As a crucial data mining problem, domain adaptation (DA) under dataset shift offers an effective paradigm for transferring knowledge in changing environments (i.e., shifting data distributions). Recent theoretical advances show that learning conditional information is crucial for successful DA, and empirical models with provable performance have been developed under discrete priors. However, for many real-world complex tasks with continuous label spaces, e.g., regression, conditional information is generally intractable, and shift correction models remain unexplored due to the high-dimensional nature of continuous variables. To deal with these challenges, we propose an information-theoretic learning principle called bi-level conditional dependence (BiCD) for continuous shift setting. BiCD characterizes the conditional information of learned representations from two aspects: conditional domain dependence and conditional label dependence, where our results prove that bi-level dependence learning is sufficient to obtain representations with desired properties and to minimize the generalization error. Moreover, BiCD also admits appealing properties for practical modeling: 1) an equivalent formulation with marginal dependence is derived to avoid the explicit computation of high-dimensional conditional dependence in practical modeling; 2) a kernel dependence-based variant is developed to serve as a guaranteed numerical proxy for the vanilla BiCD; 3) a graph Laplacian understanding is provided to justify the learning mechanism of BiCD. Empirically, BiCD is evaluated on standard DA regression datasets, where the significance of the proposed dependence learning principle is validated and SOTA performance is consistently achieved.

In teleoperated surgery, the motion scaling factor directly influences both the operator’s control precision of surgical instruments and operational comfort. Previous studies have revealed that the master manipulator state and operator’s gaze information can reflect the complexity of surgical operations and the operator’s intention to some extent. Although enabling real-time adjustment of scaling factors, they were limited by the narrow range of core parameters and the results were significantly influenced by subjective factors. To tackle these challenges, this paper presents a multi-dimensional adaptive motion scaling strategy based on the Bayesian optimization. The prediction of operator’s intention and attention is achieved by integrating multiple dimensional parameters, including master-slave manipulator states, gaze information, as well as pupillary data, all of which have been experimentally validated. Specifically, there exists a significant temporal synchronization between the Index of Pupillary Activity (IPA) and teleoperation tasks, which aligns with research on the correlation between IPA and attention levels. Furthermore, to evaluate the proposed adaptive scaling strategy, we combine subjective questionnaire surveys with objective metric assessments, effectively reducing the excessive influence of operators’ personal conditions and proficiency levels on optimization results.

Credit card fraud is the new financial fraud crime accompanied by the gradual development of the economy which causes billions of dollars of losses every year. Credit card fraud case not only seriously violated the cardholder benefits and financial institutions, but also undermined the credit management order. However, fraudsters keep exploring new crime strategies constantly which exacerbates the crime rate of fraud. Thus, a predictive model for credit card fraud detection is essential to minimize its losses. By distinguishing between fraud and non-fraud, machine learning is one of the most efficient solutions for detecting fraud. Support vector machines have proven to be a novel algorithm with excellent performance. Nevertheless, the performance of SVM depends largely on the correct choice of model parameters (C and g), which could cause that the false positive was very high if the kernel function type and parameter cannot be selected properly. In this paper, based on the real transaction data of the credit card business, firstly, it will find the optimal kernel function suitable for the data set. Secondly, this paper will propose the method of optimizing the support vector machine parameters by the cuckoo search algorithm, genetic algorithm and particle swarm optimization algorithm. Last but not least, the Linear kernel function was found to be the best kernel function with an accuracy rate of 91.56%. Furthermore, the Radial basis function is used to optimize the kernel function, which can improve the accuracy from 42.86% to the highest accuracy rate of 98.05%. Compared with CS-SVM and GA-SVM, PSO-SVM has the best overall performance.

In the cooperative vehicle infrastructure system (CVIS), due to the limitation of deployment conditions, some roadside units (RSUs) need to use renewable energy to supply power and transmit the fused sensor network’s data to the backbone network through the passing vehicles. Aiming at the problem of energy consumption and time delay guarantee of multiple self‐powered RSUs in the CVIS, a distributed packet scheduling optimization strategy for energy‐delay trade‐off in self‐powered RSUs is proposed. The strategy can minimize the system energy consumption by constraining the packet queue length of the self‐powered RSUs. A dynamic optimization model of distributed packet adaptive scheduling for multiple self‐powered RSUs is established based on the Lyapunov optimization theory. Based on the knapsack algorithm, the analytical algorithm of the optimization model is proposed. The simulation results show that the packet scheduling strategy can reduce the energy consumption and delay of the system by satisfying the upper limit of the packet queue length.

Unsupervised domain adaptation (UDA) is a representative problem in transfer learning, which aims to improve the classification performance on an unlabeled target domain by exploiting discriminant information from a labeled source domain. The optimal transport model has been used for UDA in the perspective of distribution matching. However, the transport distance cannot reflect the discriminant information from either domain knowledge or category prior. In this work, we propose an enhanced transport distance (ETD) for UDA. This method builds an attention-aware transport distance, which can be viewed as the prediction feedback of the iteratively learned classifier, to measure the domain discrepancy. Further, the Kantorovich potential variable is re-parameterized by deep neural networks to learn the distribution in the latent space. The entropy-based regularization is developed to explore the intrinsic structure of the target domain. The proposed method is optimized alternately in an end-to-end manner. Extensive experiments are conducted on four benchmark datasets to demonstrate the SOTA performance of ETD.

Unsupervised domain adaptation in person re-identification resorts to labeled source data to promote the model training on target domain, facing the dilemmas caused by large domain shift and large camera variations. The non-overlapping labels challenge that the source domain and the target domain have entirely different persons further increases the re-identification difficulty. In this paper, we propose a novel algorithm to narrow such domain gaps. We derive a camera style adaptation framework to learn the style-based mappings between different camera views, from the target domain to the source domain, and then we can transfer the identity-based distribution from the source domain to the target domain on the camera level. Target camera variations can be captured by the style adaptation method, thus, the re-identification model trained on the target domain can learn target camera-invariant features better. It indicates that the style translator approximates an appropriate metric space for improving feature matching. To overcome the non-overlapping labels challenge and guide the person re-identification model to narrow the gap further, an efficient and effective soft-labeling method is proposed to mine the intrinsic local structure of the target domain through building the connection between GAN-translated source domain and the target domain. Experiment results conducted on real benchmark datasets indicate that our method gets state-of-the-art results.

People rarely put in their papers the things that didn't work, the mistakes they made, and how they found out what went wrong. Such confessions can help others learn how to avoid similar mistakes. Twenty-six confessions were collected to form the bulk of this paper. Themes that arise are errors that result from not understanding the limitations of simulation tools in modeling physical reality, chip verification errors that result from lack of clear communication between designers, and projects that are considered in their own isolated environment of technical challenges rather than the broader context of their environment or application.

We present a 1.5V low power CMOS temperature sensor for detection of on-chip temperature gradients. The temperature sensor has measured accuracy of+/- 0.25/spl deg/C over a temperature range from 25/spl deg/C to 97/spl deg/C. The power consumption of a single temperature sensor is 96nW and the layout occupies 64.8/spl mu/m/sup 2/ in a 0.5/spl mu/m CMOS technology. A chip with spatially non-uniform heat generation devices and an array of temperature sensors was fabricated and tested. Experimental results demonstrate that the sensors successfully detect the induced temperature gradient on the chip.