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Search Results: 1 - 10 of 461841 matches for " A. Tadepalli "
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Solving Relational MDPs with Exogenous Events and Additive Rewards
S. Joshi,R. Khardon,P. Tadepalli,A. Raghavan,A. Fern
Computer Science , 2013,
Abstract: We formalize a simple but natural subclass of service domains for relational planning problems with object-centered, independent exogenous events and additive rewards capturing, for example, problems in inventory control. Focusing on this subclass, we present a new symbolic planning algorithm which is the first algorithm that has explicit performance guarantees for relational MDPs with exogenous events. In particular, under some technical conditions, our planning algorithm provides a monotonic lower bound on the optimal value function. To support this algorithm we present novel evaluation and reduction techniques for generalized first order decision diagrams, a knowledge representation for real-valued functions over relational world states. Our planning algorithm uses a set of focus states, which serves as a training set, to simplify and approximate the symbolic solution, and can thus be seen to perform learning for planning. A preliminary experimental evaluation demonstrates the validity of our approach.
Toward the Development of Virtual Surgical Tools to Aid Orthopaedic FE Analyses
Srinivas C. Tadepalli,Kiran H. Shivanna,Vincent A. Magnotta,Nicole A. Kallemeyn
EURASIP Journal on Advances in Signal Processing , 2010, DOI: 10.1155/2010/190293
Abstract: Computational models of joint anatomy and function provide a means for biomechanists, physicians, and physical therapists to understand the effects of repetitive motion, acute injury, and degenerative diseases. Finite element models, for example, may be used to predict the outcome of a surgical intervention or to improve the design of prosthetic implants. Countless models have been developed over the years to address a myriad of orthopaedic procedures. Unfortunately, few studies have incorporated patient-specific models. Historically, baseline anatomic models have been used due to the demands associated with model development. Moreover, surgical simulations impose additional modeling challenges. Current meshing practices do not readily accommodate the inclusion of implants. Our goal is to develop a suite of tools (virtual instruments and guides) which enable surgical procedures to be readily simulated and to facilitate the development of all-hexahedral finite element mesh definitions.
Indirect Tensile Characterization of Graphite Platelet Reinforced Vinyl Ester Nanocomposites at High-Strain Rate  [PDF]
Brahmananda Pramanik, P. Raju Mantena, Tezeswi Tadepalli, Arunachalam M. Rajendran
Open Journal of Composite Materials (OJCM) , 2014, DOI: 10.4236/ojcm.2014.44022
Abstract: An indirect tensile testing method is proposed for characterizing low strength graphite platelet reinforced vinyl ester nanocomposites at high-strain rate. In this technique, the traditional Brazilian disk (diametrical compression) test method for brittle materials is utilized along with conventional split-Hopkinson pressure bars (SHPB) for evaluating cylindrical disk specimens. The cylindrical disk specimen is held snugly in between two concave end fixtures attached to the incident and transmission bars. To eliminate the complexities of conventional strain gage application, a non-contact Laser Occluding Expansion Gage (LOEG) has been adapted for measuring the diametrical transverse expansion of the specimen under high-strain rate diametrical compressive loading. Failure diagnosis using high-speed digital photography validates the viability of utilizing this indirect test method for characterizing the tensile properties of xGnP (exfoliated graphite nanoplatelets) reinforced and additional CTBN (Carboxyl Terminated Butadiene Nitrile) toughened vinyl ester based nanocomposites. Also, quasi-static indirect tensile response agrees with previous investigations conducted using the traditional dog-bone specimen in direct tensile tests. Investigation of both quasi-static and dynamic indirect tensile test responses shows the strain rate effect on the tensile strength and energy absorbing capacity of the candidate materials. The contribution of reinforcement to the tensile properties of the candidate materials is presented.
A Formal Framework for Speedup Learning from Problems and Solutions
P. Tadepalli,B. K. Natarajan
Computer Science , 1996,
Abstract: Speedup learning seeks to improve the computational efficiency of problem solving with experience. In this paper, we develop a formal framework for learning efficient problem solving from random problems and their solutions. We apply this framework to two different representations of learned knowledge, namely control rules and macro-operators, and prove theorems that identify sufficient conditions for learning in each representation. Our proofs are constructive in that they are accompanied with learning algorithms. Our framework captures both empirical and explanation-based speedup learning in a unified fashion. We illustrate our framework with implementations in two domains: symbolic integration and Eight Puzzle. This work integrates many strands of experimental and theoretical work in machine learning, including empirical learning of control rules, macro-operator learning, Explanation-Based Learning (EBL), and Probably Approximately Correct (PAC) Learning.
Combinatorial Action of miRNAs Regulates Transcriptional and Post-Transcriptional Gene Silencing following in vivo PNS Injury
Tadepalli Adilakshmi, Ida Sudol, Nikos Tapinos
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0039674
Abstract: Injury response in the peripheral nervous system (PNS) is characterized by rapid alterations in the genetic program of Schwann cells. However, the epigenetic mechanisms modulating these changes remain elusive. Here we show that sciatic nerve injury in mice induces a cohort of 22 miRNAs, which coordinate Schwann cell differentiation and dedifferentiation through a combinatorial modulation of their positive and negative gene regulators. These miRNAs and their targeted mRNAs form functional complexes with the Argonaute-2 protein to mediate post-transcriptional gene silencing. MiR-138 and miR-709 show the highest affinity amongst the cohort, for binding and regulation of Egr2, Sox-2 and c-Jun expression following injury. Moreover, miR-709 participates in the formation of epigenetic silencing complexes with H3K27me3 and Argonaute-1 to induce transcriptional gene silencing of the Egr2 promoter. Collectively, we identified a discrete cohort of miRNAs as the central epigenetic regulators of the transition between differentiation and dedifferentiation during the acute phase of PNS injury.
Surface Fractal Analysis for Estimating the Fracture Energy Absorption of Nanoparticle Reinforced Composites
Brahmananda Pramanik,Tezeswi Tadepalli,P. Raju Mantena
Materials , 2012, DOI: 10.3390/ma5050922
Abstract: In this study, the fractal dimensions of failure surfaces of vinyl ester based nanocomposites are estimated using two classical methods, Vertical Section Method (VSM) and Slit Island Method (SIM), based on the processing of 3D digital microscopic images. Self-affine fractal geometry has been observed in the experimentally obtained failure surfaces of graphite platelet reinforced nanocomposites subjected to quasi-static uniaxial tensile and low velocity punch-shear loading. Fracture energy and fracture toughness are estimated analytically from the surface fractal dimensionality. Sensitivity studies show an exponential dependency of fracture energy and fracture toughness on the fractal dimensionality. Contribution of fracture energy to the total energy absorption of these nanoparticle reinforced composites is demonstrated. For the graphite platelet reinforced nanocomposites investigated, surface fractal analysis has depicted the probable ductile or brittle fracture propagation mechanism, depending upon the rate of loading.
Output Space Search for Structured Prediction
Janardhan Rao Doppa,Alan Fern,Prasad Tadepalli
Computer Science , 2012,
Abstract: We consider a framework for structured prediction based on search in the space of complete structured outputs. Given a structured input, an output is produced by running a time-bounded search procedure guided by a learned cost function, and then returning the least cost output uncovered during the search. This framework can be instantiated for a wide range of search spaces and search procedures, and easily incorporates arbitrary structured-prediction loss functions. In this paper, we make two main technical contributions. First, we define the limited-discrepancy search space over structured outputs, which is able to leverage powerful classification learning algorithms to improve the search space quality. Second, we give a generic cost function learning approach, where the key idea is to learn a cost function that attempts to mimic the behavior of conducting searches guided by the true loss function. Our experiments on six benchmark domains demonstrate that using our framework with only a small amount of search is sufficient for significantly improving on state-of-the-art structured-prediction performance.
Coactive Learning for Locally Optimal Problem Solving
Robby Goetschalckx,Alan Fern,Prasad Tadepalli
Computer Science , 2014,
Abstract: Coactive learning is an online problem solving setting where the solutions provided by a solver are interactively improved by a domain expert, which in turn drives learning. In this paper we extend the study of coactive learning to problems where obtaining a globally optimal or near-optimal solution may be intractable or where an expert can only be expected to make small, local improvements to a candidate solution. The goal of learning in this new setting is to minimize the cost as measured by the expert effort over time. We first establish theoretical bounds on the average cost of the existing coactive Perceptron algorithm. In addition, we consider new online algorithms that use cost-sensitive and Passive-Aggressive (PA) updates, showing similar or improved theoretical bounds. We provide an empirical evaluation of the learners in various domains, which show that the Perceptron based algorithms are quite effective and that unlike the case for online classification, the PA algorithms do not yield significant performance gains.
Toward the Development of Virtual Surgical Tools to Aid Orthopaedic FE Analyses
Tadepalli SrinivasC,Shivanna KiranH,Magnotta VincentA,Kallemeyn NicoleA
EURASIP Journal on Advances in Signal Processing , 2010,
Abstract: Computational models of joint anatomy and function provide a means for biomechanists, physicians, and physical therapists to understand the effects of repetitive motion, acute injury, and degenerative diseases. Finite element models, for example, may be used to predict the outcome of a surgical intervention or to improve the design of prosthetic implants. Countless models have been developed over the years to address a myriad of orthopaedic procedures. Unfortunately, few studies have incorporated patient-specific models. Historically, baseline anatomic models have been used due to the demands associated with model development. Moreover, surgical simulations impose additional modeling challenges. Current meshing practices do not readily accommodate the inclusion of implants. Our goal is to develop a suite of tools (virtual instruments and guides) which enable surgical procedures to be readily simulated and to facilitate the development of all-hexahedral finite element mesh definitions.
Stability Criteria for Uncertain Discrete-Time Systems under the Influence of Saturation Nonlinearities and Time-Varying Delay
Siva Kumar Tadepalli,V. Krishna Rao Kandanvli,Haranath Kar
ISRN Applied Mathematics , 2014, DOI: 10.1155/2014/861759
Abstract: The problem of global asymptotic stability of a class of uncertain discrete-time systems in the presence of saturation nonlinearities and interval-like time-varying delay in the state is considered. The uncertainties associated with the system parameters are assumed to be deterministic and normbounded. The objective of the paper is to propose stability criteria having considerably smaller numerical complexity. Two new delay-dependent stability criteria are derived by estimating the forward difference of the Lyapunov functional using the concept of reciprocal convexity and method of scale inequality, respectively. The presented criteria are compared with a previously reported criterion. A numerical example is provided to illustrate the effectiveness of the presented criteria. 1. Introduction During the implementation of fixed-point state-space discrete-time systems using computer or digital hardware, one encounters finite wordlength nonlinearities such as quantization and overflow. Such nonlinearities may lead to instability in the designed system [1, 2]. Saturation overflow nonlinearity is one of the well-known nonlinear phenomena studied in the real world [3]. The stability analysis of discrete-time systems with state saturation is considered to be an important subject of system theoretic study [1–19]. Physical systems may suffer from parameter uncertainties that arise due to modeling errors, variations in system parameters, or some ignored factors. The existence of parameter uncertainties may result in instability of the designed system [20]. In the modeling of physical systems, time delays are often introduced due to finite capabilities of information processing and data transmission among various parts of the system [17, 20–22]. Such delays are another source of instability in discrete-time systems. The stability criteria for time delay systems are broadly classified into delay-independent and delay-dependent. In general, delay-dependent approach [15, 17, 18, 20, 22–37] leads to less conservative results as compared to delay-independent approach [16, 19, 20, 38]. The delay partitioning approach has been utilized in [30, 31] for the stability analysis of systems with interval-like time-varying delay. The stability analysis of discrete-time systems involving overflow nonlinearities, parameter uncertainties, and state delays is an important problem. Delay-independent stability criteria for a class of discrete-time state-delayed systems with saturation nonlinearities have been presented in [16, 19]. A delay-dependent approach for the stability analysis
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