Future Frame Prediction for Anomaly Detection -- A New Baseline

Anomaly detection in videos refers to the identification of events that do not conform to expected behavior. However, almost all existing methods tackle the problem by minimizing the reconstruction errors of training data, which cannot guarantee a larger reconstruction error for an abnormal event. In this paper, we propose to tackle the anomaly detection problem within a video prediction framework. To the best of our knowledge, this is the first work that leverages the difference between a predicted future frame and its ground truth to detect an abnormal event. To predict a future frame with higher quality for normal events, other than the commonly used appearance (spatial) constraints on intensity and gradient, we also introduce a motion (temporal) constraint in video prediction by enforcing the optical flow between predicted frames and ground truth frames to be consistent, and this is the first work that introduces a temporal constraint into the video prediction task. Such spatial and motion constraints facilitate the future frame prediction for normal events, and consequently facilitate to identify those abnormal events that do not conform the expectation. Extensive experiments on both a toy dataset and some publicly available datasets validate the effectiveness of our method in terms of robustness to the uncertainty in normal events and the sensitivity to abnormal events.Comment: IEEE Conference on Computer Vision and Pattern Recognition 201

An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction

Uncertainty is common in ship construction. However, few studies have focused on scheduling problems under uncertainty in shipbuilding. This paper formulates the scheduling problem of panel block construction as a multiobjective fuzzy flow shop scheduling problem (FSSP) with a fuzzy processing time, a fuzzy due date, and the just-in-time (JIT) concept. An improved multiobjective particle swarm optimization called MOPSO-M is developed to solve the scheduling problem. MOPSO-M utilizes a ranked-order-value rule to convert the continuous position of particles into the discrete permutations of jobs, and an available mapping is employed to obtain the precedence-based permutation of the jobs. In addition, to improve the performance of MOPSO-M, archive maintenance is combined with global best position selection, and mutation and a velocity constriction mechanism are introduced into the algorithm. The feasibility and effectiveness of MOPSO-M are assessed in comparison with general MOPSO and nondominated sorting genetic algorithm-II (NSGA-II)

Range-only Target Localisation using Geometrically Constrained Optimisation

The problem of optimal range-only localisation of a single target is of considerable interest in two-dimensional search radar networking. For coping with this problem, a range-only target localisation method using synchronous measurements from radars is presented in the real ellipsoidal earth model. In the relevant radar localisation scenario, geometric relationships between the target and three radars were formed. A set of localisation equations was derived on range error in such a scenario. Using these equations, the localisation task has been formulated as a nonlinear weighted least squares problem that can be performed using the Levenberg- Marquardt (LM) algorithm to provide the optimal estimate of the target’s position. To avoid the double value solutions and to accelerate the convergence speed for the LM algorithm, the initial value was approximately given according to observations from two radars. In addition, the relative validity has been defined to evaluate the performance of the proposed method. The performance of the proposed approach is evaluated using two simulation experiments and a real-test experiment, and it has been found to possess higher localisation accuracy than the other conventional method.Defence Science Journal, Vol. 65, No. 1, January 2015, pp.70-76, DOI:http://dx.doi.org/10.14429/dsj.65.547

Open Knowledge Base Canonicalization with Multi-task Unlearning

The construction of large open knowledge bases (OKBs) is integral to many applications in the field of mobile computing. Noun phrases and relational phrases in OKBs often suffer from redundancy and ambiguity, which calls for the investigation on OKB canonicalization. However, in order to meet the requirements of some privacy protection regulations and to ensure the timeliness of the data, the canonicalized OKB often needs to remove some sensitive information or outdated data. The machine unlearning in OKB canonicalization is an excellent solution to the above problem. Current solutions address OKB canonicalization by devising advanced clustering algorithms and using knowledge graph embedding (KGE) to further facilitate the canonicalization process. Effective schemes are urgently needed to fully synergise machine unlearning with clustering and KGE learning. To this end, we put forward a multi-task unlearning framework, namely MulCanon, to tackle machine unlearning problem in OKB canonicalization. Specifically, the noise characteristics in the diffusion model are utilized to achieve the effect of machine unlearning for data in OKB. MulCanon unifies the learning objectives of diffusion model, KGE and clustering algorithms, and adopts a two-step multi-task learning paradigm for training. A thorough experimental study on popular OKB canonicalization datasets validates that MulCanon achieves advanced machine unlearning effects

Pygopus 2 promotes kidney cancer OS-RC-2 cells proliferation and invasion in vitro and in vivo

AbstractObjectiveHuman Pygopus 2 (Pygo2) was recently discovered to be a component of the Wnt signaling pathway required for β-catenin/Tcf-mediated transcription. But the role of Pygo2 in malignant cell proliferation and invasion has not yet been determined.MethodsLentivirus-mediated small interfering RNA (siRNA) and vector-based overexpression were used to study the function of Pygo2 in OS-RC-2 cells. The resulted cells were subject to Western blotting assay, MTT assay, colony formation and cell invasion assays. Furthermore, renal cell carcinoma (RCC) models were established in BALB/c nude mice inoculated with OS-RC-2 cells. Immunohistochemistry (IHC) staining of matrix metalloproteinase-7 (MMP-7), matrix metalloproteinase-9 (MMP-9) and vascular endothelial growth factor (VEGF) was performed in tumor tissue.ResultsPygo2 gene was successful knocked down and overexpressed in RCC OS-RC-2 cells by using an shRNA and overexpressing vector, respectively. Overexpression of Pygo2 effectively promoted cell proliferation, colony formation and invasion in vitro. Knockdown of Pygo2 obviously inhibited xenograft tumor growth in nude mice. In addition, overexpression of Pygo2 increased the levels of MMP-7, MMP-9 and VEGF in the xenograft tumors.ConclusionPygo2 has a role in promoting cell proliferation, invasion and metastasis, and may regulate angiogenesis via the Wnt/β-catenin signaling pathway