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Qiang Li

Publications and source records attributed to Qiang Li.

At least 271 records · Page 15Linked to original sources
arXiv2024

A Collision-Aware Cable Grasping Method in Cluttered Environment

We introduce a Cable Grasping-Convolutional Neural Network designed to facilitate robust cable grasping in cluttered environments. Utilizing physics simulations, we generate an extensive dataset that mimics the intricacies of cable grasping, factoring in potential collisions between cables and robotic grippers. We employ the Approximate Convex Decomposition technique to dissect the non-convex cable model, with grasp quality autonomously labeled based on simulated grasping attempts. The CG-CNN is refined using this simulated dataset and enhanced through domain randomization techniques. Subsequently, the trained model predicts grasp quality, guiding the optimal grasp pose to the robot controller for execution. Grasping efficacy is assessed across both synthetic and real-world settings. Given our model implicit collision sensitivity, we achieved commendable success rates of 92.3% for known cables and 88.4% for unknown cables, surpassing contemporary state-of-the-art approaches. Supplementary materials can be found at https://leizhang-public.github.io/cg-cnn/ .

cs.RO↗
arXiv2024

Robust single divacancy defects near stacking faults in 4H-SiC under resonant excitation

Color centers in silicon carbide (SiC) have demonstrated significant promise for quantum information processing. However, the undesirable ionization process that occurs during optical manipulation frequently causes fluctuations in the charge state and performance of these defects, thereby restricting the effectiveness of spin-photon interfaces. Recent predictions indicate that divacancy defects near stacking faults possess the capability to stabilize their neutral charge states, thereby providing robustness against photoionization effects. In this work, we present a comprehensive protocol for the scalable and targeted fabrication of single divacancy arrays in 4H-SiC using a high-resolution focused helium ion beam. Through photoluminescence emission (PLE) experiments, we demonstrate long-term emission stability with minimal linewidth shift ($\sim$ 50 MHz over 3 hours) for the single c-axis divacancies within stacking faults. By measuring the ionization rate for different polytypes of divacancies, we found that the divacancies within stacking faults are more robust against resonant excitation. Additionally, angle-resolved PLE spectra reveal their two resonant-transition lines with mutually orthogonal polarizations. Notably, the PLE linewidths are approximately 7 times narrower and the spin-coherent times are 6 times longer compared to divacancies generated via carbon-ion implantation. These findings highlight the immense potential of SiC divacancies for on-chip quantum photonics and the construction of efficient spin-to-photon interfaces, indicating a significant step forward in the development of quantum technologies.

quant-ph↗
arXiv2024

Cooperative Backscatter Communications with Reconfigurable Intelligent Surfaces: An APSK Approach

In this paper, a novel amplitude phase shift keying (APSK) modulation scheme for cooperative backscatter communications aided by a reconfigurable intelligent surface (RIS-CBC) is presented, according to which the RIS is configured to modulate backscatter information onto unmodulated or PSK-modulated signals impinging on its surface via APSK. We consider both passive and active RISs, with the latter including an amplification unit at each reflecting element. In the passive (resp. active) RIS-CBC-APSK, backscatter information is conveyed through the number of RIS reflecting elements being on the ON state (resp. active mode) and their phase shift values. By using the optimal APSK constellation to ensure that reflected signals from the RIS undergo APSK modulation, a bit-mapping mechanism is presented. Assuming maximum-likelihood detection, we also present closed-form upper bounds for the symbol error rate (SER) performance for both passive and active RIS-CBC-APSK schemes over Rician fading channels. In addition, we devise a low-complexity detector that can achieve flexible trade-offs between performance and complexity. Finally, we extend RIS-CBC-APSK to multiple-input single-output scenarios and present an alternating optimization approach for the joint design of transmit beamforming and RIS reflection. Our extensive simulation results on the SER performance corroborate our conducted performance analysis and showcase the superiority of the proposed RIS-CBC-APSK schemes over the state-of-the-art RIS-CBC benchmarks.

cs.IT↗
arXiv2024

SpirDet: Towards Efficient, Accurate and Lightweight Infrared Small Target Detector

In recent years, the detection of infrared small targets using deep learning methods has garnered substantial attention due to notable advancements. To improve the detection capability of small targets, these methods commonly maintain a pathway that preserves high-resolution features of sparse and tiny targets. However, it can result in redundant and expensive computations. To tackle this challenge, we propose SpirDet, a novel approach for efficient detection of infrared small targets. Specifically, to cope with the computational redundancy issue, we employ a new dual-branch sparse decoder to restore the feature map. Firstly, the fast branch directly predicts a sparse map indicating potential small target locations (occupying only 0.5\% area of the map). Secondly, the slow branch conducts fine-grained adjustments at the positions indicated by the sparse map. Additionally, we design an lightweight DO-RepEncoder based on reparameterization with the Downsampling Orthogonality, which can effectively reduce memory consumption and inference latency. Extensive experiments show that the proposed SpirDet significantly outperforms state-of-the-art models while achieving faster inference speed and fewer parameters. For example, on the IRSTD-1K dataset, SpirDet improves $MIoU$ by 4.7 and has a $7\times$ $FPS$ acceleration compared to the previous state-of-the-art model. The code will be open to the public.

cs.CV↗
arXiv2024

Correlation between magnetic domain structures and quantum anomalous Hall effect in epitaxial MnBi2Te4 thin films

We use magnetic force microscopy (MFM) to study spatial uniformity of magnetization of epitaxially grown MnBi2Te4 thin films. Compared to films which exhibit no quantum anomalous Hall effect (QAH), films with QAH are observed to have more spatial uniformity of magnetization with larger domain size. The domain evolution upon magnetic field sweeping indicates that the magnetic domains or the spatial nonuniformity of magnetization originates from the strong pinning of the inherent sample inhomogeneity. A direct correlation between the Hall resistivity and the domain size has been established by analyzing a series of thin films with and without QAH. Our observation shows that one has to suppress the spatial nonuniformity of magnetization to allow the Hall resistivity to be quantized. The fact that a sizable longitudinal resistivity remains even for the QAH sample suggests a quantized Hall insulator scenario. Our work provides important insights to the understanding of the quantization mechanism and the dissipation of the QAH state in MnBi2Te4 system.

cond-mat.mes-hall↗
arXiv2024

An efficient algorithm for multiuser sum-rate maximization of large-scale active RIS-aided MIMO system

Active reconfigurable intelligent surface (RIS) is a new RIS architecture that can reflect and amplify communication signals. It can provide enhanced performance gain compared to the conventional passive RIS systems that can only reflect the signals. On the other hand, the design problem of active RIS-aided systems is more challenging than the passive RIS-aided systems and its efficient algorithms are less studied. In this paper, we consider the sum rate maximization problem in the multiuser massive multiple-input single-output (MISO) downlink with the aid of a large-scale active RIS. Existing approaches for handling this problem usually resort to general optimization solvers and can be computationally prohibitive. We propose an efficient block successive upper bound minimization (BSUM) method, of which each step has a (semi) closed-form update. Thus, the proposed algorithm has an attractive low per-iteration complexity. By simulation, our proposed algorithm consumes much less computation than the existing approaches. In particular, when the MIMO and/or RIS sizes are large, our proposed algorithm can be orders-of-magnitude faster than existing approaches.

cs.IT↗
arXiv2024

From Beginner to Expert: Modeling Medical Knowledge into General LLMs

Recently, large language model (LLM) based artificial intelligence (AI) systems have demonstrated remarkable capabilities in natural language understanding and generation. However, these models face a significant challenge when it comes to sensitive applications, such as reasoning over medical knowledge and answering medical questions in a physician-like manner. Prior studies attempted to overcome this challenge by increasing the model size (>100B) to learn more general medical knowledge, while there is still room for improvement in LLMs with smaller-scale model sizes (<100B). In this work, we start from a pre-trained general LLM model (AntGLM-10B) and fine-tune it from a medical beginner towards a medical expert (called AntGLM-Med-10B), which leverages a 3-stage optimization procedure, i.e., general medical knowledge injection, medical domain instruction tuning, and specific medical task adaptation. Our contributions are threefold: (1) We specifically investigate how to adapt a pre-trained general LLM in medical domain, especially for a specific medical task. (2) We collect and construct large-scale medical datasets for each stage of the optimization process. These datasets encompass various data types and tasks, such as question-answering, medical reasoning, multi-choice questions, and medical conversations. (3) Specifically for multi-choice questions in the medical domain, we propose a novel Verification-of-Choice approach for prompting engineering, which significantly enhances the reasoning ability of LLMs. Remarkably, by combining the above approaches, our AntGLM-Med-10B model can outperform the most of LLMs on PubMedQA, including both general and medical LLMs, even when these LLMs have larger model size.

cs.CL↗
arXiv2024

Exploration of Adolescent Depression Risk Prediction Based on Census Surveys and General Life Issues

In contemporary society, the escalating pressures of life and work have propelled psychological disorders to the forefront of modern health concerns, an issue that has been further accentuated by the COVID-19 pandemic. The prevalence of depression among adolescents is steadily increasing, and traditional diagnostic methods, which rely on scales or interviews, prove particularly inadequate for detecting depression in young people. Addressing these challenges, numerous AI-based methods for assisting in the diagnosis of mental health issues have emerged. However, most of these methods center around fundamental issues with scales or use multimodal approaches like facial expression recognition. Diagnosis of depression risk based on everyday habits and behaviors has been limited to small-scale qualitative studies. Our research leverages adolescent census data to predict depression risk, focusing on children's experiences with depression and their daily life situations. We introduced a method for managing severely imbalanced high-dimensional data and an adaptive predictive approach tailored to data structure characteristics. Furthermore, we proposed a cloud-based architecture for automatic online learning and data updates. This study utilized publicly available NSCH youth census data from 2020 to 2022, encompassing nearly 150,000 data entries. We conducted basic data analyses and predictive experiments, demonstrating significant performance improvements over standard machine learning and deep learning algorithms. This affirmed our data processing method's broad applicability in handling imbalanced medical data. Diverging from typical predictive method research, our study presents a comprehensive architectural solution, considering a wider array of user needs.

cs.LG↗
arXiv2023

Electronic structure, magnetic and transport properties of antiferromagnetic Weyl semimetal GdAlSi

We report the topological electronic structure, magnetic, and magnetotransport properties of a noncentrosymmetric compound GdAlSi. Magnetic susceptibility shows an antiferromagnetic transition at $T_\mathrm{N}$ = 32 K. In-plane isothermal magnetization exhibits an unusual hysteresis behavior at higher magnetic field, rather than near zero field. Moreover, the hysteresis behavior is asymmetric under positive and negative magnetic fields. First-principles calculations were performed on various magnetic configurations, revealing that the antiferromagnetic state is the ground state, and the spiral antiferromagnetic state is a close competing state. The calculations also reveal that GdAlSi hosts multiple Weyl points near the Fermi energy. The band structure measured by angle-resolved photoemission spectroscopy (ARPES) shows relatively good agreement with the theory, with the possibility of Weyl nodes slightly above the Fermi energy. Within the magnetic ordered state, we observe an exceptionally large anomalous Hall conductivity (AHC) of ~ 1310 $Ω^{-1}$cm$^{-1}$ at 2 K. Interestingly, the anomalous Hall effect persists up to room temperature with a significant value of AHC (~ 155 $Ω^{-1}$cm$^{-1}$). Our analysis indicates that the large AHC originates from the Berry curvature associated with the multiple pairs of Weyl points near Fermi energy.

cond-mat.str-el↗
arXiv2023

Nano-Imaging of Landau-Phonon Polaritons in Dirac Heterostructures

Polaritons are light-matter quasiparticles that govern the optical response of quantum materials and enable their nanophotonic applications. We have studied a new type of polaritons arising in magnetized graphene encapsulated in hexagonal boron nitride (hBN). These polaritons stem from hybridization of Dirac magnetoexciton modes of graphene with waveguide phonon modes of hBN crystals. We refer to these quasiparticles as the Landau-phonon polaritons (LPPs). Using infrared magneto nanoscopy, we imaged LPPs and controlled their real-space propagation by varying the magnetic field. These LLPs have large in-plane momenta and are not bound by the conventional optical selection rules, granting us access to the "forbidden" inter-Landau level transitions (ILTs). We observed avoided crossings in the LPP dispersion - a hallmark of the strong coupling regime - occurring when the magnetoexciton and hBN phonon frequencies matched. Our LPP-based nanoscopy also enabled us to resolve two fundamental many-body effects: the graphene Fermi velocity renormalization and ILT-dependent magnetoexciton binding energies. These results indicate that magnetic-field-tuned Dirac heterostructures are promising platforms for precise nanoscale control and sensing of light-matter interaction.

physics.optics↗
arXiv2023

Functional Connectivity via Total Correlation: Analytical results in Visual Areas

Recent studies invoke the superiority of the multivariate Total Correlation concept over the conventional pairwise measures of functional connectivity in biological networks. Those seminal works certainly show that empirical measures of Total Correlation lead to connectivity patterns that differ from what is obtained using the most popular measure, linear correlation, or its higher order and nonlinear alternative Mutual Information. However, they do not provide analytical results that explain the differences beyond the obvious multivariate versus bivariate definitions. Moreover, the accuracy of the empirical estimators could not be addressed directly because no controlled scenario with known analytical result was provided either. This point is critical because empirical estimation of information theory measures is always challenging. As opposed to previous empirical approaches, in this work we present analytical results to prove the advantages of Total Correlation over Mutual Information to describe the functional connectivity. In particular, we do it in neural networks for early vision (retina-LGN-cortex) which are realistic but simple enough to get analytical results. The presented analytical setting is also useful to check empirical estimates of Total Correlation. Therefore, once certain estimate can be trusted, one can explore the behavior with natural signals where the analytical results (that assume Gaussian signals), may not be valid. In this regard, as applications (a) we explore the effect of connectivity and feedback in the analytical retina-LGN-cortex network with natural images, and (b) we assess the functional connectivity in visual areas V1-V2-V3-V4 from actual fMRI recordings.

q-bio.NC↗
arXiv2023

The physics case for neutrino-neutrino collisions

Addressing the mass origin and properties of neutrinos is of strong interest to particle physics, baryogenesis and cosmology. Popular explanations involve physics beyond the standard model, for example, the dimension-5 Weinberg operator or heavy Majorana neutrinos arising from ``seesaw'' models. The current best direct limits on the electron neutrino mass, derived from nuclei beta decay or neutrinoless double beta decay processes, are at the sub-electronvolt level. Here we propose a novel neutrino neutrino collider where the neutrino beam is generated from TeV scale muon decays. Such collisions can happen between either neutrinos and anti-neutrinos, or neutrinos and neutrinos. We find that with a tiny integrated luminosity of about $10^{-5}$/fb we can already expect to observe direct neutrino anti-neutrino annihilation, $ν\barν\rightarrow {\rm Z}$, which also opens the door to explore neutrino related resonances $ν\barν\rightarrow {\rm X}$. The low luminosity requirement can accommodate a relatively large emittance muon beam. Such a device would also allow for probing heavy Majorana neutrino and effective Majorana neutrino mass through $νν\rightarrow {\rm H H}$ to a competitive level, for both electron and muon types.

hep-ph↗
arXiv2023

Integrated Robotics Networks with Co-optimization of Drone Placement and Air-Ground Communications

Terrestrial robots, i.e., unmanned ground vehicles (UGVs), and aerial robots, i.e., unmanned aerial vehicles (UAVs), operate in separate spaces. To exploit their complementary features (e.g., fields of views, communication links, computing capabilities), a promising paradigm termed integrated robotics network emerges, which provides communications for cooperative UAVs-UGVs applications. However, how to efficiently deploy UAVs and schedule the UAVs-UGVs connections according to different UGV tasks become challenging. In this paper, we propose a sum-rate maximization problem, where UGVs plan their trajectories autonomously and are dynamically associated with UAVs according to their planned trajectories. Although the problem is a NP-hard mixed integer program, a fast polynomial time algorithm using alternating gradient descent and penalty-based binary relaxation, is devised. Simulation results demonstrate the effectiveness of the proposed algorithm.

eess.SP↗
arXiv2023

Deep Learning Assisted Multiuser MIMO Load Modulated Systems for Enhanced Downlink mmWave Communications

This paper is focused on multiuser load modulation arrays (MU-LMAs) which are attractive due to their low system complexity and reduced cost for millimeter wave (mmWave) multi-input multi-output (MIMO) systems. The existing precoding algorithm for downlink MU-LMA relies on a sub-array structured (SAS) transmitter which may suffer from decreased degrees of freedom and complex system configuration. Furthermore, a conventional LMA codebook with codewords uniformly distributed on a hypersphere may not be channel-adaptive and may lead to increased signal detection complexity. In this paper, we conceive an MU-LMA system employing a full-array structured (FAS) transmitter and propose two algorithms accordingly. The proposed FAS-based system addresses the SAS structural problems and can support larger numbers of users. For LMA-imposed constant-power downlink precoding, we propose an FAS-based normalized block diagonalization (FAS-NBD) algorithm. However, the forced normalization may result in performance degradation. This degradation, together with the aforementioned codebook design problems, is difficult to solve analytically. This motivates us to propose a Deep Learning-enhanced (FAS-DL-NBD) algorithm for adaptive codebook design and codebook-independent decoding. It is shown that the proposed algorithms are robust to imperfect knowledge of channel state information and yield excellent error performance. Moreover, the FAS-DL-NBD algorithm enables signal detection with low complexity as the number of bits per codeword increases.

eess.SP↗
arXiv2023

Muon Beam for Neutrino CP Violation: connecting energy and neutrino frontiers

We propose here a proposal to connect neutrino and energy frontiers, by exploiting collimated muon beams for neutrino oscillations, which generate symmetric neutrino and antineutrino sources: $μ^+\rightarrow e^+\,\barν_μ\, ν_{e}$ and $μ^-\rightarrow e^-\, ν_μ \,\barν_{e}$. Interfacing with long baseline neutrino detectors such as DUNE and T2K, this experiment can be applicable to measure tau neutrino properties, and also to probe neutrino CP phase, by measuring muon electron (anti-)neutrino mixing or tau (anti-)neutrino appearance, and differences between neutrino and antineutrino rates. There are several significant benefits leading to large neutrino flux and high sensitivity on CP phase, including 1) collimated and manipulable muon beams, which lead to a larger acceptance of neutrino sources in the far detector side; 2) symmetric $μ^+$ and $μ^-$ beams, and thus symmetric neutrino and antineutrino sources, which make this proposal ideally useful for measuring neutrino CP violation. More importantly, $\barν_{e,μ}\rightarrow\barν_τ$ and $ν_{e,μ}\rightarrow ν_τ$, and, $\barν_{e}\rightarrow\barν_μ$ and $ν_{e}\rightarrow ν_μ$ oscillation signals can be collected simultaneously, with no needs for separate specific runs for neutrinos or antineutrinos. Based on a simulation of neutrino oscillation experiment, we estimate $10^4$ tau (anti-) neutrinos can be collected within 5 years which makes this proposal suitable for a brighter tau neutrino factory. Moreover, more than 7 standard deviations of sensitivity can be reached for $\dcp = |π/2|$, within only five ears of data taking, by combining tau and muon (anti-) neutrino appearances. With the development of a more intensive muon beam targeting future muon collider, the neutrino potential of the current proposal will surely be further improved.

hep-ph↗
arXiv2023

Aberrant High-Order Dependencies in Schizophrenia Resting-State Functional MRI Networks

The human brain has a complex, intricate functional architecture. While many studies primarily emphasize pairwise interactions, delving into high-order associations is crucial for a comprehensive understanding of how functional brain networks intricately interact beyond simple pairwise connections. Analyzing high-order statistics allows us to explore the nuanced and complex relationships across the brain, unraveling the heterogeneity and uncovering patterns of multilevel overlap on the psychosis continuum. Here, we employed high-order independent component analysis (ICA) plus multivariate information-theoretical metrics ($O$-information and $S$-information) to estimate high-order interaction to examine schizophrenia using resting-state fMRI. The results show that multiple brain regions networks may be altered in schizophrenia, such as temporal, subcortical, and higher-cognitive brain regions, and meanwhile, it also shows that revealed synergy gives more information than redundancy in diagnosing schizophrenia. All in all, we showed that high-order dependencies were altered in schizophrenia. Identification of these aberrant patterns will give us a new window to diagnose schizophrenia.

q-bio.NC↗
arXiv2023

A Survey of Methods for Handling Disk Data Imbalance

Class imbalance exists in many classification problems, and since the data is designed for accuracy, imbalance in data classes can lead to classification challenges with a few classes having higher misclassification costs. The Backblaze dataset, a widely used dataset related to hard discs, has a small amount of failure data and a large amount of health data, which exhibits a serious class imbalance. This paper provides a comprehensive overview of research in the field of imbalanced data classification. The discussion is organized into three main aspects: data-level methods, algorithmic-level methods, and hybrid methods. For each type of method, we summarize and analyze the existing problems, algorithmic ideas, strengths, and weaknesses. Additionally, the challenges of unbalanced data classification are discussed, along with strategies to address them. It is convenient for researchers to choose the appropriate method according to their needs.

cs.LG↗