PELSI: Power-Efficient Layer-Switched Inference

Convolutional Neural Networks (CNNs) are now quintessential kernels within embedded computer vision applications deployed in edge devices. Heterogeneous Multi-Processor System-on-Chips (HMPSoCs) with Dynamic Voltage and Frequency Scaling (DVFS) capable components (CPUs and GPUs) allow for low-latency, low-power CNN inference on resource-constrained edge devices when employed efficiently. CNNs comprise several heterogeneous layer types that execute with different degrees of power efficiency on different HMPSoC components at different frequencies. We propose the first framework, PELSI, that exploits this layer-wise power efficiency heterogeneity for power-efficient CPU-GPU layer-switched CNN interference on HMPSoCs. PELSI executes each layer of a CNN on an HMPSoC component (CPU or GPU) clocked at just the right frequency for every layer such that the CNN meets its inference latency target with minimal power consumption while still accounting for the power-performance overhead of multiple switching between CPU and GPU mid-inference. PELSI incorporates a Genetic Algorithm (GA) to identify the near-optimal CPU-GPU layer-switched CNN inference configuration from within the large exponential design space that meets the given latency requirement most power efficiently. We evaluate PELSI on Rock-Pi embedded platform. The platform contains an RK3399Pro HMPSoC with DVFS-capable CPU clusters and GPU. Empirical evaluations with five different CNNs show a 44.48% improvement in power efficiency for CNN inference under PELSI over the state-of-the-art

CPU-GPU Layer-Switched Low Latency CNN Inference

Convolutional Neural Networks (CNNs) inference on Heterogeneous Multi-Processor System-on-Chips (HMPSoCs) in edge devices represent cutting-edge embedded machine learning. Embedded CPU and GPU within an HMPSoC can both perform inference using CNNs. However, common practice is to run a CNN on the HMPSoC component (CPU or GPU) provides the best performance (lowest latency) for that CNN. CNNs are not monolithic and are composed of several layers of different types. Some of these layers have lower latency on the CPU, while others execute faster on the GPU. In this work, we investigate the reason behind this observation. We also propose an execution of CNN that switches between CPU and GPU at the layer granularity, wherein a CNN layer executes on the component that provides it with the lowest latency. Switching between the CPU and the GPU back and forth mid-inference introduces additional overhead (delay) in the inference. Regardless of overhead, we show in this work that a CPU-GPU layer switched execution results in, on average, having 4.72% lower CNN inference latency on the Khadas VIM 3 board with Amlogic A311D HMPSoC

Predicting Energy Requirement for Cooling the Building Using Artificial Neural Network

This paper explores total cooling load during summers and total carbon emissions of a six storey building by using artificial neural network (ANN). Parameters used for the calculation were conduction losses, ventilation losses, solar heat gain and internal gain. The standard back-propagation learning algorithm has been used in the network. The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, the operation of sub-level components like lighting and HVAC systems, occupancy and their behavior. This complex situation makes it very difficult to accurately implement the prediction of building energy consumption. The calculated cooling load was 0.87 million kW per year. ANN application showed that data was best fit for the regression coefficient of 0.9955 with best validation performance of 0.41231 in case of conduction losses. To meet out this energy demand various fuel options are presented along with their cost and carbon emission

Impact of health education on knowledge and practices about menstruation among adolescent school girls of rural part of district Ambala, Haryana

Background: This study was undertaken to assess the impact of health education on knowledge regarding menstruation, misconceptions related to it as the prevalence of RTI is still very high in India.  Aims: To study the existing level of status of hygiene, knowledge and practices regarding menstruation among adolescent school girls and to assess the change in their knowledge level and practices after health education. Materials A community-based pre and post interventional study was conducted among 200 adolescents’ girls of class IX and X of rural part of district Ambala. Multistage random sampling technique was used to draw the representative sample. A pre-tested questionnaire was administered and later health education regarding menstruation and healthy menstrual practices was imparted to the girls. Post-test was done after 3 months to assess the impact of health education. Pre- and post-intervention, data were compared using the paired t test, z test for proportions, chi-squared test for paired proportions. Difference between Proportions of the pre-post data and its 95% confidence interval has been calculated of the findings. SPSS for Windows software version 20 (IBM, Chicago, USA) have been used for data analysis. The level of significance has been considered at p value < 0.05. Results: In the pre-test, menstrual perceptions amongst them were found to be poor and practices incorrect while in the post-test, there was a significant difference in the level of knowledge (P<0.05). There was no significant difference in pre and post-test with regard to restrictions followed during menses (P>0.05) while in the post-test preceding health education, significant improvements were observed in their practices. Conclusion: Overall significant improvement was found in knowledge and practices regarding menstruation among adolescent school girls

Performance, Power and Cooling Trade-Offs with NCFET-based Many-Cores

Negative Capacitance Field-Effect Transistor (NCFET) is an emerging technology that incorporates a ferroelectric layer within the transistor gate stack to overcome the fundamental limit of sub-threshold swing in transistors. Even though physics-based NCFET models have been recently proposed, system-level NCFET models do not exist and research is still in its infancy. In this work, we are the first to investigate the impact of NCFET on performance, energy and cooling costs in many-core processors. Our proposed methodology starts from accurate physics models all the way up to the system level, where the performance and power of a many-core are widely affected. Our new methodology and system-level models allow, for the first time, the exploration of the novel trade-offs between performance gains and power losses that NCFET now offers to system-level designers. We demonstrate that an optimal ferroelectric thickness does exist. In addition, we reveal that current state-of-the-art power management techniques fail when NCFET (with a thick ferroelectric layer) comes into play

Multi-omic Profiling Reveals Dynamics of the Phased Progression of Pluripotency

Pluripotency is highly dynamic and progresses through a continuum of pluripotent stem cell states. The two states that bookend the pluripotency continuum, naive and primed, are well characterized, but our understanding of the intermediate states and transitions between them remains incomplete. Here, we dissect the dynamics of pluripotent state transitions underlying pre- to post-implantation epiblast differentiation. Through comprehensive mapping of the proteome, phosphoproteome, transcriptome, and epigenome of embryonic stem cells transitioning from naive to primed pluripotency, we find that rapid, acute, and widespread changes to the phosphoproteome precede ordered changes to the epigenome, transcriptome, and proteome. Reconstruction of the kinase-substrate networks reveals signaling cascades, dynamics, and crosstalk. Distinct waves of global proteomic changes mark discrete phases of pluripotency, with cell-state-specific surface markers tracking pluripotent state transitions. Our data provide new insights into multi-layered control of the phased progression of pluripotency and a foundation for modeling mechanisms regulating pluripotent state transitions (www.steamcellatlas.org)

DISC1-dependent Regulation of Mitochondrial Dynamics Controls the Morphogenesis of Complex Neuronal Dendrites

The DISC1 protein is implicated in major mental illnesses including schizophrenia, depression, bipolar disorder, and autism. Aberrant mitochondrial dynamics are also associated with major mental illness. DISC1 plays a role in mitochondrial transport in neuronal axons, but its effects in dendrites have yet to be studied. Further, the mechanisms of this regulation and its role in neuronal development and brain function are poorly understood. Here we have demonstrated that DISC1 couples to the mitochondrial transport and fusion machinery via interaction with the outer mitochondrial membrane GTPase proteins Miro1 and Miro2, the TRAK1 and TRAK2 mitochondrial trafficking adaptors, and the mitochondrial fusion proteins (mitofusins). Using live cell imaging, we show that disruption of the DISC1-Miro-TRAK complex inhibits mitochondrial transport in neurons. We also show that the fusion protein generated from the originally described DISC1 translocation (DISC1-Boymaw) localizes to the mitochondria, where it similarly disrupts mitochondrial dynamics. We also show by super resolution microscopy that DISC1 is localized to endoplasmic reticulum contact sites and that the DISC1-Boymaw fusion protein decreases the endoplasmic reticulum-mitochondria contact area. Moreover, disruption of mitochondrial dynamics by targeting the DISC1-Miro-TRAK complex or upon expression of the DISC1-Boymaw fusion protein impairs the correct development of neuronal dendrites. Thus, DISC1 acts as an important regulator of mitochondrial dynamics in both axons and dendrites to mediate the transport, fusion, and cross-talk of these organelles, and pathological DISC1 isoforms disrupt this critical function leading to abnormal neuronal development

FMDV replicons encoding green fluorescent protein are replication competent

The study of replication of viruses that require high bio-secure facilities can be accomplished with less stringent containment using non-infectious 'replicon' systems. The FMDV replicon system (pT7rep) reported by Mclnerney et al. (2000) was modified by the replacement of sequences encoding chloramphenicol acetyl-transferase (CAT) with those encoding a functional L proteinase (Lpro) linked to a bi-functional fluorescent/antibiotic resistance fusion protein (green fluorescent protein/puromycin resistance, [GFP-PAC]). Cells were transfected with replicon-derived transcript RNA and GFP fluorescence quantified. Replication of transcript RNAs was readily detected by fluorescence, whilst the signal from replication-incompetent forms of the genome was >2-fold lower. Surprisingly, a form of the replicon lacking the Lpro showed a significantly stronger fluorescence signal, but appeared with slightly delayed kinetics. Replication can, therefore, be quantified simply by live-cell imaging and image analyses, providing a rapid and facile alternative to RT-qPCR or CAT assays

Which individual, social, and urban factors in early childhood predict psychopathology in later childhood, adolescence and young adulthood? A systematic review

Background: A comprehensive picture is lacking of the impact of early childhood (age 0–5) risk factors on the subsequent development of mental health symptoms. Objective: In this systematic review, we investigated which individual, social and urban factors, experienced in early childhood, contribute to the development of lateranxiety and depression, behavioural problems, and internalising and externalising symptoms in youth. Methods: Embase, MEDLINE, Scopus, and PsycInfo were searched on the 5th of January 2022. Three additional databases were retrieved from a mega-systematic review source that focused on the identification of both risk and protective indicators for the onset and maintenance of prospective depressive, anxiety and substance use disorders. A total of 46,450 records were identified and screened in ASReview, an AI-aided systematic review tool. We included studies with experimental, quasi-experimental, prospective and longitudinal study designs, while studies that focused on biological and genetical factors, were excluded. Results: Twenty studies were included. The majority of studies explored individual-level risk factors (N = 16). Eleven studies also explored social risk factors and three studied urban risk factors. We found evidence for early predictors relating to later psychopathology measures (i.e., anxiety and depression, behavioural problems, and internalising and externalising symptoms) in childhood, adolescence and early adulthood. These were: parental psychopathology, exposure to parental physical and verbal violence and social and neighbourhood disadvantage. Conclusions: Very young children are exposed to a complex mix of risk factors, which operate at different levels and influence children at different time points. The urban environment appears to have an effect on psychopathology but it is understudied compared to individual-level factors. Moreover, we need more research exploring the interaction between individual, social and urban factor

Evaluation of cultivated chickpea (Cicer arietinum L.) for agro-morphological traits and resistance to rust in Northwestern Indian Himalaya

First successful attempt was made to grow and evaluate the twenty five cultivated chickpea genotypes for agro-morphological traits to know the nature and magnitude of genetic divergence existing among genotypes alongwith resistance to rust (Uromyces ciceris arientini) in the Lahaul valley situated in northwestern Indian Himalaya. The genotypes showed highly significant differences for all the characters studied. The twenty five chickpea genotypes were grouped into seven clusters on the basis of D2-statistics. The cluster I was largest cluster with eleven genotypes followed by cluster II having seven genotypeswhile remaining clusters accomodated one genotype each. Highest intra cluster distance was observed for cluster II followed by cluster I. Highest inter cluster distance was observed between cluster III and I V. Cluster mean was found highest for days to maturity followed by days to flowering, pods per plant and seed yield per plant. Two characters viz., pods per plant followed by seed yield per plant contributed maximum in manifestation of genetic diversity. Highest range was observed for pods per plant and seed yield per plant. Number of pods per plant had highest range and maximum phenotypic and genotypic coefficient of variation (PCV and GCV), followed by seed yield per plant. High heritability was observed for pods per plant followed by seed yield per plant and plant height. High heritability coupled with high genetic advance was observed for number of pods per plant and seed yield per plant. Three genotypes viz., ICC 3137, ICCV 9675 and ICCL 87316 were found to be moderately resistant against rust. ICCV 9675 and ICCL 87316 were found to be superior for number of pods per plant and seed yield per plant, respectively. The studies revealed ICC 3137, ICCV 9675 and ICCL 87316 as diverse genotypes moderately resistant to rust (found in different clusters II, I and IV respectively). These can be utilized as promising genotypes for future breeding and hybridization program with susceptible lines which were otherwise superior for other traits