HiPSEC x-ray diffraction and infrared spectroscopy studies on energetic materials under extreme conditions

We conducted a series of experiments on the decompositions of the energetic materials NaBH4, NH3BH3, HMX, and RDX under different pressures using the x-ray diffraction (XRD) technique; we also studied the lesser known but high-performance explosive FOX-7’s behaviors under high pressures using the infrared spectroscopy (IR) technique. For the chemical decomposition of NaBH4 and NH3BH3 we discovered possible x-ray induced hydrogen gas generation; for the decomposition of HMX and RDX, we discovered that the decay rates of these two materials vary with pressure respectively; for the study of FOX-7’s high pressure behaviors we discovered potential phase changes and pressure induced chemical reactions as pressure is increased

TensorLayer: A Versatile Library for Efficient Deep Learning Development

Deep learning has enabled major advances in the fields of computer vision, natural language processing, and multimedia among many others. Developing a deep learning system is arduous and complex, as it involves constructing neural network architectures, managing training/trained models, tuning optimization process, preprocessing and organizing data, etc. TensorLayer is a versatile Python library that aims at helping researchers and engineers efficiently develop deep learning systems. It offers rich abstractions for neural networks, model and data management, and parallel workflow mechanism. While boosting efficiency, TensorLayer maintains both performance and scalability. TensorLayer was released in September 2016 on GitHub, and has helped people from academia and industry develop real-world applications of deep learning.Comment: ACM Multimedia 201

Tight and attainable quantum speed limit for open systems

We develop an intuitive geometric picture of quantum states, define a particular state distance, and derive a quantum speed limit (QSL) for open systems. Our QSL is attainable because any initial state can be driven to a final state by the particular dynamics along the geodesic. We present the general condition for dynamics along the geodesic for our QSL. As evidence, we consider the generalized amplitude damping dynamics and the dephasing dynamics to demonstrate the attainability. In addition, we also compare our QSL with others by strict analytic processes as well as numerical illustrations, and show our QSL is tight in many cases. It indicates that our work is significant in tightening the bound of evolution time

Diurnal modulation of electron recoils from DM-nucleon scattering through the Migdal effect

Halo dark matter (DM) particles could lose energy due to the scattering off nuclei within the Earth before reaching the underground detectors of DM direct detection experiments. This Earth shielding effect can result in diurnal modulation of the DM-induced recoil event rates observed underground due to the self-rotation of the Earth. For electron recoil signals from DM-electron scatterings, the current experimental constraints are very stringent such that the diurnal modulation cannot be observed for halo DM. We propose a novel type of diurnal modulation effect: diurnal modulation in electron recoil signals induced by DM-nucleon scattering via the Migdal effect. We set so far the most stringent constraints on DM-nucleon scattering cross section via the Migdal effect for sub-GeV DM using the S2-only data of PandaX-II and PandaX-4T with improved simulations of the Earth shielding effect. Based on the updated constraints, we show that the Migdal effect induced diurnal modulation of electron events can still be significant in the low energy region, and can be probed by experiments such as PandaX-4T in the near future

Family of attainable geometric quantum speed limits

We propose a quantum state distance and develop a family of geometrical quantum speed limits (QSLs) for open and closed systems. The QSL time includes an alternative function by which we derive three QSL times with particularly chosen functions. It indicates that two QSL times are exactly the ones presented in Ref. [1] and [2], respectively, and the third one can provide a unified QSL time for both open and closed systems. The three QSL times are attainable for any given initial state in the sense that there exists a dynamics driving the initial state to evolve along the geodesic. We numerically compare the tightness of the three QSL times, which typically promises a tighter QSL time if optimizing the alternative function

CD2^2: Fine-grained 3D Mesh Reconstruction with Twice Chamfer Distance

Monocular 3D reconstruction is to reconstruct the shape of object and its other information from a single RGB image. In 3D reconstruction, polygon mesh, with detailed surface information and low computational cost, is the most prevalent expression form obtained from deep learning models. However, the state-of-the-art schemes fail to directly generate well-structured meshes, and most of meshes have two severe problems Vertices Clustering (VC) and Illegal Twist (IT). By diving into the mesh deformation process, we pinpoint that the inappropriate usage of Chamfer Distance (CD) loss is the root causes of VC and IT problems in the training of deep learning model. In this paper, we initially demonstrate these two problems induced by CD loss with visual examples and quantitative analyses. Then, we propose a fine-grained reconstruction method CD$^2$ by employing Chamfer distance twice to perform a plausible and adaptive deformation. Extensive experiments on two 3D datasets and comparisons with five latest schemes demonstrate that our CD$^2$ directly generates well-structured meshes and outperforms others by alleviating VC and IT problems.Comment: under major review in TOM