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There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ ., Comment: 10 pages, 4 figures, Accepted at NeurIPS 2022

Gradient ascent pulse engineering algorithm (GRAPE) is a typical method to solve quantum optimal control problems. However, it suffers from an exponential resource in computing the time evolution of quantum systems with the increasing number of qubits, which is a barrier for its application in large-qubit systems. To mitigate this issue, we propose an iterative GRAPE algorithm (iGRAPE) for preparing a desired quantum state, where the large-scale, resource-consuming optimization problem is decomposed into a set of lower-dimensional optimization subproblems by disentanglement operations. Consequently these subproblems can be solved in parallel with less computing resources. For physical platforms such as nuclear magnetic resonance (NMR) and superconducting quantum systems, we show that iGRAPE can provide up to 13-fold speedup over GRAPE when preparing desired quantum states in systems within 12 qubits. Using a four-qubit NMR system, we also experimentally verify the feasibility of the iGRAPE algorithm.

The proliferation of smartphones has accelerated mobility studies by largely increasing the type and volume of mobility data available. One such source of mobility data is from GPS technology, which is becoming increasingly common and helps the research community understand mobility patterns of people. However, there lacks a standardized framework for studying the different mobility patterns created by the non-Work, non-Home locations of Working and Nonworking users on Workdays and Offdays using machine learning methods. We propose a new mobility metric, Daily Characteristic Distance, and use it to generate features for each user together with Origin-Destination matrix features. We then use those features with an unsupervised machine learning method, $k$-means clustering, and obtain three clusters of users for each type of day (Workday and Offday). Finally, we propose two new metrics for the analysis of the clustering results, namely User Commonality and Average Frequency. By using the proposed metrics, interesting user behaviors can be discerned and it helps us to better understand the mobility patterns of the users., Comment: 13 pages, 12 figures. To be published in IEEE Access

By utilizing quantum mechanical effects, such as superposition and entanglement, quantum metrology promises higher precision than the classical strategies. It is, however, practically challenging to realize the quantum advantages. This is mainly due to the difficulties in engineering non-classical probe state and performing nontrivial measurement in practise, particularly with a large number of particles. Here we propose a scalable scheme with a symmetrical variational quantum circuit which, same as the Loschmidt echo, consists of a forward and a backward evolution. We show that in this scheme the quantum Fisher information, which quantifies the precision limit, can be efficiently obtained from a measurement signal of the Loschmidt echo. We experimentally implement the scheme on an ensemble of 10-spin quantum processor and successfully achieves a precision near the theoretical limit which outperforms the standard quantum limit with 12.4 dB. The scheme can be efficiently implemented on various noisy intermediate-scale quantum devices which provides a promising routine to demonstrate quantum advantages.

With the increasing need for multi-robot for exploring the unknown region in a challenging environment, efficient collaborative exploration strategies are needed for achieving such feat. A frontier-based Rapidly-Exploring Random Tree (RRT) exploration can be deployed to explore an unknown environment. However, its' greedy behavior causes multiple robots to explore the region with the highest revenue, which leads to massive overlapping in exploration process. To address this issue, we present a temporal memory-based RRT (TM-RRT) exploration strategy for multi-robot to perform robust exploration in an unknown environment. It computes adaptive duration for each frontier assigned and calculates the frontier's revenue based on the relative position of each robot. In addition, each robot is equipped with a memory consisting of frontier assigned and share among fleets to prevent repeating assignment of same frontier. Through both simulation and actual deployment, we have shown the robustness of TM-RRT exploration strategy by completing the exploration in a 25.0m x 54.0m (1350.0m2) area, while the conventional RRT exploration strategy falls short., Comment: 8 pages, 10 Figures

To accomplish task efficiently in a multiple robots system, a problem that has to be addressed is Simultaneous Localization and Mapping (SLAM). LiDAR (Light Detection and Ranging) has been used for many SLAM solutions due to its superb accuracy, but its performance degrades in featureless environments, like tunnels or long corridors. Centralized SLAM solves the problem with a cloud server, which requires a huge amount of computational resources and lacks robustness against central node failure. To address these issues, we present a distributed SLAM solution to estimate the trajectory of a group of robots using Ultra-WideBand (UWB) ranging and odometry measurements. The proposed approach distributes the processing among the robot team and significantly mitigates the computation concern emerged from the centralized SLAM. Our solution determines the relative pose (also known as loop closure) between two robots by minimizing the UWB ranging measurements taken at different positions when the robots are in close proximity. UWB provides a good distance measure in line-of-sight conditions, but retrieving a precise pose estimation remains a challenge, due to ranging noise and unpredictable path traveled by the robot. To deal with the suspicious loop closures, we use Pairwise Consistency Maximization (PCM) to examine the quality of loop closures and perform outlier rejections. The filtered loop closures are then fused with odometry in a distributed pose graph optimization (DPGO) module to recover the full trajectory of the robot team. Extensive experiments are conducted to validate the effectiveness of the proposed approach., Comment: accepted by the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)

Localization of robots is vital for navigation and path planning, such as in cases where a map of the environment is needed. Ultra-Wideband (UWB) for indoor location systems has been gaining popularity over the years with the introduction of low-cost UWB modules providing centimetre-level accuracy. However, in the presence of obstacles in the environment, Non-Line-Of-Sight (NLOS) measurements from the UWB will produce inaccurate results. As low-cost UWB devices do not provide channel information, we propose an approach to decide if a measurement is within Line-Of-Sight (LOS) or not by using some signal strength information provided by low-cost UWB modules through a Neural Network (NN) model. The result of this model is the probability of a ranging measurement being LOS which was used for localization through the Weighted-Least-Square (WLS) method. Our approach improves localization accuracy by 16.93% on the lobby testing data and 27.97% on the corridor testing data using the NN model trained with all extracted inputs from the office training data., Comment: Accepted by 2022 IEEE International Conference on Automation Science and Engineering (CASE)

Autonomous robots operating in indoor and GPS denied environments can use LiDAR for SLAM instead. However, LiDARs do not perform well in geometrically-degraded environments, due to the challenge of loop closure detection and computational load to perform scan matching. Existing WiFi infrastructure can be exploited for localization and mapping with low hardware and computational cost. Yet, accurate pose estimation using WiFi is challenging as different signal values can be measured at the same location due to the unpredictability of signal propagation. Therefore, we introduce the use of WiFi fingerprint sequence for pose estimation (i.e. loop closure) in SLAM. This approach exploits the spatial coherence of location fingerprints obtained while a mobile robot is moving. This has better capability of correcting odometry drift. The method also incorporates LiDAR scans and thus, improving computational efficiency for large and geometrically-degraded environments while maintaining the accuracy of LiDAR SLAM. We conducted experiments in an indoor environment to illustrate the effectiveness of the method. The results are evaluated based on Root Mean Square Error (RMSE) and it has achieved an accuracy of 0.88m for the test environment., Comment: accepted by the 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)

While quantum state tomography plays a vital role in the verification and benchmarking of quantum systems, it is an intractable task if the controllability and measurement of quantum registers are constrained. In this paper, we study the quantum state tomography of star-topology registers, in which the individual addressability of peripheral spins is infeasible. Based on the star-symmetry, we decompose the Hilbert space to alleviate the complexity of tomography and design a compact strategy with minimum number of measurements. By optimizing the parameterized quantum circuit for information transfer, the robustness against measurement errors is also improved. Furthermore, we apply this method to a 10-spin star-topology register and demonstrate its ability to characterize large-scale systems. Our results can help future investigations of quantum systems with constrained ability of quantum control and measurement., Comment: 9 pages, 4 figures

Quantum correlations are key information about the structures and dynamics of quantum many-body systems. There are many types of high-order quantum correlations with different time orderings, but only a few of them are accessible to the existing detection methods. Recently, a quantum-sensing approach based on sequential weak measurement was proposed to selectively extract arbitrary types of correlations. However, its experimental implementation is still elusive. Here we demonstrate the extraction of arbitrary types of quantum correlations. We generalized the original weak measurement scheme to a protocol using synthesized quantum channels, which can be applied to more universal scenarios including both single and ensemble quantum systems. In this quantum channel method, various controls on the sensors are superimposed to select the sensor-target evolution along a specific path for measuring a desired quantum correlation. Using the versatility of nuclear magnetic resonance techniques, we successfully extract the second- and fourth-order correlations of a nuclear-spin target by another nuclear-spin sensor. The full characterization of quantum correlations provides a new tool for understanding quantum many-body systems, exploring fundamental quantum physics, and developing quantum technologies., Comment: 9 pages, 4 figures in main text

Complex time-varying systems are often studied by abstracting away from the dynamics of individual components to build a model of the population-level dynamics from the start. However, when building a population-level description, it can be easy to lose sight of each individual and how they contribute to the larger picture. In this paper, we present a novel transformer architecture for learning from time-varying data that builds descriptions of both the individual as well as the collective population dynamics. Rather than combining all of our data into our model at the onset, we develop a separable architecture that operates on individual time-series first before passing them forward; this induces a permutation-invariance property and can be used to transfer across systems of different size and order. After demonstrating that our model can be applied to successfully recover complex interactions and dynamics in many-body systems, we apply our approach to populations of neurons in the nervous system. On neural activity datasets, we show that our model not only yields robust decoding performance, but also provides impressive performance in transfer across recordings of different animals without any neuron-level correspondence. By enabling flexible pre-training that can be transferred to neural recordings of different size and order, our work provides a first step towards creating a foundation model for neural decoding., Comment: accepted by NeurIPS 2022

With the present situation of economic globalization, simple inter-flows of information within and between countries have become standard to meet the development needs of college and university students, who now require and demand intercultural communication to improve their understanding of the globalized world. In recent years, online platforms have dramatically changed the traditional face-to-face communication methods and provide a learning environment without time and place restrictions. With the construction of international online platforms, real intercultural communication conditions have been provided for students to improve their Intercultural Communication Competence (ICC) and English skills where they were not available before. This study outlines an online teaching method and design of a new multiform course mode that consists of two online platforms. Eleven random classes of 330 university students were observed in this study. All of them were enrolled in a four-month course of intercultural communication in the spring semester of 2017 at a science and technology university in China. Students were divided into two groups, Group A had five classes of students who participated in the new course mode and Group B was the other group which had six classes under the traditional classroom teaching mode. This study aims to find if the special course mode would help develop ICC among college students who stay in a domestic context, through an analysis of the variation of the two groups' ICC before and after the course. This research adopts a mixed methods approach that includes questionnaires, online platform observations, and students' reflective journals to test students' cultural intelligence before and after they have had the intercultural communication course. The findings provide a basis and reference for future ICC cultivation, especially regarding the construction of online ICC cultivating platforms. [For the complete volume, "Virtual Exchange in the Asia Pacific: Research and Practice," see ED610332.]