Barrier Frank-Wolfe for Marginal Inference

We introduce a globally-convergent algorithm for optimizing the tree-reweighted (TRW) variational objective over the marginal polytope. The algorithm is based on the conditional gradient method (Frank-Wolfe) and moves pseudomarginals within the marginal polytope through repeated maximum a posteriori (MAP) calls. This modular structure enables us to leverage black-box MAP solvers (both exact and approximate) for variational inference, and obtains more accurate results than tree-reweighted algorithms that optimize over the local consistency relaxation. Theoretically, we bound the sub-optimality for the proposed algorithm despite the TRW objective having unbounded gradients at the boundary of the marginal polytope. Empirically, we demonstrate the increased quality of results found by tightening the relaxation over the marginal polytope as well as the spanning tree polytope on synthetic and real-world instances.Comment: 25 pages, 12 figures, To appear in Neural Information Processing Systems (NIPS) 2015, Corrected reference and cleaned up bibliograph

Arthroscopic decompression and debridement for the treatment of anterior cruciate ligament ganglion cyst in a 45-year-old female

Anterior cruciate ligament (ACL) cysts are infrequently encountered in clinical practice, with limited reported cases. These cysts usually present with chronic knee pain and clinical examination is usually unremarkable except for knee tenderness. Due to the lack of characteristic symptoms and deficiency of precise clinical techniques to diagnose the condition, timely diagnosis requires a high index of suspicion, supported by magnetic resonance imaging (MRI). Hence management of ACL ganglion cysts poses unique challenges for healthcare providers. We present a case study of a 45-year-old female with an ACL ganglion cyst successfully treated with arthroscopic decompression and debridement. This article outlines the clinical presentation, diagnostic workup, surgical intervention, and post-operative outcomes of this case, providing insights into the effective management of this uncommon condition. Furthermore, we provide a comprehensive review of the existing literature on ACL ganglion cyst, emphasizing findings and treatment outcomes reported in previous studies. This case underscores the importance of considering ACL cysts in the differential diagnosis of knee pain and discomfort. Early identification and appropriate management, such as arthroscopic cyst excision, can lead to favorable outcomes and complete recovery

Wavelet-Based Analysis of Physical Activity and Sleep Movement Data from Wearable Sensors among Obese Adults

Decreased physical activity in obese individuals is associated with a prevalence of cardiovascular and metabolic disorders. Physicians usually recommend that obese individuals change their lifestyle, specifically changes in diet, exercise, and other physical activities for obesity management. Therefore, understanding physical activity and sleep behavior is an essential aspect of obesity management. With innovations in mobile and electronic health care technologies, wearable inertial sensors have been used extensively over the past decade for monitoring human activities. Despite significant progress with the wearable inertial sensing technology, there is a knowledge gap among researchers regarding how to analyze longitudinal multi-day inertial sensor data to explore activities of daily living (ADL) and sleep behavior. The purpose of this study was to explore new clinically relevant metrics using movement amplitude and frequency from longitudinal wearable sensor data in obese and non-obese young adults. We utilized wavelet analysis to determine movement frequencies on longitudinal multi-day wearable sensor data. In this study, we recruited 10 obese and 10 non-obese young subjects. We found that obese participants performed more low-frequency (0.1 Hz) movements and fewer movements of high frequency (1.1–1.4 Hz) compared to non-obese counterparts. Both obese and non-obese subjects were active during the 00:00–06:00 time interval. In addition, obesity affected sleep with significantly fewer transitions, and obese individuals showed low values of root mean square transition accelerations throughout the night. This study is critical for obesity management to prevent unhealthy weight gain by the recommendations of physical activity based on our results. Longitudinal multi-day monitoring using wearable sensors has great potential to be integrated into routine health care checkups to prevent obesity and promote physical activities

Effect of Electronic Secondary Markets on the Supply Chain

We present a model to investigate the competitive implications of electronic secondary markets that promote concurrent selling of new and used goods on a supply chain. In secondary markets where suppliers cannot directly utilize used goods for practicing intertemporal price discrimination and where transaction costs of resales is negligible, the threat of cannibalization of new goods by used goods become significant. We examine conditions under which it is optimal for suppliers to operate in such markets, explaining why these markets may not always be detrimental for them. Intuitively, secondary markets provide an active outlet for some highvaluation consumers to sell their used goods. The potential for such resales lead to an 05 ghose.pmd 91 8/26/2005, 1:10 PM 92 GHOSE, TELANG, AND KRISHNAN increase in consumersâ valuation for a new good, leading them to buy an additional new good. Given sufficient heterogeneity in consumerâ s affinity across multiple suppliersâ products, the â market expansion effectâ accruing from consumersâ cross-product purchase affinity can mitigate the losses incurred by suppliers from the direct â cannibalization effect.â We also highlight the strategic role that used goods commission set by the retailer plays in determining profits for suppliers. We conclude the paper by empirically testing some implications of our model using a unique data set from the online book industry, which has a flourishing secondary market.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

Durable Goods Cpmpetition in Secondary Electronic Markets

We develop a game-theoretic framework to investigate the competitive implications of consumer-to-consumer electronic marketplaces, which promote concurrent selling of new and used goods. In many e-marketplaces, where suppliers cannot directly use second-hand goods for practicing inter-temporal price discrimination, the threat of cannibalization of new goods by used goods become significant. We examine conditions under which it is optimal for suppliers to operate in such markets, explaining why used-goods markets may not be predatory for them. While a monopolist supplier is worse off in the presence of a secondary market, competition can in fact make it better off. The presence of used-goods markets provides an active outlet for some consumers to sell their second-hand goods. Such sales lead to an increase in their disposable income. This increased income can then be used to buy an additional new good. Contrary to conventional wisdom, our model predicts the reduction in the price of new goods when there are used-goods markets. We highlight the strategic role that used goods commission plays in determining optimal profits. Overall, for a wide range of parameters, there is an increase in social welfare from establishing such secondary markets

FluTO: Graded Multiscale Fluid Topology Optimization using Neural Networks

Fluid-flow devices with low dissipation, but high contact area, are of importance in many applications. A well-known strategy to design such devices is multi-scale topology optimization (MTO), where optimal microstructures are designed within each cell of a discretized domain. Unfortunately, MTO is computationally very expensive since one must perform homogenization of the evolving microstructures, during each step of the homogenization process. As an alternate, we propose here a graded multiscale topology optimization (GMTO) for designing fluid-flow devices. In the proposed method, several pre-selected but size-parameterized and orientable microstructures are used to fill the domain optimally. GMTO significantly reduces the computation while retaining many of the benefits of MTO. In particular, GMTO is implemented here using a neural-network (NN) since: (1) homogenization can be performed off-line, and used by the NN during optimization, (2) it enables continuous switching between microstructures during optimization, (3) the number of design variables and computational effort is independent of number of microstructure used, and, (4) it supports automatic differentiation, thereby eliminating manual sensitivity analysis. Several numerical results are presented to illustrate the proposed framework

The Economics of Peer-to-Peer Networks

Peer-to-Peer (P2P) networks have emerged as a significant social phenomenon for the distribution of information goods and may become an important alternative to traditional client-server network architectures for knowledge sharing within enterprises. This paper reviews and synthesizes the relevant computer science and economics literatures as they relate to P2P networks, and raises important questions for researchers interested in studying the behavior of these networks from the perspective of the economics of information technology. With regard to the economic characteristics of these networks, we show that while the characteristics of services provided over P2P networks are similar to public goods and club goods, they have many important differences and hence there is a need for new theoretical models as well as empirical and experimental analysis to understand P2P user behavior. We then identify several important areas for study with regard to the economics of P2P networks and review recent academic papers in each area