arXiv subjects

Jie Fan

Publications and source records attributed to Jie Fan.

At least 37 records · Page 2Linked to original sources

arXiv2024

A Simplified Algorithm for Joint Real-Time Synchronization, NLoS Identification, and Multi-Agent Localization

Real-time, high-precision localization in large-scale wireless networks faces two primary challenges: clock offsets caused by network asynchrony and non-line-of-sight (NLoS) conditions. To tackle these challenges, we propose a low-complexity real-time algorithm for joint synchronization and NLoS identification-based localization. For precise synchronization, we resolve clock offsets based on accumulated time-of-arrival measurements from all the past time instances, modeling it as a large-scale linear least squares (LLS) problem. To alleviate the high computational burden of solving this LLS, we introduce the blockwise recursive Moore-Penrose inverse (BRMP) technique, a generalized recursive least squares approach, and derive a simplified formulation of BRMP tailored specifically for the real-time synchronization problem. Furthermore, we formulate joint NLoS identification and localization as a robust least squares regression (RLSR) problem and address it by using an efficient iterative approach. Simulations show that the proposed algorithm achieves sub-nanosecond synchronization accuracy and centimeter-level localization precision, while maintaining low computational overhead.

eess.SP↗

arXiv2024

Chatting with a Learning Analytics Dashboard: The Role of Generative AI Literacy on Learner Interaction with Conventional and Scaffolding Chatbots

Learning analytics dashboards (LADs) simplify complex learner data into accessible visualisations, providing actionable insights for educators and students. However, their educational effectiveness has not always matched the sophistication of the technology behind them. Explanatory and interactive LADs, enhanced by generative AI (GenAI) chatbots, hold promise by enabling dynamic, dialogue-based interactions with data visualisations and offering personalised feedback through text. Yet, the effectiveness of these tools may be limited by learners' varying levels of GenAI literacy, a factor that remains underexplored in current research. This study investigates the role of GenAI literacy in learner interactions with conventional (reactive) versus scaffolding (proactive) chatbot-assisted LADs. Through a comparative analysis of 81 participants, we examine how GenAI literacy is associated with learners' ability to interpret complex visualisations and their cognitive processes during interactions with chatbot-assisted LADs. Results show that while both chatbots significantly improved learner comprehension, those with higher GenAI literacy benefited the most, particularly with conventional chatbots, demonstrating diverse prompting strategies. Findings highlight the importance of considering learners' GenAI literacy when integrating GenAI chatbots in LADs and educational technologies. Incorporating scaffolding techniques within GenAI chatbots can be an effective strategy, offering a more guided experience that reduces reliance on learners' GenAI literacy.

cs.HC↗

arXiv2024

The Effects of Generative AI Agents and Scaffolding on Enhancing Students' Comprehension of Visual Learning Analytics

Visual learning analytics (VLA) is becoming increasingly adopted in educational technologies and learning analytics dashboards to convey critical insights to students and educators. Yet many students experienced difficulties in comprehending complex VLA due to their limited data visualisation literacy. While conventional scaffolding approaches like data storytelling have shown effectiveness in enhancing students' comprehension of VLA, these approaches remain difficult to scale and adapt to individual learning needs. Generative AI (GenAI) technologies, especially conversational agents, offer potential solutions by providing personalised and dynamic support to enhance students' comprehension of VLA. This study investigates the effectiveness of GenAI agents, particularly when integrated with scaffolding techniques, in improving students' comprehension of VLA. A randomised controlled trial was conducted with 117 higher education students to compare the effects of two types of GenAI agents: passive agents, which respond to student queries, and proactive agents, which utilise scaffolding questions, against standalone scaffolding in a VLA comprehension task. The results show that passive agents yield comparable improvements to standalone scaffolding both during and after the intervention. Notably, proactive GenAI agents significantly enhance students' VLA comprehension compared to both passive agents and standalone scaffolding, with these benefits persisting beyond the intervention. These findings suggest that integrating GenAI agents with scaffolding can have lasting positive effects on students' comprehension skills and support genuine learning.

cs.HC↗

arXiv2024

Dark photon constraints from a 7.139 GHz cavity haloscope experiment

The dark photon is a promising candidate for the dark matter which comprises most of the matter in our visible Universe. Via kinetic mixing with the Standard Model it can also be resonantly converted to photons in an electromagnetic cavity, offering novel experimental possibilities for the discovery and study of dark matter. We report the results of a pathfinder dark photon dark matter cavity search experiment performed at Hunan Normal University and the Institute of Physics, Chinese Academy of Sciences, representing the first stage of the APEX (Axion and dark Photon EXperiment) program. Finding no statistically significant excess, we place an upper limit on the kinetic mixing parameter $|χ|<3.7\times 10^{-13}$ around $m_A\simeq 29.5$ $μ$eV at 90% confidence level. This result exceeds other constraints on dark photon dark matter in this frequency range by roughly an order of magnitude.

hep-ex↗

arXiv2024

The Optimal Production Transport: Model and Algorithm

In this paper, we propose the optimal production transport model, which is an extension of the classical optimal transport model. We observe in economics, the production of the factories can always be adjusted within a certain range, while the classical optimal transport does not take this situation into account. Therefore, differing from the classical optimal transport, one of the marginals is allowed to vary within a certain range in our proposed model. To address this, we introduce a multiple relaxation optimal production transport model and propose the generalized alternating Sinkhorn algorithms, inspired by the Sinkhorn algorithm and the double regularization method. By incorporating multiple relaxation variables and multiple regularization terms, the inequality and capacity constraints in the optimal production transport model are naturally satisfied. Alternating iteration algorithms are derived based on the duality of the regularized model. We also provide a theoretical analysis to guarantee the convergence of our proposed algorithms. Numerical results indicate significant advantages in terms of accuracy and efficiency. Furthermore, we apply the optimal production transport model to the coal production and transport problem. Numerical simulation demonstrates that our proposed model can save the production and transport cost by 13.17%.

math.OC↗

arXiv2024

Calibration of the Cryogenic Measurement System of a Resonant Haloscope Cavity

Possible light bosonic dark matter interactions with the Standard Model photon have been searched by microwave resonant cavities. In this paper, we demonstrate the cryogenic readout system calibration of a 7.138 GHz copper cavity with a loaded quality factor $Q_l=10^4$, operated at 22 mK temperature based on a dilution refrigerator. Our readout system consists of High Electron Mobility Transistors as cryogenic amplifiers at 4 K, plus room-temperature amplifiers and a spectrum analyzer for signal power detection. We test the system with a superconducting two-level system as a single-photon source in the microwave frequency regime and report an overall 95.6 dB system gain and -71.4 dB attenuation in the cavity's input channel. The effective noise temperature of the measurement system is 7.5 K.

hep-ex↗

arXiv2024

Fast Gradient Computation for Gromov-Wasserstein Distance

The Gromov-Wasserstein distance is a notable extension of optimal transport. In contrast to the classic Wasserstein distance, it solves a quadratic assignment problem that minimizes the pair-wise distance distortion under the transportation of distributions and thus could apply to distributions in different spaces. These properties make Gromov-Wasserstein widely applicable to many fields, such as computer graphics and machine learning. However, the computation of the Gromov-Wasserstein distance and transport plan is expensive. The well-known Entropic Gromov-Wasserstein approach has a cubic complexity since the matrix multiplication operations need to be repeated in computing the gradient of Gromov-Wasserstein loss. This becomes a key bottleneck of the method. Currently, existing methods accelerate the computation focus on sampling and approximation, which leads to low accuracy or incomplete transport plan. In this work, we propose a novel method to accelerate accurate gradient computation by dynamic programming techniques, reducing the complexity from cubic to quadratic. In this way, the original computational bottleneck is broken and the new entropic solution can be obtained with total quadratic time, which is almost optimal complexity. Furthermore, it can be extended to some variants easily. Extensive experiments validate the efficiency and effectiveness of our method.

cs.LG↗

arXiv2024

Logics of False Belief and Radical Ignorance

In the literature, the question about how to axiomatize the transitive logic of false belief is thought of as hard and left as an open problem. In this paper, among other contributions, we deal with this problem. In more details, although the standard doxastic operator is undefinable with the operator of false belief, the former is {\em almost definable} with the latter. On one hand, the involved almost definability schema guides us to find the desired core axioms for the transitive logic and the Euclidean logic of false belief. On the other hand, inspired by the schema and other considerations, we propose a suitable canonical relation, which can uniformly handle the completeness proof of various logics of false belief, including the transitive logic. We also extend the results to the logic of radical ignorance, due to the interdefinability of the operators of false belief and radical ignorance.

math.LO↗

arXiv2023

Fitchean Ignorance and First-order Ignorance: A Neighborhood Look

In a seminal work~\cite{Fine:2018}, Fine classifies several forms of ignorance, among which are Fitchean ignorance, first-order ignorance, Rumsfeld ignorance, and second-order ignorance. It is shown that there is interesting relationship among some of them, which includes that in ${\bf S4}$, all higher-order ignorance are reduced to second-order ignorance. This is thought of as a bad consequence by some researchers. It is then natural to ask how to avoid this consequence. We deal with this issue in a much more general framework. In detail, we treat the forms of Fitchean ignorance and first-order ignorance as primitive modalities and study them as first-class citizens under neighborhood semantics, in which Rumsfeld ignorance and second-order ignorance are definable. The main contributions include model-theoretical results such as expressivity and frame definability, and axiomatizations. Last but not least, by updating the neighborhood models via the intersection semantics, we extend the results to the dynamic case of public announcements, which gives us some applications to successful formulas.

cs.LO↗

arXiv2023

Unknown Truths and Unknowable Truths

Notions of unknown truths and unknowable truths are important in formal epistemology, which are related to each other in e.g. Fitch's paradox of knowability. Although there have been some logical research on the notion of unknown truths and some philosophical discussion on the two notions, there seems to be no logical research on unknowable truths. In this paper, we propose a logic of unknowable truths, investigate the logical properties of unknown truths and unknowable truths, which includes the similarities of the two notions and the relationship between the two notions, and axiomatize this logic.

cs.LO↗

arXiv2023

Towards Usable Parental Control for Voice Assistants

Voice Personal Assistants (VPA) have become a common household appliance. As one of the leading platforms for VPA technology, Amazon created Alexa and designed Amazon Kids for children to safely enjoy the rich functionalities of VPA and for parents to monitor their kids' activities through the Parent Dashboard. Although this ecosystem is in place, the usage of Parent Dashboard is not yet popularized among parents. In this paper, we conduct a parent survey to find out what they like and dislike about the current parental control features. We find that parents need more visuals about their children's activity, easier access to security features for their children, and a better user interface. Based on the insights from our survey, we present a new design for the Parent Dashboard considering the parents' expectations.

cs.HC↗

arXiv2022

Quasiparticle poisoning rate in a superconducting transmon qubit involving Majorana zero modes

Majorana zero modes have been attracting considerable attention because of their prospective applications in fault-tolerant topological quantum computing. In recent years, some schemes have been proposed to detect and manipulate Majorana zero modes using superconducting qubits. However, manipulating and reading the Majorana zero modes must be kept in the time window of quasiparticle poisoning. In this work, we study the problem of quasiparticle poisoning in a split transmon qubit containing hybrid Josephson junctions involving Majorana zero modes. We show that Majorana coupling will cause parity mixing and 4π Josephson effect. In addition, we obtained the expression of qubit parameter-dependent parity switching rate and demonstrated that quasiparticle poisoning can be greatly suppressed by reducing E_J/E_C via qubit design.

quant-ph↗

arXiv2022

A new blow-up criterion for the 2D full compressible Navier-Stokes equations without heat conduction in a bounded domain

This paper is to derive a new blow-up criterion for the 2D full compressible Navier-Stokes equations without heat conduction in terms of the density $ρ$ and the pressure $P$. More precisely, it indicates that in a bounded domain the strong solution exists globally if the norm $\|ρ||_{{L^\infty(0,t;L^{\infty})}}+||P||_{L^{p_0}(0,t;L^\infty)}<\infty$ for some constant $p_0$ satisfying $1<p_0\leq 2$. The boundary condition is imposed as a Navier-slip boundary one and the initial vacuum is permitted. Our result extends previous one which is stated as $\|ρ||_{{L^\infty(0,t;L^{\infty})}}+||P||_{L^{\infty}(0,t;L^\infty)}<\infty$.

math.AP↗

arXiv2022

BDG-Net: Boundary Distribution Guided Network for Accurate Polyp Segmentation

Colorectal cancer (CRC) is one of the most common fatal cancer in the world. Polypectomy can effectively interrupt the progression of adenoma to adenocarcinoma, thus reducing the risk of CRC development. Colonoscopy is the primary method to find colonic polyps. However, due to the different sizes of polyps and the unclear boundary between polyps and their surrounding mucosa, it is challenging to segment polyps accurately. To address this problem, we design a Boundary Distribution Guided Network (BDG-Net) for accurate polyp segmentation. Specifically, under the supervision of the ideal Boundary Distribution Map (BDM), we use Boundary Distribution Generate Module (BDGM) to aggregate high-level features and generate BDM. Then, BDM is sent to the Boundary Distribution Guided Decoder (BDGD) as complementary spatial information to guide the polyp segmentation. Moreover, a multi-scale feature interaction strategy is adopted in BDGD to improve the segmentation accuracy of polyps with different sizes. Extensive quantitative and qualitative evaluations demonstrate the effectiveness of our model, which outperforms state-of-the-art models remarkably on five public polyp datasets while maintaining low computational complexity. Code: https://github.com/zihuanqiu/BDG-Net

eess.IV↗

arXiv2021

ChemiRise: a data-driven retrosynthesis engine

We have developed an end-to-end, retrosynthesis system, named ChemiRise, that can propose complete retrosynthesis routes for organic compounds rapidly and reliably. The system was trained on a processed patent database of over 3 million organic reactions. Experimental reactions were atom-mapped, clustered, and extracted into reaction templates. We then trained a graph convolutional neural network-based one-step reaction proposer using template embeddings and developed a guiding algorithm on the directed acyclic graph (DAG) of chemical compounds to find the best candidate to explore. The atom-mapping algorithm and the one-step reaction proposer were benchmarked against previous studies and showed better results. The final product was demonstrated by retrosynthesis routes reviewed and rated by human experts, showing satisfying functionality and a potential productivity boost in real-life use cases.

physics.chem-ph↗

arXiv2021

Molecular modeling with machine-learned universal potential functions

Molecular modeling is an important topic in drug discovery. Decades of research have led to the development of high quality scalable molecular force fields. In this paper, we show that neural networks can be used to train a universal approximator for energy potential functions. By incorporating a fully automated training process we have been able to train smooth, differentiable, and predictive potential functions on large-scale crystal structures. A variety of tests have also been performed to show the superiority and versatility of the machine-learned model.

q-bio.QM↗

arXiv2021

Commonly Knowing Whether

This paper introduces the notion of `commonly knowing whether', a non-standard version of standard common knowledge which is defined on the basis of `knowing whether', instead of standard `knowing that'. After giving five possible definitions of this notion, we explore the logical relations among them in the single-agent and multi-agent cases. We propose a sound and complete axiomatization. We investigate one of the five definitions in terms of expressivity via a strategy of modal comparison games.

cs.LO↗

arXiv2020

Dimensionality-dependent type-II Weyl semimetal state in Mo$_{0.25}$W$_{0.75}$Te$_{2}$

Weyl nodes and Fermi arcs in type-II Weyl semimetals (WSMs) have led to lots of exotic transport phenomena. Recently, Mo$_{0.25}$W$_{0.75}$Te$_{2}$ has been established as a type-II WSM with Weyl points located near Fermi level, which offers an opportunity to study its intriguing band structure by electrical transport measurements. Here, by selecting a special sample with the thickness gradient across two- (2D) and three-dimensional (3D) regime, we show strong evidences that Mo$_{0.25}$W$_{0.75}$Te$_{2}$ is a type-II Weyl semimetal by observing the following two dimensionality-dependent transport features: 1) A chiral-anomaly-induced anisotropic magneto-conductivity enhancement, proportional to the square of in-plane magnetic field (B$_{in}$$^{2}$); 2) An additional quantum oscillation with thickness-dependent phase shift. Our theoretical calculations show that the observed quantum oscillation originates from a Weyl-orbit-like scenario due to the unique band structure of Mo$_{0.25}$W$_{0.75}$Te$_{2}$. The in situ dimensionality-tuned transport experiment offers a new strategy to search for type-II WSMs.

cond-mat.mes-hall↗