Advanced patch attacks (PAs) on object detection in natural images have pointed out the great safety vulnerability in methods based on deep neural networks (DNNs). However, little attention has been paid to this topic...
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Advanced patch attacks (PAs) on object detection in natural images have pointed out the great safety vulnerability in methods based on deep neural networks (DNNs). However, little attention has been paid to this topic in optical remote sensing images (O-RSIs). To this end, we focus on this research, i.e., PAs on object detection in O-RSIs, and propose a more threatening patch attack (TPA) without the scarification of the visual quality. Specifically, to address the problem of inconsistency between the local and global landscapes in existing patch selection schemes, we propose leveraging the first-order difference (FOD) of the objective function before and after masking to select the subpatches to be attacked. Furthermore, considering the problem of gradient inundation when applying existing coordinate-based loss (CBL) to PAs directly, we design an IoU-based objective function specific for PAs, dubbed bounding box (Bbox) drifting loss (BDL), which pushes the detected Bboxes far from the initial ones until there are no intersections between them. Finally, on two widely used benchmarks, i.e., DIOR and DOTA, comprehensive evaluations of our TPA with four typical detectors (faster R-CNN, FCOS, RetinaNet, and YOLO-v4) witness its remarkable effectiveness. To the best of our knowledge, this is the first attempt to study the PAs on object detection in O-RSIs, and we hope this work can get our readers interested in studying this topic.
Background: According to the World Health Organization, globally, one in seven 10- to 19-year-olds experiences a mental disorder, accounting for 13% of the global burden of disease in this age group. Half of all menta...
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Background: According to the World Health Organization, globally, one in seven 10- to 19-year-olds experiences a mental disorder, accounting for 13% of the global burden of disease in this age group. Half of all mental illnesses begin by the age of 14 years and some teenagers with severe presentations must be admitted to the hospital and assessed by highly skilled mental health care practitioners. Digital telehealth solutions can be useful for the assessment of young individuals remotely. Ultimately, this technology can save travel costs for the health service rather than assessing adolescents in person at the corresponding hospital. Especially in rural regions, where travel times can be high, this innovative approach can make a difference to patients by providing quicker assessments. Objective: The aim of this study is to share insights on how we developed a decision support tool to assign staff to days and locations where adolescent mental health patients are assessed face to face. Where possible, patients are seen through video consultation. The model not only seeks to reduce travel times and consequently carbon emissions but also can be used to find a minimum number of staff to run the service. Methods: To model the problem, we used integer linear programming, a technique that is used in mathematical modeling. The model features 2 objectives: first, we aim to find a minimum coverage of staff to provide the service and second, to reduce travel time. The constraints that are formulated algebraically are used to ensure the feasibility of the schedule. The model is implemented using an open-source solver backend. Results: In our case study, we focus on real-world demand coming from different hospital sites in the UK National Health Service (NHS). We incorporate our model into a decision support tool and solve a realistic test instance. Our results reveal that the tool is not only capable of solving this problem efficiently but also shows the benefits of using mathem
The aim of this paper is to introduce a new recurrent neural network to solve linear programming. The main characteristic of the proposed scheme is its design based on the predefined-time stability. The predefined-tim...
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The aim of this paper is to introduce a new recurrent neural network to solve linear programming. The main characteristic of the proposed scheme is its design based on the predefined-time stability. The predefined-time stability is a stronger form of finite-time stability which allows the a priori definition of a convergence time that does not depend on the network initial state. The network structure is based on the Karush-Kuhn-Tucker (KKT) conditions and the KKT multipliers are proposed as sliding mode control inputs. This selection yields to an one-layer recurrent neural network in which the only parameter to be tuned is the desired convergence time. With this features, the network can be easily scaled from a small to a higher dimension problem. The simulation of a simple example shows the feasibility of the current approach.
In this paper, we propose a linear programming based interactive method for multiobjective linear programming problems, in which fuzzy coefficients and random variable coefficients are involved in the objective functi...
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In this paper, we propose a linear programming based interactive method for multiobjective linear programming problems, in which fuzzy coefficients and random variable coefficients are involved in the objective functions simultaneously. In the proposed method, it is assumed that the decision maker has a fuzzy goal for each objective function, and such a fuzzy goal can be quantified by eliciting the membership function. Through the possibility measure and a fractile optimization model, the original problem is transformed to the well-defined multiobjective programming problem. Then, a generalized Pareto optimal concept is defined, and a linear programming based interactive algorithm is proposed to obtain a satisfactory solution from among a generalized Pareto optimal solution set.
When linear programming is used to decode low-density parity-check (LDPC) codes, the outcome is a codeword or a pseudocodeword that contains fractional symbol values. It is possible to make pseudocodewords infeasible ...
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When linear programming is used to decode low-density parity-check (LDPC) codes, the outcome is a codeword or a pseudocodeword that contains fractional symbol values. It is possible to make pseudocodewords infeasible and increase the performance of linear programming decoders by generating redundant parity check equations. In this paper, redundant parity check equations that can eliminate pseudocodewords are searched using an integer programming based optimization approach. We show that the generated parity check equations increase the performance of the adaptive linear programming decoder and that its performance can converge that of a maximum-likelihood decoder.
This paper proposes a multi-kernel linear programming support vector regression with prior knowledge in order to obtain an accurate regression model in the case of the scarcity of measured data available. In the algor...
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This paper proposes a multi-kernel linear programming support vector regression with prior knowledge in order to obtain an accurate regression model in the case of the scarcity of measured data available. In the algorithm, multi-kernel and prior knowledge which may be exact or biased from a calibrated simulator have been incorporated into the framework of linear programming support vector regression by utilizing multiple feature spaces and modifying optimization formulation. Some experiments from a synthetic example have been carried out, and the results show that the proposed algorithm is effective, and that the obtained model is sparse and accurate. The proposed algorithm shows great potential in some practical applications where the experimental data is few and the prior knowledge from a simulator is available.
Based on the Delsarte-Yudin linear programming approach, we extend Levenshtein's framework to obtain lower bounds for the minimum henergy of spherical codes of prescribed dimension and cardinality, and upper bound...
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Based on the Delsarte-Yudin linear programming approach, we extend Levenshtein's framework to obtain lower bounds for the minimum henergy of spherical codes of prescribed dimension and cardinality, and upper bounds on the maximal cardinality of spherical codes of prescribed dimension and minimum separation. These bounds are universal in the sense that they hold for a large class of potentials h and in the sense of Levenshtein. Moreover, codes attaining the bounds are universally optimal in the sense of Cohn-Kumar. Referring to Levenshtein bounds and the energy bounds of the authors as "first level", our results can be considered as "next level" universal bounds as they have the same general nature and imply necessary and sufficient conditions for their local and global optimality. For this purpose, we introduce the notion of Universal Lower Bound space (ULB-space), a space that satisfies certain quadrature and interpolation properties. While there are numerous cases for which our method applies, we will emphasize the model examples of 24 points (24-cell) and 120 points (600-cell) on S-3 . In particular, we provide a new proof that the 600-cell is universally optimal, and in so doing, we derive optimality of the 600-cell on a class larger than the absolutely monotone potentials considered by Cohn-Kumar.
The aim of this paper is to present a simple new class of recurrent neural networks, which solves linear programming. It is considered as a sliding mode control problem, where the network structure is based on the Kar...
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The aim of this paper is to present a simple new class of recurrent neural networks, which solves linear programming. It is considered as a sliding mode control problem, where the network structure is based on the Karush-Kuhn-Tucker (KKT) optimality conditions, and the KKT multipliers are the control inputs to be implemented with finite time stabilizing terms based on the unit control, instead of common used activation functions. Thus, the main feature of the proposed network is the fixed number of parameters despite of the optimization problem dimension, which means, the network can be easily scaled from a small to a higher dimension problem. The applicability of the proposed scheme is tested on real-time optimization of an electrical Microgrid prototype.
It is of great importance to calculate PV access capacity on different scene and different time division. Kmeans clustering method was used to cluster the scene, time division is carried out. Genetic algorithm is used...
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ISBN:
(数字)9798350389579
ISBN:
(纸本)9798350389586
It is of great importance to calculate PV access capacity on different scene and different time division. Kmeans clustering method was used to cluster the scene, time division is carried out. Genetic algorithm is used to solve the PV access capacity problem. Without network reconfiguration, there are three time divisions that exceed the constraints. Constraint overlimit for three exceedance period can be eliminated by network reconfiguration. The total PV access capacity increases and the active power loss decreases compared with optimization results without network reconfiguration.
The aim of this paper is to present a new dynamical system which solves linear programming. Its design is considered as a sliding mode control problem, where its structure is based on the Karush-Kuhn-Tucker optimality...
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ISBN:
(纸本)9781467360890
The aim of this paper is to present a new dynamical system which solves linear programming. Its design is considered as a sliding mode control problem, where its structure is based on the Karush-Kuhn-Tucker optimality conditions, and its multipliers are the control inputs to be implemented by using fixed time stabilizing terms with vectorial structure, based on the unit control, instead of common terms used in other approaches. Thus, the main features of the proposed system are the fixed convergence time to the programming solution and the fixed parameters number despite of the optimization problem dimension. That is, there is a time independent to the initial conditions in which the system converges to the solution and, the proposed structure can be easily scaled from a small to a higher dimension problem. The applicability of the proposed scheme is tested on real-time optimization of an electrical Microgrid prototype.
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