NeuSE: Neural SE(3)-Equivariant Embedding for Consistent Spatial Understanding with Objects

We present NeuSE, a novel Neural SE(3)-Equivariant Embedding for objects, and illustrate how it supports object SLAM for consistent spatial understanding with long-term scene changes. NeuSE is a set of latent object embeddings created from partial object observations. It serves as a compact point cloud surrogate for complete object models, encoding full shape information while transforming SE(3)-equivariantly in tandem with the object in the physical world. With NeuSE, relative frame transforms can be directly derived from inferred latent codes. Our proposed SLAM paradigm, using NeuSE for object shape and pose characterization, can operate independently or in conjunction with typical SLAM systems. It directly infers SE(3) camera pose constraints that are compatible with general SLAM pose graph optimization, while also maintaining a lightweight object-centric map that adapts to real-world changes. Our approach is evaluated on synthetic and real-world sequences featuring changed objects and shows improved localization accuracy and change-aware mapping capability, when working either standalone or jointly with a common SLAM pipeline.Comment: 15 Pages and 12 figures. Accepted to RSS 2023. Project webpage: https://neuse-slam.github.io/neuse

Robust Change Detection Based on Neural Descriptor Fields

The ability to reason about changes in the environment is crucial for robots operating over extended periods of time. Agents are expected to capture changes during operation so that actions can be followed to ensure a smooth progression of the working session. However, varying viewing angles and accumulated localization errors make it easy for robots to falsely detect changes in the surrounding world due to low observation overlap and drifted object associations. In this paper, based on the recently proposed category-level Neural Descriptor Fields (NDFs), we develop an object-level online change detection approach that is robust to partially overlapping observations and noisy localization results. Utilizing the shape completion capability and SE(3)-equivariance of NDFs, we represent objects with compact shape codes encoding full object shapes from partial observations. The objects are then organized in a spatial tree structure based on object centers recovered from NDFs for fast queries of object neighborhoods. By associating objects via shape code similarity and comparing local object-neighbor spatial layout, our proposed approach demonstrates robustness to low observation overlap and localization noises. We conduct experiments on both synthetic and real-world sequences and achieve improved change detection results compared to multiple baseline methods. Project webpage: https://yilundu.github.io/ndf_changeComment: 8 pages, 8 figures, and 2 tables. Accepted to IROS 2022. Project webpage: https://yilundu.github.io/ndf_chang

Signal Demodulation with Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset and Algorithms

In this paper, we investigate the design and implementation of machine learning (ML) based demodulation methods in the physical layer of visible light communication (VLC) systems. We build a flexible hardware prototype of an end-to-end VLC system, from which the received signals are collected as the real data. The dataset is available online, which contains eight types of modulated signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Specifically, the CNN based demodulator converts the modulated signals to images and recognizes the signals by the image classification. The proposed DBN based demodulator contains three restricted Boltzmann machines (RBMs) to extract the modulation features. The AdaBoost method includes a strong classifier that is constructed by the weak classifiers with the k-nearest neighbor (KNN) algorithm. These three demodulators are trained and tested by our online open dataset. Experimental results show that the demodulation accuracy of the three data-driven demodulators drops as the transmission distance increases. A higher modulation order negatively influences the accuracy for a given transmission distance. Among the three ML methods, the AdaBoost modulator achieves the best performance

One-for-All: Towards Universal Domain Translation with a Single StyleGAN

In this paper, we propose a novel translation model, UniTranslator, for transforming representations between visually distinct domains under conditions of limited training data and significant visual differences. The main idea behind our approach is leveraging the domain-neutral capabilities of CLIP as a bridging mechanism, while utilizing a separate module to extract abstract, domain-agnostic semantics from the embeddings of both the source and target realms. Fusing these abstract semantics with target-specific semantics results in a transformed embedding within the CLIP space. To bridge the gap between the disparate worlds of CLIP and StyleGAN, we introduce a new non-linear mapper, the CLIP2P mapper. Utilizing CLIP embeddings, this module is tailored to approximate the latent distribution in the P space, effectively acting as a connector between these two spaces. The proposed UniTranslator is versatile and capable of performing various tasks, including style mixing, stylization, and translations, even in visually challenging scenarios across different visual domains. Notably, UniTranslator generates high-quality translations that showcase domain relevance, diversity, and improved image quality. UniTranslator surpasses the performance of existing general-purpose models and performs well against specialized models in representative tasks. The source code and trained models will be released to the public

Field-of-view optimized and constrained undistorted single-shot study of intravoxel incoherent motion and diffusion-weighted imaging of the uterus during the menstrual cycle: a prospective study

PURPOSEThis study aimed to compare the variability of the uterus during the menses phase (MP), follicular phase (FP), and luteal phase (LP) of the menstrual cycle using intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI).METHODSThis prospective study was conducted at the Guangdong Provincial Hospital of Traditional Chinese Medicine between January 2022 and January 2023. Women of childbearing age (18–45 years) with appropriate progesterone levels were included in this study. Conventional magnetic resonance imaging and IVIM-DWI scans were performed during the MP, FP, and LP. The differences in IVIM-DWI-derived parameters between these phases were then compared, and the overlap was quantitatively described.RESULTSThe apparent diffusion coefficient (ADC) and pure molecular diffusion coefficient (D) values from the endometrium, uterine junctional zone (UJZ), and myometrium indicated statistical differences between the MP and FP and the MP and LP (ADC: endometrium, both P < 0.001; UJZ, P = 0.008 and P < 0.001, respectively; myometrium, P = 0.033 and P = 0.006, respectively; D: endometrium, both P < 0.001; UJZ, P = 0.008 and P = 0.006, respectively; myometrium, P = 0.041 and P = 0.045, respectively). The perfusion-related diffusion coefficient (D*) values from the myometrium indicated statistical differences between the FP and MP and the FP and LP (D*: myometrium, P = 0.049 and P = 0.009, respectively). The overlapping endometrium ratios between the MP and FP or LP were lower than 50% in the ADC and D values (ADC: overlapping of MP and FP: 33.33%, overlapping of MP and LP: 23.33%; D: overlapping of MP and FP: 40.00%, overlapping of MP and LP: 43.33%).CONCLUSIONThe ADC and IVIM-derived parameters indicated differences in the uterus in diverse phases of the menstrual cycle, especially in the endometrium in relation to ADC and D values

Coordinated economic dispatch of the primary and secondary heating systems considering the boiler’s supplemental heating

District heating systems have been widely used in large and medium-sized cities. Typical district heating systems consist of the primary heating system (PHS) and the secondary heating system (SHS) operating in isolation. However, the isolated dispatch of the PHS and the SHS has poor adjustability and large losses, resulting in unnecessary operation costs. To address these issues, a coordinated economic dispatching model (CEDM) for the primary and secondary heating systems considering the boiler’s supplemental heating is proposed in this study, which characterized the physical properties of the PHS and the SHS in detail. Considering that the PHS and the SHS are controlled separately without central operators in practice, it is difficult to dispatch them in a centralized method. Thus, the master-slave splitting algorithm is innovatively introduced to solve the CEDM in a decentralized way. Finally, a P6S12 system is utilized to analyze and verify the effectiveness and optimality of the proposed algorithm

Developing a new treatment for superficial fungal infection using antifungal Collagen-HSAF dressing

Fungal pathogens are common causes of superficial clinical infection. Their increasing drug resistance gradually makes existing antifungal drugs ineffective. Heat stable antifungal factor (HSAF) is a novel antifungal natural product with a unique structure. However, the application of HSAF has been hampered by very low yield in the current microbial producers and from extremely poor solubility in water and common solvents. In this study, we developed an effective mode of treatment applying HSAF to superficial fungal infections. The marine-derived Lysobacter enzymogenes YC36 contains the HSAF biosynthetic gene cluster, which we activated by the interspecific signaling molecule indole. An efficient extraction strategy was used to significantly improve the purity to 95.3%. Scanning electron microscopy images revealed that the Type I collagen-based HSAF (Col-HSAF) has a transparent appearance and good physical properties, and the in vitro sustained-release effect of HSAF was maintained for more than 2 weeks. The effective therapeutic concentration of Col-HSAF against superficial fungal infection was explored, and Col-HSAF showed good biocompatibility, lower clinical scores, mild histological changes, and antifungal capabilities in animals with Aspergillus fumigatus keratitis and cutaneous candidiasis. In conclusion, Col-HSAF is an antifungal reagent with significant clinical value in the treatment of superficial fungal infections