@article{1010, abstract = {We prove a local law in the bulk of the spectrum for random Gram matrices XX∗, a generalization of sample covariance matrices, where X is a large matrix with independent, centered entries with arbitrary variances. The limiting eigenvalue density that generalizes the Marchenko-Pastur law is determined by solving a system of nonlinear equations. Our entrywise and averaged local laws are on the optimal scale with the optimal error bounds. They hold both in the square case (hard edge) and in the properly rectangular case (soft edge). In the latter case we also establish a macroscopic gap away from zero in the spectrum of XX∗. }, author = {Alt, Johannes and Erdös, László and Krüger, Torben H}, issn = {10836489}, journal = {Electronic Journal of Probability}, publisher = {Institute of Mathematical Statistics}, title = {{Local law for random Gram matrices}}, doi = {10.1214/17-EJP42}, volume = {22}, year = {2017}, } @inproceedings{1009, abstract = {A standard objective in partially-observable Markov decision processes (POMDPs) is to find a policy that maximizes the expected discounted-sum payoff. However, such policies may still permit unlikely but highly undesirable outcomes, which is problematic especially in safety-critical applications. Recently, there has been a surge of interest in POMDPs where the goal is to maximize the probability to ensure that the payoff is at least a given threshold, but these approaches do not consider any optimization beyond satisfying this threshold constraint. In this work we go beyond both the “expectation” and “threshold” approaches and consider a “guaranteed payoff optimization (GPO)” problem for POMDPs, where we are given a threshold t and the objective is to find a policy σ such that a) each possible outcome of σ yields a discounted-sum payoff of at least t, and b) the expected discounted-sum payoff of σ is optimal (or near-optimal) among all policies satisfying a). We present a practical approach to tackle the GPO problem and evaluate it on standard POMDP benchmarks.}, author = {Chatterjee, Krishnendu and Novotny, Petr and Pérez, Guillermo and Raskin, Jean and Zikelic, Djordje}, booktitle = {Proceedings of the 31st AAAI Conference on Artificial Intelligence}, location = {San Francisco, CA, United States}, pages = {3725 -- 3732}, publisher = {AAAI Press}, title = {{Optimizing expectation with guarantees in POMDPs}}, volume = {5}, year = {2017}, } @misc{9859, abstract = {Lists of all differentially expressed genes in the different priming-challenge treatments (compared to the fully naïve control; xlsx file). Relevant columns include the following: sample_1 and sample_2 – treatment groups being compared; Normalised FPKM sample_1 and sample_2 – FPKM of samples being compared; log2(fold_change) – log2(FPKM sample 2/FPKM sample 1), i.e. negative means sample 1 upregulated compared with sample 2, positive means sample 2 upregulated compared with sample 1; cuffdiff test_statistic – test statistic of differential expression test; p_value – p-value of differential expression test; q_value (FDR correction) – adjusted P-value of differential expression test. (XLSX 598 kb)}, author = {Greenwood, Jenny and Milutinovic, Barbara and Peuß, Robert and Behrens, Sarah and Essar, Daniela and Rosenstiel, Philip and Schulenburg, Hinrich and Kurtz, Joachim}, publisher = {Springer Nature}, title = {{Additional file 1: Table S1. of Oral immune priming with Bacillus thuringiensis induces a shift in the gene expression of Tribolium castaneum larvae}}, doi = {10.6084/m9.figshare.c.3756974_d1.v1}, year = {2017}, } @misc{9860, author = {Greenwood, Jenny and Milutinovic, Barbara and Peuß, Robert and Behrens, Sarah and Essar, Daniela and Rosenstiel, Philip and Schulenburg, Hinrich and Kurtz, Joachim}, publisher = {Springer Nature}, title = {{Additional file 5: Table S3. of Oral immune priming with Bacillus thuringiensis induces a shift in the gene expression of Tribolium castaneum larvae}}, doi = {10.6084/m9.figshare.c.3756974_d5.v1}, year = {2017}, } @inproceedings{1002, abstract = { We present an interactive design system to create functional mechanical objects. Our computational approach allows novice users to retarget an existing mechanical template to a user-specified input shape. Our proposed representation for a mechanical template encodes a parameterized mechanism, mechanical constraints that ensure a physically valid configuration, spatial relationships of mechanical parts to the user-provided shape, and functional constraints that specify an intended functionality. We provide an intuitive interface and optimization-in-the-loop approach for finding a valid configuration of the mechanism and the shape to ensure that higher-level functional goals are met. Our algorithm interactively optimizes the mechanism while the user manipulates the placement of mechanical components and the shape. Our system allows users to efficiently explore various design choices and to synthesize customized mechanical objects that can be fabricated with rapid prototyping technologies. We demonstrate the efficacy of our approach by retargeting various mechanical templates to different shapes and fabricating the resulting functional mechanical objects. }, author = {Zhang, Ran and Auzinger, Thomas and Ceylan, Duygu and Li, Wilmot and Bickel, Bernd}, issn = {07300301}, location = {Los Angeles, CA, United States }, number = {4}, publisher = {ACM}, title = {{Functionality-aware retargeting of mechanisms to 3D shapes}}, doi = {10.1145/3072959.3073710}, volume = {36}, year = {2017}, }