Estimating neuron location from extracellular recordings is essential for developing advanced brain-machine interfaces. Accurate neuron localization improves spike sorting, which involves detecting action potentials and assigning them to individual neurons. It also assists in monitoring probe drift, which affects long-term probe reliability. Although several localization algorithms are currently in use, the field is nascent and arguments for using one algorithm over another are largely theoretical or based on visual inspection of clustering results. We present a first-of-its-kind benchmarking of commonly used neuron localization algorithms. We assess these algorithms using two ground truth datasets: a biophysically realistic simulated dataset, and an experimental dataset pairing patch-clamp and extracellular Neuropixels recording data. We systematically evaluate the accuracy, robustness, and runtime of these algorithms in ideal recording conditions and long-term recording conditions with electrode degradation. Our findings highlight significant performance differences; while more complex and physically realistic models perform better in ideal conditions, models relying on simpler heuristics demonstrate superior robustness to noise and electrode degradation, making them more suitable for long-term neural recordings. This work provides a framework for assessing localization algorithms and developing robust, biologically grounded algorithms to advance the development of brain-machine interfaces.

In today's digital era, data visualization (DV) technology has become indispensable for tasks involving data processing and graphical reasoning. In this demonstration, we introduce a novel automatic DV system named VoiceVisSystem. VoiceVisSystem is designed for transforming speech-form natural language questions (NLQs) into visual data representations, a task formally known as Speech-to-Vis. Unlike the existing cascaded method (e.g., Sevi), the core component of our system relies on an advanced end-to-end speech-to-vis model named SpeechVisNet, eliminating the need for text as an intermediate medium and directly facilitating the conversion from Speech-form to DV. Specifically, the speech encoder and the text encoder of the SpeechVisNet respectively take the user's NLQs and the corresponding database information as inputs and convert them into hidden representations. Then, a grammar-based decoder generates the corresponding DVs as the output. As a result, our system enjoys the benefits of avoiding error propagation, thereby enhancing accuracy. By offering a seamless solution for the speech-to-vis task, VoiceVisSystem presents a promising tool for practical applications in various domains. The demonstration video is available at https://1drv.ms/v/s!Ah2vhbolPBFMiSNPZLunJ6Qp6jqU?e=Shyq8R.

Unmanned aerial vehicle(UAV)-aided mobile edge computing network can help resource-constrained users to complete time-sensitive tasks. In recent years, UAV has attracted extensive attention due to their flexibility and low cost. However, UAV's computing power is limited, and how to provide reliable low-delay services for edge users is one of the critical issues. To solve this question, we design a scheme in which UAV and ground base stations cooperate to serve users. When the user offloads the task to the UAV, the UAV can relay some tasks to the ground base station through millimeter-wave to obtain lower calculation delay. We jointly optimize user offload strategy, relay ratio, UAV trajectory, and bit allocation to minimize computational delay for all users. We propose an improved alternating optimization algorithm, which transforms the non-convex problem into three subproblems and obtains the optimal solution through multiple iterations. Simulation results show that the proposed scheme can effectively enhance the computing power of the system and reduce the user's delay significantly.