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Researcher profile

Tong Li

Tong Li contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
hep-phArtificial IntelligenceComputation and LanguageMachine Learninghep-exCryptography and SecurityInformation Retrievalgr-qcnucl-thphysics.soc-phastro-ph.COastro-ph.HEComputer Visioncs.CYmath.APNetworking and Internet Architecturephysics.data-anphysics.opticsquant-phSocial and Information Networks

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Published work

36 published item(s)

preprint2026arXiv

Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation

We present a comprehensive evaluation framework for assessing Large Language Models' (LLMs) capabilities in suicide prevention, focusing on two critical aspects: the Identification of Implicit Suicidal ideation (IIS) and the Provision of Appropriate Supportive responses (PAS). We introduce \ourdata, a novel dataset of 1,308 test cases built upon psychological frameworks including D/S-IAT and Negative Automatic Thinking, alongside real-world scenarios. Through extensive experiments with 8 widely used LLMs under different contextual settings, we find that current models struggle significantly with detecting implicit suicidal ideation and providing appropriate support, highlighting crucial limitations in applying LLMs to mental health contexts. Our findings underscore the need for more sophisticated approaches in developing and evaluating LLMs for sensitive psychological applications.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning

Large Language Models (LLMs) are increasingly deployed as long-term interactive agents, yet their limited context windows make it difficult to sustain coherent behavior over extended interactions. Existing memory systems often store isolated records and retrieve fragments, limiting their ability to consolidate evolving user states and resolve conflicts. We introduce EverMemOS, a self-organizing memory operating system that implements an engram-inspired lifecycle for computational memory. Episodic Trace Formation converts dialogue streams into MemCells that capture episodic traces, atomic facts, and time-bounded Foresight signals. Semantic Consolidation organizes MemCells into thematic MemScenes, distilling stable semantic structures and updating user profiles. Reconstructive Recollection performs MemScene-guided agentic retrieval to compose the necessary and sufficient context for downstream reasoning. Experiments on LoCoMo and LongMemEval show that EverMemOS achieves state-of-the-art performance on memory-augmented reasoning tasks. We further report a profile study on PersonaMem v2 and qualitative case studies illustrating chat-oriented capabilities such as user profiling and Foresight. Code is available at https://github.com/EverMind-AI/EverMemOS.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Practical Poisoning Attacks against Retrieval-Augmented Generation

Large language models (LLMs) have demonstrated impressive natural language processing abilities but face challenges such as hallucination and outdated knowledge. Retrieval-Augmented Generation (RAG) has emerged as a state-of-the-art approach to mitigate these issues. While RAG enhances LLM outputs, it remains vulnerable to poisoning attacks. Recent studies show that injecting poisoned text into the knowledge database can compromise RAG systems, but most existing attacks assume that the attacker can insert a sufficient number of poisoned texts per query to outnumber correct-answer texts in retrieval, an assumption that is often unrealistic. To address this limitation, we propose CorruptRAG, a practical poisoning attack against RAG systems in which the attacker injects only a single poisoned text, enhancing both feasibility and stealth. Extensive experiments conducted on multiple large-scale datasets demonstrate that CorruptRAG achieves higher attack success rates than existing baselines.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Production of Light Dark Particles from Nonlinear Compton Scattering Between Intense Laser and Muon or Proton Beam

The laser of an intense electromagnetic field promotes the studies of strong-field particle physics in high-intensity frontier. Particle accelerator facilities in the world produce high-quality muon and proton beams. In this work, we propose the nonlinear Compton scattering to light dark particles through the collision between intense laser pulse and muon or proton beam. We take light dark photon and axion-like particle as illustrative dark particles. The cross sections of relevant nonlinear Compton scattering to dark photon or axion-like particle are calculated. We also analyze the background processes with missing neutrinos. The prospective sensitivity shows that the laser-induced process provides a complementary and competitive search of new invisible particles lighter than about 1 MeV.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Prompt Segmentation and Annotation Optimisation: Controlling LLM Behaviour via Optimised Segment-Level Annotations

Prompt engineering is crucial for effective interaction with generative artificial intelligence systems, yet existing optimisation methods often operate over an unstructured and vast prompt space, leading to high computational costs and potential distortions of the original intent. We introduce Prompt Segmentation and Annotation Optimisation (PSAO), a structured prompt optimisation framework designed to improve prompt optimisation controllability and efficiency. PSAO decomposes a prompt into interpretable segments (e.g., sentences) and augments each with human-readable annotations (e.g., {not important}, {important}, {very important}). These annotations guide large language models (LLMs) in allocating focus and clarifying confusion during response generation. We formally define the segmentations and annotations and demonstrate that optimised segment-level annotations can lead to improved LLM responses, with the original prompt retained as a candidate in the optimisation space to prevent performance degradation. Empirical evaluations indicate that PSAO benefits from annotations in terms of improved reasoning accuracy and self-consistency. However, developing efficient methods for identifying optimal segmentations and annotations remains challenging and is reserved for future investigation. This work is intended as a proof of concept, demonstrating the feasibility and potential of segment-level annotation optimisation.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Solutions to axion electrodynamics with electric-magnetic duality in supersymmetric Seiberg-Witten theory

Axion and magnetic monopole are among the most fascinating candidates for physics beyond the Standard Model. The potential connection between axion and magnetic monopole stems from the Witten effect and is revealed by non-standard axion electrodynamics. Non-standard axion electrodynamics under electric-magnetic duality modifies conventional axion Maxwell equations and motivates intriguing axion-photon phenomenology. A calculable ultraviolet model of Peccei-Quinn axion coupled to magnetic monopoles and electric charges was proposed based on $\mathcal{N}=2$ supersymmetric Seiberg-Witten (SW) theory with manifest electric-magnetic duality. In this work, we aim to investigate the solutions to the non-linear axion electrodynamics from SW axion model and propose relevant detection strategies for non-trivial axion-photon couplings. Based on the infrared Lagrangian of SW axion, we derive the electromagetic (EM) equations of motion. We also analyze the moduli space coordinate in SW theory and find out the reliabe parameter space. We then solve the resultant axion Maxwell equations with an external EM field. The observable axion-induced EM fields are obtained analytically and then numerically computed. Finally, we propose the detection strategy with an LC circuit and show the prospective sensitivity to SW axion-photon couplings.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

ESG Beliefs of Large Language Models: Evidence and Impact

We examine whether large language models (LLMs) hold systematic beliefs about environmental, social, and governance (ESG) issues and how these beliefs compare with-and potentially influence-those of human market participants. Based on established surveys originally administered to professional and retail investors, we show that major LLMs exhibit a strong pro-ESG orientation. Compared with human investors, LLMs assign greater financial relevance for ESG performance, expect larger return premia for high-ESG firms, and display a stronger willingness to sacrifice financial returns for ESG improvements. These preferences are highly uniform and values-driven, in contrast to heterogeneous human views. Using a large dataset of analyst reports, we further show that sell-side analysts become significantly more optimistic about high-ESG firms after adopting LLMs for research. Our findings reveal that LLMs embed distinct, coherent ESG beliefs and that these beliefs can shape human judgments, highlighting a new channel through which AI adoption may influence financial markets.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Searching for high-frequency axion in quantum electromagnetodynamics through interface haloscopes

The so-called Witten effect implies the existence of electromagnetic interactions between axion and magnetic monopole due to the axion-photon coupling. A sound quantization in the presence of magnetic monopoles, called quantum electromagnetodynamics (QEMD), was utilized to construct a more generic axion-photon Lagrangian in the low-energy axion effective field theory. This generic axion-photon Lagrangian introduces the interactions between axion and two four-potentials, and leads to new axion-modified Maxwell equations. The interface haloscopes place an interface between two electromagnetic media with different properties and are desirable to search for high-mass axions $m_a\gtrsim \mathcal{O}(10)~μ{\rm eV}$. In this work, for the generic axion-photon couplings built under QEMD, we perform comprehensive calculations of the axion-induced propagating waves and energy flux densities in different interface setups. We also obtain the sensitivity to new axion-photon couplings for high-mass axions.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Towards simultaneous coherent radiation in the visible and microwave bands with doped molecular crystals

Coherent sources exploiting the stimulated emission of non-equilibrium quantum systems, i.e. gain media, have proven indispensable for advancing fundamental research and engineering. The operating electromagnetic bands of such coherent sources have been continuously enriched for increasing demands.Nevertheless, for a single bench top coherent source, simultaneous generation of radiation in multiple bands, especially when the bands are widely separated, present formidable challenges with a single gain medium. Here, we propose a mechanism of simultaneously realizing the stimulated emission of radiation in the visible and microwave bands, i.e. lasing and masing actions, at ambient conditions by utilizing photoexcited singlet and triplet states of the pentacene molecules that are doped in p-terphenyl. The possibility is validated by the observed amplified spontaneous emission (ASE) at 645 nm with a narrow linewidth around 1 nm from the pentacene-doped p-terphenyl crystal used for masing at 1.45 GHz and consolidated by a 20 fold lower threshold of ASE compared to the reported masing threshold. The overall threshold of the pentacene-based multiband coherent source can be optimized by appropriate alignment of the pump-light polarization with the pentacene's transition dipole moment. Our work not only shows a great promise on immediate realization of multiband coherent sources but also establishes an intriguing solid-state platform for fundamental research of quantum optics in multiple frequency domains.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Scalable Workflow to Build Machine Learning Classifiers with Clinician-in-the-Loop to Identify Patients in Specific Diseases

Clinicians may rely on medical coding systems such as International Classification of Diseases (ICD) to identify patients with diseases from Electronic Health Records (EHRs). However, due to the lack of detail and specificity as well as a probability of miscoding, recent studies suggest the ICD codes often cannot characterise patients accurately for specific diseases in real clinical practice, and as a result, using them to find patients for studies or trials can result in high failure rates and missing out on uncoded patients. Manual inspection of all patients at scale is not feasible as it is highly costly and slow. This paper proposes a scalable workflow which leverages both structured data and unstructured textual notes from EHRs with techniques including NLP, AutoML and Clinician-in-the-Loop mechanism to build machine learning classifiers to identify patients at scale with given diseases, especially those who might currently be miscoded or missed by ICD codes. Case studies in the MIMIC-III dataset were conducted where the proposed workflow demonstrates a higher classification performance in terms of F1 scores compared to simply using ICD codes on gold testing subset to identify patients with Ovarian Cancer (0.901 vs 0.814), Lung Cancer (0.859 vs 0.828), Cancer Cachexia (0.862 vs 0.650), and Lupus Nephritis (0.959 vs 0.855). Also, the proposed workflow that leverages unstructured notes consistently outperforms the baseline that uses structured data only with an increase of F1 (Ovarian Cancer 0.901 vs 0.719, Lung Cancer 0.859 vs 0.787, Cancer Cachexia 0.862 vs 0.838 and Lupus Nephritis 0.959 vs 0.785). Experiments on the large testing set also demonstrate the proposed workflow can find more patients who are miscoded or missed by ICD codes. Moreover, interpretability studies are also conducted to clinically validate the top impact features of the classifiers.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Axion-Like Particles at High Energy Muon Colliders -- A White paper for Snowmass 2021

We study the discovery potential for heavy axion-like particles (ALPs) and the perspectives for determining their coupling properties at a muon collider. Focusing on their couplings to the Standard Model (SM) gauge bosons $γ, Z, W^\pm$, we show that a high-energy muon collider can substantially extend the mass coverage, essentially reaching the kinematic limit of the collider energy. The unique kinematics allow for non-ambiguous determination of the individual coupling strengths. The associated production via $μ^+μ^-$ annihilation and the VBF processes with the tagged outgoing muons can be utilized to verify the CP property of the ALPs. We illustrate our results for a muon collider running at 3 TeV and 10 TeV.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Dark magnetic dipole property in fermionic absorption by nucleus and electrons

The fermionic dark matter (DM) absorption by nucleus or electron targets provides a distinctive signal to search for sub-GeV DM. We consider a Dirac fermion DM charged under a dark gauge group and with the dark magnetic dipole operator. The DM field mixes with right-handed neutrino and interacts with the ordinary electromagnetic charge current via the kinetic mixing term of gauge fields. As a result, the incoming DM is absorbed and converted into neutrino in final state through the dipole-charge interaction. For the DM absorption by nucleus, the recoil energy spectrum exhibit a peak at $m_χ^2/2m_N$ for each isotope in the target. XENON1T can probe the DM mass above 27 MeV and the projected constraint on the inelastic DM-nucleon cross section becomes $10^{-49}$ cm$^2$. CRESSTIII with lower energy threshold would be sensitive to the DM mass above 2 MeV. We also check that the contribution from the nuclear magnetic dipole is negligible for $^{131}{\rm Xe}$ target. The absorption of DM by bound electron target induces ionization signal and is sensitive to sub-MeV DM mass. The involvement of the ionization form factor spreads out the localized recoil energy. We show the future prospect for the constraint on the magnetic dipole coupling from the electron ionization of $^{131}{\rm Xe}$.

0 citations0 reviewsNo code linkedOpen access
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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Enhancing Collaborative Filtering Recommender with Prompt-Based Sentiment Analysis

Collaborative Filtering(CF) recommender is a crucial application in the online market and ecommerce. However, CF recommender has been proven to suffer from persistent problems related to sparsity of the user rating that will further lead to a cold-start issue. Existing methods address the data sparsity issue by applying token-level sentiment analysis that translate text review into sentiment scores as a complement of the user rating. In this paper, we attempt to optimize the sentiment analysis with advanced NLP models including BERT and RoBERTa, and experiment on whether the CF recommender has been further enhanced. We build the recommenders on the Amazon US Reviews dataset, and tune the pretrained BERT and RoBERTa with the traditional fine-tuned paradigm as well as the new prompt-based learning paradigm. Experimental result shows that the recommender enhanced with the sentiment ratings predicted by the fine-tuned RoBERTa has the best performance, and achieved 30.7% overall gain by comparing MAP, NDCG and precision at K to the baseline recommender. Prompt-based learning paradigm, although superior to traditional fine-tune paradigm in pure sentiment analysis, fail to further improve the CF recommender.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Indirect constraints on lepton-flavour-violating quarkonium decays

Within an effective-field-theory framework, we present a model-independent analysis of the potential of discovering new physics by searching for lepton flavour violation in heavy quarkonium decays and, more in general, we study the phenomenology of lepton-flavour-violating (LFV) 2 quark - 2 lepton ($2q2\ell$) operators with two charm or bottom fields. We compute the constraints from LFV muon and tau decays on the new-physics operators that can induce LFV processes involving $c\bar c$ and $b\bar b$ systems, thus providing a comprehensive list of indirect upper limits on processes such as $J/ψ\to \ell\ell^\prime$, $Υ(nS) \to \ell\ell^\prime$, $Υ(nS) \to \ell\ell^\prime γ$ etc., which can be sought at BESIII, Belle II, and the proposed super tau-charm factory. We show that such indirect constraints are so stringent that they prevent the detection of quarkonium decays into $eμ$. In the case of decays of quarkonia into $\ellτ$ ($\ell=e,μ$), we find that an improvement by 2-3 orders of magnitude on the current sensitivities is in general required in order to discover or further constrain new physics. However, we show that cancellations among different contributions to the LFV tau decay rates are possible, such that $Υ(nS)\to \ellτ$ can saturate the present experimental bounds. We also find that, interestingly, searches for LFV $Z$ decays, $Z\to\ellτ$, at future $e^+e^-$ colliders are complementary probes of $2q2\ell$ operators with third generation quarks.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Interpretation of XENON1T excess with MeV boosted dark matter

The XENON1T excess of keV electron recoil events may be induced by the scattering of electrons and long-lived particles with MeV mass and high-speed. We consider a tangible model composed of two scalar MeV dark matter (DM) particles $S_A$ and $S_B$ to interpret the XENON1T keV excess via boosted $S_B$. A small mass splitting $m_{S_A}-m_{S_B}>0$ is introduced and the boosted $S_B$ can be produced by the dark annihilation process of $S_A S_A^\dagger \to ϕ\to S_B S_B^\dagger$ via a resonant scalar $ϕ$. The $S_B-$electron scattering is intermediated by a vector boson $X$. Although the constraints from BBN, CMB and low-energy experiments set the $X-$mediated $S_B-$electron scattering cross section to be $\lesssim 10^{-35} \mathrm{cm}^2$, the MeV scale DM with a resonance enhanced dark annihilation today can still provide enough boosted $S_B$ and induce the XENON1T keV excess. The relic density of $S_B$ is significantly reduced by the $s$-wave process of $S_B S_B^\dagger \to X X$ which is allowed by the constraints from CMB and 21-cm absorption. A very small relic fraction of $S_B$ is compatible with the stringent bounds on un-boosted $S_B$-electron scattering in DM direct detection and the $S_A$-electron scattering is also allowed.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Measurement of Higgs boson self-couplings through $2\rightarrow 3$ vector bosons scattering in future muon colliders

The $2\rightarrow 3$ scattering of longitudinal vector bosons (VBS) has been proven to be a complementary channel to measure the Higgs self-couplings in the Standard Model (SM) and the SM effective field theory (SMEFT) at colliders. We perform the first comprehensive study of all main $2\rightarrow 3$ VBS processes at high-energy muon colliders, especially including background analysis. The main contributing channels turn out to be the scattering of $W^+W^-\rightarrow W^+W^-h$, $ZZh$ and $hhh$. We obtain the constraints on $c_6$ and $c_{Φ_1}$ which are the Wilson coefficients of the dimension-6 operators relevant for Higgs self-couplings in the SMEFT. With the center-of-mass energies of 10 TeV and 30 TeV, we find the expected sensitivity to the coefficients $c_6/Λ^2$ and $c_{Φ_1}/Λ^2$ can reach the level of 0.01 TeV$^{-2}$. The tightest constraints come from the $hhh$ channel, while the constraints from $WWh$ are also comparable. Our results crucially depend on selecting the longitudinal polarizations for the final $W$ and $Z$ bosons. We then study how the sensitivities change by varying the efficiency of tagging longitudinal polarizations, and find that the significance remains consistently high.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Medical Scientific Table-to-Text Generation with Human-in-the-Loop under the Data Sparsity Constraint

Structured (tabular) data in the preclinical and clinical domains contains valuable information about individuals and an efficient table-to-text summarization system can drastically reduce manual efforts to condense this data into reports. However, in practice, the problem is heavily impeded by the data paucity, data sparsity and inability of the state-of-the-art natural language generation models (including T5, PEGASUS and GPT-Neo) to produce accurate and reliable outputs. In this paper, we propose a novel table-to-text approach and tackle these problems with a novel two-step architecture which is enhanced by auto-correction, copy mechanism and synthetic data augmentation. The study shows that the proposed approach selects salient biomedical entities and values from structured data with improved precision (up to 0.13 absolute increase) of copying the tabular values to generate coherent and accurate text for assay validation reports and toxicology reports. Moreover, we also demonstrate a light-weight adaptation of the proposed system to new datasets by fine-tuning with as little as 40\% training examples. The outputs of our model are validated by human experts in the Human-in-the-Loop scenario.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Mid-level Representation Enhancement and Graph Embedded Uncertainty Suppressing for Facial Expression Recognition

Facial expression is an essential factor in conveying human emotional states and intentions. Although remarkable advancement has been made in facial expression recognition (FER) task, challenges due to large variations of expression patterns and unavoidable data uncertainties still remain. In this paper, we propose mid-level representation enhancement (MRE) and graph embedded uncertainty suppressing (GUS) addressing these issues. On one hand, MRE is introduced to avoid expression representation learning being dominated by a limited number of highly discriminative patterns. On the other hand, GUS is introduced to suppress the feature ambiguity in the representation space. The proposed method not only has stronger generalization capability to handle different variations of expression patterns but also more robustness to capture expression representations. Experimental evaluation on Aff-Wild2 have verified the effectiveness of the proposed method.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Three-dimensional Skyrme Hartree-Fock-Bogoliubov solver in coordinate-space representation

The coordinate-space representation of the Hartree-Fock-Bogoliubov theory is the method of choice to study weakly bound nuclei whose properties are affected by the quasiparticle continuum space. To describe such systems, we developed a three-dimensional Skyrme-Hartree-Fock-Bogoliubov solver HFBFFT based on the existing, highly optimized and parallelized Skyrme-Hartree-Fock code Sky3D. The code does not impose any self-consistent spatial symmetries such as mirror inversions or parity. The underlying equations are solved in HFBFFT directly in the canonical basis using the fast Fourier transform. To remedy the problems with pairing collapse, we implemented the soft energy cutoff and pairing annealing. The convergence of HFB solutions was improved by a sub-iteration method. The Hermiticity violation of differential operators brought by Fourier-transform-based differentiation has also been solved. The accuracy and performance of HFBFFT were tested by benchmarking it against other HFB codes, both spherical and deformed, for a set of nuclei, both well-bound and weakly-bound.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Building Blocks of Sharding Blockchain Systems: Concepts, Approaches, and Open Problems

Sharding is the prevalent approach to breaking the trilemma of simultaneously achieving decentralization, security, and scalability in traditional blockchain systems, which are implemented as replicated state machines relying on atomic broadcast for consensus on an immutable chain of valid transactions. Sharding is to be understood broadly as techniques for dynamically partitioning nodes in a blockchain system into subsets (shards) that perform storage, communication, and computation tasks without fine-grained synchronization with each other. Despite much recent research on sharding blockchains, much remains to be explored in the design space of these systems. Towards that aim, we conduct a systematic analysis of existing sharding blockchain systems and derive a conceptual decomposition of their architecture into functional components and the underlying assumptions about system models and attackers they are built on. The functional components identified are node selection, epoch randomness, node assignment, intra-shard consensus, cross-shard transaction processing, shard reconfiguration, and motivation mechanism. We describe interfaces, functionality, and properties of each component and show how they compose into a sharding blockchain system. For each component, we systematically review existing approaches, identify potential and open problems, and propose future research directions. We focus on potential security attacks and performance problems, including system throughput and latency concerns such as confirmation delays. We believe our modular architectural decomposition and in-depth analysis of each component, based on a comprehensive literature study, provides a systematic basis for conceptualizing state-of-the-art sharding blockchain systems, proving or improving security and performance properties of components, and developing new sharding blockchain system designs.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

C-Learning: Horizon-Aware Cumulative Accessibility Estimation

Multi-goal reaching is an important problem in reinforcement learning needed to achieve algorithmic generalization. Despite recent advances in this field, current algorithms suffer from three major challenges: high sample complexity, learning only a single way of reaching the goals, and difficulties in solving complex motion planning tasks. In order to address these limitations, we introduce the concept of cumulative accessibility functions, which measure the reachability of a goal from a given state within a specified horizon. We show that these functions obey a recurrence relation, which enables learning from offline interactions. We also prove that optimal cumulative accessibility functions are monotonic in the planning horizon. Additionally, our method can trade off speed and reliability in goal-reaching by suggesting multiple paths to a single goal depending on the provided horizon. We evaluate our approach on a set of multi-goal discrete and continuous control tasks. We show that our method outperforms state-of-the-art goal-reaching algorithms in success rate, sample complexity, and path optimality. Our code is available at https://github.com/layer6ai-labs/CAE, and additional visualizations can be found at https://sites.google.com/view/learning-cae/.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Microscopic origin of reflection-asymmetric nuclear shapes

Background: The presence of nuclear ground states with stable reflection-asymmetric shapes is supported by rich experimental evidence. Theoretical surveys of odd-multipolarity deformations predict the existence of pear-shaped isotopes in several fairly localized regions of the nuclear landscape in the vicinity of near-lying single-particle shells with $Δ\ell=Δj=3$. Purpose: We analyze the role of isoscalar, isovector, neutron-proton, neutron-neutron, and proton-proton multipole interaction energies in inducing the onset of reflection-asymmetric ground-state deformations. Methods: The calculations are performed in the framework of axial reflection-asymmetric Hartree-Fock-Bogoliubov theory using two Skyrme energy density functionals and density-dependent pairing force. Results: We show that reflection-asymmetric ground-state shapes of atomic nuclei are driven by the odd-multipolarity neutron-proton (or isoscalar) part of the nuclear interaction energy. This result is consistent with the particle-vibration picture, in which the main driver of octupole instability is the isoscalar octupole-octupole interaction giving rise to large $E3$ polarizability.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records

E-commerce business is revolutionizing our shopping experiences by providing convenient and straightforward services. One of the most fundamental problems is how to balance the demand and supply in market segments to build an efficient platform. While conventional machine learning models have achieved great success on data-sufficient segments, it may fail in a large-portion of segments in E-commerce platforms, where there are not sufficient records to learn well-trained models. In this paper, we tackle this problem in the context of market segment demand prediction. The goal is to facilitate the learning process in the target segments by leveraging the learned knowledge from data-sufficient source segments. Specifically, we propose a novel algorithm, RMLDP, to incorporate a multi-pattern fusion network (MPFN) with a meta-learning paradigm. The multi-pattern fusion network considers both local and seasonal temporal patterns for segment demand prediction. In the meta-learning paradigm, transferable knowledge is regarded as the model parameter initialization of MPFN, which are learned from diverse source segments. Furthermore, we capture the segment relations by combining data-driven segment representation and segment knowledge graph representation and tailor the segment-specific relations to customize transferable model parameter initialization. Thus, even with limited data, the target segment can quickly find the most relevant transferred knowledge and adapt to the optimal parameters. We conduct extensive experiments on two large-scale industrial datasets. The results justify that our RMLDP outperforms a set of state-of-the-art baselines. Besides, RMLDP has been deployed in Taobao, a real-world E-commerce platform. The online A/B testing results further demonstrate the practicality of RMLDP.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Bluetooth-based COVID-19 Proximity Tracing Proposals: An Overview

Large-scale COVID-19 infections have occurred worldwide, which has caused tremendous impact on the economy and people's lives. The traditional method for tracing contagious virus, for example, determining the infection chain according to the memory of infected people, has many drawbacks. With the continuous spread of the pandemic, many countries or organizations have started to study how to use mobile devices to trace COVID-19, aiming to help people automatically record information about incidents with infected people through technologies, reducing the manpower required to determine the infection chain and alerting people at risk of infection. This article gives an overview on various Bluetooth-based COVID-19 proximity tracing proposals including centralized and decentralized proposals. We discussed the basic workflow and the differences between them before providing a survey of five typical proposals with explanations of their design features and benefits. Then, we summarized eight security and privacy design goals for Bluetooth-based COVID-19 proximity tracing proposals and applied them to analyze the five proposals. Finally, open problems and future directions are discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Complementarity of the future $e^+ e^-$ colliders and gravitational waves in the probe of complex singlet extension to the Standard Model

In this work, we study the future probes of the complex singlet extension to the Standard Model (cxSM). This model is possible to realize a strongly first-order electroweak phase transition (SFOEWPT). The cxSM naturally provides dark matter (DM) candidate, with or without an exact $\mathbb{Z}_2$ symmetry in the scalar sector. The benchmark models which can realize the SFOEWPT are selected, and passed to the current observational constraints to the DM candidates, including the relic densities and the direct detection limits set by the latest XENON1T results. We then calculate the one-loop corrections to the SM-like Higgs boson decays and the precision electroweak parameters due to the cxSM scalar sector. We perform a global fit to the benchmark models and study the extent to which they can be probed by the future high-energy $e^+ e^-$ colliders, such as CEPC and FCC-ee. Besides, the gravitational wave (GW) signals generated by the benchmark models are also evaluated. We further find that the future GW detector, such as LISA, is complementary in probing the benchmark models that are beyond the sensitivity of the future precision tests at the $e^+ e^-$ colliders.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Designing the Business Conversation Corpus

While the progress of machine translation of written text has come far in the past several years thanks to the increasing availability of parallel corpora and corpora-based training technologies, automatic translation of spoken text and dialogues remains challenging even for modern systems. In this paper, we aim to boost the machine translation quality of conversational texts by introducing a newly constructed Japanese-English business conversation parallel corpus. A detailed analysis of the corpus is provided along with challenging examples for automatic translation. We also experiment with adding the corpus in a machine translation training scenario and show how the resulting system benefits from its use.

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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.

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preprint2020arXiv

General neutrino interactions with sterile neutrinos in light of coherent neutrino-nucleus scattering and meson invisible decays

In this work we study the current bounds from the CEνNS process and meson invisible decays on generic neutrino interactions with sterile neutrinos in effective field theories. The interactions between quarks and left-handed SM neutrinos and/or right-handed neutrinos are first described by the low-energy effective field theory (LNEFT) between the electroweak scale and the chiral symmetry breaking scale. We complete the independent operator basis for the LNEFT up to dimension-6 by including both the lepton-number-conserving (LNC) and lepton-number-violating (LNV) operators involving right-handed neutrinos. We translate the bounds on the LNEFT Wilson coefficients from the COHERENT observation and calculate the branching fractions of light meson invisible decays. The bounds on LNEFT are then mapped onto the SM effective field theory with sterile neutrinos (SMNEFT) to constrain new physics above the electroweak scale. We find that the meson invisible decays can provide the only sensitive probe for τ neutrino flavor component and s quark component in the quark-neutrino interactions involving two (one) active neutrinos and for the effective operators without any active neutrino fields. The CEνNS process places the most stringent bound on all other Wilson coefficients. By assuming one dominant Wilson coefficient at a time in SMNEFT and negligible sterile neutrino mass, the most stringent limits on the new physics scale are 2.7 - 10 TeV from corresponding dipole operator in LNEFT and 0.5 - 1.5 TeV from neutrino-quark operator in LNEFT.

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preprint2020arXiv

Implication of $K\to πν\barν$ for generic neutrino interactions in effective field theories

In this work we investigate the implication of $K\to πν\barν$ from the recent KOTO and NA62 measurements for generic neutrino interactions and the new physics scale in effective field theories. The interactions between quarks and left-handed Standard Model (SM) neutrinos are first described by the low energy effective field theory (LEFT) below the electroweak scale. We match them to the chiral perturbation theory ($χ$PT) at the chiral symmetry breaking scale to calculate the branching fractions of Kaon semi-invisible decays and match them up to the SM effective field theory (SMEFT) to constrain new physics above the electroweak scale. In the framework of effective field theories, we prove that the Grossman-Nir bound is valid for both dim-6 and dim-7 LEFT operators, and the dim-6 vector and scalar operators dominantly contribute to Kaon semi-invisible decays based on LEFT and chiral power counting rules. They are induced by multiple dim-6 lepton-number-conserving operators and one dim-7 lepton-number-violating operator in the SMEFT, respectively. In the lepton-number-conserving $s\to d$ transition, the $K\to πν\barν$ decays provide the most sensitive probe for the operators with $ττ$ component and point to a corresponding new physics scale of $Λ_{\rm NP} \in[47~\text{TeV},~72~\text{TeV}]$ associated with a single effective coefficient. The lepton-number-violating operator can also explain the observed $K\toπν\barν$ discrepancy with the SM prediction within a narrow range $Λ_{\rm NP}\in [19.4~\text{TeV},~21.5~\text{TeV}]$, which is consistent with constraints from Kaon invisible decays.

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preprint2020arXiv

Large Time Behavior and Diffusion Limit for a System of Balance Laws From Chemotaxis in Multi-dimensions

We consider the Cauchy problem for a system of balance laws derived from a chemotaxis model with singular sensitivity in multiple space dimensions. Utilizing energy methods, we first prove the global well-posedness of classical solutions to the Cauchy problem when only the energy of the first order spatial derivatives of the initial data is sufficiently small, and the solutions are shown to converge to the prescribed constant equilibrium states as time goes to infinity. Then we prove that the solutions of the fully dissipative model converge to those of the corresponding partially dissipative model when the chemical diffusion coefficient tends to zero.

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preprint2020arXiv

Sensitivity of future lepton colliders and low-energy experiments to charged lepton flavor violation from bileptons

The observation of charged lepton flavor violation is a clear sign of physics beyond the Standard Model (SM). In this work, we investigate the sensitivity of future lepton colliders to charged lepton flavor violation via on-shell production of bileptons, and compare their sensitivity with current constraints and future sensitivities of low-energy experiments. Bileptons couple to two charged leptons with possibly different flavors and are obtained by expanding the general SM gauge invariant Lagrangians with or without lepton number conservation. We find that future lepton colliders will provide complementary sensitivity to the charged-lepton-flavor-violating couplings of bileptons compared with low-energy experiments. The future improvements of muonium-antimuonium conversion, lepton flavor non-universality in leptonic τ decays, electroweak precision observables and the anomalous magnetic moments of charged leptons will also be able to probe similar parameter space.

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preprint2020arXiv

The gravitational waves from the collapsing domain walls in the complex singlet model

We study the CP domain walls and the consequent gravitational waves induced by the spontaneous breaking of the CP symmetry in the complex singlet extension to the Standard Model. We impose the constraints from the unitarity, stability and the global minimal of the vacuum solutions on the model parameter space. The CP domain wall profiles and tensions are obtained by numerically solving the relevant field equations. The explicit CP violation terms are then introduced to the potential as biased terms to make the domain walls unstable and collapse, The BBN bound on the magnitude of the energy bias is taken into account. To achieve sufficiently strong gravitational wave signals, the domain wall tension $σ$ is required to be at least $σ/{\rm TeV}^3 \sim \mathcal{O}(10^3)$. We find that the gravitational wave spectrum can be probed in the future SKA and/or DECIGO programs, when the typical mass scale is at least $\sim \mathcal{O}(10)$ TeV and the explicit CP violation terms are as small as $\mathcal{O}(10^{-29}) - \mathcal{O}(10^{-27}) $. The gravitational waves from collapsing domain walls thus provide a complementarity to the probe of extremely small CP violation at high-energy scale.

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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.

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preprint2019arXiv

Identifying significant edges via neighborhood information

Heterogeneous nature of real networks implies that different edges play different roles in network structure and functions, and thus to identify significant edges is of high value in both theoretical studies and practical applications. We propose the so-called second-order neighborhood (SN) index to quantify an edge's significance in a network. We compare SN index with many other benchmark methods based on 15 real networks via edge percolation. Results show that the proposed SN index outperforms other well-known methods.

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preprint2019arXiv

Simplified dark matter models with loop effects in direct detection and the constraints from indirect detection and collider search

We reexamine the simplified dark matter (DM) models with fermionic DM particle and spin-0 mediator. The DM-nucleon scattering cross sections of these models are low-momentum suppressed at tree-level, but receive sizable loop-induced spin-independent contribution. We perform one-loop calculation for scalar-type and twist-2 DM-quark operators and complete two-loop calculation for scalar-type DM-gluon operator. By analyzing the loop-level contribution from new operators, we find that future direct detection experiments can be sensitive to a fraction of parameter space. The indirect detection and collider search also provide complementary constraints on these models.

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