A study on social-based cooperative sensing in cognitive radio networks

A cognitive radio (CR) is an intelligent radio that reuses frequency band based on dynamic spectrum access (DSA). CR implements spectrum sensing to detect primary users' (PU) presence, and exploits available spectrum without interfering PU. In contrast with local spectrum sensing, cooperative sensing which is implemented by multiple CRs, is more efficient and effective generally. Previous work on cooperative spectrum sensing in cognitive radio (CR) assumes a default mode that CRs are willing to cooperate for others unconditionally. While this situation does not always hold, the requested CR might reject the cooperation request due to its insufficient energy, or security concerns. In this thesis, we propose a social-based cooperative sensing scheme (SBC) that exploits social ties of CRs on their cooperative sensing. Simulation results show that SBC fulfills improved sensing quality, and the sensing performance of CRs correlate to the social degree and social network topology

Visualizing Drug Release from a Stimuli-Responsive Soft Material Based on Amine-Thiol Displacement

In this research, we developed a photoluminescent platform using amine-coupled fluorophores, generated from a single conjugate acceptor containing bis-vinylogous thioesters. Based on the experimental and computational results, the fluorescence turn-on mechanism was proposed to be charge separated induced energy radiative transition for the amine-coupled fluorophore, while the sulfur-containing precursor was not fluorescent since the energy internal conversion occurred through vibrational 2RS- (R represents alkyl groups) as energy acceptor(s). Further utilizing the conjugate acceptor, we establish a new fluorogenic approach via a highly cross-linked soft material to selectively detect cysteine under neutral aqueous conditions. Turn-on fluorescence emission and macroscopic degradation occurred in the presence of cysteine as the stimuli, which can be visually tracked due to the generation of an optical indicator and the cleavage of linkers within the matrix. Furthermore, a novel drug delivery system was constructed, achieving controlled release of sulfhydryl drug (6-mercaptopurine) which was tracked by photoluminescence and high-performance liquid chromatography. The photoluminescent molecules developed herein are suitable for visualizing polymeric degradation, making them suitable for additional “smart” material applications.</p

Visualizing Drug Release from a Stimuli-Responsive Soft Material Based on Amine-Thiol Displacement

In this research, we developed a photoluminescent platform using amine-coupled fluorophores, generated from a single conjugate acceptor containing bis-vinylogous thioesters. Based on the experimental and computational results, the fluorescence turn-on mechanism was proposed to be charge separated induced energy radiative transition for the amine-coupled fluorophore, while the sulfur-containing precursor was not fluorescent since the energy internal conversion occurred through vibrational 2RS- (R represents alkyl groups) as energy acceptor(s). Further utilizing the conjugate acceptor, we establish a new fluorogenic approach via a highly cross-linked soft material to selectively detect cysteine under neutral aqueous conditions. Turn-on fluorescence emission and macroscopic degradation occurred in the presence of cysteine as the stimuli, which can be visually tracked due to the generation of an optical indicator and the cleavage of linkers within the matrix. Furthermore, a novel drug delivery system was constructed, achieving controlled release of sulfhydryl drug (6-mercaptopurine) which was tracked by photoluminescence and high-performance liquid chromatography. The photoluminescent molecules developed herein are suitable for visualizing polymeric degradation, making them suitable for additional “smart” material applications.</p

Knee loading inhibits osteoclast lineage in a mouse model of osteoarthritis

Osteoarthritis (OA) is a whole joint disorder that involves cartilage degradation and periarticular bone response. Changes of cartilage and subchondral bone are associated with development and activity of osteoclasts from subchondral bone. Knee loading promotes bone formation, but its effects on OA have not been well investigated. Here, we hypothesized that knee loading regulates subchondral bone remodeling by suppressing osteoclast development, and prevents degradation of cartilage through crosstalk of bone-cartilage in osteoarthritic mice. Surgery-induced mouse model of OA was used. Two weeks application of daily dynamic knee loading significantly reduced OARSI scores and CC/TAC (calcified cartilage to total articular cartilage), but increased SBP (subchondral bone plate) and B.Ar/T.Ar (trabecular bone area to total tissue area). Bone resorption of osteoclasts from subchondral bone and the differentiation of osteoclasts from bone marrow-derived cells were completely suppressed by knee loading. The osteoclast activity was positively correlated with OARSI scores and negatively correlated with SBP and B.Ar/T.Ar. Furthermore, knee loading exerted protective effects by suppressing osteoclastogenesis through Wnt signaling. Overall, osteoclast lineage is the hyper responsiveness of knee loading in osteoarthritic mice. Mechanical stimulation prevents OA-induced cartilage degeneration through crosstalk with subchondral bone. Knee loading might be a new potential therapy for osteoarthritis patients

Detecting Backdoors in Pre-trained Encoders

Self-supervised learning in computer vision trains on unlabeled data, such as images or (image, text) pairs, to obtain an image encoder that learns high-quality embeddings for input data. Emerging backdoor attacks towards encoders expose crucial vulnerabilities of self-supervised learning, since downstream classifiers (even further trained on clean data) may inherit backdoor behaviors from encoders. Existing backdoor detection methods mainly focus on supervised learning settings and cannot handle pre-trained encoders especially when input labels are not available. In this paper, we propose DECREE, the first backdoor detection approach for pre-trained encoders, requiring neither classifier headers nor input labels. We evaluate DECREE on over 400 encoders trojaned under 3 paradigms. We show the effectiveness of our method on image encoders pre-trained on ImageNet and OpenAI's CLIP 400 million image-text pairs. Our method consistently has a high detection accuracy even if we have only limited or no access to the pre-training dataset.Comment: Accepted at CVPR 2023. Code is available at https://github.com/GiantSeaweed/DECRE

LOTUS: Evasive and Resilient Backdoor Attacks through Sub-Partitioning

Backdoor attack poses a significant security threat to Deep Learning applications. Existing attacks are often not evasive to established backdoor detection techniques. This susceptibility primarily stems from the fact that these attacks typically leverage a universal trigger pattern or transformation function, such that the trigger can cause misclassification for any input. In response to this, recent papers have introduced attacks using sample-specific invisible triggers crafted through special transformation functions. While these approaches manage to evade detection to some extent, they reveal vulnerability to existing backdoor mitigation techniques. To address and enhance both evasiveness and resilience, we introduce a novel backdoor attack LOTUS. Specifically, it leverages a secret function to separate samples in the victim class into a set of partitions and applies unique triggers to different partitions. Furthermore, LOTUS incorporates an effective trigger focusing mechanism, ensuring only the trigger corresponding to the partition can induce the backdoor behavior. Extensive experimental results show that LOTUS can achieve high attack success rate across 4 datasets and 7 model structures, and effectively evading 13 backdoor detection and mitigation techniques. The code is available at https://github.com/Megum1/LOTUS.Comment: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024

Variable-Permeability Well-Testing Models and Pressure Response in Low-Permeability Reservoirs with non-Darcy Flow

This paper proposes the concept of variable-permeability effect and sets up the one-dimensional and two-dimensional non-Darcy well testing models. The finite difference algorithm is employed to solve the differential equations of the variable-permeability model, and the non-convergence of the numerical solutions is solved by using the geometric mean of permeability. The type curves of pressure and pressure derivative with variable-permeability effect are obtained, and sensitivity analysis is conducted. The results show that the type curves upturn in the middle and late sections, and the curves turn more upward with the severer of the variable-permeability effect. The severer the non-Darcy effect is, the less obviously the curve upturns caused by boundary effect. Furthermore, the boundary effect is increased by increasing the number of impermeable boundaries or decreasing the distance between the well and boundary