Structured Epipolar Matcher for Local Feature Matching

Local feature matching is challenging due to the textureless and repetitive pattern. Existing methods foucs on using appearance features and global interaction and matching, while the importance of geometry prior in local feature matching has not been fully exploited. Different from these methods, in this paper, we delve into the importance of geometry prior and propose Structured Epipolar Matcher (SEM) for local feature matching, which can leverage the geometric information in a iterative matching way. The proposed model enjoys several merits. First, our proposed Structured Feature Extractor can model the relative positional relationship between pixels and high-confidence anchor points. Second, our proposed Epipolar Attention and Matching can filter out irrelevant areas by utilizing the epipolar constraint. Extensive experimental results on five standard benchmarks demonstrate the superior performance of our SEM compared to state-of-the-art methods

Multiplexed control scheme for scalable quantum information processing with superconducting qubits

The advancement of scalable quantum information processing relies on the accurate and parallel manipulation of a vast number of qubits, potentially reaching into the millions. Superconducting qubits, traditionally controlled through individual circuitry, currently face a formidable scalability challenge due to the excessive use of wires. This challenge is nearing a critical point where it might soon surpass the capacities of on-chip routing, I/O packaging, testing platforms, and economically feasible solutions. Here we introduce a multiplexed control scheme that efficiently utilizes shared control lines for operating multiple qubits and couplers. By integrating quantum hardware-software co-design, our approach utilizes advanced techniques like frequency multiplexing and individual tuning. This enables simultaneous and independent execution of single- and two-qubit gates with significantly simplified wiring. This scheme has the potential to diminish the number of control lines by one to two orders of magnitude in the near future, thereby substantially enhancing the scalability of superconducting quantum processors

Multi-Factor Spatio-Temporal Prediction based on Graph Decomposition Learning

Spatio-temporal (ST) prediction is an important and widely used technique in data mining and analytics, especially for ST data in urban systems such as transportation data. In practice, the ST data generation is usually influenced by various latent factors tied to natural phenomena or human socioeconomic activities, impacting specific spatial areas selectively. However, existing ST prediction methods usually do not refine the impacts of different factors, but directly model the entangled impacts of multiple factors. This amplifies the modeling complexity of ST data and compromises model interpretability. To this end, we propose a multi-factor ST prediction task that predicts partial ST data evolution under different factors, and combines them for a final prediction. We make two contributions to this task: an effective theoretical solution and a portable instantiation framework. Specifically, we first propose a theoretical solution called decomposed prediction strategy and prove its effectiveness from the perspective of information entropy theory. On top of that, we instantiate a novel model-agnostic framework, named spatio-temporal graph decomposition learning (STGDL), for multi-factor ST prediction. The framework consists of two main components: an automatic graph decomposition module that decomposes the original graph structure inherent in ST data into subgraphs corresponding to different factors, and a decomposed learning network that learns the partial ST data on each subgraph separately and integrates them for the final prediction. We conduct extensive experiments on four real-world ST datasets of two types of graphs, i.e., grid graph and network graph. Results show that our framework significantly reduces prediction errors of various ST models by 9.41% on average (35.36% at most). Furthermore, a case study reveals the interpretability potential of our framework

Accurate Single Stage Detector Using Recurrent Rolling Convolution

Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible regions were proposed in the first stage then followed by a second stage for decision refinement. Despite the simplicity of training and the efficiency in deployment, the single stage detection methods have not been as competitive when evaluated in benchmarks consider mAP for high IoU thresholds. In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation. We achieved this by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are "deep in context". We evaluated our method in the challenging KITTI dataset which measures methods under IoU threshold of 0.7. We showed that with RRC, a single reduced VGG-16 based model already significantly outperformed all the previously published results. At the time this paper was written our models ranked the first in KITTI car detection (the hard level), the first in cyclist detection and the second in pedestrian detection. These results were not reached by the previous single stage methods. The code is publicly available.Comment: CVPR 201

Assessing Prompt Injection Risks in 200+ Custom GPTs

In the rapidly evolving landscape of artificial intelligence, ChatGPT has been widely used in various applications. The new feature: customization of ChatGPT models by users to cater to specific needs has opened new frontiers in AI utility. However, this study reveals a significant security vulnerability inherent in these user-customized GPTs: prompt injection attacks. Through comprehensive testing of over 200 user-designed GPT models via adversarial prompts, we demonstrate that these systems are susceptible to prompt injections. Through prompt injection, an adversary can not only extract the customized system prompts but also access the uploaded files. This paper provides a first-hand analysis of the prompt injection, alongside the evaluation of the possible mitigation of such attacks. Our findings underscore the urgent need for robust security frameworks in the design and deployment of customizable GPT models. The intent of this paper is to raise awareness and prompt action in the AI community, ensuring that the benefits of GPT customization do not come at the cost of compromised security and privacy

TIFace: Improving Facial Reconstruction through Tensorial Radiance Fields and Implicit Surfaces

This report describes the solution that secured the first place in the "View Synthesis Challenge for Human Heads (VSCHH)" at the ICCV 2023 workshop. Given the sparse view images of human heads, the objective of this challenge is to synthesize images from novel viewpoints. Due to the complexity of textures on the face and the impact of lighting, the baseline method TensoRF yields results with significant artifacts, seriously affecting facial reconstruction. To address this issue, we propose TI-Face, which improves facial reconstruction through tensorial radiance fields (T-Face) and implicit surfaces (I-Face), respectively. Specifically, we employ an SAM-based approach to obtain the foreground mask, thereby filtering out intense lighting in the background. Additionally, we design mask-based constraints and sparsity constraints to eliminate rendering artifacts effectively. The experimental results demonstrate the effectiveness of the proposed improvements and superior performance of our method on face reconstruction. The code will be available at https://github.com/RuijieZhu94/TI-Face.Comment: 1st place solution in the View Synthesis Challenge for Human Heads (VSCHH) at the ICCV 2023 worksho

Investigation of the Frequency Shift of a SAD Circuit Loop and the Internal Micro-Cantilever in a Gas Sensor

Micro-cantilever sensors for mass detection using resonance frequency have attracted considerable attention over the last decade in the field of gas sensing. For such a sensing system, an oscillator circuit loop is conventionally used to actuate the micro-cantilever, and trace the frequency shifts. In this paper, gas experiments are introduced to investigate the mechanical resonance frequency shifts of the micro-cantilever within the circuit loop(mechanical resonance frequency, MRF) and resonating frequency shifts of the electric signal in the oscillator circuit (system working frequency, SWF). A silicon beam with a piezoelectric zinc oxide layer is employed in the experiment, and a Self-Actuating-Detecting (SAD) circuit loop is built to drive the micro-cantilever and to follow the frequency shifts. The differences between the two resonating frequencies and their shifts are discussed and analyzed, and a coefficient α related to the two frequency shifts is confirmed

Adaptive Spot-Guided Transformer for Consistent Local Feature Matching

Local feature matching aims at finding correspondences between a pair of images. Although current detector-free methods leverage Transformer architecture to obtain an impressive performance, few works consider maintaining local consistency. Meanwhile, most methods struggle with large scale variations. To deal with the above issues, we propose Adaptive Spot-Guided Transformer (ASTR) for local feature matching, which jointly models the local consistency and scale variations in a unified coarse-to-fine architecture. The proposed ASTR enjoys several merits. First, we design a spot-guided aggregation module to avoid interfering with irrelevant areas during feature aggregation. Second, we design an adaptive scaling module to adjust the size of grids according to the calculated depth information at fine stage. Extensive experimental results on five standard benchmarks demonstrate that our ASTR performs favorably against state-of-the-art methods. Our code will be released on https://astr2023.github.io.Comment: Accepted to CVPR 2023. Project page: https://astr2023.github.io

A Perspective on Carotenoids: Z/E-Isomerization, Extraction by Deep Eutectic Solvents and Applications

Carotenoids are used commercially for nutraceutical products because of their low toxicity, antioxidant activity, association with a reduction in various diseases and high coloring capacity. However, low stability and bioavailability limited their applications. Alterations in the physicochemical properties of carotenoids by Z-isomerization are beneficial for their extraction and bioavailability. The main strategies applied for enhancing their Z-isomerization include adding a catalyst, especially natural or heterogeneous sulfur-containing compounds. Consumers’ interest in products with carotenoids of natural origin has increased, which has emphasized a need for improved methods for their extraction from food waste. The green extraction methods for carotenoid recovery, especially using natural deep eutectic solvents combined with some novel extraction techniques showed a rapid increase and excellent application prospects. Health problems faced by the older population boost the demand for carotenoid diet supplements for skin health, anti-aging, treating eye disorders, preventing cancer (prostate) and obesity, thereby driving the growth of the carotenoids industry. However, the expansion of the carotenoid worldwide market is hampered by strict regulatory and approval standards. Relevant standards of carotenoids, especially Z-carotenoids, need to be improved