Dynamic semantic cognition : Characterising coherent and controlled conceptual retrieval through time using magnetoencephalography and chronometric transcranial magnetic stimulation

Distinct neural processes are thought to support the retrieval of semantic information that is (i) coherent with strongly-encoded aspects of knowledge, and (ii) non-dominant yet relevant for the current task or context. While the brain regions that support readily coherent and more controlled patterns of semantic retrieval are relatively well-characterised, the temporal dynamics of these processes are not well-understood. This study used magnetoencephalography (MEG) and dual-pulse chronometric transcranial magnetic stimulation (cTMS) in two separate experiments to examine temporal dynamics during the retrieval of strong and weak associations. MEG results revealed a dissociation within left temporal cortex: anterior temporal lobe (ATL) showed greater oscillatory response for strong than weak associations, while posterior middle temporal gyrus (pMTG) showed the reverse pattern. Left inferior frontal gyrus (IFG), a site associated with semantic control and retrieval, showed both patterns at different time points. In the cTMS experiment, stimulation of ATL at ∼150 msec disrupted the efficient retrieval of strong associations, indicating a necessary role for ATL in coherent conceptual activations. Stimulation of pMTG at the onset of the second word disrupted the retrieval of weak associations, suggesting this site may maintain information about semantic context from the first word, allowing efficient engagement of semantic control. Together these studies provide converging evidence for a functional dissociation within the temporal lobe, across both tasks and time

Video Question Answering via Attribute-Augmented Attention Network Learning

Video Question Answering is a challenging problem in visual information retrieval, which provides the answer to the referenced video content according to the question. However, the existing visual question answering approaches mainly tackle the problem of static image question, which may be ineffectively for video question answering due to the insufficiency of modeling the temporal dynamics of video contents. In this paper, we study the problem of video question answering by modeling its temporal dynamics with frame-level attention mechanism. We propose the attribute-augmented attention network learning framework that enables the joint frame-level attribute detection and unified video representation learning for video question answering. We then incorporate the multi-step reasoning process for our proposed attention network to further improve the performance. We construct a large-scale video question answering dataset. We conduct the experiments on both multiple-choice and open-ended video question answering tasks to show the effectiveness of the proposed method.Comment: Accepted for SIGIR 201

Imaging of Alignment, Deformation and Dissociation of CS2 Molecules using Ultrafast Electron Diffraction

Imaging the structure of molecules in transient excited states remains a challenge due to the extreme requirements for spatial and temporal resolution. Ultrafast electron diffraction from aligned molecules (UEDAM) provides atomic resolution and allows for the retrieval of structural information without the need to rely on theoretical models. Here we use UEDAM and femtosecond laser mass spectrometry (FLMS) to investigate the dynamics in carbon disulfide (CS2) following the interaction with an intense femtosecond laser pulse. We have retrieved images of ground state and excited molecules with 0.03 {\AA} precision. We have observed that the degree of alignment reaches an upper limit at laser intensities below the ionization threshold, and found evidence of structural deformation, dissociation, and ionization at higher laser intensities

Temporal dynamics in information retrieval

The passage of time is unrelenting. Time is an omnipresent feature of our existence, serving as a context to frame change driven by events and phenomena in our personal lives and social constructs. Accordingly, various elements of time are woven throughout information itself, and information behaviours such as creation, seeking and utilisation. Time plays a central role in many aspects of information retrieval (IR). It can not only distinguish the interpretation of information, but also profoundly influence the intentions and expectations of users' information seeking activity. Many time-based patterns and trends - namely temporal dynamics - are evident in streams of information behaviour by individuals and crowds. A temporal dynamic refers to a periodic regularity, or, a one-off or irregular past, present or future of a particular element (e.g., word, topic or query popularity) - driven by predictable and unpredictable time-based events and phenomena. Several challenges and opportunities related to temporal dynamics are apparent throughout IR. This thesis explores temporal dynamics from the perspective of query popularity and meaning, and word use and relationships over time. More specifically, the thesis posits that temporal dynamics provide tacit meaning and structure of information and information seeking. As such, temporal dynamics are a ‘two-way street’ since they must be supported, but also conversely, can be exploited to improve time-aware IR effectiveness. Real-time temporal dynamics in information seeking must be supported for consistent user satisfaction over time. Uncertainty about what the user expects is a perennial problem for IR systems, further confounded by changes over time. To alleviate this issue, IR systems can: (i) assist the user to submit an effective query (e.g., error-free and descriptive), and (ii) better anticipate what the user is most likely to want in relevance ranking. I first explore methods to help users formulate queries through time-aware query auto-completion, which can suggest both recent and always popular queries. I propose and evaluate novel approaches for time-sensitive query auto-completion, and demonstrate state-of-the-art performance of up to 9.2% improvement above the hard baseline. Notably, I find results are reflected across diverse search scenarios in different languages, confirming the pervasive and language agnostic nature of temporal dynamics. Furthermore, I explore the impact of temporal dynamics on the motives behind users' information seeking, and thus how relevance itself is subject to temporal dynamics. I find that temporal dynamics have a dramatic impact on what users expect over time for a considerable proportion of queries. In particular, I find the most likely meaning of ambiguous queries is affected over short and long-term periods (e.g., hours to months) by several periodic and one-off event temporal dynamics. Additionally, I find that for event-driven multi-faceted queries, relevance can often be inferred by modelling the temporal dynamics of changes in related information. In addition to real-time temporal dynamics, previously observed temporal dynamics offer a complementary opportunity as a tacit dimension which can be exploited to inform more effective IR systems. IR approaches are typically based on methods which characterise the nature of information through the statistical distributions of words and phrases. In this thesis I look to model and exploit the temporal dimension of the collection, characterised by temporal dynamics, in these established IR approaches. I explore how the temporal dynamic similarity of word and phrase use in a collection can be exploited to infer temporal semantic relationships between the terms. I propose an approach to uncover a query topic's "chronotype" terms -- that is, its most distinctive and temporally interdependent terms, based on a mix of temporal and non-temporal evidence. I find exploiting chronotype terms in temporal query expansion leads to significantly improved retrieval performance in several time-based collections. Temporal dynamics provide both a challenge and an opportunity for IR systems. Overall, the findings presented in this thesis demonstrate that temporal dynamics can be used to derive tacit structure and meaning of information and information behaviour, which is then valuable for improving IR. Hence, time-aware IR systems which take temporal dynamics into account can better satisfy users consistently by anticipating changing user expectations, and maximising retrieval effectiveness over time

Estimation of biophysical parameters in boreal forests from ERS and JERS SAR interferometry

The thesis describes investigations concerning the evaluation of ERS and JERS SAR images and repeat-pass interferometric SAR images for the retrieval of biophysical parameters in boreal forests. The availability of extensive data sets of images over several test sites located in Sweden, Finland and Siberia has allowed analysis of temporal dynamics of ERS and JERS backscatter and coherence, and of ERS interferometric phase. Modelling of backscatter, coherence and InSAR phase has been performed by means of the Water Cloud Model (WCM) and the Interferometric Water Cloud Model (IWCM); sensitivity analysis and implications for the retrieval of forest biophysical parameters have been thoroughly discussed. Model inversion has been carried out for stem volume retrieval using ERS coherence, ERS backscatter and JERS backscatter, whereas for tree height estimation the ERS interferometric phase has been used. Multi-temporal combination of ERS coherence images, and to a lesser extent of JERS backscatter images, can provide stem volume estimates comparable to stand-wise ground-based measurements. Since the information content of the interferometric phase is strongly degraded by phase noise and uncorrected atmospheric artefacts, the retrieved tree height shows large errors

Linear stability analysis of retrieval state in associative memory neural networks of spiking neurons

We study associative memory neural networks of the Hodgkin-Huxley type of spiking neurons in which multiple periodic spatio-temporal patterns of spike timing are memorized as limit-cycle-type attractors. In encoding the spatio-temporal patterns, we assume the spike-timing-dependent synaptic plasticity with the asymmetric time window. Analysis for periodic solution of retrieval state reveals that if the area of the negative part of the time window is equivalent to the positive part, then crosstalk among encoded patterns vanishes. Phase transition due to the loss of the stability of periodic solution is observed when we assume fast alpha-function for direct interaction among neurons. In order to evaluate the critical point of this phase transition, we employ Floquet theory in which the stability problem of the infinite number of spiking neurons interacting with alpha-function is reduced into the eigenvalue problem with the finite size of matrix. Numerical integration of the single-body dynamics yields the explicit value of the matrix, which enables us to determine the critical point of the phase transition with a high degree of precision.Comment: Accepted for publication in Phys. Rev.

Temporal Cross-Media Retrieval with Soft-Smoothing

Multimedia information have strong temporal correlations that shape the way modalities co-occur over time. In this paper we study the dynamic nature of multimedia and social-media information, where the temporal dimension emerges as a strong source of evidence for learning the temporal correlations across visual and textual modalities. So far, cross-media retrieval models, explored the correlations between different modalities (e.g. text and image) to learn a common subspace, in which semantically similar instances lie in the same neighbourhood. Building on such knowledge, we propose a novel temporal cross-media neural architecture, that departs from standard cross-media methods, by explicitly accounting for the temporal dimension through temporal subspace learning. The model is softly-constrained with temporal and inter-modality constraints that guide the new subspace learning task by favouring temporal correlations between semantically similar and temporally close instances. Experiments on three distinct datasets show that accounting for time turns out to be important for cross-media retrieval. Namely, the proposed method outperforms a set of baselines on the task of temporal cross-media retrieval, demonstrating its effectiveness for performing temporal subspace learning.Comment: To appear in ACM MM 201

Magnon dark modes and gradient memory

Extensive efforts have been expended in developing hybrid quantum systems to overcome the short coherence time of superconducting circuits by introducing the naturally long-lived spin degree of freedom. Among all the possible materials, single-crystal yttrium iron garnet has shown up very recently as a promising candidate for hybrid systems, and various highly coherent interactions, including strong and even ultra-strong coupling, have been demonstrated. One distinct advantage of these systems is that the spins are in the form of well-defined magnon modes, which allows flexible and precise tuning. Here we demonstrate that by dissipation engineering, a non-Markovian interaction dynamics between the magnon and the microwave cavity photon can be achieved. Such a process enables us to build a magnon gradient memory to store information in the magnon dark modes, which decouple from the microwave cavity and thus preserve a long life-time. Our findings provide a promising approach for developing long-lifetime, multimode quantum memories.Comment: 18 pages, 12 figure