Cryptanalysis of a multi-party quantum key agreement protocol with single particles

Recently, Sun et al. [Quant Inf Proc DOI: 10.1007/s11128-013-0569-x] presented an efficient multi-party quantum key agreement (QKA) protocol by employing single particles and unitary operations. The aim of this protocol is to fairly and securely negotiate a secret session key among $N$ parties with a high qubit efficiency. In addition, the authors claimed that no participant can learn anything more than his/her prescribed output in this protocol, i.e., the sub-secret keys of the participants can be kept secret during the protocol. However, here we points out that the sub-secret of a participant in Sun et al.'s protocol can be eavesdropped by the two participants next to him/her. In addition, a certain number of dishonest participants can fully determine the final shared key in this protocol. Finally, we discuss the factors that should be considered when designing a really fair and secure QKA protocol.Comment: 7 page

Dense-Coding Attack on Three-Party Quantum Key Distribution Protocols

Cryptanalysis is an important branch in the study of cryptography, including both the classical cryptography and the quantum one. In this paper we analyze the security of two three-party quantum key distribution protocols (QKDPs) proposed recently, and point out that they are susceptible to a simple and effective attack, i.e. the dense-coding attack. It is shown that the eavesdropper Eve can totally obtain the session key by sending entangled qubits as the fake signal to Alice and performing collective measurements after Alice's encoding. The attack process is just like a dense-coding communication between Eve and Alice, where a special measurement basis is employed. Furthermore, this attack does not introduce any errors to the transmitted information and consequently will not be discovered by Alice and Bob. The attack strategy is described in detail and a proof for its correctness is given. At last, the root of this insecurity and a possible way to improve these protocols are discussed.Comment: 6 pages, 3 figure

Identification of a New γ\gamma-ray-emitting narrow-line Seyfert 1 Galaxy, at Redshift ∼1\sim1

We report on the identification of a new $\gamma$-ray-emitting narrow-line Seyfert 1 (NLS1) galaxy, SDSS J122222.55+041315.7, which increases the number of known objects of this remarkable but rare type of active galactic nuclei (AGN) to seven. Its optical spectrum, obtained in the Sloan Digital Sky Survey-Baryon Oscillation Spectroscopic Survey, reveals a broad H $\beta$ emission line with a width (FWHM) of 1734$\pm$104 km s$^{-1}$. This, along with strong optical Fe II multiplets [$R_{4570}=0.9$] and a weak [O III] $\lambda 5007$ emission line, makes the object a typical NLS1. On the other hand, the source exhibits a high radio brightness temperature, rapid infrared variability, and a flat X-ray spectrum extending up to $\sim$200 keV. It is associated with a luminous $\gamma$-ray source detected significantly with {\it Fermi}/LAT. Correlated variability with other wavebands has not yet been tested. The spectral energy distribution can be well modelled by a one-zone leptonic jet model. This new member is by far the most distant $\gamma$-ray-emitting NLS1, at a redshift of $z=0.966$.Comment: 5 pages, published on MNRA

KINEMATICS ANALYSIS ON THE CHINESE ELITE ATHLETES TAN SIXIN'S BALANCE BEAM MOVEMENT OF CHANGE LEG RING LEAP

The purpose of this study was to obtain a series of kinematics parameters of Tan Sixin's change leg ring leap with the help of software and to make kinematics analysis, in order to reveal the kinematic rules and technical characteristics of the change leg ring leap movement, thus providing theoretical basis and reference for athletes to consummate technique and coaches to guide training

Integrating Per-Stream Stat Tracking into Accel-Sim

Accel-Sim is a widely used computer architecture simulator that models the behavior of modern NVIDIA GPUs in great detail. However, although Accel-Sim and the underlying GPGPU-Sim model many of the features of real GPUs, thus far it has not been able to track statistics separately per stream. Instead, Accel-Sim combines statistics (e.g., cycles and cache hits/misses) across all simultaneously running streams. This can prevent users from properly identifying the behavior of specific kernels and streams and potentially lead to incorrect conclusions. Thus, in this work we extend Accel-Sim's and GPGPU-Sim's statistic tracking support to track per-stream statistics. To validate this support, we designed a series of multi-stream microbenchmarks and checked their reported per-kernel, per-stream counts.Comment: 13 page