Introducing anisotropic tensor to high order variational model for image restoration

Second order total variation (SOTV) models have advantages for image restoration over their first order counterparts including their ability to remove the staircase artefact in the restored image. However, such models tend to blur the reconstructed image when discretised for numerical solution [1–5]. To overcome this drawback, we introduce a new tensor weighted second order (TWSO) model for image restoration. Specifically, we develop a novel regulariser for the SOTV model that uses the Frobenius norm of the product of the isotropic SOTV Hessian matrix and an anisotropic tensor. We then adapt the alternating direction method of multipliers (ADMM) to solve the proposed model by breaking down the original problem into several subproblems. All the subproblems have closed-forms and can be solved efficiently. The proposed method is compared with state-of-the-art approaches such as tensor-based anisotropic diffusion, total generalised variation, and Euler's elastica. We validate the proposed TWSO model using extensive experimental results on a large number of images from the Berkeley BSDS500. We also demonstrate that our method effectively reduces both the staircase and blurring effects and outperforms existing approaches for image inpainting and denoising applications

Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning

In-context learning, a paradigm bridging the gap between pre-training and fine-tuning, has demonstrated high efficacy in several NLP tasks, especially in few-shot settings. Despite being widely applied, in-context learning is vulnerable to malicious attacks. In this work, we raise security concerns regarding this paradigm. Our studies demonstrate that an attacker can manipulate the behavior of large language models by poisoning the demonstration context, without the need for fine-tuning the model. Specifically, we design a new backdoor attack method, named ICLAttack, to target large language models based on in-context learning. Our method encompasses two types of attacks: poisoning demonstration examples and poisoning demonstration prompts, which can make models behave in alignment with predefined intentions. ICLAttack does not require additional fine-tuning to implant a backdoor, thus preserving the model's generality. Furthermore, the poisoned examples are correctly labeled, enhancing the natural stealth of our attack method. Extensive experimental results across several language models, ranging in size from 1.3B to 180B parameters, demonstrate the effectiveness of our attack method, exemplified by a high average attack success rate of 95.0% across the three datasets on OPT models

Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information

Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images

Optimizing ADMM and Over-Relaxed ADMM Parameters for Linear Quadratic Problems

The Alternating Direction Method of Multipliers (ADMM) has gained significant attention across a broad spectrum of machine learning applications. Incorporating the over-relaxation technique shows potential for enhancing the convergence rate of ADMM. However, determining optimal algorithmic parameters, including both the associated penalty and relaxation parameters, often relies on empirical approaches tailored to specific problem domains and contextual scenarios. Incorrect parameter selection can significantly hinder ADMM's convergence rate. To address this challenge, in this paper we first propose a general approach to optimize the value of penalty parameter, followed by a novel closed-form formula to compute the optimal relaxation parameter in the context of linear quadratic problems (LQPs). We then experimentally validate our parameter selection methods through random instantiations and diverse imaging applications, encompassing diffeomorphic image registration, image deblurring, and MRI reconstruction.Comment: Accepted to AAAI 202

The combination of 2d layered graphene oxide and 3d porous cellulose heterogeneous membranes for nanofluidic osmotic power generation

Salinity gradient energy, as a type of blue energy, is a promising sustainable energy source. Its energy conversion efficiency is significantly determined by the selective membranes. Recently, nanofluidic membrane made by two-dimensional (2D) nanomaterials (e.g., graphene) with densely packed nanochannels has been considered as a high-efficient membrane in the osmotic power generation research field. Herein, the graphene oxide-cellulose acetate (GO–CA) heterogeneous membrane was assembled by combining a porous CA membrane and a layered GO membrane; the combination of 2D nanochannels and 3D porous structures make it show high surface-charge-governed property and excellent ion transport stability, resulting in an efficient osmotic power harvesting. A power density of about 0.13 W/m2 is achieved for the sea–river mimicking system and up to 0.55 W/m2 at a 500-fold salinity gradient. With different functions, the CA and GO membranes served as ion storage layer and ion selection layer, respectively. The GO–CA heterogeneous membrane open a promising avenue for fabrication of porous and layered platform for wide potential applications, such as sustainable power generation, water purification, and seawater desalination

Autoantibodies against the Catalytic Domain of BRAF Are Not Specific Serum Markers for Rheumatoid Arthritis

BACKGROUND: Autoantibodies to the catalytic domain of v-raf murine sarcoma viral oncogene homologue B1 (BRAF) have been recently identified as a new family of autoantibodies involved in rheumatoid arthritis (RA). The objective of this study was to determine antibody responses to the catalytic domain of BRAF in RA and other autoimmune diseases. The association between RA-related clinical indices and these antibodies was also assessed. METHODOLOGY/PRINCIPAL FINDINGS: The presence of autoantibodies to the catalytic domain of BRAF (anti-BRAF) or to peptide P25 (amino acids 656-675 of the catalytic domain of BRAF; anti-P25) was determined in serum samples from patients with RA, primary Sjögren's syndrome (pSS), systemic lupus erythematosus (SLE), and healthy controls by using indirect enzyme-linked immunosorbent assays (ELISAs) based on the recombinant catalytic domain of BRAF or a synthesized peptide, respectively. Associations of anti-BRAF or anti-P25 with disease variables of RA patients were also evaluated. Our results show that the BRAF-specific antibodies anti-BRAF and anti-P25 are equally present in RA, pSS, and SLE patients. However, the erythrocyte sedimentation rate (ESR) used to detect inflammation was significantly different between patients with and without BRAF-specific antibodies. The anti-BRAF-positive patients were found to have prolonged disease, and active disease occurred more frequently in anti-P25-positive patients than in anti-P25-negative patients. A weak but significant correlation between anti-P25 levels and ESRs was observed (r = 0.319, p = 0.004). CONCLUSIONS/SIGNIFICANCE: The antibody response against the catalytic domain of BRAF is not specific for RA, but the higher titers of BRAF-specific antibodies may be associated with increased inflammation in RA

A RFID-Based Monitoring System for Characterization of Perching Behaviors of Individual Poultry

Perching is a natural behavior of poultry. However, it is difficult to distinguish individual birds in a large group in order to relate perching behavior to health condition or productivity. To enable such research, this study developed and validated a radio frequency identification (RFID)-based automated perching monitoring system (APMS) for characterizing individual perching behaviors of group-housed poultry. The APMS consisted of a RFID module, a load cell module, and a round wooden perch. The RFID module was comprised of a high-frequency RFID reader, three customized rectangular antennas, and multiple RFID transponders. The load cell module was comprised of a data acquisition system and two load cells supporting the two ends of the perch. Daily number of perch visits (PV) and perching duration (PD) of individual birds were used to delineate perching behavior. Three identical experimental pens, five hens per pen, were equipped with the monitoring system. Two RFID transponders were attached to each hen (one per leg) and a distinct color was marked on the bird‘s head for video or visual identification. Performance of the APMS was validated by comparing the system outputs with manual observation/labeling over an entire day. Sensitivity and specificity of the system were shown to improve from 97.77% and 99.88%, respectively, when using only the RFID module, to 99.83% and 99.93%, respectively, when incorporating weight information from the load cell module. This study revealed that the APMS has an excellent performance in measuring perching behaviors of individual birds in a group. The APMS offers great potentials for delineating differences in perching behavior among hens with different social status or health conditions in a group setting