Learning to Augment for Data-Scarce Domain BERT Knowledge Distillation

Despite pre-trained language models such as BERT have achieved appealing performance in a wide range of natural language processing tasks, they are computationally expensive to be deployed in real-time applications. A typical method is to adopt knowledge distillation to compress these large pre-trained models (teacher models) to small student models. However, for a target domain with scarce training data, the teacher can hardly pass useful knowledge to the student, which yields performance degradation for the student models. To tackle this problem, we propose a method to learn to augment for data-scarce domain BERT knowledge distillation, by learning a cross-domain manipulation scheme that automatically augments the target with the help of resource-rich source domains. Specifically, the proposed method generates samples acquired from a stationary distribution near the target data and adopts a reinforced selector to automatically refine the augmentation strategy according to the performance of the student. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art baselines on four different tasks, and for the data-scarce domains, the compressed student models even perform better than the original large teacher model, with much fewer parameters (only ${\sim}13.3\%$) when only a few labeled examples available.Comment: AAAI202

Deep3DSketch+: Rapid 3D Modeling from Single Free-hand Sketches

The rapid development of AR/VR brings tremendous demands for 3D content. While the widely-used Computer-Aided Design (CAD) method requires a time-consuming and labor-intensive modeling process, sketch-based 3D modeling offers a potential solution as a natural form of computer-human interaction. However, the sparsity and ambiguity of sketches make it challenging to generate high-fidelity content reflecting creators' ideas. Precise drawing from multiple views or strategic step-by-step drawings is often required to tackle the challenge but is not friendly to novice users. In this work, we introduce a novel end-to-end approach, Deep3DSketch+, which performs 3D modeling using only a single free-hand sketch without inputting multiple sketches or view information. Specifically, we introduce a lightweight generation network for efficient inference in real-time and a structural-aware adversarial training approach with a Stroke Enhancement Module (SEM) to capture the structural information to facilitate learning of the realistic and fine-detailed shape structures for high-fidelity performance. Extensive experiments demonstrated the effectiveness of our approach with the state-of-the-art (SOTA) performance on both synthetic and real datasets

Reality3DSketch: Rapid 3D Modeling of Objects from Single Freehand Sketches

The emerging trend of AR/VR places great demands on 3D content. However, most existing software requires expertise and is difficult for novice users to use. In this paper, we aim to create sketch-based modeling tools for user-friendly 3D modeling. We introduce Reality3DSketch with a novel application of an immersive 3D modeling experience, in which a user can capture the surrounding scene using a monocular RGB camera and can draw a single sketch of an object in the real-time reconstructed 3D scene. A 3D object is generated and placed in the desired location, enabled by our novel neural network with the input of a single sketch. Our neural network can predict the pose of a drawing and can turn a single sketch into a 3D model with view and structural awareness, which addresses the challenge of sparse sketch input and view ambiguity. We conducted extensive experiments synthetic and real-world datasets and achieved state-of-the-art (SOTA) results in both sketch view estimation and 3D modeling performance. According to our user study, our method of performing 3D modeling in a scene is $>$5x faster than conventional methods. Users are also more satisfied with the generated 3D model than the results of existing methods.Comment: IEEE Transactions on MultiMedi

High-sensitive and rapid detection of Mycobacterium tuberculosis infection by IFN-γ release assay among HIV-infected individuals in BCG-vaccinated area

<p>Abstract</p> <p>Background</p> <p>An accurate test for <it>Mycobacterium tuberculosis </it>infection is urgently needed in immunosuppressed populations. The aim of this study was to investigate the diagnostic power of enzyme-linked immunospot (ELISPOT)-based IFN-γ release assay in detecting active and latent tuberculosis in HIV-infected population in <it>bacillus Calmette-Guerin </it>(BCG)-vaccinated area. A total of 100 HIV-infected individuals including 32 active tuberculosis patients were recruited. An ELISPOT-based IFN-γ release assay, T-SPOT.TB, was used to evaluate the <it>M. tuberculosis </it>ESAT-6 and CFP-10 specific IFN-γ response. Tuberculin skin test (TST) was performed for all recruited subjects.</p> <p>Results</p> <p>The subjects were divided into group HIV+ATB (HIV-infected individuals with active tuberculosis, n = 32), group HIV+LTB (HIV-infected individuals with positive results of T-SPOT.TB assay, n = 46) and group HIV only (HIV-infected individuals with negative results of T-SPOT.TB assay and without evidence of tuberculosis infection, n = 22). In group HIV+ATB and HIV+LTB, T-SPOT.TB positive rate in subjects with TST <5 mm were 50% (16/32) and 41.3% (19/46), respectively. Individuals in group HIV+ATB and HIV+LTB with CD4+ T cells <500/μl, T-SPOT.TB showed a higher sensitivity than TST (64.5% vs. 22.6% and 62.2% vs. 29.7%, respectively, both <it>P </it>< 0.0001). In addition, the sensitivity of T-SPOT.TB assay in group HIV+ATB increased to >85% in patients with TB treatment for less than 1 month and CD4+ T cells ≥200/μl, while for patients treated for more than 3 months and CD4+ T cells <200/μl, the sensitivity was decreased to only 33.3%. Furthermore, the results could be generated by T-SPOT.TB assay within 24 hours, which was more rapid than TST with 48–72 hours.</p> <p>Conclusion</p> <p>ELISPOT-based IFN-γ release assay is more sensitive and rapid for the diagnosis of TB infection in Chinese HIV-infected individuals with history of BCG vaccination, and could be an effective tool for guiding preventive treatment with isoniazid in latently infected people and for TB control in China.</p

SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, and More

The emergence of large models, also known as foundation models, has brought significant advancements to AI research. One such model is Segment Anything (SAM), which is designed for image segmentation tasks. However, as with other foundation models, our experimental findings suggest that SAM may fail or perform poorly in certain segmentation tasks, such as shadow detection and camouflaged object detection (concealed object detection). This study first paves the way for applying the large pre-trained image segmentation model SAM to these downstream tasks, even in situations where SAM performs poorly. Rather than fine-tuning the SAM network, we propose \textbf{SAM-Adapter}, which incorporates domain-specific information or visual prompts into the segmentation network by using simple yet effective adapters. Our extensive experiments show that SAM-Adapter can significantly elevate the performance of SAM in challenging tasks and we can even outperform task-specific network models and achieve state-of-the-art performance in the task we tested: camouflaged object detection and shadow detection. We believe our work opens up opportunities for utilizing SAM in downstream tasks, with potential applications in various fields, including medical image processing, agriculture, remote sensing, and more

A study on a feasible no-root approach on Android

National Research Foundation (NRF) Singapor

Present-day activity and seismic potential of the north Qinling fault, southern ordos block, central China, as revealed from GPS data and seismicity

The North Qinling Fault, located at the boundary of the North China Block and the South China Block, represents an important tectonic structure between the Weihe Basin and the Qinling Mountains, and controls the subsidence and expansion of the Weihe Basin. This fault has been highly active and has caused strong earthquakes since the Holocene and in a pre-seismic stage currently, as indicated by the many paleoearthquake traces found along it. To determine the present-day activity and seismic potential of the North Qinling Fault, by inverting GPS data, we produced fault locking depth, slip rate, and regional strain fields maps; moreover, based on seismicity, we produced a seismic b-value map. Combining this information with modern seismicity, we were able to comprehensively analyze the seismic potential of different fault segments. Our inversion of GPS data showed that the slip rate of the western segment of the fault (Qingjiangkou–Xitangyu) and the correspondent locking depth are 1.33 mm/a and 13.54 km, respectively, while the slip rate of the middle segment (Xitangyu–Fengyukou) and the correspondent locking depth are 0.45 mm/a and 8.58 km, respectively; finally, the slip rate of the eastern segment (Xitangyu–Daiyu) and the correspondent locking depth are 0.36 mm/a and 21.46 km, respectively. The locking depths of the western and middle segments of the fault are shallower than 90% of the seismic cutoff depth, while the locking depth of the eastern segment of the fault is similar to 90% of the seismic cutoff depth, indicating that “deep creep” occurs in the western and middle segments, while the eastern segment is locked. Modern small earthquakes have involved the western and middle segments of the fault, while the eastern segment has acted as a seismic gap with weak seismicity, characterized by a higher shear strain value and a lower b-value. These characteristics reflect the relationship between the locking depth and seismicity distribution. The results of our comprehensive analysis, combined with field geological surveys, show that the eastern segment of the North Qinling Fault has a strong seismic potential and is presently locked