Syntactic complexity representation in sentence production reveals a fronto-temporal syntactic network.

Drug–drug interactions (DDIs) remain a major concern in medication safety. Although advanced artificial intelligence methods such as deep learning have improved DDI prediction, their adoption is limited by the need for specialized expertise and complex model development. This study introduces the first application of the AutoGluon AutoML framework to DDI prediction using molecular features, aiming to automate and simplify model development. A curated subset of 100,000 drug pairs from DrugBank was used, employing three molecular representations: 2D molecular descriptors, 2048-bit Morgan fingerprints, and their combination. Models were trained using AutoGluon-Tabular with no manual hyperparameter tuning. The descriptor-only model achieved the best performance, with 84.4% test accuracy and an AUC of 0.916, outperforming fingerprint-based and hybrid models. Feature importance analysis identified key physicochemical and topological descriptors—such as drug-likeness, electrotopological indices, and hydrophobic surface area—as critical predictors of DDIs. These results demonstrate that AutoML can extract chemically meaningful patterns while reducing technical barriers. Overall, our results validate AutoGluon as a scalable approach to DDI prediction that provides chemically meaningful, feature-level interpretability, and lay the groundwork for future applications involving larger datasets and more complex chemical representations.

Neural rendering has been the disruptive technology in the creation of very realistic content of the visual multimedia production that the computer graphics and deep learning have substituted. This paper examines the neural rendering systems that can be trained to produce hyper-realistic artistic images through the acquisition of the complex representations of scenes based on multi-view image representations. The suggested architecture compiles the neural radiance field modeling, deep neural networks as well as volumetric rendering to reproduce detailed three-dimensional scenes as well as produce photorealistic images in new perspectives. Multi-view data acquisition, neural feature encoding, and radiance field estimation are the system architecture elements based on deep learning models that capture geometry, lighting, texture and color interaction within a scene. Experimental analysis of neural rendering methods has shown that they render visual fidelity, geometric consistency and rendering realism by a wide margin than the standard computer graphics pipelines. The quantitative investigation of the measures of the quality of rendering, such as the similarity index of the structure, the perceptual realism scores, and the reconstruction accuracy, reveals the significant progress of visual detail and scene modeling.

ABSTRACT The Fourier-Legendre (FL) model employs Legendre polynomials to characterize radar wave propagation within vegetation. Compared to empirical functions used in conventional models, it has greater flexibility and adaptability, enabling a more accurate representation of complex scattering mechanisms. When applied to Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) forest height inversion, the FL model can significantly improve retrieval accuracy. In theory, higher polynomial orders provide more precise descriptions of radar scattering, however, increasing polynomial order introduces excessive unknown parameters, leading to ill-posed problems and reduced estimation stability. Given this, this paper proposes a multi-baseline fusion PolInSAR forest height inversion method based on the FL model and linear least squares estimation theory. Additionally, the regularization method is incorporated to mitigate ill-posed influence and enhance parameter stability. By constructing a regularization matrixforest height inversion experiments were then conducted using multi-baseline PolInSAR data. Finally, the results show that the conventional single-baseline inversion method achieves an optimal forest height estimate with an root mean square error (RMSE) of 6.73 m and an coefficient of determination (R 2) of 0.84, while the new method yielded an RMSE of 5.53 m and an R 2 of 0.89, representing an accuracy improvement of 18%. Therefore, the new method can effectively enhance the accuracy of forest height parameter inversion and is a practical and effective PolInSAR forest height inversion approach.

This article examines the prospect of clinical acceptability of the automated diagnosis of histopathological images by reviewing and critically analyzing the extant literature devoted to context-aware deep learning approaches. Analogous to unboxing the black-boxes, the context-aware methods in the deep learning models introduce transparency in the process, which builds trust and thus encourages acceptance of the method among the clinicians. This review highlights the context-aware approaches researchers adopt to disentangle the convoluted strands of complex algorithms and their efficacy as a whole. To detect cancers, classical machine learning algorithms employ handcrafted features, while deep learning algorithms generate complex representations of objects from simple features. Analysis of a histological image for cancer detection, using morphological and textural features of cells, can be enhanced by including contextual information. In medical image analysis, the focus has primarily been on handcrafted or learned features, largely devoid of explicit context for object representation. To delve into this scantily addressed domain of “context", this study rests on knowledge-driven and adaptive smart features for classifying histological images. This work explores available algorithms that incorporate contextual features for diagnosing breast cancer. Accordingly, a high-level block diagram is devised to aid in selecting an algorithm vis-à-vis an application that would fulfill a vital need for researchers and industry professionals alike. Furthermore, this study proposes the upcoming trend of automated diagnosis in the domain of cancer.

Despite longstanding research highlighting the disproportionate representation of multilingual learners in special education, disparities specific to intellectual and developmental disabilities (IDD)—including autism spectrum disorder (ASD), intellectual disability (ID), and developmental delay (DD)—remain underexamined. This systematic review critically analyzed the extent and nature of these disparities through an intersectional and dis/ability critical race studies (DisCrit) lens. Following PRISMA guidelines, 294 articles published since 2004 were screened across eight databases, yielding 10 that met inclusion criteria. Findings revealed that multilingual learners were generally underrepresented in ASD and overrepresented in ID, while limited evidence suggested underrepresentation in DD. These results highlight the need to understand IDD disparities as intersectional and contextually mediated phenomena. Implications for research, policy, and practice emphasize equity-oriented, contextually grounded approaches to addressing the complex representation patterns of multilingual learners in special education.

The representations of the face and oral cavity occupy a large but understudied portion of primary somatosensory cortex (S1 or area 3b) in primates. These studies are clearly important in processing food, taste, and vocalizations. Little is known about the neuronal representations of the teeth and tongue, their relationship with myeloarchitecture, and their cortical and thalamic connections in area 3b of strepsirrhine galagos. In owl and squirrel monkeys, and likely in macaque monkeys, the most lateral portion of S1 represents the face, teeth, and tongue in a series of myelin-dense ovals, including ovals for the contralateral teeth and tongue and ipsilateral teeth and tongue. As this complex representation of the teeth and tongue may differ among mammals and even primates, we sought to evaluate these representations in the galagos, representing an early branch of primates. Using a combination of neuronal responses to touch, neuroanatomical tracer injections, and tissue processing to reveal anatomical area borders, we found that lateral S1 in galagos resembles those of studied monkeys by having a series of ovals that double the representation of the tongue and teeth, along with similar intracortical, callosal, and thalamic connections, suggesting that these features of S1 emerged in the first primates.

In an era where girlhood is increasingly commodified in postfeminist and digital cultures, queer girlhood, particularly sapphic and trans forms remains contested, underrepresented, and structurally silenced. This paper examines the complex literary representation of intersectional queer girlhoods in two contemporary Anglophone novels: Emily M. Danforth’s The Miseducation of Cameron Post (2012) and Akwaeke Emezi’s Pet (2019). Drawing on the frameworks of queer temporality (Halberstam 2005), intersectionality (Crenshaw 1991), and affect theory (Ahmed 2004; Muñoz 2009), the paper interrogates how these texts disrupt normative paradigms of development, innocence, and heteronormativity that often define girlhood in literature. In The Miseducation of Cameron Post, the repression of sapphic desire under Christian moralism is juxtaposed with moments of subversive agency, silence, and queer kinship. In Pet, Emezi constructs a speculative future where a Black trans girl protagonist must confront concealed structural violence despite narratives of supposed safety. By analysing how both novels deploy genre (realism and speculative fiction), voice, and affect to articulate queer resistance, this study foregrounds girlhood not as a universal or stable category, but as a site of becoming, inflected by race, sexuality, gender identity, and sociocultural power. Ultimately, the paper argues for a reading practice that attends to the pluralities of queer girlhoods, not merely to represent but to reimagine the political stakes of visibility, recognition, and refusal in contemporary literature.

Electroencephalography (EEG) provides rich information and a representation of brain activity for intelligent healthcare applications. This study proposes a framework for automated emotion recognition and epileptic seizure classification using machine learning (ML) and deep learning (DL) techniques. The framework was tested on three EEG datasets to cover areas of anomaly detection, multi-class emotion recognition, and binary seizure prediction. EEG signals were first normalized and then segmented into fixed-length windows depending on each participant's recording traits, thus maintaining subject-specific temporal patterns. Models’ evaluation was done with properly separated training and testing data to guarantee a trustworthy performance assessment. For emotion recognition, the model based on Gated Recurrent Units (GRU) obtained 96% accuracy on the test data, whereas ensemble learning with Random Forest got 98%, thereby proving its excellent discriminative power on structured EEG features. Anomaly detection without supervision through Histogram-Based Outlier Score (HBOS) was able to detect the abnormal single-channel EEG segments accurately. In seizure classification, a convolutional neural network (CNN) trained on log, scaled time, frequency spectrograms yielded 95. 75% accuracy with an AUC of 0. 996 on the test dataset, thus successfully differentiating interictal and preictal states. The findings confirm that ML models provide robust and computationally efficient performance on engineered EEG features, whereas DL models effectively capture complex temporal and spectral representations across multiple EEG analysis tasks

Ambient backscatter systems enable passive sensing and information transfer by utilizing the reflection and modulation of incident radio-frequency (RF) signals. However, in real-world scenarios involving non-cooperative targets such as off-the-shelf printed circuit boards (PCBs), the backscattered signal is extremely weak and often obscured by strong direct-path self-interference (SI) at the receiver. This issue becomes even more severe when unintentional PCB structures act as radiating elements. In this work, we explore ambient backscatter leakage from a compromised PCB using a realistic measurement setup that includes separated transmit and receive antennas and a direct-conversion Universal Software Radio Peripheral (USRP)-based receiver. We demonstrate that residual carrier frequency offset (CFO), caused by oscillator mismatch and hardware imperfections, can spread the dominant SI in the baseband and completely mask the weak backscattered signal. To solve this problem, a software-based post-processing framework is applied. This method leverages the complex baseband representation enabled by the homodyne receiver to jointly manage the carrier and SI components without relying on intermediate-frequency processing or prior knowledge of the target signal parameters. Experimental results show that this approach significantly improves the detectability of weak backscattered baseband information that would otherwise be concealed within the raw I/Q data. This study emphasizes the importance of CFO-aware digital processing in ambient backscatter systems and offers new insights into unintended electromagnetic leakage mechanisms from commercial PCB platforms.

We present T-Rex2++, a unified and highly practical framework for generic open-set object perception, encompassing both object detection and instance segmentation. Previous methods relying on text prompts effectively encapsulate the abstract concept of common objects, but struggle with rare or complex object representation due to data scarcity and descriptive limitations. Conversely, visual prompts excel in depicting novel objects through concrete visual examples, but fall short in conveying the abstract concept of objects as effectively as text prompts. Recognizing these complementary strengths, we introduce a text-visual synergy mechanism that aligns both modalities within a single feature space via contrastive learning. Crucially, T-Rex2++ advances beyond the passive perception paradigm of its predecessor by introducing a novel Universal Prompt. This learnable component models generic objectness, empowering the system to autonomously discover and localize arbitrary objects without any user-provided cues, thereby closing the loop between human-guided interaction and fully automatic perception. Furthermore, we extend the synergy verification to the pixel level by integrating a zero-shot instance segmentation module, demonstrating that our contrastive alignment generalizes robustly to fine-grained masks. Comprehensive experiments demonstrate that T-Rex2++ exhibits strong zero-shot object perception capabilities across a wide spectrum of scenarios, validating T-Rex2++ as a versatile foundation for generic object perception.

Time is a central dimension of episodic memory which allows us to remember not only what happened and where from past events, but also when those events occurred and how they relate to one another. Adults can form hierarchical knowledge derived from episodic experience that includes precise timing details about individual events and information about temporal patterns that encode regularities across experiences, alongside factual knowledge about time (e.g., the months of the year). Young children's temporal memory is more constrained, lacking both the level of local detail and limited global knowledge relative to adult temporal representation. Despite behavioral evidence for such developmental differences in temporal memory, we lack a unified model that explains how local and global temporal representation abilities emerge, interact, and are organized across development. Here, we propose a three-stage neurocognitive framework for the hierarchical development of temporal memory, resulting from increasing representational capacity across the hippocampal-frontoparietal memory system. Reviewing behavioral and neuroimaging evidence, we propose that: 1) young children's temporal memory is initially local and event-specific due to functional immaturity of hippocampus; 2) older children and adolescents form and reinstate global knowledge of temporal regularities resulting from enhanced interactions between hippocampus and lateral frontoparietal cortex; and 3) adults flexibly deploy hierarchical knowledge of local details and generalities in new environments mediated by hippocampus and medial frontoparietal cortex interactions. This framework thus provides a unified, empirically-grounded model of temporal memory development, supporting increasingly complex temporal representations that enable adaptive behaviors at a variety of temporal resolutions.

Mental imagery and visual perception can both give rise to vivid visual experiences, yet the extent to which they can functionally influence each other remains an open question. Previous research has shown that imagining a stimulus before viewing a rivalrous display can bias perception towards the imagined content. However, this effect has been demonstrated primarily with simple, low-level stimuli such as oriented gratings. Here, we investigated whether imagery of more complex representations-people and buildings-can influence perception, using the binocular rivalry paradigm. Participants in our study imagined either a personally familiar person or personally familiar building before viewing a rivalrous face-house stimulus. We measured their perceptual dominance and imagery vividness on each trial. Their overall imagery ability was assessed using the Vividness of Visual Imagery Questionnaire (VVIQ). We found that participants were significantly more likely to perceive the imagined stimulus; however, this priming effect was driven by person imagery. Greater vividness of person imagery on each trial significantly increased dominance of the face stimulus, but this effect did not extend to building imagery and the house stimulus. Furthermore, the VVIQ did not predict individual differences in priming magnitude. These results extend previous work by showing that mental imagery can influence perception beyond simple stimuli, but that this functional link is shaped by stimulus-specific features. Our findings highlight the need for future research to examine the conditions under which imagining more complex representations affects seeing.

This work extends previous investigations into the relationship between the Einsteinian ‎Hamiltonian formulation and the Musakhail aether-based Lagrangian description of ‎dynamics. While earlier studies established their simultaneous role in the Newtonian-‎Einsteinian framework, the present paper focuses specifically on a formal Lagrangian ‎dynamical analysis in order to derive the corresponding equation of motion. Within the ‎proposed framework, the resulting dynamics suggest a correspondence in which the ‎classical relation F=ma transitions naturally toward the relativistic energy expression E=‎mc^2, interpreted here through the restoration of Newtonian behavior during the so-called ‎Reverse Higgs process. In this regime, the effective mass remains constant (m=m_e ) ‎rather than velocity-dependent, permitting a force-based description of particle-wave ‎interaction. The analysis further introduces a rotating Einstein energy vector derived from ‎the invariant relation E^2=(pc)^2+(m_0 c^2 )^2, which is employed to describe the ‎cyclic interaction between fermionic constituents and electromagnetic wave structure. This ‎approach yields a dual interpretative framework in which either photon energy extraction or ‎spin measurement may occur, depending on the observational configuration. The ‎formalism also explores a complex representation in which the orthogonal axis is treated as ‎imaginary, producing a geometrical interpretation associated with oscillatory spin states of ‎fermions (±1/2) and photons (0,±1). The resulting model suggests an underlying ‎symmetry between fermionic and bosonic spin states within the proposed aether-‎dynamical environment, providing a phenomenological bridge between classical force ‎dynamics and relativistic energy relations.‎

In Chinese, intransitive verbs can take direct objects in certain constructions, and transitive verbs can also be used without objects. These characteristics have long sparked debates about whether verbs can be divided into intransitive and transitive verbs in Chinese. Using E-Prime software (3.0 version) and functional magnetic resonance imaging (fMRI) technology, we investigated the behavioral responses and neural activities of native speakers when processing Chinese intransitive and transitive verbs. Behavioral data showed that the accuracy rate for Chinese intransitive verbs was significantly higher than that for transitive verbs, while the reaction time was significantly shorter. fMRI data revealed that compared with Chinese intransitive verbs, transitive verbs elicited significantly stronger activation in brain regions such as the bilateral angular gyri (BA39), left supramarginal gyrus (BA40), and left inferior frontal gyrus (BA44). The bilateral angular gyri and left supramarginal gyrus may be associated with more intricate argument semantic representation of the Chinese transitive verb, while the left inferior frontal gyrus may reflect their more complex syntactic structure representation. The above experimental results indicate that processing Chinese transitive verbs requires greater cognitive effort and involves more complex neural activities compared to intransitive verbs, which demonstrates that verbs in Chinese should be subdivided into intransitive and transitive verbs.

Recent years have seen an increasing amount of attention devoted to the subject of structural representation. Is there one type of structural representation or many? How do they differ from other types of representation? Are they really a genuine type of representation in the first place? All good questions, which I will address indirectly by arguing that structural representations are nothing more than analog representations. Understanding them as such provides some much needed theoretical clarity about this type of representation. Typical analog representations (e.g., liquid thermometers or analog clocks) are often "one-dimensional;" the corresponding one-dimensional characterization of these representations can be extended into multiple dimensions, which elucidates the structure of more complex analog representations, such as photographs, maps, or three-dimensional models. However, this analysis applies to structural representations without remainder. The upshot is that we can directly apply what we have learned about analog representation to our understanding of structural representation, which, if not directly answering these recent questions, greatly adds to our theoretical resources for doing so. The analog wheel has already been invented; we need not reinvent it for structural representation.

This paper presents the Ze → Twistor → Spin Network framework, a unified conceptual pathway from a primitive discrete ontology to the emergence of relativistic spacetime. The framework begins with fundamental events, denoted ΔC_i, each characterized by dual aspects: a temporal component C_i^{temporal} governing participation in sequential causal chains, and a spatial component C_i^{spatial} governing participation in parallel structural configurations. The first transition, Ze → Twistor, encodes these aspects in a complex representation Z_i = C_i^{temporal} + i C_i^{spatial}, drawing on Penrose's twistor theory. The Hermitian norm |Z_i|^2 = (ΔC_i^{spatial})^2 - γ(ΔC_i^{temporal})^2 carries the Minkowski signature intrinsically, emerging from the SU(2,2) invariant structure of twistor space rather than being inserted by hand. This addresses the fundamental question of how a discrete substrate can give rise to a Lorentzian manifold without violating Lorentz invariance. The second transition, Twistor → Spin Network, discretizes the twistor representation into a labeled graph. Nodes correspond to events (antichains representing spatial slices), edges correspond to causal links, and each edge carries a spin label j determined by j(j+1) ∝ |Z_i|^2, connecting directly to loop quantum gravity where spin networks provide an orthonormal basis for kinematical states. From this structure, relativistic spacetime emerges in the continuum limit: proper time along a worldline is given by the sum of spin labels τ = Σ √[j(j+1)] × τ_Planck, velocity emerges from the ratio of accumulated spatial to temporal increments, and the twin paradox resolves combinatorially through different total spin sums along distinct worldlines. The framework synthesizes insights from causal set theory, twistor theory, and loop quantum gravity, demonstrating how these approaches complement rather than compete with one another. It offers resolutions to long-standing puzzles including the origin of the Lorentzian signature, the compatibility of discreteness with Lorentz invariance, and the combinatorial definition of proper time. Open questions regarding dynamics, the quantum measure, and phenomenological predictions are discussed as directions for future investigation.

This paper examines how caste, class and gender intersect to shape Indian consciousness in Aravind Adiga’s The White Tiger. Focusing on Balram Halwai’s first‑person narrative of his journey from a lower‑caste village boy to a Bangalore entrepreneur, it argues that his evolving self‑understanding is produced within, and against, India’s entrenched hierarchies and rapidly changing economic order. Through close reading of key episodes in Laxmangarh, Delhi and Bangalore, the study explores how Balram internalises and then contests normative beliefs about family loyalty, servitude, masculinity and success, revealing a consciousness marked by resentment, ambition and ethical ambivalence. Central to the analysis is the “rooster coop” metaphor, which is read as a condensed image of intersecting structures of caste subordination, class exploitation and patriarchal control that keep the poor “guarded from the inside.” By tracing how Balram both exposes and reproduces this system—ultimately embracing violence and corruption to “break out”—the paper contends that The White Tiger offers not a simple rags‑to‑riches fable but a complex representation of Indian consciousness under liberalisation. The novel thus unsettles celebratory narratives of a “shining” India by foregrounding the psychological captivity, moral compromises and fractured agency of those at the bottom of its social order.

Auditory areas on the superior temporal plane and lateral convexity are key initial stages of speech processing in the human cortex, representing acoustic and phonetic attributes in a temporally precise manner. More complex representations in auditory-related cortex along the ventral and dorsal processing streams and prefrontal cortex are associated with perception and action. In this study, we used intracranial electroencephalography (iEEG) to clarify where and how activity leading to perceptually driven behavioral events emerges. Participants were patients undergoing iEEG monitoring for medically intractable epilepsy. Stimuli were monosyllabic words, and participants pressed a button in response to a semantic target category. Significant high gamma activity after stimulus onset and immediately prior to motor response defined stimulus- and behavior-related activity patterns, respectively. The stimulus-related pattern was more common than behavior-related throughout the cortical auditory hierarchy as well as sensorimotor cortex. Behavior-related activity was sparsely represented, with the highest prevalence in the prefrontal cortex and a more limited representation in anterior temporal and parieto-occipital cortex. Hemispheric asymmetries included a higher prevalence of stimulus-related activity in the right sensorimotor cortex and a higher prevalence of the behavior-related pattern in the left prefrontal cortex. Faster behavioral responses were associated with greater stimulus-locked high gamma power in non-core auditory, prefrontal, and premotor cortex. Results reveal the cortical distribution of sensory stimulus-driven responses and activity time-locked to behavior and provide insights into neural substrates of speech perception.

Urban housing conflicts are increasingly shaping the social and spatial dynamics of cities, yet they remain difficult to analyze systematically due to their multi-actor complexity and fragmented representation across textual and spatial data. Existing studies tend to focus on either narrative or spatial aspects, rarely capturing the structural, temporal, and geographic dimensions of these conflicts in an integrated way. This paper addresses this gap by proposing a reproducible methodological framework that combines Large Language Models (LLMs), graph databases, and Geographic Information Systems (GIS) to analyze housing conflicts in Montréal between 2001 and 2024. The study aims to demonstrate how urban housing conflicts can be systematically extracted, classified, and analyzed across time and space using AI-based methods, and how their structural patterns reflect underlying socio-political dynamics. The resulting framework offers new insights into the evolution of conflicts linked to gentrification, economic vulnerability, and shifting governance, while contributing a replicable, scalable methodology for studying complex urban phenomena at the intersection of AI, spatial analysis, and social science.