Constraining the Mass of the Photon with Gamma-Ray Bursts

One of the cornerstones of modern physics is Einstein's special relativity, with its constant speed of light and zero photon mass assumptions. Constraint on the rest mass m_{\gamma} of photons is a fundamental way to test Einstein's theory, as well as other essential electromagnetic and particle theories. Since non-zero photon mass can give rise to frequency-(or energy-) dependent dispersions, measuring the time delay of photons with different frequencies emitted from explosive astrophysical events is an important and model-independent method to put such a constraint. The cosmological gamma-ray bursts (GRBs), with short time scales, high redshifts as well as broadband prompt and afterglow emissions, provide an ideal testbed for m_{\gamma} constraints. In this paper we calculate the upper limits of the photon mass with GRB early time radio afterglow observations as well as multi-band radio peaks, thus improve the results of Schaefer (1999) by nearly half an order of magnitude.Comment: 25 pages, 2 tables, Accepted by Journal of High Energy Astrophysic

Energy Management for a User Interactive Smart Community: A Stackelberg Game Approach

This paper studies a three party energy management problem in a user interactive smart community that consists of a large number of residential units (RUs) with distributed energy resources (DERs), a shared facility controller (SFC) and the main grid. A Stackelberg game is formulated to benefit both the SFC and RUs, in terms of incurred cost and achieved utility respectively, from their energy trading with each other and the grid. The properties of the game are studied and it is shown that there exists a unique Stackelberg equilibrium (SE). A novel algorithm is proposed that can be implemented in a distributed fashion by both RUs and the SFC to reach the SE. The convergence of the algorithm is also proven, and shown to always reach the SE. Numerical examples are used to assess the properties and effectiveness of the proposed scheme.Comment: 6 pages, 4 figure

Feasibility of Using Discriminate Pricing Schemes for Energy Trading in Smart Grid

This paper investigates the feasibility of using a discriminate pricing scheme to offset the inconvenience that is experienced by an energy user (EU) in trading its energy with an energy controller in smart grid. The main objective is to encourage EUs with small distributed energy resources (DERs), or with high sensitivity to their inconvenience, to take part in the energy trading via providing incentive to them with relatively higher payment at the same time as reducing the total cost to the energy controller. The proposed scheme is modeled through a two-stage Stackelberg game that describes the energy trading between a shared facility authority (SFA) and EUs in a smart community. A suitable cost function is proposed for the SFA to leverage the generation of discriminate pricing according to the inconvenience experienced by each EU. It is shown that the game has a unique sub-game perfect equilibrium (SPE), under the certain condition at which the SFA's total cost is minimized, and that each EU receives its best utility according to its associated inconvenience for the given price. A backward induction technique is used to derive a closed form expression for the price function at SPE, and thus the dependency of price on an EU's different decision parameters is explained for the studied system. Numerical examples are provided to show the beneficial properties of the proposed scheme.Comment: 7 pages, 4 figures, 3 tables, conference pape

On the microstructure and mechanical property of as-extruded Mg-Gd-Y-Zn alloy with Sr addition

In this study, microstructure evolutions of Mg-6Gd-3Y-0.1Zn-xSr (x=0, 0.2, 0.6) alloys (named as sample 0Sr, 0.2Sr, 0.6Sr) during heat-treatment and extrusion were investigated. As-cast sample 0Sr contains typical long period stacking ordered (LPSO) phases and MgRE. With Sr addition, amounts of LPSO phases decrease and are gradually replaced by the MgSr phases. After homogenization and annealing treatment, profuse strip LPSO phases readily precipitate in grain interiors of sample 0Sr, while only MgSr and MgRE phases are detected in samples 0.2Sr and 0.6Sr. It suggests that the Sr addition would inhibit LPSO phases. After extrusion, the bimodal grain structures, the bulk and strip LPSO phases are detected in sample 0Sr, which can contribute to providing strengthening and extra strain hardening. In as-extruded sample 0.2Sr, finer recrystallized grain size, bulk MgSr and LPSO phases (micron-scale) and MgRE phase (nano-scale) are found due to the pre-annealing treatment. However, lower amounts of both nano-sized and macro-sized LPSO phases lead to the low ultimate strength (300 MPa). In sample 0.6Sr, the strip LPSO phases are readily formed even though the length and total amounts of LPSO phases decrease. More bulk MgSr phases and LPSO phases are also precipitated, which lead to the more superior yield and ultimate strengths of 0.6Sr sample under higher temperature, as compared with the 0Sr sample

Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model

Sequence-to-sequence models provide a simple and elegant solution for building speech recognition systems by folding separate components of a typical system, namely acoustic (AM), pronunciation (PM) and language (LM) models into a single neural network. In this work, we look at one such sequence-to-sequence model, namely listen, attend and spell (LAS), and explore the possibility of training a single model to serve different English dialects, which simplifies the process of training multi-dialect systems without the need for separate AM, PM and LMs for each dialect. We show that simply pooling the data from all dialects into one LAS model falls behind the performance of a model fine-tuned on each dialect. We then look at incorporating dialect-specific information into the model, both by modifying the training targets by inserting the dialect symbol at the end of the original grapheme sequence and also feeding a 1-hot representation of the dialect information into all layers of the model. Experimental results on seven English dialects show that our proposed system is effective in modeling dialect variations within a single LAS model, outperforming a LAS model trained individually on each of the seven dialects by 3.1 ~ 16.5% relative.Comment: submitted to ICASSP 201

Linking in situ LAI and Fine Resolution Remote Sensing Data to Map Reference LAI over Cropland and Grassland Using Geostatistical Regression Method

Leaf Area Index (LAI) is an important parameter of vegetation structure. A number of moderate resolution LAI products have been produced in urgent need of large scale vegetation monitoring. High resolution LAI reference maps are necessary to validate these LAI products. This study used a geostatistical regression (GR) method to estimate LAI reference maps by linking in situ LAI and Landsat TM/ETM+ and SPOT-HRV data over two cropland and two grassland sites. To explore the discrepancies of employing different vegetation indices (VIs) on estimating LAI reference maps, this study established the GR models for different VIs, including difference vegetation index (DVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI). To further assess the performance of the GR model, the results from the GR and Reduced Major Axis (RMA) models were compared. The results show that the performance of the GR model varies between the cropland and grassland sites. At the cropland sites, the GR model based on DVI provides the best estimation, while at the grassland sites, the GR model based on DVI performs poorly. Compared to the RMA model, the GR model improves the accuracy of reference LAI maps in terms of root mean square errors (RMSE) and bia

N-(4-Hydroxy­pheneth­yl)acetamide

In the title compound, C10H13NO2, the occurrence of inter­molecular N—H⋯O and O—H⋯O hydrogen bonds between the hydr­oxy and acetamido groups results in the formation of tetra­mers with an R 4 4(25) graph-set motif. These tetra­mers are further assembled, building up a corrugated sheet parallel to (001)

Resource-Efficient Transfer Learning From Speech Foundation Model Using Hierarchical Feature Fusion

Self-supervised pre-training of a speech foundation model, followed by supervised fine-tuning, has shown impressive quality improvements on automatic speech recognition (ASR) tasks. Fine-tuning separate foundation models for many downstream tasks are expensive since the foundation model is usually very big. Parameter-efficient fine-tuning methods (e.g. adapter, sparse update methods) offer an alternative paradigm where a small set of parameters are updated to adapt the foundation model to new tasks. However, these methods still suffer from a high computational memory cost and slow training speed because they require backpropagation through the entire neural network at each step. In the paper, we analyze the performance of features at different layers of a foundation model on the speech recognition task and propose a novel hierarchical feature fusion method for resource-efficient transfer learning from speech foundation models. Experimental results show that the proposed method can achieve better performance on speech recognition task than existing algorithms with fewer number of trainable parameters, less computational memory cost and faster training speed. After combining with Adapters at all layers, the proposed method can achieve the same performance as fine-tuning the whole model with $97\%$ fewer trainable encoder parameters and $53\%$ faster training speed

N-(2-Chloro­benzo­yl)-N′-(3-pyrid­yl)thio­urea

In the mol­ecule of the title compound, C13H10ClN3OS, the dihedral angles between the plane through the thio­urea group and the pyridine and benzene rings are 53.08 (3) and 87.12 (3)°, respectively. The mol­ecules are linked by inter­molecular N—H⋯N hydrogen-bonding inter­actions to form a supra­molecular chain structure along the a axis. An intra­mol­ecular N—H⋯O hydrogen bond is also present