Bridge Diffusion Model: bridge non-English language-native text-to-image diffusion model with English communities

Text-to-Image generation (TTI) technologies are advancing rapidly, especially in the English language communities. However, English-native TTI models inherently carry biases from English world centric training data, which creates a dilemma for development of other language-native TTI models. One common choice is fine-tuning the English-native TTI model with translated samples from non-English communities. It falls short of fully addressing the model bias problem. Alternatively, training non-English language native models from scratch can effectively resolve the English world bias, but diverges from the English TTI communities, thus not able to utilize the strides continuously gaining in the English TTI communities any more. To build non-English language native TTI model meanwhile keep compatability with the English TTI communities, we propose a novel model structure referred as "Bridge Diffusion Model" (BDM). The proposed BDM employs a backbone-branch network structure to learn the non-English language semantics while keep the latent space compatible with the English-native TTI backbone, in an end-to-end manner. The unique advantages of the proposed BDM are that it's not only adept at generating images that precisely depict non-English language semantics, but also compatible with various English-native TTI plugins, such as different checkpoints, LoRA, ControlNet, Dreambooth, and Textual Inversion, etc. Moreover, BDM can concurrently generate content seamlessly combining both non-English native and English-native semantics within a single image, fostering cultural interaction. We verify our method by applying BDM to build a Chinese-native TTI model, whereas the method is generic and applicable to any other language

What Makes Good Open-Vocabulary Detector: A Disassembling Perspective

Open-vocabulary detection (OVD) is a new object detection paradigm, aiming to localize and recognize unseen objects defined by an unbounded vocabulary. This is challenging since traditional detectors can only learn from pre-defined categories and thus fail to detect and localize objects out of pre-defined vocabulary. To handle the challenge, OVD leverages pre-trained cross-modal VLM, such as CLIP, ALIGN, etc. Previous works mainly focus on the open vocabulary classification part, with less attention on the localization part. We argue that for a good OVD detector, both classification and localization should be parallelly studied for the novel object categories. We show in this work that improving localization as well as cross-modal classification complement each other, and compose a good OVD detector jointly. We analyze three families of OVD methods with different design emphases. We first propose a vanilla method,i.e., cropping a bounding box obtained by a localizer and resizing it into the CLIP. We next introduce another approach, which combines a standard two-stage object detector with CLIP. A two-stage object detector includes a visual backbone, a region proposal network (RPN), and a region of interest (RoI) head. We decouple RPN and ROI head (DRR) and use RoIAlign to extract meaningful features. In this case, it avoids resizing objects. To further accelerate the training time and reduce the model parameters, we couple RPN and ROI head (CRR) as the third approach. We conduct extensive experiments on these three types of approaches in different settings. On the OVD-COCO benchmark, DRR obtains the best performance and achieves 35.8 Novel AP$_{50}$, an absolute 2.8 gain over the previous state-of-the-art (SOTA). For OVD-LVIS, DRR surpasses the previous SOTA by 1.9 AP$_{50}$ in rare categories. We also provide an object detection dataset called PID and provide a baseline on PID

Efficacy and safety of tigecycline monotherapy vs. imipenem/cilastatin in Chinese patients with complicated intra-abdominal infections: a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Tigecycline, a first-in-class broad-spectrum glycylcycline antibiotic, has broad-spectrum in vitro activity against bacteria commonly encountered in complicated intra-abdominal infections (cIAIs), including aerobic and facultative Gram-positive and Gram-negative bacteria and anaerobic bacteria. In the current trial, tigecycline was evaluated for safety and efficacy vs. imipenem/cilastatin in hospitalized Chinese patients with cIAIs.</p> <p>Methods</p> <p>In this phase 3, multicenter, open-label study, patients were randomly assigned to receive IV tigecycline or imipenem/cilastatin for ≤2 weeks. The primary efficacy endpoints were clinical response at the test-of-cure visit (12-37 days after therapy) for the microbiologic modified intent-to-treat and microbiologically evaluable populations. Because the study was not powered to demonstrate non-inferiority between tigecycline and imipenem/cilastatin, no formal statistical analysis was performed. Two-sided 95% confidence intervals (CIs) were calculated for the response rates in each treatment group and for differences between treatment groups for descriptive purposes.</p> <p>Results</p> <p>One hundred ninety-nine patients received ≥1 dose of study drug and comprised the modified intent-to-treat population. In the microbiologically evaluable population, 86.5% (45 of 52) of tigecycline- and 97.9% (47 of 48) of imipenem/cilastatin-treated patients were cured at the test-of-cure assessment (12-37 days after therapy); in the microbiologic modified intent-to-treat population, cure rates were 81.7% (49 of 60) and 90.9% (50 of 55), respectively. The overall incidence of treatment-emergent adverse events was 80.4% for tigecycline vs. 53.9% after imipenem/cilastatin therapy (<it>P </it>< 0.001), primarily due to gastrointestinal-related events, especially nausea (21.6% vs. 3.9%; <it>P </it>< 0.001) and vomiting (12.4% vs. 2.0%; <it>P </it>= 0.005).</p> <p>Conclusions</p> <p>Clinical cure rates for tigecycline were consistent with those found in global cIAI studies. The overall safety profile was also consistent with that observed in global studies of tigecycline for treatment of cIAI, as well as that observed in analyses of Chinese patients in those studies; no novel trends were observed.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov NCT00136201</p

Macro-Texture and Acoustic Performance Characterization of Concrete Pavements with Various Types of Surface Textures

8th International Conference on Maintenance and Rehabilitation of Pavements, MAIREPAV 2016, Singapore, 27-29 July 2016With the ever-increasing cost and ever-decreasing resource of petroleum, concrete pavements are becoming a more competitive paving alternative compared to asphalt pavements and have gained increasing interest of highway agencies. Different surface texturing methods may lead to different surface characteristics of the concrete pavements, such as skid resistance, drainage and road-Tire noise. The main objective of this study is to assess the surface characteristics of concrete pavements with four common types of surface textures, namely wire brushing, exposed aggregate, transverse tining, and artificial turf dragging. To achieve this objective, full-scale concrete panels (3.65mx4mx0.25m), with the four different textures were first constructed following the standard construction procedure adopted in Hong Kong. 250mm-diameter cores were then extracted from the hardened concrete panels for various surface characteristic tests, including macro-Texture profile tests and mirco-Texture profile tests with associated road-Tyre noise prediction. To assess the durability of the surface characteristics of each type of surface texture, each of the aforementioned tests was repeated six times after the test samples were subjected to 0, 16, 33, 100, 200, and 300mins of real tire polishing applied by the advanced Aachen Polishing Machine (APM). The test results indicated that among the four types of surface textures evaluated in this study, exposed aggregate provides the best overall performance in terms of macro-Texture depth and tyre-road noise, while the performances of the others are relatively similar. Recommendations on further improving the surface characteristics of each type of surface texture were also provided.Department of Civil and Environmental Engineerin

Effects of Material Composition on Mechanical and Acoustic Performance of PoroElastic Road Surface (PERS)

Poroelastic road surface (PERS) is a type of low-noise pavement surface, which was derived from porous asphalt (PA). Because of its high elasticity, large air void content and an inter-connected air void structure, PERS provides excellent performance in traffic noise reduction. However, it also faces the challenging problem of poor ravelling resistance, which limits its more widespread application. The main objective of this study is to investigate the effects of various composition factors on the ravelling resistance of PERS so as to provide recommendations on appropriate PERS composition. To this end, 20 PERS mixtures with different compositions were designed and characterized after various stages of polishing applied by the Aachener-Raveling-Tester (ARTe). The ravelling resistance of PERS was quantified by measuring the material loss after polishing, while their acoustic performance and rutting resistance were also tested for validation purpose. It was concluded that binder content and degree of compaction are the critical factors affecting the ravelling resistance of PERS. To ensure sufficient durability, a minimum binder content of 15% and a minimum compaction degree of 98% were recommended.Department of Civil and Environmental Engineerin

Extraction of chondroitin sulfate and type II collagen from sturgeon (Acipenser gueldenstaedti) notochord and characterization of their hybrid fibrils

Chondroitin sulfate (CS) and undenatured type II collagen (Col II) are the two major, biological macromolecules of cartilage-related tissues. In this study, a new extraction. process was developed to obtain CS and Col II simultaneously. By this process, CS. and undenatured Col II were extracted from sturgeon notochord with the yields of 5.34, ± 0.74% and 45.25 ± 5.25%, respectively. The SEC-RI-MALLS result showed that the, average molecular weight of notochord CS was 38.4 kDa. FTIR NMR, and SAX-HPLC, results indicated the notochord CS was mainly composed of CS-A. The new extraction, process had no effect on the triple helical structure of Col II. To analyze the interaction, between the two macromolecules, the effect of CS on Col II fibril formation was, examined using turbidity assay and SEM observation. CS accelerated the completion, of Col II self-assembly and inhibited the lateral aggregation of fibrils. The results of this, study suggested that the sturgeon notochord is a valuable source of CS and Col II. The. new extraction method not only improves the utilization rate of sturgeon notochord, but,also reduces the waste of aquatic resources. CS and Col II derived from sturgeon, notochord have the potential for use in biomedical material