Generating Linear programming Instances with Controllable Rank and Condition Number

Instances generation is crucial for linear programming algorithms, which is necessary either to find the optimal pivot rules by training learning method or to evaluate and verify corresponding algorithms. This study proposes a general framework for designing linear programming instances based on the preset optimal solution. First, we give a constraint matrix generation method with controllable condition number and rank from the perspective of matrix decomposition. Based on the preset optimal solution, the bounded feasible linear programming instance is generated with the right-hand side and objective coefficient satisfying the original and dual feasibility. In addition, we provide three kind of neighborhood exchange operators and prove that instances generated under this method can fill the whole feasible and bounded case space of linear programming. We experimentally validate that the proposed schedule can generate more controllable linear programming instances, while neighborhood exchange operator can construct more complex instances.Comment: 28 page

Towards Consistent Video Editing with Text-to-Image Diffusion Models

Existing works have advanced Text-to-Image (TTI) diffusion models for video editing in a one-shot learning manner. Despite their low requirements of data and computation, these methods might produce results of unsatisfied consistency with text prompt as well as temporal sequence, limiting their applications in the real world. In this paper, we propose to address the above issues with a novel EI$^2$ model towards \textbf{E}nhancing v\textbf{I}deo \textbf{E}diting cons\textbf{I}stency of TTI-based frameworks. Specifically, we analyze and find that the inconsistent problem is caused by newly added modules into TTI models for learning temporal information. These modules lead to covariate shift in the feature space, which harms the editing capability. Thus, we design EI$^2$ to tackle the above drawbacks with two classical modules: Shift-restricted Temporal Attention Module (STAM) and Fine-coarse Frame Attention Module (FFAM). First, through theoretical analysis, we demonstrate that covariate shift is highly related to Layer Normalization, thus STAM employs a \textit{Instance Centering} layer replacing it to preserve the distribution of temporal features. In addition, {STAM} employs an attention layer with normalized mapping to transform temporal features while constraining the variance shift. As the second part, we incorporate {STAM} with a novel {FFAM}, which efficiently leverages fine-coarse spatial information of overall frames to further enhance temporal consistency. Extensive experiments demonstrate the superiority of the proposed EI$^2$ model for text-driven video editing

DiffBFR: Bootstrapping Diffusion Model Towards Blind Face Restoration

Blind face restoration (BFR) is important while challenging. Prior works prefer to exploit GAN-based frameworks to tackle this task due to the balance of quality and efficiency. However, these methods suffer from poor stability and adaptability to long-tail distribution, failing to simultaneously retain source identity and restore detail. We propose DiffBFR to introduce Diffusion Probabilistic Model (DPM) for BFR to tackle the above problem, given its superiority over GAN in aspects of avoiding training collapse and generating long-tail distribution. DiffBFR utilizes a two-step design, that first restores identity information from low-quality images and then enhances texture details according to the distribution of real faces. This design is implemented with two key components: 1) Identity Restoration Module (IRM) for preserving the face details in results. Instead of denoising from pure Gaussian random distribution with LQ images as the condition during the reverse process, we propose a novel truncated sampling method which starts from LQ images with part noise added. We theoretically prove that this change shrinks the evidence lower bound of DPM and then restores more original details. With theoretical proof, two cascade conditional DPMs with different input sizes are introduced to strengthen this sampling effect and reduce training difficulty in the high-resolution image generated directly. 2) Texture Enhancement Module (TEM) for polishing the texture of the image. Here an unconditional DPM, a LQ-free model, is introduced to further force the restorations to appear realistic. We theoretically proved that this unconditional DPM trained on pure HQ images contributes to justifying the correct distribution of inference images output from IRM in pixel-level space. Truncated sampling with fractional time step is utilized to polish pixel-level textures while preserving identity information

Identification of multi-drug resistant genes in P. aeruginosa isolates from patients under mechanical ventilation and respiratory support in an intensive care unit

Purpose: To determine multi-drug resistant (MDR) and metallo β-lactamase (MBL)-resistant genes from Pseudomonas aeruginosa isolated from intensive care unit (ICU) patients under mechanical ventilation and respiratory support.Methods: P. aeruginosa was isolated from 387 purulent tracheobronchial secretions collected from ICU patients who were intubated and mechanically ventilated for at least 48 h. Antibiotic resistance was determined by minimum inhibitory concentration (MIC) assay while MDR genes, viz, blaTEM, blaOXA, blaVIM, blaCTX-M-15 were determined by polymerase chain reaction (PCR).Results: A total of 144 (37.2 %) P. aeruginosa were isolated from the purulent tracheobronchial secretions. A majority of the isolates (51.4 %) were resistant to gentamicin. Meropenem-gentamicin was the predominant (35.4 %) resistant combination. Out of the 144 isolates, 102 (70.8 %) were positive for blaTEM gene, 51 (35.4 %) for were positive for blaOXA gene, 22 (15.3 %) were positive for blaVIM gene, while 19 (13.2 %) were positive for blaCTX-M gene.Conclusion: The high prevalence of MDR P. aeruginosa indicates the need for continued monitoring of MDR P. aeruginosa especially in ICU patients who are under mechanical respiratory support.Keywords: Multi-drug resistance genes, Mechanical ventilator, Respiratory support, Pseudomonas aeruginos

DropKey

In this paper, we focus on analyzing and improving the dropout technique for self-attention layers of Vision Transformer, which is important while surprisingly ignored by prior works. In particular, we conduct researches on three core questions: First, what to drop in self-attention layers? Different from dropping attention weights in literature, we propose to move dropout operations forward ahead of attention matrix calculation and set the Key as the dropout unit, yielding a novel dropout-before-softmax scheme. We theoretically verify that this scheme helps keep both regularization and probability features of attention weights, alleviating the overfittings problem to specific patterns and enhancing the model to globally capture vital information; Second, how to schedule the drop ratio in consecutive layers? In contrast to exploit a constant drop ratio for all layers, we present a new decreasing schedule that gradually decreases the drop ratio along the stack of self-attention layers. We experimentally validate the proposed schedule can avoid overfittings in low-level features and missing in high-level semantics, thus improving the robustness and stableness of model training; Third, whether need to perform structured dropout operation as CNN? We attempt patch-based block-version of dropout operation and find that this useful trick for CNN is not essential for ViT. Given exploration on the above three questions, we present the novel DropKey method that regards Key as the drop unit and exploits decreasing schedule for drop ratio, improving ViTs in a general way. Comprehensive experiments demonstrate the effectiveness of DropKey for various ViT architectures, e.g. T2T and VOLO, as well as for various vision tasks, e.g., image classification, object detection, human-object interaction detection and human body shape recovery.Comment: Accepted by CVPR202

Early cretaceous ridge subduction in the Shandong Peninsula, Eastern China, indicated by Laoshan A-type granite

Early Cretaceous A-type granites are widespread in the Shandong Peninsula, which can be used to elucidate the tectonic evolution of the eastern China and the destruction of the North China Craton. However, their genesis is still controversial. Several competing models, ranging from slab break-off, postorogenic extension, foundering of the lower crust and ridge subduction, were proposed. Here, we report zircon U–Pb ages, whole-rock and apatite geochemical compositions of the Laoshan granite and discuss its tectonic implications. The Laoshan granite has typical characteristics of A-type granite with high FeOT/(FeOT + MgO) ratios (0.90–0.97) and 10000*Ga/Al ratios (2.70–3.36) and high total alkali (Na2O + K2O: 7.95–8.70 wt%) contents and Zr+Nb+Ce+Y (most >350 ppm) concentrations. The Laoshan granite is further classified as A1-type based on the Yb/Ta-Y/Nb and Ce/Nb-Y/Nb diagrams and the Nb-Y-3Ga and Nb-Y-Ce triangular discriminant diagrams. Zircon U–Pb dating of two Laoshan granite samples yielded emplacement ages of 117.8 ± 1.0 Ma and 120.1 ± 1.3 Ma, respectively. The oxygen fugacity of the Laoshan granite magma is low, as indicated by zircon Ce4+/Ce3+ ratios (most <300). The crystallization temperature of zircon varies significantly, ranging from 652 to 830°C. The apatite compositions show that the Laoshan granite has high F (2.09–2.72 wt%) and low Cl (0.01–0.09 wt%) contents, consistent with influence by fluid released from the decomposition of phengite. Apatite rare earth elements show that mantle sources are also involved in Laoshan A-type granite. Combined previous studies of A-type granitic plutons in the Shandong Province and the Lower Yangtze River belt with the drifting history of the Pacific plate, we propose that the flat subduction of the spreading ridge between the Pacific and the Izanagi plates was responsible for the formation of Laoshan A-type granite