Conditional Entropies as Over-Segmentation and Under-Segmentation Metrics for Multi-Part Image Segmentation

In this paper, we define two conditional entropy measures for performance evaluation of general image segmentation. Given a segmentation label map and a ground truth label map, our measures describe their compatibility in two ways. The first one is the conditional entropy of the segmentation given the ground truth, which indicates the oversegmentation rate. The second one is that of the ground truth given the segmentation, which indicates the under-segmentation rate. The two conditional entropies indicate the trade-off between smaller and larger granularities like false positive rate and false negative rate in ROC, and precision and recall in PR curve. Our measures are easy to implement, and involve no threshold or other parameter, have very intuitive explanation and many good theoretical properties, e.g., good bounds, monotonicity, continuity. Experiments show that our measures work well on Berkeley Image Segmentation Benchmark using three segmentation algorithms, Efficient Graph- Based segmentation, Mean Shift and Normalized Cut. We also give an asymmetric similarity measure based on the two entropies and compared it with Variation of Information. The comparison revealled that our method has advantages in many situations.We also checked the coarse-to-fine compatibility of segmentation results with changing parameters and ground truths from different annotators

Learning Active Basis Models by EM-Type Algorithms

EM algorithm is a convenient tool for maximum likelihood model fitting when the data are incomplete or when there are latent variables or hidden states. In this review article we explain that EM algorithm is a natural computational scheme for learning image templates of object categories where the learning is not fully supervised. We represent an image template by an active basis model, which is a linear composition of a selected set of localized, elongated and oriented wavelet elements that are allowed to slightly perturb their locations and orientations to account for the deformations of object shapes. The model can be easily learned when the objects in the training images are of the same pose, and appear at the same location and scale. This is often called supervised learning. In the situation where the objects may appear at different unknown locations, orientations and scales in the training images, we have to incorporate the unknown locations, orientations and scales as latent variables into the image generation process, and learn the template by EM-type algorithms. The E-step imputes the unknown locations, orientations and scales based on the currently learned template. This step can be considered self-supervision, which involves using the current template to recognize the objects in the training images. The M-step then relearns the template based on the imputed locations, orientations and scales, and this is essentially the same as supervised learning. So the EM learning process iterates between recognition and supervised learning. We illustrate this scheme by several experiments.Comment: Published in at http://dx.doi.org/10.1214/09-STS281 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Evaluation of corrosion expansion of reinforced concrete specimen using fiber optical Brillouin sensing technique

This paper investigated the evaluation of the concrete damage degree due to steel bar corrosion for reinforced concrete structures. Brillouin optical fiber time domain analysis (BOTDA) sensors were developed to monitor the steel bar corrosion expansion strain. Electrochemical accelerating experimental results showed the sensors could be used for early detection and the lifelong monitoring. The damage factor was proposed to quantitatively evaluate the concrete damage degree before initial cracking and during the development of cracks. Finite element analysis was performed on concrete specimens to map the monitoring results with the damage factor, which supported the capability of the damage factor

An integrated background model for video surveillance based on primal sketch and 3D scene geometry

This paper presents a novel integrated background model for video surveillance. Our model uses a primal sketch representation for image appearance and 3D scene geometry to capture the ground plane and major surfaces in the scene. The primal sketch model divides the background image into three types of regions — flat, sketchable and textured. The three types of regions are modeled respectively by mixture of Gaussians, image primitives and LBP histograms. We calibrate the camera and recover important planes such as ground, horizontal surfaces, walls, stairs in the 3D scene, and use geometric information to predict the sizes and locations of foreground blobs to further reduce false alarms. Compared with the state-of-theart background modeling methods, our approach is more effective, especially for indoor scenes where shadows, highlights and reflections of moving objects and camera exposure adjusting usually cause problems. Experiment results demonstrate that our approach improves the performance of background/foreground separation at pixel level, and the integrated video surveillance system at the object and trajectory level. 1

Simple and Efficient FE for Quadratic Functions

This paper presents the first functional encryption schemes for quadratic functions (or degree-2 polynomials) achieving simulation-based security in the semi-adaptive model with constant-size secret key. The unique prior construction with the same security guarantee by Gay [PKC 20] has secret keys of size linear in the message size. They also enjoy shorter ciphertexts: - our first scheme is based on bilateral DLIN (decisional linear) assumption as Gay\u27s scheme and the ciphertext is 15% shorter; - our second scheme based on SXDH assumption and bilateral DLIN assumption is more efficient; it has 67% shorter ciphertext than previous SXDH-based scheme with selective indistinguishability security by Baltico et al. [CRYPTO 17]; the efficiency is comparable to their second scheme in the generic group model. Technically, we roughly combine Wee\u27s ``secret-key-to-public-key\u27\u27 compiler [TCC 17] with Gay\u27s paradigm [PKC 20]. We avoid (partial) function-hiding inner-product functional encryption used in Gay\u27s work and make our schemes conceptually simpler

BLT: Bidirectional Layout Transformer for Controllable Layout Generation

Creating visual layouts is a critical step in graphic design. Automatic generation of such layouts is essential for scalable and diverse visual designs. To advance conditional layout generation, we introduce BLT, a bidirectional layout transformer. BLT differs from previous work on transformers in adopting non-autoregressive transformers. In training, BLT learns to predict the masked attributes by attending to surrounding attributes in two directions. During inference, BLT first generates a draft layout from the input and then iteratively refines it into a high-quality layout by masking out low-confident attributes. The masks generated in both training and inference are controlled by a new hierarchical sampling policy. We verify the proposed model on six benchmarks of diverse design tasks. Experimental results demonstrate two benefits compared to the state-of-the-art layout transformer models. First, our model empowers layout transformers to fulfill controllable layout generation. Second, it achieves up to 10x speedup in generating a layout at inference time than the layout transformer baseline. Code is released at https://shawnkx.github.io/blt.Comment: ECCV 202

Landau-Zener-Stuckelberg-Majorana interference in a 3D transmon driven by a chirped microwave

By driving a 3D transmon with microwave fields, we generate an effective avoided energy-level crossing. Then we chirp microwave frequency, which is equivalent to driving the system through the avoided energy-level crossing by sweeping the avoided crossing. A double-passage chirp produces Landau-Zener-St\"uckelberg-Majorana interference that agree well with the numerical results. Our method is fully applicable to other quantum systems that contain no intrinsic avoided level crossing, providing an alternative approach for quantum control and quantum simulation

Case report: Chronic lymphocytic leukemia/small lymphocytic lymphoma and monomorphic epitheliotropic intestinal T-cell lymphoma: A composite lymphoma

Background: Composite lymphomas involving B-cell and T-cell lymphomas is very rare.Case presentation: We reported a 63-year-old gentleman with composite chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) and monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL). The patient was admitted to our hospital due to abdominal pain, and was diagnosed with CLL/SLL after bone marrow (BM) biopsy, BM aspiration, and flow cytometry. Two weeks later, he was diagnosed with MEITL based on pathological analysis after intestine excision. Next gene sequencing (NGS) findings identified two hotspot mutation sites (STAT5B and DNMT3A) closely related with the pathogenesis of CLL/SLL and MEILT. Additionally, BCOR mutation was only detected in the CLL/SLL area. The likely pathogenic mutations of CLL were SETD2, NOTCH1, SF3B1, and PTPN11, while the likely pathogenic mutations related with the MEILT were TET2 and ZRSR2. Mutations of GATA3, PLCG2, and FAT1 were identified in both CLL/SLL and MEITL areas, but the clinical significance was unknown. Finally, the patient died in the 12-month follow-up after surgery.Conclusion: We report a rare case of composite CLL/SLL and MEITL that highlights the importance of careful inspection of hematologic neoplasms. We also present the results of NGS of different gene mutations in CLL and MEITL tissues