Assessing the effect of noise-reduction to the intelligibility of low-pass filtered speech

Given the fact that most hearing-impaired listeners have low-frequency residual hearing, the present work assessed the effect of applying commonly-used singlechannel noise-reduction (NR) algorithms to improve the intelligibility of low-pass filtered speech, which simulates the effect of understanding speech with low-frequency residual hearing of hearing-impaired patients. In addition, this study was performed with Mandarin speech, which is characterized by its significant contribution of information present in (low-frequency dominated) vowels to speech intelligibility. Mandarin sentences were corrupted by steady-state speech-shaped noise and processed by four types (i.e., subspace, statistical-modeling, spectral-subtractive, and Wiener-filtering) of single-channel NR algorithms. The processed sentences were played to normal-hearing listeners for recognition. Experimental results showed that existing single-channel NR algorithms were unable to improve the intelligibility of low-pass filtered Mandarin sentences. Wiener-filtering had the least negative influence to the intelligibility of low-pass filtered speech among the four types of single-channel NR algorithms examined

Data Interpreter: An LLM Agent For Data Science

Large Language Model (LLM)-based agents have demonstrated remarkable effectiveness. However, their performance can be compromised in data science scenarios that require real-time data adjustment, expertise in optimization due to complex dependencies among various tasks, and the ability to identify logical errors for precise reasoning. In this study, we introduce the Data Interpreter, a solution designed to solve with code that emphasizes three pivotal techniques to augment problem-solving in data science: 1) dynamic planning with hierarchical graph structures for real-time data adaptability;2) tool integration dynamically to enhance code proficiency during execution, enriching the requisite expertise;3) logical inconsistency identification in feedback, and efficiency enhancement through experience recording. We evaluate the Data Interpreter on various data science and real-world tasks. Compared to open-source baselines, it demonstrated superior performance, exhibiting significant improvements in machine learning tasks, increasing from 0.86 to 0.95. Additionally, it showed a 26% increase in the MATH dataset and a remarkable 112% improvement in open-ended tasks. The solution will be released at https://github.com/geekan/MetaGPT

Photonic Synapses for Ultrahigh-Speed Neuromorphic Computing

Photonic Synapses for Ultrahigh-Speed Neuromorphic Computin

Hybrid oxide brain-inspired neuromorphic devices for hardware implementation of artificial intelligence

The state-of-the-art artificial intelligence technologies mainly rely on deep learning algorithms based on conventional computers with classical von Neumann computing architectures, where the memory and processing units are separated resulting in an enormous amount of energy and time consumed in the data transfer process. Inspired by the human brain acting like an ultra-highly efficient biological computer, neuromorphic computing is proposed as a technology for hardware implementation of artificial intelligence. Artificial synapses are the main component of a neuromorphic computing architecture. Memristors are considered to be a relatively ideal candidate for artificial synapse applications due to their high scalability and low power consumption. Oxides are most widely used in memristors due to the ease of fabrication and high compatibility with complementary metal-oxide-semiconductor processes. However, oxide memristors suffer from unsatisfactory stability and reliability. Oxide-based hybrid structures can effectively improve the device stability and reliability, therefore providing a promising prospect for the application of oxide memristors to neuromorphic computing. This work reviews the recent advances in the development of hybrid oxide memristive synapses. The discussion is organized according to the blending schemes as well as the working mechanisms of hybrid oxide memristors

Memristive Synapses for Brain-Inspired Computing

Memristive Synapses for Brain-Inspired Computin

Processing Of Polymer Nanocomposites

We demonstrate a single-pixel color display based on voltage-stretchable liquid crystal (LC) droplet. The gray scale is induced by stretching a dye-doped LC droplet from a small circular visible area to different extent through dielectrophoretic force. This polarization-insensitive liquid display shows a relatively low operating voltage, fast response, wide viewing angle and good contrast ratio. Both transmissive and reflective modes can be configured

Ultralow operation voltages of a transparent memristor based on bilayer ITO

Traditional memristors based on metal/insulator/metal sandwich structures generally require the operation voltages of several volts to switch the device between different resistance states. In this work, we report the ultralow set and reset voltages of 14mV and 0.3V in a simple bilayer device, respectively, which is composed of the widely used indium tin oxide (ITO) solely. Such low operation voltages might be ascribed to the synergistic effect of the loose porous structure in the upper ITO layer deposited by electron beam evaporation, the amorphous interface between two ITO layers, and the formation of an oxygen concentration gradient triggered by an initiation process. Based on the superior resistive switching properties of this kind bilayer device, synaptic functions and image memorization are achieved by applying ultralow electrical stimulus. The prototype device not only paves the way for simplifying the device structure and the fabrication process but also offers possibilities to develop transparent multifunctional devices with low power consumption