Specific Representation Research Articles

On component-wise dissimilarity measures and metric properties in pattern recognition.

In many real-world applications concerning pattern recognition techniques, it is of utmost importance the automatic learning of the most appropriate dissimilarity measure to be used in object comparison. Real-world objects are often complex entities and need a specific representation grounded on a composition of different heterogeneous features, leading to a non-metric starting space where Machine Learning algorithms operate. However, in the so-called unconventional spaces a family of dissimilarity measures can be still exploited, that is, the set of component-wise dissimilarity measures, in which each component is treated with a specific sub-dissimilarity that depends on the nature of the data at hand. These dissimilarities are likely to be non-Euclidean, hence the underlying dissimilarity matrix is not isometrically embeddable in a standard Euclidean space because it may not be structurally rich enough. On the other hand, in many metric learning problems, a component-wise dissimilarity measure can be defined as a weighted linear convex combination and weights can be suitably learned. This article, after introducing some hints on the relation between distances and the metric learning paradigm, provides a discussion along with some experiments on how weights, intended as mathematical operators, interact with the Euclidean behavior of dissimilarity matrices.

University freshmen's excessive smartphone use and psychological safety during the COVID-19 pandemic.

Although excessive smartphone use has been confirmed as being associated with specific representations of mental health (e. g., anxiety, depression, wellbeing, etc.) throughout the COVID-19 pandemic, the relationship between excessive smartphone use and cognitive representations of mental health (i.e., psychological safety) is not yet fully understood. This study aimed to identify the association between excessive smartphone use and psychological safety among university freshmen during the COVID-19 pandemic; in addition, we examined the mediation effects of hardiness and interpersonal distress in this relationship. In this study, 1,224 university freshmen were selected at random from several universities in Guizhou Province of China. The Psychological Safety Scale was used to evaluate the mental health of university freshmen; the Mobile Phone Dependence Scale was used to evaluate excessive smartphone use; the Hardiness Questionnaire was used to evaluate hardiness; and the Interpersonal Relation Synthetic Diagnose Test was used to evaluate interpersonal distress. The findings showed that: (1) the greater the degree of excessive smartphone use, the more serious respondents' interpersonal distress and the lower their hardiness; (2) excessive smartphone use was not only directly related to the psychological safety of university freshmen but also indirectly related to their psychological safety through the independent mediation of hardiness and interpersonal distress, as well as through the chain mediation of hardiness and interpersonal distress. In general, excessive smartphone use in university freshmen could lead to a decline in their psychological safety. Also, hardiness and interpersonal distress play a complex role in this relationship. During the COVID-19 pandemic, interventions on the mental health of college freshmen should not only provide guidance on how to use their smartphone responsibly but also to provide them with support and guidance for the enhancement of their hardiness and improvement of their interpersonal relationships.

Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation From Depth Maps

Knowing the exact 3D location of workers and robots in a collaborative environment enables several real applications, such as the detection of unsafe situations or the study of mutual interactions for statistical and social purposes. In this letter, we propose a non-invasive and light-invariant framework based on depth devices and deep neural networks to estimate the 3D pose of robots from an external camera. The method can be applied to any robot without requiring hardware access to the internal states. We introduce a novel representation of the predicted pose, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Semi-Perspective Decoupled Heatmaps</i> (SPDH), to accurately compute 3D joint locations in world coordinates adapting efficient deep networks designed for the 2D Human Pose Estimation. The proposed approach, which takes as input a depth representation based on XYZ coordinates, can be trained on synthetic depth data and applied to real-world settings without the need for domain adaptation techniques. To this end, we present the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SimBa</i> dataset, based on both synthetic and real depth images, and use it for the experimental evaluation. Results show that the proposed approach, made of a specific depth map representation and the SPDH, overcomes the current state of the art.

Stanley–Reisner rings and the occurrence of the Steinberg representation in the hit problem

A result of G. Walker and R. Wood states that the space of indecomposable elements in degree 2 n -1-n of the polynomial algebra 𝔽 2 [x 1 ,...,x n ], considered as a module over the mod 2 Steenrod algebra, is isomorphic to the Steinberg representation of GL n (𝔽 2 ). We generalize this result to all finite fields by studying a family of finite quotient rings R n,k , k∈ℕ * , of 𝔽 q [x 1 ,...,x n ], where each R n,k is defined as a quotient of the Stanley–Reisner ring of a matroid complex. By considering a variant of R n,k , we also show that the space of indecomposable elements of 𝔽 q [x 1 ,...,x n ] in degree q n-1 -n has dimension equal to that of a complex cuspidal representation of GL n (𝔽 q ), that is (q-1)(q 2 -1)⋯(q n-1 -1).

Prediction of delay discounting in intertemporal decisions by future thinking: Accounting for fluency, contents, and functions of future thoughts.

PurposeTo study the variations of delay discounting rates as a function of fluency, contents, and functions of future thoughts in healthy subjects.BackgroundDelay discounting (DD) is a concept that can measure a frequent tendency toward smaller, yet immediate rewards, while a delayed reward is greater in value. DD describes people's choices in intertemporal decisions and is associated with self‐control. Future thinking (FT) and having a vivid imagination of the future can reduce individuals’ DD rates. However, constructing a specific episodic future representation was merely studied in relation to DD. Although fluency and contents of future thoughts have been reported related to various disorders and behaviors, their association with DD has not been previously addressed.MethodsThe present study applies a verbal fluency task named the personal future task (PFT), the functions of future thinking scale (FoFTS), and the 27‐item delay discounting questionnaire (DDQ) in order to assess fluency, contents, and functions of future thoughts, and delay discounting in healthy subjects (N = 114, Female = 64%, Male = 36%, Mage = 34.22, SDage = 7.15).ResultsFindings indicate that fluency of future thoughts is associated with DD. Among the contents of FT categories, financial contents (future thoughts about money and real estate matters), and regarding functions of FT, engaging in FT for planning are related to DD. Due to the final model, the above‐mentioned correlated variables can be considered as significant predictors of intertemporal choices when controlling for education and gender (R 2 = 0.4, Adjusted R 2 = 0.33, F = 5.186, p‐value = 0.001).ConclusionThe frequency of future thoughts one can generate, specifically future thoughts about financial contents, is associated with less short‐sighted intertemporal decisions. The former relationship is enhanced for longer delays (e.g., 5–10 years). Besides, individuals who frequently engage in FT for planning (planning out sequences of actions) discount future rewards to a lesser extent.

Troi

Emotional Self-Awareness (ESA) plays a vital role in physical and mental well-being. Recent advancements in artificial intelligence technologies have shown promising emotion recognition results, opening new opportunities to build systems to support ESA. However, little research has been done to understand users' perspectives on artificial-intelligence-based emotion recognition systems. We introduce Troi, an automatic emotion recognition mobile app using wearable signals. With Troi, we ran a multi-day user study with 12 users to understand user preference parameters, such as perceived accuracy, confidence, preferred emotion representations, effect of self-awareness of emotions, and real-time use cases. Further, we extend our study to evaluate the machine learning model in-the-wild to understand behaviours in-the-wild. We found that users perceived accuracy of the emotion recognition model is higher than the actual model prediction accuracy; there was no strong preference for one specific emotion representation, and users' self-awareness of emotions improved over time.

Simulation of real entangled systems based on the model of a pair of entangled solitons

ABSTRACT The features of the propagation of optical solitons in a dielectric are currently actively being applied in the fields of applied use of quantum phenomena. When creating new technologies based on the theory of solitons, the topical issue arises of developing soliton models accessible for practical implementation, suitable for subsequent modelling of real entangled systems. We chose a nematic liquid crystal as a dielectric medium for considering optical 1D envelopes of solitons. On the basis of a mathematical model of the behaviour of a pair of entangled solitons in a medium, a special stochastic representation of the wave function is constructed for subsequent modelling of entangled states of photons. Based on a qualitative analysis, a constructive addition to the entangled photon detector is proposed. The complete replacement of classical elements is discussed with a view to the possible use of the idea in the creation of quantum computers.

Impact of Skin Disinfection on Cutaneous Microbiota, before and after Peripheral Venous Catheter Insertion.

Introduction. Patients with invasive medical devices are at high risk for infection. Skin colonization is the initial stage of these infections, leading to the recommendation of practices requiring disinfection using antiseptics. Microbial communities playing a major role in skin health could be impacted by antiseptic procedures. Aim. To characterize and compare the bacterial communities of skin samples from patients before an antisepsis procedure, and after removal of the medical device itself, according to the nature of the antiseptic molecule (povidone iodine or chlorhexidine). Methods. The study focused on alterations in bacterial communities depending on the nature of the antiseptic procedure and type of intravascular device. After amplification of 16S rDNA, libraries (n = 498 samples) were sequenced using MiSeq platform. Results. Using an in-house pipeline (QIIME2 modules), while no alteration in skin microbiota diversity was associated with antiseptic procedure or PVC type, according to culture results (p < 0.05), alterations were at times associated with restricted diversity and higher dissimilarity (p < 0.05). Antiseptic procedures and PVC types were associated with the modification of specific bacterial representations with modulation of the Bacillota/Bacteroidota (Firmicutes/Bacteroidetes) ratio (modulation of C. acnes, Prevotella, Lagierella, and Actinomyces spp.) (p < 0.05). At baseline, the microbiota shows certain bacteria that are significantly associated with future PVC colonization and/or bacteremia (p < 0.05). All of these modulations were associated with altered expression of metabolic pathways (p < 0.05). Discussion. Finally, this work highlights the need to optimize the management of patients requiring intravascular devices, possibly by modulating the skin microbiota.

Relations among Ramanujan-type congruences I

We prove that Ramanujan-type congruences for integral weight modular forms away from the level and the congruence prime are equivalent to specific congruences for Hecke eigenvalues. In particular, we show that Ramanujan-type congruences are preserved by the action of the shallow Hecke algebra. More generally, we show for weakly holomorphic modular forms of integral weight, that Ramanujan-type congruences naturally occur for shifts in the union of two square-classes as opposed to single square-classes that appear in the literature on the partition function. We also rule out the possibility of square-free periods, whose scarcity in the case of the partition function was investigated recently. We complement our obstructions on maximal Ramanujan-type congruences with several existence statements. Our results are based on a framework that leverages classical results on integral models of modular curves via modular representation theory, and applies to congruences of all weakly holomorphic modular forms. Steinberg representations govern all maximal Ramanujan-type congruences for integral weights. We discern the scope of our framework in the case of half-integral weights through example calculations.

Adult age differences in specific and gist associative episodic memory across short- and long-term retention intervals.

Age-related deficits in associative episodic memory have been widely reported, but recent research suggests that some of these deficits occur for highly specific but not gist representations. It remains undetermined whether older adults' deficits in specific associative episodic memory, observed in long-term memory, are also present in short-term memory. We used a continuous associative recognition task to address this question. Fifty young and 50 older adults studied face-scene pairs, with memory tests occurring in both short-term and long-term memory. Memory tests featured intact (old) pairs, related (similar) pairs, and unrelated (dissimilar) pairs. On short-term memory tests, older adults were less accurate in classifying related pairs, which was manifest by age-related reductions in the probability of retrieving specific memory to engage in recollection rejection. However, older adults were capable of remembering specific details in short-term memory for intact probes but were less likely to remember specific details in long-term memory. Finally, older adults were always as capable as younger adults of remembering gist details. Results suggest that older adults do at least partially encode specific representations in short-term memory, and their access to these specific representations is cue dependent-they can do so when there is a large correspondence between encoding and retrieval conditions but are less likely to engage in deeper elaboration at retrieval. This limits their ability to remember specific details of associations to suppress false recognitions in short-term memory and to engage in veridical recognition in long-term memory. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Under-reporting and under-representation of non-Hispanic Black subjects in lipid-lowering atherosclerotic cardiovascular disease outcomes trials: A systematic review

BackgroundNon-Hispanic (NH) Black participants have been under-represented in studies of cardiovascular disease. ObjectiveWe sought to determine the trends of reporting and representation of NH Black subjects in randomized controlled trials (RCTs) of lipid-lowering therapies demonstrating atherosclerotic cardiovascular disease (ASCVD) risk reduction benefit. MethodsThe electronic databases of MEDLINE, EMBASE and ClinicalTrials.gov were searched from 1990-2020. Studies of lipid-lowering therapies (i.e., statins, ezetimibe, proprotein convertase subtilisin/kexin type 9 inhibitors [PCSK9], and icosapent ethyl) with proven ASCVD benefit, sample sizes of at least 1000 subjects and follow-up of at least 1 year were included (40 RCTs, N=306 747 total participants). We examined articles and supplementary material for participant-level race data. Using United States disease prevalence data, the participation-to-prevalence ratio (PPR) metric was used to estimate the representation of NH Black subjects compared with their reported disease burden (i.e., < 0.8 indicated under-representation; > 1.2, over-representation; and 0.8 to <1.2, adequate representation). ResultsThe median (interquartile range) number of participants per trial was 4871 (2434-10077). NH Black enrollees comprised 7.3% (95% CI, 0.9%-15.4%) of the total number of subjects reported. During the time intervals 1990-1995, 1996-2000, 2001-2005, 2006-2010, 2011-2015 and 2016-2020, NH Black participation was 0%, 1.1%, 4.4%, 4.8%, 0.2% and 0.7% respectively (P for trend <0.001). For statin trials, the participation of NH Black subjects was reported in 0 studies between 1990-1995 and in 9 of 28 trials from 1996-2020. For ezetimibe and icosapent ethyl, NH Black participants were reported in 0 of 3 and 0 of 1 studies, respectively. For trials of PCSK9 inhibitors, NH Black subjects were reported in 2 of 5 (40%). NH Black participants were under-represented compared with their disease burden in studies evaluating subjects with diabetes, hypercholesterolemia, stable coronary artery disease, and acute coronary syndrome (PPR < 0.8 for all). ConclusionNH Black participants are markedly under-represented, and results are under-reported. The inclusion of population and disease specific representation of NH Black persons and their related social determinants of health will help to address the disparity in preventive care for this historically undertreated population.

Sampling Rates for $$\ell ^1$$-Synthesis

This work investigates the problem of signal recovery from undersampled noisy sub-Gaussian measurements under the assumption of a synthesis-based sparsity model. Solving the \(\ell ^1\)-synthesis basis pursuit allows for a simultaneous estimation of a coefficient representation as well as the sought-for signal. However, due to linear dependencies within redundant dictionary atoms, it might be impossible to identify a specific representation vector, although the actual signal is still successfully recovered. The present manuscript studies both estimation problems from a non-uniform, signal-dependent perspective. By utilizing recent results on the convex geometry of linear inverse problems, the sampling rates describing the phase transitions of each formulation are identified. In both cases, they are given by the conic Gaussian mean width of an \(\ell ^1\)-descent cone that is linearly transformed by the dictionary. In general, this expression does not allow for a simple calculation by following the polarity-based approach commonly found in the literature. Hence, two upper bounds involving the sparsity structure of coefficient representations are provided: The first one is based on a local condition number and the second one on a geometric analysis that makes use of the thinness of high-dimensional polyhedral cones with not too many generators. It is furthermore revealed that both recovery problems can differ dramatically with respect to robustness to measurement noise—a fact that seems to have gone unnoticed in most of the related literature. All insights are carefully validated through numerical simulations.

Distinct neural representations for prosocial and self-benefiting effort

Prosocial behaviors-actions that benefit others-are central to individual and societal well-being. Although the mechanisms underlying the financial and moral costs of prosocial behaviors are increasingly understood, this work has often ignored a key influence on behavior: effort. Many prosocial acts are effortful, and people are averse to the costs of exerting them. However, how the brain encodes effort costs when actions benefit others is unknown. During fMRI, participants completed a decision-making task where they chose in each trial whether to "work" and exert force (30%-70% of maximum grip strength) or "rest" (no effort) for rewards (2-10 credits). Crucially, on separate trials, they made these decisions either to benefit another person or themselves. We used a combination of multivariate representational similarity analysis and model-based univariate analysis to reveal how the costs of prosocial and self-benefiting efforts are processed. Strikingly, we identified a unique neural signature of effort in the anterior cingulate gyrus (ACCg) for prosocial acts, both when choosing to help others and when exerting force to benefit them. This pattern was absent for self-benefiting behaviors. Moreover, stronger, specific representations of prosocial effort in the ACCg were linked to higher levels of empathy and higher subsequent exerted force to benefit others. In contrast, the ventral tegmental area and ventral insula represented value preferentially when choosing for oneself and not for prosocial acts. These findings advance our understanding of the neural mechanisms of prosocial behavior, highlighting the critical role that effort has in the brain circuits that guide helping others.

Specific Relation Attention-Guided Graph Neural Networks for Joint Entity and Relation Extraction in Chinese EMR

Electronic medical records (EMRs) contain a variety of valuable medical entities and their relations. The extraction of medical entities and their relations has important application value in the structuring of EMR and the development of various types of intelligent assistant medical systems, and hence is a hot issue in intelligent medicine research. In recent years, most research aims to firstly identify entities and then to recognize the relations between the entities, and often suffers from many redundant operations. Furthermore, the challenge remains of identifying overlapping relation triplets along with the entire medical entity boundary and detecting multi-type relations. In this work, we propose a Specific Relation Attention-guided Graph Neural Networks (SRAGNNs) model to jointly extract entities and their relations in Chinese EMR, which uses sentence information and attention-guided graph neural networks to perceive the features of every relation in a sentence and then to extract those relations. In addition, a specific sentence representation is constructed for every relation, and sequence labeling is performed to extract its corresponding head and tail entities. Experiments on a medical evaluation dataset and a manually labeled Chinese EMR dataset show that our model improves the performance of Chinese medical entities and relation extraction.

Structure enhanced deep clustering network via a weighted neighbourhood auto-encoder

Structural deep clustering involves the use of neural networks for fusing semantic and structural representations for clustering tasks, and it has been receiving increasing attention. In some pioneering works, auto-encoder (AE)-specific representations were integrated with a graph convolutional network (GCN)-specific representation by delivering semantic information to the GCN module layer-by-layer. Although promising performance has been achieved in various applications, we observed that a vital aspect was overlooked in these works: the structural information may vanish in the learning process because of the over-smoothing problem of the GCN module, leading to non-representative features and, thus, deteriorating clustering performance. In this study, we address this issue by proposing a structure enhanced deep clustering network. The GCN-specific structural data representation is enhanced and supervised by its structural information. Specifically, the GCN-specific structural data representation is strengthened during the learning process by combining it with a structure enhanced semantic (SES) representation. A novel structure enhanced AE, named the weighted neighbourhood AE (wNAE), is employed to learn the SES representation for each data sample. Finally, we design a joint supervision strategy to uniformly guide the simultaneous learning of the wNAE and GCN modules and the clustering assignment. Experimental results for different datasets empirically validate the importance of semantic and neighbour-wise structure learning.

Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing data.

Single-cell RNA sequencing (scRNA-seq) data provides unprecedented opportunities to reconstruct gene regulatory networks (GRNs) at fine-grained resolution. Numerous unsupervised or self-supervised models have been proposed to infer GRN from bulk RNA-seq data, but few of them are appropriate for scRNA-seq data under the circumstance of low signal-to-noise ratio and dropout. Fortunately, the surging of TF-DNA binding data (e.g. ChIP-seq) makes supervised GRN inference possible. We regard supervised GRN inference as a graph-based link prediction problem that expects to learn gene low-dimensional vectorized representations to predict potential regulatory interactions. In this paper, we present GENELink to infer latent interactions between transcription factors (TFs) and target genes in GRN using graph attention network. GENELink projects the single-cell gene expression with observed TF-gene pairs to a low-dimensional space. Then, the specific gene representations are learned to serve for downstream similarity measurement or causal inference of pairwise genes by optimizing the embedding space. Compared to eight existing GRN reconstruction methods, GENELink achieves comparable or better performance on seven scRNA-seq datasets with four types of ground-truth networks. We further apply GENELink on scRNA-seq of human breast cancer metastasis and reveal regulatory heterogeneity of Notch and Wnt signalling pathways between primary tumour and lung metastasis. Moreover, the ontology enrichment results of unique lung metastasis GRN indicate that mitochondrial oxidative phosphorylation (OXPHOS) is functionally important during the seeding step of the cancer metastatic cascade, which is validated by pharmacological assays. The code and data are available at https://github.com/zpliulab/GENELink. Supplementary data are available at Bioinformatics online.

Different intuitive rules in mathematical thinking

This study examines the behavior of individuals when two intuitive rules in their minds lead to two different outcomes concerning a problem. In the absence of the formal knowledge, intuitive rules can affect the mathematical thinking. Hence, studies have usually compared the correct answer of the related formal knowledge with the incorrect one obtained from one intuitive rule. What has been rather less explored is the situation where both correct and incorrect answers are obtained from intuitive rules. In so doing, comparing infinite sets as a challenging issue for learning was selected as the theme of the study. Two specific geometric representations regarding the comparison of two intervals were employed, one in line with the intuitive rule ‘More A - More B’ and the other in line with the ‘Same A - Same B’ rule, so that two different results are produced by each of the rules. 85 first grader high school students who had no formal knowledge about comparing infinite sets were participated in the study, and the data were collected through interview. Analysis of the data showed that when the two intended different intuitive rules can be activated in mind and, in turn, create a cognitive conflict, individuals rank intuitive rules based on the degree of transparency of the concepts they have already known.

Attention-like feature explanation for tabular data

A new method for local and global explanation of the machine learning black-box model predictions by tabular data is proposed. It is implemented as a system called AFEX (Attention-like Feature EXplanation) and consisting of two main parts. The first part is a set of the one-feature neural subnetworks, which aim to get a specific representation for every feature in the form of a basis of shape functions. The subnetworks use shortcut connections with trainable parameters to improve the network training performance. The second part of AFEX produces shape functions of features as the weighted sum of the basis shape functions where weights are computed by using an attention-like mechanism. The most important advantage of AFEX is that it identifies pairwise interactions between features based on pairwise multiplications of shape functions corresponding to different features. A modification of AFEX with incorporating an additional surrogate model, which approximates the black-box model, is proposed. AFEX is trained end-to-end on a whole dataset only once such that it does not require to train neural networks again in the explanation stage. Numerical experiments with synthetic and real data illustrate AFEX. The corresponding code implementing the method is publicly available.

Protesting the Protest Paradigm: TikTok as a Space for Media Criticism

Though news representations of protest have been studied extensively, little is known about how media audiences critique such representations. Focusing on TikTok as a space for media criticism, this article examines how users employ the app to respond to representations of protest in mainstream news media. Content collected in the spring of 2021 illuminated two very distinct foci of discussion about news representations of protest: the Black Lives Matter movement and the Capitol riot. Our qualitative content analysis of TikTok videos and their related comments demonstrates how users employed TikTok’s creative affordances to dissect specific news representations, critique the media apparatus, and expand news narratives. These findings shed light on the complex role of TikTok as a platform for media criticism—one that can be used for both democratic and non-democratic ends.

Supplemental Material for The Formation of Specific and Gist Associative Episodic Memory Representations During Encoding: Effects of Rate of Presentation

Supplemental Material for The Formation of Specific and Gist Associative Episodic Memory Representations During Encoding: Effects of Rate of Presentation