On the photofragmentation of SF2+_2^+: Experimental evidence for a predissociation channel

We report on the first observation of the photofragmentation dynamics of SF$_2^+$. With the aid of state-of-the-art ab initio calculations on the low-lying excited cationic states of SF$_2^+$ performed by Lee et al. [J. Chem. Phys. 125, 104304 (2006)], a predissociation channel of SF$_2^+$ is evidenced by means of resonance-enhanced multilphoton ionization spectroscopy. This work represents a second experimental investigation on the low-lying excited cationic states of SF$_2^+$. [The first one is the He I photoelectron spectrum of SF$_2^+$ reported by de Leeuw et al. three decades ago, see Chem. Phys. 34, 287 (1978).]Comment: 7 pages, 3 figures, submitted to JCP as a Not

Consistent and Flexible Selectivity Estimation for High-dimensional Data

Selectivity estimation aims at estimating the number of database objects that satisfy a selection criterion. Answering this problem accurately and efficiently is essential to many applications, such as density estimation, outlier detection, query optimization, and data integration. The estimation problem is especially challenging for large-scale high-dimensional data due to the curse of dimensionality, the large variance of selectivity across different queries, and the need to make the estimator consistent (i.e., the selectivity is non-decreasing in the threshold). We propose a new deep learning-based model that learns a query-dependent piecewise linear function as selectivity estimator, which is flexible to fit the selectivity curve of any query object and threshold, while guaranteeing that the output is non-decreasing in the threshold. To improve the accuracy for large datasets, we propose to partition the dataset into multiple disjoint subsets and build a local model on each of them. We perform experiments on real datasets and show that the proposed model significantly outperforms state-of-the-art models in accuracy and is competitive in efficiency

Counterexample-Preserving Reduction for Symbolic Model Checking

The cost of LTL model checking is highly sensitive to the length of the formula under verification. We observe that, under some specific conditions, the input LTL formula can be reduced to an easier-to-handle one before model checking. In our reduction, these two formulae need not to be logically equivalent, but they share the same counterexample set w.r.t the model. In the case that the model is symbolically represented, the condition enabling such reduction can be detected with a lightweight effort (e.g., with SAT-solving). In this paper, we tentatively name such technique "Counterexample-Preserving Reduction" (CePRe for short), and finally the proposed technquie is experimentally evaluated by adapting NuSMV

SAFA : a semi-asynchronous protocol for fast federated learning with low overhead

Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of FL considering the unreliable nature of end devices while the cost of device-server communication cannot be neglected. In this paper, we propose SAFA, a semi-asynchronous FL protocol, to address the problems in federated learning such as low round efficiency and poor convergence rate in extreme conditions (e.g., clients dropping offline frequently). We introduce novel designs in the steps of model distribution, client selection and global aggregation to mitigate the impacts of stragglers, crashes and model staleness in order to boost efficiency and improve the quality of the global model. We have conducted extensive experiments with typical machine learning tasks. The results demonstrate that the proposed protocol is effective in terms of shortening federated round duration, reducing local resource wastage, and improving the accuracy of the global model at an acceptable communication cost

Uniform rotundity in every direction of Orlicz-Sobolev spaces

Abstract In this paper, we study the extreme points and rotundity of Orlicz-Sobolev spaces. Analyzing and combining the properties of both Orlicz spaces and Sobolev spaces, we get the sufficient and necessary criteria for Orlicz-Sobolev spaces equipped with a modular norm to be uniformly rotund in every direction