arXiv subjects

Tian Li

Publications and source records attributed to Tian Li.

At least 109 records · Page 6Linked to original sources

arXiv2021

Unusual Magnetic Properties in Layered Magnetic Topological Insulator EuSn2As2

EuSn2As2 with layered rhombohedral crystal structure is proposed to be a candidate of intrinsic antiferromagnetic (AFM) topological insulator. Here, we have investigated systematic magnetoresistance (MR) and magnetization measurements on the high quality EuSn2As2 single crystal with the magnetic field both parallel and perpendicular to (00l) plane. Both the kink of magnetic susceptibility and longitudinal resistivity reveal that EuSn2An2 undergoes an AFM transition at TN = 21 K. At T = 2 K, the magnetization exhibits two successive plateaus of ~ 5.6 μB/Eu and ~ 6.6 μB/Eu at the corresponding critical magnetic fields. Combined with the negative longitudinal MR and abnormal Hall resistance, we demonstrate that EuSn2An2 undergoes complicated magnetic transitions from an AFM state to a canted ferromagnetic (FM) state at Hc and then to a polarized FM state at Hs as the magnetic field increase.

cond-mat.mtrl-sci↗

arXiv2021

Efficient all-optical helicity dependent switching of spins in a Pt/Co/Pt film by a dual-pulse excitation

All-optical helicity dependent switching (AO-HDS), deterministic control of magnetization by circularly polarized laser pulses, allows to efficiently manipulate spins without the need of a magnetic field. However, AO-HDS in ferromagnetic metals so far requires many laser pulses for fully switching their magnetic states. Using a combination of a short, 90-fs linearly polarized pulse and a subsequent longer, 3-ps circularly polarized pulse, we demonstrate that the number of pulses for full magnetization reversal can be reduced to 4 pulse pairs in a single stack of Pt/Co/Pt. The obtained results suggest that the dual-pulse approach is a potential route towards realizing efficient AO-HDS in ferromagnetic metals.

cond-mat.mtrl-sci↗

arXiv2021

Intra-day variability of BL Lacertae from 2016 to 2018

We monitored BL Lacertae in the B, V, R and I bands for 14 nights during the period of 2016-2018. The source showed significant intraday variability on 12 nights. We performed colour-magnitude analysis and found that the source exhibited bluer-when-brighter chromatism. This bluer-when-brighter behavior is at least partly caused by the larger variation amplitude at shorter wavelength. The variations at different wavelengths are well correlated and show no inter-band time lag.

astro-ph.GA↗

arXiv2021

Experimental study of decoherence of the two-mode squeezed vacuum state via second harmonic generation

Decoherence remains one of the most serious challenges to the implementation of quantum technology. It appears as a result of the transformation over time of a quantum superposition state into a classical mixture due to the quantum system interacting with the environment. Since quantum systems are never completely isolated from their environment, decoherence therefore cannot be avoided in realistic situations. Decoherence has been extensively studied, mostly theoretically, because it has many important implications in quantum technology, such as in the fields of quantum information processing, quantum communication and quantum computation. Here we report a novel experimental scheme on the study of decoherence of a two-mode squeezed vacuum state via its second harmonic generation signal. Our scheme can directly extract the decoherence of the phase-sensitive quantum correlation $\langle \hat{a}\hat{b}\rangle$ between two entangled modes $a$ and $b$. Such a correlation is the most important characteristic of a two-mode squeezed state. More importantly, this is an experimental study on the decoherence effect of a squeezed vacuum state, which has been rarely investigated.

physics.optics↗

arXiv2021

A Field Guide to Federated Optimization

Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving federated optimization problems, which emphasize communication efficiency, data heterogeneity, compatibility with privacy and system requirements, and other constraints that are not primary considerations in other problem settings. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms through concrete examples and practical implementation, with a focus on conducting effective simulations to infer real-world performance. The goal of this work is not to survey the current literature, but to inspire researchers and practitioners to design federated learning algorithms that can be used in various practical applications.

cs.LG↗

arXiv2021

Ditto: Fair and Robust Federated Learning Through Personalization

Fairness and robustness are two important concerns for federated learning systems. In this work, we identify that robustness to data and model poisoning attacks and fairness, measured as the uniformity of performance across devices, are competing constraints in statistically heterogeneous networks. To address these constraints, we propose employing a simple, general framework for personalized federated learning, Ditto, that can inherently provide fairness and robustness benefits, and develop a scalable solver for it. Theoretically, we analyze the ability of Ditto to achieve fairness and robustness simultaneously on a class of linear problems. Empirically, across a suite of federated datasets, we show that Ditto not only achieves competitive performance relative to recent personalization methods, but also enables more accurate, robust, and fair models relative to state-of-the-art fair or robust baselines.

cs.LG↗

arXiv2021

Quantum Advantage with Seeded Squeezed Light for Absorption Measurement

Absorption measurement is an exceptionally versatile tool for many applications in science and engineering. For absorption measurements using laser beams of light, the sensitivity is theoretically limited by the shot noise due to the fundamental Poisson distribution of photon number in laser radiation. In practice, the shot-noise limit can only be achieved when all other sources of noise are eliminated. \textcolor{black}{Here, we use seeded squeezed light to demonstrate that direct absorption measurement can be performed with sensitivity beyond the shot-noise limit. We present a practically realizable scheme, where intensity squeezed beams are generated by a seeded four-wave mixing process in an atomic rubidium vapor cell. More than 1.2~dB quantum advantage for the measurement sensitivity is obtained at faint absorption levels ($\leq 10\%$). We also present a detailed theoretical analysis to show that the observed quantum advantage when corrected for optical loss would be equivalent to 3~dB. Our experiment demonstrates a direct sub-shot-noise measurement of absorption that requires neither homodyne/lock-in nor logic coincidence detection schemes. It is therefore very applicable in many circumstances where sub-shot-noise-level absorption measurements are highly desirable.

physics.optics↗

arXiv2021

You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module

3D point-cloud-based perception is a challenging but crucial computer vision task. A point-cloud consists of a sparse, unstructured, and unordered set of points. To understand a point-cloud, previous point-based methods, such as PointNet++, extract visual features through hierarchically aggregation of local features. However, such methods have several critical limitations: 1) Such methods require several sampling and grouping operations, which slow down the inference speed. 2) Such methods spend an equal amount of computation on each points in a point-cloud, though many of points are redundant. 3) Such methods aggregate local features together through downsampling, which leads to information loss and hurts the perception performance. To overcome these challenges, we propose a novel, simple, and elegant deep learning model called YOGO (You Only Group Once). Compared with previous methods, YOGO only needs to sample and group a point-cloud once, so it is very efficient. Instead of operating on points, YOGO operates on a small number of tokens, each of which summarizes the point features in a sub-region. This allows us to avoid computing on the redundant points and thus boosts efficiency.Moreover, YOGO preserves point-wise features by projecting token features to point features although the computation is performed on tokens. This avoids information loss and can improve point-wise perception performance. We conduct thorough experiments to demonstrate that YOGO achieves at least 3.0x speedup over point-based baselines while delivering competitive classification and segmentation performance on the ModelNet, ShapeNetParts and S3DIS datasets.

cs.RO↗

arXiv2021

Tilted Empirical Risk Minimization

Empirical risk minimization (ERM) is typically designed to perform well on the average loss, which can result in estimators that are sensitive to outliers, generalize poorly, or treat subgroups unfairly. While many methods aim to address these problems individually, in this work, we explore them through a unified framework -- tilted empirical risk minimization (TERM). In particular, we show that it is possible to flexibly tune the impact of individual losses through a straightforward extension to ERM using a hyperparameter called the tilt. We provide several interpretations of the resulting framework: We show that TERM can increase or decrease the influence of outliers, respectively, to enable fairness or robustness; has variance-reduction properties that can benefit generalization; and can be viewed as a smooth approximation to a superquantile method. We develop batch and stochastic first-order optimization methods for solving TERM, and show that the problem can be efficiently solved relative to common alternatives. Finally, we demonstrate that TERM can be used for a multitude of applications, such as enforcing fairness between subgroups, mitigating the effect of outliers, and handling class imbalance. TERM is not only competitive with existing solutions tailored to these individual problems, but can also enable entirely new applications, such as simultaneously addressing outliers and promoting fairness.

cs.LG↗

arXiv2021

CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs

Deploying deep learning models on embedded systems has been challenging due to limited computing resources. The majority of existing work focuses on accelerating image classification, while other fundamental vision problems, such as object detection, have not been adequately addressed. Compared with image classification, detection problems are more sensitive to the spatial variance of objects, and therefore, require specialized convolutions to aggregate spatial information. To address this need, recent work introduces dynamic deformable convolution to augment regular convolutions. However, this will lead to inefficient memory accesses of inputs with existing hardware. In this work, we harness the flexibility of FPGAs to develop a novel object detection pipeline with deformable convolutions. We show the speed-accuracy tradeoffs for a set of algorithm modifications including irregular-access versus limited-range and fixed-shape. We then Co-Design a Network CoDeNet with the modified deformable convolution and quantize it to 4-bit weights and 8-bit activations. With our high-efficiency implementation, our solution reaches 26.9 frames per second with a tiny model size of 0.76 MB while achieving 61.7 AP50 on the standard object detection dataset, Pascal VOC. With our higher accuracy implementation, our model gets to 67.1 AP50 on Pascal VOC with only 2.9 MB of parameters-20.9x smaller but 10% more accurate than Tiny-YOLO.

cs.CV↗

arXiv2020

Cross-Domain Sentiment Classification with In-Domain Contrastive Learning

Contrastive learning (CL) has been successful as a powerful representation learning method. In this paper, we propose a contrastive learning framework for cross-domain sentiment classification. We aim to induce domain invariant optimal classifiers rather than distribution matching. To this end, we introduce in-domain contrastive learning and entropy minimization. Also, we find through ablation studies that these two techniques behaviour differently in case of large label distribution shift and conclude that the best practice is to choose one of them adaptively according to label distribution shift. The new state-of-the-art results our model achieves on standard benchmarks show the efficacy of the proposed method.

cs.CL↗

arXiv2020

Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information Maximization

Contrastive learning (CL) has been successful as a powerful representation learning method. In this work we propose CLIM: Contrastive Learning with mutual Information Maximization, to explore the potential of CL on cross-domain sentiment classification. To the best of our knowledge, CLIM is the first to adopt contrastive learning for natural language processing (NLP) tasks across domains. Due to scarcity of labels on the target domain, we introduce mutual information maximization (MIM) apart from CL to exploit the features that best support the final prediction. Furthermore, MIM is able to maintain a relatively balanced distribution of the model's prediction, and enlarges the margin between classes on the target domain. The larger margin increases our model's robustness and enables the same classifier to be optimal across domains. Consequently, we achieve new state-of-the-art results on the Amazon-review dataset as well as the airlines dataset, showing the efficacy of our proposed method CLIM.

cs.CL↗

arXiv2020

Squeezed Light Induced Two-photon Absorption Fluorescence of Fluorescein Biomarkers

Two-photon absorption (TPA) fluorescence of biomarkers has been decisive in advancing the fields of biosensing and deep-tissue in vivo imaging of live specimens. However, due to the extremely small TPA cross section and the quadratic dependence on the input photon flux, extremely high peak-intensity pulsed lasers are imperative, which can result in significant photo- and thermal-damage. Previous works on entangled TPA (ETPA) with spontaneous parametric down-conversion (SPDC) light sources found a linear dependence on the input photon-pair flux, but are limited by low optical powers, along with a very broad spectrum. We report that by using a high-flux squeezed light source for TPA, a fluorescence enhancement of 47 is achieved in fluorescein biomarkers as compared to classical TPA. Moreover, a polynomial behavior of the TPA rate is observed in the DCM laser dye.

physics.optics↗

arXiv2020

Temporal Quantum Noise Reduction Acquired by an Electron-Multiplying Charge-Coupled-Device Camera

Electron-multiplying charge-coupled-device cameras (EMCCDs) have been used to observe quantum noise reductions in beams of light in the transverse spatial degree of freedom. For the quantum noise reduction in the temporal domain, "bucket detectors," usually composed of photodiodes with operational amplifiers, are used to register the intensity fluctuations in beams of light within the bandwidth of the detectors. Spatial information, however, is inevitably washed off by the detector. In this paper, we report on measurements of the temporal quantum noise reduction in bright twin beams using an EMCCD camera. The four-wave mixing process in an atomic rubidium vapor cell is used to generate the bright twin beams of light. We observe more than 25% of temporal quantum noise reduction with respect to the shot-noise limit in images captured by the EMCCD camera. Compared with bucket detectors, EMCCD makes it possible to take advantage of the spatial and temporal quantum properties of light simultaneously, which would greatly benefit many applications using quantum protocols.

physics.optics↗

arXiv2020

Federated Optimization in Heterogeneous Networks

Federated Learning is a distributed learning paradigm with two key challenges that differentiate it from traditional distributed optimization: (1) significant variability in terms of the systems characteristics on each device in the network (systems heterogeneity), and (2) non-identically distributed data across the network (statistical heterogeneity). In this work, we introduce a framework, FedProx, to tackle heterogeneity in federated networks. FedProx can be viewed as a generalization and re-parametrization of FedAvg, the current state-of-the-art method for federated learning. While this re-parameterization makes only minor modifications to the method itself, these modifications have important ramifications both in theory and in practice. Theoretically, we provide convergence guarantees for our framework when learning over data from non-identical distributions (statistical heterogeneity), and while adhering to device-level systems constraints by allowing each participating device to perform a variable amount of work (systems heterogeneity). Practically, we demonstrate that FedProx allows for more robust convergence than FedAvg across a suite of realistic federated datasets. In particular, in highly heterogeneous settings, FedProx demonstrates significantly more stable and accurate convergence behavior relative to FedAvg---improving absolute test accuracy by 22% on average.

cs.LG↗

arXiv2020

Outage Behaviors of NOMA-based Satellite Network over Shadowed-Rician Fading Channels

This paper investigates the application of non-orthogonal multiple access (NOMA) to satellite communication network over Shadowed-Rician fading channels. The impact of imperfect successive interference cancellation (ipSIC) on NOMA-based satellite network is taken into consideration from the perspective of practical scenarios. We first derive new exact expressions of outage probability for the p-th terrestrial user and provide the corresponding asymptotic analysis results. The diversity order of zero and p are achieved by the p-th terrestrial user with ipSIC and perfect successive interference cancellation (pSIC), respectively. Finally, the presented simulation results show that: 1) On the condition of pSIC, the outage behaviors of NOMA-based satellite network are superior to that of orthogonal multiple access; 2) With the value of residual interference increasing, the outage performance of terrestrial users with ipSIC is becoming worse seriously; and 3) Infrequent light shadowing of Shadowed-Rician fading brings the better outage probability compared to frequent heavy and average shadowing.

cs.IT↗

arXiv2020

Fair Resource Allocation in Federated Learning

Federated learning involves training statistical models in massive, heterogeneous networks. Naively minimizing an aggregate loss function in such a network may disproportionately advantage or disadvantage some of the devices. In this work, we propose q-Fair Federated Learning (q-FFL), a novel optimization objective inspired by fair resource allocation in wireless networks that encourages a more fair (specifically, a more uniform) accuracy distribution across devices in federated networks. To solve q-FFL, we devise a communication-efficient method, q-FedAvg, that is suited to federated networks. We validate both the effectiveness of q-FFL and the efficiency of q-FedAvg on a suite of federated datasets with both convex and non-convex models, and show that q-FFL (along with q-FedAvg) outperforms existing baselines in terms of the resulting fairness, flexibility, and efficiency.

cs.LG↗

arXiv2020

FedDANE: A Federated Newton-Type Method

Federated learning aims to jointly learn statistical models over massively distributed remote devices. In this work, we propose FedDANE, an optimization method that we adapt from DANE, a method for classical distributed optimization, to handle the practical constraints of federated learning. We provide convergence guarantees for this method when learning over both convex and non-convex functions. Despite encouraging theoretical results, we find that the method has underwhelming performance empirically. In particular, through empirical simulations on both synthetic and real-world datasets, FedDANE consistently underperforms baselines of FedAvg and FedProx in realistic federated settings. We identify low device participation and statistical device heterogeneity as two underlying causes of this underwhelming performance, and conclude by suggesting several directions of future work.

cs.LG↗