Temporal Object Research Articles

Rhythmic Pruning of Perceptual Noise for Object Recognition in the Hippocampal Memory Systems

Neurons in the temporal cortex signal object familiarity by modulating their spiking activity as the object is repeatedly experienced. However, the neural mechanisms underlying this ‘repetition effect’ and its functional significance remain unknown. We investigated this process in a goal-directed, object-recognition task in which rats were required to recognize familiar and novel objects. Single-unit spiking activity and local field potential were recorded simultaneously from the hippocampus and perirhinal cortex (PER) as rats performed the task. Repetition effects were detected in both the hippocampus and PER. However, phase locking to the theta rhythm was strengthened with object repetitions in the hippocampus, but not in the PER, whereas stronger phase locking was observed with gamma rhythm in the PER, but not in the hippocampus. Our findings suggest that the repetition effect occurs in sync with region-specific rhythms and manifests as neural “pruning” of noise that facilitates object recognition.

Generalized Multi-Instance Control Mapping for Interactive Media Systems

We articulate a need for the representation of temporal objects reflecting dynamic, short-lived mapping connections instantiated from a template, in tools for designing and using interactive media systems. A list of requirements is compiled from an examination of existing tools, practical use cases, and abstract considerations of node connectivity and information propagation within a graph of connected devices. We validate the concept through implementation in the open source software libmapper, and explore its application by integration with existing controller/synthesizer software and hardware.

현대중국어 시량보어와 목적어 어순제약의 화용·인지적 원인 연구 — “我学了汉语一年”과 “我等了三天他”의 오류원인 분석을 중심으로 —

현대중국어 시량보어와 목적어 어순제약의 화용·인지적 원인 연구 — “我学了汉语一年”과 “我等了三天他”의 오류원인 분석을 중심으로 —

Reconstruction of scaffolds from a photogrammetric point cloud of construction sites using a novel 3D local feature descriptor

Scaffolds always act as disturbances when reconstructing the 3D scene of the construction site due to occlusions, similarities with buildings in color and height as well as their adjacent positions to wall surfaces. Since scaffolds are commonly utilized to assist the construction and maintenance of building structures, professionals can estimate the overall progress and temporal objects of construction projects by assessing the status or arrangement of the scaffolds. Its thin, repeating and complex structures also make it a valuable dataset for testing related algorithms and approaches for the reconstruction of 3D construction site scene. To this end, we present a data-driven workflow for the detection and reconstruction of scaffolding components, including tubes, toeboards, and decks, given a photogrammetric point cloud. Our workflow consists of two parts: one part concerns the strategy based on projection and methods of grouping and slicing planar surfaces for detecting and extracting points of scaffolds from the construction site. The other part relates to the point feature derivation using a novel 3D local feature descriptor LSSHOT, designed for extracting features in the classification of points. Specifically, our workflow is implemented by five major steps, including preprocessing of the point cloud, division of building facades, classification of points, geometric modeling and refinement of results. To evaluate our proposed descriptor, a series of simulated experiments using synthetic datasets is conducted via shape matching tests. A real application is also carried out to validate the feasibility and effectiveness of our workflow using the photogrammetric point cloud of a construction site. Results of simulated experiments reveal that our proposed descriptor outperforms the original SHOT descriptor in the simulated test, especially when dealing with point clouds having a large percentage of noise. Regarding the real application of reconstructing scaffolds, points of scaffolds are successfully detected, extracted, and reconstructed. For a facade having enough points, over 70% of the scaffolding elements are reconstructed. For the classification of points using LSSHOT descriptor and a random forest classifier, the accuracy of results for the points of two major scaffolding elements reaches more than 70% in our test examples.

The Dopamine D5 Receptor Is Involved in Working Memory.

Pharmacological studies indicate that dopamine D1-like receptors (D1 and D5) are critically involved in cognitive function. However, the lack of pharmacological ligands selective for either the D1 or D5 receptors has made it difficult to determine the unique contributions of the D1-like family members. To circumvent these pharmacological limitations, we used D5 receptor homozygous (-/-) and heterozygous (+/-) knockout mice, to identify the specific role of this receptor in higher order cognitive functions. We identified a novel role for D5 receptors in the regulation of spatial working memory and temporal order memory function. The D5 mutant mice acquired a discrete paired-trial variable-delay T-maze task at normal rates. However, both and mice exhibited impaired performance compared to littermates when a higher burden on working memory faculties was imposed. In a temporal order object recognition task, exhibited significant memory deficits. No D5-dependent differences in locomotor functions and interest in exploring objects were evident. Molecular biomarkers of dopaminergic functions within the prefrontal cortex (PFC) revealed a selective gene-dose effect on Akt phosphorylation at Ser473 with increased levels in knockout mice. A trend toward reduced levels in CaMKKbeta brain-specific band (64 kDa) in compared to was also evident. These findings highlight a previously unidentified role for D5 receptors in working memory function and associated molecular signatures within the PFC.

Toward an iconology for temporal object

Abstract The advent of cinema brought with it a different kind of image montage, to which Warburg’s iconological project was strangely oblivious. Are we meant to believe, then, that the cinema does not produce icons? Is iconology destined to be only a science of classical culture, or can it also evolve to integrate into its own study new forms of kinematic art? We would like to ask the question of organogenesis of iconology here, that is to say, the invention of an iconological science for temporal objects by means of appropriate instruments for this new medium. The instrument is both conceived as a means of acting upon the real, and as a means of activating noetic capacities. The aim of our study will be to first outline the iconographic and iconological consequences stemming from animation and flux that have governed images since the birth of cinema, precipitating a crisis in classical iconology. Secondly, through the analysis of the noetic function of animated GIFs (Graphics Interchange Format) in digital streams, we will introduce the notion of ‘temporal gesture’ – as a tertiarized retentional technology, which can be reused for imagining an iconology of temporal suspension and insistence. This will be presented in the third part of this work to reflect on an iconological device for temporal objects by the grammatization and the engramming of ‘temporal gesture’, as an art of delay and repetition. This thinking will be based on the video artwork produced in collaboration with Gregory Dassié titled Iconologie pour objet temporel.

Migration of the Temporal RDB into Temporal ORDB including

This paper proposes an approach for migrating existing relational database (TRDB) according to SQL: 2011 standard into temporal object relational database (TORDB) including Bitemporal data. This is done with methods that can extract various functions from a TRDB, which is based on the different kind of the relationships between tables. To do that we’re going to enhance a representation of a varying time database’s structure in order to make hidden semantic explicit. In contrast to other studies, our main goal here is to offer a first and better solution to mentioned limits to existing works, in order to provide the efficient method for the translation from TRDB to TORDB. We are going to take an existing RDB with bitemporal data features as input, we provide our meta-model in order to get a correct description of a data structure, enriches its metadata representation, and generates a bitemporal data Model (BTDM), which captures essential characteristics of temporal databases for migration. A prototype has been developed which proves the effectiveness of this solution, and temporal queries has been made with help of Oracle 12C.

Differential ghost imaging in time domain

Differential ghost imaging is attempted in time domain, i.e., temporal differential ghost imaging (TDGI), using pseudo-randomized light pulses and a temporal object consisting of no-return-to-zero bit patterns of varying duty. Evaluation of the signal-to-noise characteristics by taking into account errors due to false cross-correlation between the reference and the bucket detector readings indicates that the TDGI outperforms its non-differential counterpart, i.e., time-domain GI, in terms of consistently high and even duty-independent signal-to-noise ratios that are achieved.

Adaptation of the Arizona Cognitive Task Battery for use with the Ts65Dn mouse model (Mus musculus) of Down syndrome.

We propose and validate a clear strategy to efficiently and comprehensively characterize neurobehavioral deficits in the Ts65Dn mouse model of Down syndrome. This novel approach uses neurocognitive theory to design and select behavioral tasks that test specific hypotheses concerning the results of Down syndrome. In this article, we model the Arizona Cognitive Task Battery, used to study human populations with Down syndrome, in Ts65Dn mice. We observed specific deficits for spatial memory, impaired long-term memory for visual objects, acquisition and reversal of motor responses, reduced motor dexterity, and impaired adaptive function as measured by nesting and anxiety tasks. The Ts65Dn mice showed intact temporal ordering, novelty detection, and visual object recognition with short delays. These results phenocopy the performance of participants with Down syndrome on the Arizona Cognitive Task Battery. This approach extends the utility of mouse models of Down syndrome by integrating the expertise of clinical neurology and cognitive neuroscience into the mouse behavioral laboratory. Further, by directly emphasizing the reciprocal translation of research between human disease states and the associated mouse models, we demonstrate that it is possible for both groups to mutually inform each other's research to more efficiently generate hypotheses and elucidate treatment strategies. (PsycINFO Database Record

Object-based cloud detection of multitemporal high-resolution stationary satellite images

Satellite remote sensing that utilizes highly accurate cloud detection is important for monitoring natural disasters. The GaoFen-4, China’s first high-resolution stationary satellite, was recently launched and acquires imagery at a spatial resolution of 50 m and a high temporal resolution (up to 10 min). An object-based cloud detection method was conducted for a time series of GaoFen-4 images. The cloudy objects were obtained from the individual images, and the outlier detection of multiple temporal objects was further processed for refinement. In the initial cloud detection, the objects were segmented by the mean-shift algorithm, and their morphological features were extracted by extended attribute profiles. The threshold-detected cloudy objects were trained according to spectral and morphological features, and the initial objects were classified as cloudy or clear by a regularized least-squares classifier. Furthermore, the medians and standard deviations of the classified cloudy and clear objects were calculated and subsequently refined by the outlier detection of multiple temporal images. The clear object features deviated more than a multiple of standard deviations from the medians of the clear objects that were classified as cloudy objects. Additionally, the refined clear objects were obtained by a similar outlier detection method. Flood event monitoring using GaoFen-4 images showed that the average overall accuracy of the initial cloud detection was 83.4% and increased to 93.3% after refinement. This object-based cloud detection method was insensitive to variations in land objects and can effectively improve cloud detection within small or thin areas, which can be helpful for the monitoring of natural disasters.

Brain activity related to working memory for temporal order and object information

Maintaining items in an appropriate sequence is important for many daily activities; however, remarkably little is known about the neural basis of human temporal working memory. Prior work suggests that the prefrontal cortex (PFC) and medial temporal lobe (MTL), including the hippocampus, play a role in representing information about temporal order. The involvement of these areas in successful temporal working memory, however, is less clear. Additionally, it is unknown whether regions in the PFC and MTL support temporal working memory across different timescales, or at coarse or fine levels of temporal detail. To address these questions, participants were scanned while completing 3 working memory task conditions (Group, Position and Item) that were matched in terms of difficulty and the number of items to be actively maintained. Group and Position trials probed temporal working memory processes, requiring the maintenance of hierarchically organized coarse and fine temporal information, respectively. To isolate activation related to temporal working memory, Group and Position trials were contrasted against Item trials, which required detailed working memory maintenance of visual objects. Results revealed that working memory encoding and maintenance of temporal information relative to visual information was associated with increased activation in dorsolateral PFC (DLPFC), and perirhinal cortex (PRC). In contrast, maintenance of visual details relative to temporal information was characterized by greater activation of parahippocampal cortex (PHC), medial and anterior PFC, and retrosplenial cortex. In the hippocampus, a dissociation along the longitudinal axis was observed such that the anterior hippocampus was more active for working memory encoding and maintenance of visual detail information relative to temporal information, whereas the posterior hippocampus displayed the opposite effect. Posterior parietal cortex was the only region to show sensitivity to temporal working memory across timescales, and was particularly involved in the encoding and maintenance of fine temporal information relative to maintenance of temporal information at more coarse timescales. Collectively, these results highlight the involvement of PFC and MTL in temporal working memory processes, and suggest a dissociation in the type of working memory information represented along the longitudinal axis of the hippocampus.

ASSESSING TEMPORAL BEHAVIOR IN LIDAR POINT CLOUDS OF URBAN ENVIRONMENTS

Abstract. Self-driving cars and robots that run autonomously over long periods of time need high-precision and up-to-date models of the changing environment. The main challenge for creating long term maps of dynamic environments is to identify changes and adapt the map continuously. Changes can occur abruptly, gradually, or even periodically. In this work, we investigate how dense mapping data of several epochs can be used to identify the temporal behavior of the environment. This approach anticipates possible future scenarios where a large fleet of vehicles is equipped with sensors which continuously capture the environment. This data is then being sent to a cloud based infrastructure, which aligns all datasets geometrically and subsequently runs scene analysis on it, among these being the analysis for temporal changes of the environment. Our experiments are based on a LiDAR mobile mapping dataset which consists of 150 scan strips (a total of about 1 billion points), which were obtained in multiple epochs. Parts of the scene are covered by up to 28 scan strips. The time difference between the first and last epoch is about one year. In order to process the data, the scan strips are aligned using an overall bundle adjustment, which estimates the surface (about one billion surface element unknowns) as well as 270,000 unknowns for the adjustment of the exterior orientation parameters. After this, the surface misalignment is usually below one centimeter. In the next step, we perform a segmentation of the point clouds using a region growing algorithm. The segmented objects and the aligned data are then used to compute an occupancy grid which is filled by tracing each individual LiDAR ray from the scan head to every point of a segment. As a result, we can assess the behavior of each segment in the scene and remove voxels from temporal objects from the global occupancy grid.

Building Skill in Videogames: A Play of Bodies, Controllers and Game-Guides

In his now-seminal book, Pilgrim in the Microworld (1983), David Sudnow details his process of learning to play the game Breakout on the Atari 2600. Sudnow develops an account of his graduation from a novice (having never played a videogame prior, and middle-aged at time of writing) to being able to fluidly perform the various configurative processes involved in an acclimated Breakout player’s repertoire.

Temporal depth imaging

Temporal optics is an emerging field in which optical signals are considered similarly to objects in spatial optics. Indeed, temporal magnifications, temporal Fourier transform, and temporal signal processing have been demonstrated by adopting optical schemes from space to time. However, temporal imaging has so far focused on the equivalent of two-dimensional spatial imaging schemes, while ignoring the depth of the input. Here we adopt the concept of three-dimensional (3D) imaging into the time domain, providing a new dimension in temporal optics. We developed the concept of temporal 3D objects and demonstrated temporal depth imaging. First, we define signals with temporal depth as signals where each point in time has a different dispersion value. Next, we demonstrate how to measure these signals with a moving time lens. Finally, we present a time lens array and use it to realize temporal depth imaging with a single measurement. Our temporal depth imaging concept will enable measurements of ultrafast nonperiodic phenomena, such as optical rogue waves and the evolution of ultrafast pulses in fiber lasers, with temporal resolution that was not possible until now.

Making sense of the temporal dimension of intellectual capital: A critical case study

Abstract We attempt to answer the following research question: how is time understood and acted upon in connection to different types of ICoriented management accounting processes within organizations? In other words, what role does time play in IC-related management accounting practices? Alvesson and Deetz’s (2000) critical management research framework was selected for the examination. The temporal dimensions of IC and its related measurement and management accounting processes are discussed and questioned from a time-oriented micro-process (practice) perspective. The main findings are the following. First, IC can be considered as a “temporal boundary object”. Second, the development of IC time-related indicators for IC should be problematized to increase understanding. Moreover, this study lends sharp focus to the need for considering more than just the amount of time spent engaging in IC activities, but the quality of that time as well. Third, we confirm that IC tends to change over time. Consequently, IC concepts, methods and tools are not stable in practice, but rather are reflective of a specific time horizon. Forth, time-lags relating to IC creation and development tend to be overlooked and, hence, are not as clearly understood.

Temporal constrained objects: Application and implementation

We present a novel programming concept called temporal constrained objects for modeling of dynamic systems. It is an extension of the paradigm of constrained objects which provides a principled approach to modeling complex engineering systems based upon two main principles: a compositional specification of the structure of the system, using objects, and a declarative specification of its behavior, using constraints. A novel feature of temporal constrained objects is the series variable, the sequence of whose values is determined by constraints that include metric temporal operators. The emergent behavior of a dynamic system is determined through a process of time-based simulation and constraint satisfaction at each step. Each class definition for a temporal constrained object is translated into a Prolog rule with constraint-solving capability. In order to improve the performance for long simulations, a partial evaluation technique is adopted for optimized code generation. This paper describes the syntax of a language called TCOB along with several examples, our objective being to demonstrate the benefits of the programming paradigm. TCOB has been implemented and all examples presented in this paper were tested using this implementation. An overview of the translation as well as partial execution and its performance improvements are presented in the paper.

Spatio-Temporal parsing in spatial hypermedia

Since 2011 Thomas Schedel has been working as a research assistant at the Institute of Information Systems (iisys) at Hof University. In 2016 he successfully finished the Ph.D. program "Computer Science and Engineering" (CSE) at Aalborg University in Denmark. In his thesis - supervised by Henrik Legind Larsen (Aalborg University) and Claus Atzenbeck (Institute of Information Systems, Hof University) - he claims that considering temporal aspects in addition to spatial and visual properties in spatial parser design will lead to significant increase in parsing accuracy, detection of richer structures and thus higher parser performance. For the purpose of providing evidence, parsers for recognizing spatial, visual and temporal object relations have been implemented and tested in a series of user surveys. It turned out, that in none of the test cases pure spatial or visual parser could outperform the spatio-temporal parser. Instead, the spatio-temporal parser was able to compensate limitations of conventional parsers. Furthermore, differences in parsing accuracy were successfully tested for statistical significance. The results indicate a non-trivial effect that is recognizable by humans. We have shown that the addition of a temporal parser shifts machine detected structures significantly closer to what knowledge workers intend to express. Due to its significant contributions to the research area of spatial hypermedia the thesis has been awarded with the Informatikpreis 2016 (given by the German Fachbereichstag Informatik, FBTI).

A Cycle Deep Belief Network Model for Multivariate Time Series Classification

Multivariate time series (MTS) data is an important class of temporal data objects and it can be easily obtained. However, the MTS classification is a very difficult process because of the complexity of the data type. In this paper, we proposed a Cycle Deep Belief Network model to classify MTS and compared its performance with DBN and KNN. This model utilizes the presentation learning ability of DBN and the correlation between the time series data. The experimental results showed that this model outperforms other four algorithms: DBN, KNN_ED, KNN_DTW, and RNN.

Chinese temporal relation resolution based on Chinese-English parallel corpus

This paper studies the annotation of temporal objects such as EVENT-type and TIMEX3-type elements in Chinese text based on Chinese-English bilingual parallel corpus. We propose and implement a temporal relation resolution (TRR) engine for Chinese text. We use temporal recognition and normalisation tools to annotate the English text, and then map the annotations of English text to the Chinese text by word alignment. The constructed Chinese TimeBank could be used for TRR research based on TimeML in Chinese text.

A Hybrid Storage Strategy to Manage the Evolution of an OWL 2 DL Domain Ontology

Ontology is mainly used to capture “the knowledge semantics”. As knowledge is continually evolving over time, an ontology should evolve accordingly. Its previous states/versions should also be kept in order to allow the history access. Existing solutions for versions storage provide a trade-off between storage space efficiency and “knowledge semantics” keeping. Indeed, some storage strategies try to capture the evolving “knowledge semantics”, but unfortunately suffer from space overhead due to the storage of redundant information. Other strategies propose efficient storage solutions while losing the semantics of knowledge. These issues are addressed by our hybrid storage strategy which combines the benefits of the ontology and the database technologies. A reference ontology version is used to allow capturing the semantics along the whole ontology versioning process. A Temporal Object Oriented Database (TOODB) is used for storing and retrieving the ontology evolution history.