Quantum correlations across two octaves from combined up and down conversion

We propose and analyse a cascaded optical parametric system which involves three interacting modes across two octaves of frequency difference. Our system, combining degenerate optical parametric oscillation (OPO) with second harmonic generation (SHG), promises to be a useful source of squeezed and entangled light at three differing frequencies. We show how changes in damping rates and the ratio of the two concurrent nonlinearities affect the quantum correlations in the output fields. We analyse the threshold behaviour, showing how the normal OPO threshold is changed by the addition of the SHG interactions. We also find that the inclusion of the OPO interaction removes the self-pulsing behaviour found in normal SHG. Finally, we show how the Einstein-Podolsky-Rosen correlations can be controlled by the injection of a coherent seed field at the lower frequency.Comment: 23 pages, 11 figures, theor

Surface-modified PLGA nanoparticles for targeted drug delivery to neurons

The blood-brain barrier (BBB), which protects the central nervous system (CNS) from unnecessary substances, is a challenging obstacle in the treatment of CNS disease such as Parkinson’s Disease (PD). Many therapeutic agents such as hydrophilic and macromolecular drugs cannot overcome the BBB. One promising solution is the employment of polymeric nanoparticles (NPs) such as poly (lactic-co-glycolic acid) (PLGA) NPs as drug carrier. Over the past few years, significant breakthroughs have been made in developing suitable poly (lactic-co-glycolic acid) (PLGA) and poly (lactic acid) (PLA) nanoparticles for drug delivery across the BBB. Recent advances on PLGA/PLA NPs enhanced neural delivery of drugs were reviewed in the second chapter. Both in vitro and in vivo studies were included. In these papers, enhanced cellular uptake and therapeutic efficacy of drugs delivered with modified PLGA/PLA NPs compared to free drugs or drugs delivered by unmodified PLGA NPs was shown; no significant in vitro cytotoxicity was observed for PLGA NPs and PLA NPs. Surface modification of PLGA/PLA NPs by coating with surfactants/polymers or covalently conjugating with targeting ligands has been confirmed to enhance drug delivery across the BBB. Most unmodified PLGA NPs showed low brain uptake (\u3c1%), which confirms the safety of PLGA/PLA NPs used for other purposes than treating CNS diseases. For the second part of the study, wheat germ agglutinin (WGA), a lectin was conjugated to PLGA nanoparticles (PLGA-tWGA NPs, 221 nm) to improve DAergic neuron delivery in C.elegans. PLGA-tWGA NPs did not show a significant effect on pumping rate and life span of C. elegans at low concentration (\u3c3 mg/ml). Fluorescent studies of GFP-DAergic neurons revealed that area of GFP-DAergic neurons of worms treated with high concentrations PLGA-tWGA NPs (\u3e3mg/ml) was significantly decreased. Number and mean intensity of GFP-DAergic neurons also decreased, but no significant difference was found compared with control group. Co-localization of the fluorescent particles with the GFP-DAergic neurons of treated worms proved targeting property of PLGA-tWGA nanoparticles to DAergic neurons. Enhanced targeted delivery of PLGA-tWGA NPs to neurons compared with tWGA and PLGA-t NPs made PLGA-tWGA NPs potential targeted neural delivery systems for the treatment of PD

Recycling of solvent used in a solvent extraction of petroleum hydrocarbons contaminated soil.

The application of water washing technology for recycling an organic composite solvent consisting of hexane and pentane (4:1; TU-A solvent) was investigated for extracting total petroleum hydrocarbons (TPH) from contaminated soil. The effects of water volume, water temperature, washing time and initial concentration of solvent were evaluated using orthogonal experiments followed by single factor experiments. Our results showed that the water volume was a statistically significant factor influencing greatly the water washing efficiency. Although less important, the other three factors have all increased the efficacy of water washing treatment. Based on a treatment of 20g of contaminated soil with a TPH concentration of 140mgg(-1), optimal conditions were found to be at 40°C, 100mL water, 5min washing time and 660mgg(-1) solvent. Semi-continuous water extraction method showed that the concentration of the composite solvent TU-A was reduced below 15mgg(-1) d.w. soil with a recovery extraction efficiency >97%. This finding suggests that water washing is a promising technology for recycling solvent used in TPH extraction from contaminated soil

JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution

Recent years have witnessed a rapid growth of deep-network based services and applications. A practical and critical problem thus has emerged: how to effectively deploy the deep neural network models such that they can be executed efficiently. Conventional cloud-based approaches usually run the deep models in data center servers, causing large latency because a significant amount of data has to be transferred from the edge of network to the data center. In this paper, we propose JALAD, a joint accuracy- and latency-aware execution framework, which decouples a deep neural network so that a part of it will run at edge devices and the other part inside the conventional cloud, while only a minimum amount of data has to be transferred between them. Though the idea seems straightforward, we are facing challenges including i) how to find the best partition of a deep structure; ii) how to deploy the component at an edge device that only has limited computation power; and iii) how to minimize the overall execution latency. Our answers to these questions are a set of strategies in JALAD, including 1) A normalization based in-layer data compression strategy by jointly considering compression rate and model accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall execution latency; and 3) An edge-cloud structure adaptation strategy that dynamically changes the decoupling for different network conditions. Experiments demonstrate that our solution can significantly reduce the execution latency: it speeds up the overall inference execution with a guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE

The magnitude homology of a hypergraph

The magnitude homology, introduced by R. Hepworth and S. Willerton, offers a topological invariant that enables the study of graph properties. Hypergraphs, being a generalization of graphs, serve as popular mathematical models for data with higher-order structures. In this paper, we focus on describing the topological characteristics of hypergraphs by considering their magnitude homology. We begin by examining the distances between hyperedges in a hypergraph and establish the magnitude homology of hypergraphs. Additionally, we explore the relationship between the magnitude and the magnitude homology of hypergraphs. Furthermore, we derive several functorial properties of the magnitude homology for hypergraphs. Lastly, we present the K\"{u}nneth theorem for the simple magnitude homology of hypergraphs

Constructing Activity-Mobility Patterns of Students Based On UB (University at Buffalo) Card Transactions

Activity-mobility patterns have been widely used to represent the movement of traveling entities in time and space. In previous studies, researchers generated various mobility patterns using a broad range of positioning technologies such as Global System Mobile, Global Positioning System, traffic sensors and smart phone data. In this research, we propose to use UB cards as a convenient source of data in order to define a UB campus-wide model for students’ activity-mobility patterns generation in time-space dimension A UB Card is a student’s official ID at the University at Buffalo and is used across campus for various reason including Stampedes (on-campus bus system), facilities access, dining and shopping. Therefore, it could be a reliable source of data to identify time, location and activity types of individual students. The research project has two different stages. In the first stage, we develop algorithms to construct students’ continuous paths in space-time dimension using a set of UB card transaction data points as input. The base algorithm will construct of activity-mobility patterns with no prior knowledge. The modified algorithm will construct activity-mobility patterns with prior knowledge of students’ prior pattern as they have similar patterns for certain days of the week. In the second stage, a survey will be conducted to provide detailed information of students’ daily activity participation and travel decisions. Based on the survey data, the algorithm results will be compared to analyze the performance of the algorithms

ProDis-ContSHC: learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrieval

<p>Abstract</p> <p>Background</p> <p>The need to retrieve or classify protein molecules using structure or sequence-based similarity measures underlies a wide range of biomedical applications. Traditional protein search methods rely on a pairwise dissimilarity/similarity measure for comparing a pair of proteins. This kind of pairwise measures suffer from the limitation of neglecting the distribution of other proteins and thus cannot satisfy the need for high accuracy of the retrieval systems. Recent work in the machine learning community has shown that exploiting the global structure of the database and learning the contextual dissimilarity/similarity measures can improve the retrieval performance significantly. However, most existing contextual dissimilarity/similarity learning algorithms work in an unsupervised manner, which does not utilize the information of the known class labels of proteins in the database.</p> <p>Results</p> <p>In this paper, we propose a novel protein-protein dissimilarity learning algorithm, ProDis-ContSHC. ProDis-ContSHC regularizes an existing dissimilarity measure <it>d_ij</it>by considering the contextual information of the proteins. The context of a protein is defined by its neighboring proteins. The basic idea is, for a pair of proteins (<it>i</it>, <it>j</it>), if their context <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2105-13-S7-S2-i1"><m:mi mathvariant="script">N</m:mi><m:mrow><m:mo class="MathClass-open">(</m:mo><m:mrow><m:mi>i</m:mi></m:mrow><m:mo class="MathClass-close">)</m:mo></m:mrow></m:math></inline-formula> and <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2105-13-S7-S2-i2"><m:mi mathvariant="script">N</m:mi><m:mrow><m:mo class="MathClass-open">(</m:mo><m:mrow><m:mi>j</m:mi></m:mrow><m:mo class="MathClass-close">)</m:mo></m:mrow></m:math></inline-formula> is similar to each other, the two proteins should also have a high similarity. We implement this idea by regularizing <it>d_ij</it>by a factor learned from the context <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2105-13-S7-S2-i3"><m:mi mathvariant="script">N</m:mi><m:mrow><m:mo class="MathClass-open">(</m:mo><m:mrow><m:mi>i</m:mi></m:mrow><m:mo class="MathClass-close">)</m:mo></m:mrow></m:math></inline-formula> and <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2105-13-S7-S2-i4"><m:mi mathvariant="script">N</m:mi><m:mrow><m:mo class="MathClass-open">(</m:mo><m:mrow><m:mi>j</m:mi></m:mrow><m:mo class="MathClass-close">)</m:mo></m:mrow></m:math></inline-formula>.</p> <p>Moreover, we divide the context to hierarchial sub-context and get the contextual dissimilarity vector for each protein pair. Using the class label information of the proteins, we select the relevant (a pair of proteins that has the same class labels) and irrelevant (with different labels) protein pairs, and train an SVM model to distinguish between their contextual dissimilarity vectors. The SVM model is further used to learn a supervised regularizing factor. Finally, with the new <b>S</b>upervised learned <b>Dis</b>similarity measure, we update the <b>Pro</b>tein <b>H</b>ierarchial <b>Cont</b>ext <b>C</b>oherently in an iterative algorithm--<b>ProDis-ContSHC</b>.</p> <p>We test the performance of ProDis-ContSHC on two benchmark sets, i.e., the ASTRAL 1.73 database and the FSSP/DALI database. Experimental results demonstrate that plugging our supervised contextual dissimilarity measures into the retrieval systems significantly outperforms the context-free dissimilarity/similarity measures and other unsupervised contextual dissimilarity measures that do not use the class label information.</p> <p>Conclusions</p> <p>Using the contextual proteins with their class labels in the database, we can improve the accuracy of the pairwise dissimilarity/similarity measures dramatically for the protein retrieval tasks. In this work, for the first time, we propose the idea of supervised contextual dissimilarity learning, resulting in the ProDis-ContSHC algorithm. Among different contextual dissimilarity learning approaches that can be used to compare a pair of proteins, ProDis-ContSHC provides the highest accuracy. Finally, ProDis-ContSHC compares favorably with other methods reported in the recent literature.</p