further improvements are demanded to meet performance requirements of these applications. We participated in Multiple Biometric Evaluation Still test conducted by National Institute of Standards and Technology (NIST) in 2010. In this evaluation, our algorithm achieved the best performance among all participants, with the highest identification rate of 95% among 1.8 million enrolled population, the lowest false match rate of 0.3% at false non-match rate 0.1%. In this demonstration, we show a real-time face recognition system using the above algorithm.