We present new measurements of the scintillation and ionization yields in liquid xenon for low energy electronic (about 3--7 keV$_{ee}$) and nuclear recoils (about 8--20 keV$_{nr}$) at different drift fields from 236 V/cm to 3.93 kV/cm, using a three-dimensional sensitive liquid xenon time projection chamber with high energy and position resolutions. Our measurement of signal responses to nuclear recoils agrees with predictions from the NEST model. However, our measured ionization (scintillation) yields for electronic recoils are consistently higher (lower) than those from the NEST model by about 5 e$^-$/keV$_{ee}$ (ph/keV$_{ee}$) at all scanned drift fields. New recombination parameters based on the Thomas-Imel box model are derived from our data. Given the lack of precise measurement of scintillation and ionization yields for low energy electronic recoils in liquid xenon previously, our new measurement provides so far the best available data covering low energy region at different drift fields for liquid xenon detectors relevant to dark matter searches.
When building large-scale machine learning (ML) programs, such as big topic models or deep neural nets, one usually assumes such tasks can only be attempted with industrial-sized clusters with thousands of nodes, which are out of reach for most practitioners or academic researchers. We consider this challenge in the context of topic modeling on web-scale corpora, and show that with a modest cluster of as few as 8 machines, we can train a topic model with 1 million topics and a 1-million-word vocabulary (for a total of 1 trillion parameters), on a document collection with 200 billion tokens -- a scale not yet reported even with thousands of machines. Our major contributions include: 1) a new, highly efficient O(1) Metropolis-Hastings sampling algorithm, whose running cost is (surprisingly) agnostic of model size, and empirically converges nearly an order of magnitude faster than current state-of-the-art Gibbs samplers; 2) a structure-aware model-parallel scheme, which leverages dependencies within the topic model, yielding a sampling strategy that is frugal on machine memory and network communication; 3) a differential data-structure for model storage, which uses separate data structures for high- and low-frequency words to allow extremely large models to fit in memory, while maintaining high inference speed; and 4) a bounded asynchronous data-parallel scheme, which allows efficient distributed processing of massive data via a parameter server. Our distribution strategy is an instance of the model-and-data-parallel programming model underlying the Petuum framework for general distributed ML, and was implemented on top of the Petuum open-source system. We provide experimental evidence showing how this development puts massive models within reach on a small cluster while still enjoying proportional time cost reductions with increasing cluster size, in comparison with alternative options.
Based on quantum encryption, we present a new idea for quantum public-key cryptography (QPKC) and construct a whole theoretical framework of a QPKC system. We show that the quantum-mechanical nature renders it feasible and reasonable to use symmetric keys in such a scheme, which is quite different from that in conventional public-key cryptography. The security of our scheme is analyzed and some features are discussed. Furthermore, the state-estimation attack to a prior QPKC scheme is demonstrated.
From the perspective of information theory and cryptography, we analyze the security of two quantum dialogue protocols and a bidirectional quantum secure direct communication (QSDC) protocol, and point out that the transmitted information would be partly leaked out in them. That is, any eavesdropper can elicit some information about the secrets from the public annunciations of the legal users. This phenomenon should have been strictly forbidden in a quantum secure communication. In fact, this problem exists in quite a few recent proposals and, therefore, it deserves more research attention in the following related study.
The participant attack is the most serious threat for quantum secret-sharing protocols. We present a method to analyze the security of quantum secret-sharing protocols against this kind of attack taking the scheme of Hillery, Buzek, and Berthiaume (HBB) [Phys. Rev. A 59 1829 (1999)] as an example. By distinguishing between two mixed states, we derive the necessary and sufficient conditions under which a dishonest participant can attain all the information without introducing any error, which shows that the HBB protocol is insecure against dishonest participants. It is easy to verify that the attack scheme of Karlsson, Koashi, and Imoto [Phys. Rev. A 59, 162 (1999)] is a special example of our results. To demonstrate our results further, we construct an explicit attack scheme according to the necessary and sufficient conditions. Our work completes the security analysis of the HBB protocol, and the method presented may be useful for the analysis of other similar protocols.
In a recent Letter [G. Chiribella et al., Phys. Rev. Lett. 98, 120501 (2007)], four protocols were proposed to secretly transmit a reference frame. Here We point out that in these protocols an eavesdropper can change the transmitted reference frame without being detected, which means the consistency of the shared reference frames should be reexamined. The way to check the above consistency is discussed. It is shown that this problem is quite different from that in previous protocols of quantum cryptography.
