Text Cube: Computing IR Measures for Multidimensional Text Database Analysis

Since Jim Gray introduced the concept of ”data cube” in 1997, data cube, associated with online analytical processing (OLAP), has become a driving engine in data warehouse industry. Because the boom of Internet has given rise to an ever increasing amount of text data associated with other multidimensional information, it is natural to propose a data cube model that integrates the power of traditional OLAP and IR techniques for text. In this paper, we propose a Text-Cube model on multidimensional text database and study effective OLAP over such data. Two kinds of hierarchies are distinguishable inside: dimensional hierarchy and term hierarchy. By incorporating these hierarchies, we conduct systematic studies on efficient text-cube implementation, OLAP execution and query processing. Our performance study shows the high promise of our methods.

Disruption of splicing-regulatory elements using CRISPR/Cas9 to rescue spinal muscular atrophy in human iPSCs and mice

We here report a genome-editing strategy to correct spinal muscular atrophy (SMA). Rather than directly targeting the pathogenic exonic mutations, our strategy employed Cas9 and guide-sgRNA for the targeted disruption of intronic splicing-regulatory elements. We disrupted intronic splicing silencers (ISSs, including ISS-N1 and ISS + 100) of survival motor neuron (SMN) 2, a key modifier gene of SMA, to enhance exon 7 inclusion and full-length SMN expression in SMA iPSCs. Survival of splicing-corrected iPSC-derived motor neurons was rescued with SMN restoration. Furthermore, co-injection of Cas9 mRNA from Streptococcus pyogenes (SpCas9) or Cas9 from Staphylococcus aureus (SaCas9) alongside their corresponding sgRNAs targeting ISS-N1 into zygotes rescued 56% and 100% of severe SMA transgenic mice (Smn , SMN2 ). The median survival of the resulting mice was extended to >400 days. Collectively, our study provides proof-of-principle for a new strategy to therapeutically intervene in SMA and other RNA-splicing-related diseases. -/- tg/

A Visual Navigation Method of Mobile Robot Using a Sketched Semantic Map

A new visual navigation method for a mobile robot is proposed in this paper. Its originality lies in integrating a sketched map with a semantic map together for the robot's navigation and in using unified tags to help recognize landmarks. In this sketched semantic map, the outline and semantic information of key referenced objects are used to represent themselves and a rough route for the robot's navigation is also sketched. Over the course of robot navigation along the route, and in order to easily recognize the referenced objects from the complex background, a kind of unified label is designed and pasted on the potential referenced objects in advance. A recognition method based on the Pseudo-Zernike Moment and the Normalized Moment of Inertia is used to compute the matching degree between the real-time image of the referenced object and its similar outline in a database. In addition, the odometer information is also fused so as to roughly localize the robot. Finally, through a series of experiments, the advantage and efficiency of the new navigation method with real-time dynamic obstacle avoidance are testified with the help of the imprecise real map and route

Electrophoretic Deposition of Co₃O₄ Particles/Reduced Graphene Oxide Composites for Efficient Non-Enzymatic H₂O₂ Sensing

In this work, the facile fabrication of Co3O4 particles/reduced graphene oxide (Co3O4/rGO) composites on Indium tin oxide (ITO) slide was achieved by an electrophoretic deposition and annealing process. The deposition time and ratio of the precursors were optimized. Structural characterization and chemical composition investigation indicated successful loading of Co3O4 particles on graphene sheets. When applied as a non-enzymatic H2O2 sensor, Co3O4/rGO showed significant electrocatalytic activity, with a wide linear range (0.1–19.5 mM) and high sensitivity (0.2247 mA mM−1 cm−2). The good anti-interference ability, reproducibility, and long-term stability of the constructed sensor were also presented. The application of Co3O4/rGO in real sample analysis was evaluated in human urine sample with satisfactory results, indicating the feasibility of the sensor in physiological and medical applications

Real-time reading system for pointer meter based on YolactEdge

Despite the extensive deployment of digital instruments in modern times, their stability is challenging to maintain in adverse environmental conditions such as extreme temperatures, pressure, or powerful electromagnetic radiation. Analog meters, owing to their mechanical resilience and electromagnetic impedance, persistently find usage across nuclear power plants, petroleum, and chemical industries. However, under these harsh conditions, manual reading of the instruments may prove to be difficult and dangerous while failing to meet the requirements of real-time monitoring. In recent years, several machine vision-based meter reading systems have been proposed, however, achieving high accuracy through camera-based methods under varying angles and lighting conditions poses significant challenges. Cloud deployment may compromise plant privacy, while edge computing faces limitations in real-time meter reading due to limited computing power. To address these issues, we propose a real-time reading system based on the YolactEdge instance segmentation framework for single-point analog meters. Our system is more accurate than previous studies and is implemented and deployed on the Jetson Xavier NX edge computing device. Our performance evaluation shows that our model outperforms other baselines, with low reference values and relative errors of 0.0237% and 0.0300%, respectively, and an average inference speed of 10.26 FPS with INIT 8 linear acceleration on Nvidia Jetson NX

Short-term power load forecasting based on combined kernel Gaussian process hybrid model

As one of the countries with the most energy consumption in the world, electricity accounts for a large proportion of the energy supply in our country. According to the national basic policy of energy conservation and emission reduction, it is urgent to realize the intelligent distribution and management of electricity by prediction. Due to the complex nature of electricity load sequences, the traditional model predicts poor results. As a kernel-based machine learning model, Gaussian Process Mixing (GPM) has high predictive accuracy, can multi-modal prediction and output confidence intervals. However, the traditional GPM often uses a single kernel function, and the prediction effect is not optimal. Therefore, this paper will combine a variety of existing kernel to build a new kernel, and use it for load sequence prediction. In the electricity load prediction experiments, the prediction characteristics of the load sequences are first analyzed, and then the prediction is made based on the optimal hybrid kernel function constructed by GPM and compared with the traditional prediction model. The results show that the GPM based on the hybrid kernel is not only superior to the single kernel GPM but also superior to some traditional prediction models such as ridge regression, kernel regression and GP

Properties of oriented carbon fiber/polyamide 12 composite parts fabricated by fused deposition modeling

This paper reports the thermal and mechanical properties of carbon fiber (CF) reinforced polyamide 12 (PA12) composites for fused deposition modeling (FDM) process. The printable filaments of carbon fiber/PA12 composites with different mass fraction were fabricated and applied in FDM. The results indicate that the tensile strength and flexural strength of 10 wt% CF/PA12 composites are enhanced by 102.2% and 251.1% respectively. The laser-flash diffusivity analysis measurements exhibit remarkable improvements on thermal conductivity (lambda) of carbon fiber/PA12 composites. Moreover, the carbon fiber/PA12 composites mechanical properties are greatly improved. Our work presents a kind of anisotropic high performance composite for FDM. (C) 2017 Elsevier Ltd. All rights reserved

Environmental Acoustic Enrichment Promotes Recovery from Developmentally Degraded Auditory Cortical Processing

It has previously been shown that environmental enrichment can enhance structural plasticity in the brain and thereby improve cognitive and behavioral function. In this study, we reared developmentally noise-exposed rats in an acoustic-enriched environment for ∼4 weeks to investigate whether or not enrichment could restore developmentally degraded behavioral and neuronal processing of sound frequency. We found that noise-exposed rats had significantly elevated sound frequency discrimination thresholds compared with age-matched naive rats. Environmental acoustic enrichment nearly restored to normal the behavioral deficit resulting from early disrupted acoustic inputs. Signs of both degraded frequency selectivity of neurons as measured by the bandwidth of frequency tuning curves and decreased long-term potentiation of field potentials recorded in the primary auditory cortex of these noise-exposed rats also were reversed partially. The observed behavioral and physiological effects induced by enrichment were accompanied by recovery of cortical expressions of certain NMDA and GABAA receptor subunits and brain-derived neurotrophic factor. These studies in a rodent model show that environmental acoustic enrichment promotes recovery from early noise-induced auditory cortical dysfunction and indicate a therapeutic potential of this noninvasive approach for normalizing neurological function from pathologies that cause hearing and associated language impairments in older children and adults

Perceptual Training Restores Impaired Cortical Temporal Processing Due to Lead Exposure.

Low-level lead exposure is a risk factor for cognitive and learning disabilities in children and has been specifically associated with deficits in auditory temporal processing that impair aural language and reading abilities. Here, we show that rats exposed to low levels of lead in early life display a significant behavioral impairment in an auditory temporal rate discrimination task. Lead exposure also results in a degradation of the neuronal repetition-rate following capacity and response synchronization in primary auditory cortex. A modified go/no-go repetition-rate discrimination task applied in adult animals for ∼50 days nearly restores to normal these lead-induced deficits in cortical temporal fidelity. Cortical expressions of parvalbumin, brain-derived neurotrophic factor, and NMDA receptor subunits NR2a and NR2b, which are down-regulated in lead-exposed animals, are also partially reversed with training. These studies in an animal model identify the primary auditory cortex as a novel target for low-level lead exposure and demonstrate that perceptual training can ameliorate lead-induced deficits in cortical discrimination between sound sequences