arXiv subjects

Mingyi Chen

Publications and source records attributed to Mingyi Chen.

6 recordsLinked to original sources

arXiv2025

A Multi-Channel Auditory Signal Encoder with Adaptive Resolution Using Volatile Memristors

We demonstrate and experimentally validate an end-to-end hybrid CMOS-memristor auditory encoder that realises adaptive-threshold, asynchronous delta-modulation (ADM)-based spike encoding by exploiting the inherent volatility of HfTiOx devices. A spike-triggered programming pulse rapidly raises the ADM threshold Delta (desensitisation); the device's volatility then passively lowers Delta when activity subsides (resensitisation), emphasising onsets while restoring sensitivity without static control energy. Our prototype couples an 8-channel 130 nm encoder IC to off-chip HfTiOx devices via a switch interface and an off-chip controller that monitors spike activity and issues programming events. An on-chip current-mirror transimpedance amplifier (TIA) converts device current into symmetric thresholds, enabling both sensitive and conservative encoding regimes. Evaluated with gammatone-filtered speech, the adaptive loop-at matched spike budget-sharpens onsets and preserves fine temporal detail that a fixed-Delta baseline misses; multi-channel spike cochleagrams show the same trend. Together, these results establish a practical hybrid CMOS-memristor pathway to onset-salient, spike-efficient neuromorphic audio front-ends and motivate low-power single-chip integration.

eess.AS↗

arXiv2025

MMHMER:Multi-viewer and Multi-task for Handwritten Mathematical Expression Recognition

Handwritten Mathematical Expression Recognition (HMER) methods have made remarkable progress, with most existing HMER approaches based on either a hybrid CNN/RNN-based with GRU architecture or Transformer architectures. Each of these has its strengths and weaknesses. Leveraging different model structures as viewers and effectively integrating their diverse capabilities presents an intriguing avenue for exploration. This involves addressing two key challenges: 1) How to fuse these two methods effectively, and 2) How to achieve higher performance under an appropriate level of complexity. This paper proposes an efficient CNN-Transformer multi-viewer, multi-task approach to enhance the model's recognition performance. Our MMHMER model achieves 63.96%, 62.51%, and 65.46% ExpRate on CROHME14, CROHME16, and CROHME19, outperforming Posformer with an absolute gain of 1.28%, 1.48%, and 0.58%. The main contribution of our approach is that we propose a new multi-view, multi-task framework that can effectively integrate the strengths of CNN and Transformer. By leveraging the feature extraction capabilities of CNN and the sequence modeling capabilities of Transformer, our model can better handle the complexity of handwritten mathematical expressions.

cs.CV↗

arXiv2023

Unlocking the Structural Mystery of Vaterite CaCO3

Calcium carbonate (CaCO3), the most abundant biogenic mineral on earth, plays a crucial role in various fields. Of the four polymorphs, calcite, aragonite, vaterite, and amorphous CaCO3, vaterite is the most enigmatic one due to an ongoing debate regarding its structure that has persisted for nearly a century. In this work, based on systematic transmission electron microscopy characterizations, elaborate crystallographic analysis and machine learning aided molecular dynamics simulations with ab initio accuracy, we reveal that vaterite can be regarded as a polytypic structure. The basic phase is a monoclinic lattice possessing pseudohexagonal symmetry. Direct imaging and atomic-scale simulations provide evidence that a single grain of vaterite can have three orientation variants. Additionally, we find that vaterite undergoes a second-order phase transition. These atomic scale insights provide a comprehensive understanding of the structure of vaterite and offer new perspectives on the biomineralization process of calcium carbonate.

cond-mat.mtrl-sci↗

arXiv2023

Imperfectly coordinated water molecules pave the way for homogeneous ice nucleation

Water freezing is ubiquitous on Earth, affecting many areas from biology to climate science and aviation technology. Probing the atomic structure in the homogeneous ice nucleation process from scratch is of great value but still experimentally unachievable. Theoretical simulations have found that ice originates from the low-mobile region with increasing abundance and persistence of tetrahedrally coordinated water molecules. However, a detailed microscopic picture of how the disordered hydrogen-bond network rearranges itself into an ordered network is still unclear. In this work, we use a deep neural network (DNN) model to "learn" the interatomic potential energy from quantum mechanical data, thereby allowing for large-scale and long molecular dynamics (MD) simulations with ab initio accuracy. The nucleation mechanism and dynamics at atomic resolution, represented by a total of 36 $\mu$s-long MD trajectories, are deeply affected by the structural and dynamical heterogeneity in supercooled water. We find that imperfectly coordinated (IC) water molecules with high mobility pave the way for hydrogen-bond network rearrangement, leading to the growth or shrinkage of the ice nucleus. The hydrogen-bond network formed by perfectly coordinated (PC) molecules stabilizes the nucleus, thus preventing it from vanishing and growing. Consequently, ice is born through competition and cooperation between IC and PC molecules. We anticipate that our picture of the microscopic mechanism of ice nucleation will provide new insights into many properties of water and other relevant materials.

cond-mat.mtrl-sci↗

arXiv2022

Tele-Knowledge Pre-training for Fault Analysis

In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents. To organize this knowledge from experts uniformly, we propose to create a Tele-KG (tele-knowledge graph). Using this valuable data, we further propose a tele-domain language pre-training model TeleBERT and its knowledge-enhanced version, a tele-knowledge re-training model KTeleBERT. which includes effective prompt hints, adaptive numerical data encoding, and two knowledge injection paradigms. Concretely, our proposal includes two stages: first, pre-training TeleBERT on 20 million tele-related corpora, and then re-training it on 1 million causal and machine-related corpora to obtain KTeleBERT. Our evaluation on multiple tasks related to fault analysis in tele-applications, including root-cause analysis, event association prediction, and fault chain tracing, shows that pre-training a language model with tele-domain data is beneficial for downstream tasks. Moreover, the KTeleBERT re-training further improves the performance of task models, highlighting the effectiveness of incorporating diverse tele-knowledge into the model.

cs.AI↗

arXiv2022

Minimizing Trust with Exclusively-Used Physically-Isolated Hardware

Smartphone owners often need to run security-critical programs on the same device as other untrusted and potentially malicious programs. This requires users to trust hardware and system software to correctly sandbox malicious programs, trust that is often misplaced. Our goal is to minimize the number and complexity of hardware and software components that a smartphone owner needs to trust to withstand adversarial inputs. We present a multi-domain hardware design composed of statically-partitioned, physically-isolated trust domains. We introduce a few simple, formally-verified hardware components to enable a program to gain provably exclusive and simultaneous access to both computation and I/O on a temporary basis. To manage this hardware, we present OctopOS, an OS composed of mutually distrustful subsystems. We present a prototype of this machine (hardware and OS) on a CPU-FPGA board and show that it incurs a small hardware cost compared to modern SoCs. For security-critical programs, we show that this machine significantly reduces the required trust compared to mainstream TEEs while achieving decent performance. For normal programs, performance is similar to a legacy machine.

cs.CR↗