FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices

Deep neural networks show great potential as solutions to many sensing application problems, but their excessive resource demand slows down execution time, pausing a serious impediment to deployment on low-end devices. To address this challenge, recent literature focused on compressing neural network size to improve performance. We show that changing neural network size does not proportionally affect performance attributes of interest, such as execution time. Rather, extreme run-time nonlinearities exist over the network configuration space. Hence, we propose a novel framework, called FastDeepIoT, that uncovers the non-linear relation between neural network structure and execution time, then exploits that understanding to find network configurations that significantly improve the trade-off between execution time and accuracy on mobile and embedded devices. FastDeepIoT makes two key contributions. First, FastDeepIoT automatically learns an accurate and highly interpretable execution time model for deep neural networks on the target device. This is done without prior knowledge of either the hardware specifications or the detailed implementation of the used deep learning library. Second, FastDeepIoT informs a compression algorithm how to minimize execution time on the profiled device without impacting accuracy. We evaluate FastDeepIoT using three different sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus. FastDeepIoT further reduces the neural network execution time by $48\%$ to $78\%$ and energy consumption by $37\%$ to $69\%$ compared with the state-of-the-art compression algorithms.Comment: Accepted by SenSys '1

Developmental stage related patterns of codon usage and genomic GC content: searching for evolutionary fingerprints with models of stem cell differentiation

Developmental-stage-related patterns of gene expression correlate with codon usage and genomic GC content in stem cell hierarchies

Derivation and Characterization of Hepatic Progenitor Cells from Human Embryonic Stem Cells

The derivation of hepatic progenitor cells from human embryonic stem (hES) cells is of value both in the study of early human liver organogenesis and in the creation of an unlimited source of donor cells for hepatocyte transplantation therapy. Here, we report for the first time the generation of hepatic progenitor cells derived from hES cells. Hepatic endoderm cells were generated by activating FGF and BMP pathways and were then purified by fluorescence activated cell sorting using a newly identified surface marker, N-cadherin. After co-culture with STO feeder cells, these purified hepatic endoderm cells yielded hepatic progenitor colonies, which possessed the proliferation potential to be cultured for an extended period of more than 100 days. With extensive expansion, they co-expressed the hepatic marker AFP and the biliary lineage marker KRT7 and maintained bipotential differentiation capacity. They were able to differentiate into hepatocyte-like cells, which expressed ALB and AAT, and into cholangiocyte-like cells, which formed duct-like cyst structures, expressed KRT19 and KRT7, and acquired epithelial polarity. In conclusion, this is the first report of the generation of proliferative and bipotential hepatic progenitor cells from hES cells. These hES cell–derived hepatic progenitor cells could be effectively used as an in vitro model for studying the mechanisms of hepatic stem/progenitor cell origin, self-renewal and differentiation

Eugene: Towards deep intelligence as a service

National Research Foundation (NRF) Singapore under its International Research Centres in Singapore Funding Initiativ

STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks

Recent advances in deep learning motivate the use of deep neural networks in Internet-of-Things (IoT) applications. These networks are modelled after signal processing in the human brain, thereby leading to significant advantages at perceptual tasks such as vision and speech recognition. IoT applications, however, often measure physical phenomena, where the underlying physics (such as inertia, wireless signal propagation, or the natural frequency of oscillation) are fundamentally a function of signal frequencies, offering better features in the frequency domain. This observation leads to a fundamental question: For IoT applications, can one develop a new brand of neural network structures that synthesize features inspired not only by the biology of human perception but also by the fundamental nature of physics? Hence, in this paper, instead of using conventional building blocks (e.g., convolutional and recurrent layers), we propose a new foundational neural network building block, the Short-Time Fourier Neural Network (STFNet). It integrates a widely-used time-frequency analysis method, the Short-Time Fourier Transform, into data processing to learn features directly in the frequency domain, where the physics of underlying phenomena leave better foot-prints. STFNets bring additional flexibility to time-frequency analysis by offering novel nonlinear learnable operations that are spectral-compatible. Moreover, STFNets show that transforming signals to a domain that is more connected to the underlying physics greatly simplifies the learning process. We demonstrate the effectiveness of STFNets with extensive experiments. STFNets significantly outperform the state-of-the-art deep learning models in all experiments. A STFNet, therefore, demonstrates superior capability as the fundamental building block of deep neural networks for IoT applications for various sensor inputs

Lomerizine attenuates LPS-induced acute lung injury by inhibiting the macrophage activation through reducing Ca2+ influx

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are life-threatening lung diseases with high mortality rates, predominantly attributable to acute and severe pulmonary inflammation. Lomerizine (LMZ) is a calcium channel blocker previously used in preventing and treating migraine. Here, we found that LMZ inhibited inflammatory responses and lung pathological injury by reducing pulmonary edema, neutrophil infiltration and pro-inflammatory cytokine production in lipopolysaccharide (LPS)-induced ALI mice. In vitro experiments, upon treating with LMZ, the expression of interleukin (IL)-1β, IL-6 and tumor necrosis factor (TNF)-α was attenuated in macrophages. The phosphorylation of p38 MAPK, ERK1/2, JNK, and NF-κB p65 was inhibited after LMZ treatment. Furthermore, LPS-induced Ca2+ influx was reduced by treating with LMZ, which correlated with inhibition of pro-inflammatory cytokine production. And L-type Ca2+ channel agonist Bay K8644 (BK) could restore cytokine generation. In conclusion, our study demonstrated that LMZ alleviates LPS-induced ALI and is a potential agent for treating ALI/ARDS

Human Hepatocytes with Drug Metabolic Function Induced from Fibroblasts by Lineage Reprogramming

SummaryObtaining fully functional cell types is a major challenge for drug discovery and regenerative medicine. Currently, a fundamental solution to this key problem is still lacking. Here, we show that functional human induced hepatocytes (hiHeps) can be generated from fibroblasts by overexpressing the hepatic fate conversion factors HNF1A, HNF4A, and HNF6 along with the maturation factors ATF5, PROX1, and CEBPA. hiHeps express a spectrum of phase I and II drug-metabolizing enzymes and phase III drug transporters. Importantly, the metabolic activities of CYP3A4, CYP1A2, CYP2B6, CYP2C9, and CYP2C19 are comparable between hiHeps and freshly isolated primary human hepatocytes. Transplanted hiHeps repopulate up to 30% of the livers of Tet-uPA/Rag2−/−/γc−/− mice and secrete more than 300 μg/ml human ALBUMIN in vivo. Our data demonstrate that human hepatocytes with drug metabolic function can be generated by lineage reprogramming, thus providing a cell resource for pharmaceutical applications

Association between occlusal support and cognitive impairment in older Chinese adults: a community-based study

IntroductionThe loss of occlusal support due to tooth loss is associated with systemic diseases. However, there was little about the association between occlusal support and cognitive impairment. The cross-sectional study aimed to investigate their association.MethodsCognitive function was assessed and diagnosed in 1,225 community-dwelling adults aged 60 years or older in Jing’an District, Shanghai. Participants were diagnosed with mild cognitive impairment (MCI) by Peterson’s criteria, or dementia, according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. We determined the number of functional occlusal supporting areas according to Eichner classifications. We used multivariate logistic regression models to analyze the relationship between occlusal support and cognitive impairment and mediation effect models to analyze the mediation effect of age.ResultsSix hundred sixty participants were diagnosed with cognitive impairment, averaging 79.92 years old. After adjusting age, sex, education level, cigarette smoking, alcohol drinking, cardiovascular disease, and diabetes, individuals with poor occlusal support had an OR of 3.674 (95%CI 1.141–11.829) for cognitive impairment compared to those with good occlusal support. Age mediated 66.53% of the association between the number of functional occlusal supporting areas and cognitive impairment.DiscussionIn this study, cognitive impairment was significantly associated with the number of missing teeth, functional occlusal areas, and Eichner classifications with older community residents. Occlusal support should be a significant concern for people with cognitive impairment

Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions.

We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual guide RNAs in three cell lines, comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified, and these patterns replicated with combinatorial drugs at 75% precision. From these results, we anticipate that cellular context will be critical to synthetic-lethal therapies