Evolutionary-inspired probabilistic search for enhancing sampling of local minima in the protein energy surface

<p>Abstract</p> <p>Background</p> <p>Despite computational challenges, elucidating conformations that a protein system assumes under physiologic conditions for the purpose of biological activity is a central problem in computational structural biology. While these conformations are associated with low energies in the energy surface that underlies the protein conformational space, few existing conformational search algorithms focus on explicitly sampling low-energy local minima in the protein energy surface.</p> <p>Methods</p> <p>This work proposes a novel probabilistic search framework, PLOW, that explicitly samples low-energy local minima in the protein energy surface. The framework combines algorithmic ingredients from evolutionary computation and computational structural biology to effectively explore the subspace of local minima. A greedy local search maps a conformation sampled in conformational space to a nearby local minimum. A perturbation move jumps out of a local minimum to obtain a new starting conformation for the greedy local search. The process repeats in an iterative fashion, resulting in a trajectory-based exploration of the subspace of local minima.</p> <p>Results and conclusions</p> <p>The analysis of PLOW's performance shows that, by navigating only the subspace of local minima, PLOW is able to sample conformations near a protein's native structure, either more effectively or as well as state-of-the-art methods that focus on reproducing the native structure for a protein system. Analysis of the actual subspace of local minima shows that PLOW samples this subspace more effectively that a naive sampling approach. Additional theoretical analysis reveals that the perturbation function employed by PLOW is key to its ability to sample a diverse set of low-energy conformations. This analysis also suggests directions for further research and novel applications for the proposed framework.</p

Beyond Single-Model Views for Deep Learning: Optimization versus Generalizability of Stochastic Optimization Algorithms

Despite an extensive body of literature on deep learning optimization, our current understanding of what makes an optimization algorithm effective is fragmented. In particular, we do not understand well whether enhanced optimization translates to improved generalizability. Current research overlooks the inherent stochastic nature of stochastic gradient descent (SGD) and its variants, resulting in a lack of comprehensive benchmarking and insight into their statistical performance. This paper aims to address this gap by adopting a novel approach. Rather than solely evaluating the endpoint of individual optimization trajectories, we draw from an ensemble of trajectories to estimate the stationary distribution of stochastic optimizers. Our investigation encompasses a wide array of techniques, including SGD and its variants, flat-minima optimizers, and new algorithms we propose under the Basin Hopping framework. Through our evaluation, which encompasses synthetic functions with known minima and real-world problems in computer vision and natural language processing, we emphasize fair benchmarking under a statistical framework, comparing stationary distributions and establishing statistical significance. Our study uncovers several key findings regarding the relationship between training loss and hold-out accuracy, as well as the comparable performance of SGD, noise-enabled variants, and novel optimizers utilizing the BH framework. Notably, these algorithms demonstrate performance on par with flat-minima optimizers like SAM, albeit with half the gradient evaluations. We anticipate that our work will catalyze further exploration in deep learning optimization, encouraging a shift away from single-model approaches towards methodologies that acknowledge and leverage the stochastic nature of optimizers

Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets

Detecting anomalies over real-world datasets remains a challenging task. Data annotation is an intensive human labor problem, particularly in sequential datasets, where the start and end time of anomalies are not known. As a result, data collected from sequential real-world processes can be largely unlabeled or contain inaccurate labels. These characteristics challenge the application of anomaly detection techniques based on supervised learning. In contrast, Multiple Instance Learning (MIL) has been shown effective on problems with incomplete knowledge of labels in the training dataset, mainly due to the notion of bags. While largely under-leveraged for anomaly detection, MIL provides an appealing formulation for anomaly detection over real-world datasets, and it is the primary contribution of this paper. In this paper, we propose an MIL-based formulation and various algorithmic instantiations of this framework based on different design decisions for key components of the framework. We evaluate the resulting algorithms over four datasets that capture different physical processes along different modalities. The experimental evaluation draws out several observations. The MIL-based formulation performs no worse than single instance learning on easy to moderate datasets and outperforms single-instance learning on more challenging datasets. Altogether, the results show that the framework generalizes well over diverse datasets resulting from different real-world application domains.Comment: 9 pages,5 figures, Anomaly and Novelty Detection, Explanation and Accommodation (ANDEA 2022

Essays on Fiscal Institutions, Public Expenditures, and Debt

This three-essay dissertation focuses on the political economy of fiscal rules in a comparative context and highlights their unintended consequences – an issue that has received relatively little attention in public financial management literature. The first essay examines whether numerical limits on deficits, or balanced budget rules, influence the composition of public spending, particularly in the social sector. Using a combination of fixed effects and GMM regressions on a large panel of developed and developing economies, this essay finds that while deficit targets are effective in improving fiscal balances, they also tend to reduce social spending on health and social protection. This effect is particularly prominent in democratic countries, which often witness overspending problems. Countries that are considering adoption of such rules should carefully examine the effects of these requirements on expenditures that may have long-term positive externalities. Policymakers should explore mechanisms to minimize the distortionary effects of fiscal limits on spending composition. The second essay focusses on whether the adoption of deficit targets by subnational governments in India influenced the composition of public spending. Using a combination of fixed effects and GMM regressions, this essay finds that the adoption of Fiscal Responsibility and Budget Management (FRBM) legislation by Indian states improved their budget balances significantly. However, the post-FRBM period also witnessed significant cuts in development spending. Furthermore, states have reduced their capital outlay and social spending after the adoption of fiscal responsibility laws. Reduced expenditure on development, and capital projects may affect long-term economic growth, therefore future amendments to the FRBM law should explore mechanisms to minimize the distortionary impacts of fiscal targets on the composition of subnational spending. The third essay shifts attention to the effect of supermajority voting requirements on credit ratings and borrowing costs in the subnational debt market in the United States. Using a combination of generalized ordered logit and linear regression analyses on a sample of general obligation bonds issued by American state governments between 2001 and 2014, this essay finds that states with supermajority voting requirements for tax increases are more likely to receive a lower credit rating on their bonds. Furthermore, on average, the states with a supermajority voting requirement pay a premium of 18 to 21 basis points in true interest cost for their bonds. States that are considering adopting supermajority requirements should consider the unintended effects in terms of lower credit ratings and higher borrowing costs while adopting or designing such fiscal rules. The findings of this dissertation inform the policy debate on the subject and improve our understanding of the impact of fiscal institutions that are being increasingly adopted to regulate the behavior of governments across the world

Menthol Binding and Inhibition of a7-Nicotinic Acetylcholine Receptors

Menthol is a common compound in pharmaceutical and commercial products and a popular additive to cigarettes. The molecular targets of menthol remain poorly defined. In this study we show an effect of menthol on the α7 subunit of the nicotinic acetylcholine (nACh) receptor function. Using a two-electrode voltage-clamp technique, menthol was found to reversibly inhibit α7-nACh receptors heterologously expressed in Xenopus oocytes. Inhibition by menthol was not dependent on the membrane potential and did not involve endogenous Ca2+-dependent Cl− channels, since menthol inhibition remained unchanged by intracellular injection of the Ca2+ chelator BAPTA and perfusion with Ca2+-free bathing solution containing Ba2+. Furthermore, increasing ACh concentrations did not reverse menthol inhibition and the specific binding of [125I] α-bungarotoxin was not attenuated by menthol. Studies of α7- nACh receptors endogenously expressed in neural cells demonstrate that menthol attenuates α7 mediated Ca2+ transients in the cell body and neurite. In conclusion, our results suggest that menthol inhibits α7-nACh receptors in a noncompetitive manner