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

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

Sevoflurane induces lung cancer cell apoptosis via inhibition of the expression of miRNA155 gene

Purpose: To determine the apoptotic effect of sevoflurane on lung cancer cells, and the underlying mechanism of action.Methods: Lung adenocarcinoma A549 cells were cultured for 24 h and divided into control group, 1% sevoflurane group and 3% sevoflurane group. The two levels of sevoflurane were provided through a gas monitor connected to each of the sevoflurane groups. The control group was not treated. Flow cytometry was used to analyze A549 cell apoptosis, while qRT-PCR was used for assay of the levels of miRNA155 in A549 cells. The protein expression of Bcl-2 was determined with immunoblotting. The percentage of apoptosis and levels of miRNA155 and Bcl-2 in the two cell lines were compared.Results: Significant differences in miRNA146a level were seen between the 3 % sevoflurane and control groups at 3 h. There was higher apoptosis in the 3 % sevoflurane group, relative to control, but miRNA155 levels in the 3 % sevoflurane group were generally less than that of the control (p &lt; 0.05). There was lower Bcl-2 content in the 3 % sevoflurane group than in control group (p &lt; 0.05).Conclusion: Sevoflurane exerts strong apoptotic and anti-proliferative effects on lung adenocarcinoma A549 cells via a mechanism which may be related to the downregulation of miRNA155, thereby inhibiting the expression of anti-apoptotic protein Bcl-2. This provides a new direction for research on anti-lung adenocarcinoma drugs. Keywords: Sevoflurane, Lung cancer cells, Apoptosis, Inhibition, miRNA155, Expression, Inductio

Multiphoton detachment of H-. II. Intensity-dependent photodetachment rates and threshold behavior—complex-scaling generalized pseudospectral method

This is the published version, also available here: http://dx.doi.org/10.1103/PhysRevA.50.3208.We extend our previous perturbative study of the multiphoton detachment of H- [Phys. Rev. A 48, 4654 (1993)] to stronger fields by considering the intensity-dependent photodetachment rates and threshold behavior. An accurate one-electron model potential, which reproduces exactly the known H- binding energy and the low-energy e-H(1s) elastic-scattering phase shifts, is employed. A computational technique, the complex-scaling generalized pseudospectral method, is developed for accurate and efficient treatment of the time-independent non-Hermitian Floquet Hamiltonian H^F. The method is simple to implement, does not require the computation of potential matrix elements, and is computationally more efficient than the traditional basis-set-expansion–variational method. We present detailed nonperturbative results of the intensity- and frequency-dependent complex quasienergies (ER,-Γ/2), the complex eigenvalues of H^F, providing directly the ac Stark shifts and multiphoton detachment rates of H-. The laser intensity considered ranges from 1 to 40 GW/cm2 and the laser frequency covers 0.20–0.42 eV (in the c.m. frame). Finally we perform a simulation of intensity-averaged multiphoton detachment rates by considering the experimental conditions of the laser and H- beams. The results (without any free parameters) are in good agreement with experimental data, both in absolute magnitude and in the threshold behavior

Two-color phase control of high-order harmonic generation in intense laser fields

This is the published version, also available here: http://dx.doi.org/10.1103/PhysRevA.52.3988.We present a time-independent generalized Floquet approach for nonperturbative treatment of high-order harmonic generation (HG) in intense onea (i) determination of the complex quasienergy eigenvalue and eigenfunction by means of the non-Hermitian Floquet formalism, wherein the Floquet Hamiltonian is discretized by the complex-scaling generalized pseudospectral technique [Wang, Chu, and Laughlin, Phys. Rev. A 50, 3208 (1994)], and (ii) calculation of the HG rates based on the approach that implies the classical treatment of the electromagnetic field and quantal treatment of the atom. The method is applied to the nonperturbative study of HG by the hydrogen atom in strong laser fields with the fundamental frequencies 532 and 775 nm and their third harmonics. The results show a strong dependence on the relative phase δ between the fundamental frequency field and its harmonic. For the intensities used in calculations (1×1013 and 5×1013 W/cm2 for the fundamental frequency 532 nm and 1×1013 and 3×1013 W/cm2 for the fundamental frequency 775 nm, the harmonic intensity being 10 and 100 times weaker), the total photon emission rate has its maximum at δ=0 and minimum at δ=π. However, this tendency, while valid for the first several HG peaks, is reversed for the higher HG peaks. The HG spectrum for δ=π is broader and the peak heights decrease more slowly compared to the case of δ=0. These results have their analog in the multiphoton above-threshold detachment study performed recently for H- ions [Telnov, Wang, and Chu, Phys. Rev. A 51, 4797 (1995)]