arXiv subjects

Cheng Shen

Publications and source records attributed to Cheng Shen.

At least 19 recordsLinked to original sources

arXiv2026

Low-complexity Frequency Domain Equalization for filtered-AFDM over General Physical Channels

Affine frequency division multiplexing (AFDM) has emerged as a promising waveform for high-mobility communications. However, its equalization remains a practical challenge under general physical channels with off-grid delay and Doppler effects. In this paper, we investigate frequency domain equalization for AFDM by considering a practical filtered-AFDM waveform. We analyze the input-output relations of filtered-AFDM across various domains and show that off-grid effects lead to severe inter-symbol interference in the DAFT domain, limiting the effectiveness of DAFT domain equalization. Motivated by the compactness of the frequency domain channel matrix in wideband systems, we propose a low-complexity two-stage frequency domain equalization scheme. Numerical results demonstrate that the proposed approach achieves performance close to full-block LMMSE equalization with significantly reduced computational complexity, and offers clear advantages over time domain equalization in wideband scenarios.

eess.SP↗

arXiv2026

A Novel One-tap Equalizer for Zero-Padded AFDM System over Doubly Selective Channels

Recently, affine frequency division multiplexing (AFDM) has gained traction as a robust solution for doubly selective channels. In this paper, we present a novel low-complexity one-tap equalizer for zero-padded AFDM (ZP-AFDM) systems. We first select the AFDM parameters, $c_1$ and $c_2$, such that $c_1$ has a relatively high value, and $c_2$ depends on $c_1$, which simplifies the affine domain input-output relation (IOR). This selection also demonstrates that a phase term that varies slowly along the affine domain is experienced by all affine domain symbols and this variation is significantly slower compared to that experienced by the time domain symbols over doubly selective channels. To simplify the equalization, we then introduce zero padding to the transmitted affine domain symbols and reconstruction operation on the received affine domain symbols. By doing so, we convert the effective affine domain IOR of our ZP-AFDM system to be characterized using approximately circular convolution. Next, we transform the resulting affine domain symbols into a new domain called the frequency-of-affine (FoA) domain. We propose our one-tap equalizer in this FoA domain to efficiently recover the transmitted symbols. Numerical results demonstrate the effectiveness of our proposed one-tap equalizer, particularly when $c_1$ is high, without compromising performance robustness.

eess.SP↗

arXiv2025

Strong Correlations and Superconductivity in the Supermoir\'e Lattice

The supermoir\'e lattice, arising from the interference of multiple moir\'e patterns, dramatically reshapes the electronic band structure by introducing new minibands and modifying band dispersion. Concurrently, strong electronic interactions within moir\'e flat bands lead to the emergence of various correlated states. However, the impact of the supermoir\'e lattice on the flat band systems with strong interactions remains largely unexplored. Here, we report the existence of the supermoir\'e lattice in the mirror-symmetry-broken twisted trilayer graphene, elucidating its role in generating mini-flat bands and mini-Dirac bands. Furthermore, we demonstrate interaction-induced symmetry-broken phases in the supermoir\'e mini-flat bands alongside the cascade of superconductor-insulator transitions enabled by the supermoir\'e lattice. Our work shows that robust superconductivity can exist in the mirror-symmetry-broken TTG and underscores the significance of the supermoir\'e lattice as an additional degree of freedom for tuning the electronic properties in twisted multilayer systems, sheds light on the correlated quantum phases such as superconductivity in the original moir\'e flat bands, and highlights the potential of using the supermoir\'e lattice to design and simulate novel quantum phases.

cond-mat.str-el↗

arXiv2025

Optimal Corpus Aware Training for Neural Machine Translation

Corpus Aware Training (CAT) leverages valuable corpus metadata during training by injecting corpus information into each training example, and has been found effective in the literature, commonly known as the "tagging" approach. Models trained with CAT inherently learn the quality, domain and nuance between corpora directly from data, and can easily switch to different inference behavior. To achieve the best evaluation, CAT models pre-define a group of high quality data before training starts which can be error-prone and inefficient. In this work, we propose Optimal Corpus Aware Training (OCAT), which fine-tunes a CAT pre-trained model by freezing most of the model parameters and only tuning small set of corpus-related parameters. We show that OCAT is lightweight, resilient to overfitting, and effective in boosting model accuracy. We use WMT23 English to Chinese and English to German translation tasks as our test ground and show +3.6 and +1.8 chrF improvement, respectively, over vanilla training. Furthermore, our approach is on-par or slightly better than other state-of-the-art fine-tuning techniques while being less sensitive to hyperparameter settings.

cs.LG↗

arXiv2025

Conformal Segmentation in Industrial Surface Defect Detection with Statistical Guarantees

In industrial settings, surface defects on steel can significantly compromise its service life and elevate potential safety risks. Traditional defect detection methods predominantly rely on manual inspection, which suffers from low efficiency and high costs. Although automated defect detection approaches based on Convolutional Neural Networks(e.g., Mask R-CNN) have advanced rapidly, their reliability remains challenged due to data annotation uncertainties during deep model training and overfitting issues. These limitations may lead to detection deviations when processing the given new test samples, rendering automated detection processes unreliable. To address this challenge, we first evaluate the detection model's practical performance through calibration data that satisfies the independent and identically distributed (i.i.d) condition with test data. Specifically, we define a loss function for each calibration sample to quantify detection error rates, such as the complement of recall rate and false discovery rate. Subsequently, we derive a statistically rigorous threshold based on a user-defined risk level to identify high-probability defective pixels in test images, thereby constructing prediction sets (e.g., defect regions). This methodology ensures that the expected error rate (mean error rate) on the test set remains strictly bounced by the predefined risk level. Additionally, we observe a negative correlation between the average prediction set size and the risk level on the test set, establishing a statistically rigorous metric for assessing detection model uncertainty. Furthermore, our study demonstrates robust and efficient control over the expected test set error rate across varying calibration-to-test partitioning ratios, validating the method's adaptability and operational effectiveness.

cs.LG↗

arXiv2025

Superconductivity Favored Anisotropic Phase Stiffness in Infinite-Layer Nickelates

In unconventional superconductors such as cuprates and iron pnictides and chalcogenides, phase stiffness - a measure of the energy cost associated with superconducting phase variations - is on the same order of magnitude as the strength of Cooper pairing, translating to superconductivity governed by phase fluctuations. However, due to a lack of a direct experimental probe, there remains a fundamental gap in establishing microscopic picture between unconventional superconductivity and phase fluctuations. Here we show a vector current technique that allows for in-situ angle-resolved transport measurements, providing exclusive evidence suggesting an anisotropic nature of phase stiffness in infinite-layer nickelate superconductors. Pronounced anisotropy of in-plane resistance manifests itself in both normal and superconducting transition states, indicating crystal symmetry breaking. Remarkably, the electric conductivity of Nd0.8Sr0.2NiO2 peaks at 125{\deg} between the direction of the current and crystal principal axis, but this angle evolves to 160{\deg} near zero-resistance temperature. Further measurements reveal that the superconductivity is favored along a direction with minimized phase fluctuations, an orientation strikingly deviating from the symmetric direction imposed by both electronic anisotropy and the underlying crystal lattice. Identical measurements conducted on a prototypical cuprate superconductor yield consistent results, suggesting that this previously unknown behavior could be ubiquitous. By shielding insight into the contrasting anisotropy between electron fluid and superfluid, our findings provide clues for a unified framework for understanding unconventional superconductors

cond-mat.supr-con↗

arXiv2024

Strongly correlated Hofstadter subbands in minimally twisted bilayer graphene

Moir\'e superlattice in twisted bilayer graphene has been proven to be a versatile platform for exploring exotic quantum phases. Extensive investigations have been invoked focusing on the zero-magnetic-field phase diagram at the magic twist angle around $\theta=1.1\degree$, which has been indicated to be an exclusive regime for exhibiting flat band with the interplay of strong electronic correlation and untrivial topology in the experiment so far. In contrast, electronic bands in non-magic-angle twisted bilayer graphene host dominant electronic kinetic energy compared to Coulomb interaction. By quenching the kinetic energy and enhancing Coulomb exchange interactions by means of an applied perpendicular magnetic field, here we unveil gapped flat Hofstadter subbands at large magnetic flux that yield correlated insulating states in minimally twisted bilayer graphene at $\theta=0.41\degree$. These states appear with isospin symmetry breaking due to strong Coulomb interactions. Our work provides a platform for studying the phase transition of the strongly correlated Hofstadter spectrum.

cond-mat.mes-hall↗

arXiv2024

Featuring nuanced electronic band structure in gapped multilayer graphene

Moir\'e systems featuring flat electronic bands exhibit a vast landscape of emergent exotic quantum states, making them one of the resourceful platforms in condensed matter physics in recent times. Tuning these systems via twist angle and the electric field greatly enhances our comprehension of their strongly correlated ground states. Here, we report a technique to investigate the nuanced intricacies of band structures in dual-gated multilayer graphene systems. We utilize the Landau levels of a decoupled monolayer graphene to extract the electric field-dependent bilayer graphene charge neutrality point gap. Then, we extend this method to analyze the evolution of the band gap and the flat bandwidth in twisted mono-bilayer graphene. The band gap maximizes at the same displacement field where the flat bandwidth minimizes, indicating the strongest electron-electron correlation, which is supported by directly observing the emergence of a strongly correlated phase. Moreover, we extract integer and fractional gaps to further demonstrate the strength of this method. Our technique gives a new perspective and paves the way for improving the understanding of electronic band structure in versatile flat band systems.

cond-mat.mes-hall↗

arXiv2023

High-resolution, large field-of-view label-free imaging via aberration-corrected, closed-form complex field reconstruction

Computational imaging methods empower modern microscopy with the ability of producing high-resolution, large field-of-view, aberration-free images. One of the dominant computational label-free imaging methods, Fourier ptychographic microscopy (FPM), effectively increases the spatial-bandwidth product of conventional microscopy by using multiple tilted illuminations to achieve high-throughput imaging. However, its iterative reconstruction method is prone to parameter selection, can be computationally expensive and tends to fail under excessive aberrations. Recently, spatial Kramers-Kronig methods show it is possible to analytically reconstruct complex field but lacks the ability of correcting aberrations or providing extended resolution enhancement. Here, we present a closed-form method, termed APIC, which weds the strengths of both methods. A new analytical phase retrieval framework is established in APIC, which demonstrates, for the first time, the feasibility of analytically reconstructing the complex field associated with darkfield measurements. In addition, APIC can analytically retrieve complex aberrations of an imaging system with no additional hardware. By avoiding iterative algorithms, APIC requires no human designed convergence metric and always obtains a closed-form complex field solution. The faithfulness and correctness of APIC's reconstruction are guaranteed due to its analytical nature. We experimentally demonstrate that APIC gives correct reconstruction result while FPM fails to do so when constrained to the same number of measurements. Meanwhile, APIC achieves 2.8 times faster computation using image tile size of 256 (length-wise). We also demonstrate APIC is unprecedentedly robust against aberrations compared to FPM - APIC is capable of addressing aberration whose maximal phase difference exceeds 3.8${\pi}$ when using a NA 0.25 objective in experiment.

physics.optics↗

arXiv2022

Error Performance of Rectangular Pulse-shaped OTFS with Practical Receivers

In this letter, we investigate error performance of rectangular pulse-shaped orthogonal time frequency space (OTFS) modulation with a practical receiver. Specifically, we consider an essential bandpass filter at receiver RF front-end, which has been ignored in existing works. We analyse the effect of rectangular pulses on practical OTFS receiver performance, and derive the exact forms of interference in delay-Doppler (DD) domain. We demonstrate that the transmitted information symbols in certain regions of the DD domain are severely contaminated. As a result, there is an error floor in the receiver error performance, which needs to be addressed for such OTFS waveform in practical systems.

eess.SP↗

arXiv2022

Dirac cone spectroscopy of strongly correlated phases in twisted trilayer graphene

Mirror-symmetric magic-angle twisted trilayer graphene (MATTG) hosts flat electronic bands close to zero energy, and has been recently shown to exhibit abundant correlated quantum phases with flexible electrical tunability. However studying these phases proved challenging as these are obscured by intertwined Dirac bands. In this work, we demonstrate a novel spectroscopy technique, that allows to quantify the energy gaps and Chern numbers of the correlated states in MATTG by driving band crossings between Dirac cone Landau levels and the energy gaps in the flat bands. We uncover hard correlated gaps with Chern numbers of C = 0 at integer moire unit cell fillings of nu = 2 and 3 and reveal novel charge density wave states originating from van Hove singularities at fractional fillings of nu = 5/3 and 11/3. In addition, we demonstrate the existence of displacement field driven first-order phase transitions at charge neutrality and half fillings of the moire unit cell nu = 2, which is consistent with a theoretical strong-coupling analysis, implying the breaking of the C2T symmetry. Overall these properties establish the diverse and electrically tunable phase diagram of MATTG and provide an avenue for investigating electronic quantum phases and strong correlations in multiple-band moire systems.

cond-mat.mes-hall↗

arXiv2021

Isospin competitions and valley polarized correlated insulators in twisted double bilayer graphene

New phase of matter usually emerges when a given symmetry breaks spontaneously, which can involve charge, spin, and valley degree of freedoms. Here, we report an observation of new correlated insulators evolved from spin polarized states to valley polarized states in AB-BA stacked twisted double bilayer graphene (TDBG). The transition of the isospin polarization is a result of the competition between spin and valley, driven by the displacement field (D). At a high field |D| > 0.7 V/nm, we observe valley polarized correlated insulators with a big Zeeman g factor of ~10, both at v = 2 in the moir\'e conduction band and more surprisingly at v = -2 in the moir\'e valence band. At a medium field |D| < 0.6 V/nm, by contrast, it is a conventional spin polarized correlated insulator at v = 2 and a featureless metal at v = -2. Moreover, we observe a valley polarized Chern insulator with C = 2 emanating at v = 2 in the electron side and a valley polarized Fermi surface around v = -2 in the hole side. The valley Chern insulator with C = 2 is evident from a well quantized Hall conductance plateau at 2e^2/h and correspondingly a vanishing longitudinal component. The valley polarized Fermi surface is topologically trivial with C = 0, and it shows a series of quantized Landau levels with v_LL = 0, 1, 2, 3, 4 and others. These observations are in good agreements with our band and topology calculations. Our results demonstrate a feasible way to realize isospin control and to obtain new phases of matter in TDBG by the displacement field, and might benefit other twisted or non-twisted multilayer systems.

cond-mat.mes-hall↗

arXiv2021

Observation of re-entrant correlated insulators and interaction driven Fermi surface reconstructions at one magnetic flux quantum per moir\'e unit cell in magic-angle twisted bilayer graphene

The discovery of flat bands with non-trivial band topology in magic angle twisted bi-layer graphene (MATBG) has provided a unique platform to study strongly correlated phe-nomena including superconductivity, correlated insulators, Chern insulators and magnetism. A fundamental feature of the MATBG, so far unexplored, is its high magnetic field Hof-stadter spectrum. Here we report on a detailed magneto-transport study of a MATBG de-vice in external magnetic fields of up to B = 31 T, corresponding to one magnetic flux quan-tum per moir\'e unit cell {\Phi}0. At {\Phi}0, we observe a re-entrant correlated insulator at a flat band filling factor of {\nu} = +2, and interaction-driven Fermi surface reconstructions at other fillings, which are identified by new sets of Landau levels originating from these. These ex-perimental observations are supplemented by theoretical work that predicts a new set of 8 well-isolated flat bands at {\Phi}0 , of comparable band width but with different topology than in zero field. Overall, our magneto-transport data reveals a qualitatively new Hofstadter spec-trum in MATBG, which arises due to the strong electronic correlations in the re-entrant flat bands.

cond-mat.str-el↗

arXiv2021

Stain-free Detection of Embryo Polarization using Deep Learning

Polarization of the mammalian embryo at the right developmental time is critical for its development to term and would be valuable in assessing the potential of human embryos. However, tracking polarization requires invasive fluorescence staining, impermissible in the in vitro fertilization clinic. Here, we report the use of artificial intelligence to detect polarization from unstained time-lapse movies of mouse embryos. We assembled a dataset of bright-field movie frames from 8-cell-stage embryos, side-by-side with corresponding images of fluorescent markers of cell polarization. We then used an ensemble learning model to detect whether any bright-field frame showed an embryo before or after onset of polarization. Our resulting model has an accuracy of 85% for detecting polarization, significantly outperforming human volunteers trained on the same data (61% accuracy). We discovered that our self-learning model focuses upon the angle between cells as one known cue for compaction, which precedes polarization, but it outperforms the use of this cue alone. By compressing three-dimensional time-lapsed image data into two-dimensions, we are able to reduce data to an easily manageable size for deep learning processing. In conclusion, we describe a method for detecting a key developmental feature of embryo development that avoids clinically impermissible fluorescence staining.

q-bio.QM↗

arXiv2021

Optically and magnetically addressable valley pseudospin of interlayer excitons in bilayer MoS2

Interlayer valley excitons in bilayer MoS2 feature concurrently large oscillator strength and long lifetime, and hence represent an advantageous scenario for valleytronic applications. However, control of valley pseudospin of interlayer excitons in pristine bilayer MoS2, which lies at the heart of valleytronics, has remained elusive. Here we report the observation of highly circularly polarized photoluminescence from interlayer excitons of bilayer MoS2 with both optical and magnetic addressability. Under excitation of circularly polarized light near exciton resonance, interlayer excitons of bilayer MoS2 show a near-unity, but negative circular polarization. Significantly, by breaking time-reversal symmetry with an out-of-plane magnetic field, a record level of spontaneous valley polarization (7.7%/Tesla) is identified for interlayer excitons in bilayer MoS2. The giant valley polarization of the interlayer excitons in bilayer MoS2, together with the feasibility of electrical/optical/magnetic control and strong oscillator strength, provides a firm basis for the development of next-generation electronic and optoelectronic applications.

cond-mat.mes-hall↗

arXiv2021

BAM: A Balanced Attention Mechanism for Single Image Super Resolution

Recovering texture information from the aliasing regions has always been a major challenge for Single Image Super Resolution (SISR) task. These regions are often submerged in noise so that we have to restore texture details while suppressing noise. To address this issue, we propose a Balanced Attention Mechanism (BAM), which consists of Avgpool Channel Attention Module (ACAM) and Maxpool Spatial Attention Module (MSAM) in parallel. ACAM is designed to suppress extreme noise in the large scale feature maps while MSAM preserves high-frequency texture details. Thanks to the parallel structure, these two modules not only conduct self-optimization, but also mutual optimization to obtain the balance of noise reduction and high-frequency texture restoration during the back propagation process, and the parallel structure makes the inference faster. To verify the effectiveness and robustness of BAM, we applied it to 10 SOTA SISR networks. The results demonstrate that BAM can efficiently improve the networks performance, and for those originally with attention mechanism, the substitution with BAM further reduces the amount of parameters and increases the inference speed. Moreover, we present a dataset with rich texture aliasing regions in real scenes, named realSR7. Experiments prove that BAM achieves better super-resolution results on the aliasing area.

eess.IV↗

arXiv2021

Temperature-linear Resistivity in Twisted Double Bilayer Graphene

We report an experimental study of carrier density (n), displacement field (D) and twist angle ({\theta}) dependence of temperature (T)-linear resistivity in twisted double bilayer graphene (TDBG). For a large twist angle ({\theta}>1.5{\deg}) where correlated insulating states are absent, we observe a T-linear resistivity (with the slope of the order ~10{\Omega}/K) over a wide range of carrier density and its slope decreases with increasing of n, in agreement with acoustic phonon scattering model semi-quantitatively. The slope of T-linear resistivity is non-monotonically dependent on the displacement field with a single peak structure. For device with {\theta}~1.23{\deg} at which correlated states emerge, the slope of T-linear resistivity is found maximum (~100{\Omega}/K) at the boundary of the halo structure where phase transition occurs, with signatures of continuous phase transition, Planckian dissipation, and the diverging effective mass; these observations are in line with quantum critical behaviors, which might be due to the symmetry-breaking instability at the critical points. Our results shed new light on correlated physics in TDBG and other twisted moir\'e systems.

cond-mat.mes-hall↗

arXiv2021

An Experiment Study on Federated LearningTestbed

While the Internet of Things (IoT) can benefit from machine learning by outsourcing model training on the cloud, user data exposure to an untrusted cloud service provider can pose threat to user privacy. Recently, federated learning is proposed as an approach for privacy-preserving machine learning (PPML) for the IoT, while its practicability remains unclear. This work presents the evaluation on the efficiency and privacy performance of a readily available federated learning framework based on PySyft, a Python library for distributed deep learning. It is observed that the training speed of the framework is significantly slower than of the centralized approach due to communication overhead. Meanwhile, the framework bears some vulnerability to potential man-in-the-middle attacks at the network level. The report serves as a starting point for PPML performance analysis and suggests the future direction for PPML framework development.

cs.CR↗