Distributed Algorithms for Scheduling on Line and Tree Networks

We have a set of processors (or agents) and a set of graph networks defined over some vertex set. Each processor can access a subset of the graph networks. Each processor has a demand specified as a pair of vertices $$, along with a profit; the processor wishes to send data between $u$ and $v$. Towards that goal, the processor needs to select a graph network accessible to it and a path connecting $u$ and $v$ within the selected network. The processor requires exclusive access to the chosen path, in order to route the data. Thus, the processors are competing for routes/channels. A feasible solution selects a subset of demands and schedules each selected demand on a graph network accessible to the processor owning the demand; the solution also specifies the paths to use for this purpose. The requirement is that for any two demands scheduled on the same graph network, their chosen paths must be edge disjoint. The goal is to output a solution having the maximum aggregate profit. Prior work has addressed the above problem in a distibuted setting for the special case where all the graph networks are simply paths (i.e, line-networks). Distributed constant factor approximation algorithms are known for this case. The main contributions of this paper are twofold. First we design a distributed constant factor approximation algorithm for the more general case of tree-networks. The core component of our algorithm is a tree-decomposition technique, which may be of independent interest. Secondly, for the case of line-networks, we improve the known approximation guarantees by a factor of 5. Our algorithms can also handle the capacitated scenario, wherein the demands and edges have bandwidth requirements and capacities, respectively.Comment: Accepted to PODC 2012, full versio

Malunion of Pediatric Forearm Shaft Fractures: Management Principles and Techniques.

PURPOSE OF REVIEW: Clinically significant malunion of forearm diaphyseal fractures is an uncommon but potentially disabling condition amongst children and adolescents. We present the preoperative evaluation, including imaging, and discuss surgical indications and contemporary approaches to manage such patients, including an illustrative case. RECENT FINDINGS: While advances in three-dimensional (3D) simulation, modeling, and patient-specific instrumentation have expanded the surgical armamentarium, their impact on long-term outcomes compared to traditional methods remains unknown. Successful outcome following surgical correction of malunion following a both-bone forearm fracture can be achieved with careful patient selection, appropriate indications, and a well-planned surgical execution

Distributed and Parallel Algorithms for Set Cover Problems with Small Neighborhood Covers

In this paper, we study a class of set cover problems that satisfy a special property which we call the {\em small neighborhood cover} property. This class encompasses several well-studied problems including vertex cover, interval cover, bag interval cover and tree cover. We design unified distributed and parallel algorithms that can handle any set cover problem falling under the above framework and yield constant factor approximations. These algorithms run in polylogarithmic communication rounds in the distributed setting and are in NC, in the parallel setting.Comment: Full version of FSTTCS'13 pape

Chylous Leak During Posterior Approach to Juvenile Scoliosis Surgery: A Case Report.

CaseWe report the first documented case of chylous leak recognized intraoperatively during posterior spinal instrumentation and fusion for juvenile scoliosis in a female patient with a history of thoracotomy and decortication for an empyema.ConclusionsThoracic duct injury can lead to severe morbidity and mortality because of chylothorax formation. Although chylous leaks are a well-documented complication of the anterior approach to spine surgery, leaks during the posterior approach are rarely reported. When these chylous leaks are recognized intraoperatively, the likelihood of serious complications may be minimized by drain placement before closure

On Optimizing Distributed Tucker Decomposition for Dense Tensors

The Tucker decomposition expresses a given tensor as the product of a small core tensor and a set of factor matrices. Apart from providing data compression, the construction is useful in performing analysis such as principal component analysis (PCA)and finds applications in diverse domains such as signal processing, computer vision and text analytics. Our objective is to develop an efficient distributed implementation for the case of dense tensors. The implementation is based on the HOOI (Higher Order Orthogonal Iterator) procedure, wherein the tensor-times-matrix product forms the core routine. Prior work have proposed heuristics for reducing the computational load and communication volume incurred by the routine. We study the two metrics in a formal and systematic manner, and design strategies that are optimal under the two fundamental metrics. Our experimental evaluation on a large benchmark of tensors shows that the optimal strategies provide significant reduction in load and volume compared to prior heuristics, and provide up to 7x speed-up in the overall running time.Comment: Preliminary version of the paper appears in the proceedings of IPDPS'1