arXiv subjects

Fei Liang

Publications and source records attributed to Fei Liang.

18 recordsLinked to original sources

arXiv2025

Extreme-ultraviolet laser generation at 118 nm via adaptive random additional periodic-phase engineering in a LiF crystal

Extreme ultraviolet (EUV) coherent sources below 120nm are of paramount significance for promoting next-generation nano-scale lithography,precision spectroscopy, and exploring the emerging physical phenomena in quantum materials. Nonlinear optical conversion serves as the only feasible approach to obtain solid state EUV lasers, yet the intrinsic strong absorption at EUV and giant phase mismatch among light waves have hindered the realization of highly-efficient EUV light sources. Herein, we propose a random additional periodic phase (RAPP) strategy in third-order nonlinear crystals to overcome these problems, that an artificially designed random phase grating at micrometer-scales is embedded in the homogeneous bulk crystal, thus adaptively compensating the phase mismatch between fundamental-wave and third-harmonic waves. For the first time, the EUV laser at 118nm is demonstrated in the RAPP lithium fluoride (LiF) crystals with wide period distributions, where the highest output power is over 90uW. To the best our knowledge, this is the shortest wavelength among all solid-state laser systems, which represents a significant advance in nonlinear optical materials and opens new roadmap toward high-brightness EUV sources.

physics.optics↗

arXiv2024

Ancient DNA from 120-Million-Year-Old Lycoptera Fossils Reveals Evolutionary Insights

High quality ancient DNA (aDNA) is essential for molecular paleontology. Due to DNA degradation and contamination by environmental DNA (eDNA), current research is limited to fossils less than 1 million years old. The study successfully extracted DNA from Lycoptera davidi fossils from the Early Cretaceous period, dating 120 million years ago. Using high-throughput sequencing, 1,258,901 DNA sequences were obtained. We established a rigorous protocol known as the mega screen method. Using this method, we identified 243 original in situ DNA (oriDNA) sequences, likely from the Lycoptera genome. These sequences have an average length of over 100 base pairs and show no signs of deamination. Additionally, 10 transposase coding sequences were discovered, shedding light on a unique self-renewal mechanism in the genome. This study provides valuable DNA data for understanding ancient fish evolution and advances paleontological research.

q-bio.GN↗

arXiv2024

DNA Fragments in Crude Oil Reveals Earth's Hidden History

This groundbreaking research extracted DNA from petroleum using nanoparticle affinity bead technology, yielding 3,159,020 petroleum DNA (pDNA) sequences, primarily environmental DNA. While most original in situ DNA (oriDNA) was lost, ancient DNA (aDNA) from petroleum offers an important source of ecological and evolutionary information, surpassing traditional fossils. This study reveals that oil, mainly sourced from algae and lower aquatic plants, now serves as a new type of fossil, providing detailed insights into Earth's hidden history, including unclassified species and ancient events, revolutionizing petroleum geology and paleontology.

q-bio.GN↗

arXiv2023

Accelerated Screening of Ternary Chalcogenides for High-Performance Optoelectronic Materials

Chalcogenides, which refer to chalcogen anions, have attracted considerable attention in multiple fields of applications, such as optoelectronics, thermoelectrics, transparent contacts, and thin film transistors. In comparison to oxide counterparts, chalcogenides have demonstrated higher mobility and \textit{p}-type dopability, owing to larger orbital overlaps between metal-X covalent chemical bondings and higher-energy valence bands derived by p-orbitals. Despite the potential of chalcogenides, the number of successfully synthesized compounds remains relatively low compared to oxides, suggesting the presence of numerous unexplored chalcogenides with fascinating physical characteristics. In this study, we implemented a systematic high-throughput screening process combined with first-principles calculations on ternary chalcogenides using 34 crystal structure prototypes. We generated a computational material database containing over 400,000 compounds by exploiting the ion-substitution approach at different atomic sites with elements in the periodic table. The thermodynamic stabilities of the candidates were validated using the chalcogenides included in the Open Quantum Materials Database. Moreover, we trained a model based on Crystal Graph Convolutional Neural Networks to predict the thermodynamic stability of novel materials. Furthermore, we theoretically evaluated the electronic structures of the stable candidates using accurate hybrid functionals. A series of in-depth characteristics, including the carrier effective masses, electronic configuration, and photovoltaic conversion efficiency, was also investigated. Our work provides useful guidance for further experimental research in the synthesis and characterization of such chalcogenides as promising candidates, as well as charting the stability and optoelectronic performance of ternary chalcogenides.

cond-mat.mtrl-sci↗

arXiv2022

Learning Quantization in LDPC Decoders

Finding optimal message quantization is a key requirement for low complexity belief propagation (BP) decoding. To this end, we propose a floating-point surrogate model that imitates quantization effects as additions of uniform noise, whose amplitudes are trainable variables. We verify that the surrogate model closely matches the behavior of a fixed-point implementation and propose a hand-crafted loss function to realize a trade-off between complexity and error-rate performance. A deep learning-based method is then applied to optimize the message bitwidths. Moreover, we show that parameter sharing can both ensure implementation-friendly solutions and results in faster training convergence than independent parameters. We provide simulation results for 5G low-density parity-check (LDPC) codes and report an error-rate performance within 0.2 dB of floating-point decoding at an average message quantization bitwidth of 3.1 bits. In addition, we show that the learned bitwidths also generalize to other code rates and channels.

cs.IT↗

arXiv2021

Artificial Neural Network and Its Application Research Progress in Chemical Process

Most chemical processes, such as distillation, absorption, extraction, and catalytic reactions, are extremely complex processes that are affected by multiple factors. The relationships between their input variables and output variables are non-linear, and it is difficult to optimize or control them using traditional methods. Artificial neural network (ANN) is a systematic structure composed of multiple neuron models. Its main function is to simulate multiple basic functions of the nervous system of living organisms. ANN can achieve nonlinear control without relying on mathematical models, and is especially suitable for more complex control objects. This article will introduce the basic principles and development history of artificial neural networks, and review its application research progress in chemical process control, fault diagnosis, and process optimization.

eess.SY↗

arXiv2021

Application of Neural Network in Optimization of Chemical Process

Artificial neural network (ANN) has been widely used due to its strong nonlinear mapping ability, fault tolerance and self-learning ability. This article summarizes the development history of artificial neural networks, introduces three common neural network types, BP neural network, RBF neural network and convolutional neural network, and focuses on the practical application in chemical process optimization, especially the results achieved in multi-objective control optimization and process parameter improvement.

eess.SY↗

arXiv2021

Vector spaces as Kripke frames

In recent years, the compositional distributional approach in computational linguistics has opened the way for an integration of the \emph{lexical} aspects of meaning into Lambek's type-logical grammar program. This approach is based on the observation that a sound semantics for the associative, commutative and unital Lambek calculus can be based on vector spaces by interpreting fusion as the tensor product of vector spaces. In this paper, we build on this observation and extend it to a `vector space semantics' for the \emph{general} Lambek calculus, based on \emph{algebras over a field} $\mathbb{K}$ (or $\mathbb{K}$-algebras), i.e. vector spaces endowed with a bilinear binary product. Such structures are well known in algebraic geometry and algebraic topology, since they are important instances of Lie algebras and Hopf algebras. Applying results and insights from duality and representation theory for the algebraic semantics of nonclassical logics, we regard $\mathbb{K}$-algebras as `Kripke frames' the complex algebras of which are complete residuated lattices. This perspective makes it possible to establish a systematic connection between vector space semantics and the standard Routley-Meyer semantics of (modal) substructural logics.

cs.LO↗

arXiv2019

Deep-ultraviolet second harmonic generation in a conventional crystal quartz by freeing phase-matching in nonlinear optics

Nonlinear frequency conversion in optics can originate the coherent light at the wavelength where it is hard or unlikely to achieve by directly lasing and is a fundamental topic in science and engineering covering both classical and quantum regions. The critical requirement for efficient nonlinear frequency conversion is phase-matching, a momentum conservation relation between the fundamental and harmonic light. It is dreaming of a technique for compensating the phase-mismatching in universal nonlinear materials and wavelength ranges, since the phase-matching was proposed in 1962. Here, an additional periodic phase (APP) concept was proposed for the phase-matching in nonlinear optics and experimentally demonstrated the APP phase-matched second harmonic generation with a conventional crystal quartz at the wavelength deep to vacuum-ultraviolet 177.3 nm. This study may not only develop a universal way to resuscitate the nonlinear optical materials for efficient nonlinear frequency conversion, but also may revolutionize the nonlinear photonics and their further applications.

physics.optics↗

arXiv2019

Towards Optimal Power Control via Ensembling Deep Neural Networks

A deep neural network (DNN) based power control method is proposed, which aims at solving the non-convex optimization problem of maximizing the sum rate of a multi-user interference channel. Towards this end, we first present PCNet, which is a multi-layer fully connected neural network that is specifically designed for the power control problem. PCNet takes the channel coefficients as input and outputs the transmit power of all users. A key challenge in training a DNN for the power control problem is the lack of ground truth, i.e., the optimal power allocation is unknown. To address this issue, PCNet leverages the unsupervised learning strategy and directly maximizes the sum rate in the training phase. Observing that a single PCNet does not globally outperform the existing solutions, we further propose ePCNet, a network ensemble with multiple PCNets trained independently. Simulation results show that for the standard symmetric multi-user Gaussian interference channel, ePCNet can outperform all state-of-the-art power control methods by 1.2%-4.6% under a variety of system configurations. Furthermore, the performance improvement of ePCNet comes with a reduced computational complexity.

eess.SP↗

arXiv2018

Semi De Morgan logic properly displayed

In the present paper, we endow a family of axiomatic extensions of semi De Morgan logic with proper multi-type display calculi which are sound, complete, conservative, and enjoy cut elimination and subformula property. Our proposal builds on an algebraic analysis of semi De Morgan algebras and its subvarieties and applies the guidelines of the multi-type methodology in the design of display calculi.

math.LO↗

arXiv2018

Proper Multi-Type Display Calculi for Rough Algebras

In the present paper, we endow the logics of topological quasi Boolean algebras, topological quasi Boolean algebras 5, intermediate algebras of types 1-3, and pre-rough algebras with proper multi-type display calculi which are sound, complete, conservative, and enjoy cut elimination and subformula property. Our proposal builds on an algebraic analysis and applies the principles of the multi-type methodology in the design of display calculi.

math.LO↗

arXiv2018

Algebraic proof theory for LE-logics

In this paper we extend the research programme in algebraic proof theory from axiomatic extensions of the full Lambek calculus to logics algebraically captured by certain varieties of normal lattice expansions (normal LE-logics). Specifically, we generalise the residuated frames in [34] to arbitrary signatures of normal lattice expansions (LE). Such a generalization provides a valuable tool for proving important properties of LE-logics in full uniformity. We prove semantic cut elimination for the display calculi D.LE associated with the basic normal LE-logics and their axiomatic extensions with analytic inductive axioms. We also prove the finite model property (FMP) for each such calculus D.LE, as well as for its extensions with analytic structural rules satisfying certain additional properties.

math.LO↗

arXiv2018

Kleene algebras, adjunction and structural control

In the present paper, we introduce a multi-type calculus for the logic of measurable Kleene algebras, for which we prove soundness, completeness, conservativity, cut elimination and subformula property. Our proposal imports ideas and techniques developed in formal linguistics around the notion of structural control.

math.LO↗

arXiv2017

Bilattice Logic Properly Displayed

We introduce a proper multi-type display calculus for bilattice logic (with conflation) for which we prove soundness, completeness, conservativity, standard subformula property and cut-elimination. Our proposal builds on the product representation of bilattices and applies the guidelines of the multi-type methodology in the design of display calculi.

math.LO↗

arXiv2017

An Iterative BP-CNN Architecture for Channel Decoding

Inspired by recent advances in deep learning, we propose a novel iterative BP-CNN architecture for channel decoding under correlated noise. This architecture concatenates a trained convolutional neural network (CNN) with a standard belief-propagation (BP) decoder. The standard BP decoder is used to estimate the coded bits, followed by a CNN to remove the estimation errors of the BP decoder and obtain a more accurate estimation of the channel noise. Iterating between BP and CNN will gradually improve the decoding SNR and hence result in better decoding performance. To train a well-behaved CNN model, we define a new loss function which involves not only the accuracy of the noise estimation but also the normality test for the estimation errors, i.e., to measure how likely the estimation errors follow a Gaussian distribution. The introduction of the normality test to the CNN training shapes the residual noise distribution and further reduces the BER of the iterative decoding, compared to using the standard quadratic loss function. We carry out extensive experiments to analyze and verify the proposed framework. The iterative BP-CNN decoder has better BER performance with lower complexity, is suitable for parallel implementation, does not rely on any specific channel model or encoding method, and is robust against training mismatches. All of these features make it a good candidate for decoding modern channel codes.

stat.ML↗

arXiv2016

Sequent Calculi for Semi-De Morgan and De Morgan Algebras

A contraction-free and cut-free sequent calculus $\msf{G3SDM}$ for semi-De Morgan algebras, and a structural-rule-free and single-succedent sequent calculus $\msf{G3DM}$ for De Morgan algebras are developed. The cut rule is admissible in both sequent calculi. Both calculi enjoy the decidability and Craig interpolation. The sequent calculi are applied to prove some embedding theorems: $\msf{G3DM}$ is embedded into $\msf{G3SDM}$ via Gödel-Gentzen translation. $\msf{G3DM}$ is embedded into a sequent calculus for classical propositional logic. $\msf{G3SDM}$ is embedded into the sequent calculus $\msf{G3ip}$ for intuitionistic propositional logic.

math.LO↗

arXiv2011

Stochastic Wave Equations with Nonlinear Damping and Source Terms

In this paper, we discuss an initial boundary value problem for the stochastic wave equation involving the nonlinear damping term $|u_t|^{q-2}u_t$ and a source term of the type $|u|^{p-2}u$. We firstly establish the local existence and uniqueness of solution by the Galerkin approximation method and show that the solution is global for $q\geq p$. Secondly, by an appropriate energy inequality, the local solution of the stochastic equations will blow up with positive probability or explosive in energy sense for $p>q$.

math.AP↗