When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing

Defense strategies have been well studied to combat Byzantine attacks that aim to disrupt cooperative spectrum sensing by sending falsified versions of spectrum sensing data to a fusion center. However, existing studies usually assume network or attackers as passive entities, e.g., assuming the prior knowledge of attacks is known or fixed. In practice, attackers can actively adopt arbitrary behaviors and avoid pre-assumed patterns or assumptions used by defense strategies. In this paper, we revisit this security vulnerability as an adversarial machine learning problem and propose a novel learning-empowered attack framework named Learning-Evaluation-Beating (LEB) to mislead the fusion center. Based on the black-box nature of the fusion center in cooperative spectrum sensing, our new perspective is to make the adversarial use of machine learning to construct a surrogate model of the fusion center's decision model. We propose a generic algorithm to create malicious sensing data using this surrogate model. Our real-world experiments show that the LEB attack is effective to beat a wide range of existing defense strategies with an up to 82% of success ratio. Given the gap between the proposed LEB attack and existing defenses, we introduce a non-invasive method named as influence-limiting defense, which can coexist with existing defenses to defend against LEB attack or other similar attacks. We show that this defense is highly effective and reduces the overall disruption ratio of LEB attack by up to 80%

Observation of Viruses, Bacteria, and Fungi in Clinical Skin Samples under Transmission Electron Microscopy

The highlight of this chapter is the description of the clinical manifestation and its pathogen and the host tissue damage observed under the transmission electron microscopy, which helps the clinician understand the pathogen’s ultrastructure, the change of host sub-cell structure, and helps the laboratory workers understand the pathogen-induced human skin lesions’ clinical characteristics, to establish a two-way learning exchange database with vivid images

Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection in OCTA Images

Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature. Ophthalmologists commonly use region-based analysis, such as the ETDRS grid, to study OCTA image biomarkers and understand the correlation with AD. However, existing studies have used general deep computer vision methods, which present challenges in providing interpretable results and leveraging clinical prior knowledge. To address these challenges, we propose a novel deep-learning framework called Polar-Net. Our approach involves mapping OCTA images from Cartesian coordinates to polar coordinates, which allows for the use of approximate sector convolution and enables the implementation of the ETDRS grid-based regional analysis method commonly used in clinical practice. Furthermore, Polar-Net incorporates clinical prior information of each sector region into the training process, which further enhances its performance. Additionally, our framework adapts to acquire the importance of the corresponding retinal region, which helps researchers and clinicians understand the model's decision-making process in detecting AD and assess its conformity to clinical observations. Through evaluations on private and public datasets, we have demonstrated that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD. In addition, we also show that the two innovative modules introduced in our framework have a significant impact on improving overall performance.Comment: Accepted by MICCAI202

A combination of phospholipids and long chain polyunsaturated fatty acids supports neurodevelopmental outcomes in infants: a randomized, double-blind, controlled clinical trial

Phospholipids (PLs) and long-chain polyunsaturated fatty acids (LCPUFAs) are naturally present in breast milk and play important roles in promoting the growth of the infant. Several studies have investigated the effects of the combination of PLs and LCPUFAs on neurodevelopment. However, data on the effectiveness of infant formula containing both PLs and LCPUFAs on the neurodevelopment of infants is still scarce. This randomized, double-blind, controlled clinical study was designed to evaluate the effect of an infant formula enriched with PLs and LCPUFAs on growth parameters and neurodevelopmental outcomes in term infants up to 365 days of age. Infants were enrolled within 30 days of birth who were then randomly assigned to either a control group (n = 150) or an investigational group (n = 150). Both groups consist of cow’s milk-based formula which were generally identical in terms of composition, except that the investigational formula was additionally supplemented with PLs and LCPUFAs. The infants were followed for the first year of life. Breastfed infants were the reference (n = 150). Bayley Scales of Infant Development [3rd edition (Bayley-III)], Carey Toddler Temperament Scales (TTS), MacArthur-Bates Communicative Development Inventories (CDI), Single Object Attention and Free Play Tasks were used to evaluate neurodevelopmental outcomes of infant at 365 days of age. In addition, Ages and Stages Questionnaires (ASQ) were also conducted at 120, 180, and 275 days of age. Compared to breastfeeding, both infant formulas were well-tolerated and provided adequate growth, with no adverse events being reported throughout the study. Infants of the investigational group showed higher mean scores in Bayley-III cognitive performance (104.3 vs. 99.0, p < 0.05), language (106.9 vs. 104.5, p < 0.05), and motor skills (109.2 vs. 103.9, p < 0.05) compared the control group. Similar results were being reported for other developmental scales including TTS and ASQ. Notably, the test scores of infants fed the investigational formula were similar to those who were breastfed. Our results indicate that PL and LCPUFA supplementation may be beneficial for neurodevelopment of infants throughout the first year of life. Further studies are needed to investigation long-term effects PL and LCPUFA on neurodevelopment in early life

Preparation and properties of antistatic high-strength aramid III/MWCNTs-OH fibers

Composite fibers made from aramid III and hydroxylated multiwalled carbon nanotubes (MWCNTs-OH) combine the excellent mechanical and electrical properties of both components, resulting in strong antistatic performance. However, it is of paramount importance to ensure the homogeneous dispersion of multi-walled carbon nanotubes functionalized with hydroxyl groups (MWCNTs-OH) within the aramid III spinning solution and optimize the compatibility between the two constituents to augment the overall performance of the composite fibers. To this end, this investigation successfully accomplished the dispersion of MWCNTs-OH in the spinning solution and probed the dispersion mechanism using molecular dynamics simulations. Moreover, composite fibers, comprising 2.4 weight percent MWCNTs-OH, were initially fabricated using the wet spinning method. These fibers displayed a uniform texture and a tensile strength of 1.210 GPa, signifying a noteworthy enhancement of 113.25% in comparison to the strength prior to modification. With respect to thermal behavior, the fibers exhibited a mass reduction of 21.24% within the temperature range of 0°C–538°C. In the temperature interval from 538°C to 800°C, the mass loss diminished to 10.31%, representing a substantial 71.03% reduction when compared to the unmodified state. Remarkably, even when subjected to temperatures exceeding 800°C, the composite fibers retained a residual mass of 68.45%, indicating a notable 61.17% increase from their initial condition. In terms of electrical properties, the fibers exhibited a specific resistance (ρ) of 3.330 × 109 Ω cm, demonstrating effective antistatic behavior. In summary, the antistatic composite fibers studied in this paper can effectively mitigate the hazards of static electricity in various applications, including military protection and engineering equipment in both military and civilian fields