Abstract
This paper presents an empirical assessment of the General Lot-Sizing and Scheduling Problem (GLSP) under demand uncertainty by means of a budget-uncertainty set robust optimization and a two-stage stochastic programming with recourse model. We have also developed a systematic procedure based on Monte Carlo simulation to compare both models in terms of protection against uncertainty and computational tractability. The extensive computational experiments cover different instances characteristics, a considerable number of combinations between budgets of uncertainty and variability levels for the robust optimization model, as well as an increasing number of scenarios and probability distribution functions for the stochastic programming model. Furthermore, we have devised some guidelines for decision-makers to evaluate a priori the most suitable uncertainty modeling approach according to their preferences.

Abstract
Machine learning algorithms have evolved by exchanging simplicity and interpretability for accuracy, which prevents their adoption in critical tasks such as healthcare. Progress can be made by improving interpretability of complex models while preserving performance. This work introduces an extension of interpretable mimic learning which teaches in-terpretable models to mimic predictions of complex deep neural networks, not only on binary problems but also in ordinal settings. The results show that the mimic models have comparative performance to Deep Neural Network models, with the advantage of being interpretable.

Abstract
In this paper, we present YAKE!, a novel feature-based system for multi-lingual keyword extraction from single documents, which supports texts of different sizes, domains or languages. Unlike most systems, YAKE! does not rely on dictionaries or thesauri, neither it is trained against any corpora. Instead, we follow an unsupervised approach which builds upon features extracted from the text, making it thus applicable to documents written in many different languages without the need for external knowledge. This can be beneficial for a large number of tasks and a plethora of situations where the access to training corpora is either limited or restricted. In this demo, we offer an easy to use, interactive session, where users from both academia and industry can try our system, either by using a sample document or by introducing their own text. As an add-on, we compare our extracted keywords against the output produced by the IBM Natural Language Understanding (IBM NLU) and Rake system. YAKE! demo is available at http://bit.ly/YakeDemoECIR2018. A python implementation of YAKE! is also available at PyPi repository (https://pypi.python.org/pypi/yake/).

Abstract
It is becoming more and more important to study methods for protecting sensitive data in computer and communication systems from unauthorized access, use, modification, destruction or deletion. Sensitive data include intellectual properties, payment information, personal files, personal credit card and other information depending on the business and the industry. Therefore, data leakage is considered an emerging security threat to organizations and companies. In this paper we present a static analysis method for information flow analysis in Java bytecode with exceptions. Exceptions are special events that break the normal execution flow. They can be used as a device to leak high security data since exception throwing can be accurately driven. The proposed analysis is capable of tracing information flow caused by exceptions by identifying instruction handler protected instructions as virtual control instructions. A malicious Java applet that clones the user secret PIN through exceptions is shown.

Abstract
We develop a technique for proving lower bounds in the setting of asymmetric communication, a model that was introduced in the famous works of Miltersen (STOC'94) and Miltersen, Nisan, Safra and Wigderson (STOC'95). At the core of our technique is a novel simulation theorem: Alice gets a p x n matrix x over F-2 and Bob gets a vector y is an element of F-2(n). Alice and Bob need to evaluate f (x center dot y) for a Boolean function f : {0, 1}(p) -> {0, 1}. Our simulation theorems show that a deterministic/randomized communication protocol exists for this problem, with cost C center dot n for Alice and C for Bob, if and only if there exists a deterministic/randomized parity decision tree of cost Theta(C) for evaluating f. As applications of this technique, we obtain the following results: (i) The first strong lower-bounds against randomized data-structure schemes for the Vector-Matrix-Vector product problem over F-2. Moreover, our method yields strong lower bounds even when the data-structure scheme has tiny advantage over random guessing. (ii) The first lower bounds against randomized data-structures schemes for two natural Boolean variants of Orthogonal Vector Counting. (iii) We construct an asymmetric communication problem and obtain a deterministic lower-bound for it which is provably better than any lower-bound that may be obtained by the classical Richness Method of Miltersen et al.. This seems to be the first known limitation of the Richness Method in the context of proving deterministic lower bounds.

Abstract
Transcriptional dysregulation induced by aberrant transcription factors (TF) is a key feature of cancer, but its global influence on drug sensitivity has not been examined. Here, we infer the transcriptional activity of 127 TFs through analysis of RNA-seq gene expression data newly generated for 448 cancer cell lines, combined with publicly available datasets to survey a total of 1,056 cancer cell lines and 9,250 primary tumors. Predicted TF activities are supported by their agreement with independent shRNA essentiality profiles and homozygous gene deletions, and recapitulate mutant-specific mechanisms of transcriptional dysregulation in cancer. By analyzing cell line responses to 265 compounds, we uncovered numerous TFs whose activity interacts with anticancer drugs. Importantly, combining existing pharmacogenomic markers with TF activities often improves the stratification of cell lines in response to drug treatment. Our results, which can be queried freely at dorothea.opentargets.io, offer a broad foundation for discovering opportunities to refine personalized cancer therapies. Significance: Systematic analysis of transcriptional dysregulation in cancer cell lines and patient tumor specimens offers a publicly searchable foundation to discover new opportunities to refine personalized cancer therapies. © 2017 American Association for Cancer Research.