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

OCT4B1 Regulates the Cellular Stress Response of Human Dental Pulp Cells with Inflammation

Introduction. Infection and apoptosis are combined triggers for inflammation in dental tissues. Octamer-binding transcription factor 4-B1 (OCT4B1), a novel spliced variant of OCT4 family, could respond to the cellular stress and possess antiapoptotic property. However, its specific role in dental pulpitis remains unknown. Methods. To investigate the effect of OCT4B1 on inflammation of dental pulp cells (DPCs), its expression in inflamed dental pulp tissues and DPCs was examined by in situ hybridization, real-time PCR, and FISH assay. OCT4B1 overexpressed DPCs model was established, confirmed by western blot and immunofluorescence staining, and then stimulated with Lipopolysaccharide (LPS). Apoptotic rate was determined by Hoechst/PI staining and FACS. Cell survival rate was calculated by CCK8 assay. Results. In situ hybridization, real-time PCR, and FISH assay revealed that OCT4B1 was extensively expressed in inflamed dental pulp tissues and DPCs with LPS stimulation. Western blot and immunofluorescence staining showed the expression of OCT4B1 and OCT4B increased after OCT4B1 transfection. Hoechst/PI staining and FACS demonstrated that less red/blue fluorescence was detected and apoptotic percentage decreased (3.45%) after transfection. CCK8 demonstrated that the survival rate of pCDH-OCT4B1-flag cells increased. Conclusions. OCT4B1 plays an essential role in inflammation and apoptosis of DPCs. OCT4B might operate synergistically with OCT4B1 to reduce apoptosis

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

Imidazole-Based pH-Sensitive Convertible Liposomes for Anticancer Drug Delivery

In efforts to enhance the activity of liposomal drugs against solid tumors, three novel lipids that carry imidazole-based headgroups of incremental basicity were prepared and incorporated into the membrane of PEGylated liposomes containing doxorubicin (DOX) to render pH-sensitive convertible liposomes (ICL). The imidazole lipids were designed to protonate and cluster with negatively charged phosphatidylethanolamine-polyethylene glycol when pH drops from 7.4 to 6.0, thereby triggering ICL in acidic tumor interstitium. Upon the drop of pH, ICL gained more positive surface charges, displayed lipid phase separation in TEM and DSC, and aggregated with cell membrane-mimetic model liposomes. The drop of pH also enhanced DOX release from ICL consisting of one of the imidazole lipids, sn-2-((2,3-dihexadecyloxypropyl)thio)-5-methyl-1H-imidazole. ICL demonstrated superior activities against monolayer cells and several 3D MCS than the analogous PEGylated, pH-insensitive liposomes containing DOX, which serves as a control and clinical benchmark. The presence of cholesterol in ICL enhanced their colloidal stability but diminished their pH-sensitivity. ICL with the most basic imidazole lipid showed the highest activity in monolayer Hela cells; ICL with the imidazole lipid of medium basicity showed the highest anticancer activity in 3D MCS. ICL that balances the needs of tissue penetration, cell-binding, and drug release would yield optimal activity against solid tumors

Progressive Neural Compression for Adaptive Image Offloading under Timing Constraints

IoT devices are increasingly the source of data for machine learning (ML) applications running on edge servers. Data transmissions from devices to servers are often over local wireless networks whose bandwidth is not just limited but, more importantly, variable. Furthermore, in cyber-physical systems interacting with the physical environment, image offloading is also commonly subject to timing constraints. It is, therefore, important to develop an adaptive approach that maximizes the inference performance of ML applications under timing constraints and the resource constraints of IoT devices. In this paper, we use image classification as our target application and propose progressive neural compression (PNC) as an efficient solution to this problem. Although neural compression has been used to compress images for different ML applications, existing solutions often produce fixed-size outputs that are unsuitable for timing-constrained offloading over variable bandwidth. To address this limitation, we train a multi-objective rateless autoencoder that optimizes for multiple compression rates via stochastic taildrop to create a compression solution that produces features ordered according to their importance to inference performance. Features are then transmitted in that order based on available bandwidth, with classification ultimately performed using the (sub)set of features received by the deadline. We demonstrate the benefits of PNC over state-of-the-art neural compression approaches and traditional compression methods on a testbed comprising an IoT device and an edge server connected over a wireless network with varying bandwidth.Comment: IEEE the 44th Real-Time System Symposium (RTSS), 202

Two new genera of Apsilocephalidae from mid-Cretaceous Burmese amber

Apsilocephalidae is an enigmatic dipteran family erected by Nagatomi et al. (1991), including three extant genera and three additional extinct genera from the Eocene Baltic amber, Eocene Florissant, and mid-Cretaceous Burmese amber. We describe herein two new taxa, Myanmarpsilocephala grimaldii gen. et sp. nov. and Irwinimyia spinosa gen. et sp. nov., from mid-Cretaceous Burmese amber. The female genitalia of Myanmarpsilocephala gen. nov. and male genitalia of Irwinimyia gen. nov. are described and illustrated. The distribution of all Apsilocephalidae species and a key to all genera of Apsilocephalidae is provided. The described diversity of Apsilocephalidae in Burmese amber strongly suggests that apsilocephalid flies diversified at least by the mid-Cretaceous.This research was supported by the National Natural Science Foundation of China (41572010, 41622201, 41688103), the Chinese Academy of Sciences (XDPB05), and Youth Innovation Promotion Association of CAS (No. 2011224)