The response of the Convolutional Neural Network to the transient noise in Gravitational wave detection

In recent years, much work have studied the use of convolutional neural networks for gravitational-wave detection. However little work pay attention to whether the transient noise can trigger the CNN model or not. In this paper, we study the responses of the sine-Gaussian glitches, the Gaussian glitches and the ring-down glitches in the trained convolutional neural network classifier. We find that the network is robust to the sine-Gaussian and Gaussian glitches, whose false alarm probabilities are close to that of the LIGO-like noises, in contrast to the case of the ring-down glitches, in which the false alarm probability is far larger than that of the LIGO-like noises. We also investigate the responses of the glitches with different frequency. We find that when the frequency of the glitches falls in that of the trained GW signals, the false alarm probability of the glitches will be much larger than that of the LIGO-like noises, and the probability of the glitches being misjudged as the GW signals may even exceed 30%.

TEQUILA: Temporal Question Answering over Knowledge Bases

Question answering over knowledge bases (KB-QA) poses challenges in handling complex questions that need to be decomposed into sub-questions. An important case, addressed here, is that of temporal questions, where cues for temporal relations need to be discovered and handled. We present TEQUILA, an enabler method for temporal QA that can run on top of any KB-QA engine. TEQUILA has four stages. It detects if a question has temporal intent. It decomposes and rewrites the question into non-temporal sub-questions and temporal constraints. Answers to sub-questions are then retrieved from the underlying KB-QA engine. Finally, TEQUILA uses constraint reasoning on temporal intervals to compute final answers to the full question. Comparisons against state-of-the-art baselines show the viability of our method.

Prediction of Topological Crystalline Insulator and Topological Phase Transitions in Two-dimensional PbTe Films

Topological phases, especially topological crystalline insulators (TCIs), have been intensively explored observed experimentally in three-dimensional (3D) materials. However, the two-dimensional (2D) films are explored much less than 3D TCI, and even 2D topological insulators. Based on ab initio calculations, here we investigate the electronic and topological properties of 2D PbTe(001) few-layers. The monolayer and trilayer PbTe are both intrinsic 2D TCIs with a large band gap reaching 0.27 eV, indicating a high possibility for room-temperature observation of quantized conductance. The origin of TCI phase can be attributed to the p band inversion,which is determined by the competitions of orbital hybridization and quantum confinement. We also observe a semimetal-TCI-normal insulator transition under biaxial strains, whereas a uniaxial strains lead to Z2 nontrivial states. Especially, the TCI phase of PbTe monolayer remains when epitaxial grow on NaI semiconductor substrate. Our findings on the controllable quantum states with sizable band gaps present an ideal platform for realizing future topological quantum devices with ultralow dissipation.

Structure, hydrolysis and diffusion of aqueous vanadium ions from Car-Parrinello molecular dynamics

A molecular level understanding of the properties of electroactive vanadium species in aqueous solution is crucial for enhancing the performance of vanadium redox flow batteries (RFB). Here, we employ Car-Parrinello molecular dynamics (CPMD) simulations based on density functional theory to investigate the hydration structures, first hydrolysis reaction and diffusion of aqueous V$^{2+}$, V$^{3+}$, VO$^{2+}$, and VO$_2^+$ ions at 300 K. The results indicate that the first hydration shell of both V$^{2+}$ and V$^{3+}$ contains six water molecules, while VO$^{2+}$ is coordinated to five and VO$_2^+$ to three water ligands. The first acidity constants (p$K_mathrm{a}$) estimated using metadynamics simulations are 2.47, 3.06 and 5.38 for aqueous V$^{3+}$, VO$_2^+$ and VO$^{2+}$, respectively, while V$^{2+}$ is predicted to be a fairly weak acid in aqueous solution with a p$K_mathrm{a}$ value of 6.22. We also show that the presence of chloride ions in the first coordination sphere of the aqueous VO$_2^+$ ion has a significant impact on water hydrolysis leading to a much higher p$K_mathrm{a}$ value of 4.8. This should result in a lower propensity of aqueous VO$_2^+$ for oxide precipitation reaction in agreement with experimental observations for chloride-based electrolyte solutions. The computed diffusion coefficients of vanadium species in water at room temperature are found to increase as V$^{3+}$ $<$ VO$_2^+$ $<$ VO$^{2+}$ $<$ V$^{2+}$ and thus correlate with the simulated hydrolysis constants, namely, the higher the p$K_mathrm{a}$ value, the greater the diffusion coefficient.

Characterizing and Subsetting Big Data Workloads

Big data benchmark suites must include a diversity of data and workloads to be useful in fairly evaluating big data systems and architectures. However, using truly comprehensive benchmarks poses great challenges for the architecture community. First, we need to thoroughly understand the behaviors of a variety of workloads. Second, our usual simulation-based research methods become prohibitively expensive for big data. As big data is an emerging field, more and more software stacks are being proposed to facilitate the development of big data applications, which aggravates hese challenges. In this paper, we first use Principle Component Analysis (PCA) to identify the most important characteristics from 45 metrics to characterize big data workloads from BigDataBench, a comprehensive big data benchmark suite. Second, we apply a clustering technique to the principle components obtained from the PCA to investigate the similarity among big data workloads, and we verify the importance of including different software stacks for big data benchmarking. Third, we select seven representative big data workloads by removing redundant ones and release the BigDataBench simulation version, which is publicly available from http://prof.ict.ac.cn/BigDataBench/simulatorversion/.