We consider parameter estimation in ordinary differential equations ( ODEs ) from completely observed systems , and describe an improved version of our previously reported heuristic algorithm ( IET Syst . Biol . , 2007 ) . This has developed our understanding of the contrasting regulatory mechanisms found in high and low copy number plasmids . The combination of mathematical modeling with robust statistical methods for parameter estimation can integrate experimental data into the model , leading to more realistically parameterized mathematical models . These have greater predictive power and are likely to play a crucial future role in the rational design of plasmids for use in biotechnology and bioprocessing . For a range of models , the marginal and conditional distributions are derived . We discuss likelihood based inference as well as parameter estimation using the method of moments , conduct a simulation study for the important special case of renewal processes and analyse a data set collected by Diggle and Hawtin . The regression model was the linearized version of the diffusion model for four pollution sources and unknown background source . Because the diffusion model used in the study was a large scale model , measurements separated by distances smaller than a threshold value distance appeared to be correlated in the corresponding parameter estimation problem . In most spatial experiments , after the sites are selected measurements are usually taken on some regular schedule , for instance , several times a day , or they are continuously recorded . The dynamics of the system is described by a time-convolutionless master equation where the unknown damping rate function contains all the information of the environment . By expressing the function as a polynomial of time with unknown coefficients , we convert the identification problem into a parameter estimation problem . We transform the master equation into a Bloch differential equation , and obtain a reduced system whose output is the time trace of an observable . In this paper , we present two parameterized performance models for estimation of execution time of training convolutional neural networks on the Intel many integrated core architecture . While for the first performance model we minimally use measurement techniques for parameter value estimation , in the second model we estimate more parameters based on measurements . We evaluate the prediction accuracy of performance models in the context of training three different convolutional neural network architectures on the Intel Xeon Phi . In this paper , we address the problem of parameter estimation of systems driven by chaotic signal We propose an expectation maximization ( EM ) based unscented Kalman smoother ( UKS ) to simultaneously estimate parameters of system along with the equalized chaotic signal . The proposed method can be applied to both linear and nonlinear systems driven by chaotic signals . We extend the existing SBL framework to accommodate constraints that enforce certain system properties , such as nonnegative state variables or bounded state trajectories . These properties are vital parts of a dynamical model in biology and chemistry but are often overlooked in the parameter estimation literature . As a result of this work , the extended framework can automatically build “ proper ” dynamical models from time-series data . We are interested in the first tier problem of locating peripheral sensors relative to their cluster head . We mainly exploit target measurements received by the cluster head in a parameter estimation setting which involves Sequential Monte Carlo methods , and is known to have many difficulties in practice , including particle deficiency , sensitivity to initialisation , and high computational complexity . These difficulties are exacerbated by the bearing-only modality which provides a relatively poor target observability . This non - linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research . We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation . Despite widespread use in language modeling and economics , the multinomial likelihood receives less attention in the recommender systems literature . We first present a simple algorithm for counting perfect matchings in an n-vertex graph in O*(2^{n/2 } ) time and polynomial space ; our algorithm matches the complexity bounds of the algorithm of Bjorklund [ SODA 2012 ] , but relies on inclusion-exclusion principle instead of algebraic transformations . Building upon this result , we show that the number of perfect matchings in an n-vertex graph with average degree bounded by d can be computed in O*(2^{(1-\eps_{2d})n/2 } ) time and exponential space , where \eps_{2d } is the constant obtained by us for the Traveling Salesman Problem in graphs of average degree at most 2d . Moreover we obtain a simple algorithm that counts the number of perfect matchings in an n-vertex bipartite graph of average degree at most d in O*(2^{(1 - 1/(3.55d))n/2 } ) time , improving and simplifying the recent result of Izumi and Wadayama [ FOCS 2012 ] . This study introduces a variant of the Traveling Salesman Problem , named Traveling Salesman Problem with Optional Bonus Collection , Pickup Time and Passengers ( PCVP-BoTc ) . It is a variant that incorporates elements of the Prize Collecting Traveling Salesman Problem and Ridesharing into the PCV . The linear ordering problem ( LOP ) is an $ \mathcal{NP}$-hard combinatorial optimization problem with a wide range of applications in economics , archaeology , the social sciences , scheduling , and biology . It has , however , drawn little attention compared to other closely related problems such as the quadratic assignment problem and the traveling salesman problem . Due to its computational complexity , it is essential in practice to develop solution approaches to rapidly search for solution of high-quality . The algorithms are discussed both in continuous and discrete time domain , and their validity is proved . It is also shown that the solution of this facility location problem is the set of points that are the geometric medians of their corresponding Voronoi cells . It is equally important for both private as well as the public sector . The k-center problem is one of the basic problems in facility location . The aim is to locate a set of k facilities for a given set of demand points , such that for any demand point the nearest facility is as close as possible . Facility layout presents a complex design challenge . It is particularly regarded as a vital issue in Chinese society and throughout Asian countries where Feng Shui , the ancient and revered Chinese practice governing environmental esthetics , has played and continues to play a vital role in man-made design . The best region search ( BRS ) is to find an optimal area with fixed range size , in which the object set has the maximum submodular function value . The BRS problem has long been studied because of its wide application in spatial data mining , facility locating and so on . However , most of the existing work focus on either Euclidean space or motionless objects , which is not applicable in many real-life cases . The network design problems can be generalized in a natural way by considering a related problem relative to a given partition of the nodes of the graph into node sets ( clusters ) , while he feasibility constraints are expressed in terms of the clusters . Several applications of the generalized network design problems arise in fields of telecommunications , facility and locations , transportation , energy distribution , etc . In this paper we present a new approach to the generalized network design problems based on distinguishing between global connections ( connections between clusters ) and local connections ( connections between nodes from different clusters ) . " This paper presents the use of a facility location case to stimulate the class experience . This case includes the quantitative topics of regression analysis , experimental designs , ANOVA , sensitivity analysis , cost analysis , and above all the use of a computer program to solve a very sophisticated and large warehouse location problem . Although both the theory and practice of integrated logistics have recognized the fact that strategic and tactical decisions are interrelated , to the best of our knowledge , our paper is the first to offer closed-form results demonstrating the relationship explicitly . We propose an efficient solution approach utilizing the implied facility assignment costs and we demonstrate that significant savings are realizable when the inventory decisions and cargo costs are modelled explicitly for facility location purposes . Timing optimization for logic circuits is one of the key steps in logic synthesis . Extant research data are mainly proposed based on various intelligence algorithms . A new printability model is proposed to handle partially coherent illuminations . The complex printability and timing optimization problem is solved in a two-phase approach . The difficulty of the printability optimization due to its multimodal nature is handled with a sensitivity-based heuristic . The method involves modeling of the pull-up and pull-down resistances using approximated drain saturation current device equations for the buffers together with lumped resistive capacitive elements for the interconnects . Compared to circuit simulation results , the analytical model provides more than four orders of magnitude speedup while maintaining an average error of 0.26 % with 3.0 % standard deviation over the entire range of power supply and circuit parameters variations , making it suitable for timing analysis and optimization . However , with DPL , adjacent features can have distinct mean CDs , and uncorrelated CD variations . This introduces a new set of ‘ bimodal ’ challenges for timing analysis and optimization . We assess the potential impact of DPL on timing analysis error and guardbanding , and find that the traditional ‘ unimodal ’ characterization and analysis framework may not be viable for DPL . However , with DPL , adjacent features can have distinct mean CDs , and uncorrelated CD variations . This introduces a new set of “ bimodal ” challenges for timing analysis and optimization . We assess the potential impact of bimodal CD distribution on timing analysis and guardbanding , and find that the traditional “ unimodal ” characterization and analysis framework may not be viable for DPL . In the detailed placement stage , we develop a constraint-monitoring and satisfaction mechanism in a network ( n/w ) flow based detailed placement framework proposed recently , and empirically show its near-optimality . We establish the effectiveness of our general constraint-satisfaction methods by applying them to the problem of timing-driven optimization under power constraints . We overlay our algorithms on a recently developed unconstrained timing-driven incremental placement method flow-place . Incremental path-based timing analysis ( PBA ) is a pivotal step in the timing optimization flow . A core building block analyzes the timing path-by-path subject to a critical amount of incremental changes on the design . By partitioning all the dead spaces into blocks while doing the packing , the buffer allocation can be handled as an integral part in the floorplanning process . Our method is based on a simulated annealing approach which is divided into two phases : timing optimization phase and buffer insertion phase . Since there is more freedom for floorplan optimization , the floorplanning algorithm integrated with buffer planning can result in better time performance and chip area . In programme dynamic analysis , control flow integrity ( CFI ) is an efficient way to investigate programme 's behaviour . By detecting these CF instructions , researchers can obtain programme 's runtime information and execution status accurately . We describe an approach to control-flow integrity protection for real-time systems . We present TrackOS , a security-aware real-time operating system . TrackOS checks a task ’s control stack against a statically-generated call graph , generated by an abstract interpretation-based tool that requires no source code . The monitoring is done from a dedicated task , the schedule of which is controlled by the real-time operating system scheduler . Finally , we implement a version of software-based attestation ( SWATT ) to ensure program-data integrity to strengthen our control-flow integrity checks . We demonstrate the feasibility of our approach by monitoring an open source autopilot in flight . Compiler techniques have been deployed to prevent various security attacks . Examples include mitigating memory access corruption , control flow integrity checks , race detection , software diversity , etc . Binary rewriting is changing the semantics of a program without having the source code at hand . It is used for diverse purposes , such as emulation ( e.g. , QEMU ) , optimization ( e.g. , DynInst ) , observation ( e.g. , Valgrind ) , and hardening ( e.g. , Control flow integrity enforcement ) . This survey gives detailed insight into the development and state-of-the-art in binary rewriting by reviewing 67 publications from 1966 to 2018 . It takes full advantage of the Trusted Platform Module ( TPM ) and gives full consideration to the memory organization of the JVM . TCFI4J takes the integrity of part of JVMs memory image into account for the control flow integrity enforcement . The method presented in this paper can provide the user information about an applications behavior . Many existing codereuse defenses have relied upon a particular attribute of the attack in question ( e.g. , the frequency of ret instructions in a return-oriented attack ) , which leads to an incomplete protection , while a smaller number of efforts in protecting all exploitable control flow transfers suffer from limited deploy-ability due to high performance overhead . In this paper , we present a novel cost-effective defense technique called control flow locking , which allows for effective enforcement of control flow integrity with a small performance overhead . Specifically , instead of immediately determining whether a control flow violation happens before the control flow transfer takes place , control flow locking lazily detects the violation after the transfer . Dynamic Binary Modification ( DBM ) tools are useful for cross-platform execution of binaries and are powerful run time environments that allow execution optimizations , instrumentation and profiling . These tools have also been used as enablers for control-flow integrity verification , a process that consists in the observation and analysis of a program 's execution path focusing on the detection of anomalies , such as those arising from flow corruption based software attacks . Even though this class of tools helps us in identifying a myriad of attacks , it is typically expensive at run time and introduce significant overhead to the program execution . Large scale optimization is a very challenging task in optimization area . The variable interaction in non-separable problems is a primary source of performance loss , especially for large scale problems . Scheduling is also well-known as a difficult problem area in Operations Research , optimization and computer science domains . Due to the complexity in solving , scheduling problems attracted more attention in the literature than any other optimization problem areas . Although small-scale scheduling problems can be solved optimally using mathematical programming approaches and exact methods , the computational effort required using such methodologies for any medium to large-sized problem is beyond the capability of the current computational resources . Surrogate-assisted optimization has proven to be very successful if applied to industrial problems . The use of a data-driven surrogate model of an objective function during an optimization cycle has many bene ts , such as being cheap to evaluate and further providing both information about the objective landscape and the parameter space . In preliminary work , it was researched how surrogate-assisted optimization can help to optimize the structure of a neural network ( NN ) controller . Finally , the models developed are manipulated together to provide an accurate graphical representation for the startup condition and design constraints lines where the interdependence of different design variables is taken into account . The approach is flexible in the sense that it enables designers to track and tune different design parameters during the optimization cycle . It can also be extended by analogy to other existing different topologies . Complexity of the maintenance of pharmacology subject , its big theoretical material , need of application of integrative knowledge both across and down , oblige to systematic search concerning teaching optimization , target organization of classroom and out-of-class independent work of students . To give a explanation for the necessity and conditions of Teaching Optimization according to the development of Modern Educational Technology , and the Application of Internet and Mobile Internet . And , to propose the key points of courseware making according to the teaching experience by oneself . The individuals with strong physique can continue to grow , while the individuals with weak physique stop growing . Results show that the algorithm has characteristics of global convergence and high convergence speed for the complicated functions optimization problems , especially for high dimensional function optimization problems . In the first problem , the inverse of an unknown covariance matrix of a multivariate normal distribution is estimated , under the assumption that it is sparse . To this end , an l1 regularized log-determinant optimization problem needs to be solved . This task is challenging especially for large-scale datasets , due to time and memory limitations . Then , the approximated reconstruction error function is minimized as the optimization objective function while the frequency selectivity performance metric function of the sub-filter is taken as the constraint function . Further , the optimal step-size optimization problem for each iteration is solved using a similar iterative strategy . Additionally , the proposed technique can also be utilized to optimize the coefficients of a class of structurally perfect reconstruction two-channel LP FIR bi-orthogonal filter bank , as well as a class of two-channel graph filter bank . In this paper , a single-objective firefly algorithm is used for the optimization of two- and five-degree-of-freedom vehicle vibration models , considering the two and five conflicting objective functions , simultaneously . The conflicting objective functions for the two-degree-of-freedom model that have been considered for minimization involve the sprung mass acceleration and the relative displacement between the sprung mass and the tire . The contribution of the paper lies on the fact that for the first time genetic optimization and simulated annealing methods are involved in traffic engineering problems . In addition , a novel hybrid method based on genetic optimization is proposed with performance comparable to that obtained by linear programming techniques ( Simplex ) , which are the optimum solvers in the case of linear cost functions optimization under linear constraints as it takes place in the herein proposed traffic engineering problem formulations . Finally , the contribution of the paper is that for the first time genetic optimization and simulated annealing techniques are used to solve real world problems of thousands of variables , achieving in the case of genetic algorithms , near optimal results . This thesis addresses the research gap by establishing a modelling framework that extends the current SoS frameworks to link the different views , and to satisfy real-time and reliability requirements . We provide a methodology that uses design exploration techniques and tools for SoS architecture optimization . The use of architecture patterns is introduced to enhance the model re-usability and facilitate the model evolution . Five enhancements are introduced here ( multiple weighted roulettes , multiple cross over points , multiple mates , utilizing the D4 wavelets and using normal distribution for selecting the initial population ) . Then the new enhanced GA was applied to a dynamic large optimization problem that uses symbolic data to differentiate between edible and poisonous mushrooms using twenty two different characteristics . Results was obtained using the enhanced GA as well as the standard GA . The particular problem statement is the steep rise of competition in the rowing sport that requires looking for modern means and methods of the training process as a system-forming factor for the highest sport achievements . Constant search of optimization ways of training requires the use of modern technical means and methods for improvement of the conditional parameters of athletes in the rowing sport . One of them is the usage of special rowing machines that meet the requirements of the competitive exercises structure on spatial construction of the stroke and its power component . The original work on filter flow focused on the problem of estimating an appropriately regularized / constrained flow between a given pair of images . This yielded convex but impractically large optimization problems ( e.g. , hours of computation to compute a single flow ) . Instead of solving for an optimal filter flow , we propose to directly predict a filter flow given an input image using a convolutional neural net ( CNN ) to regress the filter weights . [ reference ] , the shortcut connections are the most direct paths for the information to propagate . Multiplicative manipulations ( scaling , gating , 1 1 convolutions , and dropout ) on the shortcuts can hamper information propagation and lead to optimization problems . It is noteworthy that the gating and 1 1 convolutional shortcuts introduce more parameters , and should have stronger representational abilities than identity shortcuts . Therefore , only a zero - padded shortcut or projection shortcut can be used for all the residual units . However , as discussed in , a projection shortcut can hamper information propagation and lead to optimization problems , especially for very deep networks . On the other hand , we found that the zero - padded shortcut does not lead to the overfitting problem because no additional parameters exist , and surprisingly , it shows significant generalization ability compared to other shortcuts . Convolution and deconvolution are combined , modeling the restoration problem by extracting primary image content and recovering details . More importantly , we propose to use skip connections , which helps on recovering clean images and tackles the optimization difficulty caused by gradient vanishing , and thus obtains performance gains when the network goes deeper . Experimental results and our analysis show that our network achieves better performance than state - of - the - art methods on image denoising , image super - resolution , JPEG deblocking and image inpainting . There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand . In this work , we consider this problem through the framework of Bayesian optimization , in which a learning algorithm ’s generalization performance is modeled as a sample from a Gaussian process ( GP ) . We show that certain choices for the nature of the GP , such as the type of kernel and the treatment of its hyperparameters , can play a crucial role in obtaining a good optimizer that can achieve expertlevel performance . section : C Experiment Setup For all experiments , we use Adam [ reference ] for online optimization and apply batch normalization [ reference ] after most layers , the details of which are specified for each experiment . We use an up - convolutional architecture for the generator networks Although TED ( [ reference ] ) is well modeled — very accurate and intuitive , there are two bottlenecks . First , the objective is a combinatorial optimization problem . It is NP - hard to exhaustively search the optimal subset from . section : A.2 OPTIMAL CONDITIONS OF EBGAN In [ reference ] , the training objectives of the generator and the discriminator can not be written as a single minimax optimization problem since the margin structure is only applied to the objective of the discriminator . In addition , the discriminator is designed to produce the mean squared reconstruction error of an auto - encoder structure . We can formulate the problem for the finetuning decoder , as either a collection of single binary problems or multiple problems put together . The single binary problem formulation allows for a focus on one class and end - to - end optimization will only have one error signal . However , because the label classes are imbalanced in all categories , this may lead to a sparse gradient signal for the positive label , which may impact recall and precision . All experiments we report in this paper are run with this option activated . Classifier Optimization : In PyDCI we use scikit - learn ’s implementation of linear SVMs ( LinearSVC , which is in turn based on the liblinear package ) , instead of using Joachims ’ SVM package as we had done in JaDCI . To reduce high variance effect due to ZO gradient estimator , we further introduce another RNN to learn the Gaussian sampling rule and dynamically guide the query direction sampling . Our learned optimizer outperforms hand-designed algorithms in terms of convergence rate and final solution on both synthetic and practical ZO optimization tasks ( in particular , the black-box adversarial attack task , which is one of the most widely used tasks of ZO optimization ) . We finally conduct extensive analytical experiments to demonstrate the effectiveness of our proposed optimizer . On the application side , we will highlight one appealing application of ZO optimization to studying the robustness of deep neural networks - practical and efficient adversarial attacks that generate adversarial examples from a black-box machine learning model . We will also summarize potential research directions regarding ZO optimization , big data challenges and some open-ended data mining and machine learning problems . This paper briefly introduces the kinds and structural frames of LAN , and expounds the optimization of LAN including the optimization of flow control , the optimization of input medium , and the optimization of server and work station ) .
We consider parameter estimation in ordinary differential equations ( ODEs ) from completely observed systems , and describe an improved version of our previously reported heuristic algorithm ( IET Syst . Biol . , 2007 ) .
This has developed our understanding of the contrasting regulatory mechanisms found in high and low copy number plasmids . The combination of mathematical modeling with robust statistical methods for parameter estimation can integrate experimental data into the model , leading to more realistically parameterized mathematical models . These have greater predictive power and are likely to play a crucial future role in the rational design of plasmids for use in biotechnology and bioprocessing .
For a range of models , the marginal and conditional distributions are derived . We discuss likelihood based inference as well as parameter estimation using the method of moments , conduct a simulation study for the important special case of renewal processes and analyse a data set collected by Diggle and Hawtin .
The regression model was the linearized version of the diffusion model for four pollution sources and unknown background source . Because the diffusion model used in the study was a large scale model , measurements separated by distances smaller than a threshold value distance appeared to be correlated in the corresponding parameter estimation problem . In most spatial experiments , after the sites are selected measurements are usually taken on some regular schedule , for instance , several times a day , or they are continuously recorded .
The dynamics of the system is described by a time-convolutionless master equation where the unknown damping rate function contains all the information of the environment . By expressing the function as a polynomial of time with unknown coefficients , we convert the identification problem into a parameter estimation problem . We transform the master equation into a Bloch differential equation , and obtain a reduced system whose output is the time trace of an observable .
In this paper , we present two parameterized performance models for estimation of execution time of training convolutional neural networks on the Intel many integrated core architecture . While for the first performance model we minimally use measurement techniques for parameter value estimation , in the second model we estimate more parameters based on measurements . We evaluate the prediction accuracy of performance models in the context of training three different convolutional neural network architectures on the Intel Xeon Phi .
In this paper , we address the problem of parameter estimation of systems driven by chaotic signal We propose an expectation maximization ( EM ) based unscented Kalman smoother ( UKS ) to simultaneously estimate parameters of system along with the equalized chaotic signal . The proposed method can be applied to both linear and nonlinear systems driven by chaotic signals .
We extend the existing SBL framework to accommodate constraints that enforce certain system properties , such as nonnegative state variables or bounded state trajectories . These properties are vital parts of a dynamical model in biology and chemistry but are often overlooked in the parameter estimation literature . As a result of this work , the extended framework can automatically build “ proper ” dynamical models from time-series data .
We are interested in the first tier problem of locating peripheral sensors relative to their cluster head . We mainly exploit target measurements received by the cluster head in a parameter estimation setting which involves Sequential Monte Carlo methods , and is known to have many difficulties in practice , including particle deficiency , sensitivity to initialisation , and high computational complexity . These difficulties are exacerbated by the bearing-only modality which provides a relatively poor target observability .
This non - linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research . We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation . Despite widespread use in language modeling and economics , the multinomial likelihood receives less attention in the recommender systems literature .
We first present a simple algorithm for counting perfect matchings in an n-vertex graph in O*(2^{n/2 } ) time and polynomial space ; our algorithm matches the complexity bounds of the algorithm of Bjorklund [ SODA 2012 ] , but relies on inclusion-exclusion principle instead of algebraic transformations . Building upon this result , we show that the number of perfect matchings in an n-vertex graph with average degree bounded by d can be computed in O*(2^{(1-\eps_{2d})n/2 } ) time and exponential space , where \eps_{2d } is the constant obtained by us for the Traveling Salesman Problem in graphs of average degree at most 2d . Moreover we obtain a simple algorithm that counts the number of perfect matchings in an n-vertex bipartite graph of average degree at most d in O*(2^{(1 - 1/(3.55d))n/2 } ) time , improving and simplifying the recent result of Izumi and Wadayama [ FOCS 2012 ] .
This study introduces a variant of the Traveling Salesman Problem , named Traveling Salesman Problem with Optional Bonus Collection , Pickup Time and Passengers ( PCVP-BoTc ) . It is a variant that incorporates elements of the Prize Collecting Traveling Salesman Problem and Ridesharing into the PCV .
The linear ordering problem ( LOP ) is an $ \mathcal{NP}$-hard combinatorial optimization problem with a wide range of applications in economics , archaeology , the social sciences , scheduling , and biology . It has , however , drawn little attention compared to other closely related problems such as the quadratic assignment problem and the traveling salesman problem . Due to its computational complexity , it is essential in practice to develop solution approaches to rapidly search for solution of high-quality .
The algorithms are discussed both in continuous and discrete time domain , and their validity is proved . It is also shown that the solution of this facility location problem is the set of points that are the geometric medians of their corresponding Voronoi cells .
It is equally important for both private as well as the public sector . The k-center problem is one of the basic problems in facility location . The aim is to locate a set of k facilities for a given set of demand points , such that for any demand point the nearest facility is as close as possible .
Facility layout presents a complex design challenge . It is particularly regarded as a vital issue in Chinese society and throughout Asian countries where Feng Shui , the ancient and revered Chinese practice governing environmental esthetics , has played and continues to play a vital role in man-made design .
The best region search ( BRS ) is to find an optimal area with fixed range size , in which the object set has the maximum submodular function value . The BRS problem has long been studied because of its wide application in spatial data mining , facility locating and so on . However , most of the existing work focus on either Euclidean space or motionless objects , which is not applicable in many real-life cases .
The network design problems can be generalized in a natural way by considering a related problem relative to a given partition of the nodes of the graph into node sets ( clusters ) , while he feasibility constraints are expressed in terms of the clusters . Several applications of the generalized network design problems arise in fields of telecommunications , facility and locations , transportation , energy distribution , etc . In this paper we present a new approach to the generalized network design problems based on distinguishing between global connections ( connections between clusters ) and local connections ( connections between nodes from different clusters ) .
" This paper presents the use of a facility location case to stimulate the class experience . This case includes the quantitative topics of regression analysis , experimental designs , ANOVA , sensitivity analysis , cost analysis , and above all the use of a computer program to solve a very sophisticated and large warehouse location problem .
Although both the theory and practice of integrated logistics have recognized the fact that strategic and tactical decisions are interrelated , to the best of our knowledge , our paper is the first to offer closed-form results demonstrating the relationship explicitly . We propose an efficient solution approach utilizing the implied facility assignment costs and we demonstrate that significant savings are realizable when the inventory decisions and cargo costs are modelled explicitly for facility location purposes .
Timing optimization for logic circuits is one of the key steps in logic synthesis . Extant research data are mainly proposed based on various intelligence algorithms .
A new printability model is proposed to handle partially coherent illuminations . The complex printability and timing optimization problem is solved in a two-phase approach . The difficulty of the printability optimization due to its multimodal nature is handled with a sensitivity-based heuristic .
The method involves modeling of the pull-up and pull-down resistances using approximated drain saturation current device equations for the buffers together with lumped resistive capacitive elements for the interconnects . Compared to circuit simulation results , the analytical model provides more than four orders of magnitude speedup while maintaining an average error of 0.26 % with 3.0 % standard deviation over the entire range of power supply and circuit parameters variations , making it suitable for timing analysis and optimization .
However , with DPL , adjacent features can have distinct mean CDs , and uncorrelated CD variations . This introduces a new set of ‘ bimodal ’ challenges for timing analysis and optimization . We assess the potential impact of DPL on timing analysis error and guardbanding , and find that the traditional ‘ unimodal ’ characterization and analysis framework may not be viable for DPL .
However , with DPL , adjacent features can have distinct mean CDs , and uncorrelated CD variations . This introduces a new set of “ bimodal ” challenges for timing analysis and optimization . We assess the potential impact of bimodal CD distribution on timing analysis and guardbanding , and find that the traditional “ unimodal ” characterization and analysis framework may not be viable for DPL .
In the detailed placement stage , we develop a constraint-monitoring and satisfaction mechanism in a network ( n/w ) flow based detailed placement framework proposed recently , and empirically show its near-optimality . We establish the effectiveness of our general constraint-satisfaction methods by applying them to the problem of timing-driven optimization under power constraints . We overlay our algorithms on a recently developed unconstrained timing-driven incremental placement method flow-place .
Incremental path-based timing analysis ( PBA ) is a pivotal step in the timing optimization flow . A core building block analyzes the timing path-by-path subject to a critical amount of incremental changes on the design .
By partitioning all the dead spaces into blocks while doing the packing , the buffer allocation can be handled as an integral part in the floorplanning process . Our method is based on a simulated annealing approach which is divided into two phases : timing optimization phase and buffer insertion phase . Since there is more freedom for floorplan optimization , the floorplanning algorithm integrated with buffer planning can result in better time performance and chip area .
In programme dynamic analysis , control flow integrity ( CFI ) is an efficient way to investigate programme 's behaviour . By detecting these CF instructions , researchers can obtain programme 's runtime information and execution status accurately .
We describe an approach to control-flow integrity protection for real-time systems . We present TrackOS , a security-aware real-time operating system . TrackOS checks a task ’s control stack against a statically-generated call graph , generated by an abstract interpretation-based tool that requires no source code . The monitoring is done from a dedicated task , the schedule of which is controlled by the real-time operating system scheduler . Finally , we implement a version of software-based attestation ( SWATT ) to ensure program-data integrity to strengthen our control-flow integrity checks . We demonstrate the feasibility of our approach by monitoring an open source autopilot in flight .
Compiler techniques have been deployed to prevent various security attacks . Examples include mitigating memory access corruption , control flow integrity checks , race detection , software diversity , etc .
Binary rewriting is changing the semantics of a program without having the source code at hand . It is used for diverse purposes , such as emulation ( e.g. , QEMU ) , optimization ( e.g. , DynInst ) , observation ( e.g. , Valgrind ) , and hardening ( e.g. , Control flow integrity enforcement ) . This survey gives detailed insight into the development and state-of-the-art in binary rewriting by reviewing 67 publications from 1966 to 2018 .
It takes full advantage of the Trusted Platform Module ( TPM ) and gives full consideration to the memory organization of the JVM . TCFI4J takes the integrity of part of JVMs memory image into account for the control flow integrity enforcement . The method presented in this paper can provide the user information about an applications behavior .
Many existing codereuse defenses have relied upon a particular attribute of the attack in question ( e.g. , the frequency of ret instructions in a return-oriented attack ) , which leads to an incomplete protection , while a smaller number of efforts in protecting all exploitable control flow transfers suffer from limited deploy-ability due to high performance overhead . In this paper , we present a novel cost-effective defense technique called control flow locking , which allows for effective enforcement of control flow integrity with a small performance overhead . Specifically , instead of immediately determining whether a control flow violation happens before the control flow transfer takes place , control flow locking lazily detects the violation after the transfer .
Dynamic Binary Modification ( DBM ) tools are useful for cross-platform execution of binaries and are powerful run time environments that allow execution optimizations , instrumentation and profiling . These tools have also been used as enablers for control-flow integrity verification , a process that consists in the observation and analysis of a program 's execution path focusing on the detection of anomalies , such as those arising from flow corruption based software attacks . Even though this class of tools helps us in identifying a myriad of attacks , it is typically expensive at run time and introduce significant overhead to the program execution .
Large scale optimization is a very challenging task in optimization area . The variable interaction in non-separable problems is a primary source of performance loss , especially for large scale problems .
Scheduling is also well-known as a difficult problem area in Operations Research , optimization and computer science domains . Due to the complexity in solving , scheduling problems attracted more attention in the literature than any other optimization problem areas . Although small-scale scheduling problems can be solved optimally using mathematical programming approaches and exact methods , the computational effort required using such methodologies for any medium to large-sized problem is beyond the capability of the current computational resources .
Surrogate-assisted optimization has proven to be very successful if applied to industrial problems . The use of a data-driven surrogate model of an objective function during an optimization cycle has many bene ts , such as being cheap to evaluate and further providing both information about the objective landscape and the parameter space . In preliminary work , it was researched how surrogate-assisted optimization can help to optimize the structure of a neural network ( NN ) controller .
Finally , the models developed are manipulated together to provide an accurate graphical representation for the startup condition and design constraints lines where the interdependence of different design variables is taken into account . The approach is flexible in the sense that it enables designers to track and tune different design parameters during the optimization cycle . It can also be extended by analogy to other existing different topologies .
Complexity of the maintenance of pharmacology subject , its big theoretical material , need of application of integrative knowledge both across and down , oblige to systematic search concerning teaching optimization , target organization of classroom and out-of-class independent work of students .
To give a explanation for the necessity and conditions of Teaching Optimization according to the development of Modern Educational Technology , and the Application of Internet and Mobile Internet . And , to propose the key points of courseware making according to the teaching experience by oneself .
The individuals with strong physique can continue to grow , while the individuals with weak physique stop growing . Results show that the algorithm has characteristics of global convergence and high convergence speed for the complicated functions optimization problems , especially for high dimensional function optimization problems .
In the first problem , the inverse of an unknown covariance matrix of a multivariate normal distribution is estimated , under the assumption that it is sparse . To this end , an l1 regularized log-determinant optimization problem needs to be solved . This task is challenging especially for large-scale datasets , due to time and memory limitations .
Then , the approximated reconstruction error function is minimized as the optimization objective function while the frequency selectivity performance metric function of the sub-filter is taken as the constraint function . Further , the optimal step-size optimization problem for each iteration is solved using a similar iterative strategy . Additionally , the proposed technique can also be utilized to optimize the coefficients of a class of structurally perfect reconstruction two-channel LP FIR bi-orthogonal filter bank , as well as a class of two-channel graph filter bank .
In this paper , a single-objective firefly algorithm is used for the optimization of two- and five-degree-of-freedom vehicle vibration models , considering the two and five conflicting objective functions , simultaneously . The conflicting objective functions for the two-degree-of-freedom model that have been considered for minimization involve the sprung mass acceleration and the relative displacement between the sprung mass and the tire .
The contribution of the paper lies on the fact that for the first time genetic optimization and simulated annealing methods are involved in traffic engineering problems . In addition , a novel hybrid method based on genetic optimization is proposed with performance comparable to that obtained by linear programming techniques ( Simplex ) , which are the optimum solvers in the case of linear cost functions optimization under linear constraints as it takes place in the herein proposed traffic engineering problem formulations . Finally , the contribution of the paper is that for the first time genetic optimization and simulated annealing techniques are used to solve real world problems of thousands of variables , achieving in the case of genetic algorithms , near optimal results .
This thesis addresses the research gap by establishing a modelling framework that extends the current SoS frameworks to link the different views , and to satisfy real-time and reliability requirements . We provide a methodology that uses design exploration techniques and tools for SoS architecture optimization . The use of architecture patterns is introduced to enhance the model re-usability and facilitate the model evolution .
Five enhancements are introduced here ( multiple weighted roulettes , multiple cross over points , multiple mates , utilizing the D4 wavelets and using normal distribution for selecting the initial population ) . Then the new enhanced GA was applied to a dynamic large optimization problem that uses symbolic data to differentiate between edible and poisonous mushrooms using twenty two different characteristics . Results was obtained using the enhanced GA as well as the standard GA .
The particular problem statement is the steep rise of competition in the rowing sport that requires looking for modern means and methods of the training process as a system-forming factor for the highest sport achievements . Constant search of optimization ways of training requires the use of modern technical means and methods for improvement of the conditional parameters of athletes in the rowing sport . One of them is the usage of special rowing machines that meet the requirements of the competitive exercises structure on spatial construction of the stroke and its power component .
The original work on filter flow focused on the problem of estimating an appropriately regularized / constrained flow between a given pair of images . This yielded convex but impractically large optimization problems ( e.g. , hours of computation to compute a single flow ) . Instead of solving for an optimal filter flow , we propose to directly predict a filter flow given an input image using a convolutional neural net ( CNN ) to regress the filter weights .
[ reference ] , the shortcut connections are the most direct paths for the information to propagate . Multiplicative manipulations ( scaling , gating , 1 1 convolutions , and dropout ) on the shortcuts can hamper information propagation and lead to optimization problems . It is noteworthy that the gating and 1 1 convolutional shortcuts introduce more parameters , and should have stronger representational abilities than identity shortcuts .
Therefore , only a zero - padded shortcut or projection shortcut can be used for all the residual units . However , as discussed in , a projection shortcut can hamper information propagation and lead to optimization problems , especially for very deep networks . On the other hand , we found that the zero - padded shortcut does not lead to the overfitting problem because no additional parameters exist , and surprisingly , it shows significant generalization ability compared to other shortcuts .
Convolution and deconvolution are combined , modeling the restoration problem by extracting primary image content and recovering details . More importantly , we propose to use skip connections , which helps on recovering clean images and tackles the optimization difficulty caused by gradient vanishing , and thus obtains performance gains when the network goes deeper . Experimental results and our analysis show that our network achieves better performance than state - of - the - art methods on image denoising , image super - resolution , JPEG deblocking and image inpainting .
There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand . In this work , we consider this problem through the framework of Bayesian optimization , in which a learning algorithm ’s generalization performance is modeled as a sample from a Gaussian process ( GP ) . We show that certain choices for the nature of the GP , such as the type of kernel and the treatment of its hyperparameters , can play a crucial role in obtaining a good optimizer that can achieve expertlevel performance .
section : C Experiment Setup For all experiments , we use Adam [ reference ] for online optimization and apply batch normalization [ reference ] after most layers , the details of which are specified for each experiment . We use an up - convolutional architecture for the generator networks
Although TED ( [ reference ] ) is well modeled — very accurate and intuitive , there are two bottlenecks . First , the objective is a combinatorial optimization problem . It is NP - hard to exhaustively search the optimal subset from .
section : A.2 OPTIMAL CONDITIONS OF EBGAN In [ reference ] , the training objectives of the generator and the discriminator can not be written as a single minimax optimization problem since the margin structure is only applied to the objective of the discriminator . In addition , the discriminator is designed to produce the mean squared reconstruction error of an auto - encoder structure .
We can formulate the problem for the finetuning decoder , as either a collection of single binary problems or multiple problems put together . The single binary problem formulation allows for a focus on one class and end - to - end optimization will only have one error signal . However , because the label classes are imbalanced in all categories , this may lead to a sparse gradient signal for the positive label , which may impact recall and precision .
All experiments we report in this paper are run with this option activated . Classifier Optimization : In PyDCI we use scikit - learn ’s implementation of linear SVMs ( LinearSVC , which is in turn based on the liblinear package ) , instead of using Joachims ’ SVM package as we had done in JaDCI .
To reduce high variance effect due to ZO gradient estimator , we further introduce another RNN to learn the Gaussian sampling rule and dynamically guide the query direction sampling . Our learned optimizer outperforms hand-designed algorithms in terms of convergence rate and final solution on both synthetic and practical ZO optimization tasks ( in particular , the black-box adversarial attack task , which is one of the most widely used tasks of ZO optimization ) . We finally conduct extensive analytical experiments to demonstrate the effectiveness of our proposed optimizer .
On the application side , we will highlight one appealing application of ZO optimization to studying the robustness of deep neural networks - practical and efficient adversarial attacks that generate adversarial examples from a black-box machine learning model . We will also summarize potential research directions regarding ZO optimization , big data challenges and some open-ended data mining and machine learning problems .
This paper briefly introduces the kinds and structural frames of LAN , and expounds the optimization of LAN including the optimization of flow control , the optimization of input medium , and the optimization of server and work station ) .
Improved Parameter Estimation for Completely Observed Ordinary Differential Equations with Application to Biological Systems Modeling Plasmid Regulatory Systems State estimation for temporal point processes 16 Design of spatial experiments: Model fitting and prediction A Linear Least Squares Method to Identify the Damping Rate Function for a Non-Markovian Single Qubit System Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures Chaos based semi-blind system identification using an EM-UKS estimator An Automatic Sparse Model Estimation Method Guided by Constraints That Encode System Properties Target aided online sensor localisation in bearing only clusters Variational Autoencoders for Collaborative Filtering Faster exponential-time algorithms in graphs of bounded average degree Traveling Salesman Problem with Optional Bonus Collection, Pickup Time and Passengers Revised GRASP with path-relinking for the linear ordering problem Facility Location Optimization via Multi-Agent Robotic Systems Heuristic Approaches for K-Center Problem Fuzzy AHP and fuzzy TOPSIS integrated multicriteria decision-making scheme employing Chinese environmental esthetics for facility layout design evaluation Monitoring best region in spatial data streams in road networks A new approach to generalized network design problems A Facility Location Case to Stimulate Classroom Interaction Characterization of facility assignment costs for a location-inventory model under truckload distribution Novel Verification Method for Timing Optimization Based on DPSO Wire Sizing and Spacing for Lithographic Printability and Timing Optimization Accurate analytical delay modeling of CMOS clock buffers considering power supply variations Timing analysis and optimization implications of bimodal CD distribution in double patterning lithography Timing Yield-Aware Color Reassignment and Detailed Placement Perturbation for Bimodal CD Distribution in Double Patterning Lithography Constraint satisfaction in incremental placement with application to performance optimization under power constraints On fast timing closure: speeding up incremental path-based timing analysis with mapreduce An integrated floorplanning with an efficient buffer planning algorithm RTTV: a dynamic CFI measurement tool based on TPM TrackOS: A Security-Aware Real-Time Operating System New Opportunities for Compilers in Computer Security From Hack to Elaborate Technique—A Survey on Binary Rewriting Trusted Control Flow Integrity for JVM-Based Application Mitigating code-reuse attacks with control-flow locking Leveraging Optimization Methods for Dynamically Assisted Control-Flow Integrity Mechanisms Cooperative coevolution with dependency identification grouping for large scale global optimization Modern heuristics for scheduling Surrogate-Assisted Learning of Neural Networks Behavior modelling for LC tank based oscillators Преподавание фармакологии студентам, обучающимся по специальности «Фармация» The Brief Introduction to the Application of TV Courseware in Teaching Optimization SIS epidemic model-based optimization A Multilevel Framework for Sparse Optimization with Application to Inverse Covariance Estimation and Logistic Regression Iterative technique for optimizing two-channel: Quadrature mirror, bi-orthogonal and graph filter bank Firefly algorithm based optimum design of vehicle suspension systems Traffic engineering in multi-service networks comparing genetic and simulated annealing optimization techniques A modelling framework for systems-of-systems with real-time and reliability requirements Application of Enhanced Genetic Algorithm:To Symbolic Audubon Data Specialių treniruoklių taikymo eksperimentinis pagrindimas rengiant kanojininkus Image Reconstruction with Predictive Filter Flow Identity Mappings in Deep Residual Networks Deep Pyramidal Residual Networks Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections Practical Bayesian Optimization of Machine Learning Algorithms InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets 10, 000+ Times Accelerated Robust Subset Selection (ARSS) Calibrating Energy-based Generative Adversarial Networks Practical Text Classification With Large Pre-Trained Language Models Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New Experiments Learning to Learn by Zeroth-Order Oracle Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Data Mining and Machine Learning Structural Frame and Optimization of Local Area Network (LAN)
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CMSB Design and analysis of experiments SMC SMC ECC WWW ICNSC ISCAS ASP-DAC ICCD SLIP ISPD RV LCPC ACSAC SBAC-PAD CEC ISCAS (4) ArXiv ECCV 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv NIPS NIPS AAAI 2015 ICLR ArXiv ArXiv KDD
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