Physical Representation-based Predicate Optimization for a Visual Analytics Database

Querying the content of images, video, and other non-textual data sources requires expensive content extraction methods. Modern extraction techniques are based on deep convolutional neural networks (CNNs) and can classify objects within images with astounding accuracy. Unfortunately, these methods are slow: processing a single image can take about 10 milliseconds on modern GPU-based hardware. As massive video libraries become ubiquitous, running a content-based query over millions of video frames is prohibitive. One promising approach to reduce the runtime cost of queries of visual content is to use a hierarchical model, such as a cascade, where simple cases are handled by an inexpensive classifier. Prior work has sought to design cascades that optimize the computational cost of inference by, for example, using smaller CNNs. However, we observe that there are critical factors besides the inference time that dramatically impact the overall query time. Notably, by treating the physical representation of the input image as part of our query optimization---that is, by including image transforms, such as resolution scaling or color-depth reduction, within the cascade---we can optimize data handling costs and enable drastically more efficient classifier cascades. In this paper, we propose Tahoma, which generates and evaluates many potential classifier cascades that jointly optimize the CNN architecture and input data representation. Our experiments on a subset of ImageNet show that Tahoma's input transformations speed up cascades by up to 35 times. We also find up to a 98x speedup over the ResNet50 classifier with no loss in accuracy, and a 280x speedup if some accuracy is sacrificed.Comment: Camera-ready version of the paper submitted to ICDE 2019, In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE 2019

THE EMERGING FOOD STORE DELICATESSEN: SOME PRELIMINARY ECONOMICS

Agribusiness,

Hybrid anion exchange nanotechnology (HAIX-Nano) for concurrent trace contaminant removal with partial desalination: laboratory and field-scale investigations

Millions of people across the Indian subcontinent are at risk of chronic exposure to arsenic and fluoride in aquifers with excess TDS (\u3e500 mg/L). Hybrid anion exchange resins with nanoparticles of zirconium oxide (HAIX-NanoZr) were created for effective fluoride removal; zirconium oxide selectively bound fluoride, silica, and phosphate. The zeta potential of HAIX-NanoZr was directly correlated to the fluoride removal capacity. Thus, to increase fluoride capacity and achieve partial desalination concurrently, a weak acid cation (WAC) exchange pretreatment was used to lower alkalinity, hardness, pH, and TDS. Consistent partial desalination (~50%) occurred in proportion to influent alkalinity and hardness. With regular acid conditioning of WAC, the fluoride capacity of HAIX-NanoZr increased 10x, 300 bed volumes (BVs) to 3500 BVs. Upon regeneration with 2% NaOH, \u3e95% of capacity was restored; fluoride treatment was identical over three consecutive cycles with water from a fluoride-contaminated well (Nakuru, Kenya). Passive pH adjustment (pH 6.5-8.5) of HAIX-NanoZr effluent was achieved with WAC-Na form and/or dolomite.Pilot-scale production of HAIX-NanoZr was researched and developed in Kolkata; analysis of zirconium content and fluoride capacity were performed at Lehigh. Modifications to washing/drying steps were critical for high fluoride capacity. Concurrently, Drinkwell (WIST, Inc., USA; WIST Systems Pvt. Ltd., India) was founded to scale-up HAIX-NanoZr across the Indian subcontinent. An exclusive licensing agreement for HAIX-NanoZr to an Indian partner company was created to pursue Indian government grants and tenders for arsenic and fluoride treatment. Under government supervision, HAIX-NanoZr was piloted for fluoride/TDS removal in Andhra Pradesh, Assam, Madhya Pradesh, Odisha, and West Bengal; HAIX-NanoFe/Zr was piloted for arsenic removal in Chhattisgarh, Jharkhand, and West Bengal. After initial lessons, treatment systems have met Indian drinking water standards.A cash flow model of HAIX-Nano as an entrepreneur-owned small business was developed and Monte Carlo analysis was performed. After being capitalized, self-sustaining operations with market-rate wages were achievable under a range of scenarios. But, capitalization will be difficult for most operations. Price of water and number of customers (TR = P x Q) were the most sensitive input variables, i.e., the most critical factor to financially sustainable water treatment was good business practices

Set-up of Digital Image Correlation Apparatus

Digital Image Correlation (DIC) is a valuable and customizable experimental technique employed to analyze localized strain regions on materials by tracking the displacement of points on the surface of the studied material under applied stress. To investigate materials behavior, it is vital to correctly set-up the DIC apparatus so work has been done to ready the equipment to start measurements on two distinct projects. On the first project, the fatigue crack behavior of a high-strength aluminum alloy will be studied by cyclic loading, testing necessary for the safe design of aircraft parts utilizing this novel alloy. DIC will be carried out ahead of the fatigue crack and to accomplish this, a MATLAB code was developed to synchronize the loading machine with the DIC equipment and camera, and to automate the capture of images. On the second project, electronic microscopy will be utilized to carry out DIC at high resolutions to study the relationship of the microstructure of structural alloys and the strain fields generated on the material. A gold nanoparticle speckling method was adapted from literature to create a speckle pattern on the specimens with the desired length scale and density for this study. A satisfactory conclusion of the preparatory work of the DIC equipment and protocols will enable the testing to start for the two projects aforementioned

A new familial form of a late-onset, persistent hyperinsulinemic hypoglycemia of infancy caused by a novel mutation in KCNJ11.

The ATP-sensitive potassium channel (KATP) functions as a metabo-electric transducer in regulating insulin secretion from pancreatic β-cells. The pancreatic KATP channel is composed of a pore-forming inwardly-rectifying potassium channel, Kir6.2, and a regulatory subunit, sulphonylurea receptor 1 (SUR1). Loss-of-function mutations in either subunit often lead to the development of persistent hyperinsulinemic hypoglycemia of infancy (PHHI). PHHI is a rare genetic disease and most patients present with immediate onset within the first few days after birth. In this study, we report an unusual form of PHHI, in which the index patient developed hyperinsulinemic hypoglycemia after 1 year of age. The patient failed to respond to routine medication for PHHI and underwent a complete pancreatectomy. Genotyping of the index patient and his immediate family members showed that the patient and other family members with hypoglycemic episodes carried a heterozygous novel mutation in KCNJ11 (C83T), which encodes Kir6.2 (A28V). Electrophysiological and cell biological experiments revealed that A28V hKir6.2 is a dominant-negative, loss-of-function mutation and that KATP channels carrying this mutation failed to reach the cell surface. De novo protein structure prediction indicated that this A28V mutation reoriented the ER retention motif located at the C-terminal of the hKir6.2, and this result may explain the trafficking defect caused by this point mutation. Our study is the first report of a novel form of late-onset PHHI that is caused by a dominant mutation in KCNJ11 and exhibits a defect in proper surface expression of Kir6.2

Lepton-flavour-violating gluonic operators: constraints from the LHC and low energy experiments

Effective operators provide a model-independent description of physics beyond the stan- dard model that is particularly useful given the absence of any signs of new physics at the Large Hadron Collider (LHC). We recast previous LHC analyses to set limits on lepton-flavour-violating gluonic effective operators of dimension 8 and compare our results to existing limits from low-energy precision experiments. Current LHC data constrains the scale $\Lambda$ of the effective operators to be larger than $\Lambda\gtrsim 0.5 - 1.6$ TeV depending on the flavour and thus provides the most stringent limit for all operators apart from parity-conserving operators of the form $GG\bar\mu P_{L,R} e$, where $\mu - e$ conversion in nuclei poses the most stringent constraint.Comment: 14 pages, 6 figures; v2: corrected normalization of operators in cross section calculation and matrix elements for tau decays, main conclusions unchanged, accepted by JHE