139 research outputs found
Exploring a Hybrid Algorithm for Price Volatility Prediction of Bitcoin
In recent years, the Bitcoin investment market has become increasingly popular. We collected existing literature on Bitcoin and found that predictions about the role of Bitcoin in investment portfolios and the volatility of Bitcoin price as well as return have become advanced research topics. This study shows our current work on the prediction of Bitcoin price volatility and proposes an idea for predicting the price volatility. We have designed an experiment that compares different combinations of machine learning algorithms with GARCH-type models, intending to compare the effects of these models in the prediction of Bitcoin time series and finally implement an optimized algorithm
Converting beam polarizations into entanglement and classical correlation
The nonclassicality of a macroscopic single-mode optical superposition state
is potentially convertible into entanglement, when the state is mixed with the
vacuum on a beam splitter. Considering light beams with polarization degree of
freedom in Euclidean space as coherent product states in a bipartite Hilbert
space, we propose a method to convert the polarization amplitudes into
entanglement and classical correlation through generating nonclassicality in
the superpositions of coherent and displaced Fock states. Equivalent Bell state
emerges from the resulted superpositions and the proportion of mixed
entanglement and correlation, quantified by the metric pair of negativity and
Schmidt number, is determined by the two displacements along the polarization
directions. We further characterize the constructed states with Wigner
functions and propose an experimental method for generating these states and
measuring them via homodyne tomography
An asymmetric supercapacitor with excellent cycling performance realized by hierarchical porous NiGa2O4 nanosheets
Rational design of composition and electrochemically favorable structure configuration of electrode materials are highly required to develop high-performance supercapacitors. Here, we report our findings on the design of interconnected NiGa2O4 nanosheets as advanced cathode electrodes for supercapacitors. Rietveld refinement analysis demonstrates that the incorporation of Ga in NiO leads to a larger cubic lattice parameter that promotes faster charge-transfer kinetics, enabling significantly improved electrochemical performance. The NiGa2O4 electrode delivers a specific capacitance of 1508 F g−1 at a current density of 1 A g−1 with the capacitance retention of 63.7% at 20 A g−1, together with excellent cycling stability after 10000 charge–discharge cycles (capacitance retention of 102.4%). An asymmetric supercapacitor device was assembled by using NiGa2O4 and Fe2O3 as cathode and anode electrodes, respectively. The ASC delivers a high energy density of 45.2 Wh kg−1 at a power density of 1600 W kg−1 with exceptional cycling stability (94.3% cell capacitance retention after 10000 cycles). These results suggest that NiGa2O4 can serve as a new class cathode material for advanced electrochemical energy storage applications
Self-driven Grounding: Large Language Model Agents with Automatical Language-aligned Skill Learning
Large language models (LLMs) show their powerful automatic reasoning and
planning capability with a wealth of semantic knowledge about the human world.
However, the grounding problem still hinders the applications of LLMs in the
real-world environment. Existing studies try to fine-tune the LLM or utilize
pre-defined behavior APIs to bridge the LLMs and the environment, which not
only costs huge human efforts to customize for every single task but also
weakens the generality strengths of LLMs. To autonomously ground the LLM onto
the environment, we proposed the Self-Driven Grounding (SDG) framework to
automatically and progressively ground the LLM with self-driven skill learning.
SDG first employs the LLM to propose the hypothesis of sub-goals to achieve
tasks and then verify the feasibility of the hypothesis via interacting with
the underlying environment. Once verified, SDG can then learn generalized
skills with the guidance of these successfully grounded subgoals. These skills
can be further utilized to accomplish more complex tasks which fail to pass the
verification phase. Verified in the famous instruction following task
set-BabyAI, SDG achieves comparable performance in the most challenging tasks
compared with imitation learning methods that cost millions of demonstrations,
proving the effectiveness of learned skills and showing the feasibility and
efficiency of our framework
UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy
In this work, we tackle the problem of learning universal robotic dexterous
grasping from a point cloud observation under a table-top setting. The goal is
to grasp and lift up objects in high-quality and diverse ways and generalize
across hundreds of categories and even the unseen. Inspired by successful
pipelines used in parallel gripper grasping, we split the task into two stages:
1) grasp proposal (pose) generation and 2) goal-conditioned grasp execution.
For the first stage, we propose a novel probabilistic model of grasp pose
conditioned on the point cloud observation that factorizes rotation from
translation and articulation. Trained on our synthesized large-scale dexterous
grasp dataset, this model enables us to sample diverse and high-quality
dexterous grasp poses for the object point cloud.For the second stage, we
propose to replace the motion planning used in parallel gripper grasping with a
goal-conditioned grasp policy, due to the complexity involved in dexterous
grasping execution. Note that it is very challenging to learn this highly
generalizable grasp policy that only takes realistic inputs without oracle
states. We thus propose several important innovations, including state
canonicalization, object curriculum, and teacher-student distillation.
Integrating the two stages, our final pipeline becomes the first to achieve
universal generalization for dexterous grasping, demonstrating an average
success rate of more than 60\% on thousands of object instances, which
significantly outperforms all baselines, meanwhile showing only a minimal
generalization gap.Comment: Accepted to CVPR 202
Wheat Rhizosphere Metagenome Reveals Newfound Potential Soil Zn-Mobilizing Bacteria Contributing to Cultivars’ Variation in Grain Zn Concentration
An effective solution to global human zinc (Zn) deficiency is Zn biofortification of staple food crops, which has been hindered by the low available Zn in calcareous soils worldwide. Many culturable soil microbes have been reported to increase Zn availability in the laboratory, while the status of these microbes in fields and whether there are unculturable Zn-mobilizing microbes remain unexplored. Here, we use the culture-independent metagenomic sequencing to investigate the rhizosphere microbiome of three high-Zn (HZn) and three low-Zn (LZn) wheat cultivars in a field experiment with calcareous soils. The average grain Zn concentration of HZn was higher than the Zn biofortification target 40 mg kg–1, while that of LZn was lower than 40 mg kg–1. Metagenomic sequencing and analysis showed large microbiome difference between wheat rhizosphere and bulk soil but small difference between HZn and LZn. Most of the rhizosphere-enriched microbes in HZn and LZn were in common, including many of the previously reported soil Zn-mobilizing microbes. Notably, 30 of the 32 rhizosphere-enriched species exhibiting different abundances between HZn and LZn possess the functional genes involved in soil Zn mobilization, especially the synthesis and exudation of organic acids and siderophores. Most of the abundant potential Zn-mobilizing species were positively correlated with grain Zn concentration and formed a module with strong interspecies relations in the co-occurrence network of abundant rhizosphere-enriched microbes. The potential Zn-mobilizing species, especially Massilia and Pseudomonas, may contribute to the cultivars’ variation in grain Zn concentration, and they deserve further investigation in future studies on Zn biofortification
A comprehensive multimodal dataset for contactless lip reading and acoustic analysis
Small-scale motion detection using non-invasive remote sensing techniques has recently garnered significant interest in the field of speech recognition. Our dataset paper aims to facilitate the enhancement and restoration of speech information from diverse data sources for speakers. In this paper, we introduce a novel multimodal dataset based on Radio Frequency, visual, text, audio, laser and lip landmark information, also called RVTALL. Specifically, the dataset consists of 7.5 GHz Channel Impulse Response (CIR) data from ultra-wideband (UWB) radars, 77 GHz frequency modulated continuous wave (FMCW) data from millimeter wave (mmWave) radar, visual and audio information, lip landmarks and laser data, offering a unique multimodal approach to speech recognition research. Meanwhile, a depth camera is adopted to record the landmarks of the subject’s lip and voice. Approximately 400 minutes of annotated speech profiles are provided, which are collected from 20 participants speaking 5 vowels, 15 words, and 16 sentences. The dataset has been validated and has potential for the investigation of lip reading and multimodal speech recognition
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