Researcher profile

Yong Li

Yong Li contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author

82works

0followers

33topics

4close collaborators

Actions

Decide how to stay connected

Follow researcher0

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

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

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.

preprint2026arXiv

AgentEconomist: An End-to-end Agentic System Translating Economic Intuitions into Executable Computational Experiments

A long-standing challenge in economics lies not in the lack of intuition, but in the difficulty of translating intuitive insights into verifiable research. To address this challenge, we introduce AgentEconomist, an end-to-end interactive system designed to translate abstract intuitions into executable computational experiments. Grounded in a domain-specific knowledge base covering over 13,000 high-quality academic papers, the system employs a modular multi-stage architecture. Specifically, the Idea Development Stage generates literature-grounded hypotheses, the Experimental Design Stage configures simulator-aligned experimental parameters and protocols, and the Experimental Execution Stage runs experiments and returns structured analyses. Together, these stages form a human-in-the-loop, iterative workflow that translates economic intuitions into executable computational experiments. Through extensive experiments involving human expert evaluation and large language models (LLMs) as judges, we show that the system generates research ideas with stronger literature grounding and higher novelty and insight than state-of-the-art generic LLMs. Overall, AgentEconomist adopts a human-AI collaboration paradigm that enables researchers to focus on high-level intuitions, while delegating the labor-intensive processes of translation and computational execution to agents.

preprint2026arXiv

SkillMaster: Toward Autonomous Skill Mastery in LLM Agents

Skills provide an effective mechanism for improving LLM agents on complex tasks, yet in existing agent frameworks, their creation, refinement, and selection are typically governed by external teachers, hand-designed rules, or auxiliary modules. As a result, skills remain external resources to be invoked, rather than capabilities that agents can develop, adapt, and internalize through experience. To endow LLM agents with autonomous skill mastery, we propose SkillMaster, a training framework that teaches agents to create new skills, refine existing skills, and select accumulated skills during task solving. This capability is achieved through three key designs. First, we train agents through trajectory-informed skill review, teaching agents to propose, update, or retain skills based on evidence from completed episodes. Second, each candidate skill edit is designed to be evaluated by its counterfactual utility on related probe tasks, providing a direct learning signal for training skill-editing decisions. Third, we introduce DualAdv-GRPO, which separately estimates advantages for task-solving actions and skill-editing decisions, stabilizing joint training across task solving and skill management. Experiments on ALFWorld and WebShop show that SkillMaster improves the overall success rate over state-of-the-art baselines by 8.8% and 9.3%, respectively, achieving the best performance among all compared methods. Further analysis reveals a marked shift in agent capability: agents trained with SkillMaster can identify skill failures, refine procedural knowledge from trajectory evidence, and transfer improvements to future tasks with limited skill-bank edits. Overall, SkillMaster moves LLM agents beyond mere skill use toward self-improving agents capable of developing, adapting, and applying their own skill repertoires.

preprint2026arXiv

UniVLR: Unifying Text and Vision in Visual Latent Reasoning for Multimodal LLMs

Multimodal large language models are increasingly expected to perform thinking with images, yet existing visual latent reasoning methods still rely on explicit textual chain-of-thought interleaved with visual latent tokens. This interleaved design limits efficiency and keeps reasoning fragmented across separate text and vision channels. We propose UniVLR, a unified visual latent reasoning framework that treats textual reasoning and auxiliary visual evidence as a shared visual workspace. Instead of preserving text CoT as an independent inference-time path, UniVLR renders reasoning traces together with auxiliary images and learns to compress this unified representation into compact visual latent tokens. At inference time, the model reasons only through visual latents and directly decodes the final answer, avoiding both external tool calls and verbose text reasoning. Experiments on real-world perception and visual reasoning tasks show that UniVLR outperforms prior visual latent reasoning methods while using substantially fewer generated reasoning tokens, suggesting a more unified and efficient paradigm for visual thinking in MLLMs.

preprint2023arXiv

A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

Recommender system is one of the most important information services on today's Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems. We first introduce the background and the history of the development of both recommender systems and graph neural networks. For recommender systems, in general, there are four aspects for categorizing existing works: stage, scenario, objective, and application. For graph neural networks, the existing methods consist of two categories, spectral models and spatial ones. We then discuss the motivation of applying graph neural networks into recommender systems, mainly consisting of the high-order connectivity, the structural property of data, and the enhanced supervision signal. We then systematically analyze the challenges in graph construction, embedding propagation/aggregation, model optimization, and computation efficiency. Afterward and primarily, we provide a comprehensive overview of a multitude of existing works of graph neural network-based recommender systems, following the taxonomy above. Finally, we raise discussions on the open problems and promising future directions in this area. We summarize the representative papers along with their code repositories in \url{https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems}.

preprint2023arXiv

Control the qubit-qubit coupling in the superconducting circuit with double-resonator couplers

We propose a scheme of using two fixed frequency resonator couplers to tune the coupling strength between two Xmon qubits. The induced indirect qubit-qubit interactions by two resonators could offset with each other, and the direct coupling between two qubits are not necessarily for switching off. The small direct qubit-quibt coupling could effectively suppress the frequency interval between switching off and switching on, and globally suppress the second and third-order static ZZ couplings. The frequencies differences between resonator couplers and qubits readout resonators are very large, this might be helpful for suppressing the qubits readout errors. The cross-kerr resonant processes between a qubit and two resonators might induce pole and affect the crosstalks between qubits. The double resonator couplers could unfreeze the restrictions on capacitances and coupling strengths in the superconducting circuit, and it can also reduce the flux noises and globally suppress the crosstalks.

preprint2023arXiv

KAM Theorems for Multi-scale Torus

In present paper, from the viewpoint of physical intuition we introduce a Hamiltonian system with multiscale rotation, which describes many systems, for example, the forced pendulum with fast rotation, weakly coupled $N$-oscillators with quasiperiodic force and so on. We study the persistence of invariant tori for this Hamiltonian system, and establish some KAM type results including the isoenergetic type. As consequences, we can show that Boltzmann's ergodicity hypothesis is also not true for this Hamiltonian system.

preprint2023arXiv

Modular Mix-and-Match Complementation of Büchi Automata (Technical Report)

Complementation of nondeterministic Büchi automata (BAs) is an important problem in automata theory with numerous applications in formal verification, such as termination analysis of programs, model checking, or in decision procedures of some logics. We build on ideas from a recent work on BA determinization by Li et al. and propose a new modular algorithm for BA complementation. Our algorithm allows to combine several BA complementation procedures together, with one procedure for a subset of the BA's strongly connected components (SCCs). In this way, one can exploit the structure of particular SCCs (such as when they are inherently weak or deterministic) and use more efficient specialized algorithms, regardless of the structure of the whole BA. We give a general framework into which partial complementation procedures can be plugged in, and its instantiation with several algorithms. The framework can, in general, produce a complement with an Emerson-Lei acceptance condition, which can often be more compact. Using the algorithm, we were able to establish an exponentially better new upper bound of $O(4n)$ for complementation of the recently introduced class of elevator automata. We implemented the algorithm in a prototype and performed a comprehensive set of experiments on a large set of benchmarks, showing that our framework complements well the state of the art and that it can serve as a basis for future efficient BA complementation and inclusion checking algorithms.

preprint2022arXiv

A General Framework of Bound States in the Continuum in an Open Acoustic Resonator

Bound states in the continuum (BICs) provide a viable way of achieving high-Q resonances in both photonics and acoustics. In this work, we proposed a general method of constructing Friedrich-Wintgen (FW) BICs and accidental BICs in a coupled acoustic waveguide-resonator system. We demonstrated that FW BICs can be achieved with arbitrary two degenerate resonances in a closed resonator regardless of whether they have the same or opposite parity. Moreover, their eigenmode profiles can be arbitrarily engineered by adjusting the position of attached waveguide. That suggests an effective way of continuous switching the nature of BIC from FW BIC to symmetry-protected BIC or accidental BICs. Also, such BICs are sustained in the coupled waveguide-resonator system with shapes such as rectangle, ellipse, and rhomboid. These interesting phenomena are well explained by the two-level effective non Hermitian Hamiltonian, where two strongly coupled degenerate modes play a major role in forming such FW BICs. Besides, we found that such an open system also supports accidental BICs in geometry space instead of momentum space via tuning the position of attached waveguide, which are attributed to the quenched coupling between the waveguide and eigenmodes of the closed cavity. Finally, we fabricated a series of 3D coupled-resonator-waveguide and experimentally verified the existence of FW BICs and accidental BICs by measuring the transmission spectra. Our results complement the current BIC library in acoustics and provide new routes for designing novel acoustic devices, such as in acoustic absorbers, filters and sensors.

preprint2022arXiv

A Mixed-Methods Analysis of the Algorithm-Mediated Labor of Online Food Deliverers in China

In recent years, China has witnessed the proliferation and success of the online food delivery industry, an emerging type of the gig economy. Online food deliverers who deliver the food from restaurants to customers play a critical role in enabling this industry. Mediated by algorithms and coupled with interactions with multiple stakeholders, this emerging kind of labor has been taken by millions of people. In this paper, we present a mixed-methods analysis to investigate this labor of online food deliverers and uncover how the mediation of algorithms shapes it. Combining large-scale quantitative data-driven investigations of 100,000 deliverers' behavioral data with in-depth qualitative interviews with 15 online food deliverers, we demonstrate their working activities, identify how algorithms mediate their delivery procedures, and reveal how they perceive their relationships with different stakeholders as a result of their algorithm-mediated labor. Our findings provide important implications for enabling better experiences and more humanized labor of deliverers as well as workers in gig economies of similar kinds.

preprint2022arXiv

A Review-aware Graph Contrastive Learning Framework for Recommendation

Most modern recommender systems predict users preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the auxiliary review information accompanied with user ratings, many of the existing review-based recommendation models enriched user/item embedding learning ability with historical reviews or better modeled user-item interactions with the help of available user-item target reviews. Though significant progress has been made, we argue that current solutions for review-based recommendation suffer from two drawbacks. First, as review-based recommendation can be naturally formed as a user-item bipartite graph with edge features from corresponding user-item reviews, how to better exploit this unique graph structure for recommendation? Second, while most current models suffer from limited user behaviors, can we exploit the unique self-supervised signals in the review-aware graph to guide two recommendation components better? To this end, in this paper, we propose a novel Review-aware Graph Contrastive Learning (RGCL) framework for review-based recommendation. Specifically, we first construct a review-aware user-item graph with feature-enhanced edges from reviews, where each edge feature is composed of both the user-item rating and the corresponding review semantics. This graph with feature-enhanced edges can help attentively learn each neighbor node weight for user and item representation learning. After that, we design two additional contrastive learning tasks (i.e., Node Discrimination and Edge Discrimination) to provide self-supervised signals for the two components in recommendation process. Finally, extensive experiments over five benchmark datasets demonstrate the superiority of our proposed RGCL compared to the state-of-the-art baselines.

preprint2022arXiv

Beyond Virtual Bazaar: How Social Commerce Promotes Inclusivity for the Traditionally Underserved Community in Chinese Developing Regions

The disadvantaged population is often underserved and marginalized in technology engagement: prior works show they are generally more reluctant and experience more barriers in adopting and engaging with mainstream technology. Here, we contribute to the HCI4D and ICTD literature through a novel "counter" case study on Chinese social commerce (e.g., Pinduoduo), which 1) first prospers among the traditionally underserved community from developing regions ahead of the more technologically advantaged communities, and 2) has been heavily engaged by this community. Through 12 in-depth interviews with social commerce users from the traditionally underserved community in Chinese developing regions, we demonstrate how social commerce, acting as a "counter", brings online the traditional offline socioeconomic lives the community has lived for ages, fits into the community's social, cultural, and economic context, and thus effectively promotes technology inclusivity. Our work provides novel insights and implications for building inclusive technology for the "next billion" population.

preprint2022arXiv

Canonical Mean Filter for Almost Zero-Shot Multi-Task classification

The support set is a key to providing conditional prior for fast adaption of the model in few-shot tasks. But the strict form of support set makes its construction actually difficult in practical application. Motivated by ANIL, we rethink the role of adaption in the feature extractor of CNAPs, which is a state-of-the-art representative few-shot method. To investigate the role, Almost Zero-Shot (AZS) task is designed by fixing the support set to replace the common scheme, which provides corresponding support sets for the different conditional prior of different tasks. The AZS experiment results infer that the adaptation works little in the feature extractor. However, CNAPs cannot be robust to randomly selected support sets and perform poorly on some datasets of Meta-Dataset because of its scattered mean embeddings responded by the simple mean operator. To enhance the robustness of CNAPs, Canonical Mean Filter (CMF) module is proposed to make the mean embeddings intensive and stable in feature space by mapping the support sets into a canonical form. CMFs make CNAPs robust to any fixed support sets even if they are random matrices. This attribution makes CNAPs be able to remove the mean encoder and the parameter adaptation network at the test stage, while CNAP-CMF on AZS tasks keeps the performance with one-shot tasks. It leads to a big parameter reduction. Precisely, 40.48\% parameters are dropped at the test stage. Also, CNAP-CMF outperforms CNAPs in one-shot tasks because it addresses inner-task unstable performance problems. Classification performance, visualized and clustering results verify that CMFs make CNAPs better and simpler.

preprint2022arXiv

Consecutive topological phase transitions and colossal magnetoresistance in a magnetic topological semimetal

The combination of magnetic symmetries and electronic band topology provides a promising route for realizing topologically nontrivial quasiparticles, and the manipulation of magnetic structures may enable the switching between topological phases, with the potential for achieving functional physical properties. Here, we report measurements of the electrical resistivity of EuCd$_2$As$_2$ under pressure, which show an intriguing insulating dome at pressures between $p_{\rm c1}\sim1.0$~GPa and $p_{\rm c2}\sim2.0$~GPa, situated between two regimes with metallic transport. The insulating state can be fully suppressed by a small magnetic field, leading to a colossal negative magnetoresistance on the order of $10^5$\%, accessible via a modest field of $\sim0.2$~T. First-principles calculations reveal that the dramatic evolution of the resistivity under pressure is due to consecutive transitions of EuCd$_2$As$_2$ from a magnetic topological insulator to a trivial insulator, and then to a Weyl semimetal, with the latter resulting from a pressure-induced change in the magnetic ground state. Similarly, the colossal magnetoresistance results from a field-induced polarization of the magnetic moments, transforming EuCd$_2$As$_2$ from a trivial insulator to a Weyl semimetal. These findings underscore weak magnetic exchange couplings and spin anisotropy as ingredients for discovering tunable magnetic topological materials with desirable functionalities.

preprint2022arXiv

DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction

Spatiotemporal activity prediction, aiming to predict user activities at a specific location and time, is crucial for applications like urban planning and mobile advertising. Existing solutions based on tensor decomposition or graph embedding suffer from the following two major limitations: 1) ignoring the fine-grained similarities of user preferences; 2) user's modeling is entangled. In this work, we propose a hypergraph neural network model called DisenHCN to bridge the above gaps. In particular, we first unify the fine-grained user similarity and the complex matching between user preferences and spatiotemporal activity into a heterogeneous hypergraph. We then disentangle the user representations into different aspects (location-aware, time-aware, and activity-aware) and aggregate corresponding aspect's features on the constructed hypergraph, capturing high-order relations from different aspects and disentangles the impact of each aspect for final prediction. Extensive experiments show that our DisenHCN outperforms the state-of-the-art methods by 14.23% to 18.10% on four real-world datasets. Further studies also convincingly verify the rationality of each component in our DisenHCN.

preprint2022arXiv

Disentangling Long and Short-Term Interests for Recommendation

Modeling user's long-term and short-term interests is crucial for accurate recommendation. However, since there is no manually annotated label for user interests, existing approaches always follow the paradigm of entangling these two aspects, which may lead to inferior recommendation accuracy and interpretability. In this paper, to address it, we propose a Contrastive learning framework to disentangle Long and Short-term interests for Recommendation (CLSR) with self-supervision. Specifically, we first propose two separate encoders to independently capture user interests of different time scales. We then extract long-term and short-term interests proxies from the interaction sequences, which serve as pseudo labels for user interests. Then pairwise contrastive tasks are designed to supervise the similarity between interest representations and their corresponding interest proxies. Finally, since the importance of long-term and short-term interests is dynamically changing, we propose to adaptively aggregate them through an attention-based network for prediction. We conduct experiments on two large-scale real-world datasets for e-commerce and short-video recommendation. Empirical results show that our CLSR consistently outperforms all state-of-the-art models with significant improvements: GAUC is improved by over 0.01, and NDCG is improved by over 4%. Further counterfactual evaluations demonstrate that stronger disentanglement of long and short-term interests is successfully achieved by CLSR. The code and data are available at https://github.com/tsinghua-fib-lab/CLSR.

preprint2022arXiv

Divide-and-Conquer Determinization of Büchi Automata based on SCC Decomposition

The determinization of a nondeterministic Büchi automaton (NBA) is a fundamental construction of automata theory, with applications to probabilistic verification and reactive synthesis. The standard determinization constructions, such as the ones based on the Safra-Piterman's approach, work on the whole NBA. In this work we propose a divide-and-conquer determinization approach. To this end, we first classify the strongly connected components (SCCs) of the given NBA as inherently weak, deterministic accepting, and nondeterministic accepting. We then present how to determinize each type of SCC independently from the others; this results in an easier handling of the determinization algorithm that takes advantage of the structure of that SCC. Once all SCCs have been determinized, we show how to compose them so to obtain the final equivalent deterministic Emerson-Lei automaton, which can be converted into a deterministic Rabin automaton without blow-up of states and transitions. We implement our algorithm in a our tool COLA and empirically evaluate COLA with the state-of-the-art tools Spot and OWL on a large set of benchmarks from the literature. The experimental results show that our prototype COLA outperforms Spot and OWL regarding the number of states and transitions.

preprint2022arXiv

DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias

Recommender systems are prone to be misled by biases in the data. Models trained with biased data fail to capture the real interests of users, thus it is critical to alleviate the impact of bias to achieve unbiased recommendation. In this work, we focus on an essential bias in micro-video recommendation, duration bias. Specifically, existing micro-video recommender systems usually consider watch time as the most critical metric, which measures how long a user watches a video. Since videos with longer duration tend to have longer watch time, there exists a kind of duration bias, making longer videos tend to be recommended more against short videos. In this paper, we empirically show that commonly-used metrics are vulnerable to duration bias, making them NOT suitable for evaluating micro-video recommendation. To address it, we further propose an unbiased evaluation metric, called WTG (short for Watch Time Gain). Empirical results reveal that WTG can alleviate duration bias and better measure recommendation performance. Moreover, we design a simple yet effective model named DVR (short for Debiased Video Recommendation) that can provide unbiased recommendation of micro-videos with varying duration, and learn unbiased user preferences via adversarial learning. Extensive experiments based on two real-world datasets demonstrate that DVR successfully eliminates duration bias and significantly improves recommendation performance with over 30% relative progress. Codes and datasets are released at https://github.com/tsinghua-fib-lab/WTG-DVR.

preprint2022arXiv

Enantiodiscrimination of chiral molecules via quantum correlation function

We propose a method to realize enantiodiscrimination of chiral molecules based on quantum correlation function in a driven cavity-molecule system, where the chiral molecule is coupled with a quantized cavity field and two classical light fields to form a cyclic three-level model. According to the inherent properties of electric-dipole transition moments of chiral molecules, there is a $π$-phase difference in the overall phase of the cyclic three-level model for the left- and right-handed chiral molecules. Thus, the correlation function depends on this overall phase and is chirality-dependent. The analytical and numerical results indicate that the left- and right-handed chiral molecules can be discriminated by detecting quantum correlation function. Our work opens up a promising route to discriminate molecular chirality, which is an extremely important task in pharmacology and biochemistry.

preprint2022arXiv

Enhancement of molecular coherent anti-Stokes Raman scattering with silicon nano-antennas

Surface-enhanced coherent anti-Stokes Raman scattering (SE-CARS) takes advantage of surface plasmon resonances supported on metallic nanostructures to amplify the coherent Raman response of target molecules. While these metallic antennas have found significant success in SE-CARS studies, photo-induced morphological changes to the nanoantenna under ultrafast excitation introduce significant hurdles in terms of stability and reproducilibty. These hurdles need to be overcome in order to establish SE-CARS as a reliable tool for rapid biomolecular sensing. Here we address this challenge by performing molecular CARS measurements enhanced by nanoantennas made from high-index dielectric particles with more favorable thermal properties. We present the first experimental demonstration of enhanced molecular CARS signals observed at Si nano-antennas, which offer much improved thermal stability compared to their metallic counterparts.

preprint2022arXiv

Enhancing the force sensitivity of squeezed light optomechanical interferometer

Application of frequency-dependent squeezed vacuum improves the force sensitivity of optomechanical interferometer beyond the standard quantum limit by a factor of $e^{-r}$, where $r$ is the squeezing parameter. In this work, we show that the application of squeezed light along with quantum optical restoring force can enhance the sensitivity beyond the standard quantum limit by a factor of $\sqrt{e^{-2r}ζ/4Δ}$, where $0< ζ/Δ<1$, with $ζ$ as the optomechanical cavity decay rate and $Δ$ as the detuning between cavity eigenfrequency and driving field. The technique described in this article is restricted to frequencies much smaller than the resonance frequency of the optomechanical mirror.

preprint2022arXiv

Experimental Verification of the Acoustic Geometric Phase

The optical geometric phase encoded by the in-plane spatial orientation of microstructures has promoted the rapid development of numerous new-type optical meta-devices. However, pushing the concept of the geometric phase toward the acoustic community still faces challenges. In this work, we take advantage of two acoustic nonlocal metagratings that could support the direct conversion between plane wave and designated vortex mode, of which the orbital angular momentum conversion process plays a vital role in obtaining the acoustic geometric phase. We obtain the acoustic geometric phases of different orders by merely varying the orientation angle of one of the acoustic nonlocal metagratings. Intriguingly, according to our developed theory, we reveal that the reflective acoustic geometric phase, which is twice of the transmissive one, can be readily realized by transferring the transmitted configuration to a reflected one. Both the theoretical model and experimental measurements successfully verify the announced transmissive and reflective acoustic geometric phases. Moreover, the characteristics of reconfigurability and continuous phase modulation that covers the 2π range shown by the acoustic geometric phases provide us with new possibilities in advanced acoustic wavefront control.

preprint2022arXiv

Finding similarity of orbits between two discrete dynamical systems via optimal principle

Whether there is similarity between two physical processes in the movement of objects and the complexity of behavior is an essential problem in science. How to seek similarity through the adoption of quantitative and qualitative research techniques still remains an urgent challenge we face. To this end, the concepts of similarity transformation matrix and similarity degree are innovatively introduced to describe similarity of orbits between two complicated discrete dynamical systems that seem to be irrelevant. Furthermore, we present a general optimal principle, giving a strict characterization from the perspective of dynamical systems combined with optimization theory. For well-known examples of chaotic dynamical systems, such as Lorenz attractor, Chua's circuit, R$\rm\ddot{o}$ssler attractor, Chen attractor, L$\rm\ddot{u}$ attractor and hybrid system, with using of the homotopy idea, some numerical simulation results demonstrate that similarity can be found in rich characteristics and complex behaviors of chaotic dynamics via the optimal principle we presented.

preprint2022arXiv

Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction

The exposure sequence is being actively studied for user interest modeling in Click-Through Rate (CTR) prediction. However, the existing methods for exposure sequence modeling bring extensive computational burden and neglect noise problems, resulting in an excessively latency and the limited performance in online recommenders. In this paper, we propose to address the high latency and noise problems via Gating-adapted wavelet multiresolution analysis (Gama), which can effectively denoise the extremely long exposure sequence and adaptively capture the implied multi-dimension user interest with linear computational complexity. This is the first attempt to integrate non-parametric multiresolution analysis technique into deep neural networks to model user exposure sequence. Extensive experiments on large scale benchmark dataset and real production dataset confirm the effectiveness of Gama for exposure sequence modeling, especially in cold-start scenarios. Benefited from its low latency and high effecitveness, Gama has been deployed in our real large-scale industrial recommender, successfully serving over hundreds of millions users.

preprint2022arXiv

Giant and Reversible Electronic Structure Evolution in a Magnetic Topological Material EuCd2As2

The electronic structure and the physical properties of quantum materials can be significantly altered by charge carrier doping and magnetic state transition. Here we report a discovery of a giant and reversible electronic structure evolution with doping in a magnetic topological material. By performing high-resolution angle-resolved photoemission measurements on EuCd2As2,we found that a huge amount of hole doping can be introduced into the sample surface due to surface absorption. The electronic structure exhibits a dramatic change with the hole doping which can not be described by a rigid band shift. Prominent band splitting is observed at high doping which corresponds to a doping-induced magnetic transition at low temperature (below -15 K) from an antiferromagnetic state to a ferromagnetic state. These results have established a detailed electronic phase diagram of EuCd2As2 where the electronic structure and the magnetic structure change systematically and dramatically with the doping level. They further suggest that the transport, magnetic and topological properties of EuCd2As2 can be greatly modified by doping. These work will stimulate further investigations to explore for new phenomena and properties in doping this magnetic topological material.

preprint2022arXiv

Giant Atoms in a Synthetic Frequency Dimension

Giant atoms that interact with real-space waveguides at multiple spatial points have attracted extensive attention due to their unique interference effects. Here we propose a feasible scheme for constructing giant atoms in a synthetic frequency dimension with, e.g., a dynamically modulated superconducting resonator and a tailored three-level artificial atom. Both analytical and numerical calculations show good agreement between our scheme and real-space two-level giant atoms. In particular, the symmetry of the model in momentum space can be broken by tuning the phase of the external field applied on the atom, enabling chiral interactions between the atom and the frequency lattice. We further demonstrate the possibility of simulating cascaded interaction and directional excitation transfer in the frequency dimension by directly extending our model to involve more such effective giant atoms.

preprint2022arXiv

Giant atoms with time-dependent couplings

We study the decay dynamics of a two-level giant atom that is coupled to a waveguide with time-dependent coupling strengths. In the non-Markovian regime where the retardation effect cannot be ignored, we show that the dynamics of the atom depends on the atom-waveguide coupling strengths at an earlier time. This allows one to tailor the decay dynamics of the giant atom and even realize a stationary population revival with appropriate coupling modulations. Moreover, we demonstrate the possibility of simulating the quantum Zeno and quantum anti-Zeno effects in the giant-atom model with periodic coupling quenches. These results have potential applications in quantum information processing and quantum network engineering.

preprint2022arXiv

High-throughput decoder of quasi-cyclic LDPC codes with limited precision for continuous-variable quantum key distribution systems

More than Mbps secret key rate was demonstrated for continuous-variable quantum key distribution (CV-QKD) systems, but real-time postprocessing is not allowed, which is restricted by the throughput of the error correction decoding in postprocessing. In this paper, a high-throughput FPGA-based quasi-cyclic LDPC decoder is proposed and implemented to support Mbps real-time secret key rate generation for CV-QKD for the first time. A residual bit error correction algorithm is used to solve the problem of high frame errors rate (FER) caused by the limited precision of the decoder. Specifically, real-time high-speed decoding for CV-QKD systems with typical code rates 0.2 and 0.1 is implemented on a commercial FPGA, and two throughputs of 360.92Mbps and 194.65Mbps are achieved, respectively, which can support 17.97 Mbps and 2.48 Mbps real-time generation of secret key rates under typical transmission distances of 25km and 50km, correspondingly. The proposed method paves the way for high-rate real-time CV-QKD deployment in secure metropolitan area network.

preprint2022arXiv

K-Detector: Identifying Duplicate Crash Failures in Large-Scale Software Delivery

After a developer submits code, corresponding test cases arise to ensure the quality of software delivery. Test failures would occur during this period, such as crash, error, and timeout. Since it takes time for developers to resolve them, many duplicate failures will happen during this period. In the delivery practice of SAP HANA, crash triage is considered as the most time-consuming task. If duplicate crash failures can be automatically identified, the degree of automation will be significantly enhanced. To find such duplicates, we propose a training-based mathematical model that utilizes component information of SAP HANA to achieve better crash similarity comparison. We implement our approach in a tool named Knowledge-based Detector (K-Detector), which is verified by 11,208 samples and performs 0.986 in AUC. Furthermore, we have deployed K-Detector to the production environment, and it can save 97% human efforts in crash triage as statistics.

preprint2022arXiv

KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning

While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently. To solve this problem, we first analyze the properties of different HPs and measure the transfer ability from small subgraph to the full graph. Based on the analysis, we propose an efficient two-stage search algorithm KGTuner, which efficiently explores HP configurations on small subgraph at the first stage and transfers the top-performed configurations for fine-tuning on the large full graph at the second stage. Experiments show that our method can consistently find better HPs than the baseline algorithms within the same time budget, which achieves {9.1\%} average relative improvement for four embedding models on the large-scale KGs in open graph benchmark.

preprint2022arXiv

Neighboring Backdoor Attacks on Graph Convolutional Network

Backdoor attacks have been widely studied to hide the misclassification rules in the normal models, which are only activated when the model is aware of the specific inputs (i.e., the trigger). However, despite their success in the conventional Euclidean space, there are few studies of backdoor attacks on graph structured data. In this paper, we propose a new type of backdoor which is specific to graph data, called neighboring backdoor. Considering the discreteness of graph data, how to effectively design the triggers while retaining the model accuracy on the original task is the major challenge. To address such a challenge, we set the trigger as a single node, and the backdoor is activated when the trigger node is connected to the target node. To preserve the model accuracy, the model parameters are not allowed to be modified. Thus, when the trigger node is not connected, the model performs normally. Under these settings, in this work, we focus on generating the features of the trigger node. Two types of backdoors are proposed: (1) Linear Graph Convolution Backdoor which finds an approximation solution for the feature generation (can be viewed as an integer programming problem) by looking at the linear part of GCNs. (2) Variants of existing graph attacks. We extend current gradient-based attack methods to our backdoor attack scenario. Extensive experiments on two social networks and two citation networks datasets demonstrate that all proposed backdoors can achieve an almost 100\% attack success rate while having no impact on predictive accuracy.

preprint2022arXiv

Nonreciprocal frequency conversion with chiral $Λ$-type atoms

In this paper, we begin with a model of a $Λ$-type atom whose both transitions are chirally coupled to a waveguide and then extend the model to its giant-atom version. We investigate the single-photon scatterings of the giant-atom model in both the Markovian and non-Markovian regimes. It is shown that the chiral atom-waveguide couplings enable nonreciprocal, reflectionless, and efficient frequency conversion, while the giant-atom structure introduces intriguing interference effects to the scattering behaviors, such as ultra-narrow scattering windows. The chiral giant-atom model exhibits quite different scattering spectra in the two regimes and, in particular, demonstrates non-Markovicity induced nonreciprocity under specific conditions. These phenomena can be understood from the effective detuning and decay rate of the giant-atom model. Our results have potential applications in integrated photonics and quantum network engineering.

preprint2022arXiv

PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems

The development of personalized recommendation has significantly improved the accuracy of information matching and the revenue of e-commerce platforms. Recently, it has 2 trends: 1) recommender systems must be trained timely to cope with ever-growing new products and ever-changing user interests from online marketing and social network; 2) SOTA recommendation models introduce DNN modules to improve prediction accuracy. Traditional CPU-based recommender systems cannot meet these two trends, and GPU- centric training has become a trending approach. However, we observe that GPU devices in training recommender systems are underutilized, and they cannot attain an expected throughput improvement as what it has achieved in CV and NLP areas. This issue can be explained by two characteristics of these recommendation models: First, they contain up to a thousand input feature fields, introducing fragmentary and memory-intensive operations; Second, the multiple constituent feature interaction submodules introduce substantial small-sized compute kernels. To remove this roadblock to the development of recommender systems, we propose a novel framework named PICASSO to accelerate the training of recommendation models on commodity hardware. Specifically, we conduct a systematic analysis to reveal the bottlenecks encountered in training recommendation models. We leverage the model structure and data distribution to unleash the potential of hardware through our packing, interleaving, and caching optimization. Experiments show that PICASSO increases the hardware utilization by an order of magnitude on the basis of SOTA baselines and brings up to 6x throughput improvement for a variety of industrial recommendation models. Using the same hardware budget in production, PICASSO on average shortens the walltime of daily training tasks by 7 hours, significantly reducing the delay of continuous delivery.

preprint2022arXiv

Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System Design

Recommender systems are playing an increasingly important role in alleviating information overload and supporting users' various needs, e.g., consumption, socialization, and entertainment. However, limited research focuses on how values should be extensively considered in industrial deployments of recommender systems, the ignorance of which can be problematic. To fill this gap, in this paper, we adopt Value Sensitive Design to comprehensively explore how practitioners and users recognize different values of current industrial recommender systems. Based on conceptual and empirical investigations, we focus on five values: recommendation quality, privacy, transparency, fairness, and trustworthiness. We further conduct in-depth qualitative interviews with 20 users and 10 practitioners to delve into their opinions about these values. Our results reveal the existence and sources of tensions between practitioners and users in terms of value interpretation, evaluation, and practice, which provide novel implications for designing more human-centric and value-sensitive recommender systems.

preprint2022arXiv

Resurgence and Partial Theta Series

We consider partial theta series associated with periodic sequences of coefficients, of the form $Θ(τ) := \sum_{n>0} n^νf(n) e^{iπn^2τ/M}$, with $ν$ non-negative integer and an $M$-periodic function $f : \mathbb{Z} \rightarrow \mathbb{C}$. Such a function is analytic in the half-plane $\{Im(τ)>0\}$ and as $τ$ tends non-tangentially to any $α\in\mathbb{Q}$, a formal power series appears in the asymptotic behaviour of $Θ(τ)$, depending on the parity of $ν$ and $f$. We discuss the summability and resurgence properties of these series by means of explicit formulas for their formal Borel transforms, and the consequences for the modularity properties of $Θ$, or its ``quantum modularity'' properties in the sense of Zagier's recent theory. The Discrete Fourier Transform of $f$ plays an unexpected role and leads to a number-theoretic analogue of Écalle's ``Bridge Equations''. The motto is: (quantum) modularity = Stokes phenomenon + Discrete Fourier Transform.

preprint2022arXiv

Structure Enhanced Graph Neural Networks for Link Prediction

Graph Neural Networks (GNNs) have shown promising results in various tasks, among which link prediction is an important one. GNN models usually follow a node-centric message passing procedure that aggregates the neighborhood information to the central node recursively. Following this paradigm, features of nodes are passed through edges without caring about where the nodes are located and which role they played. However, the neglected topological information is shown to be valuable for link prediction tasks. In this paper, we propose Structure Enhanced Graph neural network (SEG) for link prediction. SEG introduces the path labeling method to capture surrounding topological information of target nodes and then incorporates the structure into an ordinary GNN model. By jointly training the structure encoder and deep GNN model, SEG fuses topological structures and node features to take full advantage of graph information. Experiments on the OGB link prediction datasets demonstrate that SEG achieves state-of-the-art results among all three public datasets.

preprint2022arXiv

Threshold solutions for nonlocal reaction diffusion equations

We study the Cauchy problem for nonlocal reaction diffusion equations with bistable nonlinearity in 1D spatial domain and investigate the asymptotic behaviors of solutions with a one-parameter family of monotonically increasing and compactly supported initial data. We show that for small values of the parameter the corresponding solutions decay to 0, while for large values the related solutions converge to 1 uniformly on compacts. Moreover, we prove that the transition from extinction (converging to 0) to propagation (converging to 1) is sharp. Numerical results are provided to verify the theoretical results.

preprint2022arXiv

Topological Supercavity Resonances In the Finite System

Acoustic resonant cavities play a vital role in modern acoustical systems. They have led to many essential applications for noise control, biomedical ultrasonics, and underwater communications. The ultrahigh quality-factor resonances are highly desired for some applications like high-resolution acoustic sensors and acoustic lasers. Here, we theoretically propose and experimentally demonstrate a new class of supercavity resonances in a coupled acoustic resonators system, arising from the merged bound states in the continuum (BICs) in geometry space. We demonstrate their topological origin by explicitly calculating their topological charges before and after BIC merging, accompanied by charges annihilation. Comparing with other types of BICs, they are robust to the perturbation brought by fabrication imperfection. Moreover, we found that such supercavity modes can be linked with the Friedrich-Wintgen BICs supported by an entire rectangular (cuboid) resonator sandwiched between two rectangular (or circular) waveguides, and thus more supercavity modes are constructed. Then, we fabricate these coupled resonators and experimentally confirm such a unique phenomenon: moving, merging, and vanishing of BICs by measuring their reflection spectra, which show good agreement with the numerical simulation and theoretical prediction of mode evolution. Finally, given the similar wave nature of acoustic and electromagnetic waves, such merged BICs also can be constructed in a coupled photonic resonator system. Our results may find exciting applications in acoustic and photonics, such as enhanced acoustic emission, filtering, and sensing.

preprint2022arXiv

Whale: Efficient Giant Model Training over Heterogeneous GPUs

The scaling up of deep neural networks has been demonstrated to be effective in improving model quality, but also encompasses several training challenges in terms of training efficiency, programmability, and resource adaptability. We present Whale, a general and efficient distributed training framework for giant models. To support various parallel strategies and their hybrids, Whale generalizes the programming interface by defining two new primitives in the form of model annotations, allowing for incorporating user hints. The Whale runtime utilizes those annotations and performs graph optimizations to transform a local deep learning DAG graph for distributed multi-GPU execution. Whale further introduces a novel hardware-aware parallel strategy, which improves the performance of model training on heterogeneous GPUs in a balanced manner. Deployed in a production cluster with 512 GPUs, Whale successfully trains an industry-scale multimodal model with over ten trillion model parameters, named M6, demonstrating great scalability and efficiency.

preprint2021arXiv

AttnMove: History Enhanced Trajectory Recovery via Attentional Network

A considerable amount of mobility data has been accumulated due to the proliferation of location-based service. Nevertheless, compared with mobility data from transportation systems like the GPS module in taxis, this kind of data is commonly sparse in terms of individual trajectories in the sense that users do not access mobile services and contribute their data all the time. Consequently, the sparsity inevitably weakens the practical value of the data even it has a high user penetration rate. To solve this problem, we propose a novel attentional neural network-based model, named AttnMove, to densify individual trajectories by recovering unobserved locations at a fine-grained spatial-temporal resolution. To tackle the challenges posed by sparsity, we design various intra- and inter- trajectory attention mechanisms to better model the mobility regularity of users and fully exploit the periodical pattern from long-term history. We evaluate our model on two real-world datasets, and extensive results demonstrate the performance gain compared with the state-of-the-art methods. This also shows that, by providing high-quality mobility data, our model can benefit a variety of mobility-oriented down-stream applications.

preprint2021arXiv

Disentangling User Interest and Conformity for Recommendation with Causal Embedding

Recommendation models are usually trained on observational interaction data. However, observational interaction data could result from users' conformity towards popular items, which entangles users' real interest. Existing methods tracks this problem as eliminating popularity bias, e.g., by re-weighting training samples or leveraging a small fraction of unbiased data. However, the variety of user conformity is ignored by these approaches, and different causes of an interaction are bundled together as unified representations, hence robustness and interpretability are not guaranteed when underlying causes are changing. In this paper, we present DICE, a general framework that learns representations where interest and conformity are structurally disentangled, and various backbone recommendation models could be smoothly integrated. We assign users and items with separate embeddings for interest and conformity, and make each embedding capture only one cause by training with cause-specific data which is obtained according to the colliding effect of causal inference. Our proposed methodology outperforms state-of-the-art baselines with remarkable improvements on two real-world datasets on top of various backbone models. We further demonstrate that the learned embeddings successfully capture the desired causes, and show that DICE guarantees the robustness and interpretability of recommendation.

preprint2021arXiv

Dynamical emission of phonon pairs in optomechanical systems

Multiphonon state plays an important role in quantum information processing and quantum metrology. Here we propose a scheme to realize dynamical emission of phonon pairs based on the technique of stimulated Raman adiabatic passage in a single cavity optomechanical system, where the optical cavity is driven by two Gaussian pulse lasers. By exploring quantum trajectories of the state populations and the average phonon number, we find that the dynamical phonon-pair emission can be realized under the appropriate parameter conditions and is tunable by controlling the time interval between the consecutive pulses of pump lasers. In particular, the numerical results for the standard and generalized second-order correlation functions of the mechanical mode show that the system can behave as an antibunched phonon-pair emitter. Our proposal can be extended to achieve an antibunched $n$-phonon emitter, which has potential applications for on-chip quantum communications.

preprint2021arXiv

Enantio-conversion of chiral mixtures via optical pumping

Enantio-conversion with the help of electromagnetic fields is an essential issue due to the chirality-dependence of many chemical, biological, and pharmaceutical processes. Here, we propose a method for this issue based on a five-level double-$Δ$ model of chiral molecules. By utilizing the breaking of left-right symmetry in the two $Δ$-type sub-structures, we can establish the chiral-state-selective excitation with one chiral ground state being excited to an achiral excited state and the other one being undisturbed. In the meanwhile, the achiral excited state will relax to the two chiral ground states. The two effects simultaneously acting on the chiral mixtures can convert molecules of different chiralities to the ones of the same chirality, i.e., the enantio-conversion via optical pumping. With typical parameters in gas-phase experiments, we numerically show that highly efficient enantio-conversion can be achieved. Our method works in the appearance of decoherences and without the precise control of pulse-durations (pulse-areas) and/or pulse-shapes. These advantages offer it promising features in promoting the future exploring of enantio-conversion.

preprint2021arXiv

Enantio-detection of cyclic three-level chiral molecules in a driven cavity

We propose an enantio-detection method of chiral molecules in a cavity with external drive. The chiral molecules are coupled with a quantized cavity field and two classical light fields to form the cyclic three-level systems. The chirality-dependent cavity-assisted three-photon process in the three-level systems leads to the generation of intracavity photons. Simultaneously, the drive field also results in the chirality-independent process of the generation of intracavity photons. Based on the interference between the intracavity photons generated from these two processes, one can detect the enantiomeric excess of chiral mixture via monitoring the transmission rate of the drive field.

preprint2021arXiv

Enantio-specific state transfer for symmetric-top chiral molecules

We study the enantio-specific state transfer in a four-level model for symmetric-top chiral molecules. Such a model is formed by coupling the electric-dipole transitions among four appropriate working states with three electromagnetic fields. It includes two cyclic three-level substructures, where the overall phases differ by $π$ with enantiomers and reflect the chirality dependence of the molecule. Based on this four-level model, two dynamic ways are proposed to achieve the approximately perfect enantio-specific state transfer for symmetric-top chiral molecules.

preprint2021arXiv

Enantiodetection of chiral molecules via two-dimensional spectroscopy

Enantiodetection of chiral molecules is important to pharmaceutical drug production, chemical reaction control, and biological function designs. Traditional optical methods of enantiodetection rely on the weak magnetic-dipole or electric-quadrupole interactions, and in turn suffer from the weak signal and low sensitivity. We propose a new optical enantiodetection method to determine the enantiomeric excess via two-dimensional (2D) spectroscopy of the chiral mixture driven by three electromagnetic fields. The quantities of left- and right- handed chiral molecules are reflected by the intensities of different peaks on the 2D spectrum, separated by the chirality-dependent frequency shifts resulting from the relative strong electric-dipole interactions between the chiral molecules and the driving fields. Thus, the enantiomeric excess can be determined via the intensity ratio of the peaks for the two enantiomers.

preprint2021arXiv

Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network

In the past decade, the heterogeneous information network (HIN) has become an important methodology for modern recommender systems. To fully leverage its power, manually designed network templates, i.e., meta-structures, are introduced to filter out semantic-aware information. The hand-crafted meta-structure rely on intense expert knowledge, which is both laborious and data-dependent. On the other hand, the number of meta-structures grows exponentially with its size and the number of node types, which prohibits brute-force search. To address these challenges, we propose Genetic Meta-Structure Search (GEMS) to automatically optimize meta-structure designs for recommendation on HINs. Specifically, GEMS adopts a parallel genetic algorithm to search meaningful meta-structures for recommendation, and designs dedicated rules and a meta-structure predictor to efficiently explore the search space. Finally, we propose an attention based multi-view graph convolutional network module to dynamically fuse information from different meta-structures. Extensive experiments on three real-world datasets suggest the effectiveness of GEMS, which consistently outperforms all baseline methods in HIN recommendation. Compared with simplified GEMS which utilizes hand-crafted meta-paths, GEMS achieves over $6\%$ performance gain on most evaluation metrics. More importantly, we conduct an in-depth analysis on the identified meta-structures, which sheds light on the HIN based recommender system design.

preprint2021arXiv

Graph Jigsaw Learning for Cartoon Face Recognition

Cartoon face recognition is challenging as they typically have smooth color regions and emphasized edges, the key to recognize cartoon faces is to precisely perceive their sparse and critical shape patterns. However, it is quite difficult to learn a shape-oriented representation for cartoon face recognition with convolutional neural networks (CNNs). To mitigate this issue, we propose the GraphJigsaw that constructs jigsaw puzzles at various stages in the classification network and solves the puzzles with the graph convolutional network (GCN) in a progressive manner. Solving the puzzles requires the model to spot the shape patterns of the cartoon faces as the texture information is quite limited. The key idea of GraphJigsaw is constructing a jigsaw puzzle by randomly shuffling the intermediate convolutional feature maps in the spatial dimension and exploiting the GCN to reason and recover the correct layout of the jigsaw fragments in a self-supervised manner. The proposed GraphJigsaw avoids training the classification model with the deconstructed images that would introduce noisy patterns and are harmful for the final classification. Specially, GraphJigsaw can be incorporated at various stages in a top-down manner within the classification model, which facilitates propagating the learned shape patterns gradually. GraphJigsaw does not rely on any extra manual annotation during the training process and incorporates no extra computation burden at inference time. Both quantitative and qualitative experimental results have verified the feasibility of our proposed GraphJigsaw, which consistently outperforms other face recognition or jigsaw-based methods on two popular cartoon face datasets with considerable improvements.

preprint2021arXiv

Learning from Home: A Mixed-Methods Analysis of Live Streaming Based Remote Education Experience in Chinese Colleges During the COVID-19 Pandemic

The COVID-19 global pandemic and resulted lockdown policies have forced education in nearly every country to switch from a traditional co-located paradigm to a pure online 'distance learning from home' paradigm. Lying in the center of this learning paradigm shift is the emergence and wide adoption of distance communication tools and live streaming platforms for education. Here, we present a mixed-methods study on live streaming based education experience during the COVID-19 pandemic. We focus our analysis on Chinese higher education, carried out semi-structured interviews on 30 students, and 7 instructors from diverse colleges and disciplines, meanwhile launched a large-scale survey covering 6291 students and 1160 instructors in one leading Chinese university. Our study not only reveals important design guidelines and insights to better support current remote learning experience during the pandemic, but also provides valuable implications towards constructing future collaborative education supporting systems and experience after pandemic.

preprint2021arXiv

MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions

Deep neural networks are widely used in personalized recommendation systems. Unlike regular DNN inference workloads, recommendation inference is memory-bound due to the many random memory accesses needed to lookup the embedding tables. The inference is also heavily constrained in terms of latency because producing a recommendation for a user must be done in about tens of milliseconds. In this paper, we propose MicroRec, a high-performance inference engine for recommendation systems. MicroRec accelerates recommendation inference by (1) redesigning the data structures involved in the embeddings to reduce the number of lookups needed and (2) taking advantage of the availability of High-Bandwidth Memory (HBM) in FPGA accelerators to tackle the latency by enabling parallel lookups. We have implemented the resulting design on an FPGA board including the embedding lookup step as well as the complete inference process. Compared to the optimized CPU baseline (16 vCPU, AVX2-enabled), MicroRec achieves 13.8~14.7x speedup on embedding lookup alone and 2.5$~5.4x speedup for the entire recommendation inference in terms of throughput. As for latency, CPU-based engines needs milliseconds for inferring a recommendation while MicroRec only takes microseconds, a significant advantage in real-time recommendation systems.

preprint2021arXiv

Nano-engineering the evolution of skyrmion crystal in synthetic antiferromagnets

The evolution of skyrmion crystal encapsulates skyrmion critical behaviors, such as nucleation, deformation and annihilation. Here, we achieve a tunable evolution of artificial skyrmion crystal in nanostructured synthetic antiferromagnet multilayers, which are comprised of perpendicular magnetic multilayers and nanopatterned arrays of magnetic nanodots. The out-of-plane magnetization hysteresis loops and first-order reversal curves show that the nucleation and annihilation of the artificial skyrmion can be controlled by tuning the diameter of and spacing between the nanodots. Moreover, when the bottom layer thickness increases, the annihilation of skyrmion shifts from evolving into a ferromagnetic spin texture to evolving into an antiferromagnetic spin texture. Most significantly, non-volatile multiple states are realized at zero magnetic field via controlling the proportion of the annihilated skyrmions in the skyrmion crystal. Our results demonstrate the tunability and flexibility of the artificial skyrmion platform, providing a promising route to achieve skyrmion-based multistate devices, such as neuromorphic spintronic devices.

preprint2021arXiv

Nonreciprocal light transmission via optomechanical parametric interactions

Nonreciprocal transmission of optical or microwave signals is indispensable in various applications involving sensitive measurements. In this paper, we study optomechanically induced directional amplification and isolation in a generic setup including two cavities and two mechanical oscillators by exclusively using blue-sideband drive tones. The input and output ports defined by the two cavity modes are coupled through coherent and dissipative paths mediated by the two mechanical resonators, respectively. By choosing appropriate transfer phases and strengths of the driving fields, either a directional amplifier or an isolator can be implemented at low thermal temperature, and both of them show bi-directional nonreciprocity working at two mirrored frequencies. The nonreciprocal device can potentially be demonstrated by opto- and electro-mechanical setups in both optical and microwave domains.

preprint2021arXiv

Policy-Aware Mobility Model Explains the Growth of COVID-19 in Cities

With the continued spread of coronavirus, the task of forecasting distinctive COVID-19 growth curves in different cities, which remain inadequately explained by standard epidemiological models, is critical for medical supply and treatment. Predictions must take into account non-pharmaceutical interventions to slow the spread of coronavirus, including stay-at-home orders, social distancing, quarantine and compulsory mask-wearing, leading to reductions in intra-city mobility and viral transmission. Moreover, recent work associating coronavirus with human mobility and detailed movement data suggest the need to consider urban mobility in disease forecasts. Here we show that by incorporating intra-city mobility and policy adoption into a novel metapopulation SEIR model, we can accurately predict complex COVID-19 growth patterns in U.S. cities ($R^2$ = 0.990). Estimated mobility change due to policy interventions is consistent with empirical observation from Apple Mobility Trends Reports (Pearson's R = 0.872), suggesting the utility of model-based predictions where data are limited. Our model also reproduces urban "superspreading", where a few neighborhoods account for most secondary infections across urban space, arising from uneven neighborhood populations and heightened intra-city churn in popular neighborhoods. Therefore, our model can facilitate location-aware mobility reduction policy that more effectively mitigates disease transmission at similar social cost. Finally, we demonstrate our model can serve as a fine-grained analytic and simulation framework that informs the design of rational non-pharmaceutical interventions policies.

preprint2021arXiv

Reinforced Contact Tracing and Epidemic Intervention

The recent outbreak of COVID-19 poses a serious threat to people's lives. Epidemic control strategies have also caused damage to the economy by cutting off humans' daily commute. In this paper, we develop an Individual-based Reinforcement Learning Epidemic Control Agent (IDRLECA) to search for smart epidemic control strategies that can simultaneously minimize infections and the cost of mobility intervention. IDRLECA first hires an infection probability model to calculate the current infection probability of each individual. Then, the infection probabilities together with individuals' health status and movement information are fed to a novel GNN to estimate the spread of the virus through human contacts. The estimated risks are used to further support an RL agent to select individual-level epidemic-control actions. The training of IDRLECA is guided by a specially designed reward function considering both the cost of mobility intervention and the effectiveness of epidemic control. Moreover, we design a constraint for control-action selection that eases its difficulty and further improve exploring efficiency. Extensive experimental results demonstrate that IDRLECA can suppress infections at a very low level and retain more than 95% of human mobility.

preprint2021arXiv

You Recommend, I Buy: How and Why People Engage in Instant Messaging Based Social Commerce

As an emerging business phenomenon especially in China, instant messaging (IM) based social commerce is growing increasingly popular, attracting hundreds of millions of users and is becoming one important way where people make everyday purchases. Such platforms embed shopping experiences within IM apps, e.g., WeChat, WhatsApp, where real-world friends post and recommend products from the platforms in IM group chats and quite often form lasting recommending/buying relationships. How and why do users engage in IM based social commerce? Do such platforms create novel experiences that are distinct from prior commerce? And do these platforms bring changes to user social lives and relationships? To shed light on these questions, we launched a qualitative study where we carried out semi-structured interviews on 12 instant messaging based social commerce users in China. We showed that IM based social commerce: 1) enables more reachable, cost-reducing, and immersive user shopping experience, 2) shapes user decision-making process in shopping through pre-existing social relationship, mutual trust, shared identity, and community norm, and 3) creates novel social interactions, which can contribute to new tie formation while maintaining existing social relationships. We demonstrate that all these unique aspects link closely to the characteristics of IM platforms, as well as the coupling of user social and economic lives under such business model. Our study provides important research and design implications for social commerce, and decentralized, trusted socio-technical systems in general.

preprint2020arXiv

Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection

Objective: Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous seizures, which affects about one percent of the world's population. Most of the current seizure detection approaches strongly rely on patient history records and thus fail in the patient-independent situation of detecting the new patients. To overcome such limitation, we propose a robust and explainable epileptic seizure detection model that effectively learns from seizure states while eliminates the inter-patient noises. Methods: A complex deep neural network model is proposed to learn the pure seizure-specific representation from the raw non-invasive electroencephalography (EEG) signals through adversarial training. Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure. Results: The proposed approach is evaluated over the Temple University Hospital EEG (TUH EEG) database. The experimental results illustrate that our model outperforms the competitive state-of-the-art baselines with low latency. Moreover, the designed attention mechanism is demonstrated ables to provide fine-grained information for pathological analysis. Conclusion and significance: We propose an effective and efficient patient-independent diagnosis approach of epileptic seizure based on raw EEG signals without manually feature engineering, which is a step toward the development of large-scale deployment for real-life use.

preprint2020arXiv

Bundle Recommendation with Graph Convolutional Networks

Bundle recommendation aims to recommend a bundle of items for a user to consume as a whole. Existing solutions integrate user-item interaction modeling into bundle recommendation by sharing model parameters or learning in a multi-task manner, which cannot explicitly model the affiliation between items and bundles, and fail to explore the decision-making when a user chooses bundles. In this work, we propose a graph neural network model named BGCN (short for \textit{\textBF{B}undle \textBF{G}raph \textBF{C}onvolutional \textBF{N}etwork}) for bundle recommendation. BGCN unifies user-item interaction, user-bundle interaction and bundle-item affiliation into a heterogeneous graph. With item nodes as the bridge, graph convolutional propagation between user and bundle nodes makes the learned representations capture the item level semantics. Through training based on hard-negative sampler, the user's fine-grained preferences for similar bundles are further distinguished. Empirical results on two real-world datasets demonstrate the strong performance gains of BGCN, which outperforms the state-of-the-art baselines by 10.77\% to 23.18\%.

preprint2020arXiv

Controllable optical response and tunable sensing based on self interference in waveguide QED systems

We study the self interference effect of a resonator coupled with a bent waveguide at two separated ports. Such interference effects are shown to be similar for the cases of standing-wave and traveling-wave resonators, while in the system of two separated resonators indirectly coupled via a waveguide, the coupling forms and the related interference effects depend on which kind of resonators is chosen. Due to the self interference, controllable optical responses including tunable linewidth and frequency shift, and optical dark state can be achieved. Moreover, we consider a self-interference photon-magnon hybrid model and show phase-dependent Fano-like line shapes which have potential applications in frequency sensing. The photon-magnon hybridization can not only enhance the sensitivity and provide tunable working region, but also enables optical readout of the magnetic field strength in turn. The results in this paper provide a deeper insight into the self interference effect and its potential applications.

preprint2020arXiv

DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks

Future advances in deep learning and its impact on the development of artificial intelligence (AI) in all fields depends heavily on data size and computational power. Sacrificing massive computing resources in exchange for better precision rates of the network model is recognized by many researchers. This leads to huge computing consumption and satisfactory results are not always expected when computing resources are limited. Therefore, it is necessary to find a balance between resources and model performance to achieve satisfactory results. This article proposes a self-adaptive quality of experience (QoE) framework, DeepNetQoE, to guide the training of deep networks. A self-adaptive QoE model is set up that relates the model's accuracy with the computing resources required for training which will allow the experience value of the model to improve. To maximize the experience value when computer resources are limited, a resource allocation model and solutions need to be established. In addition, we carry out experiments based on four network models to analyze the experience values with respect to the crowd counting example. Experimental results show that the proposed DeepNetQoE is capable of adaptively obtaining a high experience value according to user needs and therefore guiding users to determine the computational resources allocated to the network models.

preprint2020arXiv

Edge Intelligence: Architectures, Challenges, and Applications

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.

preprint2020arXiv

Efficient Neural Interaction Function Search for Collaborative Filtering

In collaborative filtering (CF), interaction function (IFC) plays the important role of capturing interactions among items and users. The most popular IFC is the inner product, which has been successfully used in low-rank matrix factorization. However, interactions in real-world applications can be highly complex. Thus, other operations (such as plus and concatenation), which may potentially offer better performance, have been proposed. Nevertheless, it is still hard for existing IFCs to have consistently good performance across different application scenarios. Motivated by the recent success of automated machine learning (AutoML), we propose in this paper the search for simple neural interaction functions (SIF) in CF. By examining and generalizing existing CF approaches, an expressive SIF search space is designed and represented as a structured multi-layer perceptron. We propose an one-shot search algorithm that simultaneously updates both the architecture and learning parameters. Experimental results demonstrate that the proposed method can be much more efficient than popular AutoML approaches, can obtain much better prediction performance than state-of-the-art CF approaches, and can discover distinct IFCs for different data sets and tasks

preprint2020arXiv

Enantio-discrimination via light deflection effect

We propose a theoretical method for enantio-discrimination based on the light deflection effect in four-level models of chiral molecules. This four-level model consists of a cyclic three-level subsystem coupled by three strong driving fields and an auxiliary level connected to the cyclic three-level subsystem by a weak probe field. It is shown that the induced refractive index for the weak probe field is chirality-dependent. Thus it will lead to chirality-dependent light deflection when the intensities of two of the three strong driving fields are spatially inhomogeneous. As a result, the deflection angle of the weak probe light can be utilized to detect the chirality of pure enantiomers and enantiomeric excess of chiral mixture. Therefore, our method may act as a tool for enantio-discrimination.

preprint2020arXiv

Fast enantioconversion of chiral mixtures based on a four-level double-$Δ$ model

Based on the four-level double-$Δ$ model composed of two degenerated (left- and right-handed) chiral ground states and two achiral excited states, we propose a purely optical method for enantio-conversion of chiral mixture. By choosing appropriate parameters, the original four-level model will be simplified to two effective two-level sub-systems with each of them involving one chiral ground state. Then, with the help of well-designed optical operations, the initial unwanted and wanted chiral ground states are converted, respectively, to the wanted chiral ground state and an auxiliary chiral excited state with the wanted chirality, i.e., achieving the enantioconversion of the chiral mixture. Comparing with the original work of enantioconversion based on the four-level double-$Δ$ model with the requirement of the time-consuming relaxation step and repeated operations, our method can be three orders of magnitude faster since we use only purely optical operations. Thus, it offers a promising candidate for fast enantioconversion when the total operation time is limited due to the racemization and/or experimental conditions.

preprint2020arXiv

Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases

With the rapid development of knowledge bases (KBs), link prediction task, which completes KBs with missing facts, has been broadly studied in especially binary relational KBs (a.k.a knowledge graph) with powerful tensor decomposition related methods. However, the ubiquitous n-ary relational KBs with higher-arity relational facts are paid less attention, in which existing translation based and neural network based approaches have weak expressiveness and high complexity in modeling various relations. Tensor decomposition has not been considered for n-ary relational KBs, while directly extending tensor decomposition related methods of binary relational KBs to the n-ary case does not yield satisfactory results due to exponential model complexity and their strong assumptions on binary relations. To generalize tensor decomposition for n-ary relational KBs, in this work, we propose GETD, a generalized model based on Tucker decomposition and Tensor Ring decomposition. The existing negative sampling technique is also generalized to the n-ary case for GETD. In addition, we theoretically prove that GETD is fully expressive to completely represent any KBs. Extensive evaluations on two representative n-ary relational KB datasets demonstrate the superior performance of GETD, significantly improving the state-of-the-art methods by over 15\%. Moreover, GETD further obtains the state-of-the-art results on the benchmark binary relational KB datasets.

preprint2020arXiv

High-throughput GPU layered decoder of multi-edge type low density parity check codes in continuous-variable quantum key distribution systems

The decoding throughput in the postprocessing is one of the bottlenecks for a continuous-variable quantum key distribution (CV-QKD) system. In this paper, we propose a layered decoder to decode quasi-cyclic multi-edge type LDPC (QC-METLDPC) codes based on graphic processing unit (GPU) in continuous-variable quantum key distribution (CV-QKD) systems. We optimize the storage method of the parity check matrix, merge the sub-matrices which are unrelated, and decode multiple codewords in parallel on GPU. Simulation results demonstrate that the average decoding speed of LDPC codes with three typical code rates, i.e., 0.1, 0.05 and 0.02, is up to 64.11Mbits/s, 48.65Mbits/s and 39.51Mbits/s, respectively, when decoding 128 codewords of length 106 simultaneously without early termination.

preprint2020arXiv

Hybrid Compositional Reasoning for Reactive Synthesis from Finite-Horizon Specifications

LTLf synthesis is the automated construction of a reactive system from a high-level description, expressed in LTLf, of its finite-horizon behavior. So far, the conversion of LTLf formulas to deterministic finite-state automata (DFAs) has been identified as the primary bottleneck to the scalabity of synthesis. Recent investigations have also shown that the size of the DFA state space plays a critical role in synthesis as well. Therefore, effective resolution of the bottleneck for synthesis requires the conversion to be time and memory performant, and prevent state-space explosion. Current conversion approaches, however, which are based either on explicit-state representation or symbolic-state representation, fail to address these necessities adequately at scale: Explicit-state approaches generate minimal DFA but are slow due to expensive DFA minimization. Symbolic-state representations can be succinct, but due to the lack of DFA minimization they generate such large state spaces that even their symbolic representations cannot compensate for the blow-up. This work proposes a hybrid representation approach for the conversion. Our approach utilizes both explicit and symbolic representations of the state-space, and effectively leverages their complementary strengths. In doing so, we offer an LTLf to DFA conversion technique that addresses all three necessities, hence resolving the bottleneck. A comprehensive empirical evaluation on conversion and synthesis benchmarks supports the merits of our hybrid approach.

preprint2020arXiv

Learning to Recommend with Multiple Cascading Behaviors

Most existing recommender systems leverage user behavior data of one type only, such as the purchase behavior in E-commerce that is directly related to the business KPI (Key Performance Indicator) of conversion rate. Besides the key behavioral data, we argue that other forms of user behaviors also provide valuable signal, such as views, clicks, adding a product to shop carts and so on. They should be taken into account properly to provide quality recommendation for users. In this work, we contribute a new solution named NMTR (short for Neural Multi-Task Recommendation) for learning recommender systems from user multi-behavior data. We develop a neural network model to capture the complicated and multi-type interactions between users and items. In particular, our model accounts for the cascading relationship among different types of behaviors (e.g., a user must click on a product before purchasing it). To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task. Extensive experiments on two real-world datasets demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data. Further analysis shows that modeling multiple behaviors is particularly useful for providing recommendation for sparse users that have very few interactions.

preprint2020arXiv

Low-frequency broadband acoustic metasurface absorbing panels

A broadband sound absorption attained by a deep-subwavelength structure is of great interest to the noise control community especially for extremely low frequencies (20-100 Hz) in room acoustics. Coupling multiple different resonant unit cells has been an effective strategy to achieve a broadband sound absorption. In this paper, we report on an analytical, numerical and experimental study of a low-frequency broadband (50-63 Hz, one third octave band), high absorption (average absorption coefficient around 93%), near-omnidirectional (0°-75°) acoustic metasurface absorber composed of 4 coupled unit cells at a thickness of 15.4 cm (1/45 of the wavelength at 50 Hz). The absorption by such a deep-subwavelength structure occurs due to a strong coupling between unit cells, which is realized by carefully engineering geometric parameters of each unit cell, especially the judicious assignment of lateral size to each unit cell. To further broaden the bandwidth (50-100 Hz, one octave band), a design with 19 unit cells coupled in a supercell is analytically studied to achieve an average absorption coefficient of 85% for a wide angle range (0°-75°) at a thickness of 20 cm (1/34 of wavelength at 50 Hz). Two additional degrees of freedom, the lateral size of supercell and the number of unit cells in the supercell, are demonstrated to facilitate such a causally optimal design which is close to the ideally causal optimality. The proposed design methodology may solve the long-standing issue for low frequency absorption in room acoustics.

preprint2020arXiv

Magnetic and electronic properties of a topological nodal line semimetal candidate: HoSbTe

We report the experimental and theoretical studies of a magnetic topological nodal line semimetal candidate HoSbTe. Single crystals of HoSbTe are grown from Sb flux, crystallizing in a tetragonal layered structure (space group: P4/nmm, no.129), in which the Ho-Te bilayer is separated by the square-net Sb layer. The magnetization and specific heat present distinct anomalies at 4 K related to an antiferromagnetic (AFM) phase transition. Meanwhile, with applying magnetic field perpendicular and parallel to the crystallographic c axis, an obvious magnetic anisotropy is observed. Electrical resistivity undergoes a bad-metal-like state below 200 K and reveals a plateau at about 8 K followed by a drop due to the AFM transition. In addition, with the first-principle calculations of band structure, we find that HoSbTe is a topological nodal line semimetal or a weak topological insulator with or without taking the spin-orbit coupling into account, providing a platform to investigate the interplay between magnetic and topological fermionic properties.

preprint2020arXiv

On the Moyal Star Product of Resurgent Series

We analyze the Moyal star product in deformation quantization from the resurgence theory perspective. By putting algebraic conditions on Borel transforms, one can define the space of ``algebro-resurgent series'' (a subspace of $1$-Gevrey formal series in $i\hbar/2$ with coefficients in $C\{q,p\}$), which we show is stable under Moyal star product.

preprint2020arXiv

Optimal unidirectional amplification induced by optical gain in optomechanical systems

We propose a three-mode optomechanical system to realize optical nonreciprocal transmission with unidirectional amplification, where the system consists of two coupled cavities and one mechanical resonator which interacts with only one of the cavities. Additionally, the optical gain is introduced into the optomechanical cavity. It is found that for a strong optical input, the optical transmission coefficient can be greatly amplified in a particular direction and suppressed in the opposite direction. The expressions of the optimal transmission coefficient and the corresponding isolation ratio are given analytically. Our results pave a way to design high-quality nonreciprocal devices based on optomechanical systems.

preprint2020arXiv

Price-aware Recommendation with Graph Convolutional Networks

In recent years, much research effort on recommendation has been devoted to mining user behaviors, i.e., collaborative filtering, along with the general information which describes users or items, e.g., textual attributes, categorical demographics, product images, and so on. Price, an important factor in marketing --- which determines whether a user will make the final purchase decision on an item --- surprisingly, has received relatively little scrutiny. In this work, we aim at developing an effective method to predict user purchase intention with the focus on the price factor in recommender systems. The main difficulties are two-fold: 1) the preference and sensitivity of a user on item price are unknown, which are only implicitly reflected in the items that the user has purchased, and 2) how the item price affects a user's intention depends largely on the product category, that is, the perception and affordability of a user on item price could vary significantly across categories. Towards the first difficulty, we propose to model the transitive relationship between user-to-item and item-to-price, taking the inspiration from the recently developed Graph Convolution Networks (GCN). The key idea is to propagate the influence of price on users with items as the bridge, so as to make the learned user representations be price-aware. For the second difficulty, we further integrate item categories into the propagation progress and model the possible pairwise interactions for predicting user-item interactions. We conduct extensive experiments on two real-world datasets, demonstrating the effectiveness of our GCN-based method in learning the price-aware preference of users. Further analysis reveals that modeling the price awareness is particularly useful for predicting user preference on items of unexplored categories.

preprint2020arXiv

Proving Non-Inclusion of Büchi Automata based on Monte Carlo Sampling

The search for a proof of correctness and the search for counterexamples (bugs) are complementary aspects of verification. In order to maximize the practical use of verification tools it is better to pursue them at the same time. While this is well-understood in the termination analysis of programs, this is not the case for the language inclusion analysis of Büchi automata, where research mainly focused on improving algorithms for proving language inclusion, with the search for counterexamples left to the expensive complementation operation. In this paper, we present $\mathsf{IMC}^2$, a specific algorithm for proving Büchi automata non-inclusion $\mathcal{L}(\mathcal{A}) \not\subseteq \mathcal{L}(\mathcal{B})$, based on Grosu and Smolka's algorithm $\mathsf{MC}^2$ developed for Monte Carlo model checking against LTL formulas. The algorithm we propose takes $M = \lceil \ln δ/ \ln (1-ε) \rceil$ random lasso-shaped samples from $\mathcal{A}$ to decide whether to reject the hypothesis $\mathcal{L}(\mathcal{A}) \not\subseteq \mathcal{L}(\mathcal{B})$, for given error probability $ε$ and confidence level $1 - δ$. With such a number of samples, $\mathsf{IMC}^2$ ensures that the probability of witnessing $\mathcal{L}(\mathcal{A}) \not\subseteq \mathcal{L}(\mathcal{B})$ via further sampling is less than $δ$, under the assumption that the probability of finding a lasso counterexample is larger than $ε$. Extensive experimental evaluation shows that $\mathsf{IMC}^2$ is a fast and reliable way to find counterexamples to Büchi automata inclusion.

preprint2020arXiv

Reinforced Epidemic Control: Saving Both Lives and Economy

Saving lives or economy is a dilemma for epidemic control in most cities while smart-tracing technology raises people's privacy concerns. In this paper, we propose a solution for the life-or-economy dilemma that does not require private data. We bypass the private-data requirement by suppressing epidemic transmission through a dynamic control on inter-regional mobility that only relies on Origin-Designation (OD) data. We develop DUal-objective Reinforcement-Learning Epidemic Control Agent (DURLECA) to search mobility-control policies that can simultaneously minimize infection spread and maximally retain mobility. DURLECA hires a novel graph neural network, namely Flow-GNN, to estimate the virus-transmission risk induced by urban mobility. The estimated risk is used to support a reinforcement learning agent to generate mobility-control actions. The training of DURLECA is guided with a well-constructed reward function, which captures the natural trade-off relation between epidemic control and mobility retaining. Besides, we design two exploration strategies to improve the agent's searching efficiency and help it get rid of local optimums. Extensive experimental results on a real-world OD dataset show that DURLECA is able to suppress infections at an extremely low level while retaining 76\% of the mobility in the city. Our implementation is available at https://github.com/anyleopeace/DURLECA/.

preprint2020arXiv

Response solutions for wave equations with variable wave speed and periodic forcing

We consider a model of nonlinear wave equations with periodically varying wave speed and periodic external forcing. By imposing non-resonance conditions on the frequency, we establish the existence of the response solutions (i.e., periodic solutions with the same frequency as the forcing) for such a model in a Cantor set of asymptotically full measure. The proof relies on a Lyapunov--Schmidt reduction together with the Nash--Moser iteration.

preprint2020arXiv

Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering

Negative sampling approaches are prevalent in implicit collaborative filtering for obtaining negative labels from massive unlabeled data. As two major concerns in negative sampling, efficiency and effectiveness are still not fully achieved by recent works that use complicate structures and overlook risk of false negative instances. In this paper, we first provide a novel understanding of negative instances by empirically observing that only a few instances are potentially important for model learning, and false negatives tend to have stable predictions over many training iterations. Above findings motivate us to simplify the model by sampling from designed memory that only stores a few important candidates and, more importantly, tackle the untouched false negative problem by favouring high-variance samples stored in memory, which achieves efficient sampling of true negatives with high-quality. Empirical results on two synthetic datasets and three real-world datasets demonstrate both robustness and superiorities of our negative sampling method.

preprint2020arXiv

Switchable bipartite and genuine tripartite entanglement via an optoelectromechanical interface

Controllable multipartite entanglement is a crucial element in quantum information processing. Here we present a scheme that generates switchable bipartite and genuine tripartite entanglement between microwave and optical photons via an optoelectromechanical interface, where microwave and optical cavities are coupled to a mechanical mode with controllable coupling constants. We show that by tuning an effective gauge phase between the coupling constants to the "sweet spots", bipartite entanglement can be generated and switched between designated output photons. The bipartite entanglement is robust against the mechanical noise and the signal loss to the mechanical mode when the couplings are chosen to satisfy the impedance matching condition. When the gauge phase is tuned away from the "sweet spots", genuine tripartite entanglement can be generated and verified with homodyne measurement on the quadratures of the output fields. Our result can lead to the implementation of controllable and robust multipartite entanglement in hybrid quantum systems operated in distinctively different frequencies.

preprint2020arXiv

Ultra-broadband acoustic ventilation barriers via hybrid-functional metasurfaces

Ventilation barriers allowing simultaneous sound blocking and free airflow passage are of great challenge but necessary for particular scenarios calling for sound-proofing ventilation. Previous works based on local resonance or Fano-like interference serve a narrow working range around the resonant or destructive-interference frequency. Efforts made on broadband designs show a limited bandwidth typically smaller than half an octave. Here, we theoretically design an ultra-broadband ventilation barrier via hybridizing dissipation and interference. Confirmed by experiments, the synergistic effect from our hybrid-functional metasurface significantly expand the scope of its working frequencies, leading to an effective blocking of more than 90% of incident energy in the range of 650-2000 Hz, while its structural thickness is only 53 mm $(\sim λ/ 10)$. Our design shows great flexibility in customizing the broadband and is capable of handling sound coming from various directions, which has potential in air-permeable yet sound-proofing applications.

preprint2020arXiv

Urban Anomaly Analytics: Description, Detection, and Prediction

Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening are of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed.

preprint2020arXiv

Well-posedness for good Boussinesq equations subject to quasi-periodic initial data

This paper concerns the local well-posedness for the "good" Boussinesq equation subject to quasi-periodic initial conditions. By constructing a delicately and subtly iterative process together with an explicit combinatorial analysis, we show that there exists a unique solution for such a model in a small region of time. The size of this region depends on both the given data and the frequency vector involved. Moreover the local solution has an expansion with exponentially decaying Fourier coefficients.

preprint2019arXiv

Enhancement of Zero-Field Skyrmion Density in [Pt/Co/Fe/Ir]2 Multilayers by FORC

Magnetic skyrmions are novel topological spin textures on the nanoscale, and significant efforts have been taken to improve their zero-field density at room temperature (RT). In this work, we reported an approach of improving zero-field skyrmion density in [Pt/Co/Fe/Ir]2 multilayers at RT by using the first-order reversal curve (FORC) technique. Firstly, we investigated the nucleation and annihilation mechanism of magnetic skyrmions using polar magneto-optical Kerr effect measurement. Secondly, the FORC technique was used to obtain information on the irreversible or reversible behaviors in the magnetization switching process. It was found from FORC diagram that the magnetization reversal mechanism can be characterized into three stages: (1) reversible labyrinth stripe domains expanding or shrinking stage; (2) irreversible stripe domains fracturing stage; and (3) irreversible skyrmion annihilation stage. At the end, we demonstrated that the zero-field skyrmion density can be highly improved by choosing reversal field from the irreversible stages. Our results have established the FORC measurement as a valuable tool for investigating magnetic multilayers of high skyrmion densities.

preprint2019arXiv

Extreme Low-Frequency Ultrathin Acoustic Absorbing Metasurface

We introduce a multi-coiled acoustic metasurface providing a quasi-perfect absorption (reaching 99.99% in experiments) at extremely low-frequency of 50 Hz, and simultaneously featuring an ultrathin thickness down to λ/527 (1.3 cm). In contrast to the state of the art, this original conceived multi-coiled metasurface offers additional degrees of freedom capable to tune the acoustic impedance effectively without increasing the total thickness. We provide analytical derivation, numerical simulation and experimental demonstrations for this unique absorber concept, and discuss its physical mechanism which breaks the quarter-wavelength resonator theory. Furthermore, based on the same conceptual approach, we propose a broadband lowfrequency metasurface absorber by coupling unit cells exhibiting different properties.

preprint2019arXiv

Nonreciprocity via nonlinearity and synthetic magnetism

We propose how to realize nonreciprocity for a weak input optical field via nonlinearity and synthetic magnetism. We show that the photons transmitting from a linear cavity to a nonlinear cavity (i.e., an asymmetric nonlinear optical molecule) exhibit nonreciprocal photon blockade but no clear nonreciprocal transmission. Both nonreciprocal transmission and nonreciprocal photon blockade can be observed, when one or two auxiliary modes are coupled to the asymmetric nonlinear optical molecule to generate an artificial magnetic field. Similar method can be used to create and manipulate nonreciprocal transmission and nonreciprocal photon blockade for photons bi-directionally transport in a symmetric nonlinear optical molecule. Additionally, a photon circulator with nonreciprocal photon blockade is designed based on nonlinearity and synthetic magnetism. The combination of nonlinearity and synthetic magnetism provides us an effective way towards the realization of quantum nonreciprocal devices, e.g., nonreciprocal single-photon sources and single-photon diodes.

Yong Li

Quick read

Decide how to stay connected

How to connect with this researcher

Open a focused conversation when the fit is right

See the researcher in context

Building this graph slice

82 published item(s)

AgentEconomist: An End-to-end Agentic System Translating Economic Intuitions into Executable Computational Experiments

SkillMaster: Toward Autonomous Skill Mastery in LLM Agents

UniVLR: Unifying Text and Vision in Visual Latent Reasoning for Multimodal LLMs

A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

Control the qubit-qubit coupling in the superconducting circuit with double-resonator couplers

KAM Theorems for Multi-scale Torus

Modular Mix-and-Match Complementation of Büchi Automata (Technical Report)

A General Framework of Bound States in the Continuum in an Open Acoustic Resonator

A Mixed-Methods Analysis of the Algorithm-Mediated Labor of Online Food Deliverers in China

A Review-aware Graph Contrastive Learning Framework for Recommendation

Beyond Virtual Bazaar: How Social Commerce Promotes Inclusivity for the Traditionally Underserved Community in Chinese Developing Regions

Canonical Mean Filter for Almost Zero-Shot Multi-Task classification

Consecutive topological phase transitions and colossal magnetoresistance in a magnetic topological semimetal

DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction

Disentangling Long and Short-Term Interests for Recommendation

Divide-and-Conquer Determinization of Büchi Automata based on SCC Decomposition

DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias

Enantiodiscrimination of chiral molecules via quantum correlation function

Enhancement of molecular coherent anti-Stokes Raman scattering with silicon nano-antennas

Enhancing the force sensitivity of squeezed light optomechanical interferometer

Experimental Verification of the Acoustic Geometric Phase

Finding similarity of orbits between two discrete dynamical systems via optimal principle

Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction

Giant and Reversible Electronic Structure Evolution in a Magnetic Topological Material EuCd2As2

Giant Atoms in a Synthetic Frequency Dimension

Giant atoms with time-dependent couplings

High-throughput decoder of quasi-cyclic LDPC codes with limited precision for continuous-variable quantum key distribution systems

K-Detector: Identifying Duplicate Crash Failures in Large-Scale Software Delivery

KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning

Neighboring Backdoor Attacks on Graph Convolutional Network

Nonreciprocal frequency conversion with chiral $Λ$-type atoms

PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems

Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System Design

Resurgence and Partial Theta Series

Structure Enhanced Graph Neural Networks for Link Prediction

Threshold solutions for nonlocal reaction diffusion equations

Topological Supercavity Resonances In the Finite System

Whale: Efficient Giant Model Training over Heterogeneous GPUs

AttnMove: History Enhanced Trajectory Recovery via Attentional Network

Disentangling User Interest and Conformity for Recommendation with Causal Embedding

Dynamical emission of phonon pairs in optomechanical systems

Enantio-conversion of chiral mixtures via optical pumping

Enantio-detection of cyclic three-level chiral molecules in a driven cavity

Enantio-specific state transfer for symmetric-top chiral molecules

Enantiodetection of chiral molecules via two-dimensional spectroscopy

Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network

Graph Jigsaw Learning for Cartoon Face Recognition

Learning from Home: A Mixed-Methods Analysis of Live Streaming Based Remote Education Experience in Chinese Colleges During the COVID-19 Pandemic

MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions

Nano-engineering the evolution of skyrmion crystal in synthetic antiferromagnets

Nonreciprocal light transmission via optomechanical parametric interactions

Policy-Aware Mobility Model Explains the Growth of COVID-19 in Cities

Reinforced Contact Tracing and Epidemic Intervention

You Recommend, I Buy: How and Why People Engage in Instant Messaging Based Social Commerce

Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection

Bundle Recommendation with Graph Convolutional Networks

Controllable optical response and tunable sensing based on self interference in waveguide QED systems

DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks

Edge Intelligence: Architectures, Challenges, and Applications

Efficient Neural Interaction Function Search for Collaborative Filtering

Enantio-discrimination via light deflection effect

Fast enantioconversion of chiral mixtures based on a four-level double-$Δ$ model

Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases

High-throughput GPU layered decoder of multi-edge type low density parity check codes in continuous-variable quantum key distribution systems

Hybrid Compositional Reasoning for Reactive Synthesis from Finite-Horizon Specifications

Learning to Recommend with Multiple Cascading Behaviors

Low-frequency broadband acoustic metasurface absorbing panels

Magnetic and electronic properties of a topological nodal line semimetal candidate: HoSbTe

On the Moyal Star Product of Resurgent Series

Optimal unidirectional amplification induced by optical gain in optomechanical systems

Price-aware Recommendation with Graph Convolutional Networks

Proving Non-Inclusion of Büchi Automata based on Monte Carlo Sampling