Associating object names with descriptions of shape that distinguish possible from impossible objects.

Five experiments examine the proposal that object names are closely linked torepresentations of global, 3D shape by comparing memory for simple line drawings of structurally possible and impossible novel objects.Objects were rendered impossible through local edge violations to global coherence (cf. Schacter, Cooper, & Delaney, 1990) and supplementary observations confirmed that the sets of possible and impossible objects were matched for their distinctiveness. Employing a test of explicit recognition memory, Experiment 1 confirmed that the possible and impossible objects were equally memorable. Experiments 2–4 demonstrated that adults learn names (single-syllable non-words presented as count nouns, e.g., “This is a dax”) for possible objectsmore easily than for impossible objects, and an item-based analysis showed that this effect was unrelated to either the memorability or the distinctiveness of the individual objects. Experiment 3 indicated that the effects of object possibility on name learning were long term (spanning at least 2months), implying that the cognitive processes being revealed can support the learning of object names in everyday life. Experiment 5 demonstrated that hearing someone else name an object at presentation improves recognition memory for possible objects, but not for impossible objects. Taken together, the results indicate that object names are closely linked to the descriptions of global, 3D shape that can be derived for structurally possible objects but not for structurally impossible objects. In addition, the results challenge the view that object decision and explicit recognition necessarily draw on separate memory systems,with only the former being supported by these descriptions of global object shape. It seems that recognition also can be supported by these descriptions, provided the original encoding conditions encourage their derivation. Hearing an object named at encoding appears to be just such a condition. These observations are discussed in relation to the effects of naming in other visual tasks, and to the role of visual attention in object identification

A Survey on Joint Object Detection and Pose Estimation using Monocular Vision

In this survey we present a complete landscape of joint object detection and pose estimation methods that use monocular vision. Descriptions of traditional approaches that involve descriptors or models and various estimation methods have been provided. These descriptors or models include chordiograms, shape-aware deformable parts model, bag of boundaries, distance transform templates, natural 3D markers and facet features whereas the estimation methods include iterative clustering estimation, probabilistic networks and iterative genetic matching. Hybrid approaches that use handcrafted feature extraction followed by estimation by deep learning methods have been outlined. We have investigated and compared, wherever possible, pure deep learning based approaches (single stage and multi stage) for this problem. Comprehensive details of the various accuracy measures and metrics have been illustrated. For the purpose of giving a clear overview, the characteristics of relevant datasets are discussed. The trends that prevailed from the infancy of this problem until now have also been highlighted.Comment: Accepted at the International Joint Conference on Computer Vision and Pattern Recognition (CCVPR) 201

Progressive Text-to-3D Generation for Automatic 3D Prototyping

Text-to-3D generation is to craft a 3D object according to a natural language description. This can significantly reduce the workload for manually designing 3D models and provide a more natural way of interaction for users. However, this problem remains challenging in recovering the fine-grained details effectively and optimizing a large-size 3D output efficiently. Inspired by the success of progressive learning, we propose a Multi-Scale Triplane Network (MTN) and a new progressive learning strategy. As the name implies, the Multi-Scale Triplane Network consists of four triplanes transitioning from low to high resolution. The low-resolution triplane could serve as an initial shape for the high-resolution ones, easing the optimization difficulty. To further enable the fine-grained details, we also introduce the progressive learning strategy, which explicitly demands the network to shift its focus of attention from simple coarse-grained patterns to difficult fine-grained patterns. Our experiment verifies that the proposed method performs favorably against existing methods. For even the most challenging descriptions, where most existing methods struggle to produce a viable shape, our proposed method consistently delivers. We aspire for our work to pave the way for automatic 3D prototyping via natural language descriptions

Intra-burst firing characteristics as network state parameters

Introduction \ud In our group we are aiming to demonstrate learning and memory capabilities of cultured networks of cortical neurons. A first step is to identify parameters that accurately describe changes in the network due to learning. Usually, such parameters are calculated from the responses to test-stimuli before and after a learning experiment. We propose that parameters should be calculated from the spontaneous activity before and after a learning experiment, as the applying of test-stimuli itself may alter the network. Since bursting is dominant in our cultures, we have investigated its spatio-temporal structure. \ud \ud Methods \ud Networks of cortical neurons were cultured on a MEA. Over a period from 9 to 35 DIV, the spontaneous activity has been measured on a regular basis. Measurements on a single day are always continuous; otherwise cultures are kept in a stove under controlled conditions (37 ˚C, 5% CO2, 100% humidity). Network bursts were detected by analysing the Array-Wide Spiking Rate (AWSR, the sum of activity over all electrodes). Next, we estimated the instantaneous AWSR during a burst by convolving spike-occurrences with a Gaussian function. We investigated the changes in burst profiles over time by aligning them to their peak AWSR. In 4 hour recording sessions, we grouped the burst profiles over 1 hour, resulting in 4 average burst profiles per day. In addition, a sufficient amount of aligned bursts yielded enough data to calculate the contribution of each recording site. \ud \ud Results \ud The burst profiles, calculated over a period of 1 hour, generally show little variation (figure 1). In subsequent hours, the profiles gradually change shape. Over a period of days however, the shape can change dramatically (figure 2). The relatively slow changes over the period of hours indicate an underlying probabilistic structure in the AWSR during bursts. The apparent structure in the burst profiles result from the relationships between individual recording sites, and thus also on the connectivity in the neural network. This is revealed in more detail by showing the contributions of individual sites (figure 3). The spike envelopes have a shape that is too detailed to be described accurately by a small set of parameters. \ud \ud Discussion \ud The burst profiles prove to be stable over a period of one hour, and gradually change their shape over several hours, as has also been suggested in [1]. The day-to-day changes in burst profiles may be the result of these gradual changes, thereby suggesting an intrinsically changing network. However, they can also be the result of putting the cultures back in the stove. The spike envelopes per recording site offer more detailed descriptions of the network state than the burst profiles. This may however be the amount of detail required to reveal the changes made during learning experiments. A subsequent refinement can be made by identifying distinct subgroups of bursts, as has been suggested in [2]

Peningkatan Hasil Belajar Siswa Pada Pembelajaran Ilmu Pengetahuan Sosial Menggunakan Metode Diskusi

The purpose of the study is an attempt to improve student learning outcomes using learning methods discussion on Social Sciences in Sixth Grade Elementary School 50 Baet Kawan, Landak District. This research method is descriptive, shape classroom action research, and collaborative nature of the research, Research subject teachers, and learners Elementary scool sixth grade 50 Baet Kawan who numbered 30 students. The technique used in this study is the technique of direct observation and data collection tool used was observation. The results based on observations using the discussion method in teaching Social Studies in improving learning outcomes. Values obtained in the first cyle on average of 67,5 student in the second cycle and the averages values obtained 76,33 students. Based on these descriptions, the general use of the discussion method can improve the learning outcomes of sixth grade students of SDN 50 Baet Kawan, Landak District, thus the discussion of the method can be used during the learning process of Social Sciences to improve learning outcomes