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Min Li

Publications and source records attributed to Min Li.

At least 163 records · Page 9Linked to original sources

Relative Pose for Nonrigid Multi-Perspective Cameras: The Static Case

Multi-perspective cameras with potentially non-overlapping fields of view have become an important exteroceptive sensing modality in a number of applications such as intelligent vehicles, drones, and mixed reality headsets. In this work, we challenge one of the basic assumptions made in these scenarios, which is that the multi-camera rig is rigid. More specifically, we are considering the problem of estimating the relative pose between a static non-rigid rig in different spatial orientations while taking into account the effect of gravity onto the system. The deformable physical connections between each camera and the body center are approximated by a simple cantilever model, and inserted into the generalized epipolar constraint. Our results lead us to the important insight that the latent parameters of the deformation model, meaning the gravity vector in both views, become observable. We present a concise analysis of the observability of all variables based on noise, outliers, and rig rigidity for two different algorithms. The first one is a vision-only alternative, while the second one makes use of additional gravity measurements. To conclude, we demonstrate the ability to sense gravity in a real-world example, and discuss practical implications.

cs.RO↗

A new highly nonlinear equation modelling shallow-water waves with constant vorticity

In this paper we apply the approach of formal asymptotic expansions and perturbation theory to derive a new highly nonlinear shallow-water model from the full governing equations for two dimensional incompressible fluid with constant vorticity. This approximate model is generated by introduction of a larger scaling than the Camassa-Holm one, which is shown to be optimal in the sense that there are no non-local terms appearing in free surface equation. Moreover, we establish the local well-posedness of the Cauchy problem in Besov spaces, and give a blow-up criterion, which improve the previous corresponding results in Sobolev spaces.

math.AP↗

DTIAM: A unified framework for predicting drug-target interactions, binding affinities and activation/inhibition mechanisms

Accurate and robust prediction of drug-target interactions (DTIs) plays a vital role in drug discovery. Despite extensive efforts have been invested in predicting novel DTIs, existing approaches still suffer from insufficient labeled data and cold start problems. More importantly, there is currently a lack of studies focusing on elucidating the mechanism of action (MoA) between drugs and targets. Distinguishing the activation and inhibition mechanisms is critical and challenging in drug development. Here, we introduce a unified framework called DTIAM, which aims to predict interactions, binding affinities, and activation/inhibition mechanisms between drugs and targets. DTIAM learns drug and target representations from large amounts of label-free data through self-supervised pre-training, which accurately extracts the substructure and contextual information of drugs and targets, and thus benefits the downstream prediction based on these representations. DTIAM achieves substantial performance improvement over other state-of-the-art methods in all tasks, particularly in the cold start scenario. Moreover, independent validation demonstrates the strong generalization ability of DTIAM. All these results suggested that DTIAM can provide a practically useful tool for predicting novel DTIs and further distinguishing the MoA of candidate drugs. DTIAM, for the first time, provides a unified framework for accurate and robust prediction of drug-target interactions, binding affinities, and activation/inhibition mechanisms.

q-bio.BM↗

A novel design for 100 meter-scale water attenuation length measurement and monitoring

Water Cherenov detector is a vital part in most of neutrino or cosmic ray research. As detectors grow in size, the water attenuation length (WAL) becomes increasingly essential for detector performance. It is essential to measure or monitor the WAL. While many experiments have measured WAL in the lab or detector, only the Super-Kamiokande experiment has achieved values exceeding 50 meters in the detector with a moving light source. However, it is impractical for many experiments to place a moving light source inside the detector, necessitating an alternative method for investigating long WAL. A novel system has been proposed to address the challenge of investigating long WAL. This system focuses on ample water Cherenkov detectors and features a fixed light source and photomultiplier tubes (PMTs) at varying distances, eliminating the need for moving parts. The static setup demands high precision for accurate measurement of long WAL. Each component, including LED, diffuse ball, PMTs, and fibers, is introduced to explain uncertainty control. Based on lab tests, the system's uncertainty has been controlled within 5\%. Additionally, camera technology is also used during the evaluation of the system uncertainty, which has the potential to replace PMTs in the future for this measurement. Monte Carlo simulations have shown that the system can achieve a 5\% measurement uncertainty at WAL of 80 meters and 8\% at WAL of 100 meters. This system can be used in experiments with large Cherenkov detectors such as JUNO water veto and Hyper-K.

physics.ins-det↗

Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs

Recent work in Natural Language Processing and Computer Vision has been using textual information -- e.g., entity names and descriptions -- available in knowledge graphs to ground neural models to high-quality structured data. However, when it comes to non-English languages, the quantity and quality of textual information are comparatively scarce. To address this issue, we introduce the novel task of automatic Knowledge Graph Enhancement (KGE) and perform a thorough investigation on bridging the gap in both the quantity and quality of textual information between English and non-English languages. More specifically, we: i) bring to light the problem of increasing multilingual coverage and precision of entity names and descriptions in Wikidata; ii) demonstrate that state-of-the-art methods, namely, Machine Translation (MT), Web Search (WS), and Large Language Models (LLMs), struggle with this task; iii) present M-NTA, a novel unsupervised approach that combines MT, WS, and LLMs to generate high-quality textual information; and, iv) study the impact of increasing multilingual coverage and precision of non-English textual information in Entity Linking, Knowledge Graph Completion, and Question Answering. As part of our effort towards better multilingual knowledge graphs, we also introduce WikiKGE-10, the first human-curated benchmark to evaluate KGE approaches in 10 languages across 7 language families.

cs.AI↗

End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions

End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of deep neural networks, especially the successful use of large pre-trained models, has further led to significant progress in EToD research in recent years. In this paper, we present a thorough review and provide a unified perspective to summarize existing approaches as well as recent trends to advance the development of EToD research. The contributions of this paper can be summarized: (1) \textbf{\textit{First survey}}: to our knowledge, we take the first step to present a thorough survey of this research field; (2) \textbf{\textit{New taxonomy}}: we first introduce a unified perspective for EToD, including (i) \textit{Modularly EToD} and (ii) \textit{Fully EToD}; (3) \textbf{\textit{New Frontiers}}: we discuss some potential frontier areas as well as the corresponding challenges, hoping to spur breakthrough research in EToD field; (4) \textbf{\textit{Abundant resources}}: we build a public website\footnote{We collect the related papers, baseline projects, and leaderboards for the community at \url{https://etods.net/}.}, where EToD researchers could directly access the recent progress. We hope this work can serve as a thorough reference for the EToD research community.

cs.CL↗

Joint Location Sensing and Channel Estimation for IRS-Aided mmWave ISAC Systems

In this paper, we investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS can effectively reduce the path loss of sensing-related links, thus rendering it advantageous in ISAC systems. Aiming to jointly sense the target/scatterer/user positions as well as estimate the sensing and communication (SAC) channels in the considered system, we propose a two-phase transmission scheme, where the coarse and refined sensing/channel estimation (CE) results are respectively obtained in the first phase (using scanning-based IRS reflection coefficients) and second phase (using optimized IRS reflection coefficients). For each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is developed to solve the considered joint location sensing and CE problem. The proposed algorithm effectively exploits the partial overlapping structured (POS) sparsity and 2-dimensional (2D) block sparsity inherent in the SAC channels to enhance the overall performance. Based on the estimation results from the first phase, we formulate a Cramér-Rao bound (CRB) minimization problem for optimizing IRS reflection coefficients, and through proper reformulations, a low-complexity manifold-based optimization algorithm is proposed to solve this problem. Simulation results are provided to verify the superiority of the proposed transmission scheme and associated algorithms.

eess.SP↗

DeepSeq: Deep Sequential Circuit Learning

Circuit representation learning is a promising research direction in the electronic design automation (EDA) field. With sufficient data for pre-training, the learned general yet effective representation can help to solve multiple downstream EDA tasks by fine-tuning it on a small set of task-related data. However, existing solutions only target combinational circuits, significantly limiting their applications. In this work, we propose DeepSeq, a novel representation learning framework for sequential netlists. Specifically, we introduce a dedicated graph neural network (GNN) with a customized propagation scheme to exploit the temporal correlations between gates in sequential circuits. To ensure effective learning, we propose to use a multi-task training objective with two sets of strongly related supervision: logic probability and transition probability at each node. A novel dual attention aggregation mechanism is introduced to facilitate learning both tasks efficiently. Experimental results on various benchmark circuits show that DeepSeq outperforms other GNN models for sequential circuit learning. We evaluate the generalization capability of DeepSeq on a downstream power estimation task. After fine-tuning, DeepSeq can accurately estimate power across various circuits under different workloads.

cs.LG↗

Discovery of Two Different Full Disk Evolutionary Patterns of M-type T Tauri Stars with LAMOST DR8

The full disk, full of gas and dust, determines the upper limit of planet masses, and its lifetime is critical for planet formation, especially for giant planets. In this work, we studied the evolutionary timescales of the full disks of T Tauri stars (TTSs) and their relations to accretion. Combined with Gaia EDR3, 2MASS, and WISE data, 1077 disk-bearing TTS candidates were found in LAMOST DR8, and stellar parameters were obtained. Among them, 783 are newly classified by spectra as classical T Tauri stars (CTTSs; 169) or weak-lined T Tauri stars (WTTSs). Based on EW and FWHM of Ha, 157 TTSs in accretion were identified, with ~ 82% also having full disks. For TTSs with M<0.35 Mo, about 80% seem to already lose their full disks at ~ 0.1 Myr, which may explain their lower mass, while the remaining 20% with full disks evolve at similar rates of non-full disks within 5 Myr, possibly suffice to form giant planets. The fraction of accreting TTSs to disk-bearing TTSs is stable at ~10% and can last $\sim$ 5-10 Myr, suggesting that full disks and accretion evolve with similar rates as non-full disks. For TTSs with M>0.35Mo, almost all full disks can survive more than 0.1 Myr, most for 1 Myr and some even for 20 Myr, which implies planets are more likely to be formed in their disks than those of M<0.35 Mo, and thus M dwarfs with M>0.35Mo can have more planets. The fraction of full-disk TTSs to disk-bearing TTSs decreases with age following the relation $f\propto t^{-0.35}$, and similar relations existed in the fraction of accreting TTSs and the fraction of full-disk CTTSs, suggesting faster full disks and accretion evolution than non-full disks. For full disk stars, the ratio of accretion of lower-mass stars is systematically lower than that of higher-mass stars, confirming the dependence of accretion on stellar mass.

astro-ph.SR↗

Dual-comb spectroscopy over 100km open-air path

Satellite-based greenhouse gases (GHG) sensing technologies play a critical role in the study of global carbon emissions and climate change. However, none of the existing satellite-based GHG sensing technologies can achieve the measurement of broad bandwidth, high temporal-spatial resolution, and high sensitivity at the same time. Recently, dual-comb spectroscopy (DCS) has been proposed as a superior candidate technology for GHG sensing because it can measure broadband spectra with high temporal-spatial resolution and high sensitivity. The main barrier to DCS's display on satellites is its short measurement distance in open air achieved thus far. Prior research has not been able to implement DCS over 20 km of open-air path. Here, by developing a bistatic setup using time-frequency dissemination and high-power optical frequency combs, we have implemented DCS over a 113 km turbulent horizontal open-air path. Our experiment successfully measured GHG with 7 nm spectral bandwidth and a 10 kHz frequency and achieved a CO2 sensing precision of <2 ppm in 5 minutes and <0.6 ppm in 36 minutes. Our results represent a significant step towards advancing the implementation of DCS as a satellite-based technology and improving technologies for GHG monitoring

physics.optics↗

Zero-filter limit issue for the Camassa-Holm equation in Besov spaces

In this paper, we focus on zero-filter limit problem for the Camassa-Holm equation in the more general Besov spaces. We prove that the solution of the Camassa-Holm equation converges strongly in $L^\infty(0,T;B^s_{2,r}(\R))$ to the inviscid Burgers equation as the filter parameter $α$ tends to zero with the given initial data $u_0\in B^s_{2,r}(\R)$. Moreover, we also show that the zero-filter limit for the Camassa-Holm equation does not converges uniformly with respect to the initial data in $B^s_{2,r}(\R)$.

math.AP↗

Anchor-Intermediate Detector: Decoupling and Coupling Bounding Boxes for Accurate Object Detection

Anchor-based detectors have been continuously developed for object detection. However, the individual anchor box makes it difficult to predict the boundary's offset accurately. Instead of taking each bounding box as a closed individual, we consider using multiple boxes together to get prediction boxes. To this end, this paper proposes the \textbf{Box Decouple-Couple(BDC) strategy} in the inference, which no longer discards the overlapping boxes, but decouples the corner points of these boxes. Then, according to each corner's score, we couple the corner points to select the most accurate corner pairs. To meet the BDC strategy, a simple but novel model is designed named the \textbf{Anchor-Intermediate Detector(AID)}, which contains two head networks, i.e., an anchor-based head and an anchor-free \textbf{Corner-aware head}. The corner-aware head is able to score the corners of each bounding box to facilitate the coupling between corner points. Extensive experiments on MS COCO show that the proposed anchor-intermediate detector respectively outperforms their baseline RetinaNet and GFL method by $\sim$2.4 and $\sim$1.2 AP on the MS COCO test-dev dataset without any bells and whistles. Code is available at: https://github.com/YilongLv/AID.

cs.CV↗

Stellar outbursts and chondrite composition

The temperatures of observed protoplanetary disks are not sufficiently high to produce the accretion rate needed to form stars, nor are they sufficient to explain the volatile depletion patterns in CM, CO, and CV chondrites and terrestrial planets. We revisit the role that stellar outbursts, caused by high accretion episodes, play in resolving these two issues. These outbursts provide the necessary mass to form the star during the disk lifetime, and provide enough heat to vaporize planet-forming materials. We show that these outbursts can reproduce the observed chondrite abundances at distances near one AU. These outbursts would also affect the growth of calcium-aluminum-rich inclusions (CAIs) and the isotopic compositions of carbonaceous and non-carbonaceous chondrites.

astro-ph.EP↗

Approximation algorithms for $k$-submodular maximization subject to a knapsack constraint

In this paper, we study the problem of maximizing $k$-submodular functions subject to a knapsack constraint. For monotone objective functions, we present a $\frac{1}{2}(1-e^{-2})\approx 0.432$ greedy approximation algorithm. For the non-monotone case, we are the first to consider the knapsack problem and provide a greedy-type combinatorial algorithm with approximation ratio $\frac{1}{3}(1-e^{-3})\approx 0.317$.

cs.DS↗

Fast algorithms for k-submodular maximization subject to a matroid constraint

In this paper, we apply a Threshold-Decreasing Algorithm to maximize $k$-submodular functions under a matroid constraint, which reduces the query complexity of the algorithm compared to the greedy algorithm with little loss in approximation ratio. We give a $(\frac{1}{2} - ε)$-approximation algorithm for monotone $k$-submodular function maximization, and a $(\frac{1}{3} - ε)$-approximation algorithm for non-monotone case, with complexity $O(\frac{n(k\cdot EO + IO)}ε \log \frac{r}ε)$, where $r$ denotes the rank of the matroid, and $IO, EO$ denote the number of oracles to evaluate whether a subset is an independent set and to compute the function value of $f$, respectively. Since the constraint of total size can be looked as a special matroid, called uniform matroid, then we present the fast algorithm for maximizing $k$-submodular functions subject to a total size constraint as corollaries. corollaries.

cs.DS↗

Design optimization of JUNO-TAO plastic scintillator with WLS-fiber and SiPM readout

Plastic scintillators (PSs)embedded with wavelength-shifting fibers are widely used in high-energy particle physics, such as in muon taggers,as well as in medical physics and other applications. In this study,a simulation package was built to evaluate the effects of the diameter and layout of optical fibers on the light yield with different configurations. The optimal optical configuration was designed based on simulations and validated using two PS prototypes under certain experimental conditions. Atop veto tracker (TVT) for the JUNO-TAO experiment, comprising four layers of 160 strips of PS, was designed and evaluated. The threshold was evaluated when the muon tagging efficiency of a PS strip was >99%. The efficiency of three layer out of four layer of TVT is >99%,even with a tagging efficiency of a single strip as low as 97%, using a threshold of 10 photoelectrons and assuming a 40%silicon PM photon detection efficiency.

physics.ins-det↗

Incomplete Information Linear-Quadratic Mean-Field Games and Related Riccati Equations

We study a class of linear-quadratic mean-field games with incomplete information. For each agent, the state is given by a linear forward stochastic differential equation with common noise. Moreover, both the state and control variables can enter the diffusion coefficients of the state equation. We deduce the open-loop adapted decentralized strategies and feedback decentralized strategies by mean-field forward-backward stochastic differential equation and Riccati equations, respectively. The well-posedness of the corresponding consistency condition system is obtained and the limiting state-average turns out to be the solution of a mean-field stochastic differential equation driven by common noise. We also verify the $\varepsilon$-Nash equilibrium property of the decentralized control strategies. Finally, a network security problem is studied to illustrate our results as an application.

math.OC↗

INGB: Informed Nonlinear Granular Ball Oversampling Framework for Noisy Imbalanced Classification

In classification problems, the datasets are usually imbalanced, noisy or complex. Most sampling algorithms only make some improvements to the linear sampling mechanism of the synthetic minority oversampling technique (SMOTE). Nevertheless, linear oversampling has several unavoidable drawbacks. Linear oversampling is susceptible to overfitting, and the synthetic samples lack diversity and rarely account for the original distribution characteristics. An informed nonlinear oversampling framework with the granular ball (INGB) as a new direction of oversampling is proposed in this paper. It uses granular balls to simulate the spatial distribution characteristics of datasets, and informed entropy is utilized to further optimize the granular-ball space. Then, nonlinear oversampling is performed by following high-dimensional sparsity and the isotropic Gaussian distribution. Furthermore, INGB has good compatibility. Not only can it be combined with most SMOTE-based sampling algorithms to improve their performance, but it can also be easily extended to noisy imbalanced multi-classification problems. The mathematical model and theoretical proof of INGB are given in this work. Extensive experiments demonstrate that INGB outperforms the traditional linear sampling frameworks and algorithms in oversampling on complex datasets.

stat.ML↗