Group B: Polar Coordinate Whiteboard Writer

From the initial funding of $250.00, our group attempted to make a polar coordinate whiteboard writer that was to be used in educational settings. Market for polar coordinate whiteboard writer is a blue ocean. Having a successful prototype will allow us to find a niche place in a market that shares border with the global education market

Room temperature spin-orbit torque switching induced by a topological insulator

Recent studies on the magneto-transport properties of topological insulators (TI) have attracted great attention due to the rich spin-orbit physics and promising applications in spintronic devices. Particularly the strongly spin-moment coupled electronic states have been extensively pursued to realize efficient spin-orbit torque (SOT) switching. However, so far current-induced magnetic switching with TI has only been observed at cryogenic temperatures. It remains a controversial issue whether the topologically protected electronic states in TI could benefit spintronic applications at room temperature. In this work, we report full SOT switching in a TI/ferromagnet bilayer heterostructure with perpendicular magnetic anisotropy at room temperature. The low switching current density provides a definitive proof on the high SOT efficiency from TI. The effective spin Hall angle of TI is determined to be several times larger than commonly used heavy metals. Our results demonstrate the robustness of TI as an SOT switching material and provide a direct avenue towards applicable TI-based spintronic devices

mixiTUI:A Tangible Sequencer for Electronic Live Performances

With the rise of crowdsourcing and mobile crowdsensing techniques, a large number of crowdsourcing applications or platforms (CAP) have appeared. In the mean time, CAP-related models and frameworks based on different research hypotheses are rapidly emerging, and they usually address specific issues from a certain perspective. Due to different settings and conditions, different models are not compatible with each other. However, CAP urgently needs to combine these techniques to form a unified framework. In addition, these models needs to be learned and updated online with the extension of crowdsourced data and task types, thus requiring a unified architecture that integrates lifelong learning concepts and breaks down the barriers between different modules. This paper draws on the idea of ubiquitous operating systems and proposes a novel OS (CrowdOS), which is an abstract software layer running between native OS and application layer. In particular, based on an in-depth analysis of the complex crowd environment and diverse characteristics of heterogeneous tasks, we construct the OS kernel and three core frameworks including Task Resolution and Assignment Framework (TRAF), Integrated Resource Management (IRM), and Task Result quality Optimization (TRO). In addition, we validate the usability of CrowdOS, module correctness and development efficiency. Our evaluation further reveals TRO brings enormous improvement in efficiency and a reduction in energy consumption

Current-induced domain wall motion in compensated ferrimagnet

Due to the difficulty in detecting and manipulating magnetic states of antiferromagnetic materials, studying their switching dynamics using electrical methods remains a challenging task. In this work, by employing heavy metal/rare earth-transition metal alloy bilayers, we experimentally studied current-induced domain wall dynamics in an antiferromagnetically coupled system. We show that the current-induced domain wall mobility reaches a maximum close to the angular momentum compensation. With experiment and modelling, we further reveal the internal structures of domain walls and the underlying mechanisms for their fast motion. We show that the chirality of the ferrimagnetic domain walls remains the same across the compensation points, suggesting that spin orientations of specific sublattices rather than net magnetization determine Dzyaloshinskii-Moriya interaction in heavy metal/ferrimagnet bilayers. The high current-induced domain wall mobility and the robust domain wall chirality in compensated ferrimagnetic material opens new opportunities for high-speed spintronic devices.Comment: 13 pages, 3 figure

Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge

Multimodal Named Entity Recognition (MNER) on social media aims to enhance textual entity prediction by incorporating image-based clues. Existing studies mainly focus on maximizing the utilization of pertinent image information or incorporating external knowledge from explicit knowledge bases. However, these methods either neglect the necessity of providing the model with external knowledge, or encounter issues of high redundancy in the retrieved knowledge. In this paper, we present PGIM -- a two-stage framework that aims to leverage ChatGPT as an implicit knowledge base and enable it to heuristically generate auxiliary knowledge for more efficient entity prediction. Specifically, PGIM contains a Multimodal Similar Example Awareness module that selects suitable examples from a small number of predefined artificial samples. These examples are then integrated into a formatted prompt template tailored to the MNER and guide ChatGPT to generate auxiliary refined knowledge. Finally, the acquired knowledge is integrated with the original text and fed into a downstream model for further processing. Extensive experiments show that PGIM outperforms state-of-the-art methods on two classic MNER datasets and exhibits a stronger robustness and generalization capability.Comment: Accepted to Findings of EMNLP 202

Quantum Double Lock-in Amplifier

Quantum lock-in amplifier aims to extract an alternating signal within strong noise background by using quantum strategy. However, as the target signal usually has an unknown initial phase, we can't obtain the complete information of its amplitude, frequency and phase in a single lock-in measurement. Here, to overcome this challenge, we give a general protocol for achieving a quantum double lock-in amplifier and illustrate its realization. In analog to a classical double lock-in amplifier, our protocol is accomplished via two quantum mixers under orthogonal pulse sequences. The two orthogonal pulse sequences act the roles of two orthogonal reference signals in a classical double lock-in amplifier. Combining the output signals, the complete characteristics of the target signal can be obtained. As an example, we illustrate the realization of our quantum double lock-in amplifier via a five-level double-$\Lambda$ coherent population trapping system with $^{87}$Rb atoms, in which each $\Lambda$ structure acts as a quantum mixer and the two applied dynamical decoupling sequences take the roles of two orthogonal reference signals. Our numerical calculations show that the quantum double lock-in amplifier is robust against experimental imperfections, such as finite pulse length and stochastic noise. Our study opens an avenue for extracting complete characteristics of an alternating signal within strong noise background, which is beneficial for developing practical quantum sensing technologies

A Multiple-Layer Representation Learning Model for Network-Based Attack Detection

Accurate detection of network-based attacks is crucial to prevent security breaches of information systems. The recent application of deep learning approaches for network intrusion detection has shown promising. However, the challenges remain on how to deal with imbalance data and small samples as well as reducing false alarm rate (FAR). To address these issues, this work has proposed a multiple-layer representation learning model for accurate end-to-end network intrusion detection by combining deep convolutional neural networks (CNN) with gcForest. The contributions of this work lie in 1) a new data encoding scheme based on P-Zigzag to encode network traffic data into two-dimensional gray-scale images for representation learning without loss of original information; 2) The combination of gcForest and CNN allows accurate detection on imbalanced data and small scale data with fewer hyperparamters comparing to most existing deep learning models, which increase computational efficiency. The proposed approach is based on a multiple-layer approach consisting of a coarse layer and a fine layer, in which the coarse layer with the improved CNN model (GoogLeNetNP) focuses on identification of N abnormal classes and a normal class. While in the fine layer, an improved model based on gcForest (caXGBoost) further classifies the abnormal classes into N-1 subclasses. This ensures fine-grained detection of various attacks. The proposed framework has been compared with the existing deep learning models using three real datasets (a new dataset NBC, a combination of UNSW-NB15 and CICIDS2017 consisting of 101 classes). The experimental results show that our proposed method outperforms other single deep learning methods (i.e., AlexNet, VGG19, GoogleNet, InceptionV3, ResNet18) in terms of accuracy, detection rate, and FAR, which demonstrates its effectiveness in detecting fine-grained attacks and handling imbalanced datasets with high-precision and low FAR