Single Shot Temporal Action Detection

Temporal action detection is a very important yet challenging problem, since videos in real applications are usually long, untrimmed and contain multiple action instances. This problem requires not only recognizing action categories but also detecting start time and end time of each action instance. Many state-of-the-art methods adopt the "detection by classification" framework: first do proposal, and then classify proposals. The main drawback of this framework is that the boundaries of action instance proposals have been fixed during the classification step. To address this issue, we propose a novel Single Shot Action Detector (SSAD) network based on 1D temporal convolutional layers to skip the proposal generation step via directly detecting action instances in untrimmed video. On pursuit of designing a particular SSAD network that can work effectively for temporal action detection, we empirically search for the best network architecture of SSAD due to lacking existing models that can be directly adopted. Moreover, we investigate into input feature types and fusion strategies to further improve detection accuracy. We conduct extensive experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD significantly outperforms other state-of-the-art systems by increasing mAP from 19.0% to 24.6% on THUMOS 2014 and from 7.4% to 11.0% on MEXaction2.Comment: ACM Multimedia 201

A COMMUNITY-BASED VULNERABILITY ASSESSMENT OF FLOODS IN URBAN AREAS OF KAMPUNG MELAYU, JAKARTA

Flooding has become a serious problem in Jakarta. During floods .of 2007, Kampung Melayu, East Jakarta was the worst hit.by the floods. Community have different perceptions on disaster and have different effort to overcome the hazards. Therefore, local government and relevant institution should investigate this situation and make this information a valuable input in developing and implementing response plans in flood mitigation. This research is to explore the vulnerability of floods based on local people\u27s perception. There were 83 households interviewed using questionnaire. Certain elements at risk related with physical and socio:economic aspects were identified. Physical information concerned the building structure and building contents. Several socio-economic characteristics were used as key indicators to analyze the vulnerability of people. Generally, the result of this research shows that the ability of people to cope with the flooding i$ linked with the capacity of the people itself. The capability of people to deal withflooding was influenced by several indicators.based on their socio- . economic characteristics. For example, lower income people will experience more suffering than the wealthier, because they cannot afford the\u27 costs of repair, reconstruction. Although the wealthier are likely to experience a higher degree of economic damage due to possessions of higher value. Base on the analysis, all coping strategies and flood measures are not enough to cope with flooding in the study area

Neural Networks for Information Retrieval

Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day research has given rise to many different approaches for many different IR problems. The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions. Additionally, it is interesting to see what key insights into IR problems the new technologies are able to give us. The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they benefit IR research. It covers key architectures, as well as the most promising future directions.Comment: Overview of full-day tutorial at SIGIR 201

Anharmonic effects in the A15 compounds induced by sublattice distortions

We demonstrate that elastic anomalies and lattice instabilities in the the A15 compounds are describable in terms of first-principles LDA electronic structure calculations. We show that at T=0 V_3Si, V_3Ge, and Nb_3Sn are intrinsically unstable against shears with elastic moduli C_11-C_12 and C_44, and that the zone center phonons, Gamma_2 and Gamma_12, are either unstable or extremely soft. We demonstrate that sublattice relaxation (internal strain) effects are key to understanding the behavior of the A15 materials.Comment: 5 pages, RevTex, 3 postscript figures, Submitted to Phys. Rev. Lett. Apr. 23, 1997 July 7, 1997: minor corrections, final accepted versio

Geometric deep learning

The goal of these course notes is to describe the main mathematical ideas behind geometric deep learning and to provide implementation details for several applications in shape analysis and synthesis, computer vision and computer graphics. The text in the course materials is primarily based on previously published work. With these notes we gather and provide a clear picture of the key concepts and techniques that fall under the umbrella of geometric deep learning, and illustrate the applications they enable. We also aim to provide practical implementation details for the methods presented in these works, as well as suggest further readings and extensions of these ideas

Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions

We explore the potential of feed-forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the Super Parameterized Community Atmospheric Model. To identify the network architecture of greatest skill, we formally optimize hyperparameters using ~250 trials. Our DNN explains over 70 percent of the temporal variance at the 15-minute sampling scale throughout the mid-to-upper troposphere. Autocorrelation timescale analysis compared against DNN skill suggests the less good fit in the tropical, marine boundary layer is driven by neural network difficulty emulating fast, stochastic signals in convection. However, spectral analysis in the temporal domain indicates skillful emulation of signals on diurnal to synoptic scales. A close look at the diurnal cycle reveals correct emulation of land-sea contrasts and vertical structure in the heating and moistening fields, but some distortion of precipitation. Sensitivity tests targeting precipitation skill reveal complementary effects of adding positive constraints vs. hyperparameter tuning, motivating the use of both in the future. A first attempt to force an offline land model with DNN emulated atmospheric fields produces reassuring results further supporting neural network emulation viability in real-geography settings. Overall, the fit skill is competitive with recent attempts by sophisticated Residual and Convolutional Neural Network architectures trained on added information, including memory of past states. Our results confirm the parameterizability of superparameterized convection with continents through machine learning and we highlight advantages of casting this problem locally in space and time for accurate emulation and hopefully quick implementation of hybrid climate models.Comment: 32 Pages, 13 Figures, Revised Version Submitted to Journal of Advances in Modeling Earth Systems April 202