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Reconstructing geometric shapes from point clouds is a common task that is often accomplished by experts manually modeling geometries in CAD-capable software. State-of-the-art workflows based on fully automatic geometry extraction are limited by point cloud density and memory constraints, and require pre- and post-processing by the user. In this work, we present a framework for interactive, user-driven, feature-assisted geometry reconstruction from arbitrarily sized point clouds. Based on seeded region-growing point cloud segmentation, the user interactively extracts planar pieces of geometry and utilizes contextual suggestions to point out plane surfaces, normal and tangential directions, and edges and corners. We implement a set of feature-assisted tools for high-precision modeling tasks in architecture and urban surveying scenarios, enabling instant-feedback interactive point cloud manipulation on large-scale data collected from real-world building interiors and facades. We evaluate our results through systematic measurement of the reconstruction accuracy, and interviews with domain experts who deploy our framework in a commercial setting and give both structured and subjective feedback., Comment: 13 pages, submitted to Computers & Graphics Journal

Finite state machines (FSM's) are implemented with sequential circuits and are used to orchestrate the operation of hardware designs. Sequential obfuscation schemes aimed at preventing IP theft often operate by augmenting a design's FSM post-synthesis. Many such schemes are based on the ability to recover the FSM's topology from the synthesized design. In this paper, we present two tools which can improve the performance of topology extraction: RECUT, which extracts the FSM implementation from a netlist, and REFSM-SAT, which solves topology enumeration as a series of SAT problems. In some cases, these tools can improve performance significantly over current methods, attaining up to a 99\% decrease in runtime., Comment: 7 pages, 2 figures, 2 algorithms

We propose a framework for expressing and analyzing the Quality of Service (QoS) of message-passing systems using a choreographic model that consists of g-choreographies and Communicating Finite State machines (CFSMs). The following are our three main contributions: (I) an extension of CFSMs with non-functional contracts to specify quantitative constraints of local computations, (II) a dynamic temporal logic capable of expressing QoS, properties of systems relative to the g-choreography that specifies the communication protocol, (III) the semi-decidability of our logic which enables a bounded model-checking approach to verify QoS property of communicating systems., Comment: 20 pages, Accepted for publication at International Conference on Theoretical Aspects of Computing 2023

We present MoCheQoS, a tool to analyse quality of service (QoS) properties of message-passing systems. Building on the logic and the choreographic model we defined in recently published work, MoCheQoS implements a bounded model checking algorithm. We discuss strengths and weaknesses of MoCheQoS through some case studies., Comment: 29 pages

Being able to efficiently obtain an accurate estimate of the failure probability of SRAM components has become a central issue as model circuits shrink their scale to submicrometer with advanced technology nodes. In this work, we revisit the classic norm minimization method. We then generalize it with infinite components and derive the novel optimal manifold concept, which bridges the surrogate-based and importance sampling (IS) yield estimation methods. We then derive a sub-optimal manifold, optimal hypersphere, which leads to an efficient sampling method being aware of the failure boundary called onion sampling. Finally, we use a neural coupling flow (which learns from samples like a surrogate model) as the IS proposal distribution. These combinations give rise to a novel yield estimation method, named Optimal Manifold Important Sampling (OPTIMIS), which keeps the advantages of the surrogate and IS methods to deliver state-of-the-art performance with robustness and consistency, with up to 3.5x in efficiency and 3x in accuracy over the best of SOTA methods in High-dimensional SRAM evaluation., Comment: 2023 60th ACM/IEEE Design Automation Conference(DAC)

Despite the fast advances in high-sigma yield analysis with the help of machine learning techniques in the past decade, one of the main challenges, the curse of dimensionality, which is inevitable when dealing with modern large-scale circuits, remains unsolved. To resolve this challenge, we propose an absolute shrinkage deep kernel learning, ASDK, which automatically identifies the dominant process variation parameters in a nonlinear-correlated deep kernel and acts as a surrogate model to emulate the expensive SPICE simulation. To further improve the yield estimation efficiency, we propose a novel maximization of approximated entropy reduction for an efficient model update, which is also enhanced with parallel batch sampling for parallel computing, making it ready for practical deployment. Experiments on SRAM column circuits demonstrate the superiority of ASDK over the state-of-the-art (SOTA) approaches in terms of accuracy and efficiency with up to 10.3x speedup over SOTA methods.

Tenuous subgraph finding aims to detect a subgraph with few social interactions and weak relationships among nodes. Despite significant efforts have been made on this task, they are mostly carried out in view of graph-structured data. These methods depend on calculating the shortest path and need to enumerate all the paths between nodes, which suffer the combinatorial explosion. Moreover, they all lack the integration of neighborhood information. To this end, we propose a novel model named Graph Neural Network with Motif-aware for tenuous subgraph finding (GNNM), a neighborhood aggregation based GNNs framework which can capture the latent relationship between nodes. Specially, we design a GNN module to project nodes into a low dimensional vector combining the higher-order correlation within nodes based on a motif-aware module. And then design greedy algorithms in vector space to obtain a tenuous subgraph whose size is greater than a specified constraint. Particularly, considering existing evaluation indicators cannot capture the latent friendship between nodes, we introduce a novel Potential Friend(PF) concept to measure the tenuity of a graph from a new perspective. Experimental results on the real-world and synthetic datasets demonstrate that our proposed method GNNM outperforms existing algorithms in efficiency and subgraph quality., Comment: 15 pages, 14 figures

Significant effort has been made to solve computationally expensive optimization problems in the past two decades, and various optimization methods incorporating surrogates into optimization have been proposed. Most research focuses on either exploiting the surrogate by defining a utility optimization problem or customizing an existing optimization method to use one or multiple approximation models. However, only a little attention has been paid to generic concepts applicable to different types of algorithms and optimization problems simultaneously. Thus this paper proposes a generalized probabilistic surrogate-assisted framework (GPSAF), applicable to a broad category of unconstrained and constrained, single- and multi-objective optimization algorithms. The idea is based on a surrogate assisting an existing optimization method. The assistance is based on two distinct phases, one facilitating exploration and another exploiting the surrogates. The exploration and exploitation of surrogates are automatically balanced by performing a probabilistic knockout tournament among different clusters of solutions. A study of multiple well-known population-based optimization algorithms is conducted with and without the proposed surrogate assistance on single- and multi-objective optimization problems with a maximum solution evaluation budget of 300 or less. The results indicate the effectiveness of applying GPSAF to an optimization algorithm and the competitiveness with other surrogate-assisted algorithms.

We describe a novel approach for exploring, visualizing and modelling of complex textile structures. We show this approach in action for the exploration of weft-knitted spacer fabrics. The modelling tools are readily available and represent a fast-growing approach in communities of generative design. The approach helps solving the problem that spacer fabrics are complex structures which are hard to imagine and to visualize. The explorations turn out insight-full and flexible and are complementary to what can be done with existing tools., Comment: 11 pages, 7 figures

The modeling and control of complex physical systems are essential in real-world problems. We propose a novel framework that is generally applicable to solving PDE-constrained optimal control problems by introducing surrogate models for PDE solution operators with special regularizers. The procedure of the proposed framework is divided into two phases: solution operator learning for PDE constraints (Phase 1) and searching for optimal control (Phase 2). Once the surrogate model is trained in Phase 1, the optimal control can be inferred in Phase 2 without intensive computations. Our framework can be applied to both data-driven and data-free cases. We demonstrate the successful application of our method to various optimal control problems for different control variables with diverse PDE constraints from the Poisson equation to Burgers' equation., Comment: 15 pages, 12 figures. Published as a conference paper at Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022)

We present an observational study to compare co-located and situated real-time visualizations in basketball free-throw training. Our goal is to understand the advantages and concerns of applying immersive visualization to real-world skill-based sports training and to provide insights for designing AR sports training systems. We design both a situated 3D visualization on a head-mounted display and a 2D visualization on a co-located display to provide immediate visual feedback on a player's shot performance. Using a within-subject study design with experienced basketball shooters, we characterize user goals, report on qualitative training experiences, and compare the quantitative training results. Our results show that real-time visual feedback helps athletes refine subsequent shots. Shooters in our study achieve greater angle consistency with our visual feedback. Furthermore, AR visualization promotes an increased focus on body form in athletes. Finally, we present suggestions for the design of future sports AR studies., Comment: To appear in the 2021 ACM Conference on Human Factors in Computing Systems (CHI 2021)

Optimal Bayesian design techniques provide an estimate for the best parameters of an experiment in order to maximize the value of measurements prior to the actual collection of data. In other words, these techniques explore the space of possible observations and determine an experimental setup that produces maximum information about the system parameters on average. Generally, optimal Bayesian design formulations result in multiple high-dimensional integrals that are difficult to evaluate without incurring significant computational costs as each integration point corresponds to solving a coupled system of partial differential equations. In the present work, we propose a novel approach for development of polynomial chaos expansion (PCE) surrogate model for the design utility function. In particular, we demonstrate how the orthogonality of PCE basis polynomials can be utilized in order to replace the expensive integration over the space of possible observations by direct construction of PCE approximation for the expected information gain. This novel technique enables the derivation of a reasonable quality response surface for the targeted objective function with a computational budget comparable to several single-point evaluations. Therefore, the proposed technique reduces dramatically the overall cost of optimal Bayesian experimental design. We evaluate this alternative formulation utilizing PCE on few numerical test cases with various levels of complexity to illustrate the computational advantages of the proposed approach., Comment: 30 pages, 9 figures. Published in Computer Methods in Applied Mechanics and Engineering

The case study analyzed in the report involves the behavioral specification and verification of a three-stage pipeline consisting of mutually concurrent modules which also compete for a shared resource. The system components are specified in terms of Concurrent State Machines (CSM) and the verification technique used is the temporal model checking in the COSMA environment., Comment: 15 pages, 11 figures, European Congress on Computational Methods in Applied Sciences and Engineering ECCOMAS 2004, Jyv\"askyl\"a, Finland, 24-28 July 2004

Environmental changes, failures, collisions or even terrorist attacks can cause serious malfunctions of the delivery systems. We have presented a novel approach improving resilience of Autonomous Moving Platforms AMPs. The approach is based on multi-level state diagrams describing environmental trigger specifications, movement actions and synchronization primitives. The upper level diagrams allowed us to model advanced interactions between autonomous AMPs and detect irregularities such as deadlocks live-locks etc. The techniques were presented to verify and analyze combined AMPs' behaviors using model checking technique. The described system, Dedan verifier, is still under development. In the near future, a graphical form of verified system representation is planned., Comment: 11 pages, 5 figures

Developing methods to design controller which at least under the situation that error exists, could continue its control procedure by decreasing operation till system attains secure situation to eliminate error, is a necessary need in a lot of industrial systems. Therefore designing error tolerable controller for industrial systems by using of bond graph theory to avoid physical and financial damages is one of the most important discussing issues in control engineering and related sciences. Based on this, in this paper we would model the system and error by using of bond graph theory and designing error tolerable controller.

Many of the proposed approaches for nonlinear systems control are developed under the assumption that all involved parameters are known. However, the system uncertainties are always unavoidable and some system parameters cannot be exactly known in the most part of the cases. In this paper, a adaptive controlscheme is proposed to system is able to achieve the control objective regardless the parametric uncertainties of the model and the lack of a priori knowledge on the system. Also, is applied the adaptive sliding mode control for synchronization of chaos in system of Rossler in presence of unknown parameters. First, suppose that there are no parameter uncertainties in the system model. Then, this condition is removed while an adaptive sliding mode control system is designed. The advantages of obtaining such a motion are twofold: firstly the system behaves as a system of reduced order with respect to the original plant; and secondly the movement on the sliding surface of the system is insensitive to a particular kind of perturbation and model uncertainties.

In this paper, we propose the concept of partial stability instead of that of global stability to deal with the stability issues of epidemic models. The partial stability is able to provide a more meaningful analysis of the problem since it only focuses on the behavior of some of the variables (infected and infectious) instead of the complete population. It has been shown that the vaccination free SEIR model can still be partially stable even when a globally stability property does not hold, for two types of nonlinear incidence rates. By introducing the concept of partial stability and by designing a control vaccination based on it. Guarantee the eradication of an epidemic disease without requiring the global stability of the epidemic model.

Excess competition in offering the goods and services in two previous decades throughout the world, is an undeniable fact. By increasing the number of rivals in the world class, the organizations were forced to improve home processes (processes within the enterprise) to stay in global competition arena. During recent years, determining appropriate suppliers for supply chain has had extreme importance. A lot of manufactures were looking for cooperation with appropriate suppliers to improve the level of management and competitive performance. The main goal in the process of selecting suppliers is to decrease the risk of purchase, maximize the important values of client and create high and close connection between buyer and supplier. Therefore, determining appropriate supplier in supply chain is a strategic key for accessing to organizational competitiveness.

To cope with the complex embedded system design, early design space exploration (DSE) is used to make design decisions early in the design phase. For early DSE it is crucial that the running time of the exploration is as small as possible. In this paper, we describe both the porting of our scenario-based DSE to the SPARC T3-4 server and the analysis of its performance behavior.

Biostatistics has become a major scientific component of biomedical research with a strong interdisciplinary basis. This endeavour is essentially interdisciplinary, therefore, training of future biostatisticians must focus its efforts on the development of successful mechanisms of communication and cooperation between researchers from different disciplines. The Biostatistics National Network, BioStatNet, has been created aiming to link together Spanish and foreign researchers in Biostatistics with an integrative and open attitude. It also intends to serve as a platform for the adequate training of biostatisticians as a means towards achieving effective interdisciplinarity., Peer Reviewed, Postprint (published version)