Scalable Bell inequalities for multiqubit systems

Based on Clauser-Horner-Shimony-Holt inequality, we show a fruitful method to exploit Bell inequalities for multipartite qubit systems. These Bell inequalities are designed with a simpler architecture tailored to experimental demonstration. Under the optimal setting we derive a set of compact Mermin-type inequalities and discuss quantum violations for generalized Greenberger-Horne-Zeilinger (GGHZ) states. Also, we reveal relationship between quantum nonlocality and four-partite entanglement for four-qubit GGHZ states.Comment: 4 pages, 1 figur

5,5′,5′′-Triphenyl-2,2′,2′′-[2,4,6-tri­methyl­benzene-1,3,5-triyltris(methyl­idene­sulfanedi­yl)]tris­(1,3,4-oxadiazole)

In the title compound, C36H30N6O3S3, the phenyl rings are twisted from the attached oxadiazole rings in the three arms by 1.5(2), 2.4 (2) and 25.7 (2)°. The crystal packing exhibits weak inter­molecular C—H⋯N inter­actions

5,5′-Diphenyl-2,2′-[butane-1,4-diylbis(sulfanedi­yl)]bis­(1,3,4-oxadiazole)

The complete mol­ecule of the title compound, C20H18N4O2S2, is generated by crystallographic inversion symmetry. The benzene ring is almost coplanar with the oxadiazole ring [dihedral angle = 7.2 (2)°]

N-(2-Chloro­phen­yl)-2-(4,6-dimethyl­pyrimidin-2-ylsulfan­yl)acetamide

In the title compound, C14H14ClN3OS, the 4,6-dimethyl­pyrimidine ring and the chloro­benzene ring subtend a dihedral angle of 80.0 (2)°. The length of the Csp 2—S bond is significantly shorter than that of the Csp 3—S bond. The crystal structure is stabilized by inter­molecular N—H⋯O, C—H⋯O and C—H⋯N hydrogen bonding, and C—H⋯π inter­actions

Achieving Covert Communication With A Probabilistic Jamming Strategy

In this work, we consider a covert communication scenario, where a transmitter Alice communicates to a receiver Bob with the aid of a probabilistic and uninformed jammer against an adversary warden's detection. The transmission status and power of the jammer are random and follow some priori probabilities. We first analyze the warden's detection performance as a function of the jammer's transmission probability, transmit power distribution, and Alice's transmit power. We then maximize the covert throughput from Alice to Bob subject to a covertness constraint, by designing the covert communication strategies from three different perspectives: Alice's perspective, the jammer's perspective, and the global perspective. Our analysis reveals that the minimum jamming power should not always be zero in the probabilistic jamming strategy, which is different from that in the continuous jamming strategy presented in the literature. In addition, we prove that the minimum jamming power should be the same as Alice's covert transmit power, depending on the covertness and average jamming power constraints. Furthermore, our results show that the probabilistic jamming can outperform the continuous jamming in terms of achieving a higher covert throughput under the same covertness and average jamming power constraints

Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering

Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either search architectures or hyperparameters while ignoring the fact they are intrinsically related and should be considered together. This motivates us to consider a joint hyperparameter and architecture search method to design CF models. However, this is not easy because of the large search space and high evaluation cost. To solve these challenges, we reduce the space by screening out usefulness yperparameter choices through a comprehensive understanding of individual hyperparameters. Next, we propose a two-stage search algorithm to find proper configurations from the reduced space. In the first stage, we leverage knowledge from subsampled datasets to reduce evaluation costs; in the second stage, we efficiently fine-tune top candidate models on the whole dataset. Extensive experiments on real-world datasets show better performance can be achieved compared with both hand-designed and previous searched models. Besides, ablation and case studies demonstrate the effectiveness of our search framework.Comment: Accepted by KDD 202