Malicious Agent Detection for Robust Multi-Agent Collaborative Perception

Recently, multi-agent collaborative (MAC) perception has been proposed and outperformed the traditional single-agent perception in many applications, such as autonomous driving. However, MAC perception is more vulnerable to adversarial attacks than single-agent perception due to the information exchange. The attacker can easily degrade the performance of a victim agent by sending harmful information from a malicious agent nearby. In this paper, we extend adversarial attacks to an important perception task -- MAC object detection, where generic defenses such as adversarial training are no longer effective against these attacks. More importantly, we propose Malicious Agent Detection (MADE), a reactive defense specific to MAC perception that can be deployed by each agent to accurately detect and then remove any potential malicious agent in its local collaboration network. In particular, MADE inspects each agent in the network independently using a semi-supervised anomaly detector based on a double-hypothesis test with the Benjamini-Hochberg procedure to control the false positive rate of the inference. For the two hypothesis tests, we propose a match loss statistic and a collaborative reconstruction loss statistic, respectively, both based on the consistency between the agent to be inspected and the ego agent where our detector is deployed. We conduct comprehensive evaluations on a benchmark 3D dataset V2X-sim and a real-road dataset DAIR-V2X and show that with the protection of MADE, the drops in the average precision compared with the best-case "oracle" defender against our attack are merely 1.28% and 0.34%, respectively, much lower than 8.92% and 10.00% for adversarial training, respectively

Lemur: Harmonizing Natural Language and Code for Language Agents

We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents. The evolution from language chat models to functional language agents demands that models not only master human interaction, reasoning, and planning but also ensure grounding in the relevant environments. This calls for a harmonious blend of language and coding capabilities in the models. Lemur and Lemur-Chat are proposed to address this necessity, demonstrating balanced proficiencies in both domains, unlike existing open-source models that tend to specialize in either. Through meticulous pre-training using a code-intensive corpus and instruction fine-tuning on text and code data, our models achieve state-of-the-art averaged performance across diverse text and coding benchmarks among open-source models. Comprehensive experiments demonstrate Lemur's superiority over existing open-source models and its proficiency across various agent tasks involving human communication, tool usage, and interaction under fully- and partially- observable environments. The harmonization between natural and programming languages enables Lemur-Chat to significantly narrow the gap with proprietary models on agent abilities, providing key insights into developing advanced open-source agents adept at reasoning, planning, and operating seamlessly across environments. https://github.com/OpenLemur/Lemu

Sub-Bottom Sediment Classification Using Reliable Instantaneous Frequency Calculation and Relaxation Time Estimation

The shift in IF (instantaneous frequency) series and the corresponding relaxation time have the potential to characterize sediment properties. However, these attributes derived from SBP (sub-bottom profiler) data are seldom used for offshore site investigations because of the unsoundness in attribute calculation. To overcome this problem, a new reliable method combining VMD (variational mode decomposition) and WVD (Wigner–Ville distribution), as well as relaxation time, is presented. Since the number of modes in classical VMD should be provided in advance, a modified VMD algorithm, MVMD (modified variational mode decomposition), is proposed here, where the distribution of the frequency domain of modes is taken into account to automatically determine the number of modes. Through the relaxation time model, the IF data of a series of pings calculated through MVMD-WVD are transformed into a relaxation time map. A robust estimation algorithm is applied to the relaxation time map to reduce the effects of interferences and obtain robust relaxation times. The final relaxation time data are used to determine the sediment types. Real data from SBP experiments, as well as borehole sampling and geotechnical analysis results, verified the good performance of the proposed method

A High-Phosphorus-Content Polyphosphonate with Combined Phosphorus Structures for Flame Retardant PET

A high-phosphorus-content polyphosphonate (PBDA), containing two phosphorus-based structures: phosphaphenanthrene (DOPO) and phenyl phosphonate groups, was synthesized and used in flame retardant polyethylene terephthalate (PET). Good self-extinguishing property (high UL 94 grade and LOI value), superior flame retardancy (lower heat/smoke release), and high quality retention (high carbon residue) were endowed to PET by PBDA. When 10 wt% PDBA was added, the peak heat release rate (pHRR), total heat release (THR), and total smoke rate (TSR) of PDBA/PET were found to be significantly reduced by 80%, 60.5%, and 21%, respectively, compared to the pure PET, and the LOI value jumped from 20.5% for pure PET to 28.7% with a UL-94 V-0 rating. The flame-retardant mode of action in PET was verified by thermogravimetric analysis-Fourier transform infrared (TGA-FTIR), pyrolysis gas chromatography/mass spectrometry (Py-GC/MS), real-time FTIR, and scanning electron microscopy (SEM). Phosphaphenanthrene and phosphonate moieties in PDBA decomposed in sequence during heating, continuously releasing and keeping high-content PO· and PO2· radicals with a quenching effect and simultaneously promoting the formation of viscous crosslinked char layers causing a high barrier effect. PDBA mainly acted in the gas phase but the condensed-phase flame retardant function was also considerable

An Integrated Horizon Picking Method for Obtaining the Main and Detailed Reflectors on Sub-Bottom Profiler Sonar Image

A sub-bottom profiler (SBP) can capture the sediment interfaces and properties of different types of sediment. Horizon picking from SBP images is one of the most crucial steps in marine sub-bottom sediment interpretation. However, traditional horizon picking methods are good at obtaining the main horizons representing the main reflectors while ignoring the detailed horizons. While detailed horizons are the prime objective, many tiny structures caused by interference echoes will also be picked. To overcome this limitation, an integrated horizon picking method for obtaining the main and detailed horizons simultaneously is proposed in this paper. A total of three main process steps: the diffusion filtering method, the enhancement filtering method as well as the local phase calculation method, are used to help obtain the main and detailed horizons. The diffusion filtering method smooths the SBP images and preserves reflectors. Enhancement filtering can eliminate outliers and enhance reflectors. The local phase can be used to highlight all of the reflections and help in the choosing of detailed horizons. A series of experiments were then performed to validate the effectiveness of the proposed method, and good performances were achieved

A Non-Local Low-Rank Algorithm for Sub-Bottom Profile Sonar Image Denoising

Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods. In this paper, a novel SBP image denoising method is developed for obtaining underlying clean images based on a non-local low-rank framework. Firstly, to take advantage of the inherent layering structures of the SBP image, a direction image is obtained and used as a guidance image. Secondly, the robust guidance weight for accurately selecting the similar patches is given. A novel denoising method combining the weight and a non-local low-rank filtering framework is proposed. Thirdly, after discussing the filtering parameter settings, the proposed method is tested in actual measurements of sub-bottom, both in deep water and shallow water. Experimental results validate the excellent performance of the proposed method. Finally, the proposed method is verified and compared with other methods quantificationally based on the synthetic images and has achieved the total average peak signal-to-noise ratio (PSNR) of 21.77 and structural similarity index (SSIM) of 0.573, which is far better than other methods

Preparation of an organometallic complex based on phosphonitrile and its flame retardant application in epoxy resin

An organo-cobalt coordination complex (Co-H4APD), based on phosphonitrile-azacycles, was prepared by hydrothermal method. The flame retardancy, smoke suppression and thermal stability of epoxy (EP) composites were investigate by means of limited oxygen index (LOI), cone calorimeter test (CONE) and thermogravimetric analysis (TGA). The flame retardant modes of action of Co-H4APD in EP were confirmed by experiments, such as thermogravimetry-Fourier transform infrared spectroscopy-gas chromatograph/mass spectrometer (TG-FTIR-GC/MS), exploring condense and gas-phase products after composites pyrolysis or combustion. Results revealed that the introduction of 6 wt.% Co-H4APD increased LOI value to 29.8% and effectively suppressed heat/smoke release of EP composites. The synergistic charring effect of Co-H4APD improved the thermal stability and char-forming ability of composites. The char strength may be well-correlated with gas release for EP/Co-H4APD, conducive to form dense and regularly expanded char layer, with more phosphorus-rich graphitic structures and cross-linking structures catalyzed by cobalt ions. This high-quality char layer was regarded as the most critical side in improving the flame retardant and smoke suppression performance of EP composites. The gas-phase function of Co-H4APD should not be overlooked due to releasing phosphorous-based radicals during pyrolysis, exhibiting flame inhibition effect in gas phase. More efficient interactions between phosphazene and cobalt within one molecule unit of Co-H4APD contributed to its more obvious reduction of the combustion and smoke production than those of the physical mixing system of CoO + H4APD

A Novel Horizon Picking Method on Sub-Bottom Profiler Sonar Images

Traditional manual horizon picking is time-consuming and laborious, while automatic picking methods often suffer from the limited scope of their applications and the discontinuity of picked results. In this paper, we propose a novel method for automatic horizon picking from sub-bottom profiles (SBP) by an improved filtering algorithm. First, a clear and fine SBP image is formed using an intensity transformation method. On this basis, a novel filtering method is proposed by improving the multi-scale enhancement filtering algorithm to obtain clear horizons from an SBP image. The improvement is performed by applying a vertical suppression weighting term based on the form of logistic function, which is constructed by using the eigenvectors from the Hessian matrix. Then, the filtered image is segmented using a threshold method, and the horizon points in the SBP image are picked. After that, a horizon linking method is applied, which uses the horizon directions to refine the picked horizon points. The proposed method has been verified experimentally, and accurate and continuous horizons were obtained. Finally, the proposed method is discussed and some conclusions are drawn