Cluster Editing: Kernelization based on Edge Cuts

Kernelization algorithms for the {\sc cluster editing} problem have been a popular topic in the recent research in parameterized computation. Thus far most kernelization algorithms for this problem are based on the concept of {\it critical cliques}. In this paper, we present new observations and new techniques for the study of kernelization algorithms for the {\sc cluster editing} problem. Our techniques are based on the study of the relationship between {\sc cluster editing} and graph edge-cuts. As an application, we present an ${\cal O}(n^2)$-time algorithm that constructs a $2k$ kernel for the {\it weighted} version of the {\sc cluster editing} problem. Our result meets the best kernel size for the unweighted version for the {\sc cluster editing} problem, and significantly improves the previous best kernel of quadratic size for the weighted version of the problem

SlimPLS: A Method for Feature Selection in Gene Expression-Based Disease Classification

A major challenge in biomedical studies in recent years has been the classification of gene expression profiles into categories, such as cases and controls. This is done by first training a classifier by using a labeled training set containing labeled samples from the two populations, and then using that classifier to predict the labels of new samples. Such predictions have recently been shown to improve the diagnosis and treatment selection practices for several diseases. This procedure is complicated, however, by the high dimensionality if the data. While microarrays can measure the levels of thousands of genes per sample, case-control microarray studies usually involve no more than several dozen samples. Standard classifiers do not work well in these situations where the number of features (gene expression levels measured in these microarrays) far exceeds the number of samples. Selecting only the features that are most relevant for discriminating between the two categories can help construct better classifiers, in terms of both accuracy and efficiency. In this work we developed a novel method for multivariate feature selection based on the Partial Least Squares algorithm. We compared the method's variants with common feature selection techniques across a large number of real case-control datasets, using several classifiers. We demonstrate the advantages of the method and the preferable combinations of classifier and feature selection technique

Near-Optimal Secret Sharing and Error Correcting Codes in AC0

We study the question of minimizing the computational complexity of (robust) secret sharing schemes and error correcting codes. In standard instances of these objects, both encoding and decoding involve linear algebra, and thus cannot be implemented in the class AC0. The feasibility of non-trivial secret sharing schemes in AC0 was recently shown by Bogdanov et al. (Crypto 2016) and that of (locally) decoding errors in AC0 by Goldwasser et al. (STOC 2007). In this paper, we show that by allowing some slight relaxation such as a small error probability, we can construct much better secret sharing schemes and error correcting codes in the class AC0. In some cases, our parameters are close to optimal and would be impossible to achieve without the relaxation. Our results significantly improve previous constructions in various parameters. Our constructions combine several ingredients in pseudorandomness and combinatorics in an innovative way. Specifically, we develop a general technique to simultaneously amplify security threshold and reduce alphabet size, using a two-level concatenation of protocols together with a random permutation. We demonstrate the broader usefulness of this technique by applying it in the context of a variant of secure broadcast

Revisiting Non-Malleable Secret Sharing

A threshold secret sharing scheme (with threshold $t$) allows a dealer to share a secret among a set of parties such that any group of $t$ or more parties can recover the secret and no group of at most $t-1$ parties learn any information about the secret. A non-malleable threshold secret sharing scheme, introduced in the recent work of Goyal and Kumar (STOC\u2718), additionally protects a threshold secret sharing scheme when its shares are subject to tampering attacks. Specifically, it guarantees that the reconstructed secret from the tampered shares is either the original secret or something that is unrelated to the original secret. In this work, we continue the study of threshold non-malleable secret sharing against the class of tampering functions that tamper each share independently. We focus on achieving greater efficiency and guaranteeing a stronger security property. We obtain the following results: - Rate Improvement. We give the first construction of a threshold non-malleable secret sharing scheme that has rate $> 0$. Specifically, for every $n,t \geq 4$, we give a construction of a $t$-out-of-$n$ non-malleable secret sharing scheme with rate $\Theta(\frac{1}{t\log ^2 n})$. In the prior constructions, the rate was $\Theta(\frac{1}{n\log m})$ where $m$ is the length of the secret and thus, the rate tends to 0 as $m \rightarrow \infty$. Furthermore, we also optimize the parameters of our construction and give a concretely efficient scheme. - Multiple Tampering. We give the first construction of a threshold non-malleable secret sharing scheme secure in the stronger setting of bounded tampering wherein the shares are tampered by multiple (but bounded in number) possibly different tampering functions. The rate of such a scheme is $\Theta(\frac{1}{k^3t\log^2 n})$ where $k$ is an apriori bound on the number of tamperings. We complement this positive result by proving that it is impossible to have a threshold non-malleable secret sharing scheme that is secure in the presence of an apriori unbounded number of tamperings. - General Access Structures. We extend our results beyond threshold secret sharing and give constructions of rate-efficient, non-malleable secret sharing schemes for more general monotone access structures that are secure against multiple (bounded) tampering attacks

The Use of an Orographic Precipitation Model to Assess the Precipitation Spatial Distribution in Lake Kinneret Watershed

A high-resolution 3-D orographic precipitation model (OPM) forced by Climate Forecast System (CFS) reanalysis fields was developed for the Lake Kinneret watershed (Israel-Syria-Lebanon territories). The OPM was tuned to represent the interaction between the advected and stratiform rainfall, and the local orographic enhancement. The OPM evaluation was focused on the densely instrumented lower part of the watershed. To evaluate the ungauged upper-elevation, bias-adjusted precipitation estimates from the Global-Hydro-Estimator were used. The OPM simulates higher rainfall amounts in the upper-elevation watershed compared to currently used rainfall estimates from an elevation dependent regression. The larger differences are during rain events with southwesterly wind flow and high moisture flux. These conditions, according to the OPM, are conducive to enhanced orographic lifting in the Hermon Mountain. A sensitivity analysis indicated that the higher wind speeds for southwesterly–northwesterly trajectories generate significant orographic lifting and increase the precipitation differences between the lower and upper elevations