Simple parameter-free self-attention approximation

The hybrid model of self-attention and convolution is one of the methods to lighten ViT. The quadratic computational complexity of self-attention with respect to token length limits the efficiency of ViT on edge devices. We propose a self-attention approximation without training parameters, called SPSA, which captures global spatial features with linear complexity. To verify the effectiveness of SPSA combined with convolution, we conduct extensive experiments on image classification and object detection tasks

General bubble expansion at strong coupling

The strongly-coupled system like the quark-hadron transition (if it is of first order) is becoming an active play-yard for the physics of cosmological first-order phase transitions. However, the traditional field theoretic approach to strongly-coupled first-order phase transitions is of great challenge, driving recent efforts from holographic dual theories with explicit numerical simulations. These holographic numerical simulations have revealed an intriguing linear correlation between the phase pressure difference (pressure difference away from the wall) to the non-relativistic terminal velocity of an expanding planar wall, which has been reproduced analytically alongside both cylindrical and spherical walls from perfect-fluid hydrodynamics in our previous study but only for a bag equation of state. We have also found in our previous study a universal quadratic correlation between the wall pressure difference (pressure difference near the bubble wall) to the non-relativistic terminal wall velocity regardless of wall geometries. In this paper, we will generalize these analytic relations between the phase/wall pressure difference and terminal wall velocity into a more realistic equation of state beyond the simple bag model, providing the most general predictions so far for future tests from holographic numerical simulations of strongly-coupled first-order phase transitionsComment: 22 pages, 10 figure

Facial Data Minimization: Shallow Model as Your Privacy Filter

Face recognition service has been used in many fields and brings much convenience to people. However, once the user's facial data is transmitted to a service provider, the user will lose control of his/her private data. In recent years, there exist various security and privacy issues due to the leakage of facial data. Although many privacy-preserving methods have been proposed, they usually fail when they are not accessible to adversaries' strategies or auxiliary data. Hence, in this paper, by fully considering two cases of uploading facial images and facial features, which are very typical in face recognition service systems, we proposed a data privacy minimization transformation (PMT) method. This method can process the original facial data based on the shallow model of authorized services to obtain the obfuscated data. The obfuscated data can not only maintain satisfactory performance on authorized models and restrict the performance on other unauthorized models but also prevent original privacy data from leaking by AI methods and human visual theft. Additionally, since a service provider may execute preprocessing operations on the received data, we also propose an enhanced perturbation method to improve the robustness of PMT. Besides, to authorize one facial image to multiple service models simultaneously, a multiple restriction mechanism is proposed to improve the scalability of PMT. Finally, we conduct extensive experiments and evaluate the effectiveness of the proposed PMT in defending against face reconstruction, data abuse, and face attribute estimation attacks. These experimental results demonstrate that PMT performs well in preventing facial data abuse and privacy leakage while maintaining face recognition accuracy.Comment: 14 pages, 11 figure

Effects of blood flow restriction training on bone turnover markers, microstructure, and biomechanics in rats

ObjectiveThe present study aimed to investigate the effects of blood flow restriction training on muscle strength, bone tissue structure material, and biomechanical properties in rats applying various exercise interventions and to analyze the process by identifying the bone turnover markers, it provides a theoretical basis for the application of BFRT in clinical rehabilitation.MethodsA total of 24, 3-month-old male SD (Sprague Dawley) rats were randomly divided into pressurized control group (CON, n=6), low-intensity training group (LIRT, n=6), high-intensity training group (HIRT, n=6), and blood flow restriction training group (LIBFR, n=6) for 8-week ladder-climbing exercises. The pressured control group were given only ischemia treatments and did not undertake any burden. The low-intensity training group was allowed to climb the ladder with 30% of the maximum voluntary carrying capacity (MVCC). The rats in the high-intensity training group were allowed to climb the ladder with 70% MVCC. The blood flow restriction training group climbed the ladder with 30% MVCC while imposing blood flow restriction. Before sampling, the final MVCC was measured using a ladder-climbing protocol with progressively increasing weight loading. The serum, muscle, and bone were removed for sampling. The concentrations of the bone turnover markers PINP, BGP, and CTX in the serum were measured using ELISA. The bone mineral density and microstructure of femur bones were measured using micro-CT. Three-point bending and torsion tests were performed by a universal testing machine to measure the material mechanics and structural mechanics indexes of the femur bone.ResultsThe results of maximum strength test showed that the MVCC in LIRT, HIRT, and LIBFR groups was significantly greater than in the CON group, while the MVCC in the HIRT group was significantly higher than that in the LIRT group (P<0.05). According to the results of the bone turnover marker test, the concentrations of bone formation indexes PINP (amino-terminal extension peptide of type I procollagen) and BGP (bone gla protein) were significantly lower in the CON group than in the HIRT group (P<0.01), while those were significantly higher in the LIRT group compared to the HIRT group (P<0.01). In terms of bone resorption indexes, significant differences were identified only between the HIRT and other groups (P<0.05). The micro-CT examination revealed that the HIRT group had significantly greater bone density index values than the CON and LIRT groups (P<0.05). The results of three-point bending and torsion test by the universal material testing machine showed that the elastic modulus and maximum load indexes of the HIRT group were significantly smaller than those of the LIBFR group (P<0.05). The fracture load indexes in the HIRT group were significantly smaller than in the LIBFR group (P<0.05).Conclusion1. LIRT, HIRT, LIBFR, and CON all have significant differences, and this training helps to improve maximum strength, with HIRT being the most effective. 2. Blood flow restriction training can improve the expression of bone turnover markers, such as PINP and BGP, which promote bone tissue formation. 3. Blood flow restriction training can improve muscle strength and increase the positive development of bone turnover markers, thereby improving bone biomechanical properties such as bone elastic modulus and maximum load

Y-Doped ZnO Nanorods by Hydrothermal Method and Their Acetone Gas Sensitivity

Pure and yttrium- (Y-) doped (1 at%, 3 at%, and 7 at%) ZnO nanorods were synthesized using a hydrothermal process. The crystallography and microstructure of the synthesized samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDX). Comparing with pure ZnO nanorods, Y-doped ZnO exhibited improved acetone sensing properties. The response of 1 at% Y-doped ZnO nanorods to 100 ppm acetone is larger than that of pure ZnO nanorods. The response and recovery times of 1 at% Y-doped ZnO nanorods to 100 ppm acetone are about 30 s and 90 s, respectively. The gas sensor based on Y-doped ZnO nanorods showed good selectivity to acetone in the interfere gases of ammonia, benzene, formaldehyde, toluene, and methanol. The formation mechanism of the ZnO nanorods was briefly analyzed