Learning Generative ConvNets via Multi-grid Modeling and Sampling

This paper proposes a multi-grid method for learning energy-based generative ConvNet models of images. For each grid, we learn an energy-based probabilistic model where the energy function is defined by a bottom-up convolutional neural network (ConvNet or CNN). Learning such a model requires generating synthesized examples from the model. Within each iteration of our learning algorithm, for each observed training image, we generate synthesized images at multiple grids by initializing the finite-step MCMC sampling from a minimal 1 x 1 version of the training image. The synthesized image at each subsequent grid is obtained by a finite-step MCMC initialized from the synthesized image generated at the previous coarser grid. After obtaining the synthesized examples, the parameters of the models at multiple grids are updated separately and simultaneously based on the differences between synthesized and observed examples. We show that this multi-grid method can learn realistic energy-based generative ConvNet models, and it outperforms the original contrastive divergence (CD) and persistent CD.Comment: CVPR 201

Domestic Activities Classification from Audio Recordings Using Multi-scale Dilated Depthwise Separable Convolutional Network

Domestic activities classification (DAC) from audio recordings aims at classifying audio recordings into pre-defined categories of domestic activities, which is an effective way for estimation of daily activities performed in home environment. In this paper, we propose a method for DAC from audio recordings using a multi-scale dilated depthwise separable convolutional network (DSCN). The DSCN is a lightweight neural network with small size of parameters and thus suitable to be deployed in portable terminals with limited computing resources. To expand the receptive field with the same size of DSCN's parameters, dilated convolution, instead of normal convolution, is used in the DSCN for further improving the DSCN's performance. In addition, the embeddings of various scales learned by the dilated DSCN are concatenated as a multi-scale embedding for representing property differences among various classes of domestic activities. Evaluated on a public dataset of the Task 5 of the 2018 challenge on Detection and Classification of Acoustic Scenes and Events (DCASE-2018), the results show that: both dilated convolution and multi-scale embedding contribute to the performance improvement of the proposed method; and the proposed method outperforms the methods based on state-of-the-art lightweight network in terms of classification accuracy.Comment: 5 pages, 2 figures, 4 tables. Accepted for publication in IEEE MMSP202

Best Practices to Increase Efficacy of Graduate School Admissions Communications at Clark University

Within the period of time that a graduate student deposits and subsequently arrives at their academic institution, receiving timely information is important for their preparation. This process has been deemed by the Deans of the Enterprise Schools at Clark University as one that needs further investigation. As such, this Capstone looks at the array of communication that goes out to each graduate student during this four-month period. The purpose of examining this communication is to analyze its effectiveness in engaging students. To analyze the effectiveness of this communication, surveys were distributed to current students in these schools to gather data surrounding their experience after applying to Clark. In addition to looking at Clark University’s current process, we conducted an analysis of trends and best practices from colleges and universities across the country. Based on findings from this research and our firsthand interviews of the aforementioned Deans and involved staff members, we have provided recommendations to improve this process. Ultimately, in order to improve student engagement our group has created recommendations that could improve some of the challenges in engaging and retaining students during this period of time

My Choice Greens

My Choice Programs for Independent Living is a nonprofit in Worcester, Massachusetts that helps individuals with special needs. In 2016, My Choice Programs added a branch to their organization called My Choice Greens. My Choice Greens is committed to growing fresh produce in their hydroponic farm. The profit generated from selling their vegetables is put back into direct programming for My Choice Programs. My Choice Greens is the first hydroponic farm in Worcester, but unfortunately not many people are aware of their program. Also, since they are a small nonprofit, they do not have sufficient funding or resources to market their programming. To rectify this situation, My Choice Greens reached out to Clark University’s School of Professional Studies to assist them with marketing. Our team was selected to help My Choice Greens as our Capstone Project. My Choice Greens wanted the team from Clark University’s School of Professional Studies to focus on creating a marketing campaign for their Open House on March 8th, as well as creating a strategic marketing plan for My Choice Greens to follow over the next two-three years

Can command-and-control policy drive low-carbon transition in energy-intensive enterprises? -a study based on evolutionary game theory

There are two views on whether command-and-control policy can promote carbon emission reduction: the “compliance cost” theory and the “innovation compensation” theory. In this paper, we construct an evolutionary game model among energy-intensive enterprises, verification agencies, and local governments from the game theory perspective to explore the impact of command-and-control policy on the low-carbon transition of energy-intensive enterprises. The interaction mechanism of the three actors and the main factors affecting the low-carbon transition of the enterprises are further analyzed with the help of the MATLAB simulation method. The study results show that command-and-control policies can promote the low-carbon transition of enterprises and have a suppressive effect on bribery behavior. In the actual game process, enterprises will compare the cost of low-carbon transition with that of no low-carbon transition. The cost of low-carbon transition is higher when the government’s incentives and penalties are small, so there is a “compliance cost” effect, and the government cannot promote low-carbon transition by increasing the intensity of regulation. On the contrary, when the government’s incentives and penalties are strong enough, enterprises will make a low-carbon transition spontaneously in the face of continuously increasing environmental regulation intensity, which supports the theory of “innovation compensation.” In addition, increasing the profitability of product sales and increasing the cost of bribes are also effective ways to promote low-carbon transition. Finally, relevant policy recommendations were proposed based on the main conclusions. This work opens up a new perspective for environmental regulation theory and provides a theoretical reference and practical basis for developing low-carbon transition

Mechanical properties of nodular natural gas hydrate-bearing sediment

Natural gas hydrate is a relatively realistic alternative energy source to conventional fossil fuels with considerable reserves. Natural gas hydrate sediments are widely distributed in marine sediment on continental margins. In this study, a numerical modeling method for sediment containing nodular gas hydrates is developed using the two-dimensional discrete element simulation software. The effects of saturation, confining pressure, and nodule radius on the mechanical properties of heterogeneous nodular gas-hydrate-bearing sediment were analyzed using the stress-strain, fracture development, and partial body strain curves, as well as force chain distribution. The results indicated that the mechanical strength of sediment containing round nodular gas hydrates was proportional to the gas hydrate saturation and simulated confining pressure. When hydrate saturation was low, the failure strength of the gas-hydrate-bearing sediment diminished as the nodule radius increased. The simulations showed that variations in sediment porosity influenced the development and evolution of the shear band, resulting in higher porosity around the shear band. These results were analyzed from the perspectives of saturation and confining pressure to determine the failure and deformation law of simple nodular gas hydrate-bearing sediment and provide theoretical support for the subsequent study of the exploitation method of shallow buried deep gas hydrates.Document Type: Original articleCited as: Jiang, Y., Zhang, R., Ye, R., Zhou, K., Gong, B., Golsanami, N. Mechanical properties of nodular natural gas hydrate-bearing sediment. Advances in Geo-Energy Research, 2024, 11(1): 41-53. https://doi.org/10.46690/ager.2024.01.0

Speech-to-Speech Translation with Discrete-Unit-Based Style Transfer

Direct speech-to-speech translation (S2ST) with discrete self-supervised representations has achieved remarkable accuracy, but is unable to preserve the speaker timbre of the source speech during translation. Meanwhile, the scarcity of high-quality speaker-parallel data poses a challenge for learning style transfer between source and target speech. We propose an S2ST framework with an acoustic language model based on discrete units from a self-supervised model and a neural codec for style transfer. The acoustic language model leverages self-supervised in-context learning, acquiring the ability for style transfer without relying on any speaker-parallel data, thereby overcoming the issue of data scarcity. By using extensive training data, our model achieves zero-shot cross-lingual style transfer on previously unseen source languages. Experiments show that our model generates translated speeches with high fidelity and style similarity. Audio samples are available at http://stylelm.github.io/ .Comment: 5 pages, 1 figure. submitted to ICASSP 202

Integration of an actor-critic model and generative adversarial networks for a Chinese calligraphy robot

As a combination of robotic motion planning and Chinese calligraphy culture, robotic calligraphy plays a significant role in the inheritance and education of Chinese calligraphy culture. Most existing calligraphy robots focus on enabling the robots to learn writing through human participation, such as human–robot interactions and manually designed evaluation functions. However, because of the subjectivity of art aesthetics, these existing methods require a large amount of implementation work from human engineers. In addition, the written results cannot be accurately evaluated. To overcome these limitations, in this paper, we propose a robotic calligraphy model that combines a generative adversarial network (GAN) and deep reinforcement learning to enable a calligraphy robot to learn to write Chinese character strokes directly from images captured from Chinese calligraphic textbooks. In our proposed model, to automatically establish an aesthetic evaluation system for Chinese calligraphy, a GAN is first trained to understand and reconstruct stroke images. Then, the discriminator network is independently extracted from the trained GAN and embedded into a variant of the reinforcement learning method, the “actor-critic model”, as a reward function. Thus, a calligraphy robot adopts the improved actor-critic model to learn to write multiple character strokes. The experimental results demonstrate that the proposed model successfully allows a calligraphy robot to write Chinese character strokes based on input stroke images. The performance of our model, compared with the state-of-the-art deep reinforcement learning method, shows the efficacy of the combination approach. In addition, the key technology in this work shows promise as a solution for robotic autonomous assembly