TBFV-M : Testing-Based Formal Verification for SysML Activity Diagrams

SysML activity diagrams are often used as models for software systems and its correctness is likely to significantly affect the reliability of the implementation. However, how to effectively verify the correctness of SysML diagrams still remains a challenge. In this paper, we propose a testing-based formal verification (TBFV) approach to the verification of SysML diagrams, called TBFV-M, by creatively applying the existing TBFV approach for code verification. We describe the principle of TBFV-M and present a case study to demonstrate its feasibility and usability. Finally, we conclude the paper and point out future research directions

An iterative data-driven turbulence modeling framework based on Reynolds stress representation

Data-driven turbulence modeling studies have reached such a stage that the fundamental framework is basically settled, but several essential issues remain that strongly affect the performance, including accuracy, smoothness, and generalization capacity. Two problems are studied in the current research: (1) the processing of the Reynolds stress tensor and (2) the coupling method between the machine learning turbulence model and CFD solver. The first determines the form of predicting targets and the resulting physical completeness and interpretability. The second determines the training process and intrinsic relevance between the mean flow features and Reynolds stress. For the Reynolds stress processing issue, we perform the theoretical derivation to extend the relevant tensor arguments of Reynolds stress in addition to the strain rate and rotation rate. Then, the tensor representation theorem is employed to give the complete irreducible invariants and integrity basis. In addition, an adaptive regularization term is employed to enhance the representation performance. For the CFD coupling issue, an iterative coupling data-driven turbulence modeling framework with consistent convergence is proposed. The training data preparation, predicting target selection, and computation platform are illustrated. The framework is then applied to a canonical separated flow for verification. The mean flow results obtained by coupling computation of the trained machine learning model and CFD solver have high consistency with the DNS true values, which proves the validity of the current approach

Enabling Full-Stack Quantum Computing with Changeable Error-Corrected Qubits

Executing quantum applications with quantum error correction (QEC) faces the gate non-universality problem imposed by the Eastin-Knill theorem. As one resource-time-efficient solution, code switching changes the encoding of logical qubits to implement universal logical gates. Unfortunately, it is still unclear how to perform full-stack fault-tolerant quantum computing (FTQC) based on the changeable logical qubit. Specifically, three critical problems remain unsolved: a) how to implement the dynamic logical qubit on hardware; b) how to determine the appropriate timing for logical qubit varying; c) how to improve the overall system performance for programs of different features. To overcome those design problems, We propose CECQ, to explore the large design space for FTQC based on changeable logical qubits. Experiments on various quantum programs demonstrate the effectiveness of CECQ

Optimal Synthesis of Stabilizer Codes via MaxSAT

Quantum Error Correction (QEC) codes are crucial for achieving fault-tolerant quantum computing in the long term. However, efficiently implementing these codes on hardware poses significant challenges, including hardware connectivity matching, efficient circuit scheduling, and fault-tolerance enforcement. In this study, we present an optimal synthesizer that stitches generic stabilizer codes onto diverse hardware structures via MaxSAT. Our evaluation demonstrates (1) the capability of our approach to be applied for various codes and devices and (2) the consistently better efficiency than the best prior heuristic approaches that only target specific QEC codes. By bridging the gap between high-level QEC code design and low-level hardware constraints, this work paves the way toward achieving long-term fault-tolerant quantum computing goals

Recurrent Contour-based Instance Segmentation with Progressive Learning

Contour-based instance segmentation has been actively studied, thanks to its flexibility and elegance in processing visual objects within complex backgrounds. In this work, we propose a novel deep network architecture, i.e., PolySnake, for contour-based instance segmentation. Motivated by the classic Snake algorithm, the proposed PolySnake achieves superior and robust segmentation performance with an iterative and progressive contour refinement strategy. Technically, PolySnake introduces a recurrent update operator to estimate the object contour iteratively. It maintains a single estimate of the contour that is progressively deformed toward the object boundary. At each iteration, PolySnake builds a semantic-rich representation for the current contour and feeds it to the recurrent operator for further contour adjustment. Through the iterative refinements, the contour finally progressively converges to a stable status that tightly encloses the object instance. Moreover, with a compact design of the recurrent architecture, we ensure the running efficiency under multiple iterations. Extensive experiments are conducted to validate the merits of our method, and the results demonstrate that the proposed PolySnake outperforms the existing contour-based instance segmentation methods on several prevalent instance segmentation benchmarks. The codes and models are available at https://github.com/fh2019ustc/PolySnake

Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation

This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to emphasize semantic regions in weakly supervised semantic segmentation. MCC adroitly incorporates concepts from masked image modeling and contrastive learning to devise Transformer blocks that induce keys to contract towards semantically pertinent regions. Unlike prevalent techniques that directly eradicate patch regions in the input image when generating masks, we scrutinize the neighborhood relations of patch tokens by exploring masks considering keys on the affinity matrix. Moreover, we generate positive and negative samples in contrastive learning by utilizing the masked local output and contrasting it with the global output. Elaborate experiments on commonly employed datasets evidences that the proposed MCC mechanism effectively aligns global and local perspectives within the image, attaining impressive performance. The source code is available at \url{https://github.com/fwu11/MCC}

Compilation for Quantum Computing on Chiplets

Chiplet architecture is an emerging architecture for quantum computing that could significantly increase qubit resources with its great scalability and modularity. However, as the computing scale increases, communication between qubits would become a more severe bottleneck due to the long routing distances. In this paper, we trade ancillary qubits for program concurrency by proposing a multi-entry communication highway mechanism, and building a compilation framework to efficiently manage and utilize the highway resources. Our evaluation shows that this framework significantly outperforms the baseline approach in both the circuit depth and the number of operations on some typical quantum benchmarks, leading to a more efficient and less error-prone compilation of quantum programs

A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks

Deep Neural Networks (DNNs) are widely used for computer vision tasks. However, it has been shown that deep models are vulnerable to adversarial attacks, i.e., their performances drop when imperceptible perturbations are made to the original inputs, which may further degrade the following visual tasks or introduce new problems such as data and privacy security. Hence, metrics for evaluating the robustness of deep models against adversarial attacks are desired. However, previous metrics are mainly proposed for evaluating the adversarial robustness of shallow networks on the small-scale datasets. Although the Cross Lipschitz Extreme Value for nEtwork Robustness (CLEVER) metric has been proposed for large-scale datasets (e.g., the ImageNet dataset), it is computationally expensive and its performance relies on a tractable number of samples. In this paper, we propose the Adversarial Converging Time Score (ACTS), an attack-dependent metric that quantifies the adversarial robustness of a DNN on a specific input. Our key observation is that local neighborhoods on a DNN's output surface would have different shapes given different inputs. Hence, given different inputs, it requires different time for converging to an adversarial sample. Based on this geometry meaning, ACTS measures the converging time as an adversarial robustness metric. We validate the effectiveness and generalization of the proposed ACTS metric against different adversarial attacks on the large-scale ImageNet dataset using state-of-the-art deep networks. Extensive experiments show that our ACTS metric is an efficient and effective adversarial metric over the previous CLEVER metric.Comment: ACM Transactions on Multimedia Computing, Communications, and Applications (ACM TOMM

Biologically Inspired Dynamic Thresholds for Spiking Neural Networks

The dynamic membrane potential threshold, as one of the essential properties of a biological neuron, is a spontaneous regulation mechanism that maintains neuronal homeostasis, i.e., the constant overall spiking firing rate of a neuron. As such, the neuron firing rate is regulated by a dynamic spiking threshold, which has been extensively studied in biology. Existing work in the machine learning community does not employ bioinspired spiking threshold schemes. This work aims at bridging this gap by introducing a novel bioinspired dynamic energy-temporal threshold (BDETT) scheme for spiking neural networks (SNNs). The proposed BDETT scheme mirrors two bioplausible observations: a dynamic threshold has 1) a positive correlation with the average membrane potential and 2) a negative correlation with the preceding rate of depolarization. We validate the effectiveness of the proposed BDETT on robot obstacle avoidance and continuous control tasks under both normal conditions and various degraded conditions, including noisy observations, weights, and dynamic environments. We find that the BDETT outperforms existing static and heuristic threshold approaches by significant margins in all tested conditions, and we confirm that the proposed bioinspired dynamic threshold scheme offers homeostasis to SNNs in complex real-world tasks