Rationale and design of the Exercise Intensity Trial (EXCITE): A randomized trial comparing the effects of moderate versus moderate to high-intensity aerobic training in women with operable breast cancer.

BACKGROUND: The Exercise Intensity Trial (EXcITe) is a randomized trial to compare the efficacy of supervised moderate-intensity aerobic training to moderate to high-intensity aerobic training, relative to attention control, on aerobic capacity, physiologic mechanisms, patient-reported outcomes, and biomarkers in women with operable breast cancer following the completion of definitive adjuvant therapy. METHODS/DESIGN: Using a single-center, randomized design, 174 postmenopausal women (58 patients/study arm) with histologically confirmed, operable breast cancer presenting to Duke University Medical Center (DUMC) will be enrolled in this trial following completion of primary therapy (including surgery, radiation therapy, and chemotherapy). After baseline assessments, eligible participants will be randomized to one of two supervised aerobic training interventions (moderate-intensity or moderate/high-intensity aerobic training) or an attention-control group (progressive stretching). The aerobic training interventions will include 150 mins.wk⁻¹ of supervised treadmill walking per week at an intensity of 60%-70% (moderate-intensity) or 60% to 100% (moderate to high-intensity) of the individually determined peak oxygen consumption (VO₂peak) between 20-45 minutes/session for 16 weeks. The progressive stretching program will be consistent with the exercise interventions in terms of program length (16 weeks), social interaction (participants will receive one-on-one instruction), and duration (20-45 mins/session). The primary study endpoint is VO₂peak, as measured by an incremental cardiopulmonary exercise test. Secondary endpoints include physiologic determinants that govern VO₂peak, patient-reported outcomes, and biomarkers associated with breast cancer recurrence/mortality. All endpoints will be assessed at baseline and after the intervention (16 weeks). DISCUSSION: EXCITE is designed to investigate the intensity of aerobic training required to induce optimal improvements in VO₂peak and other pertinent outcomes in women who have completed definitive adjuvant therapy for operable breast cancer. Overall, this trial will inform and refine exercise guidelines to optimize recovery in breast and other cancer survivors following the completion of primary cytotoxic therapy. TRIAL REGISTRATION: NCT01186367.

Research Question, Study Design and Continuous Research Education and Training Exercises (CREATE) Program.

The clinical research project starts with identifying the optimal research question, one that is ethical, impactful, feasible, scientifically sound, novel, relevant, and interesting. The project continues with the design of the study to answer the research question. Such design should be consistent with ethical and methodological principles, and make optimal use of resources in order to have the best chances of identifying a meaningful answer to the research question. Physicians and other healthcare providers are optimally positioned to identify meaningful research questions the answer to which could make significant impact on healthcare delivery. The typical medical education curriculum, however, lacks solid training in clinical research. We propose CREATE (Continuous Research Education And Training Exercises) as a peer- and group-based, interactive, analytical, customized, and accrediting program with didactic, training, mentoring, administrative, and professional support to enhance clinical research knowledge and skills among healthcare professionals, promote the generation of original research projects, increase the chances of their successful completion and potential for meaningful impact. The key features of the program are successive intra- and inter-group discussions and confrontational thematic challenges among participating peers aimed at capitalizing on the groups' collective knowledge, experience and skills, and combined intellectual processing capabilities to optimize choice of research project elements and stakeholder decision-making.

The abelianization of the Johnson kernel

We prove that the first complex homology of the Johnson subgroup of the Torelli group Tg is a non-trivial, unipotent Tg-module for all g ≥ 4 and give an explicit presentation of it as a Sym H 1(Tg,C)-module when g ≥ 6. We do this by proving that, for a finitely generated group G satisfying an assumption close to formality, the triviality of the restricted characteristic variety implies that the first homology of its Johnson kernel is a nilpotent module over the corresponding Laurent polynomial ring, isomorphic to the infinitesimal Alexander invariant of the associated graded Lie algebra of G. In this setup, we also obtain a precise nilpotence test. © European Mathematical Society 2014.

The Performance Practice of Buddhist Baiqi in Contemporary Taiwan

The baiqi (Buddhist percussive instruments), also known as faqi (dharma instruments), are mentioned in the Chinese Buddhist scriptures under many different terms: jianzhi, jiandi, jianzhui, or jianchi. The original function of baiqi in earlier monastic life was to gather people or to call an assembly. With the completion of monasticism and monastic institutions, baiqi have become multifunctional in monasteries, and many baiqi instruments have been developed for different monastic applications. In contemporary Buddhist monasteries in Taiwan, baiqi are used, on the one hand, to mark the time throughout the day, signal the beginning and end of monastic daily activities, and regulate the monastic order; and on the other hand, baiqi are indispensable to the musical practices of all Buddhist rituals, where they are used to accompany fanbai (Buddhist liturgical chants) and to articulate the whole ritual process. This study investigates multiple facets of Buddhist baiqi in their performance practice, function, application, notation, and transmission, exploring the interaction between baiqi and fanbai, baiqi and the practitioner, baiqi and the monastic space, and baiqi and various Buddhist contexts. I draw upon ideas from performance theory as it concerns different disciplines, but I maintain a sharper focus on the musicological dimension of performance practice when analyzing, interpreting, and explaining the performance and music of baiqi in terms of the monastic lifestyle and its rituals. The study not only uncovers the musical system of baiqi, but also encapsulates various issues of performed identity, social interaction, performer/audience, associated behaviors, the musical construction of space, and transmission.

Design of Scheduling Algorithms Using Game Theoretic Ideas

Scheduling a set of jobs over a collection of machines to optimize a certain quality-of-service measure is one of the most important research topics in both computer science theory and practice. In this thesis, we design algorithms that optimize {\em flow-time} (or delay) of jobs for scheduling problems that arise in a wide range of applications. We consider the classical model of unrelated machine scheduling and resolve several long standing open problems; we introduce new models that capture the novel algorithmic challenges in scheduling jobs in data centers or large clusters; we study the effect of selfish behavior in distributed and decentralized environments; we design algorithms that strive to balance the energy consumption and performance.

The technically interesting aspect of our work is the surprising connections we establish between approximation and online algorithms, economics, game theory, and queuing theory. It is the interplay of ideas from these different areas that lies at the heart of most of the algorithms presented in this thesis.

The main contributions of the thesis can be placed in one of the following categories.

1. Classical Unrelated Machine Scheduling: We give the first polygorithmic approximation algorithms for minimizing the average flow-time and minimizing the maximum flow-time in the offline setting. In the online and non-clairvoyant setting, we design the first non-clairvoyant algorithm for minimizing the weighted flow-time in the resource augmentation model. Our work introduces iterated rounding technique for the offline flow-time optimization, and gives the first framework to analyze non-clairvoyant algorithms for unrelated machines.

2. Polytope Scheduling Problem: To capture the multidimensional nature of the scheduling problems that arise in practice, we introduce Polytope Scheduling Problem (\psp). The \psp problem generalizes almost all classical scheduling models, and also captures hitherto unstudied scheduling problems such as routing multi-commodity flows, routing multicast (video-on-demand) trees, and multi-dimensional resource allocation. We design several competitive algorithms for the \psp problem and its variants for the objectives of minimizing the flow-time and completion time. Our work establishes many interesting connections between scheduling and market equilibrium concepts, fairness and non-clairvoyant scheduling, and queuing theoretic notion of stability and resource augmentation analysis.

3. Energy Efficient Scheduling: We give the first non-clairvoyant algorithm for minimizing the total flow-time + energy in the online and resource augmentation model for the most general setting of unrelated machines.

4. Selfish Scheduling: We study the effect of selfish behavior in scheduling and routing problems. We define a fairness index for scheduling policies called {\em bounded stretch}, and show that for the objective of minimizing the average (weighted) completion time, policies with small stretch lead to equilibrium outcomes with small price of anarchy. Our work gives the first linear/ convex programming duality based framework to bound the price of anarchy for general equilibrium concepts such as coarse correlated equilibrium.

Weight loss intervention for young adults using mobile technology: design and rationale of a randomized controlled trial - Cell Phone Intervention for You (CITY).

BACKGROUND: The obesity epidemic has spread to young adults, leading to significant public health implications later in adulthood. Intervention in early adulthood may be an effective public health strategy for reducing the long-term health impact of the epidemic. Few weight loss trials have been conducted in young adults. It is unclear what weight loss strategies are beneficial in this population. PURPOSE: To describe the design and rationale of the NHLBI-sponsored Cell Phone Intervention for You (CITY) study, which is a single center, randomized three-arm trial that compares the impact on weight loss of 1) a behavioral intervention that is delivered almost entirely via cell phone technology (Cell Phone group); and 2) a behavioral intervention delivered mainly through monthly personal coaching calls enhanced by self-monitoring via cell phone (Personal Coaching group), each compared to 3) a usual care, advice-only control condition. METHODS: A total of 365 community-dwelling overweight/obese adults aged 18-35 years were randomized to receive one of these three interventions for 24 months in parallel group design. Study personnel assessing outcomes were blinded to group assignment. The primary outcome is weight change at 24 [corrected] months. We hypothesize that each active intervention will cause more weight loss than the usual care condition. Study completion is anticipated in 2014. CONCLUSIONS: If effective, implementation of the CITY interventions could mitigate the alarming rates of obesity in young adults through promotion of weight loss. ClinicalTrial.gov: NCT01092364.

A new heuristic for three-machine flow shop scheduling

This paper considers the problem of sequencing n jobs in a three-machine flow shop with the objective of minimizing the makespan, which is the completion time of the last job. An O(n log n) time heuristic that is based on Johnson's algorithm is presented. It is shown to generate a schedule with length at most 5/3 times that of an optimal schedule, thereby reducing the previous best available worst-case performance ratio of 2. An application to the general flow shop is also discussed.

A novel neural/symbolic hybrid approach to heuristically optimized fuel allocation and automated revision of heuristics in nuclear engineering

FUELCON is an expert system for optimized refueling design in nuclear engineering. This task is crucial for keeping down operating costs at a plant without compromising safety. FUELCON proposes sets of alternative configurations of allocation of fuel assemblies that are each positioned in the planar grid of a horizontal section of a reactor core. Results are simulated, and an expert user can also use FUELCON to revise rulesets and improve on his or her heuristics. The successful completion of FUELCON led this research team into undertaking a panoply of sequel projects, of which we provide a meta-architectural comparative formal discussion. In this paper, we demonstrate a novel adaptive technique that learns the optimal allocation heuristic for the various cores. The algorithm is a hybrid of a fine-grained neural network and symbolic computation components. This hybrid architecture is sensitive enough to learn the particular characteristics of the ‘in-core fuel management problem’ at hand, and is powerful enough to use this information fully to automatically revise heuristics, thus improving upon those provided by a human expert.

A heuristic algorithm for two-machine re-entrant shop scheduling

This paper considers the problem of sequencing n jobs in a two‐machine re‐entrant shopwith the objective of minimizing the maximum completion time. The shop consists of twomachines, M1 and M2 , and each job has the processing route (M1 , M2 , M1 ). An O(n log n)time heuristic is presented which generates a schedule with length at most 4/3 times that ofan optimal schedule, thereby improving the best previously available worst‐case performanceratio of 3/2.

Temporal representation of state transitions

This paper describes a knowledge-based temporal representation of state transitions for industrial real-time systems. To allow expression of uncertainty, we shall define fluents as disjuncts of positive/negative time-varying properties. A state of the world is represented as a collection of fluents, which is usually incomplete in the sense that neither the positive form nor the negative form of some properties can be implied from it. The world under consideration is assumed to persist in a given state until an action(s) takes place to effect a transition of it into another state, where actions may either be instantaneous or durative. High-level causal laws are characterized in terms of relationships between actions and the involved world states. An effect completion axiom is imposed on each causal law to guarantee that all the fluents that can be affected by the performance of the corresponding action are governed. This completion requirement is practical for most industrial real-time applications and in fact provides a simple and effective treatment to the so-called frame problem.

An open shop scheduling problem with a non-bottleneck machine

This paper considers the problem of processing n jobs in a two-machine non-preemptive open shop to minimize the makespan, i.e., the maximum completion time. One of the machines is assumed to be non-bottleneck. It is shown that, unlike its flow shop counterpart, the problem is NP-hard in the ordinary sense. On the other hand, the problem is shown to be solvable by a dynamic programming algorithm that requires pseudopolynomial time. The latter algorithm can be converted into a fully polynomial approximation scheme that runs in time. An O(n log n) approximation algorithm is also designed whi finds a schedule with makespan at most 5/4 times the optimal value, and this bound is tight.

Three-machine shop scheduling with partially ordered processing routes

This paper considers the problem of sequencing n jobs in a three-machine shop with the objective of minimising the maximum completion time. The shop consists of three machines, M1,M2 and M_{3}. A job is first processed on M1 and then is assigned either the route (M2,M_{3}) or the route (M_{3},M2). Thus, for our model the processing route is given by a partial order of machines, as opposed to the linear order of machines for a job shop, or to an arbitrary sequence of machines for an open shop. The main result is on O(nlog n) time heuristic, which generates a schedule with the makespan that is at most 5/3 times the optimum value.

Implications for the scheduling of theory versus application in learning

This paper presents the findings of an experiment which looked at the effects of performing applied tasks (action learning) prior to the completion of the theoretical learning of these tasks (explanation-based learning), and vice-versa. The applied tasks took the form of laboratories for the Object-Oriented Analysis and Design (OOAD) course, theoretical learning was via lectures.

Batching decisions for assembly production systems

The two-stage assembly scheduling problem is a model for production processes that involve the assembly of final or intermediate products from basic components. In our model, there are m machines at the first stage that work in parallel, and each produces a component of a job. When all components of a job are ready, an assembly machine at the second stage completes the job by assembling the components. We study problems with the objective of minimizing the makespan, under two different types of batching that occur in some manufacturing environments. For one type, the time to process a batch on a machine is equal to the maximum of the processing times of its operations. For the other type, the batch processing time is defined as the sum of the processing times of its operations, and a setup time is required on a machine before each batch. For both models, we assume a batch availability policy, i.e., the completion times of the operations in a batch are defined to be equal to the batch completion time. We provide a fairly comprehensive complexity classification of the problems under the first type of batching, and we present a heuristic and its worst-case analysis under the second type of batching.

Approximation Algorithms for a Symmetric Quadratic Knapsack Problem and its Scheduling Applications

We consider a knapsack problem to minimize a symmetric quadratic function. We demonstrate that this symmetric quadratic knapsack problem is relevant to two problems of single machine scheduling: the problem of minimizing the weighted sum of the completion times with a single machine non-availability interval under the non-resumable scenario; and the problem of minimizing the total weighted earliness and tardiness with respect to a common small due date. We develop a polynomial-time approximation algorithm that delivers a constant worst-case performance ratio for a special form of the symmetric quadratic knapsack problem. We adapt that algorithm to our scheduling problems and achieve a better performance. For the problems under consideration no fixed-ratio approximation algorithms have been previously known.