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1
{
2
  "id": "id-1",
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  "title": "Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks",
4
  "abstract": "The aim of this paper is to present video quality prediction models for objective non-intrusive, prediction of H.264 encoded video for all content types combining parameters both in the physical and application layer over Universal Mobile Telecommunication Systems (UMTS) networks. In order to characterize the Quality of Service (QoS) level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) and a second model based on non-linear regression analysis is proposed to predict the video quality in terms of the Mean Opinion Score (MOS). The objective of the paper is two-fold. First, to find the impact of QoS parameters on end-to-end video quality for H.264 encoded video. Second, to develop learning models based on ANFIS and nonlinear regression analysis to predict video quality over UMTS networks by considering the impact of radio link loss models. The loss models considered are 2-state Markov models. Both the models are trained with a combination of physical and application layer parameters and validated with unseen dataset. Preliminary results show that good prediction accuracy was obtained from both the models. The work should help in the development of a reference-free video prediction model and QoS control methods for video over UMTS networks.",
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  "language": null,
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  "keywords": null,
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  "externalIdentifiers": {
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    "hindawi-id": "608138",
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    "doi": "10.1155/2010/608138"
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  },
11
  "journal": "International Journal of Digital Multimedia Broadcasting",
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  "year": 2010,
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  "publisher": "Hindawi Publishing Corporation",
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      "organization": "Centre for Signal Processing and Multimedia Communication, School of Computing, University of Plymouth",
771
      "country": " UK",
772
      "rawText": "Centre for Signal Processing and Multimedia Communication, School of Computing, Communications and Electronics, University of Plymouth, Plymouth PL4 8AA, UK"
773
    },
774
    {
775
      "organization": "Department of Electronics and Telecommunications, University of the Basque Country (UPV/EHU)",
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781
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789
{
790
  "id": "id-2",
791
  "title": "Stability Criteria of Fullerene-like Nanoparticles: Comparing V2O5 to Layered Metal Dichalcogenides and Dihalides",
792
  "abstract": "Numerous examples of closed-cage nanostructures, such as nested fullerene-like nanoparticles and nanotubes, formed by the folding of materials with layered structure are known. These compounds include WS2, NiCl2, CdCl2, Cs2O, and recently V2O5. Layered materials, whose chemical bonds are highly ionic in character, possess relatively stiff layers, which cannot be evenly folded. Thus, stress-relief generally results in faceted nanostructures seamed by edge-defects. V2O5, is a metal oxide compound with a layered structure. The study of the seams in nearly perfect inorganic \"fullerene-like\" hollow V2O5 nanoparticles (NIFV2O5) synthesized by pulsed laser ablation (PLA), is discussed in the present work. The relation between the formation mechanism and the seams between facets is examined. The formation mechanism of the NIF-V2O5 is discussed in comparison to fullerene-like structures of other layered materials, like IF structures of MoS2, CdCl2, and Cs2O. The criteria for the perfect seaming of such hollow closed structures are highlighted.",
793
  "language": null,
794
  "keywords": null,
795
  "externalIdentifiers": {
796
    "doi": "10.3390/ma3084428"
797
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2236
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2248
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2260
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2261
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2262
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2263
        "title": "Optical-Properties of Vanadium Pentoxide Determined from Ellipsometry and Band-Structure Calculations",
2264
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2265
          "Parker",
2266
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2268
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2269
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2282
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      "position": 56,
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2286
    }
2287
  ],
2288
  "authors": [
2289
    {
2290
      "authorFullName": "Roi Levi",
2291
      "affiliationPositions": [
2292
        1
2293
      ]
2294
    },
2295
    {
2296
      "authorFullName": "Maya Bar-Sadan",
2297
      "affiliationPositions": [
2298
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2299
        1
2300
      ]
2301
    },
2302
    {
2303
      "authorFullName": "Ana Albu-Yaron",
2304
      "affiliationPositions": [
2305
        1
2306
      ]
2307
    },
2308
    {
2309
      "authorFullName": "Ronit Popovitz-Biro",
2310
      "affiliationPositions": [
2311
        3,
2312
        1
2313
      ]
2314
    },
2315
    {
2316
      "authorFullName": "Lothar Houben",
2317
      "affiliationPositions": [
2318
        2,
2319
        1
2320
      ]
2321
    },
2322
    {
2323
      "authorFullName": "Yehiam Prior",
2324
      "affiliationPositions": [
2325
        1,
2326
        0
2327
      ]
2328
    },
2329
    {
2330
      "authorFullName": "Reshef Tenne",
2331
      "affiliationPositions": [
2332
        1
2333
      ]
2334
    }
2335
  ],
2336
  "affiliations": [
2337
    {
2338
      "organization": "Chemical Physics Department, Weizmann Institute of Science",
2339
      "country": " Israel",
2340
      "rawText": "Chemical Physics Department, Weizmann Institute of Science, Israel"
2341
    },
2342
    {
2343
      "organization": "Materials and Interfaces Department, Weizmann Institute of Science",
2344
      "country": " Israel",
2345
      "rawText": "Materials and Interfaces Department, Weizmann Institute of Science, Rehovot, Israel"
2346
    },
2347
    {
2348
      "organization": "Institute of Solid State Research and Ernst-Ruska Centre for Microscopy and Spectroscopy with Electrons, Research Centre Jülich GmbH",
2349
      "country": " Germany",
2350
      "rawText": "Institute of Solid State Research and Ernst-Ruska Centre for Microscopy and Spectroscopy with Electrons, Research Centre Jülich GmbH, Germany"
2351
    },
2352
    {
2353
      "organization": "Electron Microscopy Unit, Weizmann Institute of Science",
2354
      "country": " Israel",
2355
      "rawText": "Electron Microscopy Unit, Weizmann Institute of Science, Israel"
2356
    }
2357
  ],
2358
  "volume": "3",
2359
  "issue": null,
2360
  "pages": {
2361
    "start": "4428",
2362
    "end": "4445"
2363
  }
2364
}
2365
{
2366
  "id": "id-3",
2367
  "title": "Optimization with Sparsity-Inducing Penalties",
2368
  "abstract": "Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. They were first dedicated to linear variable selection but numerous extensions have now emerged such as structured sparsity or kernel selection. It turns out that many of the related estimation problems can be cast as convex optimization problems by regularizing the empirical risk with appropriate nonsmooth norms. The goal of this paper is to present from a general perspective optimization tools and techniques dedicated to such sparsityinducing penalties. We cover proximal methods, block-coordinate descent, reweighted ℓ 2-penalized techniques, working-set and homotopy methods, as well as non-convex formulations and extensions, and provide an extensive set of experiments to compare various algorithms from a computational point of view.",
2369
  "language": null,
2370
  "keywords": null,
2371
  "externalIdentifiers": null,
2372
  "journal": null,
2373
  "year": null,
2374
  "publisher": null,
2375
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2480
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2482
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        ],
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      "position": 5,
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      "text": "[5] C. Archambeau and F. Bach. Sparse probabilistic projections. In Advances in Neural Information Processing Systems (NIPS), 2008."
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    },
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    {
2500
      "basicMetadata": {
2501
        "title": "Convex multi-task feature learning",
2502
        "authors": [
2503
          "Argyriou, A.",
2504
          "Evgeniou, T.",
2505
          "Pontil, M."
2506
        ],
2507
        "pages": {
2508
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2509
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2510
        },
2511
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2512
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2513
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2514
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2515
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2516
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2517
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      "text": "[6] A. Argyriou, T. Evgeniou, and M. Pontil. Convex multi-task feature learning. Machine Learning, 73(3):243–272, 2008."
2523
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2524
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2525
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2526
        "title": "Consistency of the group lasso and multiple kernel learning",
2527
        "authors": [
2528
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2529
        ],
2530
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2531
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2532
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2533
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2534
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2535
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2537
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2539
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2540
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2541
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2542
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2543
      },
2544
      "position": 7,
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      "text": "[7] F. Bach. Consistency of the group lasso and multiple kernel learning. Journal of Machine Learning Research, 9:1179–1225, 2008."
2546
    },
2547
    {
2548
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2549
        "title": "Consistency of trace norm minimization",
2550
        "authors": [
2551
          "Bach, F."
2552
        ],
2553
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2567
      "position": 8,
2568
      "text": "[8] F. Bach. Consistency of trace norm minimization. Journal of Machine Learn- ing Research, 9:1019–1048, 2008."
2569
    },
2570
    {
2571
      "basicMetadata": {
2572
        "title": "Exploring large feature spaces with hierarchical multiple kernel learning",
2573
        "authors": [
2574
          "Bach, F."
2575
        ],
2576
        "pages": null,
2577
        "source": "In Advances in Neural Information Processing Systems (NIPS)",
2578
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2580
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2583
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2587
      "position": 9,
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      "text": "[9] F. Bach. Exploring large feature spaces with hierarchical multiple kernel learning. In Advances in Neural Information Processing Systems (NIPS), 2008."
2589
    },
2590
    {
2591
      "basicMetadata": {
2592
        "title": "Structured sparsity-inducing norms through submodular functions",
2593
        "authors": [
2594
          "Bach, F."
2595
        ],
2596
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2597
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2607
      "position": 10,
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2609
    },
2610
    {
2611
      "basicMetadata": {
2612
        "title": "Shaping level sets with submodular functions",
2613
        "authors": [
2614
          "Bach, F."
2615
        ],
2616
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2617
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2623
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2625
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2627
      "position": 11,
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      "text": "[11] F. Bach. Shaping level sets with submodular functions. In Advances in Neural Information Processing Systems (NIPS), 2011."
2629
    },
2630
    {
2631
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2632
        "title": "Convex optimization with sparsity-inducing norms",
2633
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2634
          "Bach, F.",
2635
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2636
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2637
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2638
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2639
        ],
2640
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2641
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2642
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2647
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2650
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2651
      "position": 12,
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      "text": "[12] F. Bach, R. Jenatton, J. Mairal, and G. Obozinski. Convex optimization with sparsity-inducing norms. In S. Sra, S. Nowozin, and S.J. Wright, editors, Optimization for Machine Learning. MIT Press, 2011."
2653
    },
2654
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2655
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2656
        "title": "Multiple kernel learning, conic duality, and the SMO algorithm",
2657
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2658
          "Bach, F.",
2659
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        ],
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      "position": 13,
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      "text": "[13] F. Bach, G. R. G. Lanckriet, and M. I. Jordan. Multiple kernel learning, conic duality, and the SMO algorithm. In Proceedings of the International Conference on Machine Learning (ICML), 2004."
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2678
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        "authors": [
2680
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2681
          "Ponce, J."
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        ],
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2773
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