A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions

High-resolution representations are important for vision-based robotic grasping problems. Existing works generally encode the input images into low-resolution representations via sub-networks and then recover high-resolution representations. This will lose spatial information, and errors introduced by the decoder will be more serious when multiple types of objects are considered or objects are far away from the camera. To address these issues, we revisit the design paradigm of CNN for robotic perception tasks. We demonstrate that using parallel branches as opposed to serial stacked convolutional layers will be a more powerful design for robotic visual grasping tasks. In particular, guidelines of neural network design are provided for robotic perception tasks, e.g., high-resolution representation and lightweight design, which respond to the challenges in different manipulation scenarios. We then develop a novel grasping visual architecture referred to as HRG-Net, a parallel-branch structure that always maintains a high-resolution representation and repeatedly exchanges information across resolutions. Extensive experiments validate that these two designs can effectively enhance the accuracy of visual-based grasping and accelerate network training. We show a series of comparative experiments in real physical environments at Youtube: https://youtu.be/Jhlsp-xzHFY

Delving into Out-of-Distribution Detection with Vision-Language Representations

Recognizing out-of-distribution (OOD) samples is critical for machine learning systems deployed in the open world. The vast majority of OOD detection methods are driven by a single modality (e.g., either vision or language), leaving the rich information in multi-modal representations untapped. Inspired by the recent success of vision-language pre-training, this paper enriches the landscape of OOD detection from a single-modal to a multi-modal regime. Particularly, we propose Maximum Concept Matching (MCM), a simple yet effective zero-shot OOD detection method based on aligning visual features with textual concepts. We contribute in-depth analysis and theoretical insights to understand the effectiveness of MCM. Extensive experiments demonstrate that MCM achieves superior performance on a wide variety of real-world tasks. MCM with vision-language features outperforms a common baseline with pure visual features on a hard OOD task with semantically similar classes by 13.1% (AUROC). Code is available at https://github.com/deeplearning-wisc/MCM.Comment: 36th Conference on Neural Information Processing Systems (NeurIPS 2022

Impact of carbon coating processing using sucrose for thick binder-free titanium niobium oxide lithium-ion battery anode

Lithium-ion batteries are increasingly important for providing energy storage solutions. In the drive to improve the energy density at the cell level, optimizing the electrode architecture is crucial in addition to researching new materials. Binder-free (BF) electrodes include porous pellets only containing battery electroactive materials. These electrodes can provide advantages with regard to mechanical stability and alleviated ion transport limitations relative to composite approaches for very thick and energy-dense electrodes. However, the absence of conductive additives often limits suitable material candidates for BF battery electrodes. TiNb2O7 (TNO) is a promising BF electrode material from a gravimetric and volumetric capacity standpoint, but phase pure TNO has relatively low electronic conductivity. Herein, a sucrose precursor coating method for TNO materials was implemented to process the TNO materials into BF electrodes. The sucrose served as a source to generate carbon in the electrodes, where the carbon coating resulted in an increase in rate capability, discharge voltage, and cycle life

Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters

In recent years, Dialogue-style Large Language Models (LLMs) such as ChatGPT and GPT4 have demonstrated immense potential in constructing open-domain dialogue agents. However, aligning these agents with specific characters or individuals remains a considerable challenge due to the complexities of character representation and the lack of comprehensive annotations. In this paper, we introduce the Harry Potter Dialogue (HPD) dataset, designed to advance the study of dialogue agents and character alignment. The dataset encompasses all dialogue sessions (in both English and Chinese) from the Harry Potter series and is annotated with vital background information, including dialogue scenes, speakers, character relationships, and attributes. These extensive annotations may empower LLMs to unlock character-driven dialogue capabilities. Furthermore, it can serve as a universal benchmark for evaluating how well can a LLM aligning with a specific character. We benchmark LLMs on HPD using both fine-tuning and in-context learning settings. Evaluation results reveal that although there is substantial room for improvement in generating high-quality, character-aligned responses, the proposed dataset is valuable in guiding models toward responses that better align with the character of Harry Potter.Comment: 14 page

Pore Microstructure Impacts on Lithium Ion Transport and Rate Capability of Thick Sintered Electrodes

Increasing electrode thickness is one route to improve the energy density of lithium-ion battery cells. However, restricted Li+ transport in the electrolyte phase through the porous microstructure of thick electrodes limits the ability to achieve high current densities and rates of charge/discharge with these high energy cells. In this work, processing routes to mitigate transport restrictions were pursued. The electrodes used were comprised of only active material sintered together into a porous pellet. For one of the electrodes, comparisons were done between using ice-templating to provide directional porosity and using sacrificial particles during processing to match the geometric density without pore alignment. The ice-templated electrodes retained much greater discharge capacity at higher rates of cycling, which was attributed to improved transport properties provided by the processing. The electrodes were further characterized using an electrochemical model of the cells evaluated and neutron imaging of a cell containing the ice-templated pellet. The results indicate that significant improvements can be made to electrochemical cell properties via templating the electrode microstructure for situations where the rate limiting step includes ion transport limitations in the cell

Scalable mode division multiplexed transmission over a 10-km ring-core fiber using high-order orbital angular momentum modes

We propose and demonstrate a scalable mode division multiplexing scheme based on orbital angular momentum modes in ring core fibers. In this scheme, the high-order mode groups of a ring core fiber are sufficiently de-coupled by the large differential effective refractive index so that multiple-input multiple-output (MIMO) equalization is only used for crosstalk equalization within each mode group. We design and fabricate a graded-index ring core fiber that supports 5 mode groups with low inter-mode-group coupling, small intra-mode-group differential group delay, and small group velocity dispersion slope over the C-band for the high-order mode groups. We implement a two-dimensional wavelength- and mode-division multiplexed transmission experiment involving 10 wavelengths and 2 mode groups each with 4 OAM modes, transmitting 32 GBaud Nyquist QPSK signals over all 80 channels. An aggregate capacity of 5.12 Tb/s and an overall spectral efficiency of 9 bit/s/Hz over 10 km are realized, only using modular 4x4 MIMO processing with 15 taps to recover signals from the intra-mode-group mode coupling. Given the fixed number of modes in each mode group and the low inter-mode-group coupling in ring core fibres, our scheme strikes a balance in the trade-off between system capacity and digital signal processing complexity, and therefore has good potential for capacity upscaling at an expense of only modularly increasing the number of mode-groups with fixed-size (4x4) MIMO blocks

ErbB2 Signaling Increases Androgen Receptor Expression in Abiraterone-Resistant Prostate Cancer

Purpose: ErbB2 signaling appears to be increased and may enhance AR activity in a subset of CRPC, but agents targeting ErbB2 have not been effective. This study was undertaken to assess ErbB2 activity in abiraterone-resistant prostate cancer (PCa), and determine whether it may contribute to androgen receptor (AR) signaling in these tumors. Experimental Design: AR activity and ErbB2 signaling were examined in the radical prostatectomy specimens from a neoadjuvant clinical trial of leuprolide plus abiraterone, and in the specimens from abiraterone-resistant CRPC xenograft models. The effect of ErbB2 signaling on AR activity was determined in two CRPC cell lines. Moreover, the effect of combination treatment with abiraterone and an ErbB2 inhibitor was assessed in a CRPC xenograft model. Results: We found that ErbB2 signaling was elevated in residual tumor following abiraterone treatment in a subset of patients, and was associated with higher nuclear AR expression. In xenograft models, we similarly demonstrated that ErbB2 signaling was increased and associated with AR reactivation in abiraterone-resistant tumors, while ERBB2 message level was not changed. Mechanistically, we show that ErbB2 signaling and subsequent activation of the PI3K/AKT signaling stabilizes AR protein. Inhibitors targeting ErbB2/PI3K/AKT pathway disrupt AR transcriptional activity. Furthermore, concomitantly treating CRPC xenograft with abiraterone and an ErbB2 inhibitor, lapatinib, blocked AR reactivation and suppressed tumor progression. Conclusions: ErbB2 signaling is elevated in a subset of abiraterone-resistant prostate cancer patients and stabilizes AR protein. Combination therapy with abiraterone and ErbB2 antagonists may be effective for treating the subset of CRPC with elevated ErbB2 activity

Integrated optical vortex beam receivers

A simple and ultra-compact integrated optical vortex beam receiver device is presented. The device is based on the coupling between the optical vortex modes and whispering gallery modes in a micro-ring resonator via embedded angular gratings, which provides the selective reception of optical vortex modes with definitive total angular momentum (summation of spin and orbital angular momentum) through the phase matching condition in the coupling process. Experimental characterization confirms the correct detection of the total angular momentum carried by the vortex beams incident on the device. In addition, photonic spin-controlled unidirectional excitation of whispering-gallery modes in the ring receiver is also observed, and utilized to differentiate between left- and right-circular polarizations and therefore unambiguously identify the orbital angular momentum of incident light. Such characteristics provide an effective mode-selective receiver for the eigen-modes in orbital angular momentum fiber transmission where the circularly polarized OAM modes can be used as data communications channels in multiplexed communications or as photonic states in quantum information applications