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In this paper, we consider the question of determining whether a function f has property P or is e-far from any function with property P. A property testing algorithm is given a sample of the value of f on instances drawn according to some distribution. In some cases, it is also allowed to query f on instances of its choice. We study this question for different properties and establish some connections to problems in learning theory and approximation.In particular, we focus our attention on testing graph properties. Given access to a graph G in the form of being able to query whether an edge exists or not between a pair of vertices, we devise algorithms to test whether the underlying graph has properties such as being bipartite, k-Colorable, or having a p-Clique (clique of density p with respect to the vertex set). Our graph property testing algorithms are probabilistic and make assertions that are correct with high probability, while making a number of queries that is independent of the size of the graph. Moreover, the property testing algorithms can be used to efficiently (i.e., in time linear in the number of vertices) construct partitions of the graph that correspond to the property being tested, if it holds for the input graph.

Property Testing and its connection to Learning and Approximation

Automatic skin lesion segmentation in dermoscopic images is a challenging task due to the low contrast between lesion and the surrounding skin, the irregular and fuzzy lesion borders, the existence of various artifacts, and various imaging acquisition conditions. In this paper, we present a fully automatic method for skin lesion segmentation by leveraging 19-layer deep convolutional neural networks that is trained end-to-end and does not rely on prior knowledge of the data. We propose a set of strategies to ensure effective and efficient learning with limited training data. Furthermore, we design a novel loss function based on Jaccard distance to eliminate the need of sample re-weighting, a typical procedure when using cross entropy as the loss function for image segmentation due to the strong imbalance between the number of foreground and background pixels. We evaluated the effectiveness, efficiency, as well as the generalization capability of the proposed framework on two publicly available databases. One is from ISBI 2016 skin lesion analysis towards melanoma detection challenge, and the other is the PH2 database. Experimental results showed that the proposed method outperformed other state-of-the-art algorithms on these two databases. Our method is general enough and only needs minimum pre- and post-processing, which allows its adoption in a variety of medical image segmentation tasks.

Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance

Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network.

Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks.

Oblivious RAM (ORAM), first introduced in the ground-breaking work of Goldreich and Ostrovsky (STOC ’87 and J. ACM ’96) is a technique for provably obfuscating programs’ access patterns, such that the access patterns leak no information about the programs’ secret inputs. To compile a general program to an oblivious counterpart, it is well-known that \(\varOmega (\log N)\) amortized blowup is necessary, where N is the size of the logical memory. This was shown in Goldreich and Ostrovksy’s original ORAM work for statistical security and in a somewhat restricted model (the so called balls-and-bins model), and recently by Larsen and Nielsen (CRYPTO ’18) for computational security.

OptORAMa: Optimal Oblivious RAM.

We present a new technique for melanocytic lesion segmentation, Mimicking Expert Dermatologists' Segmentations (MEDS), and extensive tests of its accuracy, speed, and robustness. MEDS combines a thresholding scheme reproducing the cognitive process of dermatologists with a number of optimizations that may be of independent interest. MEDS is simple, with a single parameter tuning its “tightness”. It is extremely fast, segmenting medium-resolution images in a fraction of a second even with the modest computational resources of a cell phone-an improvement of an order of magnitude or more over state-of-the-art techniques. And it is extremely accurate: very experienced dermatologists disagree with its segmentations less than they disagree with the segmentations of state-of-the-art techniques, and in fact less than they disagree with the segmentations of dermatologists of moderate experience.

Simpler, Faster, More Accurate Melanocytic Lesion Segmentation Through MEDS

This paper investigates some computational problems associated with probabilistic translation models that have recently been adopted in the literature on machine translation. These models can be viewed as pairs of probabilistic context-free grammars working in a 'synchronous' way. Two hardness results for the class NP are reported, along with an exponential time lower-bound for certain classes of algorithms that are currently used in the literature.

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Some Computational Complexity Results for Synchronous Context-Free Grammars

Choose the damping, choose the ranking?

This work studies delay and throughput achievable in delay-tolerant networks with ballistic mobility -- informally, when the average distance a node travels before changing direction does not become vanishingly small as the number of nodes in the deployment area grows. Ballistic mobility is a simple condition satisfied by a large number of well-studied mobility models, including the i.i.d. model, the random waypoint model, the uniform mobility model and Levy walks with exponent less than 1. Our contribution is twofold. First, we show that, under some very mild and natural hypotheses satisfied by all models in the literature, ballistic mobility is strictly necessary to achieve simultaneously, as the number of nodes grows, a) per-node throughput that does not become vanishingly small and b) communication delay that does not become infinitely large. Any network whose nodes exhibit a more "local" mobility pattern (e.g. Levy walks with exponent greater than 1, or Brownian motion) must sacrifice either a) or b), regardless of the communication scheme adopted -- even with network coding.Second, we present a novel packet routing scheme. Our scheme is relatively simple and does not rely on centralized control, replication, or static base stations. At the same time it achieves both non-vanishing throughput and bounded delay as the number of nodes grows, on any network with ballistic mobility (i.e. whenever they can be simultaneously achieved), asymptotically outperforming any existing communication scheme that exploits node mobility to boost throughput.

Enoch Peserico

Papers

OptORAMa: Optimal Oblivious RAM.

Simpler, Faster, More Accurate Melanocytic Lesion Segmentation Through MEDS

Some Computational Complexity Results for Synchronous Context-Free Grammars

Choose the damping, choose the ranking?

Optimal throughput and delay in delay-tolerant networks with ballistic mobility