Chance-Constrained Outage Scheduling using a Machine Learning Proxy

Outage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its objective is to minimize operation-cost expectation while satisfying reliability-related constraints. We propose a distributed scenario-based chance-constrained optimization formulation for this problem. To tackle tractability issues arising in large networks, we use machine learning to build a proxy for predicting outcomes of power system operation processes in this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains cheaper and more reliable plans than other candidates

Classifying pairs with trees for supervised biological network inference

Networks are ubiquitous in biology and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local approach, which trains a separate model for each network node, and the global approach, which trains a single model over pairs of nodes. Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference. We first formalize the problem of network inference as classification of pairs, unifying in the process homogeneous and bipartite graphs and discussing two main sampling schemes. We then present the global and the local approaches, extending the later for the prediction of interactions between two unseen network nodes, and discuss their specializations to tree-based ensemble methods, highlighting their interpretability and drawing links with clustering techniques. Extensive computational experiments are carried out with these methods on various biological networks that clearly highlight that these methods are competitive with existing methods.Comment: 22 page

Contingency severity assessment for voltage security using non-parametric regression techniques

peer reviewedThis paper proposes a novel approach to power system voltage security assessment exploiting nonparametric regression techniques to extract simple, and at the same time reliable, models of the severity of a contingency, defined as the difference between pre- and post-contingency load power margins. The regression techniques extract information from large sets of possible operating conditions of a power system screened offline via massive random sampling, whose voltage security with respect to contingencies is pre-analyzed using an efficient voltage stability simulation. In particular, regression trees are used to identify the most salient parameters of the pre-contingency topology and electrical state which influence the severity of a given contingency, and to provide a first guess transparent approximation of the contingency severity in terms of these latter parameters. Multilayer perceptrons are exploited to further refine this information. The approach is demonstrated on a realistic model of a large scale voltage stability limited power system, where it shows to provide valuable physical insight and reliable contingency evaluation. Various potential uses in power system planning and operation are discusse

TARGET VALIDATION OF UK-101 AND FUNCTIONAL STUDIES OF β1i

β1i is a major catalytic subunit of the immunoproteasome, an alternative form of the constitutive proteasome, and its upregulation has been demonstrated in a variety of disease states including cancer. Our lab has developed a small molecule inhibitor of β1i, dubbed UK-101. While UK-101 causes apoptosis in cancer cell lines, it was not clear whether this apoptotic effect was directly mediated by its irreversible inhibition of β1i. Since off-target effects are major roadblocks for the development of new and effective pharmaceuticals, target validation studies in this system would assist in the further progression of β1i inhibitors towards preclinical trials. Our hypothesis was that the expression and catalytic activity of β1i is important for the growth and proliferation of the PC-3 prostate cancer cell line, therefore the apoptotic effect seen upon treatment of PC-3 cells with UK-101 was due solely to its covalent inhibition of β1i. To test this hypothesis, a number of complementary approaches were used. The expression of β1i in PC-3 cells was increased by the treatment of these cells with interferon-gamma or tumor necrosis factor-alpha, natural inducers of the immunoproteasome. The expression of β1i in PC-3 cells was decreased using small interfering RNA or short hairpin RNA, in a transient or stable manner, respectively. All of these cells were then treated with UK-101. The efficacy of UK-101 decreased in the interferon-gamma treated cells but did not change in any other the other cell lines, suggesting that UK-101 was not specific for β1i. This was confirmed using a molecular probe of the proteasome and demonstrated that UK-101 bound to other proteasome catalytic subunits. Additional experiments were performed to determine the effect of β1i on the proliferation of PC-3 cells. Simply removing the β1i using small interfering RNA reduces the viability of these cells. Other studies demonstrated that a mutation of β1i which inhibited its catalytic activity reduced the viability of cells when compared to those containing the wild type protein. Overall, our data indicate that β1i is a potential therapeutic target in prostate cancer. Further medicinal chemistry efforts will be required develop UK-101 into a truly selective proteasome inhibitor

Sensitivity-based approaches for handling discrete variables in optimal power flow computations

peer reviewedThis paper proposes and compares three iterative approaches for handling discrete variables in optimal power flow (OPF) computations. The first two approaches rely on the sensitivities of the objective and inequality constraints with respect to discrete variables. They set the discrete variables values either by solving a mixed-integer linear programming (MILP) problem or by using a simple procedure based on a merit function. The third approach relies on the use of Lagrange multipliers corresponding to the discrete variables bound constraints at the OPF solution. The classical round-off technique and a progressive round-off approach have been also used as a basis of comparison. We provide extensive numerical results with these approaches on four test systems with up to 1203 buses, and for two OPF problems: loss minimization and generation cost minimization, respectively. These results show that the sensitivity-based approach combined with the merit function clearly outperforms the other approaches in terms of: objective function quality, reliability, and computational times. Furthermore, the objective value obtained with this approach has been very close to that provided by the continuous relaxation OPF. This approach constitutes therefore a viable alternative to other methods dealing with discrete variables in an OPF

Diffusion Priors In Variational Autoencoders

Among likelihood-based approaches for deep generative modelling, variational autoencoders (VAEs) offer scalable amortized posterior inference and fast sampling. However, VAEs are also more and more outperformed by competing models such as normalizing flows (NFs), deep-energy models, or the new denoising diffusion probabilistic models (DDPMs). In this preliminary work, we improve VAEs by demonstrating how DDPMs can be used for modelling the prior distribution of the latent variables. The diffusion prior model improves upon Gaussian priors of classical VAEs and is competitive with NF-based priors. Finally, we hypothesize that hierarchical VAEs could similarly benefit from the enhanced capacity of diffusion priors