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The autonomous driving industry is rapidly advancing, with Vehicle-to-Vehicle (V2V) communication systems highlighting as a key component of enhanced road safety and traffic efficiency. This paper introduces a novel Real-time Vehicle-to-Vehicle Communication Based Network Cooperative Control System (VVCCS), designed to revolutionize macro-scope traffic planning and collision avoidance in autonomous driving. Implemented on Quanser Car (Qcar) hardware platform, our system integrates the distributed databases into individual autonomous vehicles and an optional central server. We also developed a comprehensive multi-modal perception system with multi-objective tracking and radar sensing. Through a demonstration within a physical crossroad environment, our system showcases its potential to be applied in congested and complex urban environments., Comment: ICICT 2024, 18 pages

Recent advances in video compression have seen significant coding performance improvements with the development of new standards and learning-based video codecs. However, most of these works focus on application scenarios that allow a certain amount of system delay (e.g., Random Access mode in MPEG codecs), which is not always acceptable for live delivery. This paper conducts a comparative study of state-of-the-art conventional and learned video coding methods based on a low delay configuration. Specifically, this study includes two MPEG standard codecs (H.266/VVC VTM and JVET ECM), two AOM codecs (AV1 libaom and AVM), and two recent neural video coding models (DCVC-DC and DCVC-FM). To allow a fair and meaningful comparison, the evaluation was performed on test sequences defined in the AOM and MPEG common test conditions in the YCbCr 4:2:0 color space. The evaluation results show that the JVET ECM codecs offer the best overall coding performance among all codecs tested, with a 16.1% (based on PSNR) average BD-rate saving over AOM AVM, and 11.0% over DCVC-FM. We also observed inconsistent performance with the learned video codecs, DCVC-DC and DCVC-FM, for test content with large background motions.

Deep learning is now playing an important role in enhancing the performance of conventional hybrid video codecs. These learning-based methods typically require diverse and representative training material for optimization in order to achieve model generalization and optimal coding performance. However, existing datasets either offer limited content variability or come with restricted licensing terms constraining their use to research purposes only. To address these issues, we propose a new training dataset, named BVI-AOM, which contains 956 uncompressed sequences at various resolutions from 270p to 2160p, covering a wide range of content and texture types. The dataset comes with more flexible licensing terms and offers competitive performance when used as a training set for optimizing deep video coding tools. The experimental results demonstrate that when used as a training set to optimize two popular network architectures for two different coding tools, the proposed dataset leads to additional bitrate savings of up to 0.29 and 2.98 percentage points in terms of PSNR-Y and VMAF, respectively, compared to an existing training dataset, BVI-DVC, which has been widely used for deep video coding. The BVI-AOM dataset is available at https://github.com/fan-aaron-zhang/bvi-aom, Comment: 5 pages, 5 figures. Swapped the PSNR-HVS plot in Fig. 3 for a PSNR-YUV plot. Updated Fig. 3 (SI/TI/CF plots) and added the URL to the dataset

Rocket recycling is a crucial pursuit in aerospace technology, aimed at reducing costs and environmental impact in space exploration. The primary focus centers on rocket landing control, involving the guidance of a nonlinear underactuated rocket with limited fuel in real-time. This challenging task prompts the application of reinforcement learning (RL), yet goal-oriented nature of the problem poses difficulties for standard RL algorithms due to the absence of intermediate reward signals. This paper, for the first time, significantly elevates the success rate of rocket landing control from 8% with a baseline controller to 97% on a high-fidelity rocket model using RL. Our approach, called Random Annealing Jump Start (RAJS), is tailored for real-world goal-oriented problems by leveraging prior feedback controllers as guide policy to facilitate environmental exploration and policy learning in RL. In each episode, the guide policy navigates the environment for the guide horizon, followed by the exploration policy taking charge to complete remaining steps. This jump-start strategy prunes exploration space, rendering the problem more tractable to RL algorithms. The guide horizon is sampled from a uniform distribution, with its upper bound annealing to zero based on performance metrics, mitigating distribution shift and mismatch issues in existing methods. Additional enhancements, including cascading jump start, refined reward and terminal condition, and action smoothness regulation, further improve policy performance and practical applicability. The proposed method is validated through extensive evaluation and Hardware-in-the-Loop testing, affirming the effectiveness, real-time feasibility, and smoothness of the proposed controller., Comment: IROS 2024 Oral

Reinforcement learning (RL) has proven highly effective in addressing complex decision-making and control tasks. However, in most traditional RL algorithms, the policy is typically parameterized as a diagonal Gaussian distribution with learned mean and variance, which constrains their capability to acquire complex policies. In response to this problem, we propose an online RL algorithm termed diffusion actor-critic with entropy regulator (DACER). This algorithm conceptualizes the reverse process of the diffusion model as a novel policy function and leverages the capability of the diffusion model to fit multimodal distributions, thereby enhancing the representational capacity of the policy. Since the distribution of the diffusion policy lacks an analytical expression, its entropy cannot be determined analytically. To mitigate this, we propose a method to estimate the entropy of the diffusion policy utilizing Gaussian mixture model. Building on the estimated entropy, we can learn a parameter $\alpha$ that modulates the degree of exploration and exploitation. Parameter $\alpha$ will be employed to adaptively regulate the variance of the added noise, which is applied to the action output by the diffusion model. Experimental trials on MuJoCo benchmarks and a multimodal task demonstrate that the DACER algorithm achieves state-of-the-art (SOTA) performance in most MuJoCo control tasks while exhibiting a stronger representational capacity of the diffusion policy., Comment: NeurIPS2024 Accepted

Knowledge distillation (KD) has emerged as a promising technique in deep learning, typically employed to enhance a compact student network through learning from their high-performance but more complex teacher variant. When applied in the context of image super-resolution, most KD approaches are modified versions of methods developed for other computer vision tasks, which are based on training strategies with a single teacher and simple loss functions. In this paper, we propose a novel Multi-Teacher Knowledge Distillation (MTKD) framework specifically for image super-resolution. It exploits the advantages of multiple teachers by combining and enhancing the outputs of these teacher models, which then guides the learning process of the compact student network. To achieve more effective learning performance, we have also developed a new wavelet-based loss function for MTKD, which can better optimize the training process by observing differences in both the spatial and frequency domains. We fully evaluate the effectiveness of the proposed method by comparing it to five commonly used KD methods for image super-resolution based on three popular network architectures. The results show that the proposed MTKD method achieves evident improvements in super-resolution performance, up to 0.46dB (based on PSNR), over state-of-the-art KD approaches across different network structures. The source code of MTKD will be made available here for public evaluation.

Deep learning techniques have been applied in the context of image super-resolution (SR), achieving remarkable advances in terms of reconstruction performance. Existing techniques typically employ highly complex model structures which result in large model sizes and slow inference speeds. This often leads to high energy consumption and restricts their adoption for practical applications. To address this issue, this work employs a three-stage workflow for compressing deep SR models which significantly reduces their memory requirement. Restoration performance has been maintained through teacher-student knowledge distillation using a newly designed distillation loss. We have applied this approach to two popular image super-resolution networks, SwinIR and EDSR, to demonstrate its effectiveness. The resulting compact models, SwinIRmini and EDSRmini, attain an 89% and 96% reduction in both model size and floating-point operations (FLOPs) respectively, compared to their original versions. They also retain competitive super-resolution performance compared to their original models and other commonly used SR approaches. The source code and pre-trained models for these two lightweight SR approaches are released at https://pikapi22.github.io/CDISM/.

Large-scale cloud systems play a pivotal role in modern IT infrastructure. However, incidents occurring within these systems can lead to service disruptions and adversely affect user experience. To swiftly resolve such incidents, on-call engineers depend on crafting domain-specific language (DSL) queries to analyze telemetry data. However, writing these queries can be challenging and time-consuming. This paper presents a thorough empirical study on the utilization of queries of KQL, a DSL employed for incident management in a large-scale cloud management system at Microsoft. The findings obtained underscore the importance and viability of KQL queries recommendation to enhance incident management. Building upon these valuable insights, we introduce Xpert, an end-to-end machine learning framework that automates KQL recommendation process. By leveraging historical incident data and large language models, Xpert generates customized KQL queries tailored to new incidents. Furthermore, Xpert incorporates a novel performance metric called Xcore, enabling a thorough evaluation of query quality from three comprehensive perspectives. We conduct extensive evaluations of Xpert, demonstrating its effectiveness in offline settings. Notably, we deploy Xpert in the real production environment of a large-scale incident management system in Microsoft, validating its efficiency in supporting incident management. To the best of our knowledge, this paper represents the first empirical study of its kind, and Xpert stands as a pioneering DSL query recommendation framework designed for incident management., Comment: Accepted as a reseach paper at ICSE 2024

Shear wave elastography (SWE) has emerged as a new imaging modality that brings tissue mechanical properties as biomarkers potentially useful for early and precise diagnosis. While different SWE methods have been proposed, how to relate the frequency SWE measurements to quasi-static stiffnesses of tissues sensed by cells when prestresses are involved remains challenging. Here we suggest an incremental dynamics theory for prestressed viscoelastic solids and investigate its application in SWE across a broad frequency range. To model the power-law dispersion relation with minimal parameters, we introduce the Kelvin-Voigt fractional derivation model (KVFD) in the constitutive modeling of material viscoelasticity. To validate the usefulness of the theory, we performed experiments on prestressed soft materials and biological tissues. The results show that the theoretical solution fits the experimental dispersion curve well over a broad frequency range and accurately captures the effect of prestress. The theory also reveals the correlation of phase velocities and attenuations of shear waves with principal stresses and leads to a method for probing the prestress in a viscoelastic solid without prior knowledge of the constitutive parameters as validated by our numerical experiments. Taken together, our results show that the theory presented here enables the development of spatially resolved SWE when high-frequency shear waves get involved, and provides insights into wave motions in soft materials subject to prestresses.

Existing learning-based autonomous driving (AD) systems face challenges in comprehending high-level information, generalizing to rare events, and providing interpretability. To address these problems, this work employs Large Language Models (LLMs) as a decision-making component for complex AD scenarios that require human commonsense understanding. We devise cognitive pathways to enable comprehensive reasoning with LLMs, and develop algorithms for translating LLM decisions into actionable driving commands. Through this approach, LLM decisions are seamlessly integrated with low-level controllers by guided parameter matrix adaptation. Extensive experiments demonstrate that our proposed method not only consistently surpasses baseline approaches in single-vehicle tasks, but also helps handle complex driving behaviors even multi-vehicle coordination, thanks to the commonsense reasoning capabilities of LLMs. This paper presents an initial step toward leveraging LLMs as effective decision-makers for intricate AD scenarios in terms of safety, efficiency, generalizability, and interoperability. We aspire for it to serve as inspiration for future research in this field. Project page: https://sites.google.com/view/llm-mpc

Motion prediction is crucial for autonomous vehicles to operate safely in complex traffic environments. Extracting effective spatiotemporal relationships among traffic elements is key to accurate forecasting. Inspired by the successful practice of pretrained large language models, this paper presents SEPT, a modeling framework that leverages self-supervised learning to develop powerful spatiotemporal understanding for complex traffic scenes. Specifically, our approach involves three masking-reconstruction modeling tasks on scene inputs including agents' trajectories and road network, pretraining the scene encoder to capture kinematics within trajectory, spatial structure of road network, and interactions among roads and agents. The pretrained encoder is then finetuned on the downstream forecasting task. Extensive experiments demonstrate that SEPT, without elaborate architectural design or manual feature engineering, achieves state-of-the-art performance on the Argoverse 1 and Argoverse 2 motion forecasting benchmarks, outperforming previous methods on all main metrics by a large margin.

This is a companion report for the OOPSLA 2023 paper of the same title, presenting a detailed end-to-end account of the $\lambda^*_{\mathsf{G}}$ graph IR, at a level of detail beyond a regular conference paper. Our first concern is adequacy and soundness of $\lambda^*_{\mathsf{G}}$, which we derive from a direct-style imperative functional language (a variant of Bao et al.'s $\lambda^*$-calculus with reachability types and a simple effect system) by a series of type-preserving translations into a calculus in monadic normalform (MNF). Static reachability types and effects entirely inform $\lambda^*_{\mathsf{G}}$'s dependency synthesis. We argue for its adequacy by proving its functional properties along with dependency safety via progress and preservation lemmas with respect to a notion of call-by-value (CBV) reduction that checks the observed order of effects. Our second concern is establishing the correctness of $\lambda^*_{\mathsf{G}}$'s equational rules that drive compiler optimizations (e.g., DCE, $\lambda$-hoisting, etc.), by proving contextual equivalence using logical relations. A key insight is that the functional properties of dependency synthesis permit a logical relation on $\lambda^*_{\mathsf{G}}$ in MNF in terms of previously developed logical relations for the direct-style $\lambda^*$-calculus. Finally, we also include a longer version of the conference paper's section on code generation and code motion for $\lambda^*_{\mathsf{G}}$ as implemented in Scala~LMS., Comment: arXiv admin note: text overlap with arXiv:2309.05885

Band gap is known as an effective parameter for tuning the Lande $g$-factor in semiconductors and can be manipulated in a wide range through the bowing effect in ternary alloys. In this work, using the recently developed virtual substrate technique, high-quality InAsSb alloys throughout the whole Sb composition range are fabricated and a large $g$-factor of $g\approx -90$ at the minimum band gap of $\sim 0.1$ eV, which is almost twice that in bulk InSb is found. Further analysis to the zero gap limit reveals a possible gigantic $g$-factor of $g\approx -200$ with a peculiar relativistic Zeeman effect that disperses as the square root of magnetic field. Such a $g$-factor enhancement toward the narrow gap limit cannot be quantitatively described by the conventional Roth formula, as the orbital interaction effect between the nearly triply degenerated bands becomes the dominant source for the Zeeman splitting. These results may provide new insights into realizing large $g$-factors and spin polarized states in semiconductors and topological materials.

Using optical magneto-spectroscopy, we investigate the magnetic excitations of Na$_2$Co$_2$TeO$_6$ in a broad magnetic field range ($0\ \rm{T}\leq B\leq 17.5\ \rm{T}$) at low temperature. Our measurements reveal rich spectra of in-plane magnetic excitations with a surprisingly large number of modes, even in the high-field spin-polarized state. Theoretical calculations find that the Na-occupation disorder in \NCTO plays a crucial role in generating these modes. Our work demonstrates the necessity to consider disorder in the spin environment in the search for Kitaev quantum spin liquid states in practicable materials.

We report the temperature evolution of the Dirac band in semiconducting zirconium pentatelluride (ZrTe$_5$) using magneto-infrared spectroscopy. We find that the band gap is temperature independent at low temperatures and increases with temperature at elevated temperatures. Although such an observation seems to support a weak topological insulator phase at all temperatures and defy the previously reported topological phase transition (TPT) at an intermediate temperature in ZrTe$_5$, we show that it is also possible to explain the observation by considering the effect of conduction-valence band mixing and band inversion with a strong topological insulator phase at low temperatures. Our work provides an alternative picture of the band gap evolution across TPT.

Realizing a large Land\'{e} $g$-factor of electrons in solid-state materials has long been thought of as a rewarding task as it can trigger abundant immediate applications in spintronics and quantum computing. Here, by using metamorphic InAsSb/InSb superlattices (SLs), we demonstrate an unprecedented high value of $g\approx 104$, twice larger than that in bulk InSb, and fully spin-polarized states at low magnetic fields. In addition, we show that the $g$-factor can be tuned on demand from 20 to 110 via varying the SL period. The key ingredients of such a wide tunability are the wavefunction mixing and overlap between the electron and hole states, which have drawn little attention in prior studies. Our work not only establishes metamorphic InAsSb/InSb as a promising and competitive material platform for future quantum devices but also provides a new route toward $g$-factor engineering in semiconductor structures.

The identification of a non-trivial band topology usually relies on directly probing the protected surface/edge states. But, it is difficult to achieve electronically in narrow-gap topological materials due to the small (meV) energy scales. Here, we demonstrate that band inversion, a crucial ingredient of the non-trivial band topology, can serve as an alternative, experimentally accessible indicator. We show that an inverted band can lead to a four-fold splitting of the non-zero Landau levels, contrasting the two-fold splitting (spin splitting only) in the normal band. We confirm our predictions in magneto-transport experiments on a narrow-gap strong topological insulator, zirconium pentatelluride (ZrTe$_5$), with the observation of additional splittings in the quantum oscillations and also an anomalous peak in the extreme quantum limit. Our work establishes an effective strategy for identifying the band inversion as well as the associated topological phases for future topological materials research.

We investigate the sideband spectra of a driven nonlinear mode with its eigenfrequency being modulated at a low frequency (< 1 kHz). This additional parametric modulation leads to prominent antiresonance lineshapes in the sideband spectra, which can be controlled through the vibration state of the driven mode. We also establish a direct connection between the antiresonance frequency and the squeezing of thermal fluctuation in the system. Our work not only provides a simple and robust method for squeezing characterization but also opens a new possibility toward sideband applications.

DNA methylation is a well-studied genetic modification that regulates gene transcription of Eukaryotes. Its alternations have been recognized as a significant component of cancer development. In this study, we use the DNA methylation 450k data from The Cancer Genome Atlas to evaluate the efficacy of DNA methylation data on cancer classification for 30 cancer types. We propose a new method for gene selection in high dimensional data(over 450 thousand). Variance filtering is first introduced for dimension reduction and Recursive feature elimination (RFE) is then used for feature selection. We address the problem of selecting a small subsets of genes from large number of methylated sites, and our parsimonious model is demonstrated to be efficient, achieving an accuracy over 91%, outperforming other studies which use DNA micro-arrays and RNA-seq Data . The performance of 20 models, which are based on 4 estimators (Random Forest, Decision Tree, Extra Tree and Support Vector Machine) and 5 classifiers (k-Nearest Neighbours, Support Vector Machine, XGboost, Light GBM and Multi-Layer Perceptron), is compared and robustness of the RFE algorithm is examined. Results suggest that the combined model of extra tree plus catboost classifier offers the best performance in cancer identification, with an overall validation accuracy of 91% , 92.3%, 93.3% and 93.5% for 20, 30, 40 and 50 features respectively. The biological functions in cancer development of 50 selected genes is also explored through enrichment analysis and the results show that 12 out of 16 of our top features have already been identified to be specific with cancer and we also propose some more genes to be tested for future studies. Therefore, our method may be utilzed as an auxiliary diagnostic method to determine the actual clinicopathological status of a specific cancer., Comment: 39 pages

Broken symmetry and tilting effects are ubiquitous in Weyl semimetals (WSMs). Therefore, it is crucial to understand their impacts on the materials' electronic and optical properties. Here, using a realistic four-band model for WSMs that incorporates both the symmetry breaking and tilting effects, we study its Landau quantization and the associated magneto-absorption spectrum. We show that the Landau levels in tilted Weyl bands can be obtained by considering a non-tilt Hamiltonian through Lorentz boost. However, broken symmetry effects can generate an additional term in the Hamiltonian, which equivalently leads to band reconstruction. Our work provides a more realistic view of the magnetic field response of WSMs that shall be taken into account in relevant future device applications.

The clinical and economic burden of cardiovascular diseases (CVDs) poses a global challenge. Growing evidence suggests an early assessment of arterial stiffness can provide insights into the pathogenesis of CVDs. However, it remains difficult to quantitatively characterize the arterial stiffness in vivo. Here we utilize the guided elastic waves continuously excited and detected by ultrasound as a tool to probe the blood pressure (BP) and mechanical properties of the common carotid artery (CCA) simultaneously. In a pilot study of 17 healthy volunteers, we obverse a ~20% variation in the group velocity (5.16 m/s in systole and 4.31 m/s in diastole) induced by variation of the BP. A linear relationship between the square of the group velocity and the BP is revealed by our experimental data and finite element analysis, which enables us to measure the waveform of the BP. Furthermore, we propose to use the wavelet analysis to extract the dispersion relation of the guided waves, which provides a quantitative measurement of the arterial stiffness. The results and methods reported in this study show the group velocity and dispersion relation of the guided waves can be adopted to probe BP and arterial stiffness noninvasively and thus promising for early diagnosis of the CVDs., Comment: 19 pages, 5 figures

When surface states (SSs) form in topological insulators (TIs), they inherit the properties of bulk bands, including the electron-hole (e-h) asymmetry but with much more profound impacts. Here, via combining magneto-infrared spectroscopy with theoretical analysis, we show that e-h asymmetry significantly modifies the SS electronic structures when interplaying with the quantum confinement effect. Compared to the case without e-h asymmetry, the SSs now bear not only a band asymmetry as that in the bulk but also a shift of the Dirac point relative to the bulk bands and a reduction of the hybridization gap up to 70%. Our results signify the importance of e-h asymmetry in band engineering of TIs in the thin film limit., Comment: 10 pages,6 figures, including supporting information. Submission version. Added two references and revised acknowledgment. Accepted in Nano Lett