Robust Total Least Mean M-Estimate normalized subband filter Adaptive Algorithm for impulse noises and noisy inputs

When the input signal is correlated input signals, and the input and output signal is contaminated by Gaussian noise, the total least squares normalized subband adaptive filter (TLS-NSAF) algorithm shows good performance. However, when it is disturbed by impulse noise, the TLS-NSAF algorithm shows the rapidly deteriorating convergence performance. To solve this problem, this paper proposed the robust total minimum mean M-estimator normalized subband filter (TLMM-NSAF) algorithm. In addition, this paper also conducts a detailed theoretical performance analysis of the TLMM-NSAF algorithm and obtains the stable step size range and theoretical steady-state mean squared deviation (MSD) of the algorithm. To further improve the performance of the algorithm, we also propose a new variable step size (VSS) method of the algorithm. Finally, the robustness of our proposed algorithm and the consistency of theoretical and simulated values are verified by computer simulations of system identification and echo cancellation under different noise models

A Bearing-Angle Approach for Unknown Target Motion Analysis Based on Visual Measurements

Vision-based estimation of the motion of a moving target is usually formulated as a bearing-only estimation problem where the visual measurement is modeled as a bearing vector. Although the bearing-only approach has been studied for decades, a fundamental limitation of this approach is that it requires extra lateral motion of the observer to enhance the target's observability. Unfortunately, the extra lateral motion conflicts with the desired motion of the observer in many tasks. It is well-known that, once a target has been detected in an image, a bounding box that surrounds the target can be obtained. Surprisingly, this common visual measurement especially its size information has not been well explored up to now. In this paper, we propose a new bearing-angle approach to estimate the motion of a target by modeling its image bounding box as bearing-angle measurements. Both theoretical analysis and experimental results show that this approach can significantly enhance the observability without relying on additional lateral motion of the observer. The benefit of the bearing-angle approach comes with no additional cost because a bounding box is a standard output of object detection algorithms. The approach simply exploits the information that has not been fully exploited in the past. No additional sensing devices or special detection algorithms are required

model-based script synthesis for fuzzing

Kernel fuzzing is important for finding critical kernel vulnerabilities. Close-source (e.g., Windows) operating system kernel fuzzing is even more challenging due to the lack of source code. Existing approaches fuzz the kernel by modeling syscall sequences from traces or static analysis of system codes. However, a common limitation is that they do not learn and mutate the syscall sequences to reach different kernel states, which can potentially result in more bugs or crashes. In this paper, we propose WinkFuzz, an approach to learn and mutate traced syscall sequences in order to reach different kernel states. WinkFuzz learns syscall dependencies from the trace, identifies potential syscalls in the trace that can have dependent subsequent syscalls, and applies the dependencies to insert more syscalls while preserving the dependencies into the trace. Then WinkFuzz fuzzes the synthesized new syscall sequence to find system crashes. We applied WinkFuzz to four seed applications and found a total increase in syscall number of 70.8\%, with a success rate of 61\%, within three insert levels. The average time for tracing, dependency analysis, recovering model script, and synthesizing script was 600, 39, 34, and 129 seconds respectively. The instant fuzzing rate is 3742 syscall executions per second. However, the average fuzz efficiency dropped to 155 syscall executions per second when the initializing time, waiting time, and other factors were taken into account. We fuzzed each seed application for 24 seconds and, on average, obtained 12.25 crashes within that time frame.Comment: 12 pages, conference pape

VulMatch: Binary-level Vulnerability Detection Through Signature

Similar vulnerability repeats in real-world software products because of code reuse, especially in wildly reused third-party code and libraries. Detecting repeating vulnerabilities like 1-day and N-day vulnerabilities is an important cyber security task. Unfortunately, the state-of-the-art methods suffer from poor performance because they detect patch existence instead of vulnerability existence and infer the vulnerability signature directly from binary code. In this paper, we propose VulMatch to extract precise vulnerability-related binary instructions to generate the vulnerability-related signature. VulMatch detects vulnerability existence based on binary signatures. Unlike previous approaches, VulMatch accurately locates vulnerability-related instructions by utilizing source and binary codes. Our experiments were conducted using over 1000 vulnerable instances across seven open-source projects. VulMatch significantly outperformed the baseline tools Asm2vec and Palmtree. Besides the performance advantages over the baseline tools, VulMatch offers a better feature by providing explainable reasons during vulnerability detection. Our empirical studies demonstrate that VulMatch detects fine-grained vulnerability that the state-of-the-art tools struggle with. Our experiment on commercial firmware demonstrates VulMatch is able to find vulnerabilities in real-world scenario.Comment: 15 pages IEEE journal templat

3D printed architected hollow sphere foams with low-frequency phononic band gaps

We experimentally and numerically investigate elastic wave propagation in a class of lightweight architected materials composed of hollow spheres and binders. Elastic wave transmission tests demonstrate the existence of vibration mitigation capability in the proposed architected foams, which is validated against the numerically predicted phononic band gap. We further describe that the phononic band gap properties can be significantly altered through changing hollow sphere thickness and binder size in the architected foams. Importantly, our results indicate that by increasing the stiffness contrast between hollow spheres and binders, the phononic band gaps are broadened and shifted toward a low-frequency range. At the threshold stiffness contrast of 50, the proposed architected foam requires only a volume fraction of 10.8% while exhibiting an omnidirectional band gap size exceeding 130%. The proposed design paradigm and physical mechanisms are robust and applicable to architected foams with other topologies, thus providing new opportunities to design phononic metamaterials for low-frequency vibration control

Reactive oxygen species activated by mitochondria-specific camptothecin prodrug for enhanced chemotherapy

Camptothecin (CPT) has attracted much attention due to its potent antitumor activities. However, the undesirable physicochemical properties, including poor water-solubility, unstable lactone ring and severe adverse effects limit its further application. In this study, two water-soluble prodrugs, CPT-lysine (CPTK) and CPT-arginine (CPTR), were designed and synthesized by conjugating lysine or arginine with CPT, improving its solubility, pharmacokinetic properties and tumor penetration. Importantly, the introduction of arginine into CPTR contributed to the mitochondria-specific delivery, which increased mitochondrial reactive oxygen species (ROS) generation, induced mitochondria dysfunction and enhanced cell apoptosis and in vivo anti-cancer effect. This strategy is believed to hold great potential for organelle-specific synergistic anti-tumor therapy

GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images

As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident. In our work, we aim to train performant 3D generative models that synthesize textured meshes which can be directly consumed by 3D rendering engines, thus immediately usable in downstream applications. Prior works on 3D generative modeling either lack geometric details, are limited in the mesh topology they can produce, typically do not support textures, or utilize neural renderers in the synthesis process, which makes their use in common 3D software non-trivial. In this work, we introduce GET3D, a Generative model that directly generates Explicit Textured 3D meshes with complex topology, rich geometric details, and high-fidelity textures. We bridge recent success in the differentiable surface modeling, differentiable rendering as well as 2D Generative Adversarial Networks to train our model from 2D image collections. GET3D is able to generate high-quality 3D textured meshes, ranging from cars, chairs, animals, motorbikes and human characters to buildings, achieving significant improvements over previous methods.Comment: NeurIPS 2022, Project Page: https://nv-tlabs.github.io/GET3D

Diverse Feature Blend Based on Filter-Bank Common Spatial Pattern and Brain Functional Connectivity for Multiple Motor Imagery Detection

Motor imagery (MI) based brain-computer interface (BCI) is a research hotspot and has attracted lots of attention. Within this research topic, multiple MI classification is a challenge due to the difficulties caused by time-varying spatial features across different individuals. To deal with this challenge, we tried to fuse brain functional connectivity (BFC) and one-versus-the-rest filter-bank common spatial pattern (OVR-FBCSP) to improve the robustness of classification. The BFC features were extracted by phase locking value (PLV), representing the brain inter-regional interactions relevant to the MI, whilst the OVR-FBCSP is used to extract the spatial-frequency features related to the MI. These diverse features were then fed into a multi-kernel relevance vector machine (MK-RVM). The dataset with three motor imagery tasks (left hand MI, right hand MI, and feet MI) was used to assess the proposed method. Experimental results not only showed that the cascade structure of diverse feature fusion and MK-RVM achieved satisfactory classification performance (average accuracy: 83.81%, average kappa: 0.76), but also demonstrated that BFC plays a supplementary role in the MI classification. Moreover, the proposed method has a potential to be integrated into multiple MI online detection owing to the advantage of strong time-efficiency of RVM

Genetic analyses of the bidirectional associations between common mental disorders and asthma

ObjectiveAlthough extensive research has explored the link between mental disorders and asthma, the characteristics and patterns of this association are still unclear. Our study aims to examine the genetic causal links between common mental disorders (specifically, anxiety and depression) and asthma.MethodsWe conducted genetic analyses including linkage disequilibrium score regression (LDSC) and bidirectional two-sample Mendelian randomization (MR) analyses, and utilized summary statistics from recent large-scale Genome-Wide Association Studies (GWASs) in European populations, covering sensation of anxiety or depression, anxiety sensation, depression sensation, anxiety disorders, major depression disorder (MDD), and asthma.ResultsLDSC revealed significant genetic correlations among sensation of anxiety or depression, MDD and asthma (P < 0.017), highlighting potential genetic correlation between anxiety disorders and asthma (P < 0.05 yet > 0.017). In bidirectional two-sample MR, inverse-variance weighted (IVW) analyses suggested that genetic liability to asthma was significantly associated with an increased risk of sensation of anxiety or depression (OR = 4.760, 95%CI: 1.645–13.777), and MDD (OR = 1.658, 95%CI: 1.477–1.860). Conversely, IVW analyses indicated that genetic liability to anxiety disorders was not associated with an increased risk of asthma (P > 0.01), nor was genetic liability to asthma associated with an increased risk of anxiety disorders (P > 0.01). Furthermore, no significant genetic causal relationships were observed for other studied traits. Multivariate MR, after adjusting for body mass index and alcohol consumption, further corroborated the independent causal effect of genetic predisposition to MDD on the risk of asthma (OR = 1.460, 95% CI: 1.285–1.660).ConclusionOur study establishes MDD as a predisposing factor for asthma. Meanwhile, anxiety disorders are not causal risk factors for asthma, nor is the reverse true. It is recommended to closely monitor asthma symptoms in patients with MDD

Flight training changes the brain functional pattern in cadets

IntroductionTo our knowledge, this is the first study to use MRI (Magnetic Resonance Imaging) before and after an intensive flight training. This study aimed to investigate the effectiveness of flight training in civil flying cadets.MethodsThe civil flying cadets and controls completed two study visits. Visit 1 was performed in 2019, and high spatial resolution structural image and resting-state functional MRI data were collected. The second visit was completed in 2022. In addition to the MRI data mentioned above, participants completed the cognitive function assessment at the second visit.ResultsMixed-effect regression model analysis found that flight training enhanced the degree centrality (DC) values of the left middle frontal gyrus and left lingual gyrus. The subsequent correlation calculation analysis suggested a possible relationship between these alterations and cognitive function.DiscussionThese results suggest that flight training might promote the DC value of the prefrontal and occipital cortices and, in turn, enhance their executive function