Representation In Order Research Articles

Multimodal sentiment analysis based on disentangled representation learning and cross-modal-context association mining

Multimodal sentiment analysis aims to extract sentiment information expressed by users from multimodal data, including linguistic, acoustic, and visual cues. However, the heterogeneity of multimodal data leads to disparities in modal distribution, thereby impacting the model’s ability to effectively integrate complementarity and redundancy across modalities. Additionally, existing approaches often merge modalities directly after obtaining their representations, overlooking potential emotional correlations between them. To tackle these challenges, we propose a Multiview Collaborative Perception (MVCP) framework for multimodal sentiment analysis. This framework consists primarily of two modules: Multimodal Disentangled Representation Learning (MDRL) and Cross-Modal Context Association Mining (CMCAM). The MDRL module employs a joint learning layer comprising a common encoder and an exclusive encoder. This layer maps multimodal data to a hypersphere, learning common and exclusive representations for each modality, thus mitigating the semantic gap arising from modal heterogeneity. To further bridge semantic gaps and capture complex inter-modal correlations, the CMCAM module utilizes multiple attention mechanisms to mine cross-modal and contextual sentiment associations, yielding joint representations with rich multimodal semantic interactions. In this stage, the CMCAM module only discovers the correlation information among the common representations in order to maintain the exclusive representations of different modalities. Finally, a multitask learning framework is adopted to achieve parameter sharing between single-modal tasks and improve sentiment prediction performance. Experimental results on the MOSI and MOSEI datasets demonstrate the effectiveness of the proposed method.

Abstract geometrical computation 12: generating representation of infinite countable linear orderings

Abstract geometrical computation 12: generating representation of infinite countable linear orderings

Survey Paper on Quantum Deep Reinforcement Learning for Robot Navigation Tasks

Abstract: Quantum deep reinforcement learning is an integration of the principles of quantum computing with deep reinforcement learning in an effort to advance robot navigation tasks. Due to the explosion of the state and action spaces, traditionally DRL methods are limited in scalability and efficiency. QDRL, however, uses quantum superposition and entanglement for more efficient processing of big amounts of information-it enables robots to learn optimal policies for navigating complex environments. We introduce here a new QDRL framework in which quantum neural networks encode policy functions and value estimates. We demonstrate how quantum circuits can take advantage of multi-dimensional state representations in order to strengthen the power of a robot in changing its environment over time. Benchmark experiments on navigation tasks reflect phenomenal acceleration in learning performance along with better decision-making accuracy compared to classical DRL methods. We then consider integrating such quantum algorithms, as Grover's search, to improve exploration strategies in sparse reward settings. The experiments show that QDRL speeds up convergence rates and provides robots with the possibility to react more rapidly to unexpected obstacles. It discusses new applications for quantum computing of autonomous systems, potentially applied to robotics, autonomous vehicles, and other environments which require complex decision making. Some lines of work in the future are optimization of the resources needed for quantum computers and hybrid architectures for the resolution of classical problems in navigation

A reduced-order hybrid model for photobioreactor performance and biomass prediction

This paper introduces a hybrid approach for photobioreactor modeling tailored to microalgae cultivation, combining data-driven and mechanistic concepts to improve modeling efficiency and practicality for industrial scale-up applications. Most growth models for microalgae are nonlinear and require experimental measurement of several parameters. The aim of this work is to develop linear practical models for monitoring purposes. A model based on linear coefficients and polynomial features is proposed, balancing interpretability with non-linear representation focusing on model transparency. To simplify the growth model, Taylor series expansion is applied to the Monod and logistic population models. Two scale-specific models are developed and evaluated, offering practical solutions for monitoring microalgae growth in photobioreactors. Therefore, this reduced order representation allows the biomass growth rate to be dependent directly on the biomass concentration. These models do not require exhaustive data collection of substrate concentration over time, making them cost-effective and efficient for industrial applications. This work provides a step forward in photobioreactor modeling, contributing to the sustainable production of microalgae.

The Development of the Mental Timeline is Related to Temporal Memory

ABSTRACT Although we cannot see or touch time, across many cultures, we use spatial representations to think about this abstract concept. Spatial representations of time are thought to support temporal concepts that might otherwise be difficult to represent and reason about, such as the temporal component of episodic memory. One common form of spatially representing time is the mental timeline, which is a linear projection of time onto space. Adults and older children from many cultures spontaneously activate the mental timeline when remembering the order of events. However, the mental timeline develops slowly throughout early childhood as children gain increasing experience with formal schooling and cultural artifacts. Here, we explored how individual differences in the development of American children’s mental timelines relate to their memory for temporal order (N = 96, M age = 6.22 years). We first tested children’s memory for the order and location of events through a memory task involving short videos. We then tested children’s spontaneous spatial representations of temporal order using an open-ended timeline construction task. The linearity of children’s timeline arrangements predicted their memory for temporal order but not memory for locations: children who spontaneously represented time linearly had significantly better temporal memory performance than children who represented time nonlinearly, but there was no difference in location memory performance. These data provide preliminary evidence that the development of the mental timeline may support the development of temporal memory.

Identification of a Triangular Random Coefficient Model using a Correction Function

Abstract Previously, identification of triangular random coefficient models required a restriction on the dimension of the first stage heterogeneity or independence assumptions across the different sources of the heterogeneity. This note proposes a new identification strategy that does not rely on either of these restrictions but rather assumes conditional means have a conditional linear projection representation in order to construct “correction functions” to address endogeneity and gain identification of the average partial effect. This identification strategy allows for both continuous and discrete instruments. Finally, the proposed identification method is illustrated in estimating the returns to education.

Hyperspectral anomaly detection based on weighted low-rank sparse dictionary learning

The geometric model-based hyperspectral anomaly detection techniques have garnered a lot of interest recently. These methods are predicated on the assumption that the background can be represented by a dictionary of spectral vectors, but the anomaly cannot. Building an objective model with high representational capabilities and obtaining precise background modeling are therefore key aspects of this type of method. The background dictionary is also readily tainted by anomalies due to the limitations of the current approaches, making the detection findings less reliable. To address the above problems, this paper proposes a weighted low-rank sparse dictionary learning method (WLSDL). This model organically combines sparse representation with low-rank representation in order to effectively depict the intricate background distribution. The weights relating to the eigenvalues of the image are designed by mining the physical characteristics of the HSI. This can enhance the nuclear norm’s capacity for representation, resulting in a background dictionary with more representativeness. Furthermore, the capped norm constraint is incorporated in the objective function to decrease the effect of anomalies and noises on background modeling, hence minimizing anomaly pollution on the background dictionary. The residual image effectively increases the accuracy of anomaly identification since it makes it easier to distinguish between anomalies and background. We use three hyperspectral datasets to evaluate the proposed method. The experimental findings demonstrate the effectiveness and superiority of the proposed method over state-of-the-art methods.

Local organization of spatial and shape information in the primate prefrontal cortex.

The current understanding of sensory and motor cortical areas has been defined by the existence of topographical maps across the brain surface, however, higher cortical areas, such as the prefrontal cortex, seem to lack an equivalent organization, and only limited evidence of functional clustering of neurons with similar stimulus properties is evident in them. We thus sought to examine whether neurons that represent similar spatial and object information are clustered in the monkey prefrontal cortex and whether such an organization only emerges as a result of training. To this end, we analyzed neurophysiological recordings from male macaque monkeys before and after training in spatial and shape working memory tasks. Neurons with similar spatial or shape selectivity were more likely than chance to be encountered at short distances from each other. Some aspects of organization were present even in naïve animals, however other changes appeared after cognitive training. Our results reveal that prefrontal microstructure automatically supports orderly representations of spatial and object information.

Machine learning assisted prediction of organic salt structure properties

We demonstrate a machine learning-based approach which predicts the properties of crystal structures following relaxation based on the unrelaxed structure. Use of crystal graph singular values reduces the number of features required to describe a crystal by more than an order of magnitude compared to the full crystal graph representation. We construct machine learning models using the crystal graph singular value representations in order to predict the volume, enthalpy per atom, and metal versus semiconductor/insulator phase of DFT-relaxed organic salt crystals based on randomly generated unrelaxed crystal structures. Initial base models are trained to relate 89,949 randomly generated structures of salts formed by varying ratios of 1,3,5-triazine and HCl with the corresponding volumes, enthalpies per atom, and phase of the DFT-relaxed structures. We further demonstrate that the base model is able to be extended to related chemical systems (isomers, pyridine, thiophene and piperidine) with the inclusion of 2000 to 10,000 crystal structures from the additional system. After training a single model with a large number of data points, extension can be done at significantly lower cost. The constructed machine learning models can be used to rapidly screen large sets of randomly generated organic salt crystal structures and efficiently downselect the structures most likely to be experimentally realizable. The models can be used as a stand-alone crystal structure predictor, but may serve CSP efforts best as a filtering step in more sophisticated workflows.

Millimeter-Wave and High-Resolution Infrared Spectroscopy of the Ground and Seven Lowest Fundamental States of 1H-1,2,4-Triazole.

A combined analysis of millimeter-wave (70-700 GHz) and rotationally resolved infrared (400-1200 cm-1) spectra of the ground state and seven fundamental vibrational modes of 1H-1,2,4-triazole is reported. While the lowest-energy vibrationally excited state (ν18) is well-treated using a single-state distorted-rotor Hamiltonian, the second (ν17) and third (ν16) vibrationally excited states are involved in strong c-type Coriolis coupling and require an appropriate two-state Hamiltonian. The oblate nature of 1H-1,2,4-triazole is sufficiently close to the oblate symmetric-top limit that the analysis requires the use of A-reduced, sextic centrifugally distorted-rotor Hamiltonian models in the Ir representation in order to achieve low σfit values. The coupling between ν17 (A″) and ν16 (A″) resulted in many transitions with slightly perturbed frequencies, many highly displaced resonant intrastate transitions, and 165 nominal interstate transitions. Modeling the spectra of ν17 and ν16 required three c-axis Coriolis-coupling terms (Fab, FabJ, and FabK) to treat the interaction. Many of the nominal interstate transitions form clearly discernible Q-branch bands, comprising degenerate sets of a- and b-type transitions. The rotational spectra of four higher-energy vibrationally excited states (ν15, ν14, ν13, and ν12), which form a complex polyad involving Coriolis and anharmonic coupling interactions, were analyzed by single-state models, thus producing only effective spectroscopic constants. Inclusion of rotationally resolved infrared transitions enabled the accurate and precise determination of vibrational band origins for the four lowest-energy fundamental states: ν18 = 542.601 824 3 (28) cm-1, ν17 = 665.183 128 5 (43) cm-1, ν16 = 682.256 910 5 (43) cm-1, and ν15 = 847.557 400 (11) cm-1.

On second-order tensor representation of derivatives in shape optimization.

In this article, we study general properties of distributed shape derivatives admitting a volumetric tensor representation of order two. We obtain a general result providing a range of expressions for the shape derivative, with the distributed shape derivative at one end of the range and the standard Hadamard formula at the other end. We further apply this result to a cost functional depending on the solution of a fourth-order elliptic equation, and obtain the distributed shape derivative in the case of open sets, and the Hadamard formula for sets of class [Formula: see text]. We also consider the case of polygons, for which a description of the weak singularities of the solution appearing in the neighbourhood of the vertices is required to obtain the Hadamard formula. This article is part of the theme issue 'Non-smooth variational problems with applications in mechanics'.

Perturbative versus non-perturbative renormalization

Approximated functional renormalization group (FRG) equations lead to regulator-dependent β-functions, in analogy to the scheme-dependence of the perturbative renormalization group (pRG) approach. A scheme transformation redefines the couplings to relate the β-functions of the FRG method with an arbitrary regulator function to the pRG ones obtained in a given scheme. Here, we consider a periodic sine-Gordon scalar field theory in d = 2 dimensions and show that the relation of the FRG and pRG approaches is intricate. Although both the FRG and the pRG methods are known to be sufficient to obtain the critical frequency βc2=8π of the model independently of the choice of the regulator and the renormalization scheme, we show that one has to go beyond the standard pRG method (e.g. using an auxiliary mass term) or the Coulomb-gas representation in order to obtain the β-function of the wave function renormalization. This aspect makes the scheme transformation non-trivial. Comparing flow equations of the two-dimensional sine-Gordon theory without any scheme-transformation, i.e. redefinition of couplings, we find that the auxiliary mass pRG β-functions of the minimal subtraction scheme can be recovered within the FRG approach with the choice of the power-law regulator with b = 2, which therefore constitutes a preferred choice for the comparison of FRG and pRG flows.

Reading direction interacts with spatial processes of temporal order verbal working memory: evidence from Iranian right-to-left readers

ABSTRACT Reading direction affects cognitive processes such as spatial processing and attention. Testing Farsi right-to-left readers, we investigated the effect of reading direction on the involved spatial and attentional processes in a verbal working memory task. Previous research in left-to-right readers has shown that serial order in verbal WM is encoded spatially from left to right and that mechanisms of spatial attention operate on these mental representations. First, we confirmed that the spatial representation of serial information in WM follows the right-to-left reading direction of Farsi. Second, we demonstrated the influence of reading direction on the distribution of spatial attention over the reading-direction-contingent mental representation of serial information in WM. Behavioural RTs and neural markers of spatial attention shifts (EDAN and ADAN ERPs and lateralised posterior alpha activity) confirm the right-to-left representation of serial order in WM and the involvement of spatial attention in retrieving items from this mnemonic space.

Changing-state irrelevant speech disrupts visual-verbal but not visual-spatial serial recall.

In an influential article, Jones et al. (1995) provide evidence that auditory distraction by changing relative to repetitive auditory distracters (the changing-state effect) did not differ between a visual-verbal and visual-spatial serial recall task, providing evidence for an amodal mechanism for the representation of serial order in short-term memory that transcends modalities. This finding has been highly influential for theories of short-term memory and auditory distraction. However, evidence vis-à-vis the robustness of this result is sorely lacking. Here, two high-powered replications of Jones et al.'s (1995) crucial Experiment 4 were undertaken. In the first partial replication (n = 64), a fully within-participants design was adopted, wherein participants undertook both the visual-verbal and visual-spatial serial recall tasks under different irrelevant sound conditions, without a retention period. The second near-identical replication (n = 128), incorporated a retention period and implemented the task-modality manipulation as a between-participants factor, as per the original Jones et al. (1995; Experiment 4) study. In both experiments, the changing-state effect was observed for visual-verbal serial recall but not for visual-spatial serial recall. The results are consistent with modular and interference-based accounts of distraction and challenge some aspects of functional equivalence accounts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Unsupervised Content Mining in CBIR: Harnessing Latent Diffusion for Complex Text-Based Query Interpretation.

The paper demonstrates a novel methodology for Content-Based Image Retrieval (CBIR), which shifts the focus from conventional domain-specific image queries to more complex text-based query processing. Latent diffusion models are employed to interpret complex textual prompts and address the requirements of effectively interpreting the complex textual query. Latent Diffusion models successfully transform complex textual queries into visually engaging representations, establishing a seamless connection between textual descriptions and visual content. Custom triplet network design is at the heart of our retrieval method. When trained well, a triplet network will represent the generated query image and the different images in the database. The cosine similarity metric is used to assess the similarity between the feature representations in order to find and retrieve the relevant images. Our experiments results show that latent diffusion models can successfully bridge the gap between complex textual prompts for image retrieval without relying on labels or metadata that are attached to database images. This advancement sets the stage for future explorations in image retrieval, leveraging the generative AI capabilities to cater to the ever-evolving demands of big data and complex query interpretations.

Combining Savage and Laplace: a new approach to ambiguity

This paper presents a new representation of preference orderings for the study of ambiguity-related decision-making. The central feature is a preference-based decomposition of subjective probabilities that provides information about inherent ambiguity. The probability decomposition is combined with a utility function reflecting the decision-maker’s attitude toward ambiguity. The proposed theory generalizes Savage’s SEU and allows for a straightforward measurement of ambiguity and ambiguity aversion while keeping concepts for measuring risk and risk attitudes unaffected. For the measurement of ambiguity, concepts of probability theory can be used since decision acts can be interpreted as two-dimensional probability distributions. The proposed measure of ambiguity aversion exploits the properties of the utility function in the same way as the Arrow/Pratt measure of risk aversion.

Controlling response order without relying on stimulus order – evidence for flexible representations of task order

In dual-task situations, both component tasks are typically not executed simultaneously but rather one after another. Task order is usually determined based on bottom-up information provided by stimulus presentation order, but also affected by top-down factors such as instructions and/or differentially dominant component tasks (e.g., oculomotor task prioritization). Recent research demonstrated that in the context of a randomly switching stimulus order, task order representations can be integrated with specific component task information rather than being coded in a purely abstract fashion (i.e., by containing only generic order information). This conclusion was derived from observing consistently smaller task-order switch costs for a preferred (e.g., oculomotor-manual) versus a non-preferred (e.g., manual-oculomotor) task order (i.e., order-switch cost asymmetries). Since such a representational format might have been especially promoted by the sequential stimulus presentation employed, we investigated task-order representations in situations without any bottom-up influence of stimulus order. To this end, we presented task stimuli simultaneously and cued the required task-order in advance. Experiment 1 employed abstract order transition cues that only indicated a task-order repetition (vs. switch) relative to the previous trial, while Experiment 2 used explicit cues that unambiguously indicated the task-order. Experiment 1 revealed significant task-order switch costs only for the second task (of either task order) and no order-switch cost asymmetries, indicating a rather generic representation of task order. Experiment 2 revealed task-order switch costs in both component tasks with a trend toward order-switch cost asymmetries, indicating an integration of task order representations with component task information. These findings highlight an astonishing flexibility of mental task-order representations during task-order control.

Dissociable Neural Mechanisms for Human Inference Processing Predicted by Static and Contextual Language Models.

Language models (LMs) continue to reveal non-trivial relations to human language performance and the underlying neurophysiology. Recent research has characterized how word embeddings from an LM can be used to generate integrated discourse representations in order to perform inference on events. The current research investigates how such event knowledge may be coded in distinct manners in different classes of LMs and how this maps onto different forms of human inference processing. To do so, we investigate inference on events using two well-documented human experimental protocols from Metusalem et al. (2012) and McKoon and Ratcliff (1986), compared with two protocols for simpler semantic processing. Interestingly, this reveals a dissociation in the relation between local semantics versus event-inference depending on the LM. In a series of experiments, we observed that for the static LMs (word2vec/GloVe), there was a clear dissociation in the relation between semantics and inference for the two inference tasks. In contrast, for the contextual LMs (BERT/RoBERTa), we observed a correlation between semantic and inference processing for both inference tasks. The experimental results suggest that inference as measured by Metusalem and McKoon rely on dissociable processes. While the static models are able to perform Metusalem inference, only the contextual models succeed in McKoon inference. Interestingly, these dissociable processes may be linked to well-characterized automatic versus strategic inference processes in the psychological literature. This allows us to make predictions about dissociable neurophysiological markers that should be found during human inference processing with these tasks.

The role of nonlinear interactions in the onset of drag increase in flow over riblets

Characterizing the mechanisms that contribute to the onset of drag increase over micro-grooves (riblets) as the spacing increases is critical to design strategies for riblet-based drag reduction. This study decomposes the roughness function to investigate different mechanisms associated with the breakdown of drag reduction as riblet spacing is increased. We obtain the roughness function through direct numerical simulations (DNS) in a minimal channel and restricted nonlinear (RNL) models. Both the traditional RNL decomposition and an augmented RNL (ARNL) model that includes additional nonlinear interactions are employed as computationally tractable, reduced order representations of the flow field. RNL and ARNL results are compared to those of DNS in minimal channels to investigate the role of the different scale-dependent nonlinear interactions contributing to the roughness function. A comparison of the co-spectra arising from the minimal channel DNS with that from RNL and ARNL simulations indicates that general trends are captured by both reduced order models. However, the additional nonlinearity introduced in the ARNL model produces closer correspondence in the observed structural features of the DNS results. In particular, the ARNL better captures the signatures of the dispersive flow and the texture-coherent fluctuations. There is also a noticeable improvement observed in the profiles of the added stress contributions obtained with the ARNL model versus the RNL model.

Separated hands further response–response binding effects

Action control is hierarchically organized. Multiple consecutive responses can be integrated into an event representation of higher order and can retrieve each other upon repetition, resulting in so-called response–response binding effects. Previous research indicates that the spatial separation of responses can affect how easily they can be cognitively separated. In this study, we introduced a barrier between the responding hands to investigate whether the spatial separation of two responses also influences response–response binding effects. In line with previous research on stimulus–response binding, we expected an increased separability of responses to result in stronger response–response binding effects when responding hands were separated by a barrier. We indeed found stronger response–response binding effects with separated hands. Results indicate that a more distinct representation of individual actions through increased separability might benefit the control of hierarchical actions.