Field-dependent diamagnetic transition in magnetic superconductor Sm1.85Ce0.15CuO4−ySm_{1.85} Ce_{0.15} Cu O_{4-y}

The magnetic penetration depth of single crystal $\rm{Sm_{1.85}Ce_{0.15}CuO_{4-y}}$ was measured down to 0.4 K in dc fields up to 7 kOe. For insulating $\rm{Sm_2CuO_4}$, Sm$^{3+}$ spins order at the N\'{e}el temperature, $T_N = 6$ K, independent of the applied field. Superconducting $\rm{Sm_{1.85}Ce_{0.15}CuO_{4-y}}$ ($T_c \approx 23$ K) shows a sharp increase in diamagnetic screening below $T^{\ast}(H)$ which varied from 4.0 K ($H = 0$) to 0.5 K ($H =$ 7 kOe) for a field along the c-axis. If the field was aligned parallel to the conducting planes, $T^{\ast}$ remained unchanged. The unusual field dependence of $T^{\ast}$ indicates a spin freezing transition that dramatically increases the superfluid density.Comment: 4 pages, RevTex

Exactness of the Original Grover Search Algorithm

It is well-known that when searching one out of four, the original Grover's search algorithm is exact; that is, it succeeds with certainty. It is natural to ask the inverse question: If we are not searching one out of four, is Grover's algorithm definitely not exact? In this article we give a complete answer to this question through some rationality results of trigonometric functions.Comment: 8 pages, 2 figure

Preparation of GHZ states via Grover's quantum searching algorithm

In this paper we propose an approach to prepare GHZ states of an arbitrary multi-particle system in terms of Grover's fast quantum searching algorithm. This approach can be regarded as an extension of the Grover's algorithm to find one or more items in an unsorted database.Comment: 9 pages, Email address: [email protected]

Exploiting Machine Learning to Subvert Your Spam Filter

Using statistical machine learning for making security decisions introduces new vulnerabilities in large scale systems. This paper shows how an adversary can exploit statistical machine learning, as used in the SpamBayes spam filter, to render it useless—even if the adversary’s access is limited to only 1 % of the training messages. We further demonstrate a new class of focused attacks that successfully prevent victims from receiving specific email messages. Finally, we introduce two new types of defenses against these attacks.