Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper. The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result. The experiments carried on the public datasets and in the field illustrate the validation of the proposed system.Comment: Accepted by ITSC 201

NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference

The inherent diversity of computation types within individual deep neural network (DNN) models necessitates a corresponding variety of computation units within hardware processors, leading to a significant constraint on computation efficiency during neural network execution. In this study, we introduce NeuralMatrix, a framework that transforms the computation of entire DNNs into linear matrix operations, effectively enabling their execution with one general-purpose matrix multiplication (GEMM) accelerator. By surmounting the constraints posed by the diverse computation types required by individual network models, this approach provides both generality, allowing a wide range of DNN models to be executed using a single GEMM accelerator and application-specific acceleration levels without extra special function units, which are validated through main stream DNNs and their variant models.Comment: 12 pages, 4figures, Submitted to 11th International Conference on Learning Representation

Surface chemistry and structure manipulation of graphene-related materials to address the challenges of electrochemical energy storage

Energy storage devices are important components in portable electronics, electric vehicles, and the electrical distribution grid. Batteries and supercapacitors have achieved great success as the spearhead of electrochemical energy storage devices, but need to be further developed in order to meet the ever-increasing energy demands, especially attaining higher power and energy density, and longer cycling life. Rational design of electrode materials plays a critical role in developing energy storage systems with higher performance. Graphene, the well-known 2D allotrope of carbon, with a unique structure and excellent properties has been considered a “magic” material with its high energy storage capability, which can not only aid in addressing the issues of the state-of-the-art lithium-ion batteries and supercapacitors, but also be crucial in the so-called post Li-ion battery era covering different technologies, e.g., sodium ion batteries, lithium-sulfur batteries, structural batteries, and hybrid supercapacitors. In this feature article, we provide a comprehensive overview of the strategies developed in our research to create graphene-based composite electrodes with better ionic conductivity, electron mobility, specific surface area, mechanical properties, and device performance than state-of-the-art electrodes. We summarize the strategies of structure manipulation and surface modification with specific focus on tackling the existing challenges in electrodes for batteries and supercapacitors by exploiting the unique properties of graphene-related materials

Better Zero-Shot Reasoning with Role-Play Prompting

Modern large language models (LLMs), such as ChatGPT, exhibit a remarkable capacity for role-playing, enabling them to embody not only human characters but also non-human entities like a Linux terminal. This versatility allows them to simulate complex human-like interactions and behaviors within various contexts, as well as to emulate specific objects or systems. While these capabilities have enhanced user engagement and introduced novel modes of interaction, the influence of role-playing on LLMs' reasoning abilities remains underexplored. In this study, we introduce a strategically designed role-play prompting methodology and assess its performance under the zero-shot setting across twelve diverse reasoning benchmarks, encompassing arithmetic, commonsense reasoning, symbolic reasoning, and more. Leveraging models such as ChatGPT and Llama 2, our empirical results illustrate that role-play prompting consistently surpasses the standard zero-shot approach across most datasets. Notably, accuracy on AQuA rises from 53.5% to 63.8%, and on Last Letter from 23.8% to 84.2%. Beyond enhancing contextual understanding, we posit that role-play prompting serves as an implicit Chain-of-Thought (CoT) trigger, thereby improving the quality of reasoning. By comparing our approach with the Zero-Shot-CoT technique, which prompts the model to "think step by step", we further demonstrate that role-play prompting can generate a more effective CoT. This highlights its potential to augment the reasoning capabilities of LLMs

The combination of 2d layered graphene oxide and 3d porous cellulose heterogeneous membranes for nanofluidic osmotic power generation

Salinity gradient energy, as a type of blue energy, is a promising sustainable energy source. Its energy conversion efficiency is significantly determined by the selective membranes. Recently, nanofluidic membrane made by two-dimensional (2D) nanomaterials (e.g., graphene) with densely packed nanochannels has been considered as a high-efficient membrane in the osmotic power generation research field. Herein, the graphene oxide-cellulose acetate (GO–CA) heterogeneous membrane was assembled by combining a porous CA membrane and a layered GO membrane; the combination of 2D nanochannels and 3D porous structures make it show high surface-charge-governed property and excellent ion transport stability, resulting in an efficient osmotic power harvesting. A power density of about 0.13 W/m2 is achieved for the sea–river mimicking system and up to 0.55 W/m2 at a 500-fold salinity gradient. With different functions, the CA and GO membranes served as ion storage layer and ion selection layer, respectively. The GO–CA heterogeneous membrane open a promising avenue for fabrication of porous and layered platform for wide potential applications, such as sustainable power generation, water purification, and seawater desalination

Understanding the Quantum Oscillation Spectrum of Heavy-fermion Compound SmB6

SmB6 is a mysterious compound that is electrically insulating but yet it exhibits quantum oscillations, which are a telltale signature of the metallic state. Adding to the enigma is the possibility that SmB6 is a topological Kondo insulator. Here, we report first-principles, parameter-free all-electron electronic-structure calculations on SmB6, which yield the band structure and crystal-field splittings within the f-electron complex in accord with experiments. Predicted energies of several magnetic phases where charge, spin and lattice degrees of freedom are treated on an equal footing are found to be extremely close, indicating the key role of spin fluctuations in SmB6. Our results show that the topological Kondo state of SmB6 is robust regardless of its magnetic configuration. The Fermi surfaces derived from our predicted ground state explain the experimentally observed bulk quantum oscillations, and our large calculated effective mass of electrons at the Fermi surface explains how the material is essentially insulating, with a measured specific heat that is in excellent agreement with our calculations.Comment: 17 pages, 7 figures and 1 tabl