Interaction-aware Kalman Neural Networks for Trajectory Prediction

Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.) benefits the on-road motion planning for intelligent and autonomous vehicles. Complex scenes always yield great challenges in modeling the patterns of surrounding traffic. For example, one main challenge comes from the intractable interaction effects in a complex traffic system. In this paper, we propose a multi-layer architecture Interaction-aware Kalman Neural Networks (IaKNN) which involves an interaction layer for resolving high-dimensional traffic environmental observations as interaction-aware accelerations, a motion layer for transforming the accelerations to interaction aware trajectories, and a filter layer for estimating future trajectories with a Kalman filter network. Attributed to the multiple traffic data sources, our end-to-end trainable approach technically fuses dynamic and interaction-aware trajectories boosting the prediction performance. Experiments on the NGSIM dataset demonstrate that IaKNN outperforms the state-of-the-art methods in terms of effectiveness for traffic trajectory prediction.Comment: 8 pages, 4 figures, Accepted for IEEE Intelligent Vehicles Symposium (IV) 202

Resilient neural network training for accelerators with computing errors

—With the advancements of neural networks, customized accelerators are increasingly adopted in massive AI applications. To gain higher energy efficiency or performance, many hardware design optimizations such as near-threshold logic or overclocking can be utilized. In these cases, computing errors may happen and the computing errors are difficult to be captured by conventional training on general purposed processors (GPPs). Applying the offline trained neural network models to the accelerators with errors directly may lead to considerable prediction accuracy loss. To address this problem, we explore the resilience of neural network models and relax the accelerator design constraints to enable aggressive design options. First of all, we propose to train the neural network models using the accelerators’ forward computing results such that the models can learn both the data and the computing errors. In addition, we observe that some of the neural network layers are more sensitive to the computing errors. With this observation, we schedule the most sensitive layer to the attached GPP to reduce the negative influence of the computing errors. According to the experiments, the neural network models obtained from the proposed training outperform the original models significantly when the CNN accelerators are affected by computing errors

Emergent phases in a compass chain with multisite interactions

We study a dimerised spin chain with biaxial magnetic interacting ions in the presence of an externally induced three-site interactions out of equilibrium. In the general case, the three-site interactions play a role in renormalizing the effective uniform magnetic field. We find that the existence of zero-energy Majorana modes is intricately related to the sign of Pfaffian of the Bogoliubov-de Gennes Hamiltonian and the relevant $Z_2$ topological invariant. In contrast, we show that an exotic spin liquid phase can emerge in the compass limit through a Berezinskii-Kosterlitz-Thouless (BKT) quantum phase transition. Such a BKT transition is characterized by a large dynamic exponent $z=4$, and the spin-liquid phase is robust under a uniform magnetic field. We find the relative entropy and the quantum discord can signal the BKT transitions. We also uncover a few differences in deriving the correlation functions for the systems with broken reflection symmetry.Comment: 12 pages, 10 figure

Exotic Superconducting Properties in Topological Nodal Semimetal PbTaSe2_2

We report the electronic properties of superconductivity in the topological nodal-line semimetal PbTaSe$_2$. Angle-resolved photoemission measurements accompanied by band calculations confirmed the nodal-line band structure in the normal state of single crystalline PbTaSe$_2$. Resistivity, magnetic-susceptibility and specific heat measurements have also been performed on high-quality single crystals. We observed upward features and large anisotropy in upper critical field ($H_{c2}$) measured in-plane (H//\textbf{ab}) and out-plane (H//\textbf{c}), respectively. Especially, $H_{c2}$ measured in H//\textbf{ab} shows sudden upward features rather than a signal of saturation in ultralow temperatures. The specific heat measurements under magnetic field reveal a full superconducting gap with no gapless nodes. These behaviors in this clean noncentrosymmetric superconductor is possibly related to the underlying exotic physics, providing important clue for realization of topological superconductivity.Comment: 6 pages, 5 figures,1 table;Accepted for publication on PR

Effects of bleaching agents on dental restorative materials: A review of the literature and recommendation to dental practitioners and researchers

AbstractIn recent years, there has been an increased demand for improvement in the appearance of natural teeth. The conservative technique of tooth bleaching has gained attention and acceptance from both patients and clinicians. Despite increased popularity, there is controversy surrounding the adverse effects of bleaching on dental restorative materials. This article reviews the effects of bleaching agents on major categories of dental restorative materials and provides evidence-based recommendations to the clinicians and researchers. Current literature reveal that bleaching might have a detrimental effect on restorative materials. However, because of the variability in experimental design, there is a lack of consensus concerning the bleaching effects on restorative materials. A standardized and reproducible guideline for assessment of bleaching effects on restorative materials needs to be established and verified by future studies