Selected Essays on the Conflict of Laws. By Brainerd Currie.

Most subspace identication algorithms are not applicable to closed-loop identication because they require future input to be uncorrelated with pastinnovation. In this paper, we propose a new subspace identication method that remove this requirement by using a parsimonious model formulation with innovation estimation. A simulation example is included to show the effectiveness of the proposed method

Public Law by Private Bargain: Title VII Consent Decrees and the Fairness of Negotiated Institutional Reform

Large-scale Title VII remedies are typical of public law litigation, which differs in kind from the conventional compensatory lawsuit. Title VII remedies are commonly adopted by way of consent decrees. By approving these consent decrees, federal courts take responsibility for extensive institutional reforms while acting independently of the adversary process. Courts have frequently approved consent decrees without fair hearings for those whose interests are at stake. Professor Schwarzschild suggests a systematic procedure for approving Title VII consent decrees. This procedure would not discourage settlements, but would ensure that courts act on the basis of fair hearings, consistently with the quasi-legislative character of public law remedies

Foreword

In this paper, we show that the consistency of closed-loop subspace identification methods (SIMs) can be achieved through innovation estimation. Based on this analysis, a sufficient condition for the consistency of a new proposed closed-loop SIM is given, A consistent estimate of the Kalman gain under closed-loop conditions is also provided based on the algorithm. A multi-input-multi-output simulation shows that itis consistent under closed-loop conditions, when traditional SIMs fail to provide consistent estimates

Probabilistic Reduced-Dimensional Vector Autoregressive Modeling for Dynamics Prediction and Reconstruction with Oblique Projections

In this paper, we propose a probabilistic reduced-dimensional vector autoregressive (PredVAR) model with oblique projections. This model partitions the measurement space into a dynamic subspace and a static subspace that do not need to be orthogonal. The partition allows us to apply an oblique projection to extract dynamic latent variables (DLVs) from high-dimensional data with maximized predictability. We develop an alternating iterative PredVAR algorithm that exploits the interaction between updating the latent VAR dynamics and estimating the oblique projection, using expectation maximization (EM) and a statistical constraint. In addition, the noise covariance matrices are estimated as a natural outcome of the EM method. A simulation case study of the nonlinear Lorenz oscillation system illustrates the advantages of the proposed approach over two alternatives

Alternating minimization for simultaneous estimation of a latent variable and identification of a linear continuous-time dynamic system

We propose an optimization formulation for the simultaneous estimation of a latent variable and the identification of a linear continuous-time dynamic system, given a single input-output pair. We justify this approach based on Bayesian maximum a posteriori estimators. Our scheme takes the form of a convex alternating minimization, over the trajectories and the dynamic model respectively. We prove its convergence to a local minimum which verifies a two point-boundary problem for the (latent) state variable and a tensor product expression for the optimal dynamics

Genetics of primary ovarian insufficiency: new developments and opportunities

BACKGROUND Primary ovarian insufficiency (POI) is characterized by marked heterogeneity, but with a significant genetic contribution. Identifying exact causative genes has been challenging, with many discoveries not replicated. It is timely to take stock of the field, outlining the progress made, framing the controversies and anticipating future directions in elucidating the genetics of POI. METHODS A search for original articles published up to May 2015 was performed using PubMed and Google Scholar, identifying studies on the genetic etiology of POI. Studies were included if chromosomal analysis, candidate gene screening and a genome-wide study were conducted. articles identified were restricted to English language full-text papers. RESULTS Chromosomal abnormalities have long been recognized as a frequent cause of POI, with a currently estimated prevalence of 10?13%. Using the traditional karyotype methodology, monosomy X, mosaicism, X chromosome deletions and rearrangements, X-autosome translocations, and isochromosomes have been detected. Based on candidate gene studies, single gene perturbations unequivocally having a deleterious effect in at least one population include Bone morphogenetic protein 15 (BMP15), Progesterone receptor membrane component 1 (PGRMC1), and Fragile X mental retardation 1 (FMR1) premutation on the X chromosome; Growth differentiation factor 9 (GDF9), Folliculogenesis specific bHLH transcription factor (FIGLA), Newborn ovary homeobox gene (NOBOX), Nuclear receptor subfamily 5, group A, member 1 (NR5A1) and Nanos homolog 3 (NANOS3) seem likely as well, but mostly being found in no more than 1?2% of a single population studied. Whole genome approaches have utilized genome-wide association studies (GWAS) to reveal loci not predicted on the basis of a candidate gene, but it remains difficult to locate causative genes and susceptible loci were not always replicated. Cytogenomic methods (array CGH) have identified other regions of interest but studies have not shown consistent results, the resolution of arrays has varied and replication is uncommon. Whole-exome sequencing in non-syndromic POI kindreds has only recently begun, revealing mutations in the Stromal antigen 3 (STAG3), Synaptonemal complex central element 1 (SYCE1), minichromosome maintenance complex component 8 and 9 (MCM8, MCM9) and ATP-dependent DNA helicase homolog (HFM1) genes. Given the slow progress in candidate-gene analysis and relatively small sample sizes available for GWAS, family-based whole exome and whole genome sequencing appear to be the most promising approaches for detecting potential genes responsible for POI. CONCLUSION Taken together, the cytogenetic, cytogenomic (array CGH) and exome sequencing approaches have revealed a genetic causation in ?20?25% of POI cases. Uncovering the remainder of the causative genes will be facilitated not only by whole genome approaches involving larger cohorts in multiple populations but also incorporating environmental exposures and exploring signaling pathways in intragenic and intergenic regions that point to perturbations in regulatory genes and networks

Competitive Online Peak-Demand Minimization Using Energy Storage

We study the problem of online peak-demand minimization under energy storage constraints. It is motivated by an increasingly popular scenario where large-load customers utilize energy storage to reduce the peak procurement from the grid, which accounts for up to $90\%$ of their electric bills. The problem is uniquely challenging due to (i) the coupling of online decisions across time imposed by the inventory constraints and (ii) the noncumulative nature of the peak procurement. In this paper, we develop an optimal online algorithm for the problem, attaining the best possible competitive ratio (CR) among all deterministic and randomized algorithms. We show that the optimal CR can be computed in polynomial time, by solving a linear number of linear-fractional problems. More importantly, we generalize our approach to develop an \emph{anytime-optimal} online algorithm that achieves the best possible CR at any epoch, given the inputs and online decisions so far. The algorithm retains the optimal worst-case performance and achieves adaptive average-case performance. Simulation results based on real-world traces show that, under typical settings, our algorithms improve peak reduction by over $19\%$ as compared to baseline alternatives

Predictive control methods to improve energy efficiency and reduce demand in buildings

Abstract This paper presents an overview of results and future challenges on temperature control and cost optimization in building energy systems. Control and economic optimization issues are discussed and illustrated through sophisticated simulation examples. The paper concludes with effective results from model predictive control solutions and identification of important directions for future work

Real-time experimental implementation of predictive control schemes in a small-scale pasteurization plant

Model predictive control (MPC) is one of the most used optimization-based control strategies for large-scale systems, since this strategy allows to consider a large number of states and multi-objective cost functions in a straightforward way. One of the main issues in the design of multi-objective MPC controllers, which is the tuning of the weights associated to each objective in the cost function, is treated in this work. All the possible combinations of weights within the cost function affect the optimal result in a given Pareto front. Furthermore, when the system has time-varying parameters, e.g., periodic disturbances, the appropriate weight tuning might also vary over time. Moreover, taking into account the computational burden and the selected sampling time in the MPC controller design, the computation time to find a suitable tuning is limited. In this regard, the development of strategies to perform a dynamical tuning in function of the system conditions potentially improves the closed-loop performance. In order to adapt in a dynamical way the weights in the MPC multi-objective cost function, an evolutionary-game approach is proposed. This approach allows to vary the prioritization weights in the proper direction taking as a reference a desired region within the Pareto front. The proper direction for the prioritization is computed by only using the current system values, i.e., the current optimal control action and the measurement of the current states, which establish the system cost function over a certain point in the Pareto front. Finally, some simulations of a multi-objective MPC for a real multi-variable case study show a comparison between the system performance obtained with static and dynamical tuning.Peer ReviewedPostprint (author's final draft