arXiv subjects

Kai Li

Publications and source records attributed to Kai Li.

At least 379 records · Page 21Linked to original sources

arXiv2020

TextHide: Tackling Data Privacy in Language Understanding Tasks

An unsolved challenge in distributed or federated learning is to effectively mitigate privacy risks without slowing down training or reducing accuracy. In this paper, we propose TextHide aiming at addressing this challenge for natural language understanding tasks. It requires all participants to add a simple encryption step to prevent an eavesdropping attacker from recovering private text data. Such an encryption step is efficient and only affects the task performance slightly. In addition, TextHide fits well with the popular framework of fine-tuning pre-trained language models (e.g., BERT) for any sentence or sentence-pair task. We evaluate TextHide on the GLUE benchmark, and our experiments show that TextHide can effectively defend attacks on shared gradients or representations and the averaged accuracy reduction is only $1.9\%$. We also present an analysis of the security of TextHide using a conjecture about the computational intractability of a mathematical problem. Our code is available at https://github.com/Hazelsuko07/TextHide

cs.CL↗

arXiv2020

Ultraintense, ultrashort pulse x-ray scattering in small molecules

We examine x-ray scattering from an isolated organic molecule from the linear to nonlinear absorptiveregime. In the nonlinear regime, we explore the importance of both the elastic and inelastic channelsand observe the onset of nonlinear behavior as a function of pulse duration and energy. In the linearregime, we test the sensitivity of the scattering signal to molecular bonding and electronic correlationvia calculations using the independent atom model (IAM), Hartree-Fock (HF) and density functionaltheory (DFT). Finally, we describe how coherent x-ray scattering can be used to directly visualizefemtosecond charge transfer and dissociation within a single molecule undergoing x-ray multiphotonabsorption.

physics.chem-ph↗

arXiv2020

Resonant propagation of x-rays from the linear to the nonlinear regime

We present a theoretical study of temporal, spectral, and spatial reshaping of intense, ultrafast x-ray pulses propagating through a resonant medium. Our calculations are based on the solution of a 3D time-dependent Schrödinger-Maxwell equation, with the incident x-ray photon energy on resonance with the core-level 1s-3p transition in neon. We study the evolution of the combined incident and medium-generated field, including the effects of stimulated emission, absorption, ionization and Auger decay, as a function of the input pulse energy and duration. We find that stimulated Raman scattering between core-excited states $1s^{-1}3p$ and $2p^{-1}3p$ occurs at high x-ray intensity, and that the emission around this frequency is strongly enhanced when also including the similar $1s^{-1}-2p^{-1}$ response of the ion. We also explore the dependence of x-ray self-induced transparency (SIT) and self-focusing on the pulse intensity and duration, and we find that the stimulated Raman scattering plays an important role in both effects. Finally, we discuss how these nonlinear effects may potentially be exploited as control parameters for pulse properties of x-ray free-electron laser sources.

physics.atom-ph↗

arXiv2020

Cost-Effective Data Feeds to Blockchains via Workload-Adaptive Data Replication

Feeding external data to a blockchain, a.k.a. data feed, is an essential task to enable blockchain interoperability and support emerging cross-domain applications, notably stablecoins. Given the data-intensive feeds in real life (e.g., high-frequency price updates) and the high cost in using blockchain, namely Gas, it is imperative to reduce the Gas cost of data feeds. Motivated by the constant-changing workloads in finance and other applications, this work focuses on designing a dynamic, workload-aware approach for cost effectiveness in Gas. This design space is understudied in the existing blockchain research which has so far focused on static data placement. This work presents GRuB, a cost-effective data feed that dynamically replicates data between the blockchain and an off-chain cloud storage. GRuB's data replication is workload-adaptive by monitoring the current workload and making online decisions w.r.t. data replication. A series of online algorithms are proposed that achieve the bounded worst-case cost in blockchain's Gas. GRuB runs the decision-making components on the untrusted cloud off-chain for lower Gas costs, and employs a security protocol to authenticate the data transferred between the blockchain and cloud. The overall GRuB system can autonomously achieve low Gas costs with changing workloads. We built a GRuB prototype functional with Ethereum and Google LevelDB, and supported real applications in stablecoins. Under real workloads collected from the Ethereum contract-call history and mixed workloads of YCSB, we systematically evaluate GRuB's cost which shows a saving of Gas by 10% ~ 74%, with comparison to the baselines of static data-placement.

cs.CR↗

arXiv2020

Type-II quadrupole topological insulators

Modern theory of electric polarization is formulated by the Berry phase, which, when quantized, leads to topological phases of matter. Such a formulation has recently been extended to higher electric multipole moments, through the discovery of the so-called quadupole topological insulator. It has been established by a classical electromagnetic theory that in a two-dimensional material the quantized properties for the quadupole topological insulator should satisfy a basic relation. Here we discover a new type of quadrupole topological insulator (dubbed type-II) that violates this relation due to the breakdown of the correspondence that a Wannier band and an edge energy spectrum close their gaps simultaneously. We find that, similar to the previously discovered (referred to as type-I) quadrupole topological insulator, the type-II hosts topologically protected corner states carrying fractional corner charges. However, the edge polarizations only occur at a pair of boundaries in the type-II insulating phase, leading to the violation of the classical constraint. We demonstrate that such new topological phenomena can appear from quench dynamics in non-equilibrium systems, which can be experimentally observed in ultracold atomic gases. We also propose an experimental scheme with electric circuits to realize such a new topological phase of matter. The existence of the new topological insulating phase means that new multipole topological insulators with distinct properties can exist in broader contexts beyond classical constraints.

cond-mat.mes-hall↗

arXiv2020

The contact binary V344 Lacertae: is it a triple system?

The VRI passbands light curves of V344 Lac were presented and analyzed by using the latest version of the W-D code. The observed spectrum reveals that V344 Lac is not an A3 type but would be a later F type star according to the yielded temperature. The results of solution show that V344 Lac is an A-subtype contact binary, with a mediate photometric mass ratio of 0.387(0.003) and a mediate contact factor of 44.6(3.0)%. Based on the parallax given by Gaia, the parameters of the components are estimated as: M1 = 1.16 Ms, M2 = 0.45 Ms, R1 = 1.31 Rs, R2 = 0.88 Rs, L1 = 2.512 Ls, L2 = 1.057 Ls. The period investigation indicates that V344 Lac may have an eccentric orbital oscillation, with P3 = 12.4(0.5) yr, A3 = 0.0020(0.0002) d, and e = 0.38(0.16). Analysis shows such oscillation would be caused by a magnetic activity which can be explained by the Applegate mechanism. Meanwhile, according to the value of l3 and the estimated physical parameters of V344 Lac, the mass of the third companion may be 0.79 Ms. This third body could be a wide company.

astro-ph.SR↗

arXiv2020

The first photometric investigation of the contact binary OQ Cam

The first charge-coupled device photometric light curves in B, V, R${_c}$, and I${_c}$ bands of the short-period contact binary star OQ Cam are presented. Through analyzing the light curves with the Wilson-Devinney code, it is found that OQ Cam is a W-type shallow contact binary star with a contact degree of f =20.2%. Based on the O-C analyzing, the orbit period displays a long term increasing with a rate of $dP/dt=4.40\times10{^{-7}} day \cdot year{^{-1}}$. The increasing in orbit period can be interpreted by mass transfer from the less massive component to the more massive one. As the orbit period increasing, OQ Cam would evolve from the present shallow contact configuration to a none contact stage. So it may be a potential candidate to confirm the thermal relaxation oscillation theory.

astro-ph.SR↗

arXiv2020

Topological Insulators beyond Energy Band Characterization

Topological phases of matter are generally characterized by topological properties of energy bands of a system. Their transitions under preserved symmetries occur through closing a gap of energy bands, leading to topologically protected edge states in energy spectra in topological phases. Here we predict a new topological phase that emerges through closing a gap of bands constructed by energy bands, instead of through closing an energy gap with preserved symmetries. From this perspective, topological phases may arise from topological properties of the "bands of bands" associated with their gap closure and corresponding edge states. We demonstrate this idea by studying a tight-binding model. We find that the Wannier bands constructed by energy bands exhibit a gap closure associated with a change of a winding number, while the energy bands remain gapped and trivial without any zero energy modes. In addition, the topological Wannier bands give rise to quantized edge polarizations. Since the emergence of this topological phase does not involve any energy gap closure, we expect its appearance under unitary time evolution. Indeed, this phase appears as we perform a quench dynamics. Our study opens a new direction for exploring topological phases beyond conventional energy band characterization.

cond-mat.mes-hall↗

arXiv2020

Cross-Domain Document Object Detection: Benchmark Suite and Method

Decomposing images of document pages into high-level semantic regions (e.g., figures, tables, paragraphs), document object detection (DOD) is fundamental for downstream tasks like intelligent document editing and understanding. DOD remains a challenging problem as document objects vary significantly in layout, size, aspect ratio, texture, etc. An additional challenge arises in practice because large labeled training datasets are only available for domains that differ from the target domain. We investigate cross-domain DOD, where the goal is to learn a detector for the target domain using labeled data from the source domain and only unlabeled data from the target domain. Documents from the two domains may vary significantly in layout, language, and genre. We establish a benchmark suite consisting of different types of PDF document datasets that can be utilized for cross-domain DOD model training and evaluation. For each dataset, we provide the page images, bounding box annotations, PDF files, and the rendering layers extracted from the PDF files. Moreover, we propose a novel cross-domain DOD model which builds upon the standard detection model and addresses domain shifts by incorporating three novel alignment modules: Feature Pyramid Alignment (FPA) module, Region Alignment (RA) module and Rendering Layer alignment (RLA) module. Extensive experiments on the benchmark suite substantiate the efficacy of the three proposed modules and the proposed method significantly outperforms the baseline methods. The project page is at \url{https://github.com/kailigo/cddod}.

cs.CV↗

arXiv2020

Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction

Forecasting future traffic flows from previous ones is a challenging problem because of their complex and dynamic nature of spatio-temporal structures. Most existing graph-based CNNs attempt to capture the static relations while largely neglecting the dynamics underlying sequential data. In this paper, we present dynamic spatio-temporal graph-based CNNs (DST-GCNNs) by learning expressive features to represent spatio-temporal structures and predict future traffic flows from surveillance video data. In particular, DST-GCNN is a two stream network. In the flow prediction stream, we present a novel graph-based spatio-temporal convolutional layer to extract features from a graph representation of traffic flows. Then several such layers are stacked together to predict future flows over time. Meanwhile, the relations between traffic flows in the graph are often time variant as the traffic condition changes over time. To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream. Experiments on real datasets demonstrate that the proposed model achieves competitive performances compared with the other state-of-the-art methods.

cs.CV↗

arXiv2020

The first light curve modeling and orbital period change investigation of nine contact binaries around the short period cut-off

In this paper, we present the first light curve synthesis and orbital period change analysis of nine contact binaries around the short period limit. It is found that all these systems are W-subtype contact binaries. One of them is a medium contact system while the others are shallow contact ones. Four of them manifest obvious O'Connell effect explained by a dark spot or hot spot on one of the component stars. Third light was detected in three systems. By investigating orbital period variations, we found that four of the targets display a secular period decrease while the others exhibit a long-term period increase. The secular period decrease is more likely caused by angular momentum loss while the long-term period increase is due to mass transfer from the less massive component to the more massive one. Based on the statistic of 19 ultrashort period contact binaries with known orbital period changes, we found that seven of them display long-term decrease (three of them also exhibit cyclic variations), ten of them manifest long-term increase while two of them only show cyclic variation and that most of them are shallow contact binaries supporting the long timescale angular momentum loss theory suggested by Stepien. For the three deep contact systems, we found that they are probably triple systems. The tertiary companion plays an essential role during their formation and evolution.

astro-ph.SR↗

arXiv2020

Privacy-preserving Learning via Deep Net Pruning

This paper attempts to answer the question whether neural network pruning can be used as a tool to achieve differential privacy without losing much data utility. As a first step towards understanding the relationship between neural network pruning and differential privacy, this paper proves that pruning a given layer of the neural network is equivalent to adding a certain amount of differentially private noise to its hidden-layer activations. The paper also presents experimental results to show the practical implications of the theoretical finding and the key parameter values in a simple practical setting. These results show that neural network pruning can be a more effective alternative to adding differentially private noise for neural networks.

cs.LG↗

arXiv2020

Interactive Summarizing -- Automatic Slide Localization Technology as Generative Learning Tool

Making a summary is a common learning strategy in lecture learning. It is an effective way for learners to engage in both traditional and video lectures. Video summarization is an effective technology applied to enhance learners' summarizing experience in a video lecture. In this article, we propose to apply cutting-edge automatic slide localization technology to lecture video learning experience. An interactive summarizing model is designed to explain how learners are engaged in the video lecture learning process supported by convolutional neural network and the possibility of related learning analytics.

cs.CY↗

arXiv2019

Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective

Zero-shot learning (ZSL) aims to recognize instances of unseen classes solely based on the semantic descriptions of the classes. Existing algorithms usually formulate it as a semantic-visual correspondence problem, by learning mappings from one feature space to the other. Despite being reasonable, previous approaches essentially discard the highly precious discriminative power of visual features in an implicit way, and thus produce undesirable results. We instead reformulate ZSL as a conditioned visual classification problem, i.e., classifying visual features based on the classifiers learned from the semantic descriptions. With this reformulation, we develop algorithms targeting various ZSL settings: For the conventional setting, we propose to train a deep neural network that directly generates visual feature classifiers from the semantic attributes with an episode-based training scheme; For the generalized setting, we concatenate the learned highly discriminative classifiers for seen classes and the generated classifiers for unseen classes to classify visual features of all classes; For the transductive setting, we exploit unlabeled data to effectively calibrate the classifier generator using a novel learning-without-forgetting self-training mechanism and guide the process by a robust generalized cross-entropy loss. Extensive experiments show that our proposed algorithms significantly outperform state-of-the-art methods by large margins on most benchmark datasets in all the ZSL settings. Our code is available at \url{https://github.com/kailigo/cvcZSL}

cs.CV↗

arXiv2019

Polarization control of an X-ray free electron laser oscillator

High-intensity, fully coherent X-ray radiation with a tunable polarization over a wide spectral range is of great importance to many experiments. In this paper, we propose a tapered crossed-polarized undulator configuration for X-ray free electron laser oscillator (XFELO) to produce arbitrarily polarized X-ray pulses in hard X-ray region. A numerical example utilizing the parameters of the Shanghai High-Repetition-Rate XFEL and Extreme Light Facility (SHINE) is presented to demonstrate the generation of polarization controllable, fully coherent Hard X-ray pulses with 99.9% polarization degree and 20 KHz polarization switching rate. This scheme also holds the possibility to be used in cavity tunable XFELO.

physics.acc-ph↗

arXiv2019

Authenticated Key-Value Stores with Hardware Enclaves

Authenticated data storage on an untrusted platform is an important computing paradigm for cloud applications ranging from big-data outsourcing, to cryptocurrency and certificate transparency log. These modern applications increasingly feature update-intensive workloads, whereas existing authenticated data structures (ADSs) designed with in-place updates are inefficient to handle such workloads. In this paper, we address this issue and propose a novel authenticated log-structured merge tree (eLSM) based key-value store by leveraging Intel SGX enclaves. We present a system design that runs the code of eLSM store inside enclave. To circumvent the limited enclave memory (128 MB with the latest Intel CPUs), we propose to place the memory buffer of the eLSM store outside the enclave and protect the buffer using a new authenticated data structure by digesting individual LSM-tree levels. We design protocols to support query authentication in data integrity, completeness (under range queries), and freshness. The proof in our protocol is made small by including only the Merkle proofs at selective levels. We implement eLSM on top of Google LevelDB and Facebook RocksDB with minimal code change and performance interference. We evaluate the performance of eLSM under the YCSB workload benchmark and show a performance advantage of up to 4.5X speedup.

cs.CR↗

arXiv2019

Design and Implementation of Secret Key Agreement for Platoon-based Vehicular Cyber-Physical Systems

In platoon-based vehicular cyber-physical system (PVCPS), a lead vehicle that is responsible for managing the platoon's moving directions and velocity periodically disseminates control messages to the vehicles that follow. Securing wireless transmissions of the messages between the vehicles is critical for privacy and confidentiality of platoon's driving pattern. However, due to the broadcast nature of radio channels, the transmissions are vulnerable to eavesdropping. In this paper, we propose a cooperative secret key agreement (CoopKey) scheme for encrypting/decrypting the control messages, where the vehicles in PVCPS generate a unified secret key based on the quantized fading channel randomness. Channel quantization intervals are optimized by dynamic programming to minimize the mismatch of keys. A platooning testbed is built with autonomous robotic vehicles, where a TelosB wireless node is used for onboard data processing and multi-hop dissemination. Extensive real-world experiments demonstrate that CoopKey achieves significantly low secret bit mismatch rate in a variety of settings. Moreover, the standard NIST test suite is employed to verify randomness of the generated keys, where the p-values of our CoopKey pass all the randomness tests. We also evaluate CoopKey with an extended platoon size via simulations to investigate the effect of system scalability on performance.

cs.NI↗

arXiv2019

Non-Gaussianity in a general kind noncanonical warm inflation

The general kind noncanonical warm inflation is introduced and its total non-Gaussianity of perturbation on uniform energy density hypersurfaces is studied. The total non-Gaussianity contains two complementary parts: the three- and the four-point correlations. The three-point correlation part denoted by $f^{int}_{NL}$, comes from the three-point correlation of inflaton field, while the four-point correlation part, denoted by $f_{NL}^{δN}$, is the contribution due to the four-point correlation function of the inflaton field. The aforementioned two parts of non-Gaussianity in general noncanonical warm inflation are calculated and analysed respectively, and the comparisons and discussions of the two parts are finally carried out in this study.

gr-qc↗