Learning Pruned Structure and Weights Simultaneously from Scratch: an Attention based Approach

As a deep learning model typically contains millions of trainable weights, there has been a growing demand for a more efficient network structure with reduced storage space and improved run-time efficiency. Pruning is one of the most popular network compression techniques. In this paper, we propose a novel unstructured pruning pipeline, Attention-based Simultaneous sparse structure and Weight Learning (ASWL). Unlike traditional channel-wise or weight-wise attention mechanism, ASWL proposed an efficient algorithm to calculate the pruning ratio through layer-wise attention for each layer, and both weights for the dense network and the sparse network are tracked so that the pruned structure is simultaneously learned from randomly initialized weights. Our experiments on MNIST, Cifar10, and ImageNet show that ASWL achieves superior pruning results in terms of accuracy, pruning ratio and operating efficiency when compared with state-of-the-art network pruning methods

Subnatural-Linewidth Polarization-Entangled Photon Pairs with Controllable Temporal Length

We demonstrate an efficient experimental scheme for producing polarization-entangled photon pairs from spontaneous four-wave mixing (SFWM) in a laser-cooled $^{85}$Rb atomic ensemble, with a bandwidth (as low as 0.8 MHz) much narrower than the rubidium atomic natural linewidth. By stabilizing the relative phase between the two SFWM paths in a Mach-Zehnder interferometer configuration, we are able to produce all four Bell states. These subnatural-linewidth photon pairs with polarization entanglement are ideal quantum information carriers for connecting remote atomic quantum nodes via efficient light-matter interaction in a photon-atom quantum network.Comment: Title changed, published version, 5 pages + 3 pages Supplemental Materia

Synthesis of Epoxidatied Castor Oil and Its Effect on the Properties of Waterborne Polyurethane

AbstractIn this study, a new method for synthesis poxidatied castor oil (ECO) is engaged. A series of waterborne polyurethane dispersions (WPUs) were synthesized using polytetramethylene ether glycol (PTMEG), toluene diisocyanate (TDI-80), and ECO. These WPUs can be crosslinked spontaneously upon drying, without extra additives or processing steps. Moreover, the particle size, and morphology of WPUs were examined with light scattering ultrafine particle analyzer, and transmission electron microscopy. The anti-water, thermal and mechanical properties were also studied. Results reveal that the particle size of WPUs mainly depends on the concentrations of ECO. The particle size decreases when the ECO is used. Furthermore, increased amount of ECO results in an improvement of the anti-water, thermal and mechanical properties of WPU films

Segatron: Segment-Aware Transformer for Language Modeling and Understanding

Transformers are powerful for sequence modeling. Nearly all state-of-the-art language models and pre-trained language models are based on the Transformer architecture. However, it distinguishes sequential tokens only with the token position index. We hypothesize that better contextual representations can be generated from the Transformer with richer positional information. To verify this, we propose a segment-aware Transformer (Segatron), by replacing the original token position encoding with a combined position encoding of paragraph, sentence, and token. We first introduce the segment-aware mechanism to Transformer-XL, which is a popular Transformer-based language model with memory extension and relative position encoding. We find that our method can further improve the Transformer-XL base model and large model, achieving 17.1 perplexity on the WikiText-103 dataset. We further investigate the pre-training masked language modeling task with Segatron. Experimental results show that BERT pre-trained with Segatron (SegaBERT) can outperform BERT with vanilla Transformer on various NLP tasks, and outperforms RoBERTa on zero-shot sentence representation learning.Comment: Accepted by AAAI 202

A refined numerical investigation of a large equivalent shallow-depth underwater explosion

The large equivalent shallow-depth explosion problem is very significant in the field of naval architecture and ocean engineering, as such explosions can be used to attack and demolish ships and anti-ship missiles. In the current work, a refined numerical study of the flow-field characteristics of a large equivalent shallow-depth explosion is carried out using a self-developed Eulerian finite element solver. Firstly, the numerical model is validated against theoretical results and a small equivalent explosion test in a tank. The numerical results are found to agree well with the theoretical and experimental results. In the next step, the cavitation cut-off effect is added to the underwater explosion model, and the cavitation phenomenon is quantitatively analyzed through the flow-field pressure. In addition, the dynamic characteristics of the bubble and water hump under various initial conditions for different stand-off parameters are analyzed. The effect of gravity on these physical processes is also discussed. The bubble pulsation period, taking into account the free surface effect, is then quantitatively studied and compared with Cole's experimental formula for an underwater explosion. Overall, when the stand-off parameter > 2, the influence of the free surface on the empirical period of the bubble is not significant. Our investigation provides broad insights into shallow-depth underwater explosions from theoretical, experimental, and numerical perspectives

Inverse Geometry Design of Radiative Enclosures Using Particle Swarm Optimization Algorithms

Three different Particle Swarm Optimization (PSO) algorithms—standard PSO, stochastic PSO (SPSO) and differential evolution PSO (DEPSO)—are applied to solve the inverse geometry design problems of radiative enclosures. The design purpose is to satisfy a uniform distribution of radiative heat flux on the designed surface. The design surface is discretized into a series of control points, the PSO algorithms are used to optimize the locations of these points and the Akima cubic interpolation is utilized to approximate the changing boundary shape. The retrieval results show that PSO algorithms can be successfully applied to solve inverse geometry design problems and SPSO achieves the best performance on computational time. The influences of the number of control points and the radiative properties of the media on the retrieval geometry design results are also investigated