Training Neural Networks for Execution on Approximate Hardware

Approximate computing methods have shown great potential for deep learning. Due to the reduced hardware costs, these methods are especially suitable for inference tasks on battery-operated devices that are constrained by their power budget. However, approximate computing hasn't reached its full potential due to the lack of work on training methods. In this work, we discuss training methods for approximate hardware. We demonstrate how training needs to be specialized for approximate hardware, and propose methods to speed up the training process by up to 18X

PSYCHOLOGICAL ANALYSIS ON NETWORK RUMORS SPREAD IN COVID-19 EPIDEMIC

Background: Since the outbreak of the COVID-19 epidemic, related rumors have been spread rapidly on social media, which has seriously affected public normal life and caused mental panic and anxiety. This paper, by analyzing the spread of online rumors and their impact on public mental health during the COVID-19 epidemic, aims to put forward relevant suggestions for dealing with rumors and improving public mental health. Subjects and methods: According to the actual rumors collected during the epidemic period as the content of the questionnaire, an online rumor survey was carried out. A total of 1,200 questionnaires were distributed, and 1,165 valid questionnaires were collected. Results: Among the sources of information about the epidemic, 78.2% of the respondents obtained epidemic information through online channels. 633 respondents were concerned about the epidemic situation, accounting for 54.3%. The respondents have different susceptibility to the epidemic sources, epidemic spread, epidemic treatment, epidemic influence, and epidemic prevention. They account for 37.2%, 43. 4%, 45.2%, 39.4%, and 46.1% respectively. 646 respondents expressed great anxiety about the epidemic situation, accounting for 55.5%. The source (t=26.33, P<0.05), transmission (t=28.32, P<0.05), prevention (t=35.36, P<0.05), treatment (t=40.32, P<0.05), and impact of the epidemic (t=42.01, P<0.05) can significantly predict the transmission intention. Conclusions: To control the spread of epidemic rumors and reduce the negative impact of such information on public mental health, the government and various walks of life need to collaborate to solve the problem from the aspects of information disclosure system, supervision means, and network communication system

Isotopic Application in High Saline Conditions

Evaporite minerals record the hydrogeochemical conditions in which they precipitated. And therefore they can be used to reconstruct the paleoclimate and paleoenvironments. Evaporite minerals are also major sources of industrial minerals including gypsum, halite, borates, lithium concentrates, and others. Because of their scientific significance and economic importance, evaporite minerals and their isotopic hydrochemical processes linked to their formation have been the focus of many geologists and paleoclimatologists. This chapter will discuss the application of isotopes of hydrogen, oxygen, sulfur, strontium, and boron in saline conditions. This will include the following: the δ18O and δD of hydrated water of gypsum and their paleoclimate since 2.2 Ma in the Qaidam Basin, NE Tibetan Plateau; the δ18O and δD of the interlayer water of clay minerals in salar lacustrine sediments; and the 87Sr/86Sr, δ34S, and δ11B of halite from evaporite deposits in Khorat Plateau, Laos, and Yunnan and their application in the origins of brine

Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

We tackle the problem of graph out-of-distribution (OOD) generalization. Existing graph OOD algorithms either rely on restricted assumptions or fail to exploit environment information in training data. In this work, we propose to simultaneously incorporate label and environment causal independence (LECI) to fully make use of label and environment information, thereby addressing the challenges faced by prior methods on identifying causal and invariant subgraphs. We further develop an adversarial training strategy to jointly optimize these two properties for casual subgraph discovery with theoretical guarantees. Extensive experiments and analysis show that LECI significantly outperforms prior methods on both synthetic and real-world datasets, establishing LECI as a practical and effective solution for graph OOD generalization

Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns

Objective: Previous studies have shown that combing with color properties may be used as part of the display presented to BCI users in order to improve performance. Build on this, we explored the effects of combinations of face stimuli with three primary colors (RGB) on BCI performance which is assessed by classification accuracy and information transfer rate (ITR). Furthermore, we analyzed the waveforms of three patterns. Methods: We compared three patterns in which semitransparent face is overlaid three primary colors as stimuli: red semitransparent face (RSF), green semitransparent face (GSF), and blue semitransparent face (BSF). Bayesian linear discriminant analysis (BLDA) was used to construct the individual classifier model. In addition, a Repeated-measures ANOVA (RM-ANOVA) and Bonferroni correction were chosen for statistical analysis. Results: The results indicated that the RSF pattern achieved the highest online averaged accuracy with 93.89%, followed by the GSF pattern with 87.78%, while the lowest performance was caused by the BSF pattern with an accuracy of 81.39%. Furthermore, significant differences in classification accuracy and ITR were found between RSF and GSF (p < 0.05) and between RSF and BSF patterns (p < 0.05). Conclusion: The semitransparent faces colored red (RSF) pattern yielded the best performance of the three patterns. The proposed patterns based on ERP-BCI system have a clinically significant impact by increasing communication speed and accuracy of the P300-speller for patients with severe motor impairment

tRNASer(CGA) differentially regulates expression of wild-type and codon-modified papillomavirus L1 genes

Exogenous transfer RNAs (tRNAs) favor translation of bovine papillomavirus 1 wild-type (wt) L1 mRNA in in vitro translation systems (Zhou et al. 1999, J. Virol., 73, 4972-4982). We, therefore, investigated whether papillomavirus (PV) wt L1 protein expression could be enhanced in eukaryotic cells following exogenous tRNA supplementation. Both Chinese hamster ovary (CHO) and Cos1 cells, transfected with PV1 wt L1 genes, effectively transcribed the genes but did not translate them. However, L1 protein translation was demonstrated following co-transfection with the L1 gene and a gene expressing tRNA(Ser)(CGA). Cell lines, stably transfected with a bovine papillomavirus 1 (BPV1) wt L1 expression construct, produced L1 protein after the transfection of the tRNA(Ser)(CGA) gene, but not following the transfection with basal vectors, suggesting that tRNA(Ser)(CGA) gene enhanced wt L1 translation as a result of endogenous tRNA alterations and phosphorylation of translation initiation factors elF4E and elF2alpha in the tRNA(Ser)(CGA) transfected L1 cell lines. The tRNA(Ser)(CGA) gene expression significantly reduced translation of L1 proteins expressed from codon-modified (HB) PV L1 genes utilizing mammalian preferred codons, but had variable effects on translation of green fluorescent proteins (GFPs) expressed from six serine GFP variants. The changes of tRNA pools appear to match the codon composition of PV wt and HB L1 genes and serine GFP variants to regulate translation of their mRNAs. These findings demonstrate for the first time in eukaryotic cells that translation of the target genes can be differentially influenced by the provision of a single tRNA expression construct

Zinc Improves Functional Recovery by Regulating the Secretion of Granulocyte Colony Stimulating Factor From Microglia/Macrophages After Spinal Cord Injury

While zinc promotes motor function recovery after spinal cord injury (SCI), the precise mechanisms involved are not fully understood. The present study aimed to elucidate the effects of zinc and granulocyte colony stimulating factor (G-CSF) on neuronal recovery after SCI. The SCI model was established by Allen’s method. Injured animals were given glucose and zinc gluconate (ZnG; 30 mg/kg) for the first time at 2 h after injury, the same dose was given for 3 days. A cytokine antibody array was used to screen changes in inflammation at the site of SCI lesion. Immunofluorescence was used to detect the distribution of cytokines. Magnetic beads were also used to isolate cells from the site of SCI lesion. We then investigated the effect of Zinc on apoptosis after SCI by Transferase UTP Nick End Labeling (TUNEL) staining and Western Blotting. Basso Mouse Scale (BMS) scores and immunofluorescence were employed to investigate neuronal apoptosis and functional recovery. We found that the administration of zinc significantly increased the expression of 19 cytokines in the SCI lesion. Of these, G-CSF was shown to be the most elevated cytokine and was secreted by microglia/macrophages (M/Ms) via the nuclear factor-kappa B (NF-κB) signaling pathway after SCI. Increased levels of G-CSF at the SCI lesion reduced the level of neuronal apoptosis after SCI, thus promoting functional recovery. Collectively, our results indicate that the administration of zinc increases the expression of G-CSF secreted by M/Ms, which then leads to reduced levels of neuronal apoptosis after SCI