arXiv subjects

Qian Li

Publications and source records attributed to Qian Li.

At least 289 records · Page 16Linked to original sources

arXiv2020

Influence of Initialization on the Performance of Metaheuristic Optimizers

All metaheuristic optimization algorithms require some initialization, and the initialization for such optimizers is usually carried out randomly. However, initialization can have some significant influence on the performance of such algorithms. This paper presents a systematic comparison of 22 different initialization methods on the convergence and accuracy of five optimizers: differential evolution (DE), particle swarm optimization (PSO), cuckoo search (CS), artificial bee colony (ABC) algorithm and genetic algorithm (GA). We have used 19 different test functions with different properties and modalities to compare the possible effects of initialization, population sizes and the numbers of iterations. Rigorous statistical ranking tests indicate that 43.37\% of the functions using the DE algorithm show significant differences for different initialization methods, while 73.68\% of the functions using both PSO and CS algorithms are significantly affected by different initialization methods. The simulations show that DE is less sensitive to initialization, while both PSO and CS are more sensitive to initialization. In addition, under the condition of the same maximum number of function evaluations (FEs), the population size can also have a strong effect. Particle swarm optimization usually requires a larger population, while the cuckoo search needs only a small population size. Differential evolution depends more heavily on the number of iterations, a relatively small population with more iterations can lead to better results. Furthermore, ABC is more sensitive to initialization, while such initialization has little effect on GA. Some probability distributions such as the beta distribution, exponential distribution and Rayleigh distribution can usually lead to better performance. The implications of this study and further research topics are also discussed in detail.

cs.NE↗

arXiv2020

Neighborhood Information-based Probabilistic Algorithm for Network Disintegration

Many real-world applications can be modelled as complex networks, and such networks include the Internet, epidemic disease networks, transport networks, power grids, protein-folding structures and others. Network integrity and robustness are important to ensure that crucial networks are protected and undesired harmful networks can be dismantled. Network structure and integrity can be controlled by a set of key nodes, and to find the optimal combination of nodes in a network to ensure network structure and integrity can be an NP-complete problem. Despite extensive studies, existing methods have many limitations and there are still many unresolved problems. This paper presents a probabilistic approach based on neighborhood information and node importance, namely, neighborhood information-based probabilistic algorithm (NIPA). We also define a new centrality-based importance measure (IM), which combines the contribution ratios of the neighbor nodes of each target node and two-hop node information. Our proposed NIPA has been tested for different network benchmarks and compared with three other methods: optimal attack strategy (OAS), high betweenness first (HBF) and high degree first (HDF). Experiments suggest that the proposed NIPA is most effective among all four methods. In general, NIPA can identify the most crucial node combination with higher effectiveness, and the set of optimal key nodes found by our proposed NIPA is much smaller than that by heuristic centrality prediction. In addition, many previously neglected weakly connected nodes are identified, which become a crucial part of the newly identified optimal nodes. Thus, revised strategies for protection are recommended to ensure the safeguard of network integrity. Further key issues and future research topics are also discussed.

cs.SI↗

arXiv2020

Triple Memory Networks: a Brain-Inspired Method for Continual Learning

Continual acquisition of novel experience without interfering previously learned knowledge, i.e. continual learning, is critical for artificial neural networks, but limited by catastrophic forgetting. A neural network adjusts its parameters when learning a new task, but then fails to conduct the old tasks well. By contrast, the brain has a powerful ability to continually learn new experience without catastrophic interference. The underlying neural mechanisms possibly attribute to the interplay of hippocampus-dependent memory system and neocortex-dependent memory system, mediated by prefrontal cortex. Specifically, the two memory systems develop specialized mechanisms to consolidate information as more specific forms and more generalized forms, respectively, and complement the two forms of information in the interplay. Inspired by such brain strategy, we propose a novel approach named triple memory networks (TMNs) for continual learning. TMNs model the interplay of hippocampus, prefrontal cortex and sensory cortex (a neocortex region) as a triple-network architecture of generative adversarial networks (GAN). The input information is encoded as specific representation of the data distributions in a generator, or generalized knowledge of solving tasks in a discriminator and a classifier, with implementing appropriate brain-inspired algorithms to alleviate catastrophic forgetting in each module. Particularly, the generator replays generated data of the learned tasks to the discriminator and the classifier, both of which are implemented with a weight consolidation regularizer to complement the lost information in generation process. TMNs achieve new state-of-the-art performance on a variety of class-incremental learning benchmarks on MNIST, SVHN, CIFAR-10 and ImageNet-50, comparing with strong baseline methods.

cs.LG↗

arXiv2020

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation

The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. Despite the new performance highs, the recent advanced segmentation models still require large, representative, and high quality annotated datasets. However, rarely do we have a perfect training dataset, particularly in the field of medical imaging, where data and annotations are both expensive to acquire. Recently, a large body of research has studied the problem of medical image segmentation with imperfect datasets, tackling two major dataset limitations: scarce annotations where only limited annotated data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations. In this article, we provide a detailed review of the solutions above, summarizing both the technical novelties and empirical results. We further compare the benefits and requirements of the surveyed methodologies and provide our recommended solutions. We hope this survey article increases the community awareness of the techniques that are available to handle imperfect medical image segmentation datasets.

eess.IV↗

arXiv2019

Coherent transfer of spin angular momentum by evanescent spin waves within antiferromagnetic NiO

Insulating antiferromagnets are efficient and robust conductors of spin current. To realise the full potential of these materials within spintronics, the outstanding challenges are to demonstrate scalability down to nanometric lengthscales and the transmission of coherent spin currents. Here, we report the coherent transfer of spin angular momentum by excitation of evanescent spin waves of GHz frequency within antiferromagnetic NiO at room temperature. Using element-specific and phase-resolved x-ray ferromagnetic resonance, we probe the injection and transmission of ac spin current, and demonstrate that insertion of a few nanometre thick epitaxial NiO(001) layer between a ferromagnet and non-magnet can even enhance the flow of spin current. Our results pave the way towards coherent control of the phase and amplitude of spin currents at the nanoscale, and enable the realization of spin-logic devices and spin current amplifiers that operate at GHz and THz frequencies.

cond-mat.mes-hall↗

arXiv2019

Self-interaction of ultrashort pulses in an epsilon-near-zero nonlinear material at the telecom wavelength

Dynamics of femtosecond pulses with the telecom carrier wavelength is investigated numerically in a subwavelength layer of an indium tin oxide (ITO) epsilon-near-zero (ENZ) material with high dispersion and high nonlinearity. Due to the subwavelength thickness of the ITO ENZ material, and the fact that the pulse's propagation time is shorter than its temporal width, multiple reflections give rise to self-interaction in both spectral and temporal domains, especially at wavelengths longer than the ENZ point, at which the reflections are significantly stronger. A larger absolute value of the pulse's chirp strongly affects the self-interaction by redistributing energy between wavelengths, while the sign of the chirp affects the interaction in the temporal domain. It is also found that, when two identical pulses are launched simultaneously from both ends, a subwavelength counterpart of a standing-wave state can be established. It shows robust energy localization in the middle of the sample, in terms of both the spectral and temporal intensity distributions.

physics.optics↗

arXiv2019

Do Chinese Internet Users Exist Heterogeneity in Search Behavior?

Investor attention is an important concept in behavioral finance. Many articles have conducted cross-disciplinary research leading by this concept. In this paper, we use data extraction technology to collect a large number of Baidu Index keyword search volume data. After analyzing the data, we draw a conclusion that has not been paid attention to in all the past research. We find heterogeneity in searching by internet users in China. Firstly, in terms of search behavior, internet users are more inclined to use the PC end to obtain information when facing areas which need to be taken seriously by them. Secondly, attention is heterogeneous while searching. When Internet users search for information in mobile end, their attention is divergent, and search for seemingly unrelated keywords at the same time which limits their attention to information.

cs.CY↗

arXiv2019

Altering control modes of complex networks based on edge removal

Controlling a complex network is of great importance in many applications. The network can be controlled by inputting external control signals through some selected nodes, which are called input nodes. Previous works found that the majority of the nodes in dense networks are either the input nodes or not, which leads to the bimodality in controlling the complex networks. Due to the physical or economic constraints of many real control scenarios, altering the control mode of a network may be critical to many applications. Here we develop a graph-based algorithm to alter the control mode of a network. The main idea is to change the control connectivity of nodes by removing carefully selected edges. We rigorously prove the correctness of our algorithm and evaluate its performance on both synthetic and real networks. The experimental results show that the control mode of a network can be easily changed by removing few selected edges. Our methods provide the ability to design the desired control mode for different control scenarios, which may be useful in many applications.

cs.SI↗

arXiv2019

Are You for Real? Detecting Identity Fraud via Dialogue Interactions

Identity fraud detection is of great importance in many real-world scenarios such as the financial industry. However, few studies addressed this problem before. In this paper, we focus on identity fraud detection in loan applications and propose to solve this problem with a novel interactive dialogue system which consists of two modules. One is the knowledge graph (KG) constructor organizing the personal information for each loan applicant. The other is structured dialogue management that can dynamically generate a series of questions based on the personal KG to ask the applicants and determine their identity states. We also present a heuristic user simulator based on problem analysis to evaluate our method. Experiments have shown that the trainable dialogue system can effectively detect fraudsters, and achieve higher recognition accuracy compared with rule-based systems. Furthermore, our learned dialogue strategies are interpretable and flexible, which can help promote real-world applications.

cs.CL↗

arXiv2019

Incremental Transformer with Deliberation Decoder for Document Grounded Conversations

Document Grounded Conversations is a task to generate dialogue responses when chatting about the content of a given document. Obviously, document knowledge plays a critical role in Document Grounded Conversations, while existing dialogue models do not exploit this kind of knowledge effectively enough. In this paper, we propose a novel Transformer-based architecture for multi-turn document grounded conversations. In particular, we devise an Incremental Transformer to encode multi-turn utterances along with knowledge in related documents. Motivated by the human cognitive process, we design a two-pass decoder (Deliberation Decoder) to improve context coherence and knowledge correctness. Our empirical study on a real-world Document Grounded Dataset proves that responses generated by our model significantly outperform competitive baselines on both context coherence and knowledge relevance.

cs.CL↗

arXiv2019

Assessing the Validity of a a priori Patient-Trial Generalizability Score using Real-world Data from a Large Clinical Data Research Network: A Colorectal Cancer Clinical Trial Case Study

Existing trials had not taken enough consideration of their population representativeness, which can lower the effectiveness when the treatment is applied in real-world clinical practice. We analyzed the eligibility criteria of Bevacizumab colorectal cancer treatment trials, assessed their a priori generalizability, and examined how it affects patient outcomes when applied in real-world clinical settings. To do so, we extracted patient-level data from a large collection of electronic health records (EHRs) from the OneFlorida consortium. We built a zero-inflated negative binomial model using a composite patient-trial generalizability (cPTG) score to predict patients clinical outcomes (i.e., number of serious adverse events, (SAEs)). Our study results provide a body of evidence that 1) the cPTG scores can predict patient outcomes; and 2) patients who are more similar to the study population in the trials that were used to develop the treatment will have a significantly lower possibility to experience serious adverse events.

stat.AP↗

arXiv2019

Incremental Learning from Scratch for Task-Oriented Dialogue Systems

Clarifying user needs is essential for existing task-oriented dialogue systems. However, in real-world applications, developers can never guarantee that all possible user demands are taken into account in the design phase. Consequently, existing systems will break down when encountering unconsidered user needs. To address this problem, we propose a novel incremental learning framework to design task-oriented dialogue systems, or for short Incremental Dialogue System (IDS), without pre-defining the exhaustive list of user needs. Specifically, we introduce an uncertainty estimation module to evaluate the confidence of giving correct responses. If there is high confidence, IDS will provide responses to users. Otherwise, humans will be involved in the dialogue process, and IDS can learn from human intervention through an online learning module. To evaluate our method, we propose a new dataset which simulates unanticipated user needs in the deployment stage. Experiments show that IDS is robust to unconsidered user actions, and can update itself online by smartly selecting only the most effective training data, and hence attains better performance with less annotation cost.

cs.CL↗

arXiv2019

Emergent superconductivity in single crystalline $\mathrm{MgTi}_2\mathrm{O}_4$ films via structural engineering

Spinel compounds have demonstrated rich functionalities but rarely shown superconductivity. Here, we report the emergence of superconductivity in the spinel $\mathrm{MgTi}_2\mathrm{O}_4$, known to be an insulator with a complicated order. The superconducting transition is achieved by engineering a superlattice of $\mathrm{MgTi}_2\mathrm{O}_4$ and $\mathrm{SrTiO}_3$. The onset transition temperature in the $\mathrm{MgTi}_2\mathrm{O}_4$ layer can be tuned from 0 to 5 K in such geometry, concurrently with a stretched $c$-axis (from 8.51 to 8.53 Å) compared to the bulk material. Such a positive correlation without saturation suggests ample room for the further enhancement. Intriguingly, the superlattice exhibits isotropic upper critical field $H_{\mathrm{c}2}$ that breaks the Pauli limit, distinct from the highly anisotropic feature of interface superconductivity. The origin of superconductivity in the $\mathrm{MgTi}_2\mathrm{O}_4$ layer is understood in combination with the electron energy loss spectra and the first-principles electronic structure calculations, which point to the birth of superconductivity in the $\mathrm{MgTi}_2\mathrm{O}_4$ layer by preventing the Ti-Ti dimerization. Our discovery not only provides a platform to explore the interplay between the superconductivity and other exotic states, but also opens a new window to realize superconductivity in the spinel compounds as well as other titanium oxides.

cond-mat.supr-con↗

arXiv2019

On some model equations for pulsatile flow in viscoelastic vessels

Considered here is the derivation of partial differential equations arising in pulsatile flow in pipes with viscoelastic walls. The equations are asymptotic models describing the propagation of long-crested pulses in pipes with cylindrical symmetry. Additional effects due to viscous stresses in bio-fluids are also taken into account. The effects of viscoelasticity of the vessels on the propagation of solitary and periodic waves in a vessel of constant radius are being explored numerically.

physics.flu-dyn↗

arXiv2019

Joint Entity Linking with Deep Reinforcement Learning

Entity linking is the task of aligning mentions to corresponding entities in a given knowledge base. Previous studies have highlighted the necessity for entity linking systems to capture the global coherence. However, there are two common weaknesses in previous global models. First, most of them calculate the pairwise scores between all candidate entities and select the most relevant group of entities as the final result. In this process, the consistency among wrong entities as well as that among right ones are involved, which may introduce noise data and increase the model complexity. Second, the cues of previously disambiguated entities, which could contribute to the disambiguation of the subsequent mentions, are usually ignored by previous models. To address these problems, we convert the global linking into a sequence decision problem and propose a reinforcement learning model which makes decisions from a global perspective. Our model makes full use of the previous referred entities and explores the long-term influence of current selection on subsequent decisions. We conduct experiments on different types of datasets, the results show that our model outperforms state-of-the-art systems and has better generalization performance.

cs.CL↗

arXiv2019

Anomalies of upper critical field in the spinel superconductor LiTi$_2$O$_{4-δ}$

High-field electrical transport and point-contact tunneling spectroscopy were used to investigate superconducting properties of the unique spinel oxide, LiTi$_2$O$_{4-δ}$ films with various oxygen content. We find that the upper critical field $B_\mathrm{c2}$ gradually increases as more oxygen impurities are brought into the samples by carefully tuning the deposition atmosphere. It is striking that although the superconducting transition temperature and energy gap are almost unchanged, an astonishing isotropic $B_\mathrm{c2}$ up to $\sim$ 26 Tesla is observed in oxygen-rich sample, which is doubled compared to the anoxic sample and breaks the Pauli limit. Such anomalies of $B_\mathrm{c2}$ were rarely reported in other three dimensional superconductors. Combined with all the anomalies, three dimensional spin-orbit interaction induced by tiny oxygen impurities is naturally proposed to account for the remarkable enhancement of $B_\mathrm{c2}$ in oxygen-rich LiTi$_2$O$_{4-δ}$ films. Such mechanism could be general and therefore provides ideas for optimizing practical superconductors with higher $B_\mathrm{c2}$.

cond-mat.supr-con↗

arXiv2018

Effect of Polarization Field on the Photocurrent Characteristics of GaN/AlN Multi-Quantum-Well Avalanche Photodiode

Polarization is an important property of GaN/AlN multi-quantum-well (MQW) avalanche diode (MAPD) but has been ignored in recent analyses of MAPD to simplify the Monte Carlo simulation. Here, the photocurrent characteristics of GaN/AlN MAPD are investigated to understand the role of polarization field in the MQW structure. Carrier multiplication in AlN/GaN MAPD is found as a result of interfacial impact ionization not much helped from external field but instead considerably contributed by the polarization field. In addition, the movement of ionized electrons out of quantum well is proved as Fowler-Nordheim tunneling process helped by the polarization field in AlN barrier. Furthermore, the transport of ionized electrons through MQW structure is found influenced by the reverse polarization field in GaN layer, which could be suppressed by the external electric field. With all the three effects, the quick photocurrent increase of GaN/AlN MAPD is ascribed to the efficient transport of interfacial-ionized electrons step by step out of MQW at high voltages, which is in big difference from conventional APD due to avalanche breakdown.

physics.app-ph↗

arXiv2018

Inferring Brain Signals Synchronicity from a Sample of EEG Readings

Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms (EEG) is scientifically and methodologically challenging. While it is intuitively and statistically appealing to rely on readings from more than one individual in order to highlight recurrent patterns of brain activation, pooling information across subjects presents non-trivial methodological problems. We discuss some of the scientific issues associated with the understanding of synchronized neuronal activity and propose a methodological framework for statistical inference from a sample of EEG readings. Our work builds on classical contributions in time-series, clustering and functional data analysis, in an effort to reframe a challenging inferential problem in the context of familiar analytical techniques. Some attention is paid to computational issues, with a proposal based on the combination of machine learning and Bayesian techniques.

stat.AP↗