An infrastructure for delivering geospatial data to field users

Federal agencies collect and analyze data to carry out their missions. A significant portion of these activities requires geospatial data collection in the field. Models for computer-assisted survey information collection are still largely based on the client-server paradigm with symbolic data representation. Little attention has been given to digital geospatial information resources, or emerging mobile computing environments. This paper discusses an infrastructure designs for delivering geospatial data users in a mobile field computing environment. Mobile field computing environments vary widely, and generally offer extremely limited computing resources, visual display, and bandwidth relative to the usual resources required for distributed geospatial data. Key to handling heterogeneity in the field is an infrastructure design that provides flexibility in the location of computing tasks and returns information in forms appropriate for the field computing environment. A view agent based infrastructure has been developed with several components. Wrappers are used for encapsulating not only the data sources, but the mobile field environment as well, localizing the details associated with heterogeneity in data sources and field environments. Within the boundaries of the wrappers, mediators and object-oriented views implemented as mobile agents work in a relatively homogeneous environment to generate query results. Mediators receive a request from the user application via the field wrapper, and generate a sequence of mobile view agents to search for, retrieve, and process data. The internal infrastructure environment is populated with computation servers to provide a location for processing, especially for combining data from multiple locations. Each computation server has a local object-oriented data warehouse equipped with a set of data warehouse tools for working with geospatial data. Since the prospect of query reuse is likely for a field worker, we store the final and intermediate results in the data warehouse, allowing the warehouse to act as an active cache. Even when field computing capacity is ample, the warehouse is used to process data so that network traffic can be minimized

Whole transcriptome sequencing identifies tumor-specific mutations in human oral squamous cell carcinoma

Background: The accumulation of somatic mutations in genes and molecular pathways is a major factor in the evolution of oral squamous cell carcinoma (OSCC), which sparks studies to identify somatic mutations with clinical potentials. Recently, massively parallel sequencing technique has started to revolutionize biomedical studies, due to the rapid increase in its throughput and drop in cost. Hence sequencing of whole transcriptome (RNA-Seq) becomes a superior approach in cancer studies, which enables the detection of somatic mutations and accurate measurement of gene expression simultaneously. Methods: We used RNA-Seq data from tumor and matched normal samples to investigate somatic mutation spectrum in OSCC. Results: By applying a sophisticated bioinformatic pipeline, we interrogated two tumor samples and their matched normal tissues and identified 70,472 tumor somatic mutations in protein-coding regions. We further identified 515 significantly mutated genes (SMGs) and 156 tumor-specific disruptive genes (TDGs), with six genes in both sets, including ANKRA2, GTF2H5, STOML1, NUP37, PPP1R26, and TAF1L. Pathway analysis suggested that SMGs were enriched in cell adhesion pathways, which are frequently indicated in tumor development. We also found that SMGs tend to be differentially expressed between tumors and normal tissues, implying a regulatory role of accumulation of genetic aberrations in these genes. Conclusions: Our finding of known tumor genes proves of the utility of RNA-Seq in mutation screening, and functional analysis of genes detected here would help understand the molecular mechanism of OSCC

Computational Design of Flexible Electride with Nontrivial Band Topology

Electrides, with their excess electrons distributed in crystal cavities playing the role of anions, exhibit a variety of unique electronic and magnetic properties. In this work, we employ the first-principles crystal structure prediction to identify a new prototype of A3B electride in which both interlayer spacings and intralayer vacancies provide channels to accommodate the excess electrons in the crystal. This A3B type of structure is calculated to be thermodynamically stable for two alkaline metals oxides (Rb3O and K3O). Remarkably, the unique feature of multiple types of cavities makes the spatial arrangement of anionic electrons highly flexible via elastic strain engineering and chemical substitution, in contrast to the previously reported electrides characterized by a single topology of interstitial electrons. More importantly, our first-principles calculations reveal that Rb3O is a topological Dirac nodal line semimetal, which is induced by the band inversion at the general electronic k momentums in the Brillouin zone associated with the intersitial electric charges. The discovery of flexible electride in combining with topological electronic properties opens an avenue for electride design and shows great promises in electronic device applications

Robust Training under Label Noise by Over-parameterization

Recently, over-parameterized deep networks, with increasingly more network parameters than training samples, have dominated the performances of modern machine learning. However, when the training data is corrupted, it has been well-known that over-parameterized networks tend to overfit and do not generalize. In this work, we propose a principled approach for robust training of over-parameterized deep networks in classification tasks where a proportion of training labels are corrupted. The main idea is yet very simple: label noise is sparse and incoherent with the network learned from clean data, so we model the noise and learn to separate it from the data. Specifically, we model the label noise via another sparse over-parameterization term, and exploit implicit algorithmic regularizations to recover and separate the underlying corruptions. Remarkably, when trained using such a simple method in practice, we demonstrate state-of-the-art test accuracy against label noise on a variety of real datasets. Furthermore, our experimental results are corroborated by theory on simplified linear models, showing that exact separation between sparse noise and low-rank data can be achieved under incoherent conditions. The work opens many interesting directions for improving over-parameterized models by using sparse over-parameterization and implicit regularization.Comment: 25 pages, 4 figures and 6 tables. Code is available at https://github.com/shengliu66/SO

UniMOS: A Universal Framework For Multi-Organ Segmentation Over Label-Constrained Datasets

Machine learning models for medical images can help physicians diagnose and manage diseases. However, due to the fact that medical image annotation requires a great deal of manpower and expertise, as well as the fact that clinical departments perform image annotation based on task orientation, there is the problem of having fewer medical image annotation data with more unlabeled data and having many datasets that annotate only a single organ. In this paper, we present UniMOS, the first universal framework for achieving the utilization of fully and partially labeled images as well as unlabeled images. Specifically, we construct a Multi-Organ Segmentation (MOS) module over fully/partially labeled data as the basenet and designed a new target adaptive loss. Furthermore, we incorporate a semi-supervised training module that combines consistent regularization and pseudolabeling techniques on unlabeled data, which significantly improves the segmentation of unlabeled data. Experiments show that the framework exhibits excellent performance in several medical image segmentation tasks compared to other advanced methods, and also significantly improves data utilization and reduces annotation cost. Code and models are available at: https://github.com/lw8807001/UniMOS.Comment: Accepted by BIBM202

Investigation of ultra-thin Al₂O₃ film as Cu diffusion barrier on low-k (k=2.5) dielectrics

Ultrathin Al(2)O(3) films were deposited by PEALD as Cu diffusion barrier on low-k (k=2.5) material. The thermal stability and electrical properties of the Cu/low k system with Al(2)O(3) layers with different thickness were studied after annealing. The AES, TEM and EDX results revealed that the ultrathin Al(2)O(3) films are thermally stable and have excellent Cu diffusion barrier performance. The electrical measurements of dielectric breakdown and TDDB tests further confirmed that the ultrathin Al(2)O(3) film is a potential Cu diffusion barrier in the Cu/low-k interconnects system

The global landscape of intron retentions in lung adenocarcinoma

Background: The transcriptome complexity in an organism can be achieved by alternative splicing of precursor messenger RNAs. It has been revealed that alternations in mRNA splicing play an important role in a number of diseases including human cancers. Methods: In this study, we exploited whole transcriptome sequencing data from five lung adenocarcinoma tissues and their matched normal tissues to interrogate intron retention, a less studied alternative splicing form which has profound structural and functional consequence by modifying open reading frame or inserting premature stop codons. Results: Abundant intron retention events were found in both tumor and normal tissues, and 2,340 and 1,422 genes only contain tumor-specific retentions and normal-specific retentions, respectively. Combined with gene expression analysis, we showed that genes with tumor-specific retentions tend to be over-expressed in tumors, and the abundance of intron retention within genes is negatively related with gene expression, indicating the action of nonsense mediated decay. Further functional analysis demonstrated that genes with tumor-specific retentions include known lung cancer driver genes and are found enriched in pathways important in carcinogenesis. Conclusions: We hypothesize that intron retentions and consequent nonsense mediated decay may collectively counteract the over-expression of genes promoting cancer development. Identification of genes with tumor-specific retentions may also help develop targeted therapies