BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Hui Chen

Hui Chen contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
eess.SPcond-mat.supr-coneess.IVComputation and LanguageComputer VisionArtificial IntelligenceMachine Learningphysics.opticscond-mat.mes-hallphysics.app-phphysics.ins-detquant-phcond-mat.mtrl-scimath.APphysics.plasm-phcond-mat.softcond-mat.str-elCryptography and SecurityDatabasesecon.EMeess.ASHardware ArchitectureLogic in Computer Sciencephysics.class-phSound

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author
55works
0followers
25topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

55 published item(s)

preprint2026arXiv

Electronic procrystalline state in moire structures

Solid state materials can display varieties of atomic structural orders ranging from crystalline to amorphous, underlying their properties and diverse functionalities. Procrystal has emerged as a new category of solids, featuring a long-range ordered lattice framework tiled with disordered atomic or molecular structures on the lattice sites, arousing great interest due to its novel structural and physical properties. However, the electronic analogue of a procrystal, dubbed as an electronic procrystalline (EPC) state, has never been experimentally observed. Here, we report the observation of an EPC state in a moire superstructure formed between a monolayer metallic NiTe2 and a superconductor NbSe2 with incommensurate lattice wavevectors. The observed EPC state exhibits a long-range periodic charge modulation at the moire scale inlaid with short-range irregular orders within each moire cell. Strikingly, the short-range charge orders inside the moire unit cells have proximately root3*root3 quasi-period, which is absent in pristine NiTe2. Intriguingly, the EPC order is also observed in the superconducting state of the moire superstructure. Furthermore, the emergent EPC state and short-range charge order, coexisting with the proximity induced superconductivity, can be precisely modulated with the thickness of NiTe2. Our findings uncover the potential of moire platform for understanding and tuning novel correlated quantum phases with this exotic procrystalline order.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Evolution from three-dimensional charge density wave to one-dimensional stripe order in CsV$_{3-x}$Ti$_x$Sb$_5$

Understanding intertwined phases near quantum criticality is a central challenge in correlated electron systems. The kagome metal CsV$_{3-x}$Ti$_x$Sb$_5$ provides a fertile platform to investigate the interplay between charge-density-wave (CDW) and superconductivity. Here, combining x-ray diffraction (XRD) and scanning tunneling microscopy (STM), we uncover a dimensional evolution of the CDW upon Ti substitution. We find that even infinitesimal Ti doping (x = 0.009) completely suppresses the three-dimensional 2 $\times$ 2 $\times$ 4 CDW present in pristine CsV3Sb5, while reducing the remaining 2 $\times$ 2 $\times$ 2 CDW to a quasi-two-dimensional order. With further Ti substitution, although no CDW transition is discernible in resistivity measurements, our XRD and STM data reveal the emergence of a (quasi-)one-dimensional CDW with a short correlation length of $\sim$ 20 $Å$ at x = 0.2. The stripelike CDW undergoes a continuous second-order phase transition, characterized by a gradual increase in intensity and correlation length below $\sim$ 56 K. Our results elucidate the dimensional evolution of CDW order in CsV$_{3-x}$Ti$_x$Sb$_5$ and provide new insight into understanding the unconventional CDWs and their role in kagome superconductors.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

FastOCR: Dynamic Visual Fixation via KV Cache Pruning for Efficient Document Parsing

Vision-Language Models (VLMs) have shown strong promise on Optical Character Recognition (OCR), yet the sheer number of visual tokens required to encode dense documents incurs prohibitive inference cost. Existing pruning methods rely on physical eviction, e.g., permanently discarding visual tokens during the prefill stage. While effective for natural images, this strategy fundamentally breaks down on OCR, where virtually every visual token may correspond to a character or structural element, and any irreversible loss leads to catastrophic accuracy degradation. We observe that, although document images appear globally dense and seemingly unprunable, the model's attention to them is in fact temporally sparse: at each decoding step it concentrates on a small region that shifts gradually across steps, much as a human reader fixates on successive words rather than perceiving an entire page at once. Motivated by this Dynamic Visual Fixation phenomenon, we recast the intractable global pruning problem as a tractable local, dynamic one and propose FastOCR, a training-free framework with two complementary modules. Specifically, Focal-Guided Pruning identifies a small set of focal layers and selects the most task-relevant visual tokens from them at each step, while Cross-Step Fixation Reuse exploits the gradual shift of fixation to warm-start each step from the previous one. By dynamically adjusting which tokens are attended rather than evicting any from the cache, FastOCR avoids permanent information loss. Extensive experiments show that FastOCR serves as a plug-and-play acceleration module, generalizing consistently across five VLMs of varying sizes and architectures. On Qwen2.5-VL, FastOCR retains 98% of the unpruned model's accuracy while attending to only 5% of the visual tokens per decoding step, reducing attention latency by 3.0$\times$.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

MiLe Loss: a New Entropy-Weighed Loss for Mitigating the Bias of Learning Difficulties in Large Language Models

Generative language models are usually pretrained on large text corpus via predicting the next token (i.e., sub-word/word/phrase) given the previous ones. Recent works have demonstrated the impressive performance of large generative language models on downstream tasks. However, existing generative language models generally neglect an inherent challenge in text corpus during training, i.e., the imbalance between frequent tokens and infrequent ones. It can lead a language model to be dominated by common and easy-to-learn tokens, thereby overlooking the infrequent and difficult-to-learn ones. To alleviate that, we propose a MiLe Loss function for mitigating the bias of learning difficulties with tokens. During training, it can dynamically assess the learning difficulty of a to-be-learned token, according to the information entropy of the corresponding predicted probability distribution over the vocabulary. Then it scales the training loss adaptively, trying to lead the model to focus more on the difficult-to-learn tokens. On the Pile dataset, we train generative language models at different scales of 468M, 1.2B, and 6.7B parameters. Experiments reveal that models incorporating the proposed MiLe Loss can gain consistent performance improvement on downstream benchmarks.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

RSATalker: Realistic Socially-Aware Talking Head Generation for Multi-Turn Conversation

Talking head generation is increasingly important in virtual reality (VR), especially for social scenarios involving multi-turn conversation. Existing approaches face notable limitations: mesh-based 3D methods can model dual-person dialogue but lack realistic textures, while large-model-based 2D methods produce natural appearances but incur prohibitive computational costs. Recently, 3D Gaussian Splatting (3DGS) based methods achieve efficient and realistic rendering but remain speaker-only and ignore social relationships. We introduce RSATalker, the first framework that leverages 3DGS for realistic and socially-aware talking head generation with support for multi-turn conversation. Our method first drives mesh-based 3D facial motion from speech, then binds 3D Gaussians to mesh facets to render high-fidelity 2D avatar videos. To capture interpersonal dynamics, we propose a socially-aware module that encodes social relationships, including blood and non-blood as well as equal and unequal, into high-level embeddings through a learnable query mechanism. We design a three-stage training paradigm and construct the RSATalker dataset with speech-mesh-image triplets annotated with social relationships. Extensive experiments demonstrate that RSATalker achieves state-of-the-art performance in both realism and social awareness. The code and dataset will be released.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Atomic Visualization of Bulk and Surface Superconductivity in Weyl Semimetal γ-PtBi2

A bulk superconductor hosting intrinsic surface superconductivity provides a unique platform to study Majorana bound states. The superconductor, trigonal γ-PtBi2, is a promising candidate, as surface superconducting gaps and topological surface states have been observed. However, the simultaneous presence of bulk and surface superconductivity has not been resolved. Here, we directly visualize coexisting bulk and surface superconducting gaps in trigonal PtBi2 by using ultra-low-temperature scanning tunneling microscopy/spectroscopy. The bulk gap is Δ ~ 0.053 meV with a critical temperature (Tc) ~ 0.5 K and a critical field below 0.01 T, accompanied by a vortex lattice and bound states, yielding a coherence length of ~200 nm. Remarkably, certain surface regions show a much larger gap of Δ ~ 0.42 meV, persisting up to Tc ~ 3 K and surviving magnetic fields up to 2 T, yet lacking a static vortex lattice. This coexistence of robust surface and bulk superconductivity establishes γ-PtBi2 as a unique platform for investigating the interplay between bulk and surface Cooper pairings in superconducting topological materials.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Federated Neural Nonparametric Point Processes

Temporal point processes (TPPs) are effective for modeling event occurrences over time, but they struggle with sparse and uncertain events in federated systems, where privacy is a major concern. To address this, we propose \textit{FedPP}, a Federated neural nonparametric Point Process model. FedPP integrates neural embeddings into Sigmoidal Gaussian Cox Processes (SGCPs) on the client side, which is a flexible and expressive class of TPPs, allowing it to generate highly flexible intensity functions that capture client-specific event dynamics and uncertainties while efficiently summarizing historical records. For global aggregation, FedPP introduces a divergence-based mechanism that communicates the distributions of SGCPs' kernel hyperparameters between the server and clients, while keeping client-specific parameters local to ensure privacy and personalization. FedPP effectively captures event uncertainty and sparsity, and extensive experiments demonstrate its superior performance in federated settings, particularly with KL divergence and Wasserstein distance-based global aggregation.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Recent progress of scanning tunneling microscopy/spectroscopy study of pair density wave in superconductors

A pair density wave (PDW) is a superconducting state characterized by an order parameter with finite center-of-mass momentum in the absence of an external magnetic field, thereby breaking the conventional translational symmetry in homogeneous superconductors. It is proposed that PDW emerges from magnetic interactions, strong electron-electron correlations, and their interplay with competing orders. In this review, we highlight recent advances in the detection and study of PDWs using scanning tunneling microscopy and spectroscopy (STM/STS). We focus on how the signatures of PDW have been experimentally visualized across a variety of extraordinary superconductors, including iron-based superconductors, cuprate superconductors, spin-triplet superconductors, kagome-lattice superconductors, and transition metal dichalcogenides. Beginning with an introduction to the fundamental concept of PDWs and the unique capabilities of STM/STS, particularly its atomic-scale spatial resolution and advanced data analysis techniques, we discuss key experimental findings, including the direct visualization of charge density modulations associated with PDWs. Finally, we discuss emerging challenges and future directions, aiming to inspire future research into the nature of PDWs in superconductors.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Tuning Bound States of Symmetry-Breaking Vortices via Unidirectional Charge Density Wave in a Transition-Metal Dichalcogenide Superconductor

The interplay between charge density wave (CDW) and superconducting vortex bound states are crucial for fundamental physics of superconductivity and advancing quantum nanotechnologies. However, the CDW-mediated modulation of vortex bound states, which opens up a new platform for vortex engineering, remains unexplored. Here, we report spatially anisotropic vortex states modulated by the unidirectional CDWs in a transition-metal dichalcogenide superconductor 1T''-NbTe2 using ultra-low-temperature scanning tunneling microscopy/spectroscopy. The stripe-like 3x1x3 CDW order exhibits a robust three-dimensional character across step edges and coexists with superconductivity below a critical temperature of 0.4 K. Under out-of-plane magnetic fields, we observe elliptical vortices whose elongation aligns with the CDW stripes, indicating strong coupling between vortex morphology and underlying electronic order. Remarkably, CDW domain boundaries induce abrupt changes in vortex orientation and vortex bound states, enabling controllable vortex states across CDW nanodomains. These findings establish a new pathway for manipulating superconducting vortex bound states via CDW coupling.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

JrCUP: Joint RIS Calibration and User Positioning for 6G Wireless Systems

Reconfigurable intelligent surface (RIS)-assisted localization has attracted extensive attention as it can enable and enhance localization services in extreme scenarios. However, most existing works treat RISs as anchors with known positions and orientations, which is not realistic in applications with mobile or uncalibrated RISs. This work considers the joint RIS calibration and user positioning (JrCUP) problem with an active RIS. We propose a novel two-stage method to solve the considered JrCUP problem. The first stage comprises a tensor-estimation of signal parameters via rotational invariance techniques (tensorESPRIT), followed by a channel parameters refinement using least-squares. In the second stage, a two-dimensional search algorithm is proposed to estimate the three-dimensional user and RIS positions, one-dimensional RIS orientation, and clock bias from the estimated channel parameters. The Cramer-Rao lower bounds of the channel parameters and localization parameters are derived to verify the effectiveness of the proposed tensorESPRIT-based algorithms. In addition, simulation results reveal that the active RIS can significantly improve the localization performance compared to the passive case under the same system power supply in practical regions. Moreover, we observe the presence of blind areas with limited JrCUP localization performance, which can be mitigated by either leveraging more prior information or deploying extra base stations.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection

Due to their unsupervised training and uncertainty estimation, deep Variational Autoencoders (VAEs) have become powerful tools for reconstruction-based Time Series Anomaly Detection (TSAD). Existing VAE-based TSAD methods, either statistical or deep, tune meta-priors to estimate the likelihood probability for effectively capturing spatiotemporal dependencies in the data. However, these methods confront the challenge of inherent data scarcity, which is often the case in anomaly detection tasks. Such scarcity easily leads to latent holes, discontinuous regions in latent space, resulting in non-robust reconstructions on these discontinuous spaces. We propose a novel generative framework that combines VAEs with self-supervised learning (SSL) to address this issue.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Unsupervised Model-based speaker adaptation of end-to-end lattice-free MMI model for speech recognition

Modeling the speaker variability is a key challenge for automatic speech recognition (ASR) systems. In this paper, the learning hidden unit contributions (LHUC) based adaptation techniques with compact speaker dependent (SD) parameters are used to facilitate both speaker adaptive training (SAT) and unsupervised test-time speaker adaptation for end-to-end (E2E) lattice-free MMI (LF-MMI) models. An unsupervised model-based adaptation framework is proposed to estimate the SD parameters in E2E paradigm using LF-MMI and cross entropy (CE) criterions. Various regularization methods of the standard LHUC adaptation, e.g., the Bayesian LHUC (BLHUC) adaptation, are systematically investigated to mitigate the risk of overfitting, on E2E LF-MMI CNN-TDNN and CNN-TDNN-BLSTM models. Lattice-based confidence score estimation is used for adaptation data selection to reduce the supervision label uncertainty. Experiments on the 300-hour Switchboard task suggest that applying BLHUC in the proposed unsupervised E2E adaptation framework to byte pair encoding (BPE) based E2E LF-MMI systems consistently outperformed the baseline systems by relative word error rate (WER) reductions up to 10.5% and 14.7% on the NIST Hub5'00 and RT03 evaluation sets, and achieved the best performance in WERs of 9.0% and 9.7%, respectively. These results are comparable to the results of state-of-the-art adapted LF-MMI hybrid systems and adapted Conformer-based E2E systems.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

6G Radio Requirements to Support Integrated Communication, Localization, and Sensing

6G will be characterized by extreme use cases, not only for communication, but also for localization, and sensing. The use cases can be directly mapped to requirements in terms of standard key performance indicators (KPIs), such as data rate, latency, or localization accuracy. The goal of this paper is to go one step further and map these standard KPIs to requirements on signals, on hardware architectures, and on deployments. Based on this, system solutions can be identified that can support several use cases simultaneously. Since there are several ways to meet the KPIs, there is no unique solution and preferable configurations will be discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Antenna Selection in Switch-Based MIMO Arrays via DOA threshold region Approximation

Direction-of-arrival (DOA) information is vital for multiple-input-multiple-output (MIMO) systems to complete localization and beamforming tasks. Switched antenna arrays have recently emerged as an effective solution to reduce the cost and power consumption of MIMO systems. Switch-based array architectures connect a limited number of radio frequency chains to a subset of the antenna elements forming a subarray. This paper addresses the problem of antenna selection to optimize DOA estimation performance. We first perform a subarray layout alignment process to remove subarrays with identical beampatterns and create a unique subarray set. By using this set, and based on a DOA threshold region performance approximation, we propose two antenna selection algorithms; a greedy algorithm and a deep-learning-based algorithm. The performance of the proposed algorithms is evaluated numerically. The results show a significant performance improvement over selected benchmark approaches in terms of DOA estimation in the threshold region and computational complexity.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Bi-Static Sensing for Near-Field RIS Localization

We address the localization of a reconfigurable intelligent surface (RIS) for a single-input single-output multi-carrier system using bi-static sensing between a fixed transmitter and a fixed receiver. Due to the deployment of RISs with a large dimension, near-field (NF) scenarios are likely to occur, especially for indoor applications, and are the focus of this work. We first derive the Cramer-Rao bounds (CRBs) on the estimation error of the RIS position and orientation and the time of arrival (TOA) for the path transmitter-RIS-receiver. We propose a multi-stage low-complexity estimator for RIS localization purposes. In this proposed estimator, we first perform a line search to estimate the TOA. Then, we use the far-field approximation of the NF signal model to implicitly estimate the angle of arrival and the angle of departure at the RIS center. Finally, the RIS position and orientation estimate are refined via a quasi-Newton method. Simulation results reveal that the proposed estimator can attain the CRBs. We also investigate the effects of several influential factors on the accuracy of the proposed estimator like the RIS size, transmitted power, system bandwidth, and RIS position and orientation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction

Entity matching (EM) is the most critical step for entity resolution (ER). While current deep learningbased methods achieve very impressive performance on standard EM benchmarks, their realworld application performance is much frustrating. In this paper, we highlight that such the gap between reality and ideality stems from the unreasonable benchmark construction process, which is inconsistent with the nature of entity matching and therefore leads to biased evaluations of current EM approaches. To this end, we build a new EM corpus and re-construct EM benchmarks to challenge critical assumptions implicit in the previous benchmark construction process by step-wisely changing the restricted entities, balanced labels, and single-modal records in previous benchmarks into open entities, imbalanced labels, and multimodal records in an open environment. Experimental results demonstrate that the assumptions made in the previous benchmark construction process are not coincidental with the open environment, which conceal the main challenges of the task and therefore significantly overestimate the current progress of entity matching. The constructed benchmarks and code are publicly released

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Channel Model Mismatch Analysis for XL-MIMO Systems from a Localization Perspective

Radio localization is applied in high-frequency (e.g., mmWave and THz) systems to support communication and to provide location-based services without extra infrastructure. {For solving localization problems, a simplified, stationary, narrowband far-field channel model is widely used due to its compact formulation.} However, with increased array size in extra-large MIMO systems and increased bandwidth at upper mmWave bands, the effect of channel spatial non-stationarity (SNS), spherical wave model (SWM), and beam squint effect (BSE) cannot be ignored. In this case, localization performance will be affected when an inaccurate channel model deviating from the true model is adopted. In this work, we employ the MCRB (misspecified Cramér-Rao lower bound) to lower bound the localization error using a simplified mismatched model while the observed data is governed by a more complex true model. The simulation results show that among all the model impairments, the SNS has the least contribution, the SWM dominates when the distance is small compared to the array size, and the BSE has a more significant effect when the distance is much larger than the array size.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Confinement of relativistic electrons in a magnetic mirror en route to a magnetized relativistic pair plasma

Creating magnetized relativistic pair plasma in the laboratory would enable the exploration of unique plasma physics relevant to some of the most energetic events in the universe. As a step towards a laboratory pair plasma, we have demonstrated effective confinement of multi-$\mathrm{MeV}$ electrons inside a pulsed-power-driven $13$ $\mathrm{T}$ magnetic mirror field with a mirror ratio of $2.6$. The confinement is diagnosed by measuring the axial and radial losses with magnetic spectrometers. The loss spectra are consistent with $\leq 2.5$ $\mathrm{MeV}$ electrons confined in the mirror for $\sim 1$ $\mathrm{ns}$. With a source of $10^{12}$ electron-positron pairs at comparable energies, this magnetic mirror would confine a relativistic pair plasma with Lorentz factor $γ\sim 6$ and magnetization $σ\sim 40$.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Constrained Wrapped Least Squares: A Tool for High Accuracy GNSS Attitude Determination

Attitude determination is a popular application of Global Navigation Satellite Systems (GNSS). Many methods have been developed to solve the attitude determination problem with different performance offerings. We develop a constrained wrapped least-squares (C-WLS) method for high-accuracy attitude determination. This approach is built on an optimization model that leverages prior information related to the antenna array and the integer nature of the carrier-phase ambiguities in an innovative way. The proposed approach adopts an efficient search strategy to estimate the vehicle's attitude parameters using ambiguous carrier-phase observations directly, without requiring prior carrier-phase ambiguity fixing. The performance of the proposed method is evaluated via simulations and experimentally utilizing data collected using multiple GNSS receivers. The simulation and experimental results demonstrate excellent performance, with the proposed method outperforming the ambiguity function method, the constrained LAMBDA and multivariate constrained LAMBDA methods, three prominent attitude determination algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Doppler Exploitation in Bistatic mmWave Radio SLAM

Networks in 5G and beyond utilize millimeter wave (mmWave) radio signals, large bandwidths, and large antenna arrays, which bring opportunities in jointly localizing the user equipment and mapping the propagation environment, termed as simultaneous localization and mapping (SLAM). Existing approaches mainly rely on delays and angles, and ignore the Doppler, although it contains geometric information. In this paper, we study the benefits of exploiting Doppler in SLAM through deriving the posterior Cramér-Rao bounds (PCRBs) and formulating the extended Kalman-Poisson multi-Bernoulli sequential filtering solution with Doppler as one of the involved measurements. Both theoretical PCRB analysis and simulation results demonstrate the efficacy of utilizing Doppler.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Doppler-Enabled Single-Antenna Localization and Mapping Without Synchronization

Radio localization is a key enabler for joint communication and sensing in the fifth/sixth generation (5G/6G) communication systems. With the help of multipath components (MPCs), localization and mapping tasks can be done with a single base station (BS) and single unsynchronized user equipment (UE) if both of them are equipped with an antenna array. However, the antenna array at the UE side increases the hardware and computational cost, preventing localization functionality. In this work, we show that with Doppler estimation and MPCs, localization and mapping tasks can be performed even with a single-antenna mobile UE. Furthermore, we show that the localization and mapping performance will improve and then saturate at a certain level with an increased UE speed. Both theoretical Cramér-Rao bound analysis and simulation results show the potential of localization under mobility and the effectiveness of the proposed localization algorithm.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification

This paper proposes a simple yet effective interpolation-based data augmentation approach termed DoubleMix, to improve the robustness of models in text classification. DoubleMix first leverages a couple of simple augmentation operations to generate several perturbed samples for each training data, and then uses the perturbed data and original data to carry out a two-step interpolation in the hidden space of neural models. Concretely, it first mixes up the perturbed data to a synthetic sample and then mixes up the original data and the synthetic perturbed data. DoubleMix enhances models' robustness by learning the "shifted" features in hidden space. On six text classification benchmark datasets, our approach outperforms several popular text augmentation methods including token-level, sentence-level, and hidden-level data augmentation techniques. Also, experiments in low-resource settings show our approach consistently improves models' performance when the training data is scarce. Extensive ablation studies and case studies confirm that each component of our approach contributes to the final performance and show that our approach exhibits superior performance on challenging counterexamples. Additionally, visual analysis shows that text features generated by our approach are highly interpretable. Our code for this paper can be found at https://github.com/declare-lab/DoubleMix.git.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks

Convolutional Neural Networks (CNNs) demonstrate excellent performance in various applications but have high computational complexity. Quantization is applied to reduce the latency and storage cost of CNNs. Among the quantization methods, Binary and Ternary Weight Networks (BWNs and TWNs) have a unique advantage over 8-bit and 4-bit quantization. They replace the multiplication operations in CNNs with additions, which are favoured on In-Memory-Computing (IMC) devices. IMC acceleration for BWNs has been widely studied. However, though TWNs have higher accuracy and better sparsity than BWNs, IMC acceleration for TWNs has limited research. TWNs on existing IMC devices are inefficient because the sparsity is not well utilized, and the addition operation is not efficient. In this paper, we propose FAT as a novel IMC accelerator for TWNs. First, we propose a Sparse Addition Control Unit, which utilizes the sparsity of TWNs to skip the null operations on zero weights. Second, we propose a fast addition scheme based on the memory Sense Amplifier to avoid the time overhead of both carry propagation and writing back the carry to memory cells. Third, we further propose a Combined-Stationary data mapping to reduce the data movement of activations and weights and increase the parallelism across memory columns. Simulation results show that for addition operations at the Sense Amplifier level, FAT achieves 2.00X speedup, 1.22X power efficiency, and 1.22X area efficiency compared with a State-Of-The-Art IMC accelerator ParaPIM. FAT achieves 10.02X speedup and 12.19X energy efficiency compared with ParaPIM on networks with 80% average sparsity.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

High-Capacity Rechargeable $Li/Cl_2$ Batteries with Graphite Positive Electrodes

Developing new types of high-capacity and high-energy density rechargeable battery is important to future generations of consumer electronics, electric vehicles, and mass energy storage applications. Recently we reported ~ 3.5 V sodium/chlorine $(Na/Cl_2)$ and lithium/chlorine $(Li/Cl_2)$ batteries with up to 1200 mAh $g^{-1}$ reversible capacity, using either a Na or Li metal as the negative electrode, an amorphous carbon nanosphere (aCNS) as the positive electrode, and aluminum chloride $(AlCl_3)$ dissolved in thionyl chloride $(SOCl_2)$ with fluoride-based additives as the electrolyte. The high surface area and large pore volume of aCNS in the positive electrode facilitated NaCl or LiCl deposition and trapping of $Cl_2$ for reversible $NaCl/Cl_2$ or $LiCl/Cl_2$ redox reactions and battery discharge/charge cycling. Here we report an initially low surface area/porosity graphite (DGr) material as the positive electrode in a $Li/Cl_2$ battery, attaining high battery performance after activation in carbon dioxide $(CO_2)$ at 1000 °C (DGr_ac) with the first discharge capacity ~ 1910 mAh $g^{-1}$ and a cycling capacity up to 1200 mAh $g^{-1}$. Ex situ Raman spectroscopy and X-ray diffraction (XRD) revealed the evolution of graphite over battery cycling, including intercalation/de-intercalation and exfoliation that generated sufficient pores for hosting $LiCl/Cl_2$ redox. This work opens up widely available, low-cost graphitic materials for high-capacity alkali metal/$Cl_2$ batteries. Lastly, we employed mass spectrometry to probe the $Cl_2$ trapped in the graphitic positive electrode, shedding light into the $Li/Cl_2$ battery operation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

MCRB-based Performance Analysis of 6G Localization under Hardware Impairments

Location information is expected to be the key to meeting the needs of communication and context-aware services in 6G systems. User localization is achieved based on delay and/or angle estimation using uplink or downlink pilot signals. However, hardware impairments (HWIs) distort the signals at both the transmitter and receiver sides and thus affect the localization performance. While this impact can be ignored at lower frequencies where HWIs are less severe, modeling and analysis efforts are needed for 6G to evaluate the localization degradation due to HWIs. In this work, we model various types of impairments and conduct a misspecified Cramér-Rao bound analysis to evaluate the HWI-induced performance loss. Simulation results with different types of HWIs show that each HWI leads to a different level of degradation in angle and delay estimation performance.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Ruleformer: Context-aware Differentiable Rule Mining over Knowledge Graph

Rule mining is an effective approach for reasoning over knowledge graph (KG). Existing works mainly concentrate on mining rules. However, there might be several rules that could be applied for reasoning for one relation, and how to select appropriate rules for completion of different triples has not been discussed. In this paper, we propose to take the context information into consideration, which helps select suitable rules for the inference tasks. Based on this idea, we propose a transformer-based rule mining approach, Ruleformer. It consists of two blocks: 1) an encoder extracting the context information from subgraph of head entities with modified attention mechanism, and 2) a decoder which aggregates the subgraph information from the encoder output and generates the probability of relations for each step of reasoning. The basic idea behind Ruleformer is regarding rule mining process as a sequence to sequence task. To make the subgraph a sequence input to the encoder and retain the graph structure, we devise a relational attention mechanism in Transformer. The experiment results show the necessity of considering these information in rule mining task and the effectiveness of our model.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Titanium-based kagome superconductor CsTi_3Bi_5 and topological states

Since the discovery of a new family of vanadium-based kagome superconductor AV3Sb5 (A=K, Rb, and Cs) with topological band structures, extensive effort has been devoted to exploring the origin of superconducting states and the intertwined orders. Meanwhile, searching for new types of superconductors with novel physical properties and higher superconducting transition temperatures has always been a major thread in the history of superconductor research. Here we report a successful fabrication and the topological states of a Titanium-based kagome metal CsTi3Bi5 (CT3B5) crystal. The as-grown CT3B5 crystal is of high quality and possesses a perfect two-dimensional kagome net of Titanium. The superconductivity of the CT3B5 crystal shows that the critical temperature Tc is of ~4.8 K. First-principle calculations predict that the CT3B5 has robust topological surface states, implying that CT3B5 is a Z2 topological kagome superconductor. This finding provides a new type of superconductors and the base for exploring the origin of superconductivity and topological states in kagome superconductors.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms

Benefiting from the search efficiency, differentiable neural architecture search (NAS) has evolved as the most dominant alternative to automatically design competitive deep neural networks (DNNs). We note that DNNs must be executed under strictly hard performance constraints in real-world scenarios, for example, the runtime latency on autonomous vehicles. However, to obtain the architecture that meets the given performance constraint, previous hardware-aware differentiable NAS methods have to repeat a plethora of search runs to manually tune the hyper-parameters by trial and error, and thus the total design cost increases proportionally. To resolve this, we introduce a lightweight hardware-aware differentiable NAS framework dubbed LightNAS, striving to find the required architecture that satisfies various performance constraints through a one-time search (i.e., \underline{\textit{you only search once}}). Extensive experiments are conducted to show the superiority of LightNAS over previous state-of-the-art methods.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

0.8% Nyquist computational ghost imaging via non-experimental deep learning

We present a framework for computational ghost imaging based on deep learning and customized pink noise speckle patterns. The deep neural network in this work, which can learn the sensing model and enhance image reconstruction quality, is trained merely by simulation. To demonstrate the sub-Nyquist level in our work, the conventional computational ghost imaging results, reconstructed imaging results using white noise and pink noise via deep learning are compared under multiple sampling rates at different noise conditions. We show that the proposed scheme can provide high-quality images with a sampling rate of 0.8% even when the object is outside the training dataset, and it is robust to noisy environments. This method is excellent for various applications, particularly those that require a low sampling rate, fast reconstruction efficiency, or experience strong noise interference.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Deep Structural Estimation: With an Application to Option Pricing

We propose a novel structural estimation framework in which we train a surrogate of an economic model with deep neural networks. Our methodology alleviates the curse of dimensionality and speeds up the evaluation and parameter estimation by orders of magnitudes, which significantly enhances one's ability to conduct analyses that require frequent parameter re-estimation. As an empirical application, we compare two popular option pricing models (the Heston and the Bates model with double-exponential jumps) against a non-parametric random forest model. We document that: a) the Bates model produces better out-of-sample pricing on average, but both structural models fail to outperform random forest for large areas of the volatility surface; b) random forest is more competitive at short horizons (e.g., 1-day), for short-dated options (with less than 7 days to maturity), and on days with poor liquidity; c) both structural models outperform random forest in out-of-sample delta hedging; d) the Heston model's relative performance has deteriorated significantly after the 2008 financial crisis.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Generative-Adversarial-Networks-based Ghost Recognition

Nowadays, target recognition technique plays an important role in many fields. However, the current target image information based methods suffer from the influence of image quality and the time cost of image reconstruction. In this paper, we propose a novel imaging-free target recognition method combining ghost imaging (GI) and generative adversarial networks (GAN). Based on the mechanism of GI, a set of random speckles sequence is employed to illuminate target, and a bucket detector without resolution is utilized to receive echo signal. The bucket signal sequence formed after continuous detections is constructed into a bucket signal array, which is regarded as the sample of GAN. Then, conditional GAN is used to map bucket signal array and target category. In practical application, the speckles sequence in training step is employed to illuminate target, and the bucket signal array is input GAN for recognition. The proposed method can improve the problems caused by conventional recognition methods that based on target image information, and provide a certain turbulence-free ability. Extensive experiments show that the proposed method achieves promising performance.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Ghost Imaging Based on Recurrent Neural Network

Benefit from the promising features of second-order correlation, ghost imaging (GI) has received extensive attentions in recent years. Simultaneously, GI is affected by the poor trade-off between sampling rate and imaging quality. The traditional image reconstruction method in GI is to accumulate the action result of each speckle and the corresponding bucket signal. We found that the image reconstruction process of GI is very similar to the Recurrent Neural Network (RNN), which is one of the deep learning algorithm. In this paper, we proposed a novel method that effectively implements GI on the RNN architecture, called GI-RNN. The state of each layer in RNN is determined by the output of the previous layer and the input of this layer, and the output of the network is the sum of all previous states. Therefore, we take the speckle of each illumination and the corresponding bucket signal as the input of each layer, and the output of the network is the sum of all previous speckle and bucket signal, which is the image of the target. The testing results show that the proposed method can achieve image reconstruction at a very low sampling rate (0.38$\%$). Moreover, we compare GI-RNN with traditional GI algorithm and compressed sensing algorithm. The results of different targets show that GI-RNN is 6.61 dB higher than compressed sensing algorithm and 12.58 dB higher than traditional GI algorithm on average. In our view, the proposed method makes an important step to applications of GI.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Nth-order nonlinear intensity fluctuation amplifier

Stronger light intensity fluctuations are pursued by related applications such as optical resolution, image enhancement, and beam positioning. In this paper, an Nth-order light intensity fluctuation amplifier is proposed, which was demonstrated by a four-wave mixing process with different statistical distribution coupling lights. Firstly, its amplification mechanism is revealed both theoretically and experimentally. The ratio $R$ of statistical distributions and the degree of second-order coherence ${g^{(2)}}(0)$ of beams are used to characterize the affected modulations and the increased light intensity fluctuations through the four-wave mixing process. The results show that the amplification of light intensity fluctuations is caused by not only the fluctuating light fields of incident coupling beams, but also the fluctuating nonlinear coefficient of interaction. At last, we highlight the potentiality of applying such amplifier to other N-order nonlinear optical effects.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Observation of magnetic adatom-induced Majorana vortex and its hybridization with field-induced Majorana vortex in an iron-based superconductor

Braiding Majorana zero modes is essential for fault-tolerant topological quantum computing. Iron-based superconductors with nontrivial band topology have recently emerged as a surprisingly promising platform for creating distinct Majorana zero modes in magnetic vortices in a single material and at relatively high temperatures. The magnetic field-induced Abrikosov vortex lattice makes it difficult to braid a set of Majorana zero modes or to study the coupling of a Majorana doublet due to overlapping wave functions. Here we report the observation of the proposed quantum anomalous vortex with integer quantized vortex core states and the Majorana zero mode induced by magnetic Fe adatoms deposited on the surface. We observe its hybridization with a nearby field-induced Majorana vortex in iron-based superconductor FeTe0.55Se0.45. We also observe vortex-free Yu-Shiba-Rusinov bound states at the Fe adatoms with a weaker coupling to the substrate, and discover a reversible transition between Yu-Shiba-Rusinov states and Majorana zero mode by manipulating the exchange coupling strength. The dual origin of the Majorana zero modes, from magnetic adatoms and external magnetic field, provides a new single-material platform for studying their interactions and braiding in superconductors bearing topological band structures.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Photon Walk in Transparent Wood: Scattering and Absorption in Hierarchically Structured Materials

The optical response of hierarchical materials is convoluted, which hinders their direct study and property control. Transparent wood (TW) is an emerging biocomposite in this category, which adds optical function to the structural properties of wood. Nano- and microscale inhomogeneities in composition, structure and at interfaces strongly affect light transmission and haze. While interface manipulation can tailor TW properties, the realization of optically clear wood requires detailed understanding of light-TW interaction mechanisms. Here we show how material scattering and absorption coefficients can be extracted from a combination of experimental spectroscopic measurements and a photon diffusion model. Contributions from different length scales can thus be deciphered and quantified. It is shown that forward scattering dominates haze in TW, primarily caused by refractive index mismatch between the wood substrate and the polymer phase. Rayleigh scattering from the wood cell wall and absorption from residual lignin have minor effects on transmittance, but the former affects haze. Results provide guidance for material design of transparent hierarchical composites towards desired optical functionality; we demonstrate experimentally how transmittance and haze of TW can be controlled over a broad range.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Ptychography Intensity Interferometry Imaging for Dynamic Distant Object

As a promising lensless imaging method for distance objects, intensity interferometry imaging (III) had been suffering from the unreliable phase retrieval process, hindering the development of III for decades. Recently, the introduction of the ptychographic detection in III overcame this challenge, and a method called ptychographic III (PIII) was proposed. We here experimentally demonstrate that PIII can image a dynamic distance object. A reasonable image for the moving object can be retrieved with only two speckle patterns for each probe, and only 10 to 20 iterations are needed. Meanwhile, PIII exhibits robust to the inaccurate information of the probe. Furthermore, PIII successfully recovers the image through a fog obfuscating the imaging light path, under which a conventional camera relying on lenses fails to provide a recognizable image.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Unsighted deconvolution ghost imaging

Ghost imaging (GI) is an unconventional imaging method that retrieves the image of an object by correlating a series of known illumination patterns with the total reflected (or transmitted) intensity. We here demonstrate a scheme which can remove the basic requirement of knowing the incident patterns on the object, enabling GI to non-invasively image objects through turbid media. As an experimental proof, we project a set of patterns towards an object hidden inside turbid media that scramble the illumination, making the patterns falling on the object completely unknown. We theoretically prove that the spatial frequency of the object is preserved in the measurement of GI, even though the spatial information of both the object and the illumination is lost. The image is then reconstructed with phase retrieval algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Active suppression of temperature oscillation from a pulse-tube cryocooler in a cryogen-free cryostat: Part 1. Simulation modeling from thermal response characteristics

A cryogen-free cryostat cooled using a 4 K commercial GM or pulse tube cryocooler (PTC) displays temperature oscillations caused by the intrinsic working principle of the regenerative cryocooler. To dampen such oscillations usually requires either a large heat capacity or a large thermal resistance. To understand this phenomenon better and suppress it more effectively, both the step response characteristic and the intrinsic oscillation characteristic of cryostat have been used to obtain the complete transfer functions of a simulation model. The latter is used to test and optimize traditional PID feedback control. The results showed this approach has almost no effect on the temperature oscillation amplitude. Based on this simulation model, a novel active method was proposed and tested numerically. Simulation results predict the method should suppress the amplitude of the original temperature oscillation by a factor of two.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Active suppression of temperature oscillation from a pulse-tube cryocooler in a cryogen-free cryostat: Part 2. Experimental realization

A cryogen-free cryostat cooled by a closed cycle cryocooler is compact, can provide uninterrupted long-term operation (up to ten thousand hours) and is suited to temperatures from 3 K to 300 K. Its intrinsic temperature oscillation, however, limits its application in experiments requiring high thermal stability at low temperature (below 77 K). Passive suppression methods are effective but all suffer from drawbacks. We describe a novel, active suppression scheme more efficient than traditional proportional-integral (PI) control. The experimental results show that it can reduce the standard deviation of the temperature oscillation by a further 30% compared with PI feedback. To the best of our knowledge, this is the first time such active suppression of temperature oscillations has been implemented with the cryogen-free cryostat. The results also show, however, that an unwanted lower frequency thermal noise will be generated, which appears to be the limit of the method. Nevertheless, the approach could be used to improve the temperature stability in all cryogen-free cryostats.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Atomically-Precise, Custom-Design Origami Graphene Nanostructures

The construction of atomically-precise carbon nanostructures holds promise for developing novel materials for scientific study and nanotechnology applications. Here we show that graphene origami is an efficient way to convert graphene into atomically-precise, complex, and novel nanostructures. By scanning-tunneling-microscope manipulation at low temperature, we repeatedly fold and unfold graphene nanoislands (GNIs) along arbitrarily chosen direction. A bilayer graphene stack featuring a tunable twist angle and a tubular edge connection between the layers are formed. Folding single-crystal GNIs creates tubular edges with specified chirality and one-dimensional electronic features similar to those of carbon nanotubes, while folding bi-crystal GNIs creates well-defined intramolecular junctions. Both origami structural models and electronic band structures were computed to complement analysis of the experimental results. The present atomically-precise graphene origami provides a platform for constructing novel carbon nanostructures with engineered quantum properties and ultimately quantum machines.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Critical regularity criteria for Navier-Stokes equations in terms of one directional derivative of the velocity

In this paper, we consider the 3D Navier-Stokes equations in the whole space. We investigate some new inequalities and \textit{a priori} estimates to provide the critical regularity criteria in terms of one directional derivative of the velocity field, namely $\partial_3 \mathbf{u} \in L^p((0,T); L^q(\mathbb{R}^3)), ~\frac{2}{p} + \frac{3}{q} = 2, ~\frac{3}{2}<q\leq 6$. Moreover, we extend the range of $q$ while the solution is axisymmetric, i.e. the axisymmetric solution $\mathbf{m}{u}$ is regular in $(0,T]$, if $ \partial_3 u^3 \in L^p((0,T); L^q(\mathbb{R}^3)), ~\frac{2}{p} + \frac{3}{q} = 2, ~\frac{3}{2}<q< \infty$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019

Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. All methods were evaluated using the false positive rate, false negative rate, and DICE coefficient (DC). The DC ranged from 0.7354$\pm$0.1149 to 0.8372$\pm$0.0858. The DC of the best method was close to the inter-observer agreement (0.8398$\pm$0.0890). All methods were based on deep learning and categorized into two groups: multi-model method and single model method. In general, multi-model methods were significantly better ($\textit{p}$<$0.01$) than single model methods, with mean DC of 0.7966 and 0.7544, respectively. Deep learning based methods could potentially help pathologists find suspicious regions for further analysis of lung cancer in WSI.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources

For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER). While all existing methods directly transfer from source-learned model to a target language, in this paper, we propose to fine-tune the learned model with a few similar examples given a test case, which could benefit the prediction by leveraging the structural and semantic information conveyed in such similar examples. To this end, we present a meta-learning algorithm to find a good model parameter initialization that could fast adapt to the given test case and propose to construct multiple pseudo-NER tasks for meta-training by computing sentence similarities. To further improve the model&#39;s generalization ability across different languages, we introduce a masking scheme and augment the loss function with an additional maximum term during meta-training. We conduct extensive experiments on cross-lingual named entity recognition with minimal resources over five target languages. The results show that our approach significantly outperforms existing state-of-the-art methods across the board.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques and is a key step to improve the performance of machine learning algorithms. In the multi-party feature engineering scenario (features are stored in many different IoT devices), direct and unlimited multivariate feature transformations will quickly exhaust memory, power, and bandwidth of devices, not to mention the security of information threatened. Given this, we present a framework called FLFE to conduct privacy-preserving and communication-preserving multi-party feature transformations. The framework pre-learns the pattern of the feature to directly judge the usefulness of the transformation on a feature. Explored the new useful feature, the framework forsakes the encryption-based algorithm for the well-designed feature exchange mechanism, which largely decreases the communication overhead under the premise of confidentiality. We made experiments on datasets of both open-sourced and real-world thus validating the comparable effectiveness of FLFE to evaluation-based approaches, along with the far more superior efficacy.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

High-performance Coherent Optical Modulators based on Thin-film Lithium Niobate Platform

The coherent transmission technology using digital signal processing and advanced modulation formats, is bringing networks closer to the theoretical capacity limit of optical fibres, the Shannon limit. The in-phase quadrature electro-optic modulator that encodes information on both the amplitude and the phase of light, is one of the underpinning devices for the coherent transmission technology. Ideally, such modulator should feature low loss, low drive voltage, large bandwidth, low chirp and compact footprint. However, these requirements have been only met on separate occasions. Here, we demonstrate integrated thin-film lithium niobate in-phase/quadrature modulators that fulfil these requirements simultaneously. The presented devices exhibit greatly improved overall performance (half-wave voltage, bandwidth and optical loss) over traditional lithium niobate counterparts, and support modulation data rate up to 320 Gbit s-1. Our devices pave new routes for future high-speed, energy-efficient, and cost-effective communication networks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval

Enabling bi-directional retrieval of images and texts is important for understanding the correspondence between vision and language. Existing methods leverage the attention mechanism to explore such correspondence in a fine-grained manner. However, most of them consider all semantics equally and thus align them uniformly, regardless of their diverse complexities. In fact, semantics are diverse (i.e. involving different kinds of semantic concepts), and humans usually follow a latent structure to combine them into understandable languages. It may be difficult to optimally capture such sophisticated correspondences in existing methods. In this paper, to address such a deficiency, we propose an Iterative Matching with Recurrent Attention Memory (IMRAM) method, in which correspondences between images and texts are captured with multiple steps of alignments. Specifically, we introduce an iterative matching scheme to explore such fine-grained correspondence progressively. A memory distillation unit is used to refine alignment knowledge from early steps to later ones. Experiment results on three benchmark datasets, i.e. Flickr8K, Flickr30K, and MS COCO, show that our IMRAM achieves state-of-the-art performance, well demonstrating its effectiveness. Experiments on a practical business advertisement dataset, named \Ads{}, further validates the applicability of our method in practical scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Microtwist elasticity: A continuum approach to zero modes and topological polarization in Kagome lattices

The topologically polarized isostatic lattices discovered by Kane and Lubensky (2014, Nat. Phys. 10, 39-45) challenged the standard effective medium theories used in the modeling of many truss-based materials and metamaterials. As a matter of fact, these exhibit Parity (P) asymmetric distributions of zero modes that induce a P-asymmetric elastic behavior, both of which cannot be reproduced within Cauchy elasticity. Here, we propose a new effective medium theory baptized &#34;microtwist elasticity&#34; capable of rendering polarization effects on a macroscopic scale. The theory is valid for trusses on the brink of a polarized-unpolarized phase transition in which case they necessarily exhibit more periodic zero modes than they have dimensions. By mapping each periodic zero mode to a macroscopic degree of freedom, the microtwist theory ends up being a kinematically enriched theory. Microtwist elasticity is constructed thanks to leading order two-scale asymptotics and its constitutive and balance equations are derived for a fairly generic isostatic truss: the Kagome lattice. Various numerical and analytical calculations, of the shape and distribution of zero modes, of dispersion diagrams and of polarization effects, systematically show the quality of the proposed effective medium theory. Most notably, the theory is capable of producing a continuum version of Kane and Lubensky&#39;s topological polarization vector.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Nearly quantized conductance plateau of vortex zero mode in an iron-based superconductor

Majorana zero-modes (MZMs) are spatially-localized zero-energy fractional quasiparticles with non-Abelian braiding statistics that hold a great promise for topological quantum computing. Due to its particle-antiparticle equivalence, an MZM exhibits robust resonant Andreev reflection and 2e2/h quantized conductance at low temperature. By utilizing variable-tunnel-coupled scanning tunneling spectroscopy, we study tunneling conductance of vortex bound states on FeTe0.55Se0.45 superconductors. We report observations of conductance plateaus as a function of tunnel coupling for zero-energy vortex bound states with values close to or even reaching the 2e2/h quantum conductance. In contrast, no such plateau behaviors were observed on either finite energy Caroli-de Genne-Matricon bound states or in the continuum of electronic states outside the superconducting gap. This unique behavior of the zero-mode conductance reaching a plateau strongly supports the existence of MZMs in this iron-based superconductor, which serves as a promising single-material platform for Majorana braiding at a relatively high temperature.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Order of magnitude increase in laser-target coupling at near-relativistic intensities using compound parabolic concentrators

Achieving a high conversion efficiency into relativistic electrons is central to short-pulse laser application and fundamentally relies on creating interaction regions with intensities ${\gg}10^{18}$~W/cm$^2$. Small focal length optics are typically employed to achieve this goal; however, this solution is impractical for large kJ-class systems that are constrained by facility geometry, debris concerns, and component costs. We fielded target-mounted compound parabolic concentrators to overcome these limitations and achieved nearly an order of magnitude increase to the conversion efficiency and more than tripled electron temperature compared to flat targets. Particle-in-cell simulations demonstrate that plasma confinement within the cone and formation of turbulent laser fields that develop from cone wall reflections are responsible for the improved laser-to-target coupling. {These passive target components can be used to improve the coupling efficiency for all high-intensity short-pulse laser applications, particularly at large facilities with long focal length optics.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Realization of ppm level pressure stability for primary thermometry using a primary piston gauge

To achieve an uncertainty of 0.25 mK in single-pressure refractive-index gas thermometry (SPRIGT), the relative pressure variation of He-4 gas in the range 30 kPa to 90 kPa, should not exceed 4 ppm (k=1). To this end, a novel pressure control system has been developed. It consists of two main parts: a piston gauge to control the pressure, and a home-made gas compensation system to supplement the micro-leak of the piston gauge. In addition, to maintain the piston at constant height, a servo loop is used that automatically determines in real time the amount of extra gas required. At room temperature, the standard deviations of the stabilized pressure are 3.0 mPa at 30 kPa, 4.5 mPa at 60 kPa and 2 mPa at 90 kPa. For the temperature region 5 K-25 K used for SPRIGT in the present work, the relative pressure stability is better than 0.16 ppm i.e. 25 times better than required. Moreover, the same pressure stabilization system is readily transposable to other primary gas thermometers.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Tunable giant magnetoresistance in a single-molecule junction

Controlling electronic transport through a single-molecule junction is crucial for molecular electronics or spintronics. In magnetic molecular devices, the spin degree-of-freedom can be used to this end since the magnetic properties of the magnetic ion centers fundamentally impact the transport through the molecules. Here we demonstrate that the electron pathway in a single-molecule device can be selected between two molecular orbitals by varying a magnetic field, giving rise to a tunable anisotropic magnetoresistance up to 93%. The unique tunability of the electron pathways is due to the magnetic reorientation of the transition metal center, resulting in a re-hybridization of molecular orbitals. We obtain the tunneling electron pathways by Kondo effect, which manifests either as a peak or a dip line shape. The energy changes of these spin-reorientations are remarkably low and less than one millielectronvolt. The large tunable anisotropic magnetoresistance could be used to control electronic transport in molecular spintronics.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Two-photon superbunching effect of broadband chaotic stationary light at femtosecond timescale based on cascaded Michelson interferometer

It is challenging for observing superbunching effect with true chaotic light, here we propose and demonstrate a method to achieve superbunching effect of the degree of second-order coherence is 2.42 with broadband stationary chaotic light based on a cascaded Michelson interferometer (CMI), exceeding the theoretical upper limit of 2 for the two-photon bunching effect of chaotic light. The superbunching correlation peak is measured with an ultrafast two-photon absorption detector which the full width at half maximum reaches about 95 fs. Two-photon superbunching theory in a CMI is developed to interpret the effect and is in agreement with experimental results. The theory also predicts that the degree of second-order coherence can be much greater than $2$ if chaotic light propagates $N$ times in a CMI. Finally, a new type of weak signals detection setup which employs broadband chaotic light circulating in a CMI is proposed. Theoretically, it can increase the detection sensitivity of weak signals 79 times after the chaotic light circulating 100 times in the CMI.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Bifrequency 3D Ghost Imaging with Haar Wavelet Transform

Recently, ghost imaging has been attracting attentions because its mechanism would lead to many applications inaccessible to conventional imaging methods. However, it is challenging for high contrast and high resolution imaging, due to its low signal-to-noise ratio (SNR) and the demand of high sampling rate in detection. To circumvent these challenges, we here propose a ghost imaging scheme that exploits Haar wavelets as illuminating patterns with a bi-frequency light projecting system and frequency-selecting single-pixel detectors. This method provides a theoretically 100% image contrast and high detection SNR, which reduces the requirement of high dynamic range of detectors, enabling high resolution ghost imaging. Moreover, it can highly reduce the sampling rate (far below Nyquist limit) for a sparse object by adaptively abandoning unnecessary patterns during the measurement. These characteristics are experimentally verified with a resolution of 512 times 512 and a sampling rate lower than 5%. A high-resolution (1000 times 1000 times 1000) 3D reconstruction of an object is also achieved from multi-angle images.

0 citations0 reviewsNo code linkedOpen access