Weighted-Sampling Audio Adversarial Example Attack

Recent studies have highlighted audio adversarial examples as a ubiquitous threat to state-of-the-art automatic speech recognition systems. Thorough studies on how to effectively generate adversarial examples are essential to prevent potential attacks. Despite many research on this, the efficiency and the robustness of existing works are not yet satisfactory. In this paper, we propose~\textit{weighted-sampling audio adversarial examples}, focusing on the numbers and the weights of distortion to reinforce the attack. Further, we apply a denoising method in the loss function to make the adversarial attack more imperceptible. Experiments show that our method is the first in the field to generate audio adversarial examples with low noise and high audio robustness at the minute time-consuming level.Comment: https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuXL.9260.pd

Testing and selecting cosmological models with ultra-compact radio quasars

In this paper, we place constraints on four alternative cosmological models under the assumption of the spatial flatness of the Universe: CPL, EDE, GCG and MPC. A new compilation of 120 compact radio quasars observed by very-long-baseline interferometry, which represents a type of new cosmological standard rulers, are used to test these cosmological models. Our results show that the fits on CPL obtained from the quasar sample are well consistent with those obtained from BAO. For other cosmological models considered, quasars provide constraints in agreement with those derived with other standard probes at $1\sigma$ confidence level. Moreover, the results obtained from other statistical methods including Figure of Merit, $Om(z)$ and statefinder diagnostics indicate that: (1) Radio quasar standard ruler could provide better statistical constraints than BAO for all cosmological models considered, which suggests its potential to act as a powerful complementary probe to BAO and galaxy clusters. (2) Turning to $Om(z)$ diagnostics, CPL, GCG and EDE models can not be distinguished from each other at the present epoch. (3) In the framework of statefinder diagnostics, MPC and EDE will deviate from $\rm{\Lambda}$CDM model in the near future, while GCG model cannot be distinguished from $\rm{\Lambda}$CDM model unless much higher precision observations are available.Comment: 12 pages, 8 figures, 1 tabl

The Quasi-Diabatic Hamiltonian Approach to Accurate and Efficient Nonadiabatic Dynamics with Correct Treatment of Conical Intersection Seams

A method to simulate photoelectron spectra using quadratic local quasi-diabatic Hamiltonians (Hd) is generalized and augmented to enable high accuracy dynamics simulations of nonadiabatic processes that involve large amplitude motions, including dissociation. The improvement is achieved by using a flexible symmetry adapted analytical expansion to approximate the representation of electronic Hamiltonian operator in a quasi-diabatic basis, the diabaticity of which is achieved by minimization of residual coupling between quasi-diabatic states. Although previous theoretical treatments have been used to treat adiabatic dissociation and rearrangement processes with success, difficulties have been encountered in systems complicated by seams of conical intersections. Existing methods are either too expensive to be applied, or could not provide sufficient accuracy. Even for nonadiabatic reactions of very small systems, such as photodissociation of NH3, all previous theoretical treatments have been unable to accurately reproduce experimental measurements. In this work, inspired by the success of bound-state Hd approach, a rigorous and flexible framework is established to create a more robust method for accurate and efficient nonadiabatic dynamics simulations, through the construction of quasi-diabatic Hamiltonians(Hd) that correctly describes reactions. This new method requires no assumption on the properties of individual systems. The application of local intersection adapted representations and partially diagonalized representations enabled entire seams of conical intersections as well as the nearby regions to be accurately described. No ad hoc approximation is made in the diabatization procedure, and the residual coupling of the underlying quasi-diabatic representation is minimized in a least squares sense and can be exactly quantified. Polynomials of arbitrary functions of internal coordinates are used to construct an extremely flexible basis for Hd, and generic symmetry treatment allows incorporation of arbitrary point group or Complete Nuclear Permutation Inversion (CNPI) group symmetry . With the Hd constructed from the new approach, the Ã←X̃ photodissociation process of NH3 was simulated. New results, obtained using Hd constructed with the method described in this work, accurately reproduce experimental measurements, illustrating its promising potential. The method is then further enhanced to allow application to much larger systems, with the coupled potential energy surfaces of the 1,2,31A states for the photodissociation of phenol used as an example. A partially diagonalized representation approach is developed to accurately treat near degenerate points, and a null-space analysis procedure is added to guide the selection of monomial basis and to remove linear dependencies in the fitting procedure. Coupled potential energy surfaces that fully incorporate all 33 degrees of freedom, many different large amplitude motions, and multiple seams of conical intersections, are successfully constructed from ab initio data

Looking ahead: Summer offerings for all ages

The advent of ultrashort soft X-ray pulse sources permits the use of established gas-phase spectroscopy methods to investigate ultrafast photochemistry in isolated molecules with element and site specificity. In the present study, we simulate excited-state wavepacket dynamics of a prototypical process, the ultrafast photodissociation of methyl iodide. Using the simulation, we calculate time-dependent excited-state carbon edge photoelectron and Auger electron spectra. We observe distinct signatures in both types of spectra and show their direct connection to C–I bond dissociation and charge rearrangement processes in the molecule. We demonstrate at the CH3I molecule that the observed signatures allow us to map the time-dependent dynamics of ultrafast photoinduced bond breaking with unprecedented detail

PL-PatchSurfer: A Novel Molecular Local Surface-Based Method for Exploring Protein-Ligand Interactions

Structure-based computational methods have been widely used in exploring protein-ligand interactions, including predicting the binding ligands of a given protein based on their structural complementarity. Compared to other protein and ligand representations, the advantages of a surface representation include reduced sensitivity to subtle changes in the pocket and ligand conformation and fast search speed. Here we developed a novel method named PL-PatchSurfer (Protein-Ligand PatchSurfer). PL-PatchSurfer represents the protein binding pocket and the ligand molecular surface as a combination of segmented surface patches. Each patch is characterized by its geometrical shape and the electrostatic potential, which are represented using the 3D Zernike descriptor (3DZD). We first tested PL-PatchSurfer on binding ligand prediction and found it outperformed the pocket-similarity based ligand prediction program. We then optimized the search algorithm of PL-PatchSurfer using the PDBbind dataset. Finally, we explored the utility of applying PL-PatchSurfer to a larger and more diverse dataset and showed that PL-PatchSurfer was able to provide a high early enrichment for most of the targets. To the best of our knowledge, PL-PatchSurfer is the first surface patch-based method that treats ligand complementarity at protein binding sites. We believe that using a surface patch approach to better understand protein-ligand interactions has the potential to significantly enhance the design of new ligands for a wide array of drug-targets

A review of research on acoustic detection of heat exchanger tube

Leakage in heat exchanger tubes can result in unreliable products and dangerous situations, which could cause great economic losses. Along with fast development of modern acoustic detection technology, using acoustic signals to detect leakage in heat exchange tube has been gradually accepted and considered with great potential by both industrial and research societies. In order to further advance the development of acoustic signal detection technology and investigate better methods for leakage detection in heat exchange tube, in this paper, firstly, we conduct a short overview of the theory of acoustic signal detection on heat exchanger tube, which had already been continuously developed for a few decades by researchers worldwide. Thereafter, we further expound the advantages and limitations of acoustic signal detection technology on heat exchanger tube in four aspects: 1) principles of acoustic signal detection, 2) characteristics of sound wave propagation in heat exchanger tube, 3) methods of leakage detection, and 4) leakage localization in heat exchanger tube

Adversarial Samples on Android Malware Detection Systems for IoT Systems

Many IoT(Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aimed at image data. To solve this problem, we propose a \textbf{t}esting framework for \textbf{l}earning-based \textbf{A}ndroid \textbf{m}alware \textbf{d}etection systems(TLAMD) for IoT Devices. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample without affecting the features of the application. By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android Application with a success rate of nearly 100\% and can perform black-box testing on the system

Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons

Suicide is among the leading causes of death in China. However, technical approaches toward preventing suicide are challenging and remaining under development. Recently, several actual suicidal cases were preceded by users who posted microblogs with suicidal ideation to Sina Weibo, a Chinese social media network akin to Twitter. It would therefore be desirable to detect suicidal ideations from microblogs in real-time, and immediately alert appropriate support groups, which may lead to successful prevention. In this paper, we propose a real-time suicidal ideation detection system deployed over Weibo, using machine learning and known psychological techniques. Currently, we have identified 53 known suicidal cases who posted suicide notes on Weibo prior to their deaths.We explore linguistic features of these known cases using a psychological lexicon dictionary, and train an effective suicidal Weibo post detection model. 6714 tagged posts and several classifiers are used to verify the model. By combining both machine learning and psychological knowledge, SVM classifier has the best performance of different classifiers, yielding an F-measure of 68:3%, a Precision of 78:9%, and a Recall of 60:3%.Comment: 6 page