Image classification by visual bag-of-words refinement and reduction

This paper presents a new framework for visual bag-of-words (BOW) refinement and reduction to overcome the drawbacks associated with the visual BOW model which has been widely used for image classification. Although very influential in the literature, the traditional visual BOW model has two distinct drawbacks. Firstly, for efficiency purposes, the visual vocabulary is commonly constructed by directly clustering the low-level visual feature vectors extracted from local keypoints, without considering the high-level semantics of images. That is, the visual BOW model still suffers from the semantic gap, and thus may lead to significant performance degradation in more challenging tasks (e.g. social image classification). Secondly, typically thousands of visual words are generated to obtain better performance on a relatively large image dataset. Due to such large vocabulary size, the subsequent image classification may take sheer amount of time. To overcome the first drawback, we develop a graph-based method for visual BOW refinement by exploiting the tags (easy to access although noisy) of social images. More notably, for efficient image classification, we further reduce the refined visual BOW model to a much smaller size through semantic spectral clustering. Extensive experimental results show the promising performance of the proposed framework for visual BOW refinement and reduction

Robust Image Analysis by L1-Norm Semi-supervised Learning

This paper presents a novel L1-norm semi-supervised learning algorithm for robust image analysis by giving new L1-norm formulation of Laplacian regularization which is the key step of graph-based semi-supervised learning. Since our L1-norm Laplacian regularization is defined directly over the eigenvectors of the normalized Laplacian matrix, we successfully formulate semi-supervised learning as an L1-norm linear reconstruction problem which can be effectively solved with sparse coding. By working with only a small subset of eigenvectors, we further develop a fast sparse coding algorithm for our L1-norm semi-supervised learning. Due to the sparsity induced by sparse coding, the proposed algorithm can deal with the noise in the data to some extent and thus has important applications to robust image analysis, such as noise-robust image classification and noise reduction for visual and textual bag-of-words (BOW) models. In particular, this paper is the first attempt to obtain robust image representation by sparse co-refinement of visual and textual BOW models. The experimental results have shown the promising performance of the proposed algorithm.Comment: This is an extension of our long paper in ACM MM 201

FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows

Despite recent progress in open-domain dialogue evaluation, how to develop automatic metrics remains an open problem. We explore the potential of dialogue evaluation featuring dialog act information, which was hardly explicitly modeled in previous methods. However, defined at the utterance level in general, dialog act is of coarse granularity, as an utterance can contain multiple segments possessing different functions. Hence, we propose segment act, an extension of dialog act from utterance level to segment level, and crowdsource a large-scale dataset for it. To utilize segment act flows, sequences of segment acts, for evaluation, we develop the first consensus-based dialogue evaluation framework, FlowEval. This framework provides a reference-free approach for dialog evaluation by finding pseudo-references. Extensive experiments against strong baselines on three benchmark datasets demonstrate the effectiveness and other desirable characteristics of our FlowEval, pointing out a potential path for better dialogue evaluation.Comment: EMNLP 2022 camera-ready versio

Ameliorating the Courant-Friedrichs-Lewy condition in spherical coordinates: A double FFT filter method for general relativistic MHD in dynamical spacetimes

Numerical simulations of merging compact objects and their remnants form the theoretical foundation for gravitational wave and multi-messenger astronomy. While Cartesian-coordinate-based adaptive mesh refinement is commonly used for simulations, spherical-like coordinates are more suitable for nearly spherical remnants and azimuthal flows due to lower numerical dissipation in the evolution of fluid angular momentum, as well as requiring fewer numbers of computational cells. However, the use of spherical coordinates to numerically solve hyperbolic partial differential equations can result in severe Courant-Friedrichs-Lewy (CFL) stability condition timestep limitations, which can make simulations prohibitively expensive. This paper addresses this issue for the numerical solution of coupled spacetime and general relativistic magnetohydrodynamics evolutions by introducing a double FFT filter and implementing it within the fully MPI-parallelized SphericalNR framework in the Einstein Toolkit. We demonstrate the effectiveness and robustness of the filtering algorithm by applying it to a number of challenging code tests, and show that it passes these tests effectively, demonstrating convergence while also increasing the timestep significantly compared to unfiltered simulations.Comment: 15 pages, 13 figures, revtex4-

Recurrent paratesticular giant liposarcoma: A case report and literature review

BackgroundPrimary paratesticular liposarcoma is rarely diagnosed among urinary tumors. In this study, through the retrospective analysis of clinical data and literature review, a case of recurrent paratesticular liposarcoma with lymph node metastasis after radical resection has been reported to explore novel strategies for the diagnosis, treatment and prognosis of this rare disease.Case summaryThe present case involved a patient who was misdiagnosed as a left inguinal hernia for the first time two years ago, but was later diagnosed as mixed liposarcoma by using postoperative pathology. Currently, he is readmitted to the hospital with a recurrence of the left scrotal mass for more than 1 year. Combined with the patient's past medical history, we performed radical resection of the left inguinal and scrotal tumors and lymphadenectomy of left femoral vein. The postoperative pathology indicated that well-differentiated liposarcoma was accompanied by mucinous liposarcoma (about 20%), and lymph node metastasis of left femoral vein both of which occurred at the same time. After the operation, we recommended the patient to receive further radiation therapy, but the patient and his family refused, hence we followed up the patient closely for a long time. During the recent follow-up, the patient reported no complaints of discomfort, and no recurrence of mass in the left scrotum and groin area.ConclusionAfter conducting extensive review of literature, we conclude that radical resection remains the key to treat primary paratesticular liposarcoma, while the significance of the lymph node metastasis is still unclear. The potential effects of postoperative adjuvant therapy depends on the pathological type, and hence close follow-up observation is essential

Uplift of the Longmen Shan range and the Wenchuan earthquake

ABSTRACT: The 12 May 2008 Wenchuan earthquake (M-s,=8.0) struck on the Longmen Shan foreland thrust zone. The event took place within the context of long-term uplift of the Longmen Shan range is a result of the extensive eastward-extrusion of crustal materials from the Tibetan plateau against the rheologically strong crust of the Sichuan Basin. The Longmen Shan range is characterized by a Pre-Sinian crystalline complex constrained by the Maoxian-Wenchuan-Kangding ductile detachment at the western margin and the Yingxiu-Beichuan-Luding ductile thrust at the eastern margin. The Longmen Shan uplift was initiated by intracontinental subduction between the Songpan-Ganzi terrane and the Yangtze block during the Pre-Cenozoic. The uplift rate was increased considerably by the collision between the Indian and Eurasian plates since similar to 50 Ma. The Wenchuan earthquake resulted in two major NE-striking coseismic ruptures (i.e., the similar to 275 km long Yingxiu-Beichuan-Qingchuan fault and the similar to 100 km long Anxian-Guanxian fault). Field investigations combined with focal solutions and seismic reflection profiles suggest that the coseismic ruptures are steeply dipping close-to-pure reverse or right reverse oblique slip faults in the similar to 15 km thick tipper crust. These faults are unfavorably oriented for frictional slip in the horizontally compressional regime, so that they need a long recurrence interval to accumulate the tectonic stress and fluid pressure to critically high levels for the formation of strong earthquakes at a given locality. It is also found that all the large earthquakes (M-s>7.0) occurred in the fault zones across which the horizontal movement velocities measured by the GPS are markedly low (<3 mm/yr). The faults, which constitute the northeastern fronts of the enlarging Tibetan plateau against the strong Sichuan Basin, Ala Shan and Ordos blocks, are very destructive, although their average recurrence intervals are generally long