In this short article, we prove that the Halton sequence, one of the most well-known low-discrepancy sequences, is not quasi-uniform in any dimension d ≥ 2 with any pairwise relatively prime bases. We further disprove the quasi-uniformity of some Halton-type sequences, including the p -dimensional Faure sequence in base p , p ∈ P , which provides an alternative proof of the known results.

In this paper, we present some new (in-)tractability results related to the integration problem in subspaces of the Wiener algebra over the d -dimensional unit cube. We show that intractability holds for multivariate integration in the standard Wiener algebra in the deterministic setting, in contrast to polynomial tractability in an unweighted subspace of the Wiener algebra recently shown by Goda (2023). Moreover, we prove that multivariate integration in the subspace of the Wiener algebra introduced by Goda is strongly polynomially tractable if we switch to the randomized setting, where we obtain a better ε -exponent than the one implied by the standard Monte Carlo method . We also identify subspaces in which multivariate integration in the deterministic setting are (strongly) polynomially tractable and we compare these results with the bound which can be obtained via Hoeffding's inequality.

This paper presents a novel Stochastic Optimal Control (SOC) method based on Model Predictive Path Integral control (MPPI), named Stein Variational Guided MPPI (SVG-MPPI), designed to handle rapidly shifting multimodal optimal action distributions. While MPPI can find a Gaussian-approximated optimal action distribution in closed form, i.e., without iterative solution updates, it struggles with the mul-timodality of the optimal distributions. This is due to the less representative nature of the Gaussian. To overcome this limitation, our method aims to identify a target mode of the optimal distribution and guide the solution to converge to fit it. In the proposed method, the target mode is roughly estimated using a modified Stein Variational Gradient Descent (SVGD) method and embedded into the MPPI algorithm to find a closed-form "mode-seeking" solution that covers only the target mode, thus preserving the fast convergence property of MPPI. Our simulation and real-world experimental results demonstrate that SVG-MPPI outperforms both the original MPPI and other state-of-the-art sampling-based SOC algorithms in terms of path-tracking and obstacle-avoidance capabilities. https://github.com/kohonda/proj-svg_mppi

In recent years, long-range wide-area network (LoRaWAN) has attracted considerable attention due to its ability to realize massive machine-type communication (MTC); however, its throughput is limited by the duty cycle (DC). Packet-level index modulation (PLIM) can increase throughput by utilizing a data packet’s frequency channel and transmission timing as the information-bearing index. In PLIM, a node selects a specific transmission resource (frequency channel and timing) within a frame and transmits a packet. If the node cannot transmit the packet at the specific transmission resource, it discards the packet. This packet discard results in throughput degradation of each node. Thus, this paper proposes an index mapping scheme that divides a frame into multiple subframes to provide each node with multiple transmission opportunities. A node performs channel activity detection (CAD) at the selected transmission resource within a subframe; if the node cannot transmit a packet, it moves to the next subframe. The proposed scheme adaptively maps the information bit sequence onto a transmission resource within each subframe based on the wireless environment and communication quality. Computer simulation results show that the proposed scheme improves throughput by increasing the transmission opportunities and reducing the packet discard rate under the constraint of DC.

The presence or absence of cancer metastasis in the lymph-nodes using an AI-based approach has been important these days in the medical field of gastroenterological surgery. The medical field needs the introduction of machine learning to help the knowledge and skill of surgery of medical doctors who are taking operations by checking CT scans or MRI images for cancer disease. Recent machine learning based researches on lymph-node are mainly either detection of the location of lymph-nodes or classification of cancer metastasis. This paper proposes a method to perform both tasks at the same time with a single U-Net model. Obtained results in the experiments show that the classification accuracy is further improved compared with the previous approach while keeping the detection ratio as almost the same level as that of the related paper.