Topological Crystalline Insulators with C2C_2 Rotation Anomaly

Based on first-principles calculations and symmetry-based indicator analysis, we find a class of topological crystalline insulators (TCIs) with $C_2$ rotation anomaly in a family of Zintl compounds, including $\mathrm{Ba}_{3}\mathrm{Cd}_{2}\mathrm{As}_{4}$, $\mathrm{Ba}_{3}\mathrm{Zn}_{2}\mathrm{As}_{4}$ and $\mathrm{Ba}_{3}\mathrm{Cd}_{2}\mathrm{Sb}_{4}$. The nontrivial band topology protected by coexistence of $C_2$ rotation symmetry and time-reversal symmetry $T$ leads to two surface Dirac cones at generic momenta on both top and bottom surfaces perpendicular to the rotation axis. In addition, ($d-2$)-dimensional helical hinge states are also protected along the hinge formed by two side surfaces parallel with the rotation axis. We develop a method based on Wilson loop technique to prove the existence of these surface Dirac cones due to $C_2$ anomaly and precisely locate them as demonstrated in studying these TCIs. The helical hinge states are also calculated. Finally, we show that external strain can be used to tune topological phase transitions among TCIs, strong Z$_2$ topological insulators and trivial insulators.Comment: 10 pages, 10 figure

Classifying motion states of AUV based on graph representation for multivariate time series

Acknowledgement This work is supported by Natural Science Foundation of Shandong Province (ZR2020MF079) and China Scholarship Council (CSC).Peer reviewedPostprin

DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization

DL compiler's primary function is to translate DNN programs written in high-level DL frameworks such as PyTorch and TensorFlow into portable executables. These executables can then be flexibly executed by the deployed host programs. However, existing DL compilers rely on a tracing mechanism, which involves feeding a runtime input to a neural network program and tracing the program execution paths to generate the computational graph necessary for compilation. Unfortunately, this mechanism falls short when dealing with modern dynamic neural networks (DyNNs) that possess varying computational graphs depending on the inputs. Consequently, conventional DL compilers struggle to accurately compile DyNNs into executable code. To address this limitation, we propose \tool, a general approach that enables any existing DL compiler to successfully compile DyNNs. \tool tackles the dynamic nature of DyNNs by introducing a compilation mechanism that redistributes the control and data flow of the original DNN programs during the compilation process. Specifically, \tool develops program analysis and program transformation techniques to convert a dynamic neural network into multiple sub-neural networks. Each sub-neural network is devoid of conditional statements and is compiled independently. Furthermore, \tool synthesizes a host module that models the control flow of the DyNNs and facilitates the invocation of the sub-neural networks. Our evaluation demonstrates the effectiveness of \tool, achieving a 100\% success rate in compiling all dynamic neural networks. Moreover, the compiled executables generated by \tool exhibit significantly improved performance, running between $1.12\times$ and $20.21\times$ faster than the original DyNNs executed on general-purpose DL frameworks.Comment: This paper has been accepted to ISSTA 202

The changes in fractal dimension after a maximal exertion in swimming

Quite often linear variables are not sensitive enough to explain the changes in the motor behavior of elite athletes. So, non-linear variables should be selected. The aim was to compare the fractal dimension before and after a maximal bout swimming front-crawl. Twenty-four subjects performed an all-out 100m trial swimming front-crawl. Immediately before (Pre-test) and after the trial (Post-test) a speed-meter cable was attached to the swimmer’s waist to measure the hip speed from which fractal dimension was derived. The fractal dimension showed a significant decrease with a moderate effect size between pre- and post-tests. Twenty-one out of 24 swimmers decreased the fractal dimension. As a conclusion, there is a decrease in the fractal dimension and hence in the swimming behavior complexity being under fatigue after a maximal trial.This research was funded by the grant NIE AcRF 11/13 TB.info:eu-repo/semantics/publishedVersio

Changes in classical kinematics and non‐linear parameters after a maximal 100‐m front‐crawl bout

In a linear system there is proportionality between input and output. Under this framework it is expected that the amount of change in sports performance must be proportional to variations in the inputs.info:eu-repo/semantics/publishedVersio

Changes in classical kinematics and non-linear parameters after a maximal 100-m front-crawl bout

In a linear system there is proportionality between input and output. Under this framework it is expected that the amount of change in sports performance must be proportional to variations in the inputs. However, as far as elite performance goes, this is not a straightforward assumption. Sometimes the variables selected are not sensitive enough. Hence, there is the need of having non-linear concepts underpinning such analysis. The aim was to compare classical kinematics and non-linear parameters after a maximal 100-m front-crawl bout. Twenty-four subjects (12 males and 12 females; 22.38±1.68-y) were invited to perform a 100-m freestyle race at maximal pace. Before (pre-test, i.e. rested) and immediately after (post-test, i.e. under fatigue) the maximal bout, they performed two maximal 25m swims at freestyle with push-off start. A speedo-meter cord (Swim speedo-meter, Swimsportec, Hildesheim, Germany) was attached to the swimmer’s hip (Barbosa et al., 2015) in the two 25m trials collecting the instantaneous speed. It was computed the speed fluctuation (dv; Barbosa et al., 2015), approximate entropy (ApEn; Barbosa et al., 2015) and fractal dimension (FD; Higuchi, 1988). Repeated measures ANOVAs (pre-test vs. post-test; P≤0.05), effect sizes (eta squared) and 95% of confidence intervals (95CI) were computed. The speed was 1.44±0.24 and 1.28±0.23m/s in the pre- and post/test, respectively (F=55.136, P<0.001)info:eu-repo/semantics/publishedVersio

Association and prediction utilizing craniocaudal and mediolateral oblique view digital mammography and long-term breast cancer risk

UNLABELLED: Mammographic percentage of volumetric density is an important risk factor for breast cancer. Epidemiology studies historically used film images often limited to craniocaudal (CC) views to estimate area-based breast density. More recent studies using digital mammography images typically use the averaged density between craniocaudal (CC) and mediolateral oblique (MLO) view mammography for 5- and 10-year risk prediction. The performance in using either and both mammogram views has not been well-investigated. We use 3,804 full-field digital mammograms from the Joanne Knight Breast Health Cohort (294 incident cases and 657 controls), to quantity the association between volumetric percentage of density extracted from either and both mammography views and to assess the 5 and 10-year breast cancer risk prediction performance. Our results show that the association between percent volumetric density from CC, MLO, and the average between the two, retain essentially the same association with breast cancer risk. The 5- and 10-year risk prediction also shows similar prediction accuracy. Thus, one view is sufficient to assess association and predict future risk of breast cancer over a 5 or 10-year interval. PREVENTION RELEVANCE: Expanding use of digital mammography and repeated screening provides opportunities for risk assessment. To use these images for risk estimates and guide risk management in real time requires efficient processing. Evaluating the contribution of different views to prediction performance can guide future applications for risk management in routine care

NMTSloth: Understanding and Testing Efficiency Degradation of Neural Machine Translation Systems

Neural Machine Translation (NMT) systems have received much recent attention due to their human-level accuracy. While existing works mostly focus on either improving accuracy or testing accuracy robustness, the computation efficiency of NMT systems, which is of paramount importance due to often vast translation demands and real-time requirements, has surprisingly received little attention. In this paper, we make the first attempt to understand and test potential computation efficiency robustness in state-of-the-art NMT systems. By analyzing the working mechanism and implementation of 1455 public-accessible NMT systems, we observe a fundamental property in NMT systems that could be manipulated in an adversarial manner to reduce computation efficiency significantly. Our key motivation is to generate test inputs that could sufficiently delay the generation of EOS such that NMT systems would have to go through enough iterations to satisfy the pre-configured threshold. We present NMTSloth, which develops a gradient-guided technique that searches for a minimal and unnoticeable perturbation at character-level, token-level, and structure-level, which sufficiently delays the appearance of EOS and forces these inputs to reach the naturally-unreachable threshold. To demonstrate the effectiveness of NMTSloth, we conduct a systematic evaluation on three public-available NMT systems: Google T5, AllenAI WMT14, and Helsinki-NLP translators. Experimental results show that NMTSloth can increase NMT systems' response latency and energy consumption by 85% to 3153% and 86% to 3052%, respectively, by perturbing just one character or token in the input sentence. Our case study shows that inputs generated by NMTSloth significantly affect the battery power in real-world mobile devices (i.e., drain more than 30 times battery power than normal inputs).Comment: This paper has been accepted to ESEC/FSE 202