EM Wave Propagation Speed, Comments on “Measurement of Time Delay of Alternating Electrical Field in Wires” and “Physical Principles of Measuring the Speed of Alternating Electrical Field

The time shift of an electromagnetic wave at a single frequency between a transmitter and a receiver can be used to determine the phase velocity of the wave propagation only if there is no reflection at the receiver or the reflection is very small. The reflection adds additional phase shifts to the composted wave of an incident wave and a reflected wave so that the time difference of the composted wave is shifted between the transmitter and the receiver. This time difference may be either decreased or increased and even negative in a certain condition. Ignoring the phase shift and time shift induced by the reflection, the authors of two articles recently published on “Modern Physics” wrongly claim of “the speed of alternating electric field can be 20 times faster than the speed of light”. The two articles are: “Measurement of Time Delay of Alternating Electrical Field in Wires” (Modern Physics, 2015, 5, 29-34) and “Physical Principles of Measuring the Speed of Alternating Electrical Field” (Modern Physics, 2015, 5, 35-39). In this communication note, theory and experiments are presented to falsify their claim. 只有在没有反射或反射因素极小的情况下，在发射端和接收端电磁场的时间差可以用于计算电磁场从反 射端到接收端的传播相速度。由于反射，测量到的合成波的相位是入射波和反射波相位的合成，由反射 而引入的相位变化导致发射和接收端之间的同相位电磁波的时间差的移动。这种两点间的合成波的时间 差由于反射既能增加也可能减小，在一定的情况下，时间差还可以是负数。由于忽视因反射而引起的相 位差或时间差的变化，最近发表在《现代物理》上的两篇文章的作者们用带有反射的合成波的时间差计 算电场传播速度，并和光在自由空间里传播相速度比较，进而在两篇文章中错误地宣称：“交变电场的 速度超过光速20倍以上。”这两篇在《现代物理》上发表的文章是：“导线中交流电场时间延迟的测定” (现代物理，2015，5，29-34)和“交变电场速度测量的物理原理”(现代物理，2015，5，35-39)。此 评论文章用理论和实验数据推翻其文章“超光速20倍”的结论

TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments

Deep neural networks (DNNs) have become core computation components within low latency Function as a Service (FaaS) prediction pipelines: including image recognition, object detection, natural language processing, speech synthesis, and personalized recommendation pipelines. Cloud computing, as the de-facto backbone of modern computing infrastructure for both enterprise and consumer applications, has to be able to handle user-defined pipelines of diverse DNN inference workloads while maintaining isolation and latency guarantees, and minimizing resource waste. The current solution for guaranteeing isolation within FaaS is suboptimal -- suffering from "cold start" latency. A major cause of such inefficiency is the need to move large amount of model data within and across servers. We propose TrIMS as a novel solution to address these issues. Our proposed solution consists of a persistent model store across the GPU, CPU, local storage, and cloud storage hierarchy, an efficient resource management layer that provides isolation, and a succinct set of application APIs and container technologies for easy and transparent integration with FaaS, Deep Learning (DL) frameworks, and user code. We demonstrate our solution by interfacing TrIMS with the Apache MXNet framework and demonstrate up to 24x speedup in latency for image classification models and up to 210x speedup for large models. We achieve up to 8x system throughput improvement.Comment: In Proceedings CLOUD 201

Preservation of molecular fossils in carbonate concretions in cretaceous shales in the songliao basin, northeast China

Paleoenvironmental information is better preserved in carbonate concretions. In this study, carbonate concretions in the Cretaceous Nenjiang shale, Songliao Basin, were examined to determine whether molecular fossils reflective of the paleoenvironment were better preserved at these sites. Organic and inorganic geochemical characteristics of the concretions and surrounding rocks were analyzed using a series of techniques, including SEM, LA-ICP-MS, GC-MS-MS, and GC-IRMS. The concretions are composed of high content microcrystalline dolomite. The δ13Ccarb and δ18Ocarb values of the concretionary dolomite were significantly higher than those of the surrounding rocks. The dolomite show enrichment in the LREEs and have a negative Eu anomaly. The concretion biomarkers showed distribution characteristics similar to those of surrounding rocks. This suggested that the molecular fossils preserved in concretions were mainly inherited from surrounding rocks. However, the concretions contained more C27 sterane and hopanes, with the hopane/sterane ratio being significantly higher than that of surrounding rocks (1.49 v. 0.86). Moreover, the relative content of 2-methylhopane was 2.4–6.6 times that of the surrounding rocks. This indicated changes in the biological equilibrium of source organisms within and outside the concretions. It was possible that the unstable organic matter at the core increased the bacterial concentration and activity inside the concretions. Both the hydrogen index and biomarker-derived indicators implied that the transformation of organic matter in concretions was minimized when compared with their host rock. The isotope δ13C16-30 was 1‰–3‰ more prevalent in individual N-alkane hydrocarbons in the concretions than in surrounding rocks, likely owing to differences in lithology, bacterial action, and degree of weathering. The study concluded that carbonate concretions could preserve molecular fossils better than the surrounding rocks, and the in-depth organic geochemical analysis of concretions could provide a valuable reference for research into early life forms

Accelerating Reduction and Scan Using Tensor Core Units

Driven by deep learning, there has been a surge of specialized processors for matrix multiplication, referred to as TensorCore Units (TCUs). These TCUs are capable of performing matrix multiplications on small matrices (usually 4x4 or 16x16) to accelerate the convolutional and recurrent neural networks in deep learning workloads. In this paper we leverage NVIDIA's TCU to express both reduction and scan with matrix multiplication and show the benefits -- in terms of program simplicity, efficiency, and performance. Our algorithm exercises the NVIDIA TCUs which would otherwise be idle, achieves 89%-98% of peak memory copy bandwidth, and is orders of magnitude faster (up to 100x for reduction and 3x for scan) than state-of-the-art methods for small segment sizes -- common in machine learning and scientific applications. Our algorithm achieves this while decreasing the power consumption by up to 22% for reduction and16%for scan.Comment: In Proceedings of the ACM International Conference on Supercomputing (ICS '19