This paper proposes a computation-efficient quantum convolutional neural network (CE-QCNN) architecture designed for autonomous driving applications. A key contribution of this work lies in the formulation of a tri-va...
This paper proposes a computation-efficient quantum convolutional neural network (CE-QCNN) architecture designed for autonomous driving applications. A key contribution of this work lies in the formulation of a tri-value qubit encoding (TQE) scheme, which compactly embeds three-channel RGB image data into single-qubit states via a sequence of quantum rotations. This strategy enables significant qubit resource reduction while preserving the representational richness of multi-channel visual inputs. The encoded quantum states are subsequently processed through parameterized quantum circuits for convolutional feature extraction, forming the core of the proposed CE-QCNN framework. To further improve learning stability and early-stage performance, a knowledge distillation (KD) strategy is employed, transferring supervision from a pretrained classical CNN model to the quantum network. The proposed model is evaluated on the KITTI dataset, a standard benchmark for autonomous driving, where it demonstrates both competitive detection accuracy and reduced computational complexity. These results substantiate the scalability and practical applicability of CE-QCNNs for future quantum-enhanced perception systems in real-time autonomous driving scenarios.
We consider correlated equilibria in strategic games in an adversarial environment, where an adversary can compromise the public signal used by the players for choosing their strategies, while players aim at detecting...
The advent of the metaverse has sparked profound interest in its integration within the gaming domain. Games, being intrinsic components of the metaverse, have attracted considerable attention, prompting inquiries int...
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The issue of imbalanced energy consumption in electric vehicle platoons stems from fixed vehicle positions within the formation, leading to practical challenges such as uneven battery degradation and varying charging ...
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During automatic target recognition once a detector has found points of interest the classifier is then tasked with identifying target objects from non-target objects. However, occasionally the detector may find somet...
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To maximize the discovery potential of high-energy colliders, experimental searches should be sensitive to unforeseen new physics scenarios. This goal has motivated the use of machine learning for unsupervised anomaly...
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To maximize the discovery potential of high-energy colliders, experimental searches should be sensitive to unforeseen new physics scenarios. This goal has motivated the use of machine learning for unsupervised anomaly detection. In this paper, we introduce a new anomaly detection strategy called force: factorized observables for regressing conditional expectations. Our approach is based on the inductive bias of factorization, which is the idea that the physics governing different energy scales can be treated as approximately independent. Assuming factorization holds separately for signal and background processes, the appearance of nontrivial correlations between low- and high-energy observables is a robust indicator of new physics. Under the most restrictive form of factorization, a machine-learned model trained to identify such correlations will in fact converge to the optimal new physics classifier. We test force on a benchmark anomaly detection task for the Large Hadron Collider involving collimated sprays of particles called jets. By teasing out correlations between the kinematics and substructure of jets, our method can reliably extract percent-level signal fractions. This strategy for uncovering new physics adds to the growing toolbox of anomaly detection methods for collider physics with a complementary set of assumptions.
A prototype of wearable vital sensing system has been developed with Fiber-Bragg-Gratings senor and 180-nm CMOS image sensor process chip. The system can acquire vital signs by capturing reflecting light with a photod...
A prototype of wearable vital sensing system has been developed with Fiber-Bragg-Gratings senor and 180-nm CMOS image sensor process chip. The system can acquire vital signs by capturing reflecting light with a photodiode. The designed test chip contains two types analog-front-end circuit. The feasibility for a vital sensing was verified with measurements with both of current-voltage conversion type and integral analog-to-digital convertor type.
We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as th...
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We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.
Bimanual tasks performed by human agents present unique optimal control considerations compared to cyberphysical agents. These considerations include minimizing attention, distributing that attention across two isolat...
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This paper proposes a method that uses satellite data to improve adaptive sampling missions. We find and track algal bloom fronts using an autonomous underwater vehicle (AUV) equipped with a sensor that measures the c...
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