Miracle-dz/UniFAR-FTUs huggingface dataset:
Charting the social neuroscience of human attachment (SoNeAt)

ABSTRACT This introduction aims to set out the potential as well as some of the pitfalls of the newly emerging area of the Social Neuroscience of Human Attachment (SoNeAt). To organize and interconnect the burgeoning empirical studies in this line of research, including those in this special issue, we outline a programmatic framework including an extension of our conceptual proposals NAMA and NAMDA to guide future research. We hope that this special issue will act as a stimulus for redoubling our efforts advancing the newly emerging SoNeAt area bridging attachment theory and social neuroscience.
Social Determinants of Health and Symptom Burden During Cancer Treatment

Background Cancer survivors (defined as individuals from diagnosis to the end of life) in treatment experience multiple physical and psychological symptoms (e.g., fatigue, pain, depression, anxiety, disturbed sleep) that influence their well-being and treatment outcomes. Underrepresented cancer survivors may disproportionately experience greater symptom burden (number of symptoms, symptom severity, depression, anxiety). Objectives The aim of this study was to examine the relationships of social determinants of health, including age, ethnicity, education, income and whether income meets the survivor's needs, neighborhood (rural vs. urban), access to healthcare (e.g., insurance), and social isolation, with symptom burden in cancer survivors. Methods This secondary analysis included baseline data from 400 cancer survivors of solid tumor cancers undergoing chemotherapy or targeted therapy who participated in a larger randomized trial of symptom management interventions. Symptom burden was measured by the Center for Epidemiological Studies–Depression scale for depression and Patient-Reported Outcomes Measurement Information System scores for anxiety and social isolation, summed severity index of 16 symptoms from the General Symptom Distress Scale, and the total number of symptoms. Self-reported comorbid conditions were measured using the Bayliss tool. General linear models were used to relate symptom measures (one at a time) to age, number of comorbid conditions, level of education, marital status, income meeting needs, and size of metropolitan neighborhood. Additional covariates included site of cancer, its treatment, and whether the cancer was metastatic. Results Non-Hispanic White survivors (n = 191) were older and had more comorbid conditions, a higher proportion of metastatic cancers, and higher levels of education and income compared with Hispanic survivors (n = 168) and non-Hispanic survivors of other races (n = 41). Compared with the other two groups, Hispanic survivors had the lowest rate of health insurance availability, and non-Hispanic survivors of other races had the lowest social isolation. Age, number of comorbid conditions, and social isolation were significantly associated with number of symptoms, symptom severity, and depression. Age and social isolation were associated with anxiety. In addition, the symptom severity of non-Hispanic White survivors was lower than that of Hispanic survivors and non-Hispanic survivors of other races. Discussion These findings highlight the health disparities in symptom burden experienced among cancer survivors when considering their social determinants of health. Assessing these may help clinicians address health disparities in cancer care.
What influences judgments of physical attractiveness? A comprehensive perspective with implications for mental health

Abstract Judgments of physical attractiveness are based on appearance but are influenced by and influence more than just physical features of the face and body (e.g. clothing and personality traits). This is explored in a selective review of previous research, plus new analyses of data from three previously published studies: the Boston Couples Study, the Multiple Identities Questionnaire, and the Intimate Relationships Across Cultures Study, with implications for mental health. Self-ratings of attractiveness are inflated by self-esteem and confidence in self-halo effects. Partner-ratings are inflated by love and relationship satisfaction in partner-halo effects. Positive responses from others influence attractiveness-enhancing cycles, while negative responses influence attractiveness-deprecating cycles, with impacts on well-being. These influences are represented in a comprehensive Attractiveness Halo Model, which identifies Ten Components of Attractiveness that are inter-related, including physical, emotional, sexual, sensory, intellectual, behavioural, observer, situation, reciprocity, and time. Aspects of the model are supported by analyses of the three studies, generalising comprehensive attractiveness halo effects across time, identities, cultures, and relationship types.
The fixed-trace β-Hermite ensemble of random matrices and the low temperature distribution of the determinant of an N × N β-Hermite matrix

The β-Hermite ensemble (β-HE) of tridiagonal N × N random matrices of Dumitriu and Edelman (2002 J. Math. Phys. 43 5830) is a continuum of ensembles in which β, the reciprocal of the temperature in the 2D electrostatic interpretation of the eigenvalue characteristics, can take any value. The eigenvalue distributions coincide with those of the classical Gaussian ensembles (GOE, GUE, GSE) for β = 1, 2, 4. A fixed-trace β-Hermite ensemble (β-FTHE) is defined from the β-HE and is used to extend the spherical ensembles of classical symmetries to β-spherical ensembles. At low temperature, when β → ∞, for a fixed value of N, the asymptotic distributions of reduced determinants DN,β of random N × N β-H and β-FTH matrices are shown to be standard Gaussians. Accordingly, the fluctuations of the potential at the origin, −ln |DN,β|, have a generalized Gumbel distribution at low temperature. For large N and large β, a ln(N) variance results from the strongly correlated fluctuations of eigenvalues around their equilibrium positions.
Describing and Calculating Flat Outputs of Two-input Driftless Control Systems

Abstract We study the problem of flatness of two-input driftless control systems. Although a characterization of flat systems of that class is known, the problems of describing all flat outputs and of calculating them is open. We show that all x -flat outputs are parameterized by an arbitrary function of three canonically defined variables. We also construct a system of 1 st order PDE's whose solutions give all x -flat outputs of 2-input driftless systems. We illustrate our results by describing all flat outputs of models of a rolling disk and a nonholonomic car.
eIF4A1-dependent mRNAs employ purine-rich 5’UTR sequences to activate localised eIF4A1-unwinding through eIF4A1-multimerisation to facilitate translation

Abstract Altered eIF4A1 activity promotes translation of highly structured, eIF4A1-dependent oncogene mRNAs at root of oncogenic translational programmes. It remains unclear how these mRNAs recruit and activate eIF4A1 unwinding specifically to facilitate their preferential translation. Here, we show that single-stranded RNA sequence motifs specifically activate eIF4A1 unwinding allowing local RNA structural rearrangement and translation of eIF4A1-dependent mRNAs in cells. Our data demonstrate that eIF4A1-dependent mRNAs contain AG-rich motifs within their 5’UTR which specifically activate eIF4A1 unwinding of local RNA structure to facilitate translation. This mode of eIF4A1 regulation is used by mRNAs encoding components of mTORC-signalling and cell cycle progression, and renders these mRNAs particularly sensitive to eIF4A1-inhibition. Mechanistically, we show that binding of eIF4A1 to AG-rich sequences leads to multimerization of eIF4A1 with eIF4A1 subunits performing distinct enzymatic activities. Our structural data suggest that RNA-binding of multimeric eIF4A1 induces conformational changes in the RNA resulting in an optimal positioning of eIF4A1 proximal to the RNA duplex enabling efficient unwinding. Our data proposes a model in which AG-motifs in the 5’UTR of eIF4A1-dependent mRNAs specifically activate eIF4A1, enabling assembly of the helicase-competent multimeric eIF4A1 complex, and positioning these complexes proximal to stable localised RNA structure allowing ribosomal subunit scanning.
Compound Poisson Approximations for Sums of Random Variables

Abstract : This document shows that a sum of dependent random variables is approximately compound Poisson when the variables are rarely nonzero and, given they are nonzero, their conditional distributions are nearly identical. It give several upper bounds on the total-variation distance between the distribution of such a sum and a compund Poisson distribution. Included is an example for Markovian occurrences of a rare event. The bounds are consistent with those that are known for Poisson approximations for sums of uniformly small random variables. (Author)
Measurement of change in angle kappa and its correlation with ocular biometric parameters pre- and post-phacoemulsification

Purpose: Angle kappa has been considered to play a role in causing glare and haloes despite accurate centration during implantation of multifocal intraocular lenses following phacoemulsification. There is a lack of substantial data regarding whether angle kappa is a constant entity or changes following ocular surgical procedures. To answer this question, in this prospective observational study, we measured change in angle kappa following phacoemulsification, and studied the ocular biometric parameters correlating with this change. Methods: Angle kappa was measured objectively using synoptophore. Ocular Biometric parameters (Anterior Chamber Depth, Corneal White-to-White measurement, Lens Thickness, and Axial Length) using LenStar LS 900 Haag Streit Anterior Segment imaging system. outcome measures were a quantitative change in angle kappa from the preoperative value by one degree or more and observation of correlation between change in angle kappa and ocular biometric parameters. The Wilcoxin Signed Rank Test was used to determine the difference between pre-operative and post-operative measurements for angle kappa. A p-value of less than 0.05 was considered statistically significant. Pearson's correlation coefficient was employed to find the relationship between preoperative ocular biometric parameters and a change in angle kappa. A linear regression model was used to derive an equation considering corneal white-to-white measurement as the predictor and change in angle kappa as the outcome measure. Results: A significant change in angle kappa was recorded, and a significant correlation was found with corneal white to white measurements. This change could be predicted preoperatively, for a known corneal white to white measurement using the standard equation y=mx+c. Conclusion: This study explains the possible cause of dissatisfaction among seemingly ideal patients who undergo multifocal IOL implantation and the potential for better decision-making during patient selection for multifocal IOL implantation.
Effects of Sex and Cuff Pressure on Physiological Responses during Blood Flow Restriction Resistance Exercise in Young Adults

ABSTRACT Purpose The purpose of this study was to examine the physiological responses resulting from an acute blood flow restriction resistance exercise bout with two different cuff pressures in young, healthy men and women. Methods Thirty adults (18–30 yr) completed a bilateral leg extension blood flow restriction bout consisting of four sets (30–15–15–15 repetitions), with cuffs applied at pressures corresponding to 40% and 60% of the minimum arterial occlusion pressure (AOP) needed to completely collapse the femoral arteries. During each of these conditions (40% and 60% AOP), physiological measures of near-infrared spectroscopy (NIRS) and EMG amplitude (EMG AMP) were collected from the dominant or nondominant vastus lateralis. After each set, ratings of perceived exertion (RPE) were collected, whereas only at baseline and at the end of the bout, mean arterial pressure (MAP) was assessed. Separate mixed-factorial ANOVA models were used to examine mean differences in the change in EMG AMP and NIRS parameters during each set. The absolute RPE and MAP values were also examined with separate ANOVAs. A P value ≤0.05 was considered statistically significant. Results Regardless of sex or cuff pressure, the change in EMG AMP was lower in set 1 (14.8%) compared with the remaining sets (22.6%–27.0%). The 40% AOP condition elicited the greatest changes in oxy[heme] and deoxy[heme], while also providing lower RPEs. For MAP, there was an effect for time such that MAP increased from preexercise (87.5 ± 4.3 mm Hg) to postexercise (104.5 ± 4.1 mm Hg). Conclusions The major findings suggested that the 40% AOP condition permitted the greatest amount of recovery during the interset rest. In addition, there did not seem to be any meaningful sex-related difference in this sample of young healthy adults.
Subthalamic and nigral neurons are differentially modulated during parkinsonian gait

Abstract The parkinsonian gait disorder and freezing of gait are therapeutically demanding symptoms with considerable impact on quality of life. The aim of this study was to assess the role of subthalamic and nigral neurons in the parkinsonian gait control using intraoperative microelectrode recordings of basal ganglia neurons during a supine stepping task. Twelve male patients (56 ± 7 years) suffering from moderate idiopathic Parkinson’s disease (disease duration 10 ± 3 years, Hoehn and Yahr stage 2), undergoing awake neurosurgery for deep brain stimulation, participated in the study. After 10 s resting, stepping at self-paced speed for 35 s was followed by short intervals of stepping in response to random ‘start’ and ‘stop’ cues. Single- and multi-unit activity was analysed offline in relation to different aspects of the stepping task (attentional ‘start’ and ‘stop’ cues, heel strikes, stepping irregularities) in terms of firing frequency, firing pattern and oscillatory activity. Subthalamic nucleus and substantia nigra neurons responded to different aspects of the stepping task. Of the subthalamic nucleus neurons, 24% exhibited movement-related activity modulation as an increase of the firing rate, suggesting a predominant role of the subthalamic nucleus in motor aspects of the task, while 8% of subthalamic nucleus neurons showed a modulation in response to the attentional cues. In contrast, responsive substantia nigra neurons showed activity changes exclusively associated with attentional aspects of the stepping task (15%). The firing pattern of subthalamic nucleus neurons revealed gait-related firing regularization and a drop of beta oscillations during the stepping performance. During freezing episodes instead, there was a rise of beta oscillatory activity. This study shows for the first time specific, task-related subthalamic nucleus and substantia nigra single-unit activity changes during gait-like movements in humans with differential roles in motor and attentional control of gait. The emergence of perturbed firing patterns in the subthalamic nucleus indicates a disrupted information transfer within the gait network, resulting in freezing of gait.
Intersection theorems for finite general linear groups

Abstract A subset Y of the general linear group $\text{GL}(n,q)$ is called t-intersecting if $\text{rk}(x-y)\le n-t$ for all $x,y\in Y$ , or equivalently x and y agree pointwise on a t-dimensional subspace of $\mathbb{F}_q^n$ for all $x,y\in Y$ . We show that, if n is sufficiently large compared to t, the size of every such t-intersecting set is at most that of the stabiliser of a basis of a t-dimensional subspace of $\mathbb{F}_q^n$ . In case of equality, the characteristic vector of Y is a linear combination of the characteristic vectors of the cosets of these stabilisers. We also give similar results for subsets of $\text{GL}(n,q)$ that intersect not necessarily pointwise in t-dimensional subspaces of $\mathbb{F}_q^n$ and for cross-intersecting subsets of $\text{GL}(n,q)$ . These results may be viewed as variants of the classical Erdős–Ko–Rado Theorem in extremal set theory and are q-analogs of corresponding results known for the symmetric group. Our methods are based on eigenvalue techniques to estimate the size of the largest independent sets in graphs and crucially involve the representation theory of $\text{GL}(n,q)$ .
Pathologies of Predictive Diversity in Deep Ensembles

Classic results establish that encouraging predictive diversity improves performance in ensembles of low-capacity models, e.g. through bagging or boosting. Here we demonstrate that these intuitions do not apply to high-capacity neural network ensembles (deep ensembles), and in fact the opposite is often true. In a large scale study of nearly 600 neural network classification ensembles, we examine a variety of interventions that trade off component model performance for predictive diversity. While such interventions can improve the performance of small neural network ensembles (in line with standard intuitions), they harm the performance of the large neural network ensembles most often used in practice. Surprisingly, we also find that discouraging predictive diversity is often benign in large-network ensembles, fully inverting standard intuitions. Even when diversity-promoting interventions do not sacrifice component model performance (e.g. using heterogeneous architectures and training paradigms), we observe an opportunity cost associated with pursuing increased predictive diversity. Examining over 1000 ensembles, we observe that the performance benefits of diverse architectures/training procedures are easily dwarfed by the benefits of simply using higher-capacity models, despite the fact that such higher capacity models often yield significantly less predictive diversity. Overall, our findings demonstrate that standard intuitions around predictive diversity, originally developed for low-capacity ensembles, do not directly apply to modern high-capacity deep ensembles. This work clarifies fundamental challenges to the goal of improving deep ensembles by making them more diverse, while suggesting an alternative path: simply forming ensembles from ever more powerful (and less diverse) component models.
Algorithm Design for Online Meta-Learning with Task Boundary Detection

Online meta-learning has recently emerged as a marriage between batch meta-learning and online learning, for achieving the capability of quick adaptation on new tasks in a lifelong manner. However, most existing approaches focus on the restrictive setting where the distribution of the online tasks remains fixed with known task boundaries. In this work, we relax these assumptions and propose a novel algorithm for task-agnostic online meta-learning in non-stationary environments. More specifically, we first propose two simple but effective detection mechanisms of task switches and distribution shift based on empirical observations, which serve as a key building block for more elegant online model updates in our algorithm: the task switch detection mechanism allows reusing of the best model available for the current task at hand, and the distribution shift detection mechanism differentiates the meta model update in order to preserve the knowledge for in-distribution tasks and quickly learn the new knowledge for out-of-distribution tasks. In particular, our online meta model updates are based only on the current data, which eliminates the need of storing previous data as required in most existing methods. We further show that a sublinear task-averaged regret can be achieved for our algorithm under mild conditions. Empirical studies on three different benchmarks clearly demonstrate the significant advantage of our algorithm over related baseline approaches.
Sulphur-bearing molecules as tracers of shocks in interstellar clouds

We have computed two-fluid MHD models of shocks propagating in molecular clouds of density 10 4 ≤ n H ≤ 10 6 cm −3 , with speeds of 5 ≤ v s ≤ 40 km s −1 . Attention has been paid to the chemistry of sulphur-bearing species, particularly SO, SO 2 , CS and H 2 S. We find that the fractional abundance of H 2 S is enhanced within the shock, whilst those of SO and especially SO 2 can be enhanced both within the shock and for large distances (up to 10 18 cm) into the post-shock gas. The fractional abundance of CS, on the other hand, remains roughly constant. The relevance of these results to observations of outflows in molecular clouds is mentioned
History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System

With the evolution of pre-trained language models, current open-domain dialogue systems have achieved great progress in conducting one-session conversations. In contrast, Multi-Session Conversation (MSC), which consists of multiple sessions over a long term with the same user, is under-investigated. In this paper, we propose History-Aware Hierarchical Transformer (HAHT) for multi-session open-domain dialogue. HAHT maintains a long-term memory of history conversations and utilizes history information to understand current conversation context and generate well-informed and context-relevant responses. Specifically, HAHT first encodes history conversation sessions hierarchically into a history memory. Then, HAHT leverages historical information to facilitate the understanding of the current conversation context by encoding the history memory together with the current context with attention-based mechanisms. Finally, to explicitly utilize historical information, HAHT uses a history-aware response generator that switches between a generic vocabulary and a history-aware vocabulary. Experimental results on a large-scale MSC dataset suggest that the proposed HAHT model consistently outperforms baseline models. Human evaluation results support that HAHT generates more human-like, context-relevant and history-relevant responses than baseline models.
Stochastic optimal transport in Banach Spaces for regularized estimation of multivariate quantiles

We introduce a new stochastic algorithm for solving entropic optimal transport (EOT) between two absolutely continuous probability measures $\mu$ and $\nu$. Our work is motivated by the specific setting of Monge-Kantorovich quantiles where the source measure $\mu$ is either the uniform distribution on the unit hypercube or the spherical uniform distribution. Using the knowledge of the source measure, we propose to parametrize a Kantorovich dual potential by its Fourier coefficients. In this way, each iteration of our stochastic algorithm reduces to two Fourier transforms that enables us to make use of the Fast Fourier Transform (FFT) in order to implement a fast numerical method to solve EOT. We study the almost sure convergence of our stochastic algorithm that takes its values in an infinite-dimensional Banach space. Then, using numerical experiments, we illustrate the performances of our approach on the computation of regularized Monge-Kantorovich quantiles. In particular, we investigate the potential benefits of entropic regularization for the smooth estimation of multivariate quantiles using data sampled from the target measure $\nu$.