Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the human binocular visual system using multi-modal magnetic resonance imaging

Having two forward-facing eyes with slightly different viewpoints enables animals, including humans, to discriminate fine differences in depth (disparities), which can facilitate interaction with the world. The binocular visual system starts in the primary visual cortex because that is where information from the eyes is integrated for the first time. Magnetic resonance imaging (MRI) is an ideal tool to non-invasively investigate this system since it can provide a range of detailed measures about structure, function, neurochemistry and connectivity of the human brain. Since binocular disparity is used for both action and object recognition, the binocular visual system is a valuable model system in neuroscience for understanding how basic sensory cues are transformed into behaviourally relevant signals. In this review, we consider how MRI has contributed to the understanding of binocular vision and depth perception in the human brain. Firstly, MRI provides the ability to image the entire brain simultaneously to compare the contribution of specific visual areas to depth perception. A large body of work using functional MRI has led to an understanding of the extensive networks of brain areas involved in depth perception, but also the fine-scale macro-organisation for binocular processing within individual visual areas. Secondly, MRI can uncover mechanistic information underlying binocular combination with the use of MR spectroscopy. This method can quantify neurotransmitters including GABA and glutamate within restricted regions of the brain, and evaluate the role of these inhibitory and excitatory neurochemicals in binocular vision. Thirdly, it is possible to measure the nature and microstructure of pathways underlying depth perception using diffusion MRI. Understanding these pathways provides insight into the importance of the connections between areas implicated in depth perception. Finally, MRI can help to understand changes in the visual system resulting from amblyopia, a neural condition where binocular vision does not develop correctly in childhood.
Parental Self-Efficacy Among Israeli Older Adults

Abstract Parental self-efficacy (PSE) is a central aspect of parenting research. Although parenting continues in older ages, few studies have focused on PSE among older adults. In line with the parenting determinant model, the study examined the associations between psychological distress, social support, loneliness, and PSE in older age. The study used a quantitative cross-sectional survey, including 362 Israeli parents age 65 years and above. Participants were asked to complete questionnaires of psychological distress, social support, loneliness, PSE, and background information. Results revealed that higher PSE was associated with lower psychological distress, higher social support, and a lower sense of loneliness in older adults. The research model explained 36.9 per cent of the variance in PSE among older adults. Mental state and social conditions are important for PSE of older adults; therefore, professionals should ensure the availability of diverse solutions to enable, maintain, and promote well-being through social and familial engagement in this population.
Socioemotional functioning across the transition to lower secondary school: The role of temperament and relationships with mothers and teachers.

This Finnish longitudinal study investigated the role of relationships with mothers (n = 631) and teachers (n = 56), and adolescents' (n = 848; 53.9% girls, 46.1% boys) temperament in the development of the adolescents' socioemotional functioning during the transition to lower secondary school (Grade 6 to Grade 7; age in Grade 6: M = 12.32, SD = 0.36). The results showed that mother-adolescent closeness predicted a higher level and teacher-adolescent closeness predicted a smaller decrease in prosocial behavior. Teacher-adolescent conflict predicted a higher level, and mother-adolescent conflict predicted an increase in externalizing problems. Only temperament predicted internalizing problems. In addition, relationship quality both mediated and moderated some of the associations between temperament and socioemotional functioning.
Comparative spatial transcriptomic profiling of severe acute respiratory syndrome coronavirus 2 Delta and Omicron variants infections in the lungs of cynomolgus macaques

Recently emerging severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) Omicron variants are generally less pathogenic than previous strains. However, elucidating the molecular basis for pulmonary immune response alterations is challenging owing to the virus's heterogeneous distribution within complex tissue structure. Here, we revealed the spatial transcriptomic profiles of pulmonary microstructures at the SARS‐CoV‐2 infection site in the nine cynomolgus macaques upon inoculation with the Delta and Omicron variants. Delta‐ and Omicron‐infected lungs had upregulation of genes involved in inflammation, cytokine response, complement, cell damage, proliferation, and differentiation pathways. Depending on the tissue microstructures (alveoli, bronchioles, and blood vessels), there were differences in the types of significantly upregulated genes in each pathway. Notably, a limited number of genes involved in cytokine and cell damage response were differentially expressed between bronchioles of the Delta‐ and Omicron‐infection groups. These results indicated that despite a significant antigenic shift in SARS‐CoV‐2, the host immune response mechanisms induced by the variants were relatively consistent, with limited transcriptional alterations observed only in large airways. This study may aid in understanding the pathogenesis of SARS‐CoV‐2 and developing a clinical strategy for addressing immune dysregulation by identifying potential transcriptional biomarkers within pulmonary microstructures during infection with emerging variants.
Perspectives on residential involvement and engagement in everyday life–a qualitative study

Abstract Background: Worldwide, the population is ageing, and the need for nursing homes is increasing. institutionalization and a culture change from task-orientated care delivery towards increased involvement and engagement in a meaningful everyday life are evolving and, thus, contributing to nursing home residents’ quality of life and well-being. Aims/Objectives: To explore nursing home staff’s and local managers’ perspectives on everyday life with a specific focus on involvement and engagement. Material and Methods: Positioned within interpretivism and hermeneutics, a qualitative exploratory design was applied using individual and group interviews for data generation and abductive thematic analysis as the analytical method. Results: Through the analyses, three main themes appeared—A good day–Everyday life in a nursing home, Doing together–involvement in Everyday life and Involvement in Everyday life–difficult to practice—together with four subthemes—Home and people within, Knowing and relating to the person, If they can - they must and Service and Habits. Conclusions: Fulfilling the needs of both residents and the institution was found to be challenging by nursing home staff and local managers. Significance: A different approach to care, facilitated by, for example, occupational therapists, might be required to enable increased involvement and engagement in everyday life.
How Interactions Influence Users' Security Perception of Virtual Reality Authentication?

Users readily embrace the rapid advancements in virtual reality (VR) technology within various everyday contexts, such as gaming, social interactions, shopping, and commerce. In order to facilitate transactions and payments, VR systems require access to sensitive user data and assets, which consequently necessitates user authentication. However, there exists a limited understanding regarding how users' unique experiences in VR contribute to their perception of security. In our study, we adopt a research approach known as ``technology probe'' to investigate this question. Specifically, we have designed probes that explore the authentication process in VR, aiming to elicit responses from participants from multiple perspectives. These probes were seamlessly integrated into the routine payment system of a VR game, thereby establishing an organic study environment. Through qualitative analysis, we uncover the interplay between participants' interaction experiences and their security perception. Remarkably, despite encountering unique challenges in usability during VR interactions, our participants found the intuitive virtualized authentication process beneficial and thoroughly enjoyed the immersive nature of VR. Furthermore, we observe how these interaction experiences influence participants' ability to transfer their pre-existing understanding of authentication into VR, resulting in a discrepancy in perceived security. Moreover, we identify users' conflicting expectations, encompassing their desire for an enjoyable VR experience alongside the assurance of secure VR authentication. Building upon our findings, we propose recommendations aimed at addressing these expectations and alleviating potential conflicts.
Modular Transformers: Compressing Transformers into Modularized Layers for Flexible Efficient Inference

Pre-trained Transformer models like T5 and BART have advanced the state of the art on a wide range of text generation tasks. Compressing these models into smaller ones has become critically important for practical use. Common neural network compression techniques such as knowledge distillation or quantization are limited to static compression where the compression ratio is fixed. In this paper, we introduce Modular Transformers, a modularized encoder-decoder framework for flexible sequence-to-sequence model compression. Modular Transformers train modularized layers that have the same function of two or more consecutive layers in the original model via module replacing and knowledge distillation. After training, the modularized layers can be flexibly assembled into sequence-to-sequence models that meet different performance-efficiency trade-offs. Experimental results show that after a single training phase, by simply varying the assembling strategy, Modular Transformers can achieve flexible compression ratios from 1.1x to 6x with little to moderate relative performance drop.
Relieving Triplet Ambiguity: Consensus Network for Language-Guided Image Retrieval

Language-guided image retrieval enables users to search for images and interact with the retrieval system more naturally and expressively by using a reference image and a relative caption as a query. Most existing studies mainly focus on designing image-text composition architecture to extract discriminative visual-linguistic relations. Despite great success, we identify an inherent problem that obstructs the extraction of discriminative features and considerably compromises model training: \textbf{triplet ambiguity}. This problem stems from the annotation process wherein annotators view only one triplet at a time. As a result, they often describe simple attributes, such as color, while neglecting fine-grained details like location and style. This leads to multiple false-negative candidates matching the same modification text. We propose a novel Consensus Network (Css-Net) that self-adaptively learns from noisy triplets to minimize the negative effects of triplet ambiguity. Inspired by the psychological finding that groups perform better than individuals, Css-Net comprises 1) a consensus module featuring four distinct compositors that generate diverse fused image-text embeddings and 2) a Kullback-Leibler divergence loss, which fosters learning among the compositors, enabling them to reduce biases learned from noisy triplets and reach a consensus. The decisions from four compositors are weighted during evaluation to further achieve consensus. Comprehensive experiments on three datasets demonstrate that Css-Net can alleviate triplet ambiguity, achieving competitive performance on benchmarks, such as $+2.77\%$ R@10 and $+6.67\%$ R@50 on FashionIQ.
Degree-M Bethe and Sinkhorn Permanent Based Bounds on the Permanent of a Non-negative Matrix

The permanent of a non-negative square matrix can be well approximated by finding the minimum of the Bethe free energy functions associated with some suitably defined factor graph; the resulting approximation to the permanent is called the Bethe permanent. Vontobel gave a combinatorial characterization of the Bethe permanent via degree-$M$ Bethe permanents, which is based on degree-$M$ covers of the underlying factor graph. In this paper, we prove a degree-$M$-Bethe-permanent-based lower bound on the permanent of a non-negative matrix, which solves a conjecture proposed by Vontobel in [IEEE Trans. Inf. Theory, Mar. 2013]. We also prove a degree-$M$-Bethe-permanent-based upper bound on the permanent of a non-negative matrix. In the limit $M \to \infty$, these lower and upper bounds yield known Bethe-permanent-based lower and upper bounds on the permanent of a non-negative matrix. Moreover, we prove similar results for an approximation to the permanent known as the (scaled) Sinkhorn permanent.
Systematic Visual Reasoning through Object-Centric Relational Abstraction

Human visual reasoning is characterized by an ability to identify abstract patterns from only a small number of examples, and to systematically generalize those patterns to novel inputs. This capacity depends in large part on our ability to represent complex visual inputs in terms of both objects and relations. Recent work in computer vision has introduced models with the capacity to extract object-centric representations, leading to the ability to process multi-object visual inputs, but falling short of the systematic generalization displayed by human reasoning. Other recent models have employed inductive biases for relational abstraction to achieve systematic generalization of learned abstract rules, but have generally assumed the presence of object-focused inputs. Here, we combine these two approaches, introducing Object-Centric Relational Abstraction (OCRA), a model that extracts explicit representations of both objects and abstract relations, and achieves strong systematic generalization in tasks (including a novel dataset, CLEVR-ART, with greater visual complexity) involving complex visual displays.
ELEV-VISION: Automated Lowest Floor Elevation Estimation from Segmenting Street View Images

We propose an automated lowest floor elevation (LFE) estimation algorithm based on computer vision techniques to leverage the latent information in street view images. Flood depth-damage models use a combination of LFE and flood depth for determining flood risk and extent of damage to properties. We used image segmentation for detecting door bottoms and roadside edges from Google Street View images. The characteristic of equirectangular projection with constant spacing representation of horizontal and vertical angles allows extraction of the pitch angle from the camera to the door bottom. The depth from the camera to the door bottom was obtained from the depthmap paired with the Google Street View image. LFEs were calculated from the pitch angle and the depth. The testbed for application of the proposed method is Meyerland (Harris County, Texas). The results show that the proposed method achieved mean absolute error of 0.190 m (1.18 %) in estimating LFE. The height difference between the street and the lowest floor (HDSL) was estimated to provide information for flood damage estimation. The proposed automatic LFE estimation algorithm using Street View images and image segmentation provides a rapid and cost-effective method for LFE estimation compared with the surveys using total station theodolite and unmanned aerial systems. By obtaining more accurate and up-to-date LFE data using the proposed method, city planners, emergency planners and insurance companies could make a more precise estimation of flood damage.
Electron kinetics in a positive column of AC discharges in a dynamic regime

We have performed hybrid kinetic-fluid simulations of a positive column in alternating current (AC) argon discharges over a range of driving frequencies f and gas pressure p for the conditions when the spatial nonlocality of the electron energy distribution function (EEDF) is substantial. Our simulations confirmed that the most efficient conditions of plasma maintenance are observed in the dynamic regime when time modulations of mean electron energy (temperature) are substantial. The minimal values of the root mean square electric field and the electron temperature have been observed at f/p values of about 3 kHz Torr−1 in a tube of radius R = 1 cm. The ionization rate and plasma density reached maximal values under these conditions. The numerical solution of a kinetic equation allowed accounting for the kinetic effects associated with spatial and temporal nonlocality of the EEDF. Using the kinetic energy of electrons as an independent variable, we solved an anisotropic tensor diffusion equation in phase space. We clarified the role of different flux components during electron diffusion in phase space over surfaces of constant total energy. We have shown that the kinetic theory uncovers a more exciting and rich physics than the classical ambipolar diffusion (Schottky) model. Non-monotonic radial distributions of excitation rates, metastable densities, and plasma density have been observed in our simulations at pR > 6 Torr cm. The predicted off-axis plasma density peak in the dynamic regime has never been observed in experiments so far. We hope our results stimulate further experimental studies of the AC positive column. The kinetic analysis could help uncover new physics even for such a well-known plasma object as a positive column in noble gases.
Cross-Lingual Transfer with Target Language-Ready Task Adapters

Adapters have emerged as a modular and parameter-efficient approach to (zero-shot) cross-lingual transfer. The established MAD-X framework employs separate language and task adapters which can be arbitrarily combined to perform the transfer of any task to any target language. Subsequently, BAD-X, an extension of the MAD-X framework, achieves improved transfer at the cost of MAD-X's modularity by creating"bilingual"adapters specific to the source-target language pair. In this work, we aim to take the best of both worlds by (i) fine-tuning task adapters adapted to the target language(s) (so-called"target language-ready"(TLR) adapters) to maintain high transfer performance, but (ii) without sacrificing the highly modular design of MAD-X. The main idea of"target language-ready"adapters is to resolve the training-vs-inference discrepancy of MAD-X: the task adapter"sees"the target language adapter for the very first time during inference, and thus might not be fully compatible with it. We address this mismatch by exposing the task adapter to the target language adapter during training, and empirically validate several variants of the idea: in the simplest form, we alternate between using the source and target language adapters during task adapter training, which can be generalized to cycling over any set of language adapters. We evaluate different TLR-based transfer configurations with varying degrees of generality across a suite of standard cross-lingual benchmarks, and find that the most general (and thus most modular) configuration consistently outperforms MAD-X and BAD-X on most tasks and languages.
Discriminative Adversarial Privacy: Balancing Accuracy and Membership Privacy in Neural Networks

The remarkable proliferation of deep learning across various industries has underscored the importance of data privacy and security in AI pipelines. As the evolution of sophisticated Membership Inference Attacks (MIAs) threatens the secrecy of individual-specific information used for training deep learning models, Differential Privacy (DP) raises as one of the most utilized techniques to protect models against malicious attacks. However, despite its proven theoretical properties, DP can significantly hamper model performance and increase training time, turning its use impractical in real-world scenarios. Tackling this issue, we present Discriminative Adversarial Privacy (DAP), a novel learning technique designed to address the limitations of DP by achieving a balance between model performance, speed, and privacy. DAP relies on adversarial training based on a novel loss function able to minimise the prediction error while maximising the MIA's error. In addition, we introduce a novel metric named Accuracy Over Privacy (AOP) to capture the performance-privacy trade-off. Finally, to validate our claims, we compare DAP with diverse DP scenarios, providing an analysis of the results from performance, time, and privacy preservation perspectives.
Stubborn Lexical Bias in Data and Models

In NLP, recent work has seen increased focus on spurious correlations between various features and labels in training data, and how these influence model behavior. However, the presence and effect of such correlations are typically examined feature by feature. We investigate the cumulative impact on a model of many such intersecting features. Using a new statistical method, we examine whether such spurious patterns in data appear in models trained on the data. We select two tasks -- natural language inference and duplicate-question detection -- for which any unigram feature on its own should ideally be uninformative, which gives us a large pool of automatically extracted features with which to experiment. The large size of this pool allows us to investigate the intersection of features spuriously associated with (potentially different) labels. We then apply an optimization approach to *reweight* the training data, reducing thousands of spurious correlations, and examine how doing so affects models trained on the reweighted data. Surprisingly, though this method can successfully reduce lexical biases in the training data, we still find strong evidence of corresponding bias in the trained models, including worsened bias for slightly more complex features (bigrams). We close with discussion about the implications of our results on what it means to"debias"training data, and how issues of data quality can affect model bias.
Oversmoothing: A Nightmare for Graph Contrastive Learning?

Oversmoothing is a common phenomenon observed in graph neural networks (GNNs), in which an increase in the network depth leads to a deterioration in their performance. Graph contrastive learning (GCL) is emerging as a promising way of leveraging vast unlabeled graph data. As a marriage between GNNs and contrastive learning, it remains unclear whether GCL inherits the same oversmoothing defect from GNNs. This work undertakes a fundamental analysis of GCL from the perspective of oversmoothing on the first hand. We demonstrate empirically that increasing network depth in GCL also leads to oversmoothing in their deep representations, and surprisingly, the shallow ones. We refer to this phenomenon in GCL as `long-range starvation', wherein lower layers in deep networks suffer from degradation due to the lack of sufficient guidance from supervision. Based on our findings, we present BlockGCL, a remarkably simple yet effective blockwise training framework to prevent GCL from notorious oversmoothing. Without bells and whistles, BlockGCL consistently improves robustness and stability for well-established GCL methods with increasing numbers of layers on several real-world graph benchmarks.