Fitting Spectral Decay with the k-Support Norm

The spectral kk-support norm enjoys good estimation properties in low rank matrix learning problems, empirically outperforming the trace norm. Its unit ball is the convex hull of rank kk matrices with unit Frobenius norm. In this paper we generalize the norm to the spectral (k,p)(k,p)-support norm, whose additional parameter pp can be used to tailor the norm to the decay of the spectrum of the underlying model. We characterize the unit ball and we explicitly compute the norm. We further provide a conditional gradient method to solve regularization problems with the norm, and we derive an efficient algorithm to compute the Euclidean projection on the unit ball in the case p=∞p=∞. In numerical experiments, we show that allowing pp to vary significantly improves performance over the spectral kk-support norm on various matrix completion benchmarks, and better captures the spectral decay of the underlying model

Incremental learning-to-learn with statistical guarantees

In learning-to-learn the goal is to infer a learning algorithm that works well on a class of tasks sampled from an unknown metadistribution. In contrast to previous work on batch learning-to-learn, we consider a scenario where tasks are presented sequentially and the algorithm needs to adapt incrementally to improve its performance on future tasks. Key to this setting is for the algorithm to rapidly incorporate new observations into the model as they arrive, without keeping them in memory. We focus on the case where the underlying algorithm is Ridge Regression parametrised by a symmetric positive semidefinite matrix. We propose to learn this matrix by applying a stochastic strategy to minimize the empirical error incurred by Ridge Regression on future tasks sampled from the meta-distribution. We study the statistical properties of the proposed algorithm and prove non-asymptotic bounds on its excess transfer risk, that is, the generalization performance on new tasks from the same meta-distribution. We compare our online learning-to-learn approach with a state-of-the-art batch method, both theoretically and empirically

Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction

We study the interplay between surrogate methods for structured prediction and techniques from multitask learning designed to leverage relationships between surrogate outputs. We propose an efficient algorithm based on trace norm regularization which, differently from previous methods, does not require explicit knowledge of the coding/decoding functions of the surrogate framework. As a result, our algorithm can be applied to the broad class of problems in which the surrogate space is large or even infinite dimensional. We study excess risk bounds for trace norm regularized structured prediction, implying the consistency and learning rates for our estimator. We also identify relevant regimes in which our approach can enjoy better generalization performance than previous methods. Numerical experiments on ranking problems indicate that enforcing low-rank relations among surrogate outputs may indeed provide a significant advantage in practice

Facing Up to Longevity with Old Actuarial Methods: A Comparison of Pooled Funds and Income Tontines

We compare the concepts underlying modern actuarial solutions to pension insurance and present two recently developed pension products—pooled annuity overlay funds (based on actuarial fairness) and equitable income tontines (based on equitability). These two products adopt specific approaches to the management of longevity risk by mutualising it among participants rather than transferring it completely to the insurer. As the market would appear to be ready for such innovations, our study seeks to establish a general framework for their introduction. We stress that the notion of actuarial fairness, which characterises pooled annuity overlay funds, enables participants to join and exit the fund at any time. Such freedom of action is a quite remarkable feature and one that cannot be matched by lifelong contracts

The Use of a Compression Device as an Alternative to Hand-Sewn and Stapled Colorectal Anastomoses: Is Three a Crowd?

BackgroundThe NiTi CAR™ 27 is a newer device that uses compression to create an anastomosis. An analysis of this device in the creation of colorectal anastomoses in humans has yet to be reported in the USA.MethodsA non-randomized, prospective pilot study of the NiTi CAR™ 27 device in patients undergoing a left-sided colectomy between March 2008 and August 2009 was performed.ResultsTwenty-three patients (9 men and 14 women) underwent a left-sided colectomy and compression anastomosis with the CAR™ 27 device. Minor morbidities, 3 of 23 (13%) patients, included one small postoperative abscess requiring antibiotics alone and two postoperative anastomotic strictures requiring balloon dilation. Major morbidities, 1 of 23 (4%) patients, included a partial anastomotic dehiscence/leak requiring surgical dismantling of the anastomosis and diversion.ConclusionThe CAR™ 27 device shows promise as a safe and effective alternative for the creation of colorectal anastomoses. However, studies in a larger patient population are warranted to demonstrate equivalence of this device

Design and synthesis of novel small molecule CCR2 antagonists: evaluation of 4-aminopiperidine derivatives

Medicinal Chemistr

Prediction of specificity-determining residues for small-molecule kinase inhibitors

<p>Abstract</p> <p>Background</p> <p>Designing small-molecule kinase inhibitors with desirable selectivity profiles is a major challenge in drug discovery. A high-throughput screen for inhibitors of a given kinase will typically yield many compounds that inhibit more than one kinase. A series of chemical modifications are usually required before a compound exhibits an acceptable selectivity profile. Rationalizing the selectivity profile for a small-molecule inhibitor in terms of the specificity-determining kinase residues for that molecule can be an important step toward the goal of developing selective kinase inhibitors.</p> <p>Results</p> <p>Here we describe S-Filter, a method that combines sequence and structural information to predict specificity-determining residues for a small molecule and its kinase selectivity profile. Analysis was performed on seven selective kinase inhibitors where a structural basis for selectivity is known. S-Filter correctly predicts specificity determinants that were described by independent groups. S-Filter also predicts a number of novel specificity determinants that can often be justified by further structural comparison.</p> <p>Conclusion</p> <p>S-Filter is a valuable tool for analyzing kinase selectivity profiles. The method identifies potential specificity determinants that are not readily apparent, and provokes further investigation at the structural level.</p

EGFRvIV: a previously uncharacterized oncogenic mutant reveals a kinase autoinhibitory mechanism

Tumor cells often subvert normal regulatory mechanisms of signal transduction. This study shows this principle by studying yet uncharacterized mutants of the epidermal growth factor receptor (EGFR) previously identified in glioblastoma multiforme, which is the most aggressive brain tumor in adults. Unlike the well-characterized EGFRvIII mutant form, which lacks a portion of the ligand-binding cleft within the extracellular domain, EGFRvIVa and EGFRvIVb lack internal segments distal to the intracellular tyrosine kinase domain. By constructing the mutants and by ectopic expression in naive cells, we show that both mutants confer an oncogenic potential in vitro, as well as tumorigenic growth in animals. The underlying mechanisms entail constitutive receptor dimerization and basal activation of the kinase domain, likely through a mechanism that relieves a restraining molecular fold, along with stabilization due to association with HSP90. Phosphoproteomic analyses delineated the signaling pathways preferentially engaged by EGFRvIVb-identified unique substrates. This information, along with remarkable sensitivities to tyrosine kinase blockers and to a chaperone inhibitor, proposes strategies for pharmacological interception in brain tumors harboring EGFRvIV mutations.Goldhirsh FoundationNational Cancer Institute (U.S.) (CA118705)National Cancer Institute (U.S.) (CA141556)National Cancer Institute (U.S.) (U54-CA112967