Context Of Inference Research Articles

Contextualised retrieval practice: the effect of context type and working memory capacity on word retention

ABSTRACT The present study on intentional retrieval practice compared the benefits of presenting words in either informative or uninformative sentence contexts. Participants first studied a list of English words with their translations. Then, they were all exposed to half of the words with informative sentences containing meaning clues in the Context Inference (CI) condition and half with uninformative sentences devoid of such clues in the Memory Retrieval (MR) condition as part of retrieval-based practising. Participants were required to type the L1 translation for each word presented using a mobile application. Data were collected by both form-recall and meaning-recall tests immediately afterwards and then a week later. In addition, this study focused on the relationship between working memory capacity (WMC) and word retention in these two conditions to explore the suggestion that individuals may benefit differently from retrieval practices. Although the results showed that both conditions contributed to word retention, the MR condition was significantly more effective than the CI condition for the participants’ long-term retention. Further, the results revealed an overall positive effect of WMC on word retention in both conditions, with high-WMC individuals achieving higher retention scores than low-WMC individuals. However, this effect was not modulated by the type of context condition.

Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization.

A key challenge of continual reinforcement learning (CRL) in dynamic environments is to promptly adapt the reinforcement learning (RL) agent's behavior as the environment changes over its lifetime while minimizing the catastrophic forgetting of the learned information. To address this challenge, in this article, we propose DaCoRL, that is, dynamics-adaptive continual RL. DaCoRL learns a context-conditioned policy using progressive contextualization, which incrementally clusters a stream of stationary tasks in the dynamic environment into a series of contexts and opts for an expandable multihead neural network to approximate the policy. Specifically, we define a set of tasks with similar dynamics as an environmental context and formalize context inference as a procedure of online Bayesian infinite Gaussian mixture clustering on environment features, resorting to online Bayesian inference to infer the posterior distribution over contexts. Under the assumption of a Chinese restaurant process (CRP) prior, this technique can accurately classify the current task as a previously seen context or instantiate a new context as needed without relying on any external indicator to signal environmental changes in advance. Furthermore, we employ an expandable multihead neural network whose output layer is synchronously expanded with the newly instantiated context and a knowledge distillation regularization term for retaining the performance on learned tasks. As a general framework that can be coupled with various deep RL algorithms, DaCoRL features consistent superiority over existing methods in terms of stability, overall performance, and generalization ability, as verified by extensive experiments on several robot navigation and MuJoCo locomotion tasks.

Rapid context inference in a thalamocortical model using recurrent neural networks

Cognitive flexibility is a fundamental ability that enables humans and animals to exhibit appropriate behaviors in various contexts. The thalamocortical interactions between the prefrontal cortex (PFC) and the mediodorsal thalamus (MD) have been identified as crucial for inferring temporal context, a critical component of cognitive flexibility. However, the neural mechanism responsible for context inference remains unknown. To address this issue, we propose a PFC-MD neural circuit model that utilizes a Hebbian plasticity rule to support rapid, online context inference. Specifically, the model MD thalamus can infer temporal contexts from prefrontal inputs within a few trials. This is achieved through the use of PFC-to-MD synaptic plasticity with pre-synaptic traces and adaptive thresholding, along with winner-take-all normalization in the MD. Furthermore, our model thalamus gates context-irrelevant neurons in the PFC, thus facilitating continual learning. We evaluate our model performance by having it sequentially learn various cognitive tasks. Incorporating an MD-like component alleviates catastrophic forgetting of previously learned contexts and demonstrates the transfer of knowledge to future contexts. Our work provides insight into how biological properties of thalamocortical circuits can be leveraged to achieve rapid context inference and continual learning.

Comparison of micro- and macro- structural narrative features between Turkish-Kurdish bilinguals and Turkish monolinguals

Abstract Bilingualism may lead to differences in narrative micro- and macrostructural features. Creating a normative data set on the narrative characteristics of healthy Turkish-Kurdish bilinguals may allow for a more effective assessment of language disorders that may occur in bilinguals with acquired brain injury. This study aims to determine how narrative micro- and macrostructural features differ in Turkish-Kurdish bilinguals in single picture and story narratives. A total of 23 bilingual (Turkish-Kurdish) and 23 monolingual (Turkish) healthy individuals were included in the study. Narrative samples were obtained using single pictures and a story. Micro- and macrostructural features were compared between bilinguals and monolinguals matched for age, education, and gender. Mean length utterance-morpheme and clausal density variables, considered as indicators of grammatical complexity, were higher in monolinguals in both single picture and story narratives. Morphemic errors were higher for bilinguals. Effort behaviors, which can interrupt verbal efficiency, were higher in bilinguals in single picture narratives. While there was no difference between the groups in verbal fluency (phonemic and categorical), the lexical diversty in storytelling was higher in monolinguals. In terms of macrostructural features, cognitive inferences in the story context and emotional discourse in the single picture context were higher in monolinguals, while enrichment expressions in the single picture narrative were higher in bilinguals. In both groups, macrostructural features such as cognitive inferences and expressions of uncertainty in discourse were negatively correlated with grammatical complexity variables. Turkish-Kurdish bilinguals’ single picture and story narratives in Turkish may show semantic, morphosyntactic and pragmatic differences compared to Turkish-speaking monolinguals.

Different mechanisms of contextual inference govern associatively learned and sensory-evoked postural responses

Human standing balance relies on the continuous monitoring and integration of sensory signals to infer our body's motion and orientation within the environment. However, when sensory information is no longer contextually relevant to balancing the body (e.g., when sensory and motor signals are incongruent), sensory-evoked balance responses are rapidly suppressed, much earlier than any conscious perception of changes in balance control. Here, we used a robotic balance simulator to assess whether associatively learned postural responses are similarly modulated by sensorimotor incongruence and contextual relevance to postural control. Twenty-nine participants in three groups were classically conditioned to generate postural responses to whole-body perturbations when presented with an initially neutral sound cue. During catch and extinction trials, participants received only the auditory stimulus but in different sensorimotor states corresponding to their group: 1) during normal active balance, 2) while immobilized, and 3) throughout periods where the computer subtly removed active control over balance. In the balancing and immobilized states, conditioned responses were either evoked or suppressed, respectively, according to the (in)ability to control movement. Following the immobilized state, conditioned responses were renewed when balance was restored, indicating that conditioning was retained but only expressed when contextually relevant. In contrast, conditioned responses persisted in the computer-controlled state even though there was no causal relationship between motor and sensory signals. These findings suggest that mechanisms responsible for sensory-evoked and conditioned postural responses do not share a single, central contextual inference and assessment of their relevance to postural control, and may instead operate in parallel.

Decision uncertainty as a context for motor memory

The current view of perceptual decision-making suggests that once a decision is made, only a single motor programme associated with the decision is carried out, irrespective of the uncertainty involved in decision making. In contrast, we show that multiple motor programmes can be acquired on the basis of the preceding uncertainty of the decision, indicating that decision uncertainty functions as a contextual cue for motor memory. The actions learned after making certain (uncertain) decisions are only partially transferred to uncertain (certain) decisions. Participants were able to form distinct motor memories for the same movement on the basis of the preceding decision uncertainty. Crucially, this contextual effect generalizes to novel stimuli with matched uncertainty levels, demonstrating that decision uncertainty is itself a contextual cue. These findings broaden the understanding of contextual inference in motor memory, emphasizing that it extends beyond direct motor control cues to encompass the decision-making process.

Contingency inferences from base rates: A parsimonious strategy?

The pseudocontingency framework provides a parsimonious strategy for inferring the contingency between two variables by assessing the base rates. Frequently occurring levels are associated, as are rarely occurring levels. However, this strategy can lead to different contingency inferences in different contexts, depending on how the base rates vary across contexts. Here, we examine how base-rate consistency influences base-rate learning and reliance by contrasting consistent with inconsistent base rates. We hypothesized that base-rate learning is facilitated, and that people rely more on base rates if base rates are consistent. In Experiment 1, the base rates across four contexts implied the same (consistent) or different (inconsistent) contingencies. Base rates were learned equally accurately, and participants inferred contingencies that followed the base rates but deviated from the genuine contingencies within contexts, regardless of consistency. In Experiment 2, we additionally manipulated whether the context was a plausible moderator of the contingency. While we replicated the first experiment's results when the context was a plausible moderator, base-rate inferences were stronger for consistent base rates when the context was an implausible moderator. Possibly, when a moderation-by-context was implausible, participants also relied on the base-rate correlation across contexts, which implied the same contingency when base rates were consistent but was zero when the base rates were inconsistent. Thus, our findings suggest that contingency inferences from base rates involve top-down processes in which people decide how to use base-rate information.

ConvoSense: Overcoming Monotonous Commonsense Inferences for Conversational AI

Abstract Mastering commonsense understanding and reasoning is a pivotal skill essential for conducting engaging conversations. While there have been several attempts to create datasets that facilitate commonsense inferences in dialogue contexts, existing datasets tend to lack in-depth details, restate information already present in the conversation, and often fail to capture the multifaceted nature of commonsense reasoning. In response to these limitations, we compile a new synthetic dataset for commonsense reasoning in dialogue contexts using GPT, ℂonvoSense, that boasts greater contextual novelty, offers a higher volume of inferences per example, and substantially enriches the detail conveyed by the inferences. Our dataset contains over 500,000 inferences across 12,000 dialogues with 10 popular inference types, which empowers the training of generative commonsense models for dialogue that are superior in producing plausible inferences with high novelty when compared to models trained on the previous datasets. To the best of our knowledge, ℂonvoSense is the first of its kind to provide such a multitude of novel inferences at such a large scale.

Frustratives in St’át’imcets vs. Kimaragang: parameters of variation

Markers of the FRUSTRATIVE (non-realization of an expected outcome) often have a variety of uses. The Kimaragang frustrative dara also marks various related functions such as “discontinuous past”, “action narrowly averted”, frustrated intention or desire, and counterfactual conditionals. Davis & Matthewson (2022) describe a similar but not identical range of uses for the frustrative marker séna7 in St’át’imcets, and propose that the core meaning of this marker involves a type of epistemic modality. They further propose that their analysis can be extended to Kimaragang dara, suggesting that observed differences of usage may be due to differences in the tense-aspect systems of the two languages.This paper argues that the Kimaragang frustrative is different from séna7 in two respects: (1) the nature of the “frustrated expectation” inference, and (2) type of modality (epistemic vs. circumstantial). Frustrated expectation is lexically entailed in St’át’imcets, but Kimaragang dara entails only that the expected outcome cannot be asserted to be true at the time of speaking, with frustrated expectation arising as a pragmatic inference in contexts where it is reasonable to expect the speaker to know the actual outcome. Davis & Matthewson show that séna7 is purely epistemic in nature: it marks an unexpected correlation between two propositions, which need not belong to a single chain of events. Kimaragang dara is circumstantial in nature: it marks an initial situation whose expected outcome within the normal course of events is so far unrealized. It can be used to describe counterfactual situations, which are not epistemically accessible.

GCN-assisted attention-guided UNet for automated retinal OCT segmentation

With the increase in the aging population of many countries, the prevalence of neovascular age-related macular degeneration (nAMD) is expected to increase. Morphological parameters such as intraretinal fluid (IRF), subretinal fluid (SRF), subretinal hyperreflective material (SHRM), and pigment epithelium detachment (PED) of spectral-domain optical coherence tomography (SD-OCT) images are vital markers for proper treatment of nAMD, especially to get the information of treatment response to determine the proper treatment interval and switching of anti-vascular endothelial growth factor (VEGF) agents. For the precise evaluation of the change in nAMD lesions and patient-specific treatment, quantitative evaluation of the lesions in the OCT volume scans is necessary. However, manual segmentation requires many resources, and the number of studies of automatic segmentation is increasing rapidly. Improving automated segmentation performance in SD-OCT visual results requires long-range contextual inference of spatial information between retinal lesions and layers. This paper proposes a GAGUNet (graph convolution network (GCN)-assisted attention-guided UNet) model with a novel global reasoning module considering these points. The dataset used in the main experiment of this study underwent rigorous review by a retinal specialist from Konkuk University Hospital in Korea, contributing to both data preprocessing and validation to ensure a qualitative assessment. We conducted experiments on the RETOUCH dataset as well to demonstrate the scalability of the proposed model. Overall, our model demonstrates superior performance over the baseline models in both quantitative and qualitative evaluations.

MetaKernel: Learning Variational Random Features With Limited Labels.

Few-shot learning deals with the fundamental and challenging problem of learning from a few annotated samples, while being able to generalize well on new tasks. The crux of few-shot learning is to extract prior knowledge from related tasks to enable fast adaptation to a new task with a limited amount of data. In this paper, we propose meta-learning kernels with random Fourier features for few-shot learning, we call MetaKernel. Specifically, we propose learning variational random features in a data-driven manner to obtain task-specific kernels by leveraging the shared knowledge provided by related tasks in a meta-learning setting. We treat the random feature basis as the latent variable, which is estimated by variational inference. The shared knowledge from related tasks is incorporated into a context inference of the posterior, which we achieve via a long-short term memory module. To establish more expressive kernels, we deploy conditional normalizing flows based on coupling layers to achieve a richer posterior distribution over random Fourier bases. The resultant kernels are more informative and discriminative, which further improves the few-shot learning. To evaluate our method, we conduct extensive experiments on both few-shot image classification and regression tasks. A thorough ablation study demonstrates that the effectiveness of each introduced component in our method. The benchmark results on fourteen datasets demonstrate MetaKernel consistently delivers at least comparable and often better performance than state-of-the-art alternatives.

CoBjeason: Reasoning Covered Object in Image by Multi-Agent Collaboration Based on Informed Knowledge Graph

Object detection is a widely studied problem in existing works. However, in this paper, we turn to a more challenging problem of “ Covered Object Reasoning ”, aimed at reasoning the category label of target object in the given image particularly when it has been totally covered (or invisible ). To resolve this problem, we propose CoBjeason to seize the opportunity when visual reasoning meets the knowledge graph, where “ empirical cognition ” on common visual contexts have been incorporated as knowledge graph to conduct reinforced multi-hop reasoning via two collaborative agents. Such two agents, for one thing, stand at the covered object (or unknown entity ) to observe the surrounding visual cues in the given image and gradually select entities and relations from the global gallery-level knowledge graph which contains entity-pairs frequently occurring across the entire image-collection, so as to infer the main structure of image-level knowledge graph forward expanded from the unknown entity . In turn, for another, based on the reasoned image-level knowledge graph, the semantic context among entities will be aggregated backward into unknown entity to select an appropriate entity from the global gallery-level knowledge graph as the reasoning result. Moreover, such two agents will collaborate with each other, securing that the above Forward & Backward Reasoning will step towards the same destination of the higher performance on covered object reasoning. To our best knowledge, this is the first work on Covered Object Reasoning with Knowledge Graphs and reinforced Multi-Agent collaboration. Particularly, our study on Covered Object Reasoning and the proposed model CoBjeason could offer novel insights into more basic Computer Vision (CV) tasks, such as Semantic Segmentation with better understanding on the current scene when some objects are blurred or covered, Visual Question Answering with enhancement on the inference in more complicated visual context when some objects are covered or invisible, and Image Caption Generation with the augmentation on the richness of visual context for images containing partially visible objects. The improvement on the above basic CV tasks can further refine more complicated ones involved with nuanced visual interpretation like Autonomous Driving, where the recognition and reasoning on partially visible or covered object are critical. According to the experimental results, our proposed CoBjeason can achieve the best overall ranking performance on covered object reasoning compared with other models, meanwhile enjoying the advantage of lower “ exploration cost ”, with the insensitivity against the long-tail covered objects and the acceptable time complexity.

A machine learning approach for real-time cortical state estimation

Objective. Cortical function is under constant modulation by internally-driven, latent variables that regulate excitability, collectively known as ‘cortical state’. Despite a vast literature in this area, the estimation of cortical state remains relatively ad hoc, and not amenable to real-time implementation. Here, we implement robust, data-driven, and fast algorithms that address several technical challenges for online cortical state estimation. Approach. We use unsupervised Gaussian mixture models to identify discrete, emergent clusters in spontaneous local field potential signals in cortex. We then extend our approach to a temporally-informed hidden semi-Markov model (HSMM) with Gaussian observations to better model and infer cortical state transitions. Finally, we implement our HSMM cortical state inference algorithms in a real-time system, evaluating their performance in emulation experiments. Main results. Unsupervised clustering approaches reveal emergent state-like structure in spontaneous electrophysiological data that recapitulate arousal-related cortical states as indexed by behavioral indicators. HSMMs enable cortical state inferences in a real-time context by modeling the temporal dynamics of cortical state switching. Using HSMMs provides robustness to state estimates arising from noisy, sequential electrophysiological data. Significance. To our knowledge, this work represents the first implementation of a real-time software tool for continuously decoding cortical states with high temporal resolution (40 ms). The software tools that we provide can facilitate our understanding of how cortical states dynamically modulate cortical function on a moment-by-moment basis and provide a basis for state-aware brain machine interfaces across health and disease.

Fuzzy information recognition and translation processing in English interpretation based on a generalized maximum likelihood ratio algorithm.

English interpretation plays a vital role as a critical link in cross-language communication. However, there are various types of ambiguous information in many interpreting scenarios, such as ambiguity, ambiguous vocabulary, and syntactic structures, which may lead to inaccuracies and fluency issues in translation. This article proposes a method based on the generalized maximum likelihood ratio algorithm (GLR) to identify and process fuzzy information in English interpretation to improve the quality and efficiency of performance. Firstly, we systematically analyzed the common types of fuzzy information in interpretation and delved into the basic principles and applications of the generalized maximum likelihood ratio algorithm. This algorithm is widely used in natural language processing to solve uncertainty problems and has robust modeling and inference capabilities, making it suitable for handling fuzzy information in interpretation. Then, we propose a fuzzy information recognition model based on the generalized maximum likelihood ratio algorithm. This model utilizes a large-scale interpretation corpus for training and identifies potential fuzzy information in the interpretation process through statistical analysis and pattern recognition. Once fuzzy information is detected, we adopt a series of effective translation processing strategies, including contextual inference and adaptation, to ensure the accuracy and naturalness of interpretation. Finally, we conducted a series of experiments to evaluate the performance of the proposed method. The experimental results show that the fuzzy information recognition and translation processing method based on the generalized maximum likelihood ratio algorithm performs well in different interpretation scenarios, significantly improving the quality and fluency of interpretation and reducing ambiguity caused by fuzzy information.

Dual-track spatio-temporal learning for urban flow prediction with adaptive normalization

Robust urban flow prediction is crucial for transportation planning and management in urban areas. Although recent advances in modeling spatio-temporal correlations have shown potential, most models fail to adequately consider the complex spatio-temporal semantic information present in real-world scenarios. We summarize the following three primary limitations in existing models: a) The majority of existing models project overall time periods into the same latent space, neglecting the diverse temporal semantics between different time intervals. b) Existing models tend to capture spatial dependencies from a locale perspective such as surroundings but do not pay attention to the global influence factors. c) Beyond the spatio-temporal properties, the dynamics and instability of the data sequences introduce perturbations to the prediction results, potentially leading to model degradation. To address these issues, we propose a dual-track spatial-temporal learning module named DualST for accurate urban flow inference. To more effectively differentiate semantic information in the time dimension, we assign the overall time scales into closeness and periodicity. The dual-track module, which includes temporal causality inference and temporal contextual inference, simultaneously exploits the dynamic evolutionary trends and periodic traffic patterns, respectively. The proposed DualST captures global spatial features in a self-supervised manner which not only enriches the spatial semantics but also avoids introducing additional prior knowledge. To eliminate the instability caused by dynamics, we first adopt spatio-temporal adaptive normalization to learn appropriate data sequence normalization. We evaluate the proposed DualST on two typical urban flow datasets. The experiment results show that our model not only exhibits a consistent superiority over various state-of-the-art baselines but also has remarkable generalization capability.

Meta-learning with deep flow kernel network for few shot cross-domain remaining useful life prediction

Reliable prediction of the remaining useful life (RUL) is important for improving maintenance efficiency, equipment availability, and avoiding catastrophic accidents in complex industrial systems. Existing RUL prediction models have made some contribution, relying mainly on a large amount of degraded data with similar patterns or approximate distributions. However, in practical industrial systems, only a small amount of labeling data is usually available, which may also come from different devices and different working conditions, resulting in different distributions in the degraded data. This situation makes the existing RUL methods difficult to achieve satisfactory generalization performance. To address this challenge, this paper proposes a novel Meta-Learning with Deep Flow Kernel Network (MetaDFKN) model for RUL prediction under the few shot and cross-domain conditions. The model first learns kernel features in a data-driven manner and considers them as latent variables to improve the model’s representative ability of shared knowledge between tasks. Then, we introduce the conditional normalized flow technique to infer richer posterior distributions in the kernel features, which helps to obtain feature information with stronger discriminative power. Moreover, shared knowledge and task-specific information are integrated into the contextual inference process, which can mine the dependencies of related tasks and capture richer domain information. Finally, to evaluate the proposed model, we conduct extensive experiments on engine and bearings degradation data, and the results verify the superiority of the model.

Tomb mural inpainting with adaptive convolutional constraints and global context inference

Tomb mural inpainting with adaptive convolutional constraints and global context inference

Towards Semantic Smart Cities: A Study on the Conceptualization and Implementation of Semantic Context Inference Systems

Smart cities provide integrated management and operation of urban data emerging within a city, supplying the infrastructure for smart city services and resolving various urban challenges. Nevertheless, cities continue to grapple with substantial issues, such as contagious diseases and terrorism, that pose severe financial and human risks. These problems sporadically arise in various locales, and current smart city frameworks lack the capability to autonomously identify and address these issues. The challenge intensifies especially when trying to recognize and respond to unprecedented problems. The primary objective of this research is to predict potential urban issues and support their resolution proactively. To achieve this, our system makes use of semantic reasoning to understand the ongoing situations within the city. In this process, the 5W1H principles serve as inference rules, guiding the extraction and consolidation of context. Firstly, utilizing domain-specific annotation templates, we craft a semantic graph by amalgamating information from various sources available in the city, such as municipal public data and IoT platforms. Subsequently, the system autonomously infers and accumulates contexts of situations occurring in the city using 5W1H-based reasoning. As a result, the accumulated contexts allow for inferring potential urban problems by identifying repeated disruptions in city services at specific times or locations and establishing connections among them. The main contribution of this paper lies in proposing a comprehensive conceptual model for the suggested system and presenting actual implementation cases and applicable use cases. These contributions facilitate awareness among city administrators and citizens within a smart city regarding potential problem-prone areas or times, thereby aiding in the preemptive identification and mitigation of urban challenges.

EXPLORING I-YOU INDEXICALS IN GHANAIAN CHURCH MUSIC

Indexicals are important to contextual inference and generally, in semantic interpretation (Hanks, 1999). The present study set out to examine the use of the I-You indexical pronouns in Ghanaian Church Music. It was revealed that the I-Y spheres of the indexicals received relative frequencies of 4.55% and 3.35% in the data. Again, the study showed various referents of the I-You indexicals and their implication in the data based on their contextual and co-textual cues. The study extends knowledge on the subject of indexicality.

MahaEmoSen: Towards Emotion-aware Multimodal Marathi Sentiment Analysis

With the advent of the Internet, social media platforms have witnessed an enormous increase in user-generated textual and visual content. Microblogs on platforms such as Twitter are extremely useful for comprehending how individuals feel about a specific issue through their posted texts, images, and videos. Owing to the plethora of content generated, it is necessary to derive an insight of its emotional and sentimental inclination. Individuals express themselves in a variety of languages and, lately, the number of people preferring native languages has been consistently increasing. Marathi language is predominantly spoken in the Indian state of Maharashtra. However, sentiment analysis in Marathi has rarely been addressed. In light of the above, we propose an emotion-aware multimodal Marathi sentiment analysis method (MahaEmoSen). Unlike the existing studies, we leverage emotions embedded in tweets besides assimilating the content-based information from the textual and visual modalities of social media posts to perform a sentiment classification. We mitigate the problem of small training sets by implementing data augmentation techniques. A word-level attention mechanism is applied on the textual modality for contextual inference and filtering out noisy words from tweets. Experimental outcomes on real-world social media datasets demonstrate that our proposed method outperforms the existing methods for Marathi sentiment analysis in resource-constrained circumstances.