Tars: Timeliness-aware Adaptive Replica Selection for Key-Value Stores

In current large-scale distributed key-value stores, a single end-user request may lead to key-value access across tens or hundreds of servers. The tail latency of these key-value accesses is crucial to the user experience and greatly impacts the revenue. To cut the tail latency, it is crucial for clients to choose the fastest replica server as much as possible for the service of each key-value access. Aware of the challenges on the time varying performance across servers and the herd behaviors, an adaptive replica selection scheme C3 is proposed recently. In C3, feedback from individual servers is brought into replica ranking to reflect the time-varying performance of servers, and the distributed rate control and backpressure mechanism is invented. Despite of C3's good performance, we reveal the timeliness issue of C3, which has large impacts on both the replica ranking and the rate control, and propose the Tars (timeliness-aware adaptive replica selection) scheme. Following the same framework as C3, Tars improves the replica ranking by taking the timeliness of the feedback information into consideration, as well as revises the rate control of C3. Simulation results confirm that Tars outperforms C3.Comment: 10pages,submitted to ICDCS 201

Minimum Entangling Power is Close to Its Maximum

Given a quantum gate $U$ acting on a bipartite quantum system, its maximum (average, minimum) entangling power is the maximum (average, minimum) entanglement generation with respect to certain entanglement measure when the inputs are restricted to be product states. In this paper, we mainly focus on the 'weakest' one, i.e., the minimum entangling power, among all these entangling powers. We show that, by choosing von Neumann entropy of reduced density operator or Schmidt rank as entanglement measure, even the 'weakest' entangling power is generically very close to its maximal possible entanglement generation. In other words, maximum, average and minimum entangling powers are generically close. We then study minimum entangling power with respect to other Lipschitiz-continuous entanglement measures and generalize our results to multipartite quantum systems. As a straightforward application, a random quantum gate will almost surely be an intrinsically fault-tolerant entangling device that will always transform every low-entangled state to near-maximally entangled state.Comment: 26 pages, subsection III.A.2 revised, authors list updated, comments are welcom

Scalable surface code decoders with parallelization in time

Fast classical processing is essential for most quantum fault-tolerance architectures. We introduce a sliding-window decoding scheme that provides fast classical processing for the surface code through parallelism. Our scheme divides the syndromes in spacetime into overlapping windows along the time direction, which can be decoded in parallel with any inner decoder. With this parallelism, our scheme can solve the decoding throughput problem as the code scales up, even if the inner decoder is slow. When using min-weight perfect matching and union-find as the inner decoders, we observe circuit-level thresholds of $0.68\%$ and $0.55\%$, respectively, which are almost identical to $0.70\%$ and $0.55\%$ for the batch decoding.Comment: Main text: 6 pages, 3 figures. Supplementary material: 18 pages, 14 figures. V2: added data and updated general formalis

An unsupervised machine learning method for assessing quality of tandem mass spectra

<p>Abstract</p> <p>Background</p> <p>In a single proteomic project, tandem mass spectrometers can produce hundreds of millions of tandem mass spectra. However, majority of tandem mass spectra are of poor quality, it wastes time to search them for peptides. Therefore, the quality assessment (before database search) is very useful in the pipeline of protein identification via tandem mass spectra, especially on the reduction of searching time and the decrease of false identifications. Most existing methods for quality assessment are supervised machine learning methods based on a number of features which describe the quality of tandem mass spectra. These methods need the training datasets with knowing the quality of all spectra, which are usually unavailable for the new datasets.</p> <p>Results</p> <p>This study proposes an unsupervised machine learning method for quality assessment of tandem mass spectra without any training dataset. This proposed method estimates the conditional probabilities of spectra being high quality from the quality assessments based on individual features. The probabilities are estimated through a constraint optimization problem. An efficient algorithm is developed to solve the constraint optimization problem and is proved to be convergent. Experimental results on two datasets illustrate that if we search only tandem spectra with the high quality determined by the proposed method, we can save about 56 % and 62% of database searching time while losing only a small amount of high-quality spectra.</p> <p>Conclusions</p> <p>Results indicate that the proposed method has a good performance for the quality assessment of tandem mass spectra and the way we estimate the conditional probabilities is effective.</p

Is exponential gravity a viable description for the whole cosmological history?

Here we analysed a particular type of $F(R)$ gravity, the so-called exponential gravity which includes an exponential function of the Ricci scalar in the action. Such term represents a correction to the usual Hilbert-Einstein action. By using Supernovae Ia, Barionic Acoustic Oscillations, Cosmic Microwave Background and $H(z)$ data, the free parameters of the model are well constrained. The results show that such corrections to General Relativity become important at cosmological scales and at late-times, providing an alternative to the dark energy problem. In addition, the fits do not determine any significant difference statistically with respect to the $\Lambda$CDM model. Finally, such model is extended to include the inflationary epoch in the same gravitational Lagrangian. As shown in the paper, the additional terms can reproduce the inflationary epoch and satisfy the constraints from Planck data.Comment: 20 pages, 6 figures, analysis extended, version published in EPJ

Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

Identification of protein complexes fromprotein-protein interaction networks has become a key problem for understanding cellular life in postgenomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expression profiles. According to Core-Attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. The protein-complex cores are identified from these always active proteins by detecting dense subgraphs. Final protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of “closeness” and dynamic meaning. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived.The proposed algorithm DPC was applied on the data of Saccharomyces cerevisiae and the experimental results show that DPC outperforms CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment based on the validation of matching with known complexes and hF-measures

Overlap Matrix Completion for Predicting Drug-Associated Indications

Identification of potential drug-associated indications is critical for either approved or novel drugs in drug repositioning. Current computational methods based on drug similarity and disease similarity have been developed to predict drug-disease associations. When more reliable drug- or disease-related information becomes available and is integrated, the prediction precision can be continuously improved. However, it is a challenging problem to effectively incorporate multiple types of prior information, representing different characteristics of drugs and diseases, to identify promising drug-disease associations. In this study, we propose an overlap matrix completion (OMC) for bilayer networks (OMC2) and tri-layer networks (OMC3) to predict potential drug-associated indications, respectively. OMC is able to efficiently exploit the underlying low-rank structures of the drug-disease association matrices. In OMC2, first of all, we construct one bilayer network from drug-side aspect and one from disease-side aspect, and then obtain their corresponding block adjacency matrices. We then propose the OMC2 algorithm to fill out the values of the missing entries in these two adjacency matrices, and predict the scores of unknown drug-disease pairs. Moreover, we further extend OMC2 to OMC3 to handle tri-layer networks. Computational experiments on various datasets indicate that our OMC methods can effectively predict the potential drug-disease associations. Compared with the other state-of-the-art approaches, our methods yield higher prediction accuracy in 10-fold cross-validation and de novo experiments. In addition, case studies also confirm the effectiveness of our methods in identifying promising indications for existing drugs in practical applications